MAPPING URBAN LAND USE / LAND COVER USING … · mapping urban land use / land cover using...

MAPPING URBAN LAND USE LAND COVER USING QUICKBIRD NDVI IMAGERY FOR RUNOFF CURVE NUMBER DETERMINATION

Pravara Thanapura Engineering Resource Center

Suzette Burckhard Civil and Environmental Engineering Mary Orsquo Neill Engineering Resource Center

Dwight Galster and Mathematics and Statistics South Dakota State University Brookings

South Dakota 57007 USA pravarathanapurasdstateedusuzetteburckhardsdstateedu

maryoneillsdstateedudwightgalstersdstateedu

Eric Warmath

Department of Transportation State of Nevada

1263 S Stewart Street Carson City Nevada 89712 USA

ewarmathdotstatenvus

ABSTRACT

The objective of this study was to evaluate Normalized Difference Vegetation Index (NDVI) data derived from QuickBird (QB) satellite imagery to map impervious areas and open spaces for runoff curve number (CN) determination The image was collected on April 26 2004 and provided by the City of Sioux Falls SD The image was radiometrically and geometrically corrected prior to this study The imagery has four bands (including the blue green red and near-infrared) and was imaged at a 239-meter spatial resolution The red channel (band 3 630-690 nm) and the near infrared channel (band 4 760-900 nm) were used to create the QB NDVI dataset (Band 4 - Band3 Band 4 + Band3) This research employed the urban land cover classification scheme of the runoff curve number table in the TR-55 (NRCS 1986) publication An unsupervised spectral classification approach was used in this study The research hypothesis was that high-resolution NDVI could improve the efficiency and effectiveness of urban land cover data extraction For the accuracy assessment process a simple random sampling scheme was employed using a random number generator to select points from the classified QB NDVI imagery for comparison to the 06-meter spatial resolution orthoimage acquired on April 23 2004 The overall and individual Kappa coefficients were calculated The overall accuracy for the two QB NDVI thematic maps produced was 95 and 73 respectively The average CN values of homogeneous land use land cover and soil type covering approximately 30 of the study area were determined and compared to the SCS CN in order to validate the utility of QB NDVI imagery for CN calculation The overall average CN produced from the spatial modeling was 85 and the average SCS CN was 79

INTRODUCTION

Remote satellite sensing application in hydrology did not start until the first launch of Earth Resources Technology Satellite (ERTS) [later renamed Landsat 1] in July of 1970 (Schultz 1988) Remotely sensed data is rapidly becoming an important source of information in hydrologic modeling monitoring and research (Engman 1982 1993 Melesse and Graham 2004) Its advantages include spectral radiometric spatial and temporal resolutions including its ability to generate information and to provide a means of observing hydrological state variables over large areas or small complex basins It offers an alternative to conventional data collection employed in the estimation of certain hydrologic model parameters The primary benefits obtainable from using remote sensing are the reduction of manpower time and monetary resources required for data collecting and processing For some situations remote

Pecora 16 ldquoGlobal Priorities in Land Remote Sensingrdquo October 23 ndash 27 2005 Sioux Falls South Dakota

sensing provides information that was previously impossible or impractical to obtain using conventional techniques (Thomas and McCuen 1979) Therefore remote sensing technology is a very important tool for successful hydrologic model analysis prediction and validation The National Resource Conservation Service (NRCS) formally the Soil Conservation Service (SCS) has developed a series of hydrologic models in water resource planning and design The SCS Curve Number (SCS-CN) method is the best-known component of the SCS hydrologic models According to Ponce and Hawkins (1996) the origins of the CN methodology can be traced back to the thousand of infiltrometer tests carried out by SCS in the late 1930s and early 1940s The intent was to develop basic data to evaluate the effects of watershed treatment and soil conservation measures on the rainfall-runoff process A major catalyst for the development and implementation of the runoff CN methodology was the passage of the Watershed Protection and Flood Prevention Act (Public Law 83-566) in August 1954 The method and its use are described in the SCS National Engineering Handbook Section 4 Hydrology (NEH-4) [NRCS 1972] The document has since been revised in 1956 64 65 71 72 85 and 1993 The CN method is well established in hydrologic engineering and environmental impact analyses Its major advantage are (1) its simplicity (2) its predictability (3) its stability (4) its reliance on only one parameter and (5) its responsiveness to major runoff-producing watershed properties (soil type land covertreatment surface condition and antecedent condition) [Ponce and Hawkins 1996] Consequently the methodology has been widely used in numerous applications by practicing engineers and hydrologists nationally and internationally (McCuen 1982 2002 2003 Ponce and Hawkins 1996 Golding 1997 McCuen and Okunola 2002 Mishra and Singh 2003)

The CN is an index that represents the combined hydrologic effect of soil group land cover type agricultural land treatment class hydrologic condition and antecedent soil moisture (McCuen 1982) It is the best-known component of a series of SCS hydrologic models most noticeably the TR-55 (NRCS 1986) procedures and the TR-20 computer model (NRCS 1992 McCuen 2002) The CN calculation requires use of storm rainfall and associated streamflow data for annual floods to derive the best means of establishing run off curve numbers A CN of 100 represents a condition of zero potential retention or 100 runoff of incident precipitation

Determination of the CN has been traditionally a time-consuming and labor-intensive procedure A basic problem exists in quantifying detailed spatial extent and distribution of various land cover classes Therefore remote sensing technology has become one of the primary methods for acquiring land cover data for providing detailed mapping information as input into the CN method (Jackson and Ragan 1977 Engman 1982 1993 Draper and Rao 1986 Schultz 1996 Dubavah et al 2000)

In previous studies one of the main problems of using remotely sensed data for estimation of land cover is a spatial resolution issue Moderate spatial resolutions are insufficient to generate the detail necessary to utilize the published SCS land cover table used in the SCS procedures (Ragan and Jackson 1980 Engman 1982 1993) Engman in 1982 and 1993 suggested that one must develop a land cover table analogous that is compatible to the Landsat data Landsat may not be acceptable in small areas with certain land cover types Bondelid et al (1982) stated that special care should be exercised in analyzing small watersheds that contain heterogeneous land cover causing the largest CN errors Engineering designs utilizing the curve number are widely used but the curve number is sensitive to errors in the estimated input values used to derive it (Bondelid et al 1982 McCuen 2002)

Remote sensing GIS GPS and image processing technological capability have improved significantly since the previous studies Commercial high-resolution satellite imagery creates unique opportunities to address and potentially solve old problems The purpose of this applied research is to bridge remote sensing and GIS to produce scientific knowledge by designing technological methodologies for determining the composite of curve numbers in the urban hydrology for small watersheds technical release 55 (TR-55) table NRCS (1986) publication for practicing professionals in urban water resource planning The theoretical foundation is that mapping a high resolution NDVI image generated using the ISODATA algorithm is an efficient and effective information extraction approach for the composite of runoff curve number calculation

METHODS

In this research newly designed technological methodologies were proposed for mapping urban land use land cover using QuickBird NDVI imagery and developing GIS spatial modeling for generating the composite of curve numbers in the TR-55 table NRCS (1986) publication To achieve this goal a sequence for digital processing and analysis was proposed and implemented (Figure 1) In order to assess the utility of QB NDVI imagery and to validate the composite runoff curve number calculation using designed GIS spatial modeling various procedures and


comparisons of the generated runoff coefficients were presented reviewed and validated by practicing professionals including the City Drainage Engineer and the City Geographic Information Systems manager for the City of Sioux Falls South Dakota in August 2005

Digital Data

darr Pre-Processing

Data Merging and Integration darr

Decision and Classification darr

Image Classification Unsupervised ndash ISODATA Algorithm

darr Classification Output

darr Assess Accuracy

darr Reports and GIS Data

darr GIS Spatial Modeling

The Composite of Runoff Curve Number Calculation darr

Reject Accept Hypothesis

Figure 1 Sequence for digital processing and analysis

Study Area

The City of Sioux Falls is located in southeastern South Dakota Sioux Falls covers 40208 acres (162716 hectares) and has a population of approximately 138000 Climatologically Sioux Falls is in a continental climate with an average rainfall of 2386 inches (606 cm) and approximately 40 inches (1016 cm) of snow annually The majority of the precipitation comes in spring and summer with May and June being the months of maximum precipitation for the region The study area for this project is centered on 43O 31rsquo 18rdquo north latitude and 96O 44rsquo 42rsquorsquo west longitude in the southwestern part of the city This area encompasses 290142 acres (11742 hectares) with various land use land cover types and includes all or part of 26 hydrological urban sub-basins (Figure 2)

(a) (b)

Figure 2 The 2004 images of the study area at the same scale

(a) The QuickBird multi image (4-3-2) on the left and (b) The QuickBird NDVI image on the right


Digital Data amp Pre-Processing The 2004 remotely sensed data and vector GIS data layers were provided by the City of Sioux Falls They include

orthophoto mosaics with 2 ft (06 m) and 05 ft (015 m) resolution a QuickBird satellite image with 796 ft (239 m) resolution and include data layers such as parcels city limit boundary hydrography and streets The data sets were processed and merged to combine the remotely sensed data with the GIS layers for the study area The April 23 2004 orthophoto was used as a reference to integrate all data sets into the same map projection and unit with the Universal Transverse Mercator (UTM) map projection World Geodetic System (WGS) 1984 and horizontal units in feet Software used in this project consisted of ERDAS Imagine 87 ArcMap 9 ArcView 33 and Microsoft Excel 2002

The two digital orthophotographs were acquired on April 23 2004 and May 20 2002 respectively Both orthophotos were collected and scanned to meet the combined requirements of National Map Accuracy Standards 90 of all contours will be within frac12 contour interval and 90 of horizontal positions shall be within 130 of one inch at the specified map scale The specified horizontal map scale was 1 inch = 200 ft (609m) The flight height above terrain was 4000 ft (12192m) for a photo negative scale of 1 inch = 667 ft (2033m) Contour interval for the elevation data was 2 ft (061m) The radiometric density of the digital orthophotograph was 10 bits per channel with a geometric precision of 1 micron Both orthophotos were provided in UTM projection WGS84 datum and planar distance survey units in feet Additionally both orthophotos were subset to the study area and the 2002 orthophoto was degraded 3x3 using ERDAS Imagine 87 Overlaying of the subset orthophotos demonstrated good alignment between images Therefore an image to image registration was not required

The QuickBird leaf-on image was collected on April 26 2004 The image was radiometrically corrected and orthorectified prior to this study with UTM projection WGS84 datum and units in meters The imagery has four bands (including the blue green red and near-infrared) and was imaged at a 239-meter spatial resolution in unsigned 16 bit data type The subset QB image was created in unsigned 8 bit data type The image was observed with no signs of atmospheric contamination The statistical spectral information showed a normal distribution with similar mean median and mode values Geometric correction was performed The subset scene was registered to the 2004 orthophoto with 31 ground control points and the RMS was 06940 pixel Figure 3 demonstrates the result The red channel (band 3 630-690 nm) and the near infrared channel (band 4 760-900 nm) were processed to create the QB NDVI image (NDVI = Band 4 - Band3 Band 4 + Band3) in unsigned 8 bit data type for the study area

The 2004 NRCS SSURGO 124000 data set of the soil survey area (SSA) named ldquoMinnehaha County South Dakotardquo and its standard metadata files were downloaded from the Soil Data Mart at httpsoildatamartnrcsusdagov on June 7 2004 It is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey The soil layer was in a single zip file in ArcView shapefile format with a geographic coordinate system and using North American Datum (NAD) 1983 This layer was processed using ArcView 33 geoprocessing and Microsoft Excel 2002 to create a soil layer associated with hydrologic soil groups in the project coordinate system for this study area Overlaying of the soil hydro features to the subset QB image demonstrated a good fit Therefore a vector to image registration was not necessary

(a) (b)

Figure 3 The registered 2004 images comparison at same scale and location (a) The QuickBird multi image (4-3-2) on the left with the 2004 Orthoimage (1-2-3) on the right

(b) The QuickBird NDVI image on the left with the 2004 Orthoimage (1-2-3) on the right


Classification Approach With the recent development of new remote sensing systems there are an increasing number of commercially high

resolution data available There is a considerable amount of previous research that has been devoted to exploring the magnitude and impact of spatial resolution on image analysis when changing scale from coarse to fine resolutions Due to the more heterogeneous spectral-radiometric characteristics within land use land cover unit portrayed in high resolution images many applications of traditional single-resolution classification approaches have not led to satisfactory results In general traditional single-resolution classification procedures such as supervised unsupervised and hybrid training approaches are inadequate for discriminating between land use land cover classes where spectralspatial features and spatial patterns vary as a function of spatial resolution Consequently there is an increasing need of researching for more efficient approaches to process and analyze these data products (Chen and Stow 2003)

In this research the relatively simple Normalized Difference Vegetation Index (NDVI) was introduced in order to reduce heterogeneous spectral-radiometric characteristics within land use land cover surfaces portrayed in high resolution images It also was used for improving the accuracy level of details for mapping impervious surfaces and open spaces with different hydrologic conditions as used in the proposed SCS runoff curve number (CN) calculation in this study Additionally this research presented applying the principle behind NDVI using high spatial resolution QuickBird imagery based on the unique interaction differences in land use land cover surfaces with reflected or emitted electromagnetic radiation between QB Red band (630-690nm) and QB NIR band (760-900nm)

NDVI has been shown to correlate green leaf biomass and green leaf area index Chlorophylls the primary photosynthetic pigments in green plants absorb light primarily from the red and blue portions of the spectrum while a higher proportion of infrared is reflected or scattered As a result vigorously growing healthy vegetation has low red-light reflectance and high near-infrared reflectance and hence high NDVI values Impervious surfaces (eg Asphalt concrete buildings etc) and bare land (eg bare soil rock dirt etc) have similar reflectance in the red and the near infrared so these surfaces will have values near zero NDVI is calculated using the formula (Near infrared ndash red)(Near infrared + red) This relatively simply algorithm produces output values in the range of -10 to 10

Figure 4 shows a visual comparison of spectral correlation between QuickBird NDVI in unsigned 8 bit data type (0-255) for impervious areas and open spaces in the urban environment The impervious surfaces are the dark areas with low NDVI DNs and the brighter areas are vegetative covers with high NDVI DNs

(a) The 2004 QuickBird NDVI (b) The QuickBird multi image (4-3-2)

(c) The 2004 Orthophoto (1-2-3) Figure 4 The registered 2004 images visual comparison of impervious areas and vegetative open spaces at

same scale and location in residential areas


This research employed the urban land cover of the runoff curve number table in the TR-55 NRCS (1986) publication (Figure 5) The classification schemes defined from the TR-55 are detailed in Table 1 and Table 2 The defined classification schemes were used to generate two QB NDVI thematic maps using the ISODATA algorithm Gaussian Maximum Likelihood Classifier with ERDAS Imagine 87 assigning unsupervised spectral classes and assessing the accuracy of the maps The detailed map information from the second thematic map was then converted as a GIS input into a spatial modeling for calculating the composite of CNrsquos using the composite runoff index spatial model below

This spatial model based on the composite of the CNrsquos equation below can be applied for calculating the runoff index for both the SCS CN method and the rational method the CN number calculated for the SCS CN method and a runoff coefficient is the ldquocrdquo value used in the rational method recommended by the American Society of Civil Engineers and the Water Pollution Control Federation

The Composite of Runoff Indexrsquos Spatial Model (copy 2005 Pravara Thanapura Use with permission)

Runoff Indexcomposite = [ of area covered (ji) x CN(j)] Where CNcomposite is the sum of the component runoff index within an area (i) delineated by description of area of area covered (ji) is the component (j) of the area (i) divided by the total area (i) Note that the component of the area is delineated by surface characteristics of land use land cover andor hydrologic soil group CN(j) is a runoff index of the component (j) of the area (i) determined by the surface characteristics andor its hydrologic soil groups The Composite CNrsquos Weigh Average Equation

CNcomposite = [ of area covered x CN]

Where CNcomposite is the sum of composite runoff curve number within an area of area covered is a component of the area divided by the total area CN is the runoff curve number of the area

In this study the composite of CNrsquos calculation requires the combined surface characteristics and hydrologic effect

of soil groups The surface characteristic was derived from the two QB NDVI thematic maps and the hydrologic soil groups were obtained from the 2004 NRCS SSURGO 124000 data set The soil information covered by hydrologic areas such as rivers or canals are not surveyed or were not available in this data set Therefore they were excluded from the decision rules Swimming pools were considered as impervious areas since these small size water features appeared spectrally impervious on the QB NDVI Additionally they retain precipitation and act as pervious surfaces during storms thus the decision rules would result in a conservative calculation of CNrsquos

There is one heavy industrial area in the southern part of the study area This area was an oil and fuel storage facility The tanks and their associated grass covered containment areas approximately 038 of the study areas 290142 acres (11742 hectares) The tank structures themselves were impervious but they were spectrally ruled as open spaces which resulted in a conservative composite CN calculation Precipitation falling on the containment areas does not interact with the surface and groundwater since precipitation falling in these areas evaporates naturally or is pumped out and treated as wastewater


Figure 5 TR-55 runoff curve number table for urban areas

(United States Department of Agriculture 1986)


Table 1 Initial land use land cover classification scheme of QuickBird NDVI thematic map1 (Figure 6) [copy 2005 Pravara Thanapura Use with permission]

Labels Rules

Land Use Land Cover Character of Surface

Impervious Areas If land area has lt 25 covered with areas characterized by vegetative open spaces then Impervious Areas (1) If land area gt or = 75 characterized by impervious surfaces (eg Asphalt concrete buildings etc) then Impervious Areas (1) If land area gt or = 75 covered by bare land (eg Bare rock gravel silt clay dirt and sand or any other earthen materials then Impervious Areas (1) Open Spaces Else if land area lt 25 covered with areas characterized by impervious surfaces then Open Spaces (2) If land area gt 75 covered with vegetation naturally existing or planted (eg grass plants tree (leaf-on leaf-off) forest shrub and scrub etc) then Open Spaces (2) Else Impervious Areas (1)

Table 2 Secondary land use land cover classification scheme of QuickBird NDVI thematic map2 (Figure 7) [copy 2005 Pravara Thanapura Use with permission]

Labels Rules

Land Use Land Cover

Character of Surface

Open Spaces If land area = or lt 75 covered with vegetation naturally existing or planted (eg grass plants tree (leaf-on leaf-off) forest shrub and scrub etc) then Open Spaces - Poor condition (1) Else if land area gt 75 covered with vegetation naturally existing or planted (eg grass plants tree (leaf-on leaf-off) forest shrub and scrub etc) then Open Spaces - Good to fair condition (2) Else Open Space Poor condition (1)

Sampling Design amp Accuracy Assessment

A simple random sampling method was chosen to ensure unbiased sample selection To provide a statistically sound assessment of accuracy the simplified worst-case scenario equation was used to calculate the sample sizes to insure sufficient samples for filling in an error matrix for this project The Conservative Sample Size Equation (Congalton and Green 1999)

n = B 4b2

Where n = Total samples of all classes B = The upper (αk) x 100th percentile of the Chi-Squared distribution with 1 degree of freedom (α) k = number of classes b = Significant level = +- 5 accuracy (α - 095 = 005)


Over 500 sample points were generated for each thematic map using the ISODATA algorithm to insure good distribution across the study area Figure 6 and 7 show the accuracy assessment sites acquired for the entire classification of each QB NDVI thematic map To accomplish this ERDAS 87rsquos random number generator function was used to create random points from the QB NDVI thematic maps for comparison to the 2004 2 ft (06 m) and the 2002 05 ft (015 m) spatial resolution orthophotos The program automatically discarded random points not sufficiently representing relatively homogenous areas This function minimized registration errors of the image and helped to accurately interpret a true representation of each reference point on the orthophoto To assure visual interpretation consistency these reference points were individually evaluated by one team member with many years of photo interpretation experience In some cases there were conflicts due to lsquoleanrsquo in the orthophoto or mixed pixels around the edges These points were re-evaluated using the 2004 orthophoto Quality control was performed on the data set by the principal author Finally the error matrixes were generated and the overall and individual Kappa coefficients were calculated (Table 3 and 4)

Figure 6 Classification result of NDVI QuickBird thematic map 1and 502 accuracy sites

Figure 7 Classification result of NDVI QuickBird thematic map 2 and the accuracy assessment sites


Table 3 Accuracy assessment result of NDVI QuickBird thematic map1

Accuracy Assessment - Unsupervised Thematic Map1(100 spectral clusters) Map Class Name

ID

Labeling

Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct

Producers Accuracy ()

Users Accuracy ()

Kappa Statistics

11 Impervious areas 1-40 127098 4372 258 216 210 8140 9722 09429 Open spaces 41-100 163581 5628 244 286 238 9754 8322 06734 Overall Classification Accuracy = 8924 Overall Kappa Statistics = 07857





Labeling spectral clusters the lower the numbers the darker the areas are label to impervious areas (low NDVI value) Labeling spectral clusters the higher the numbers are the brighter the areas are label to open spaces (high NDVI value)


Accuracy Assessment - Unsupervised Thematic Map2 (50 spectral clusters) Map Class Name

ID Open Spaces

Labeling

Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics

21 Poor 1-25 73066 2514 242 239 164 6777 6862 04547 Good to Fair 26-50 64959 2235 328 331 253 7713 7644 0445 Overall Classification Accuracy = 7316 Overall Kappa Statistics = 04498


Labeling spectral clusters the lower the numbers the darker the areas are label to open spaces-poor condition (low NDVI value) Labeling spectral clusters the higher the numbers the brighter the areas are label to open spaces-good to fair condition (high NDVI value) GIS Spatial Modeling

A spatial modeling or analysis is a set of analytical procedures applied with a GIS A spatial model is a set of clearly defined analytical procedures that run on a spatial database to derive new information describing a new spatial relationship It is structured as a set of rules including a combination of logical expressions mathematical procedures and criteria built using analytical tools in GIS which are applied for the purpose of simulating the process (ESRI staff 1995)

In GIS there are three categories of spatial modeling functions that can be applied to geographic data objects (1) geometric models such as calculating the Euclidean distance between objects generating buffers calculating areas and perimeters and so on (2) coincidence models such as polygon overlay and (3) adjacency models (pathfinding redistricting and allocation) All three model categories support operations on geographic data objects such as points lines polygon TINS and grids (ESRI staff 1995)

In this study the geometric and coincidence modeling functions in ArcView33 geoprocessing were designed in a sequence of steps to build a spatial modeling based on the composite runoff index spatial model The process of spatial


modeling created a new data set (ArcView shapefile) of geographic features Each shapefile contained new polygons that described new spatial relationships of the data sets among surface characteristics of land use land cover and hydrologic soil groups including its CN

Next is an example of applying the composite runoff index spatial model to develop spatial modeling for the composite of CNrsquos calculation within the study area Finally the CN results were compared to the TR-55 NRCS (1986) publication (Figure 5) in order to validate the utility of QB NDVI imagery as shown in Table 8

The Composite of Runoff Indexrsquos Spatial Model for SCS CN and SCS adjusted CN (copy 2005 Pravara Thanapura Use with permission)

CNcomposite = [ of area covered (ji) x CN(j)] Where CNcomposite is the sum of the component CN within a parcel boundary (i) delineated by the land activity code of the City of Sioux Falls (Table 5) of area covered is a component (j) of the area (i) divided by the total area (i) Note that the component of the area is a subset of polygon features delineated by surface characteristics of land use land cover (GIS layer = ArcView shapefile) derived from two QB NDVI thematic maps using the ISODATA algorithm and hydrologic soil group as described previously (Figure 8) CN(j) is a runoff curve number (Table 6)of the component (polygon)[j] of the area (i) determined by the surface characteristics and its hydrologic soil groups

Table 5 The 2004 parcel ndash activity codes and a set of rules

Activity Code and Description SCS CN Cover Type1 Rule by Area 11 Single family Residential districts by average lot size - gt= 01 and lt= 0125 acre Residential 18 acre (0125 acres or 50585 sqm2) gt= 40468 and lt= 50585 sqm2

11 Single family Residential districts by average lot size - gt 0125 and lt= 025 acre Residential 14 acre (025 acres or 101171 sqm2) gt 50585 and lt= 101171 sqm2



11 Single family Residential districts by average lot size - gt 05 and lt= 1 acre Residential 1 acre (404683 sqm2) gt 202341and lt= 404683 sqm2

11 Single family Residential districts by average lot size - gt 1 and lt= 2 acres Residential 2 acres (809365 sqm2) gt 404683 and lt= 809365 sqm2

31 Banks and Financial Institutions Urban Districts - Commercial and business gt= 05 acres or 202341 sqm2

33 Other offices Urban Districts - Commercial and business gt= 05 acres or 202341 sqm2

64 Warehousing Distribution Urban Districts - Industrial gt= 05 acres or 202341 sqm2

and Wholesale 1 The samples of activity codes are similar to the cover type of the urban land cover of the runoff curve number table in the TR-55 NRCS (1986) publication [Figure 5]


Figure 8 The runoff index GIS data sets derived from the 2004 parcel layer by activity codes delineated by

NDVI QuickBird information and hydrologic soil groups

Table 6 Runoff curve numbers in Percentage for the CN calculation defined from the TR-55 NRCS (1986) Publication (Figure 5)

Land Use Land Cover Curve numbers for

hydrologic Soil Group

Character of Surface A2 B3 C4 D5

Impervious Areas 98 98 98 98 Open Spaces - Poor Condition 68 79 86 89 Open Spaces - Good to Fair Condition1 49 69 79 84

1 CN of fair condition is use for open spaces ndash good to fair condition 1 A The soil characteristics are deep sand deep loess and aggregated silts (McCuen 1982) 2 B The soil characteristics are shallow loess and sandy loam (McCuen 1982) 3 C The soil characteristics are clay loams shallow sandy loam soils low in organic content and soils usually high in clay (McCuen 1982) 4 B The soil characteristics are swell significantly when wet heavy plastic clays and certain saline soils (McCuen 1982)


Table 8 The CN Results and SCS CN comparison

GIS Activity Code SCS CN Cover Type1 Rule Samples Total CNID and by Area Areas2 Result

Description (avg) B3 Sample Area4

1 11 Single family Residential districts by average lot size - gt= 01 and lt= 0125 acres 312 3555 acres 8688 85 9994Residential 18 acre gt= 40468 and lt= 50585 sqm2 14386463 sqm2

(0125 acres or 50585 sqm2) 123 2 11 Single family Residential districts by average lot size - gt 0125 and lt= 025 acres 3284 5766 acres 8430 75 9976

Residential 14 acre gt 50585 and lt= 101171 sqm2 23333993 sqm2

(025 acres 101171 sqm2) 1987 3 11 Single family Residential districts by average lot size - gt 025 and lt= 03330 acres 357 10006 acres 8244 72 10000





Residential 1 acre (404683 sqm2) gt 202341and lt= 404683 sqm2 10562213 sqm2

09 6 11 Single family Residential districts by average lot size - gt 1 and lt= 2 acres 4 456 acres 8527 65 10000

Residential 2 acres (809365 sqm2) gt 404683 and lt= 809365 sqm2 1845352 sqm2

016 7 31 Banks and Urban Districts - Commercial gt= 05 acres 17 1496 acres 9419 92 10000

Financial and business gt= 202341 sqm2 6054050 sqm2

Institutions 052 8 33 Other offices Urban Districts - Commercial gt= 05 acres 56 6396 acres 9321 92 10000

and business gt= 202341 sqm2 25883493 sqm2

22 9 64 Warehousing Urban Districts - Industrial gt= 05 acres 11 2004 acres 8631 88 9400

Distribution and gt= 202341 sqm2 8109837 sqm2

Wholesale 07 4224 89634 acres 8572 79 9881

362733112 sqm2

3089

SCS CNHydrologic Soil Group

Total

1 The samples of activity codes are similar to the cover type of the urban land cover of the runoff curve number table in the TR-55 NRCS (1986) publication [Figure 5] 2 The study area is 290142 acres (11742 hectares) 3The total area of hydrologic soil group B of each sample layer by parcel - activity code 4 B The soil characteristics are shallow loess and sandy loam (McCuen 1982)

RESULTS amp DISCUSSION

The overall accuracy of the two QB NDVI thematic maps produced is presented in Table 3 and Table 4 using classification schemes from Table 1 and Table 2 respectively Figure 6 and 7 shows the two NDVI QuickBird thematic maps with the highest overall classification accuracy used as a GIS input into the Sioux Falls spatial modeling for the composite of CNs calculations

Table 3 shows the overall accuracy results of mapping impervious areas and open spaces obtained from five different labeling criteria when the same unsupervised image of 100 spectral clusters was used The Map ID13 with the combination labeling criteria of impervious areas (DNs range between 1-50) and open spaces (DNs range between 51-100) achieved the highest overall Kappa value of 09003 with the overall classification accuracy of 9505 Comparing the accuracy results to the accuracy results obtained from other labeling criteria showed slightly differing classification accuracy improvement increasing toward the mid DNs of QB NDVI This pattern of change in classification accuracy showed the potential correlations between increasing and decreasing of the DNs and amounts of open spaces (vegetative areas) and impervious areas (non vegetative areas) [Figure 4] The DNs range from 126 to 0 indicates increasing impervious surfaces shown in shades of dark tones The DNs range from 126 to 255 indicates increasing openspaces shown in shades of light tones


Table 4 shows the overall accuracy results of mapping two classes obtained from two different labeling criteria utilizing the same unsupervised image of 50 spectral clusters The overall Kappa value for the Map ID 21 and Map ID22 produced was only 044 and 045 respectively The results showed that the spectral classifications were not spectrally distinct among open spaces-poor and good-to-fair conditions Map ID21 was used in this study since its producer and user accuracy in percentages were similar which indicated higher map accuracy compare to Map ID22

Comparing the overall accuracy of the two thematic maps demonstrated using the labeling pixel criteria with the DNs 5050 spectral cluster thresholds for assigning labels into two classes was the best method to use This tended to improve classification accuracy with high Kappa values and similar percentages of producer and user accuracy

Finally the average CN values of different land use land cover descriptions covering homogeneous soil types were calculated and compared to the SCS CN The results of this comparison are shown in Table 8 in order to validate the utility of QB NDVI imagery for CN calculation The overall average CN covering approximately 30 of the study area produced was 85 and the average SCS CN was 79 Comparing CN value within GIS ID produced from different approaches demonstrated that the new design technological methodologies produced conservative CN results similar to those found in the SCS CN table

The finer resolution imagery used in this study allows for better discrimination in land use land cover and the accuracy results validate the hypothesis of this study This is reflected in the fact that overall accuracy for the two QB NDVI thematic maps produced was 95 and 73 respectively Additionally the average CN values among the GIS ID samples described as single family showed that decreasing average lot size produced higher CN numbers or higher runoff This indicates increasing amounts of impervious areas and decreasing amounts of green vegetation as expected

CONCLUSIONS

This applied research demonstrated that the proposed spatial modeling utilizing the fine spatial resolution remotely sensed data and a GIS approach were successful in accurately automating and mapping land use land cover surface characteristics and determining the runoff curve number in the urban hydrology for small watersheds Therefore the hypothesis of this study was accepted Mapping fine spatial resolution QuickBird NDVI imagery generated using the traditional unsupervised classification was a more accurate simpler and efficient data extraction approach that increased speed and potentially reduces costs of both the analysis and mapping process The application of this approach could be beneficial to municipal engineers involved with designing or maintaining stormwater management systems and drainage improvement projects

ACKNOWLEDGEMENTS

The author would like to thank the following employees of the City of Sioux Falls South Dakota for their contribution to the success of this research project Steve Van Aartsen GIS supervisor and his staff for all the data and information contributed for this project Jeff Dunn City Drainage Engineer for his time and the literature provided on hydrology in Sioux Falls and Sam Trebilcock Transportation Planner for his assistance and interest

The author would also like to thank the co authors for their support input effort and specialized expertise in making this project a success Additional thanks go to Mr Kevin Dalsted Director Engineering Resource Center South Dakota State University Brookings South Dakota for the use of the hardware and software that made this research possible


REFERENCES

Bondelid TR TJ Jackson and RH McCuen 1982 Estimating runoff curve numbers using remote sensing data Singh Vijay P editor Applied modeling in catchment hydrology Water Resources Publications Littleton Colorado 563 p

Congalton R and K Green 1999 Assessing the Accuracy of Remotely Sensed Data Principles and Practices CRCLewis Press Boca Raton Florida 137 p

Chen DM and D Stow 2003 Strategies for integrating information from multiple spatial resolutions into land-use land-cover classification routines Photogrammetric Engineering amp Remote Sensing 69(11)1279-1287

Draper S E and S G Rao 1986 Runoff prediction using remote sensing imagery Water Resources Bulletin 22(6) 941-949 p

Dubayah R O E F Wood E T Engman K P Czajkowski M Zion and Rhoads 2000 Remote Sensing in Hydrology and Water Management Remote Sensing in Hydrology and Water Management Schultz G A and ET Engman editors Springer Germany 483 p

Engman E T 1982 Remote sensing applications in watershed modeling Applied modeling in catchment hydrology Singh Vijay P editor Water Resources Publications Littleton Colorado 563 p

Engman E T 1993 Remote Sensing Handbook of Hydrology Maidment David R editor New York McGraw- Hill 241-2423 p

Environmental Systems Research Institute Staff 1995 Understanding GIS Version 7 for UNIX and Open VMS John Wiley ampSons New York NY 500 p

Golding B L 1997 Discussion Runoff Curve Number Has It Reached Maturity Journal of Hydrologic Engineering 2(3) 145-148 p

Jackson T J and R M Ragan 1977 State-of-the-art application of Landsat data in urban hydrology International symposium on urban hydrology hydraulics and sediment control University of Kentucky Lexington Kentucky 275-285 p

McCuen R H 1982 A guide to hydrologic analysis using SCS methods NJ Prentice-Hall Englewood Cliffs NJ 145 p

McCuen R H 2002 Approach to confidence interval estimation for curve numbers Journal of Hydrologic Engineering 7(1) 43-48 p

McCuen R H 2003 Discussion of Approach to Confidence Interval Estimation for Curve Numbers Journal of Hydrologic Engineering 8(4) 232-233 p

McCuen R H and O Okunola 2002 Extension of TR-55 for Microwatersheds Journal of Hydrologic Engineering 7(4) 319-325 p

Melesse A M and W D Graham 2004 Storm runoff prediction based on a spatially distributed travel time method utilizing remote sensing and GIS Journal of the American Water Resources Association (JAWRA) 40(4) 863-879 p

Mishra S K and V P Singh 2003 Soil Conservation Service Curve Number (SCS-CN) methodology The Netherlands Kluwer Academic Publishers PO Box 17 3300 AA Dordrecht 536 p

Natural Resources Conservation Service (NRCS) 1972 SCS National Engineering Handbook Section 4 Hydrology URL httpwwwinfousdagovCEDftpCEDneh630-ch10pdf US Department of Agriculture Natural Resources Conservation Service (last date accessed 21 August 2005)

Natural Resources Conservation Service (NRCS) 1986 Urban hydrology for small watersheds TR-55 Technical release 55 URL httpwwwwccnrcsusdagovhydrohydro-tools-models-tr55html US Department of Agriculture National Water and Climate Water (last date accessed 21 August 2005)

Natural Resources Conservation Service (NRCS) 1992 Computer program for project formulation-hydrology Tech Release No 20 URL httpwwwwccnrcsusdagovhydrohydro-tools-models-tr20html US Department of Agriculture National Water and Climate Water (last date accessed 21 August 2005)

Ponce V M and R H Hawkins 1996 Runoff curve number has it reached maturity Journal of Hydrologic Engineering 1(1) 11-19p

Ragan R M and T J Jackson 1980 Runoff synthesis using Landsat and SCS model Journal of Hydraulics division Proceedings of the American Society of Civil Engineers (ASCE) 106(5) 667-678p

Schultz G A 1988 Remote sensing in Hydrology Journal of Hydrology 100(1-3) 239-265 p


Schultz G A 1996 Remote sensing applications to hydrology Runoff Hydrological Sciences Journal 41(4) 453-475 p

Thomas J J and R H McCuen 1979 Accuracy of impervious area values estimated using remotely sensed data Water Resources Bulletin 27(3) 373-379 p


ABSTRACT

METHODS

Study Area

Digital Data amp Pre-Processing

Classification Approach

REFERENCES

Mapping Urban Land Use Land Cover Using QuickBird NDVI Imagery for Runoff Curve Number Determination

Pravara Thanapura Engineering Resource Center

Suzette Burckhard Civil and Environmental Engineering

Mary Orsquo Neill Engineering Resource Center

Dwight Galster and Mathematics and Statistics

South Dakota State University Brookings

South Dakota 57007 USA

pravarathanapurasdstateedu

suzetteburckhardsdstateedu

maryoneillsdstateedu

dwightgalstersdstateedu

Eric Warmath


1263 S Stewart Street

Carson City Nevada 89712 USA


ABSTRACT


INTRODUCTION

Remote satellite sensing application in hydrology did not start until the first launch of Earth Resources Technology Satellite (ERTS) [later renamed Landsat 1] in July of 1970 QUOTE (Schultz 1988) (Schultz 1988) Remotely sensed data is rapidly becoming an important source of information in hydrologic modeling monitoring and research QUOTE (Engman 1982 Engman 1993 Melesse and Graham 2004) (Engman 1982 1993 Melesse and Graham 2004) QUOTE ADDIN PROCITE yuml1105lsquo190200000000010000)030000F5C_Procite5CPravara_Thanapura1pdt10Engman 1982 8100102000600agraveagrave000000iquestH00agrave1400yumlyumlyumlyuml00000000010000001400000001000000000000000A000000000000ordmI_˜lsaquoEuml00010000000100000000000000tograve12000notW00yumlyumlyumlyuml QUOTE ADDIN PROCITE yuml1105lsquo190200000000010000F030000F5C_Procite5CPravara_Thanapura1pdt1AMelesse amp Graham 2004 8450104000700agraveagrave000000iquestH00 7140014000000010000000000000000000000100000000000000003000000000000AtildeAcircw00000000yumlyumlyumlyuml07AumlAcircwTHORNAcircAcircw0000200uumlntilde1200atildeAcircAcircw0600agraveagrave000000iquestH00 714001400000001000000000000000000000010000000000000000A000000000000ordmI_hUgraveEuml00030000000500000000000000tograve12000notW00yumlyumlyumlyuml Its advantages include spectral radiometric spatial and temporal resolutions including its ability to generate information and to provide a means of observing hydrological state variables over large areas or small complex basins It offers an alternative to conventional data collection employed in the estimation of certain hydrologic model parameters The primary benefits obtainable from using remote sensing are the reduction of manpower time and monetary resources required for data collecting and processing For some situations remote sensing provides information that was previously impossible or impractical to obtain using conventional techniques QUOTE (Thomas and McCuen 1979) (Thomas and McCuen 1979) Therefore remote sensing technology is a very important tool for successful hydrologic model analysis prediction and validation The National Resource Conservation Service (NRCS) formally the Soil Conservation Service (SCS) has developed a series of hydrologic models in water resource planning and design The SCS Curve Number (SCS-CN) method is the best-known component of the SCS hydrologic models According to Ponce and Hawkins (1996) the origins of the CN methodology can be traced back to the thousand of infiltrometer tests carried out by SCS in the late 1930s and early 1940s The intent was to develop basic data to evaluate the effects of watershed treatment and soil conservation measures on the rainfall-runoff process A major catalyst for the development and implementation of the runoff CN methodology was the passage of the Watershed Protection and Flood Prevention Act (Public Law 83-566) in August 1954 The method and its use are described in the SCS National Engineering Handbook Section 4 Hydrology (NEH-4) QUOTE (Soil Conservation Service (SCS) 1972) [NRCS 1972] The document has since been revised in 1956 64 65 71 72 85 and 1993 The CN method is well established in hydrologic engineering and environmental impact analyses Its major advantage are (1) its simplicity (2) its predictability (3) its stability (4) its reliance on only one parameter and (5) its responsiveness to major runoff-producing watershed properties (soil type land covertreatment surface condition and antecedent condition) [ QUOTE (Ponce and Hawkins 1996) Ponce and Hawkins 1996] Consequently the methodology has been widely used in numerous applications by practicing engineers and hydrologists nationally and internationally QUOTE (McCuen 1982 Ponce and Hawkins 1996 Golding 1997 McCuen 2002 McCuen and Okunola 2002 Mishra and Singh 2003 McCuen 2003) (McCuen 1982 2002 2003 Ponce and Hawkins 1996 Golding 1997 McCuen and Okunola 2002 Mishra and Singh 2003) QUOTE ADDIN PROCITE yuml1105lsquo19020000000001000012030000F5C_Procite5CPravara_Thanapura1pdt11Golding 1997 7870102000700agraveagrave0C0000iquestH00xB1400yumlyumlyumlyuml00000000010000001400000001000000000000000A000000000000ordmI_Agrave08Ccedil00030000000500000000000000umlograve12000notW00yumlyumlyumlyuml QUOTE ADDIN PROCITE yuml1105lsquo19020000000001000013030000F5C_Procite5CPravara_Thanapura1pdt19Ponce amp Hawkins 1996 7880104000500agraveagrave000000iquestH00agrave1400yumlyumlyumlyuml000000000100000014000000010000000000000003000000000000002000000000019notAcircw00000000yumlyumlyumlyumlFnotAcircw000000003laquoAcircw0700agraveagrave000000iquestH00agrave1400yumlyumlyumlyuml00000000010000001400000001000000000000000A000000000000ordmI_deg0BIacute00030000000500000000000000umlograve12000notW00yumlyumlyumlyuml QUOTE ADDIN PROCITE yuml1105lsquo19020000000001000015030000F5C_Procite5CPravara_Thanapura1pdt10McCuen 2003 7900102000600agraveagrave000000iquestH00xB1400yumlyumlyumlyuml00000000010000001400000001000000000000000A000000000000ordmI_deg0BIacute00050000000500000000000000umlograve12000notW00yumlyumlyumlyuml QUOTE ADDIN PROCITE yuml1105lsquo1902000000000100001E030000F5C_Procite5CPravara_Thanapura1pdt1AMcCuen amp Okunola 2002 7990104000600agraveagrave000000iquestH00xB1400yumlyumlyumlyuml000000000100000014000000010000000000000003000000000000002000000000019notAcircw00000000yumlyumlyumlyumlFnotAcircw000000003laquoAcircw0700agraveagrave000000iquestH00xB1400yumlyumlyumlyuml00000000010000001400000001000000000000000A000000000000ordmI_deg0BIacute00050000000500000000000000umlograve12000notW00yumlyumlyumlyuml QUOTE ADDIN PROCITE yuml1105lsquo190200000000010000$030000F5C_Procite5CPravara_Thanapura1pdt10McCuen 1982 8050102000600agraveagrave000000iquestH00agrave1400yumlyumlyumlyuml00000000010000001400000001000000000000000A000000000000ordmI_08Ecirc00010000000100000000000000umlograve12000notW00yumlyumlyumlyuml QUOTE ADDIN PROCITE yuml1105lsquo1902000000000100006030000F5C_Procite5CPravara_Thanapura1pdt18Mishra amp Singh 2003 8230104000600agraveagrave000000iquestH00agrave1400yumlyumlyumlyuml000000000100000014000000010000000000000003000000000000002000000000019notAcircw00000000yumlyumlyumlyumlFnotAcircw000000003laquoAcircw0500agraveagrave000000iquestH00agrave1400yumlyumlyumlyuml00000000010000001400000001000000000000000A000000000000ordmI_08Ecirc00010000000100000000000000umlograve12000notW00yumlyumlyumlyuml

The CN is an index that represents the combined hydrologic effect of soil group land cover type agricultural land treatment class hydrologic condition and antecedent soil moisture QUOTE (McCuen 1982) (McCuen 1982) It is the best-known component of a series of SCS hydrologic models most noticeably the TR-55 QUOTE (Natural Resources Conservation Service (NRCS) 1986) (NRCS 1986) QUOTE Natural Resources Conservation Service United States Department of Agriculture 1986 822 procedures and the TR-20 computer model QUOTE (Natural Resources Conservation Service (NRCS) 1984 McCuen 2002) (NRCS 1992 McCuen 2002) QUOTE ADDIN PROCITE yuml1105lsquo19020000000001000011030000F5C_Procite5CPravara_Thanapura1pdt10McCuen 2002 7860102000600agraveagrave000000iquestH00agrave1400yumlyumlyumlyuml00000000010000001400000001000000000000000A000000000000ordmI_pograveEacute00050000000500000000000000umlograve12000notW00yumlyumlyumlyuml The CN calculation requires use of storm rainfall and associated streamflow data for annual floods to derive the best means of establishing run off curve numbers A CN of 100 represents a condition of zero potential retention or 100 runoff of incident precipitation


In previous studies one of the main problems of using remotely sensed data for estimation of land cover is a spatial resolution issue Moderate spatial resolutions are insufficient to generate the detail necessary to utilize the published SCS land cover table used in the SCS procedures Egrave008000000010000000000200atilde120080000000500agraveagrave00000005lsquo|yumlyumlyumlyumlm05lsquo|THORNAcircAcircw0000010007000000`00000080000000500000000000005lsquo|yumlyumlyumlyumlm05lsquo|THORNAcircAcircw0000010007000000`00000080000000700agraveagrave00000005lsquo|yumlyumlyumlyumlm05lsquo|THORNAcircAcircw0000010007000000`000000800000007000000000000AtildeAcircw000020000000000IcircAtildeAcircw00000000yumlyumlyumlyuml07AumlAcircw0000000600agraveagrave00000005lsquo|0000200m05lsquo|THORNAcircAcircw0000010007000000latilde1200800010007000000000000AtildeAcircw000020000000000IcircAtildeAcircw00000000yumlyumlyumlyuml07AumlAcircw80000000600agraveagrave00000005lsquo|0000200m05lsquo|THORNAcircAcircw0000010007000000latilde1200800010006000000000000AtildeAcircw000020000000000IcircAtildeAcircw00000000yumlyumlyumlyuml07AumlAcircw8000000020000F5C_Procite5CPravara_Thanapura1pdt19Ragan amp Jackson 1980 6420104000500agraveagrave000000iquestH00agrave1400yumlyumlyumlyuml000000000100000014000000010000000000000003000000000000002000000000019notAcircw00000000yumlyumlyumlyumlFnotAcircw000000003laquoAcircw0700agraveagrave000000iquestH00agrave1400yumlyumlyumlyuml00000000010000001400000001000000000000000A000000000000ordmI_08Ecirc00010000000100000000000000umlograve12000notW00yumlyumlyumlyuml QUOTE (Ragan and Jackson 1980 Engman 1982 Engman 1993) (Ragan and Jackson 1980 Engman 1982 1993) QUOTE ADDIN PROCITE yuml1105lsquo190200000000010000 030000F5C_Procite5CPravara_Thanapura1pdt10Engman 1993 8010102000600agraveagrave000000iquestH00agrave1400yumlyumlyumlyuml00000000010000001400000001000000000000000A000000000000ordmI_08Ecirc00010000000100000000000000umlograve12000notW00yumlyumlyumlyuml QUOTE ADDIN PROCITE yuml1105lsquo190200000000010000)030000F5C_Procite5CPravara_Thanapura1pdt10Engman 1982 8100102000600agraveagrave000000iquestH00agrave1400yumlyumlyumlyuml00000000010000001400000001000000000000000A000000000000ordmI_08Ecirc00010000000100000000000000umlograve12000notW00yumlyumlyumlyuml Engman in 1982 and 1993 suggested that one must develop a land cover table analogous that is compatible to the Landsat data Landsat may not be acceptable in small areas with certain land cover types Bondelid et al (1982) stated that special care should be exercised in analyzing small watersheds that contain heterogeneous land cover causing the largest CN errors Engineering designs utilizing the curve number are widely used but the curve number is sensitive to errors in the estimated input values used to derive it QUOTE (Bondelid et al 1982) (Bondelid et al 1982 McCuen 2002)


METHODS

In this research newly designed technological methodologies were proposed for mapping urban land use land cover using QuickBird NDVI imagery and developing GIS spatial modeling for generating the composite of curve numbers in the TR-55 table NRCS (1986) publication To achieve this goal a sequence for digital processing and analysis was proposed and implemented (Figure 1) In order to assess the utility of QB NDVI imagery and to validate the composite runoff curve number calculation using designed GIS spatial modeling various procedures and comparisons of the generated runoff coefficients were presented reviewed and validated by practicing professionals including the City Drainage Engineer and the City Geographic Information Systems manager for the City of Sioux Falls South Dakota in August 2005

Figure 1 Sequence for digital processing and analysis

Study Area


(a) (b)

Figure 2 The 2004 images of the study area at the same scale(a) The QuickBird multi image (4-3-2) on the left and (b) The QuickBird NDVI image on the right


The 2004 remotely sensed data and vector GIS data layers were provided by the City of Sioux Falls They include orthophoto mosaics with 2 ft (06 m) and 05 ft (015 m) resolution a QuickBird satellite image with 796 ft (239 m) resolution and include data layers such as parcels city limit boundary hydrography and streets The data sets were processed and merged to combine the remotely sensed data with the GIS layers for the study area The April 23 2004 orthophoto was used as a reference to integrate all data sets into the same map projection and unit with the Universal Transverse Mercator (UTM) map projection World Geodetic System (WGS) 1984 and horizontal units in feet Software used in this project consisted of ERDAS Imagine 87 ArcMap 9 ArcView 33 and Microsoft Excel 2002



The 2004 NRCS SSURGO 124000 data set of the soil survey area (SSA) named ldquoMinnehaha County South Dakotardquo and its standard metadata files were downloaded from the Soil Data Mart at on June 7 2004 It is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey The soil layer was in a single zip file in ArcView shapefile format with a geographic coordinate system and using North American Datum (NAD) 1983 This layer was processed using ArcView 33 geoprocessing and Microsoft Excel 2002 to create a soil layer associated with hydrologic soil groups in the project coordinate system for this study area Overlaying of the soil hydro features to the subset QB image demonstrated a good fit Therefore a vector to image registration was not necessary

(a) (b)

Figure 3 The registered 2004 images comparison at same scale and location

(a) The QuickBird multi image (4-3-2) on the left with the 2004 Orthoimage (1-2-3) on the right

(b) The QuickBird NDVI image on the left with the 2004 Orthoimage (1-2-3) on the right


With the recent development of new remote sensing systems there are an increasing number of commercially high resolution data available There is a considerable amount of previous research that has been devoted to exploring the magnitude and impact of spatial resolution on image analysis when changing scale from coarse to fine resolutions Due to the more heterogeneous spectral-radiometric characteristics within land use land cover unit portrayed in high resolution images many applications of traditional single-resolution classification approaches have not led to satisfactory results In general traditional single-resolution classification procedures such as supervised unsupervised and hybrid training approaches are inadequate for discriminating between land use land cover classes where spectralspatial features and spatial patterns vary as a function of spatial resolution Consequently there is an increasing need of researching for more efficient approaches to process and analyze these data products (Chen and Stow 2003)




(a) The 2004 QuickBird NDVI (b) The QuickBird multi image (4-3-2)

(c) The 2004 Orthophoto (1-2-3)

Figure 4 The registered 2004 images visual comparison of impervious areas and vegetative open spaces at same scale and location in residential areas




Runoff Indexcomposite = ( [ of area covered (ji) x CN(j)]

Where

CNcomposite is the sum of the component runoff index within an area (i) delineated by description of area

of area covered (ji) is the component (j) of the area (i) divided by the total area (i) Note that the component of the area is delineated by surface characteristics of land use land cover andor hydrologic soil group

CN(j) is a runoff index of the component (j) of the area (i) determined by the surface characteristics andor its hydrologic soil groups

The Composite CNrsquos Weigh Average Equation

CNcomposite = ( [ of area covered x CN] Where CNcomposite is the sum of composite runoff curve number within an area

of area covered is a component of the area divided by the total area

CN is the runoff curve number of the area

In this study the composite of CNrsquos calculation requires the combined surface characteristics and hydrologic effect of soil groups The surface characteristic was derived from the two QB NDVI thematic maps and the hydrologic soil groups were obtained from the 2004 NRCS SSURGO 124000 data set The soil information covered by hydrologic areas such as rivers or canals are not surveyed or were not available in this data set Therefore they were excluded from the decision rules Swimming pools were considered as impervious areas since these small size water features appeared spectrally impervious on the QB NDVI Additionally they retain precipitation and act as pervious surfaces during storms thus the decision rules would result in a conservative calculation of CNrsquos


Figure 5 TR-55 runoff curve number table for urban areas 14001400000001000000000000000000000010000000000000000A000000000000ordmI_cedil13Eacute00030000000500000000000000tograve12000notW00yumlyumlyumlyuml QUOTE (United States Department of Agriculture 1986) (United States Department of Agriculture 1986)

Table 1 Initial land use land cover classification scheme of QuickBird NDVI thematic map1 (Figure 6)[copy 2005 Pravara Thanapura Use with permission]

Table 2 Secondary land use land cover classification scheme of QuickBird NDVI thematic map2 (Figure 7)

[copy 2005 Pravara Thanapura Use with permission]

Sampling Design amp Accuracy Assessment

A simple random sampling method was chosen to ensure unbiased sample selection To provide a statistically sound assessment of accuracy the simplified worst-case scenario equation was used to calculate the sample sizes to insure sufficient samples for filling in an error matrix for this project

The Conservative Sample Size Equation (Congalton and Green 1999)

n = B 4b2

Where

n = Total samples of all classes

B = The upper (αk) x 100th percentile of the Chi-Squared distribution with 1 degree of freedom (α) k = number of classes

b = Significant level = +- 5 accuracy (α - 095 = 005)


Figure 6 Classification result of NDVI QuickBird thematic map 1and 502 accuracy sites

Figure 7 Classification result of NDVI QuickBird thematic map 2 and the accuracy assessment sites

Table 3 Accuracy assessment result of NDVI QuickBird thematic map1

Labeling spectral clusters the lower the numbers the darker the areas are label to impervious areas (low NDVI value)

Labeling spectral clusters the higher the numbers are the brighter the areas are label to open spaces (high NDVI value)


Labeling spectral clusters the lower the numbers the darker the areas are label to open spaces-poor condition (low NDVI value)

Labeling spectral clusters the higher the numbers the brighter the areas are label to open spaces-good to fair condition (high NDVI value)

GIS Spatial Modeling

A spatial modeling or analysis is a set of analytical procedures applied with a GIS A spatial model is a set of clearly defined analytical procedures that run on a spatial database to derive new information describing a new spatial relationship It is structured as a set of rules including a combination of logical expressions mathematical procedures and criteria built using analytical tools in GIS which are applied for the purpose of simulating the process QUOTE (Environmental Systems Research Institude 1995) (ESRI staff 1995)

In GIS there are three categories of spatial modeling functions that can be applied to geographic data objects (1) geometric models such as calculating the Euclidean distance between objects generating buffers calculating areas and perimeters and so on (2) coincidence models such as polygon overlay and (3) adjacency models (pathfinding redistricting and allocation) All three model categories support operations on geographic data objects such as points lines polygon TINS and grids QUOTE (Environmental Systems Research Institude 1995) (ESRI staff 1995)

In this study the geometric and coincidence modeling functions in ArcView33 geoprocessing were designed in a sequence of steps to build a spatial modeling based on the composite runoff index spatial model The process of spatial modeling created a new data set (ArcView shapefile) of geographic features Each shapefile contained new polygons that described new spatial relationships of the data sets among surface characteristics of land use land cover and hydrologic soil groups including its CN



CNcomposite = ( [ of area covered (ji) x CN(j)]

WhereCNcomposite is the sum of the component CN within a parcel boundary (i) delineated by the land activity code of the City of Sioux Falls (Table 5)

of area covered is a component (j) of the area (i) divided by the total area (i) Note that the component of the area is a subset of polygon features delineated by surface characteristics of land use land cover (GIS layer = ArcView shapefile) derived from two QB NDVI thematic maps using the ISODATA algorithm and hydrologic soil group as described previously (Figure 8)

CN(j) is a runoff curve number (Table 6)of the component (polygon)[j] of the area (i) determined by the surface characteristics and its hydrologic soil groups

Table 5 The 2004 parcel ndash activity codes and a set of rules

1 The samples of activity codes are similar to the cover type of the urban land cover of the runoff curve number table in the TR-55 NRCS (1986) publication [Figure 5]

Figure 8 The runoff index GIS data sets derived from the 2004 parcel layer by activity codes delineated by NDVI QuickBird information and hydrologic soil groups

Table 6 Runoff curve numbers in Percentage for the CN calculation defined from the TR-55 NRCS (1986) Publication (Figure 5)

1 CN of fair condition is use for open spaces ndash good to fair condition1 A The soil characteristics are deep sand deep loess and aggregated silts (McCuen 1982)

2 B The soil characteristics are shallow loess and sandy loam (McCuen 1982)

3 C The soil characteristics are clay loams shallow sandy loam soils low in organic content and soils usually high in clay (McCuen 1982)

4 B The soil characteristics are swell significantly when wet heavy plastic clays and certain saline soils (McCuen 1982)

Table 8 The CN Results and SCS CN comparison

GISActivity Code SCS CN Cover Type1RuleSamplesTotal CN

IDand by AreaAreas2Result

Description (avg)

B

3

Sample Area

4

111 Single familyResidential districts by average lot size - gt= 01 and lt= 0125 acres3123555 acres8688859994

Residential 18 acre

gt= 40468 and lt= 50585 sqm

2

14386463 sqm

2

(0125 acres or 50585 sqm

2

)

123

211 Single familyResidential districts by average lot size - gt 0125 and lt= 025 acres32845766 acres8430759976

Residential 14 acre

gt 50585 and lt= 101171 sqm

2

23333993 sqm

2

(025 acres 101171 sqm

2

)

1987


Residential 13 acre

gt 101171 and lt= 134759 sqm

2

40492530 sqm

2

(03330 acres 134759 sqm

2

)

345


Residential 12 acre

gt 134759 and lt= 202341 sqm

2

22059243 sqm

2

(05 acres 202341 sqm

2

)

188


Residential 1 acre (404683 sqm

2

)gt 202341and lt= 404683 sqm

2

10562213 sqm

2

09


Residential 2 acres (809365 sqm

2

)gt 404683 and lt= 809365 sqm

2

1845352 sqm

2

016

731 Banks and Urban Districts - Commercial gt= 05 acres 171496 acres94199210000

Financial and business

gt= 202341 sqm

2

6054050 sqm

2

Institutions 052

833 Other officesUrban Districts - Commercialgt= 05 acres566396 acres93219210000

and business

gt= 202341 sqm

2

25883493 sqm

2

22

964 Warehousing Urban Districts - Industrialgt= 05 acres112004 acres8631889400

Distribution and

gt= 202341 sqm

2

8109837 sqm

2

Wholesale 07

422489634 acres8572799881

362733112 sqm

2

3089

SCS CN

Hydrologic Soil Group

Total


2 The study area is 290142 acres (11742 hectares)

3The total area of hydrologic soil group B of each sample layer by parcel - activity code4 B The soil characteristics are shallow loess and sandy loam (McCuen 1982)

RESULTS amp DISCUSSION







CONCLUSIONS


AcknowleDgements



References

Bondelid TR TJ Jackson and RH McCuen 1982 Estimating runoff curve numbers using remote sensing data Singh Vijay P editor Applied modeling in catchment hydrology Water Resources Publications Littleton Colorado 563 p

Congalton R and K Green 1999 Assessing the Accuracy of Remotely Sensed Data Principles and Practices CRCLewis Press Boca Raton Florida 137 p

Chen DM and D Stow 2003 Strategies for integrating information from multiple spatial resolutions into land-use land-cover classification routines Photogrammetric Engineering amp Remote Sensing 69(11)1279-1287

Draper S E and S G Rao 1986 Runoff prediction using remote sensing imagery Water Resources Bulletin 22(6) 941-949 p

Dubayah R O E F Wood E T Engman K P Czajkowski M Zion and Rhoads 2000 Remote Sensing in Hydrology and Water Management Remote Sensing in Hydrology and Water Management Schultz G A and ET Engman editors Springer Germany 483 p

Engman E T 1982 Remote sensing applications in watershed modeling Applied modeling in catchment hydrology Singh Vijay P editor Water Resources Publications Littleton Colorado 563 p

Engman E T 1993 Remote Sensing Handbook of Hydrology Maidment David R editor New York McGraw- Hill 241-2423 p


Golding B L 1997 Discussion Runoff Curve Number Has It Reached Maturity Journal of Hydrologic Engineering 2(3) 145-148 p

Jackson T J and R M Ragan 1977 State-of-the-art application of Landsat data in urban hydrology International symposium on urban hydrology hydraulics and sediment control University of Kentucky Lexington Kentucky 275-285 p

McCuen R H 1982 A guide to hydrologic analysis using SCS methods NJ Prentice-Hall Englewood Cliffs NJ 145 p

McCuen R H 2002 Approach to confidence interval estimation for curve numbers Journal of Hydrologic Engineering 7(1) 43-48 p

McCuen R H 2003 Discussion of Approach to Confidence Interval Estimation for Curve Numbers Journal of Hydrologic Engineering 8(4) 232-233 p

McCuen R H and O Okunola 2002 Extension of TR-55 for Microwatersheds Journal of Hydrologic Engineering 7(4) 319-325 p

Melesse A M and W D Graham 2004 Storm runoff prediction based on a spatially distributed travel time method utilizing remote sensing and GIS Journal of the American Water Resources Association (JAWRA) 40(4) 863-879 p

Mishra S K and V P Singh 2003 Soil Conservation Service Curve Number (SCS-CN) methodology The Netherlands Kluwer Academic Publishers PO Box 17 3300 AA Dordrecht 536 p

Natural Resources Conservation Service (NRCS) 1972 SCS National Engineering Handbook Section 4 Hydrology URL httpwwwinfousdagovCEDftpCEDneh630-ch10pdf US Department of Agriculture Natural Resources Conservation Service (last date accessed 21 August 2005)

Natural Resources Conservation Service (NRCS) 1986 Urban hydrology for small watersheds TR-55 Technical release 55 URL httpwwwwccnrcsusdagovhydrohydro-tools-models-tr55html US Department of Agriculture National Water and Climate Water (last date accessed 21 August 2005)

Natural Resources Conservation Service (NRCS) 1992 Computer program for project formulation-hydrology Tech Release No 20 URL httpwwwwccnrcsusdagovhydrohydro-tools-models-tr20html US Department of Agriculture National Water and Climate Water (last date accessed 21 August 2005)

Ponce V M and R H Hawkins 1996 Runoff curve number has it reached maturity Journal of Hydrologic Engineering 1(1) 11-19p

Ragan R M and T J Jackson 1980 Runoff synthesis using Landsat and SCS model Journal of Hydraulics division Proceedings of the American Society of Civil Engineers (ASCE) 106(5) 667-678p

Schultz G A 1988 Remote sensing in Hydrology Journal of Hydrology 100(1-3) 239-265 p

Schultz G A 1996 Remote sensing applications to hydrology Runoff Hydrological Sciences Journal 41(4) 453-475 p

Thomas J J and R H McCuen 1979 Accuracy of impervious area values estimated using remotely sensed data Water Resources Bulletin 27(3) 373-379 p

Digital Data13

darr13

Pre-Processing 13

Data Merging and Integration 13

darr13

Decision and Classification13

darr13

Image Classification13

Unsupervised ndash ISODATA Algorithm13

darr13

Classification Output13

darr13

Assess Accuracy13

darr13

Reports and GIS Data13

darr13

GIS Spatial Modeling13

The Composite of Runoff Curve Number Calculation13

darr13

Reject Accept Hypothesis13

13

13

Pecora 16 ldquoGlobal Priorities in Land Remote Sensingrdquo

October 23 ndash 27 2005 Sioux Falls South Dakota

_1186573192xls

Classification Scheme - Map1


Defined SCS CN Table

Defined SCS adjusted CN Table

FSD-AOI Activity Codes

FSD-AOI CN Result

Area Covered by Soil Type 1of2


Area Covered by Soil Type

Area Covered by Soil Type_origi

FSD-AOI CN Result Original

Map1-Results-06-10-2005 - Table


Table1

Table2

Table3

Table 91

Table 91 Runoff curve numbers for hydrologic soil-cover complexes

Cover

Land use Treatment Hydrologic Hydrologic soil group

or practice condition A B C D

Fallow Straight row ---- 77 86 91 94

Row crops Straight row Poor 72 81 88 91

Straight row Good 67 78 85 89

Contoured Poor 70 79 84 88

Contoured Good 65 75 82 86

Contoured and terraced Poor 66 74 80 82

Contoured and terraced Good 62 71 78 81

Small grain Straight row Poor 65 76 84 88






Close-seeded Straight row Poor 66 77 85 89


or Contoured Poor 64 75 83 85

rotation Contoured Good 55 69 78 83

meadow Contoured and terraced Poor 63 73 80 83


Pasture Poor 68 79 86 89

or range Fair 49 69 79 84

Good 39 61 74 80


Contoured Fair 25 59 75 83


Meadow Good 30 58 71 78

Woods Poor 45 66 77 83

Fair 36 60 73 79

Good 25 55 70 77

Farmsteads ---- 59 74 82 86

---- 72 82 87 89

---- 74 84 90 92

Table 3 Pennsylvania Landsat Runoff Curve Numbers (from Bondelid et al 1980)


Land Cover

A B C D

Woods 25 55 70 77

Agricultural 64 75 83 87

Residential 60 74 83 87

Highly Impervious 90 93 94 95

Water 98 98 98 98

Table 2 Runoff Curve Numbers for Landsat Rural Land Cover Delineations (from Slack and Welch 1980)


Land Use Description

A B C D

Agricultural vegetated 52 68 79 84

Bare Ground 77 86 91 94

Forest Land 30 58 72 78

Table 1 Runoff Curve Numbers for Landsat Land Use Delineations (from Ragan and Jackson 1980)



A B C D


Grassed Open Space 36 60 73 78

Highly Impervious

(Commercial-Industrial-Parking Lot) 90 93 94 94



Map Class Name Labeling Criteria Areas (Acres) Areas () Reference Totals Classified Totals Number Correct Producers Accuracy () Users Accuracy () Kappa Statistics

ID Open Spaces

Map 21 21 Poor 1-25 73066 2514 242 239 164 6777 6862 04547

Good to Fair 26-50 64959 2235 328 331 253 7713 7644 0445

Map 22 22 Poor 1-30 90677 3119 242 309 197 8140 6375 03701

Good to Fair 31-50 47349 1629 328 261 216 6585 8276 05939

Overall Classification Accuracy = 7246 Overall Kappa Statistics = 0456


Labeling spectral clusters the higher the numbers the brighter the areas are label to open spaces-good to fair condition (high NDVI

value)


ID

Map 13 11 Impervious areas 1-40 127098 4372 258 216 210 8140 9722 09429

Open spaces 41-100 163581 5628 244 286 238 9754 8322 06734



Open spaces 46-100 151453 5210 244 261 232 9508 8889 07838



Open spaces 51-100 138025 4748 244 241 230 9426 9544 09112


Open spaces 56-100 122922 4229 244 217 209 8566 9631 09283



Open spaces 61-100 106470 3663 244 196 189 7746 9643 09305




GIS Activity Code Rule Samples Total Total CN Adjusted

ID and by Area Areas Areas (Avg) CN

Description (avg)

1 11 Single family Residential districts by average lot size - gt= 01 and lt= 0125 acres 312 3555 acres 123 8688 7025

Residential 18 acre

(0125 acres or 50585 sqm2)

2 11 Single family Residential districts by average lot size - gt 0125 and lt= 025 acres 3284 5766 acres 1987 8430 5935

Residential 14 acre

(025 acres 101171 sqm2)


Residential 13 acre

(03330 acres 134759 sqm2)


Residential 12 acre

(05 acres 202341 sqm2)



7 31 Banks and Urban Districts - Commercial gt= 05 acres 17 1496 acres 052 9419 8351


Institutions

8 33 Other offices Urban Districts - Commercial gt= 05 acres 56 6396 acres 220 9321 8303

and business

9 64 Warehousing Urban Districts - Industrial gt= 05 acres 11 2004 acres 070 8631 6595

Distribution and

Wholesale

Total 4224 89634 3089 8572 6577

Note 1 The samples of activity codes are similar to the cover type of the urban land cover of the runoff curve number table in the TR-55 (NRCS 1986) publication [Figure 4]

2 The study area is 290142 acres

3 is Total areas of hydrologic soil group by character of surface and hydrologic condition by activity code in Percentage

GIS Activity Code Hydrologic Soil Group by Character of Surface () with CN amp Adjusted CN (CNadj)

ID and A B C D

Description Impervious Open P Open GtoF

1 11 Single family Residential districts by average lot size - 5040 3259 1694 9994 003 001 001 006

Residential 18 acre (CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)

(0125 acres or 50585 sqm2) (CNadj=65) (CNadj=65)



(025 acres 101171 sqm2) (CNadj=38) (CNadj=38)

3 11 Single family Residential districts by average lot size - 3533 3193 3271 10000

Residential 13 acre (CN=98) (CN=79) (CN=69)

(03330 acres 134759 sqm2) (CNadj=30)



(05 acres 202341 sqm2) (CNadj=25)


Residential 1 acre (404683 sqm2) (CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)

(CNadj=20) (CNadj=20)


Residential 2 acres (809365 sqm2) (CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)

(Cnadj=12) (Cnadj=12)

7 31 Banks and Urban Districts - Commercial 7304 909 682 8903 869 144 085 1096

Financial and business (CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)

Institutions (CNadj=85) (CNadj=85)

8 33 Other offices Urban Districts - Commercial 026 005 002 032 6454 1099 1349 8903 543 059 020 622 290 060 090 439

and business (CN = 98) (CN =68) (CN = 49) (CN = 98) (CN=79) (CN=69) (CN = 98) (CN =86) (CN = 79) (CN=98) (CN=89) (CN=84)

(CNadj=85) (CNadj=85) (CNadj=85) (CNadj=85)

9 64 Warehousing Urban Districts - Industrial 459 048 503 8496 1146 357 9491

Distribution and (CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)

Wholesale (CNadj=72) (CNadj=72)



3 is Total sample areas of hydrologic soil group by character of surface and hydrologic condition by activity code in Percentage

4 is Total sample areas of hydrologic soil group by activity code in Percentage

GIS Hydrologic Soil Group by Character of Surface () with CN amp Adjusted CN (CNadj)

ID A B C D

Open Total Open Total Open Total Open Total

1 5040 3259 1694 9994 003 001 001 006

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


2 4087 3217 2694 9976 010 009 005 024

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557 257 169 008 443

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


6 2610 3882 1273 7774 051 1557 154 2226

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


7 7304 909 682 8903 869 144 085 1096

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


8 026 005 002 032 6454 1099 1349 8903 543 059 020 622 290 060 090 439

(CN = 98) (CN =68) (CN = 49) (CN = 98) (CN=79) (CN=69) (CN = 98) (CN =86) (CN = 79) (CN=98) (CN=89) (CN=84)


9 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)


Note 1 is Total sample areas of hydrologic soil group by character of surface and hydrologic condition by activity code in Percentage



ID C D

Open Total Open Total

1 003 001 001 006

(CN=98) (CN=89) (CN=84)

(CNadj=65)

2 010 009 005 024

(CN=98) (CN=89) (CN=84)

(CNadj=38)

3

4

5 257 169 008 443

(CN=98) (CN=89) (CN=84)

(CNadj=20)

6 051 1557 154 2226 051 1557 154 2226

(CN=98) (CN=89) (CN=84)

(Cnadj=12)

7 869 144 085 1096

(CN=98) (CN=89) (CN=84)

(CNadj=85)

8 543 059 020 622 290 060 090 439

(CN = 98) (CN =86) (CN = 79) (CN=98) (CN=89) (CN=84)


9

Note 1 is Total sample area of hydrologic soil group by character of surface and hydrologic condition by activity code in percentage

2 is Total sample areaa of hydrologic soil group by activity code in percentage


ID A B


1 5040 3259 1694 9994

(CN=98) (CN=79) (CN=69)

(CNadj=65)

2 4087 3217 2694 9976

(CN=98) (CN=79) (CN=69)

(CNadj=38)

3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557

(CN=98) (CN=79) (CN=69)

(CNadj=20)

6 2610 3882 1273 7774 2610 3882 1273 7774

(CN=98) (CN=79) (CN=69)

(Cnadj=12)

7 7304 909 682 8903

(CN=98) (CN=79) (CN=69)

(CNadj=85)

8 026 005 002 032 6454 1099 1349 8903

(CN = 98) (CN =68) (CN = 49) (CN = 98) (CN=79) (CN=69)


9 459 048 503 8496 1146 357 9491 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)



2 is Total sample area of hydrologic soil group by activity code in percentage

GIS Activity Code SCS CN Cover Type1 Rule Samples Total CN SCS CN Adjusted

ID and by Area Areas2 Result Hydrologic Soil Group CN

Description (avg) (avg)

1 11 Single family Residential districts by average lot size - gt= 01 and lt= 0125 acres 312 3555 acres 8688 85 9994 7025

Residential 18 acre

123

2 11 Single family Residential districts by average lot size - gt 0125 and lt= 025 acres 3284 5766 acres 8430 75 9976 5935

Residential 14 acre

1987


Residential 13 acre

345


Residential 12 acre

188


09


016

7 31 Banks and Urban Districts - Commercial gt= 05 acres 17 1496 acres 9419 92 10000 8351


Institutions 052

8 33 Other offices Urban Districts - Commercial gt= 05 acres 56 6396 acres 9321 92 10000 8303

and business

22

9 64 Warehousing Urban Districts - Industrial gt= 05 acres 11 2004 acres 8631 88 9400 6595

Distribution and

Wholesale 07

Total 4224 89634 acres 8572 79 9881 6577

3089



3 Hydroloogic soil group B characteristics are shallow loess and sandy loam

4 The total area of hydrologic soil group B of each sample layer by parcel - acticity code

GIS ID Activity Code and Description Rule by Area

1 11 Single family Residential districts by average lot size - gt= 01 and lt= 0125 acre

Impervious areas of residential districts by average lot size

2 11 Single family Residential districts by average lot size - gt 0125 and lt= 025 acre




6 11 Single family Residential districts by average lot size - gt 1 and lt= 2 acres

7 31 Banks and Financial Institutions Urban Districts - Commercial and business

8 33 Other offices Urban Districts - Commercial and business

9 64 Warehousing Distribution Urban Districts - Industrial

and Wholesale

1 The samples of activity codes are similar to the cover type of the urban land cover of the runoff curve number table in the TR-55 (NRCS 1986) publication [Figure 4]

Note 1 acre = 40468 sqm frac12 acre = 20234 13 acre = 13355 frac14 acre = 10117 18 acre = 5059 sq m

Table 7 Runoff curve numbers in Percentage for the adjusted CN calculations defined from the TR-55 (NRCS 1986) Publication (Figure 4)

Land Use Land Cover Curve numbers for hydrologic Soil Group

Description of Area A B C D

Impervious areas of Urban Districts

Commercial and business 85 85 85 85

Industrial 72 72 72 72


65 65 65 65

38 38 38 38

30 30 30 30

25 25 25 25

20 20 20 20

12 12 12 12

Character of Surface A B C D

Open Spaces - Poor Condition 68 79 86 89

49 69 79 84

Note 1 ndash CN of Fair condition are use for open spaces ndash good to fair condition

Table 6 Runoff curve numbers in Percentage for the CN and adjusted CN calculations defined from the TR-55 (NRCS 1986) Publication (Figure 4)




Impervious Areas 98 98 98 98


49 69 79 84


Table 2 Classification Scheme for NDVI QuickBrid Thematic Map2

Labels Rules

Land Use Land Cover


Open Spaces If land area = or lt 75 covered with vegetation naturally existing or planted

(eg grass plants tree (leaf-on leaf-off) forest shrub and scrub etc)

Else if land area gt 75 covered with vegetation naturally existing or planted


Labels Rules

Land Use Land Cover


Impervious Areas If land area has lt 25 covered with areas characterized by

If land area gt or = 75 characterized by impervious surfaces

If land area gt or = 75 covered by bare land (eg Bare rock

gravel silt clay dirt and sand or any other earthen materials

Open Spaces Else if land area lt 25 covered with areas characterized by impervious surfaces

If land area gt 75 covered with vegetation naturally existing or planted


Land Use Land Cover

Curve numbers for



A2

B3

C4

D5

Impervious Areas

98

98

98

98

Open Spaces - Poor Condition

68

79

86

89

Open Spaces - Good to Fair Condition1

49

69

79

84

Activity Code and Description

SCS CN Cover Type1

Rule by Area

11 Single family

Residential districts by average lot size -

gt= 01 and lt= 0125 acre

Residential 18 acre (0125 acres or 50585 sqm2)

gt= 40468 and lt= 50585 sqm2

11 Single family


gt 0125 and lt= 025 acre


gt 50585 and lt= 101171 sqm2

11 Single family




gt 101171 and lt= 134759 sqm2

11 Single family




gt 134759 and lt= 202341 sqm2

11 Single family



Residential 1 acre (404683 sqm2)

gt 202341and lt= 404683 sqm2

11 Single family


gt 1 and lt= 2 acres

Residential 2 acres (809365 sqm2)

gt 404683 and lt= 809365 sqm2

31 Banks and Financial Institutions

Urban Districts - Commercial and business

gt= 05 acres or 202341 sqm2

33 Other offices



64 Warehousing Distribution

Urban Districts - Industrial


and Wholesale

Accuracy Assessment - Unsupervised Thematic Map2 (50 spectral clusters)

Map

Class Name

Labeling Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics

ID

Open Spaces

21

Poor

1-25

73066

2514

242

239

164

6777

6862

04547

Good to Fair

26-50

64959

2235

328

331

253

7713

7644

0445

Overall Classification Accuracy = 7316

Overall Kappa Statistics = 04498

22

Poor

1-30

90677

3119

242

309

197

8140

6375

03701

Good to Fair

31-50

47349

1629

328

261

216

6585

8276

05939

Overall Classification Accuracy = 7246

Overall Kappa Statistics = 0456

Accuracy Assessment - Unsupervised Thematic Map1(100 spectral clusters)

Map

Class Name

Labeling Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics

ID

11

Impervious areas

1-40

127098

4372

258

216

210

8140

9722

09429

Open spaces

41-100

163581

5628

244

286

238

9754

8322

06734



12

Impervious areas

1-45

139227

4790

258

241

229

8876

9502

08976

Open spaces

46-100

151453

5210

244

261

232

9508

8889

07838



13

Impervious areas

1-50

152654

5252

258

261

247

9574

9464

08896

Open spaces

51-100

138025

4748

244

241

230

9426

9544

09112



14

Impervious areas

1-55

167757

5771

258

285

250

9690

8772

07473

Open spaces

56-100

122922

4229

244

217

209

8566

9631

09283



15

Impervious areas

1-60

184209

6337

258

306

251

9729

8203

06302

Open spaces

61-100

106470

3663

244

196

189

7746

9643

09305



Labels

Rules

Land Use Land Cover


Open Spaces

If land area = or lt 75 covered with vegetation naturally existing or planted


then Open Spaces - Poor condition (1)



then Open Spaces - Good to fair condition (2)

Else Open Space Poor condition (1)

Labels

Rules

Land Use Land Cover


Impervious Areas

If land area has lt 25 covered with areas characterized by

vegetative open spaces then Impervious Areas (1)


(eg Asphalt concrete buildings etc) then Impervious Areas (1)



then Impervious Areas (1)

Open Spaces

Else if land area lt 25 covered with areas characterized by impervious surfaces

then Open Spaces (2)




Else Impervious Areas (1)

sensing provides information that was previously impossible or impractical to obtain using conventional techniques (Thomas and McCuen 1979) Therefore remote sensing technology is a very important tool for successful hydrologic model analysis prediction and validation The National Resource Conservation Service (NRCS) formally the Soil Conservation Service (SCS) has developed a series of hydrologic models in water resource planning and design The SCS Curve Number (SCS-CN) method is the best-known component of the SCS hydrologic models According to Ponce and Hawkins (1996) the origins of the CN methodology can be traced back to the thousand of infiltrometer tests carried out by SCS in the late 1930s and early 1940s The intent was to develop basic data to evaluate the effects of watershed treatment and soil conservation measures on the rainfall-runoff process A major catalyst for the development and implementation of the runoff CN methodology was the passage of the Watershed Protection and Flood Prevention Act (Public Law 83-566) in August 1954 The method and its use are described in the SCS National Engineering Handbook Section 4 Hydrology (NEH-4) [NRCS 1972] The document has since been revised in 1956 64 65 71 72 85 and 1993 The CN method is well established in hydrologic engineering and environmental impact analyses Its major advantage are (1) its simplicity (2) its predictability (3) its stability (4) its reliance on only one parameter and (5) its responsiveness to major runoff-producing watershed properties (soil type land covertreatment surface condition and antecedent condition) [Ponce and Hawkins 1996] Consequently the methodology has been widely used in numerous applications by practicing engineers and hydrologists nationally and internationally (McCuen 1982 2002 2003 Ponce and Hawkins 1996 Golding 1997 McCuen and Okunola 2002 Mishra and Singh 2003)

The CN is an index that represents the combined hydrologic effect of soil group land cover type agricultural land treatment class hydrologic condition and antecedent soil moisture (McCuen 1982) It is the best-known component of a series of SCS hydrologic models most noticeably the TR-55 (NRCS 1986) procedures and the TR-20 computer model (NRCS 1992 McCuen 2002) The CN calculation requires use of storm rainfall and associated streamflow data for annual floods to derive the best means of establishing run off curve numbers A CN of 100 represents a condition of zero potential retention or 100 runoff of incident precipitation


In previous studies one of the main problems of using remotely sensed data for estimation of land cover is a spatial resolution issue Moderate spatial resolutions are insufficient to generate the detail necessary to utilize the published SCS land cover table used in the SCS procedures (Ragan and Jackson 1980 Engman 1982 1993) Engman in 1982 and 1993 suggested that one must develop a land cover table analogous that is compatible to the Landsat data Landsat may not be acceptable in small areas with certain land cover types Bondelid et al (1982) stated that special care should be exercised in analyzing small watersheds that contain heterogeneous land cover causing the largest CN errors Engineering designs utilizing the curve number are widely used but the curve number is sensitive to errors in the estimated input values used to derive it (Bondelid et al 1982 McCuen 2002)


METHODS

In this research newly designed technological methodologies were proposed for mapping urban land use land cover using QuickBird NDVI imagery and developing GIS spatial modeling for generating the composite of curve numbers in the TR-55 table NRCS (1986) publication To achieve this goal a sequence for digital processing and analysis was proposed and implemented (Figure 1) In order to assess the utility of QB NDVI imagery and to validate the composite runoff curve number calculation using designed GIS spatial modeling various procedures and



Digital Data

darr Pre-Processing











Study Area


(a) (b)









(a) (b)



























Labels Rules




Labels Rules

Land Use Land Cover





n = B 4b2









ID

Labeling

Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics









ID Open Spaces

Labeling

Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics





















































Wholesale 07 4224 89634 acres 8572 79 9881

362733112 sqm2

3089


Total










CONCLUSIONS


ACKNOWLEDGEMENTS




REFERENCES



























ABSTRACT

METHODS

Study Area



REFERENCES












Eric Warmath





ABSTRACT


INTRODUCTION






METHODS



Study Area


(a) (b)







(a) (b)
















Where

















n = B 4b2

Where


































Description (avg)

B

3

Sample Area

4


Residential 18 acre

gt= 40468 and lt= 50585 sqm

2

14386463 sqm

2


2

)

123


Residential 14 acre

gt 50585 and lt= 101171 sqm

2

23333993 sqm

2

(025 acres 101171 sqm

2

)

1987


Residential 13 acre

gt 101171 and lt= 134759 sqm

2

40492530 sqm

2

(03330 acres 134759 sqm

2

)

345


Residential 12 acre

gt 134759 and lt= 202341 sqm

2

22059243 sqm

2


2

)

188



2

)gt 202341and lt= 404683 sqm

2

10562213 sqm

2

09



2

)gt 404683 and lt= 809365 sqm

2

1845352 sqm

2

016



gt= 202341 sqm

2

6054050 sqm

2

Institutions 052


and business

gt= 202341 sqm

2

25883493 sqm

2

22


Distribution and

gt= 202341 sqm

2

8109837 sqm

2

Wholesale 07

422489634 acres8572799881

362733112 sqm

2

3089

SCS CN


Total











CONCLUSIONS





References

























Digital Data13

darr13

Pre-Processing 13


darr13


darr13



darr13


darr13

Assess Accuracy13

darr13


darr13



darr13


13

13



_1186573192xls






FSD-AOI CN Result








Table1

Table2

Table3

Table 91


Cover
























Good 39 61 74 80






Fair 36 60 73 79

Good 25 55 70 77

Farmsteads ---- 59 74 82 86

---- 72 82 87 89

---- 74 84 90 92



Land Cover

A B C D

Woods 25 55 70 77




Water 98 98 98 98




A B C D







A B C D



Highly Impervious





ID Open Spaces

Map 21 21 Poor 1-25 73066 2514 242 239 164 6777 6862 04547

Good to Fair 26-50 64959 2235 328 331 253 7713 7644 0445

Map 22 22 Poor 1-30 90677 3119 242 309 197 8140 6375 03701

Good to Fair 31-50 47349 1629 328 261 216 6585 8276 05939




value)


ID


Open spaces 41-100 163581 5628 244 286 238 9754 8322 06734



Open spaces 46-100 151453 5210 244 261 232 9508 8889 07838



Open spaces 51-100 138025 4748 244 241 230 9426 9544 09112


Open spaces 56-100 122922 4229 244 217 209 8566 9631 09283



Open spaces 61-100 106470 3663 244 196 189 7746 9643 09305






Description (avg)


Residential 18 acre

(0125 acres or 50585 sqm2)


Residential 14 acre

(025 acres 101171 sqm2)


Residential 13 acre

(03330 acres 134759 sqm2)


Residential 12 acre

(05 acres 202341 sqm2)





Institutions


and business


Distribution and

Wholesale

Total 4224 89634 3089 8572 6577





ID and A B C D










(03330 acres 134759 sqm2) (CNadj=30)



(05 acres 202341 sqm2) (CNadj=25)





















ID A B C D


1 5040 3259 1694 9994 003 001 001 006

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


2 4087 3217 2694 9976 010 009 005 024

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557 257 169 008 443

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


6 2610 3882 1273 7774 051 1557 154 2226

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


7 7304 909 682 8903 869 144 085 1096

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


8 026 005 002 032 6454 1099 1349 8903 543 059 020 622 290 060 090 439



9 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)





ID C D


1 003 001 001 006

(CN=98) (CN=89) (CN=84)

(CNadj=65)

2 010 009 005 024

(CN=98) (CN=89) (CN=84)

(CNadj=38)

3

4

5 257 169 008 443

(CN=98) (CN=89) (CN=84)

(CNadj=20)

6 051 1557 154 2226 051 1557 154 2226

(CN=98) (CN=89) (CN=84)

(Cnadj=12)

7 869 144 085 1096

(CN=98) (CN=89) (CN=84)

(CNadj=85)

8 543 059 020 622 290 060 090 439

(CN = 98) (CN =86) (CN = 79) (CN=98) (CN=89) (CN=84)


9




ID A B


1 5040 3259 1694 9994

(CN=98) (CN=79) (CN=69)

(CNadj=65)

2 4087 3217 2694 9976

(CN=98) (CN=79) (CN=69)

(CNadj=38)

3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557

(CN=98) (CN=79) (CN=69)

(CNadj=20)

6 2610 3882 1273 7774 2610 3882 1273 7774

(CN=98) (CN=79) (CN=69)

(Cnadj=12)

7 7304 909 682 8903

(CN=98) (CN=79) (CN=69)

(CNadj=85)

8 026 005 002 032 6454 1099 1349 8903

(CN = 98) (CN =68) (CN = 49) (CN = 98) (CN=79) (CN=69)


9 459 048 503 8496 1146 357 9491 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)








Residential 18 acre

123


Residential 14 acre

1987


Residential 13 acre

345


Residential 12 acre

188


09


016



Institutions 052


and business

22


Distribution and

Wholesale 07

Total 4224 89634 acres 8572 79 9881 6577

3089
















and Wholesale










65 65 65 65

38 38 38 38

30 30 30 30

25 25 25 25

20 20 20 20

12 12 12 12



49 69 79 84








49 69 79 84



Labels Rules

Land Use Land Cover






Labels Rules

Land Use Land Cover









Land Use Land Cover

Curve numbers for



A2

B3

C4

D5

Impervious Areas

98

98

98

98


68

79

86

89


49

69

79

84


SCS CN Cover Type1

Rule by Area

11 Single family




gt= 40468 and lt= 50585 sqm2

11 Single family




gt 50585 and lt= 101171 sqm2

11 Single family




gt 101171 and lt= 134759 sqm2

11 Single family




gt 134759 and lt= 202341 sqm2

11 Single family




gt 202341and lt= 404683 sqm2

11 Single family




gt 404683 and lt= 809365 sqm2




33 Other offices






and Wholesale


Map

Class Name

Labeling Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics

ID

Open Spaces

21

Poor

1-25

73066

2514

242

239

164

6777

6862

04547

Good to Fair

26-50

64959

2235

328

331

253

7713

7644

0445



22

Poor

1-30

90677

3119

242

309

197

8140

6375

03701

Good to Fair

31-50

47349

1629

328

261

216

6585

8276

05939




Map

Class Name

Labeling Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics

ID

11

Impervious areas

1-40

127098

4372

258

216

210

8140

9722

09429

Open spaces

41-100

163581

5628

244

286

238

9754

8322

06734



12

Impervious areas

1-45

139227

4790

258

241

229

8876

9502

08976

Open spaces

46-100

151453

5210

244

261

232

9508

8889

07838



13

Impervious areas

1-50

152654

5252

258

261

247

9574

9464

08896

Open spaces

51-100

138025

4748

244

241

230

9426

9544

09112



14

Impervious areas

1-55

167757

5771

258

285

250

9690

8772

07473

Open spaces

56-100

122922

4229

244

217

209

8566

9631

09283



15

Impervious areas

1-60

184209

6337

258

306

251

9729

8203

06302

Open spaces

61-100

106470

3663

244

196

189

7746

9643

09305



Labels

Rules

Land Use Land Cover


Open Spaces








Labels

Rules

Land Use Land Cover


Impervious Areas








Open Spaces








Digital Data

darr Pre-Processing











Study Area


(a) (b)









(a) (b)



























Labels Rules




Labels Rules

Land Use Land Cover





n = B 4b2









ID

Labeling

Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics









ID Open Spaces

Labeling

Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics





















































Wholesale 07 4224 89634 acres 8572 79 9881

362733112 sqm2

3089


Total










CONCLUSIONS


ACKNOWLEDGEMENTS




REFERENCES



























ABSTRACT

METHODS

Study Area



REFERENCES












Eric Warmath





ABSTRACT


INTRODUCTION






METHODS



Study Area


(a) (b)







(a) (b)
















Where

















n = B 4b2

Where


































Description (avg)

B

3

Sample Area

4


Residential 18 acre

gt= 40468 and lt= 50585 sqm

2

14386463 sqm

2


2

)

123


Residential 14 acre

gt 50585 and lt= 101171 sqm

2

23333993 sqm

2

(025 acres 101171 sqm

2

)

1987


Residential 13 acre

gt 101171 and lt= 134759 sqm

2

40492530 sqm

2

(03330 acres 134759 sqm

2

)

345


Residential 12 acre

gt 134759 and lt= 202341 sqm

2

22059243 sqm

2


2

)

188



2

)gt 202341and lt= 404683 sqm

2

10562213 sqm

2

09



2

)gt 404683 and lt= 809365 sqm

2

1845352 sqm

2

016



gt= 202341 sqm

2

6054050 sqm

2

Institutions 052


and business

gt= 202341 sqm

2

25883493 sqm

2

22


Distribution and

gt= 202341 sqm

2

8109837 sqm

2

Wholesale 07

422489634 acres8572799881

362733112 sqm

2

3089

SCS CN


Total











CONCLUSIONS





References

























Digital Data13

darr13

Pre-Processing 13


darr13


darr13



darr13


darr13

Assess Accuracy13

darr13


darr13



darr13


13

13



_1186573192xls






FSD-AOI CN Result








Table1

Table2

Table3

Table 91


Cover
























Good 39 61 74 80






Fair 36 60 73 79

Good 25 55 70 77

Farmsteads ---- 59 74 82 86

---- 72 82 87 89

---- 74 84 90 92



Land Cover

A B C D

Woods 25 55 70 77




Water 98 98 98 98




A B C D







A B C D



Highly Impervious





ID Open Spaces

Map 21 21 Poor 1-25 73066 2514 242 239 164 6777 6862 04547

Good to Fair 26-50 64959 2235 328 331 253 7713 7644 0445

Map 22 22 Poor 1-30 90677 3119 242 309 197 8140 6375 03701

Good to Fair 31-50 47349 1629 328 261 216 6585 8276 05939




value)


ID


Open spaces 41-100 163581 5628 244 286 238 9754 8322 06734



Open spaces 46-100 151453 5210 244 261 232 9508 8889 07838



Open spaces 51-100 138025 4748 244 241 230 9426 9544 09112


Open spaces 56-100 122922 4229 244 217 209 8566 9631 09283



Open spaces 61-100 106470 3663 244 196 189 7746 9643 09305






Description (avg)


Residential 18 acre

(0125 acres or 50585 sqm2)


Residential 14 acre

(025 acres 101171 sqm2)


Residential 13 acre

(03330 acres 134759 sqm2)


Residential 12 acre

(05 acres 202341 sqm2)





Institutions


and business


Distribution and

Wholesale

Total 4224 89634 3089 8572 6577





ID and A B C D










(03330 acres 134759 sqm2) (CNadj=30)



(05 acres 202341 sqm2) (CNadj=25)





















ID A B C D


1 5040 3259 1694 9994 003 001 001 006

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


2 4087 3217 2694 9976 010 009 005 024

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557 257 169 008 443

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


6 2610 3882 1273 7774 051 1557 154 2226

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


7 7304 909 682 8903 869 144 085 1096

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


8 026 005 002 032 6454 1099 1349 8903 543 059 020 622 290 060 090 439



9 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)





ID C D


1 003 001 001 006

(CN=98) (CN=89) (CN=84)

(CNadj=65)

2 010 009 005 024

(CN=98) (CN=89) (CN=84)

(CNadj=38)

3

4

5 257 169 008 443

(CN=98) (CN=89) (CN=84)

(CNadj=20)

6 051 1557 154 2226 051 1557 154 2226

(CN=98) (CN=89) (CN=84)

(Cnadj=12)

7 869 144 085 1096

(CN=98) (CN=89) (CN=84)

(CNadj=85)

8 543 059 020 622 290 060 090 439

(CN = 98) (CN =86) (CN = 79) (CN=98) (CN=89) (CN=84)


9




ID A B


1 5040 3259 1694 9994

(CN=98) (CN=79) (CN=69)

(CNadj=65)

2 4087 3217 2694 9976

(CN=98) (CN=79) (CN=69)

(CNadj=38)

3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557

(CN=98) (CN=79) (CN=69)

(CNadj=20)

6 2610 3882 1273 7774 2610 3882 1273 7774

(CN=98) (CN=79) (CN=69)

(Cnadj=12)

7 7304 909 682 8903

(CN=98) (CN=79) (CN=69)

(CNadj=85)

8 026 005 002 032 6454 1099 1349 8903

(CN = 98) (CN =68) (CN = 49) (CN = 98) (CN=79) (CN=69)


9 459 048 503 8496 1146 357 9491 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)








Residential 18 acre

123


Residential 14 acre

1987


Residential 13 acre

345


Residential 12 acre

188


09


016



Institutions 052


and business

22


Distribution and

Wholesale 07

Total 4224 89634 acres 8572 79 9881 6577

3089
















and Wholesale










65 65 65 65

38 38 38 38

30 30 30 30

25 25 25 25

20 20 20 20

12 12 12 12



49 69 79 84








49 69 79 84



Labels Rules

Land Use Land Cover






Labels Rules

Land Use Land Cover









Land Use Land Cover

Curve numbers for



A2

B3

C4

D5

Impervious Areas

98

98

98

98


68

79

86

89


49

69

79

84


SCS CN Cover Type1

Rule by Area

11 Single family




gt= 40468 and lt= 50585 sqm2

11 Single family




gt 50585 and lt= 101171 sqm2

11 Single family




gt 101171 and lt= 134759 sqm2

11 Single family




gt 134759 and lt= 202341 sqm2

11 Single family




gt 202341and lt= 404683 sqm2

11 Single family




gt 404683 and lt= 809365 sqm2




33 Other offices






and Wholesale


Map

Class Name

Labeling Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics

ID

Open Spaces

21

Poor

1-25

73066

2514

242

239

164

6777

6862

04547

Good to Fair

26-50

64959

2235

328

331

253

7713

7644

0445



22

Poor

1-30

90677

3119

242

309

197

8140

6375

03701

Good to Fair

31-50

47349

1629

328

261

216

6585

8276

05939




Map

Class Name

Labeling Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics

ID

11

Impervious areas

1-40

127098

4372

258

216

210

8140

9722

09429

Open spaces

41-100

163581

5628

244

286

238

9754

8322

06734



12

Impervious areas

1-45

139227

4790

258

241

229

8876

9502

08976

Open spaces

46-100

151453

5210

244

261

232

9508

8889

07838



13

Impervious areas

1-50

152654

5252

258

261

247

9574

9464

08896

Open spaces

51-100

138025

4748

244

241

230

9426

9544

09112



14

Impervious areas

1-55

167757

5771

258

285

250

9690

8772

07473

Open spaces

56-100

122922

4229

244

217

209

8566

9631

09283



15

Impervious areas

1-60

184209

6337

258

306

251

9729

8203

06302

Open spaces

61-100

106470

3663

244

196

189

7746

9643

09305



Labels

Rules

Land Use Land Cover


Open Spaces








Labels

Rules

Land Use Land Cover


Impervious Areas








Open Spaces












(a) (b)



























Labels Rules




Labels Rules

Land Use Land Cover





n = B 4b2









ID

Labeling

Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics









ID Open Spaces

Labeling

Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics





















































Wholesale 07 4224 89634 acres 8572 79 9881

362733112 sqm2

3089


Total










CONCLUSIONS


ACKNOWLEDGEMENTS




REFERENCES



























ABSTRACT

METHODS

Study Area



REFERENCES












Eric Warmath





ABSTRACT


INTRODUCTION






METHODS



Study Area


(a) (b)







(a) (b)
















Where

















n = B 4b2

Where


































Description (avg)

B

3

Sample Area

4


Residential 18 acre

gt= 40468 and lt= 50585 sqm

2

14386463 sqm

2


2

)

123


Residential 14 acre

gt 50585 and lt= 101171 sqm

2

23333993 sqm

2

(025 acres 101171 sqm

2

)

1987


Residential 13 acre

gt 101171 and lt= 134759 sqm

2

40492530 sqm

2

(03330 acres 134759 sqm

2

)

345


Residential 12 acre

gt 134759 and lt= 202341 sqm

2

22059243 sqm

2


2

)

188



2

)gt 202341and lt= 404683 sqm

2

10562213 sqm

2

09



2

)gt 404683 and lt= 809365 sqm

2

1845352 sqm

2

016



gt= 202341 sqm

2

6054050 sqm

2

Institutions 052


and business

gt= 202341 sqm

2

25883493 sqm

2

22


Distribution and

gt= 202341 sqm

2

8109837 sqm

2

Wholesale 07

422489634 acres8572799881

362733112 sqm

2

3089

SCS CN


Total











CONCLUSIONS





References

























Digital Data13

darr13

Pre-Processing 13


darr13


darr13



darr13


darr13

Assess Accuracy13

darr13


darr13



darr13


13

13



_1186573192xls






FSD-AOI CN Result








Table1

Table2

Table3

Table 91


Cover
























Good 39 61 74 80






Fair 36 60 73 79

Good 25 55 70 77

Farmsteads ---- 59 74 82 86

---- 72 82 87 89

---- 74 84 90 92



Land Cover

A B C D

Woods 25 55 70 77




Water 98 98 98 98




A B C D







A B C D



Highly Impervious





ID Open Spaces

Map 21 21 Poor 1-25 73066 2514 242 239 164 6777 6862 04547

Good to Fair 26-50 64959 2235 328 331 253 7713 7644 0445

Map 22 22 Poor 1-30 90677 3119 242 309 197 8140 6375 03701

Good to Fair 31-50 47349 1629 328 261 216 6585 8276 05939




value)


ID


Open spaces 41-100 163581 5628 244 286 238 9754 8322 06734



Open spaces 46-100 151453 5210 244 261 232 9508 8889 07838



Open spaces 51-100 138025 4748 244 241 230 9426 9544 09112


Open spaces 56-100 122922 4229 244 217 209 8566 9631 09283



Open spaces 61-100 106470 3663 244 196 189 7746 9643 09305






Description (avg)


Residential 18 acre

(0125 acres or 50585 sqm2)


Residential 14 acre

(025 acres 101171 sqm2)


Residential 13 acre

(03330 acres 134759 sqm2)


Residential 12 acre

(05 acres 202341 sqm2)





Institutions


and business


Distribution and

Wholesale

Total 4224 89634 3089 8572 6577





ID and A B C D










(03330 acres 134759 sqm2) (CNadj=30)



(05 acres 202341 sqm2) (CNadj=25)





















ID A B C D


1 5040 3259 1694 9994 003 001 001 006

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


2 4087 3217 2694 9976 010 009 005 024

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557 257 169 008 443

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


6 2610 3882 1273 7774 051 1557 154 2226

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


7 7304 909 682 8903 869 144 085 1096

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


8 026 005 002 032 6454 1099 1349 8903 543 059 020 622 290 060 090 439



9 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)





ID C D


1 003 001 001 006

(CN=98) (CN=89) (CN=84)

(CNadj=65)

2 010 009 005 024

(CN=98) (CN=89) (CN=84)

(CNadj=38)

3

4

5 257 169 008 443

(CN=98) (CN=89) (CN=84)

(CNadj=20)

6 051 1557 154 2226 051 1557 154 2226

(CN=98) (CN=89) (CN=84)

(Cnadj=12)

7 869 144 085 1096

(CN=98) (CN=89) (CN=84)

(CNadj=85)

8 543 059 020 622 290 060 090 439

(CN = 98) (CN =86) (CN = 79) (CN=98) (CN=89) (CN=84)


9




ID A B


1 5040 3259 1694 9994

(CN=98) (CN=79) (CN=69)

(CNadj=65)

2 4087 3217 2694 9976

(CN=98) (CN=79) (CN=69)

(CNadj=38)

3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557

(CN=98) (CN=79) (CN=69)

(CNadj=20)

6 2610 3882 1273 7774 2610 3882 1273 7774

(CN=98) (CN=79) (CN=69)

(Cnadj=12)

7 7304 909 682 8903

(CN=98) (CN=79) (CN=69)

(CNadj=85)

8 026 005 002 032 6454 1099 1349 8903

(CN = 98) (CN =68) (CN = 49) (CN = 98) (CN=79) (CN=69)


9 459 048 503 8496 1146 357 9491 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)








Residential 18 acre

123


Residential 14 acre

1987


Residential 13 acre

345


Residential 12 acre

188


09


016



Institutions 052


and business

22


Distribution and

Wholesale 07

Total 4224 89634 acres 8572 79 9881 6577

3089
















and Wholesale










65 65 65 65

38 38 38 38

30 30 30 30

25 25 25 25

20 20 20 20

12 12 12 12



49 69 79 84








49 69 79 84



Labels Rules

Land Use Land Cover






Labels Rules

Land Use Land Cover









Land Use Land Cover

Curve numbers for



A2

B3

C4

D5

Impervious Areas

98

98

98

98


68

79

86

89


49

69

79

84


SCS CN Cover Type1

Rule by Area

11 Single family




gt= 40468 and lt= 50585 sqm2

11 Single family




gt 50585 and lt= 101171 sqm2

11 Single family




gt 101171 and lt= 134759 sqm2

11 Single family




gt 134759 and lt= 202341 sqm2

11 Single family




gt 202341and lt= 404683 sqm2

11 Single family




gt 404683 and lt= 809365 sqm2




33 Other offices






and Wholesale


Map

Class Name

Labeling Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics

ID

Open Spaces

21

Poor

1-25

73066

2514

242

239

164

6777

6862

04547

Good to Fair

26-50

64959

2235

328

331

253

7713

7644

0445



22

Poor

1-30

90677

3119

242

309

197

8140

6375

03701

Good to Fair

31-50

47349

1629

328

261

216

6585

8276

05939




Map

Class Name

Labeling Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics

ID

11

Impervious areas

1-40

127098

4372

258

216

210

8140

9722

09429

Open spaces

41-100

163581

5628

244

286

238

9754

8322

06734



12

Impervious areas

1-45

139227

4790

258

241

229

8876

9502

08976

Open spaces

46-100

151453

5210

244

261

232

9508

8889

07838



13

Impervious areas

1-50

152654

5252

258

261

247

9574

9464

08896

Open spaces

51-100

138025

4748

244

241

230

9426

9544

09112



14

Impervious areas

1-55

167757

5771

258

285

250

9690

8772

07473

Open spaces

56-100

122922

4229

244

217

209

8566

9631

09283



15

Impervious areas

1-60

184209

6337

258

306

251

9729

8203

06302

Open spaces

61-100

106470

3663

244

196

189

7746

9643

09305



Labels

Rules

Land Use Land Cover


Open Spaces








Labels

Rules

Land Use Land Cover


Impervious Areas








Open Spaces






























Labels Rules




Labels Rules

Land Use Land Cover





n = B 4b2









ID

Labeling

Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics









ID Open Spaces

Labeling

Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics





















































Wholesale 07 4224 89634 acres 8572 79 9881

362733112 sqm2

3089


Total










CONCLUSIONS


ACKNOWLEDGEMENTS




REFERENCES



























ABSTRACT

METHODS

Study Area



REFERENCES












Eric Warmath





ABSTRACT


INTRODUCTION






METHODS



Study Area


(a) (b)







(a) (b)
















Where

















n = B 4b2

Where


































Description (avg)

B

3

Sample Area

4


Residential 18 acre

gt= 40468 and lt= 50585 sqm

2

14386463 sqm

2


2

)

123


Residential 14 acre

gt 50585 and lt= 101171 sqm

2

23333993 sqm

2

(025 acres 101171 sqm

2

)

1987


Residential 13 acre

gt 101171 and lt= 134759 sqm

2

40492530 sqm

2

(03330 acres 134759 sqm

2

)

345


Residential 12 acre

gt 134759 and lt= 202341 sqm

2

22059243 sqm

2


2

)

188



2

)gt 202341and lt= 404683 sqm

2

10562213 sqm

2

09



2

)gt 404683 and lt= 809365 sqm

2

1845352 sqm

2

016



gt= 202341 sqm

2

6054050 sqm

2

Institutions 052


and business

gt= 202341 sqm

2

25883493 sqm

2

22


Distribution and

gt= 202341 sqm

2

8109837 sqm

2

Wholesale 07

422489634 acres8572799881

362733112 sqm

2

3089

SCS CN


Total











CONCLUSIONS





References

























Digital Data13

darr13

Pre-Processing 13


darr13


darr13



darr13


darr13

Assess Accuracy13

darr13


darr13



darr13


13

13



_1186573192xls






FSD-AOI CN Result








Table1

Table2

Table3

Table 91


Cover
























Good 39 61 74 80






Fair 36 60 73 79

Good 25 55 70 77

Farmsteads ---- 59 74 82 86

---- 72 82 87 89

---- 74 84 90 92



Land Cover

A B C D

Woods 25 55 70 77




Water 98 98 98 98




A B C D







A B C D



Highly Impervious





ID Open Spaces

Map 21 21 Poor 1-25 73066 2514 242 239 164 6777 6862 04547

Good to Fair 26-50 64959 2235 328 331 253 7713 7644 0445

Map 22 22 Poor 1-30 90677 3119 242 309 197 8140 6375 03701

Good to Fair 31-50 47349 1629 328 261 216 6585 8276 05939




value)


ID


Open spaces 41-100 163581 5628 244 286 238 9754 8322 06734



Open spaces 46-100 151453 5210 244 261 232 9508 8889 07838



Open spaces 51-100 138025 4748 244 241 230 9426 9544 09112


Open spaces 56-100 122922 4229 244 217 209 8566 9631 09283



Open spaces 61-100 106470 3663 244 196 189 7746 9643 09305






Description (avg)


Residential 18 acre

(0125 acres or 50585 sqm2)


Residential 14 acre

(025 acres 101171 sqm2)


Residential 13 acre

(03330 acres 134759 sqm2)


Residential 12 acre

(05 acres 202341 sqm2)





Institutions


and business


Distribution and

Wholesale

Total 4224 89634 3089 8572 6577





ID and A B C D










(03330 acres 134759 sqm2) (CNadj=30)



(05 acres 202341 sqm2) (CNadj=25)





















ID A B C D


1 5040 3259 1694 9994 003 001 001 006

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


2 4087 3217 2694 9976 010 009 005 024

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557 257 169 008 443

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


6 2610 3882 1273 7774 051 1557 154 2226

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


7 7304 909 682 8903 869 144 085 1096

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


8 026 005 002 032 6454 1099 1349 8903 543 059 020 622 290 060 090 439



9 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)





ID C D


1 003 001 001 006

(CN=98) (CN=89) (CN=84)

(CNadj=65)

2 010 009 005 024

(CN=98) (CN=89) (CN=84)

(CNadj=38)

3

4

5 257 169 008 443

(CN=98) (CN=89) (CN=84)

(CNadj=20)

6 051 1557 154 2226 051 1557 154 2226

(CN=98) (CN=89) (CN=84)

(Cnadj=12)

7 869 144 085 1096

(CN=98) (CN=89) (CN=84)

(CNadj=85)

8 543 059 020 622 290 060 090 439

(CN = 98) (CN =86) (CN = 79) (CN=98) (CN=89) (CN=84)


9




ID A B


1 5040 3259 1694 9994

(CN=98) (CN=79) (CN=69)

(CNadj=65)

2 4087 3217 2694 9976

(CN=98) (CN=79) (CN=69)

(CNadj=38)

3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557

(CN=98) (CN=79) (CN=69)

(CNadj=20)

6 2610 3882 1273 7774 2610 3882 1273 7774

(CN=98) (CN=79) (CN=69)

(Cnadj=12)

7 7304 909 682 8903

(CN=98) (CN=79) (CN=69)

(CNadj=85)

8 026 005 002 032 6454 1099 1349 8903

(CN = 98) (CN =68) (CN = 49) (CN = 98) (CN=79) (CN=69)


9 459 048 503 8496 1146 357 9491 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)








Residential 18 acre

123


Residential 14 acre

1987


Residential 13 acre

345


Residential 12 acre

188


09


016



Institutions 052


and business

22


Distribution and

Wholesale 07

Total 4224 89634 acres 8572 79 9881 6577

3089
















and Wholesale










65 65 65 65

38 38 38 38

30 30 30 30

25 25 25 25

20 20 20 20

12 12 12 12



49 69 79 84








49 69 79 84



Labels Rules

Land Use Land Cover






Labels Rules

Land Use Land Cover









Land Use Land Cover

Curve numbers for



A2

B3

C4

D5

Impervious Areas

98

98

98

98


68

79

86

89


49

69

79

84


SCS CN Cover Type1

Rule by Area

11 Single family




gt= 40468 and lt= 50585 sqm2

11 Single family




gt 50585 and lt= 101171 sqm2

11 Single family




gt 101171 and lt= 134759 sqm2

11 Single family




gt 134759 and lt= 202341 sqm2

11 Single family




gt 202341and lt= 404683 sqm2

11 Single family




gt 404683 and lt= 809365 sqm2




33 Other offices






and Wholesale


Map

Class Name

Labeling Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics

ID

Open Spaces

21

Poor

1-25

73066

2514

242

239

164

6777

6862

04547

Good to Fair

26-50

64959

2235

328

331

253

7713

7644

0445



22

Poor

1-30

90677

3119

242

309

197

8140

6375

03701

Good to Fair

31-50

47349

1629

328

261

216

6585

8276

05939




Map

Class Name

Labeling Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics

ID

11

Impervious areas

1-40

127098

4372

258

216

210

8140

9722

09429

Open spaces

41-100

163581

5628

244

286

238

9754

8322

06734



12

Impervious areas

1-45

139227

4790

258

241

229

8876

9502

08976

Open spaces

46-100

151453

5210

244

261

232

9508

8889

07838



13

Impervious areas

1-50

152654

5252

258

261

247

9574

9464

08896

Open spaces

51-100

138025

4748

244

241

230

9426

9544

09112



14

Impervious areas

1-55

167757

5771

258

285

250

9690

8772

07473

Open spaces

56-100

122922

4229

244

217

209

8566

9631

09283



15

Impervious areas

1-60

184209

6337

258

306

251

9729

8203

06302

Open spaces

61-100

106470

3663

244

196

189

7746

9643

09305



Labels

Rules

Land Use Land Cover


Open Spaces








Labels

Rules

Land Use Land Cover


Impervious Areas








Open Spaces













ID

Labeling

Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics









ID Open Spaces

Labeling

Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics





















































Wholesale 07 4224 89634 acres 8572 79 9881

362733112 sqm2

3089


Total










CONCLUSIONS


ACKNOWLEDGEMENTS




REFERENCES



























ABSTRACT

METHODS

Study Area



REFERENCES












Eric Warmath





ABSTRACT


INTRODUCTION






METHODS



Study Area


(a) (b)







(a) (b)
















Where

















n = B 4b2

Where


































Description (avg)

B

3

Sample Area

4


Residential 18 acre

gt= 40468 and lt= 50585 sqm

2

14386463 sqm

2


2

)

123


Residential 14 acre

gt 50585 and lt= 101171 sqm

2

23333993 sqm

2

(025 acres 101171 sqm

2

)

1987


Residential 13 acre

gt 101171 and lt= 134759 sqm

2

40492530 sqm

2

(03330 acres 134759 sqm

2

)

345


Residential 12 acre

gt 134759 and lt= 202341 sqm

2

22059243 sqm

2


2

)

188



2

)gt 202341and lt= 404683 sqm

2

10562213 sqm

2

09



2

)gt 404683 and lt= 809365 sqm

2

1845352 sqm

2

016



gt= 202341 sqm

2

6054050 sqm

2

Institutions 052


and business

gt= 202341 sqm

2

25883493 sqm

2

22


Distribution and

gt= 202341 sqm

2

8109837 sqm

2

Wholesale 07

422489634 acres8572799881

362733112 sqm

2

3089

SCS CN


Total











CONCLUSIONS





References

























Digital Data13

darr13

Pre-Processing 13


darr13


darr13



darr13


darr13

Assess Accuracy13

darr13


darr13



darr13


13

13



_1186573192xls






FSD-AOI CN Result








Table1

Table2

Table3

Table 91


Cover
























Good 39 61 74 80






Fair 36 60 73 79

Good 25 55 70 77

Farmsteads ---- 59 74 82 86

---- 72 82 87 89

---- 74 84 90 92



Land Cover

A B C D

Woods 25 55 70 77




Water 98 98 98 98




A B C D







A B C D



Highly Impervious





ID Open Spaces

Map 21 21 Poor 1-25 73066 2514 242 239 164 6777 6862 04547

Good to Fair 26-50 64959 2235 328 331 253 7713 7644 0445

Map 22 22 Poor 1-30 90677 3119 242 309 197 8140 6375 03701

Good to Fair 31-50 47349 1629 328 261 216 6585 8276 05939




value)


ID


Open spaces 41-100 163581 5628 244 286 238 9754 8322 06734



Open spaces 46-100 151453 5210 244 261 232 9508 8889 07838



Open spaces 51-100 138025 4748 244 241 230 9426 9544 09112


Open spaces 56-100 122922 4229 244 217 209 8566 9631 09283



Open spaces 61-100 106470 3663 244 196 189 7746 9643 09305






Description (avg)


Residential 18 acre

(0125 acres or 50585 sqm2)


Residential 14 acre

(025 acres 101171 sqm2)


Residential 13 acre

(03330 acres 134759 sqm2)


Residential 12 acre

(05 acres 202341 sqm2)





Institutions


and business


Distribution and

Wholesale

Total 4224 89634 3089 8572 6577





ID and A B C D










(03330 acres 134759 sqm2) (CNadj=30)



(05 acres 202341 sqm2) (CNadj=25)





















ID A B C D


1 5040 3259 1694 9994 003 001 001 006

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


2 4087 3217 2694 9976 010 009 005 024

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557 257 169 008 443

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


6 2610 3882 1273 7774 051 1557 154 2226

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


7 7304 909 682 8903 869 144 085 1096

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


8 026 005 002 032 6454 1099 1349 8903 543 059 020 622 290 060 090 439



9 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)





ID C D


1 003 001 001 006

(CN=98) (CN=89) (CN=84)

(CNadj=65)

2 010 009 005 024

(CN=98) (CN=89) (CN=84)

(CNadj=38)

3

4

5 257 169 008 443

(CN=98) (CN=89) (CN=84)

(CNadj=20)

6 051 1557 154 2226 051 1557 154 2226

(CN=98) (CN=89) (CN=84)

(Cnadj=12)

7 869 144 085 1096

(CN=98) (CN=89) (CN=84)

(CNadj=85)

8 543 059 020 622 290 060 090 439

(CN = 98) (CN =86) (CN = 79) (CN=98) (CN=89) (CN=84)


9




ID A B


1 5040 3259 1694 9994

(CN=98) (CN=79) (CN=69)

(CNadj=65)

2 4087 3217 2694 9976

(CN=98) (CN=79) (CN=69)

(CNadj=38)

3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557

(CN=98) (CN=79) (CN=69)

(CNadj=20)

6 2610 3882 1273 7774 2610 3882 1273 7774

(CN=98) (CN=79) (CN=69)

(Cnadj=12)

7 7304 909 682 8903

(CN=98) (CN=79) (CN=69)

(CNadj=85)

8 026 005 002 032 6454 1099 1349 8903

(CN = 98) (CN =68) (CN = 49) (CN = 98) (CN=79) (CN=69)


9 459 048 503 8496 1146 357 9491 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)








Residential 18 acre

123


Residential 14 acre

1987


Residential 13 acre

345


Residential 12 acre

188


09


016



Institutions 052


and business

22


Distribution and

Wholesale 07

Total 4224 89634 acres 8572 79 9881 6577

3089
















and Wholesale










65 65 65 65

38 38 38 38

30 30 30 30

25 25 25 25

20 20 20 20

12 12 12 12



49 69 79 84








49 69 79 84



Labels Rules

Land Use Land Cover






Labels Rules

Land Use Land Cover









Land Use Land Cover

Curve numbers for



A2

B3

C4

D5

Impervious Areas

98

98

98

98


68

79

86

89


49

69

79

84


SCS CN Cover Type1

Rule by Area

11 Single family




gt= 40468 and lt= 50585 sqm2

11 Single family




gt 50585 and lt= 101171 sqm2

11 Single family




gt 101171 and lt= 134759 sqm2

11 Single family




gt 134759 and lt= 202341 sqm2

11 Single family




gt 202341and lt= 404683 sqm2

11 Single family




gt 404683 and lt= 809365 sqm2




33 Other offices






and Wholesale


Map

Class Name

Labeling Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics

ID

Open Spaces

21

Poor

1-25

73066

2514

242

239

164

6777

6862

04547

Good to Fair

26-50

64959

2235

328

331

253

7713

7644

0445



22

Poor

1-30

90677

3119

242

309

197

8140

6375

03701

Good to Fair

31-50

47349

1629

328

261

216

6585

8276

05939




Map

Class Name

Labeling Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics

ID

11

Impervious areas

1-40

127098

4372

258

216

210

8140

9722

09429

Open spaces

41-100

163581

5628

244

286

238

9754

8322

06734



12

Impervious areas

1-45

139227

4790

258

241

229

8876

9502

08976

Open spaces

46-100

151453

5210

244

261

232

9508

8889

07838



13

Impervious areas

1-50

152654

5252

258

261

247

9574

9464

08896

Open spaces

51-100

138025

4748

244

241

230

9426

9544

09112



14

Impervious areas

1-55

167757

5771

258

285

250

9690

8772

07473

Open spaces

56-100

122922

4229

244

217

209

8566

9631

09283



15

Impervious areas

1-60

184209

6337

258

306

251

9729

8203

06302

Open spaces

61-100

106470

3663

244

196

189

7746

9643

09305



Labels

Rules

Land Use Land Cover


Open Spaces








Labels

Rules

Land Use Land Cover


Impervious Areas








Open Spaces




















































Wholesale 07 4224 89634 acres 8572 79 9881

362733112 sqm2

3089


Total










CONCLUSIONS


ACKNOWLEDGEMENTS




REFERENCES



























ABSTRACT

METHODS

Study Area



REFERENCES












Eric Warmath





ABSTRACT


INTRODUCTION






METHODS



Study Area


(a) (b)







(a) (b)
















Where

















n = B 4b2

Where


































Description (avg)

B

3

Sample Area

4


Residential 18 acre

gt= 40468 and lt= 50585 sqm

2

14386463 sqm

2


2

)

123


Residential 14 acre

gt 50585 and lt= 101171 sqm

2

23333993 sqm

2

(025 acres 101171 sqm

2

)

1987


Residential 13 acre

gt 101171 and lt= 134759 sqm

2

40492530 sqm

2

(03330 acres 134759 sqm

2

)

345


Residential 12 acre

gt 134759 and lt= 202341 sqm

2

22059243 sqm

2


2

)

188



2

)gt 202341and lt= 404683 sqm

2

10562213 sqm

2

09



2

)gt 404683 and lt= 809365 sqm

2

1845352 sqm

2

016



gt= 202341 sqm

2

6054050 sqm

2

Institutions 052


and business

gt= 202341 sqm

2

25883493 sqm

2

22


Distribution and

gt= 202341 sqm

2

8109837 sqm

2

Wholesale 07

422489634 acres8572799881

362733112 sqm

2

3089

SCS CN


Total











CONCLUSIONS





References

























Digital Data13

darr13

Pre-Processing 13


darr13


darr13



darr13


darr13

Assess Accuracy13

darr13


darr13



darr13


13

13



_1186573192xls






FSD-AOI CN Result








Table1

Table2

Table3

Table 91


Cover
























Good 39 61 74 80






Fair 36 60 73 79

Good 25 55 70 77

Farmsteads ---- 59 74 82 86

---- 72 82 87 89

---- 74 84 90 92



Land Cover

A B C D

Woods 25 55 70 77




Water 98 98 98 98




A B C D







A B C D



Highly Impervious





ID Open Spaces

Map 21 21 Poor 1-25 73066 2514 242 239 164 6777 6862 04547

Good to Fair 26-50 64959 2235 328 331 253 7713 7644 0445

Map 22 22 Poor 1-30 90677 3119 242 309 197 8140 6375 03701

Good to Fair 31-50 47349 1629 328 261 216 6585 8276 05939




value)


ID


Open spaces 41-100 163581 5628 244 286 238 9754 8322 06734



Open spaces 46-100 151453 5210 244 261 232 9508 8889 07838



Open spaces 51-100 138025 4748 244 241 230 9426 9544 09112


Open spaces 56-100 122922 4229 244 217 209 8566 9631 09283



Open spaces 61-100 106470 3663 244 196 189 7746 9643 09305






Description (avg)


Residential 18 acre

(0125 acres or 50585 sqm2)


Residential 14 acre

(025 acres 101171 sqm2)


Residential 13 acre

(03330 acres 134759 sqm2)


Residential 12 acre

(05 acres 202341 sqm2)





Institutions


and business


Distribution and

Wholesale

Total 4224 89634 3089 8572 6577





ID and A B C D










(03330 acres 134759 sqm2) (CNadj=30)



(05 acres 202341 sqm2) (CNadj=25)





















ID A B C D


1 5040 3259 1694 9994 003 001 001 006

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


2 4087 3217 2694 9976 010 009 005 024

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557 257 169 008 443

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


6 2610 3882 1273 7774 051 1557 154 2226

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


7 7304 909 682 8903 869 144 085 1096

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


8 026 005 002 032 6454 1099 1349 8903 543 059 020 622 290 060 090 439



9 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)





ID C D


1 003 001 001 006

(CN=98) (CN=89) (CN=84)

(CNadj=65)

2 010 009 005 024

(CN=98) (CN=89) (CN=84)

(CNadj=38)

3

4

5 257 169 008 443

(CN=98) (CN=89) (CN=84)

(CNadj=20)

6 051 1557 154 2226 051 1557 154 2226

(CN=98) (CN=89) (CN=84)

(Cnadj=12)

7 869 144 085 1096

(CN=98) (CN=89) (CN=84)

(CNadj=85)

8 543 059 020 622 290 060 090 439

(CN = 98) (CN =86) (CN = 79) (CN=98) (CN=89) (CN=84)


9




ID A B


1 5040 3259 1694 9994

(CN=98) (CN=79) (CN=69)

(CNadj=65)

2 4087 3217 2694 9976

(CN=98) (CN=79) (CN=69)

(CNadj=38)

3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557

(CN=98) (CN=79) (CN=69)

(CNadj=20)

6 2610 3882 1273 7774 2610 3882 1273 7774

(CN=98) (CN=79) (CN=69)

(Cnadj=12)

7 7304 909 682 8903

(CN=98) (CN=79) (CN=69)

(CNadj=85)

8 026 005 002 032 6454 1099 1349 8903

(CN = 98) (CN =68) (CN = 49) (CN = 98) (CN=79) (CN=69)


9 459 048 503 8496 1146 357 9491 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)








Residential 18 acre

123


Residential 14 acre

1987


Residential 13 acre

345


Residential 12 acre

188


09


016



Institutions 052


and business

22


Distribution and

Wholesale 07

Total 4224 89634 acres 8572 79 9881 6577

3089
















and Wholesale










65 65 65 65

38 38 38 38

30 30 30 30

25 25 25 25

20 20 20 20

12 12 12 12



49 69 79 84








49 69 79 84



Labels Rules

Land Use Land Cover






Labels Rules

Land Use Land Cover









Land Use Land Cover

Curve numbers for



A2

B3

C4

D5

Impervious Areas

98

98

98

98


68

79

86

89


49

69

79

84


SCS CN Cover Type1

Rule by Area

11 Single family




gt= 40468 and lt= 50585 sqm2

11 Single family




gt 50585 and lt= 101171 sqm2

11 Single family




gt 101171 and lt= 134759 sqm2

11 Single family




gt 134759 and lt= 202341 sqm2

11 Single family




gt 202341and lt= 404683 sqm2

11 Single family




gt 404683 and lt= 809365 sqm2




33 Other offices






and Wholesale


Map

Class Name

Labeling Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics

ID

Open Spaces

21

Poor

1-25

73066

2514

242

239

164

6777

6862

04547

Good to Fair

26-50

64959

2235

328

331

253

7713

7644

0445



22

Poor

1-30

90677

3119

242

309

197

8140

6375

03701

Good to Fair

31-50

47349

1629

328

261

216

6585

8276

05939




Map

Class Name

Labeling Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics

ID

11

Impervious areas

1-40

127098

4372

258

216

210

8140

9722

09429

Open spaces

41-100

163581

5628

244

286

238

9754

8322

06734



12

Impervious areas

1-45

139227

4790

258

241

229

8876

9502

08976

Open spaces

46-100

151453

5210

244

261

232

9508

8889

07838



13

Impervious areas

1-50

152654

5252

258

261

247

9574

9464

08896

Open spaces

51-100

138025

4748

244

241

230

9426

9544

09112



14

Impervious areas

1-55

167757

5771

258

285

250

9690

8772

07473

Open spaces

56-100

122922

4229

244

217

209

8566

9631

09283



15

Impervious areas

1-60

184209

6337

258

306

251

9729

8203

06302

Open spaces

61-100

106470

3663

244

196

189

7746

9643

09305



Labels

Rules

Land Use Land Cover


Open Spaces








Labels

Rules

Land Use Land Cover


Impervious Areas








Open Spaces











CONCLUSIONS


ACKNOWLEDGEMENTS




REFERENCES



























ABSTRACT

METHODS

Study Area



REFERENCES












Eric Warmath





ABSTRACT


INTRODUCTION






METHODS



Study Area


(a) (b)







(a) (b)
















Where

















n = B 4b2

Where


































Description (avg)

B

3

Sample Area

4


Residential 18 acre

gt= 40468 and lt= 50585 sqm

2

14386463 sqm

2


2

)

123


Residential 14 acre

gt 50585 and lt= 101171 sqm

2

23333993 sqm

2

(025 acres 101171 sqm

2

)

1987


Residential 13 acre

gt 101171 and lt= 134759 sqm

2

40492530 sqm

2

(03330 acres 134759 sqm

2

)

345


Residential 12 acre

gt 134759 and lt= 202341 sqm

2

22059243 sqm

2


2

)

188



2

)gt 202341and lt= 404683 sqm

2

10562213 sqm

2

09



2

)gt 404683 and lt= 809365 sqm

2

1845352 sqm

2

016



gt= 202341 sqm

2

6054050 sqm

2

Institutions 052


and business

gt= 202341 sqm

2

25883493 sqm

2

22


Distribution and

gt= 202341 sqm

2

8109837 sqm

2

Wholesale 07

422489634 acres8572799881

362733112 sqm

2

3089

SCS CN


Total











CONCLUSIONS





References

























Digital Data13

darr13

Pre-Processing 13


darr13


darr13



darr13


darr13

Assess Accuracy13

darr13


darr13



darr13


13

13



_1186573192xls






FSD-AOI CN Result








Table1

Table2

Table3

Table 91


Cover
























Good 39 61 74 80






Fair 36 60 73 79

Good 25 55 70 77

Farmsteads ---- 59 74 82 86

---- 72 82 87 89

---- 74 84 90 92



Land Cover

A B C D

Woods 25 55 70 77




Water 98 98 98 98




A B C D







A B C D



Highly Impervious





ID Open Spaces

Map 21 21 Poor 1-25 73066 2514 242 239 164 6777 6862 04547

Good to Fair 26-50 64959 2235 328 331 253 7713 7644 0445

Map 22 22 Poor 1-30 90677 3119 242 309 197 8140 6375 03701

Good to Fair 31-50 47349 1629 328 261 216 6585 8276 05939




value)


ID


Open spaces 41-100 163581 5628 244 286 238 9754 8322 06734



Open spaces 46-100 151453 5210 244 261 232 9508 8889 07838



Open spaces 51-100 138025 4748 244 241 230 9426 9544 09112


Open spaces 56-100 122922 4229 244 217 209 8566 9631 09283



Open spaces 61-100 106470 3663 244 196 189 7746 9643 09305






Description (avg)


Residential 18 acre

(0125 acres or 50585 sqm2)


Residential 14 acre

(025 acres 101171 sqm2)


Residential 13 acre

(03330 acres 134759 sqm2)


Residential 12 acre

(05 acres 202341 sqm2)





Institutions


and business


Distribution and

Wholesale

Total 4224 89634 3089 8572 6577





ID and A B C D










(03330 acres 134759 sqm2) (CNadj=30)



(05 acres 202341 sqm2) (CNadj=25)





















ID A B C D


1 5040 3259 1694 9994 003 001 001 006

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


2 4087 3217 2694 9976 010 009 005 024

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557 257 169 008 443

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


6 2610 3882 1273 7774 051 1557 154 2226

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


7 7304 909 682 8903 869 144 085 1096

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


8 026 005 002 032 6454 1099 1349 8903 543 059 020 622 290 060 090 439



9 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)





ID C D


1 003 001 001 006

(CN=98) (CN=89) (CN=84)

(CNadj=65)

2 010 009 005 024

(CN=98) (CN=89) (CN=84)

(CNadj=38)

3

4

5 257 169 008 443

(CN=98) (CN=89) (CN=84)

(CNadj=20)

6 051 1557 154 2226 051 1557 154 2226

(CN=98) (CN=89) (CN=84)

(Cnadj=12)

7 869 144 085 1096

(CN=98) (CN=89) (CN=84)

(CNadj=85)

8 543 059 020 622 290 060 090 439

(CN = 98) (CN =86) (CN = 79) (CN=98) (CN=89) (CN=84)


9




ID A B


1 5040 3259 1694 9994

(CN=98) (CN=79) (CN=69)

(CNadj=65)

2 4087 3217 2694 9976

(CN=98) (CN=79) (CN=69)

(CNadj=38)

3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557

(CN=98) (CN=79) (CN=69)

(CNadj=20)

6 2610 3882 1273 7774 2610 3882 1273 7774

(CN=98) (CN=79) (CN=69)

(Cnadj=12)

7 7304 909 682 8903

(CN=98) (CN=79) (CN=69)

(CNadj=85)

8 026 005 002 032 6454 1099 1349 8903

(CN = 98) (CN =68) (CN = 49) (CN = 98) (CN=79) (CN=69)


9 459 048 503 8496 1146 357 9491 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)








Residential 18 acre

123


Residential 14 acre

1987


Residential 13 acre

345


Residential 12 acre

188


09


016



Institutions 052


and business

22


Distribution and

Wholesale 07

Total 4224 89634 acres 8572 79 9881 6577

3089
















and Wholesale










65 65 65 65

38 38 38 38

30 30 30 30

25 25 25 25

20 20 20 20

12 12 12 12



49 69 79 84








49 69 79 84



Labels Rules

Land Use Land Cover






Labels Rules

Land Use Land Cover









Land Use Land Cover

Curve numbers for



A2

B3

C4

D5

Impervious Areas

98

98

98

98


68

79

86

89


49

69

79

84


SCS CN Cover Type1

Rule by Area

11 Single family




gt= 40468 and lt= 50585 sqm2

11 Single family




gt 50585 and lt= 101171 sqm2

11 Single family




gt 101171 and lt= 134759 sqm2

11 Single family




gt 134759 and lt= 202341 sqm2

11 Single family




gt 202341and lt= 404683 sqm2

11 Single family




gt 404683 and lt= 809365 sqm2




33 Other offices






and Wholesale


Map

Class Name

Labeling Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics

ID

Open Spaces

21

Poor

1-25

73066

2514

242

239

164

6777

6862

04547

Good to Fair

26-50

64959

2235

328

331

253

7713

7644

0445



22

Poor

1-30

90677

3119

242

309

197

8140

6375

03701

Good to Fair

31-50

47349

1629

328

261

216

6585

8276

05939




Map

Class Name

Labeling Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics

ID

11

Impervious areas

1-40

127098

4372

258

216

210

8140

9722

09429

Open spaces

41-100

163581

5628

244

286

238

9754

8322

06734



12

Impervious areas

1-45

139227

4790

258

241

229

8876

9502

08976

Open spaces

46-100

151453

5210

244

261

232

9508

8889

07838



13

Impervious areas

1-50

152654

5252

258

261

247

9574

9464

08896

Open spaces

51-100

138025

4748

244

241

230

9426

9544

09112



14

Impervious areas

1-55

167757

5771

258

285

250

9690

8772

07473

Open spaces

56-100

122922

4229

244

217

209

8566

9631

09283



15

Impervious areas

1-60

184209

6337

258

306

251

9729

8203

06302

Open spaces

61-100

106470

3663

244

196

189

7746

9643

09305



Labels

Rules

Land Use Land Cover


Open Spaces








Labels

Rules

Land Use Land Cover


Impervious Areas








Open Spaces







REFERENCES



























ABSTRACT

METHODS

Study Area



REFERENCES












Eric Warmath





ABSTRACT


INTRODUCTION






METHODS



Study Area


(a) (b)







(a) (b)
















Where

















n = B 4b2

Where


































Description (avg)

B

3

Sample Area

4


Residential 18 acre

gt= 40468 and lt= 50585 sqm

2

14386463 sqm

2


2

)

123


Residential 14 acre

gt 50585 and lt= 101171 sqm

2

23333993 sqm

2

(025 acres 101171 sqm

2

)

1987


Residential 13 acre

gt 101171 and lt= 134759 sqm

2

40492530 sqm

2

(03330 acres 134759 sqm

2

)

345


Residential 12 acre

gt 134759 and lt= 202341 sqm

2

22059243 sqm

2


2

)

188



2

)gt 202341and lt= 404683 sqm

2

10562213 sqm

2

09



2

)gt 404683 and lt= 809365 sqm

2

1845352 sqm

2

016



gt= 202341 sqm

2

6054050 sqm

2

Institutions 052


and business

gt= 202341 sqm

2

25883493 sqm

2

22


Distribution and

gt= 202341 sqm

2

8109837 sqm

2

Wholesale 07

422489634 acres8572799881

362733112 sqm

2

3089

SCS CN


Total











CONCLUSIONS





References

























Digital Data13

darr13

Pre-Processing 13


darr13


darr13



darr13


darr13

Assess Accuracy13

darr13


darr13



darr13


13

13



_1186573192xls






FSD-AOI CN Result








Table1

Table2

Table3

Table 91


Cover
























Good 39 61 74 80






Fair 36 60 73 79

Good 25 55 70 77

Farmsteads ---- 59 74 82 86

---- 72 82 87 89

---- 74 84 90 92



Land Cover

A B C D

Woods 25 55 70 77




Water 98 98 98 98




A B C D







A B C D



Highly Impervious





ID Open Spaces

Map 21 21 Poor 1-25 73066 2514 242 239 164 6777 6862 04547

Good to Fair 26-50 64959 2235 328 331 253 7713 7644 0445

Map 22 22 Poor 1-30 90677 3119 242 309 197 8140 6375 03701

Good to Fair 31-50 47349 1629 328 261 216 6585 8276 05939




value)


ID


Open spaces 41-100 163581 5628 244 286 238 9754 8322 06734



Open spaces 46-100 151453 5210 244 261 232 9508 8889 07838



Open spaces 51-100 138025 4748 244 241 230 9426 9544 09112


Open spaces 56-100 122922 4229 244 217 209 8566 9631 09283



Open spaces 61-100 106470 3663 244 196 189 7746 9643 09305






Description (avg)


Residential 18 acre

(0125 acres or 50585 sqm2)


Residential 14 acre

(025 acres 101171 sqm2)


Residential 13 acre

(03330 acres 134759 sqm2)


Residential 12 acre

(05 acres 202341 sqm2)





Institutions


and business


Distribution and

Wholesale

Total 4224 89634 3089 8572 6577





ID and A B C D










(03330 acres 134759 sqm2) (CNadj=30)



(05 acres 202341 sqm2) (CNadj=25)





















ID A B C D


1 5040 3259 1694 9994 003 001 001 006

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


2 4087 3217 2694 9976 010 009 005 024

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557 257 169 008 443

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


6 2610 3882 1273 7774 051 1557 154 2226

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


7 7304 909 682 8903 869 144 085 1096

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


8 026 005 002 032 6454 1099 1349 8903 543 059 020 622 290 060 090 439



9 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)





ID C D


1 003 001 001 006

(CN=98) (CN=89) (CN=84)

(CNadj=65)

2 010 009 005 024

(CN=98) (CN=89) (CN=84)

(CNadj=38)

3

4

5 257 169 008 443

(CN=98) (CN=89) (CN=84)

(CNadj=20)

6 051 1557 154 2226 051 1557 154 2226

(CN=98) (CN=89) (CN=84)

(Cnadj=12)

7 869 144 085 1096

(CN=98) (CN=89) (CN=84)

(CNadj=85)

8 543 059 020 622 290 060 090 439

(CN = 98) (CN =86) (CN = 79) (CN=98) (CN=89) (CN=84)


9




ID A B


1 5040 3259 1694 9994

(CN=98) (CN=79) (CN=69)

(CNadj=65)

2 4087 3217 2694 9976

(CN=98) (CN=79) (CN=69)

(CNadj=38)

3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557

(CN=98) (CN=79) (CN=69)

(CNadj=20)

6 2610 3882 1273 7774 2610 3882 1273 7774

(CN=98) (CN=79) (CN=69)

(Cnadj=12)

7 7304 909 682 8903

(CN=98) (CN=79) (CN=69)

(CNadj=85)

8 026 005 002 032 6454 1099 1349 8903

(CN = 98) (CN =68) (CN = 49) (CN = 98) (CN=79) (CN=69)


9 459 048 503 8496 1146 357 9491 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)








Residential 18 acre

123


Residential 14 acre

1987


Residential 13 acre

345


Residential 12 acre

188


09


016



Institutions 052


and business

22


Distribution and

Wholesale 07

Total 4224 89634 acres 8572 79 9881 6577

3089
















and Wholesale










65 65 65 65

38 38 38 38

30 30 30 30

25 25 25 25

20 20 20 20

12 12 12 12



49 69 79 84








49 69 79 84



Labels Rules

Land Use Land Cover






Labels Rules

Land Use Land Cover









Land Use Land Cover

Curve numbers for



A2

B3

C4

D5

Impervious Areas

98

98

98

98


68

79

86

89


49

69

79

84


SCS CN Cover Type1

Rule by Area

11 Single family




gt= 40468 and lt= 50585 sqm2

11 Single family




gt 50585 and lt= 101171 sqm2

11 Single family




gt 101171 and lt= 134759 sqm2

11 Single family




gt 134759 and lt= 202341 sqm2

11 Single family




gt 202341and lt= 404683 sqm2

11 Single family




gt 404683 and lt= 809365 sqm2




33 Other offices






and Wholesale


Map

Class Name

Labeling Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics

ID

Open Spaces

21

Poor

1-25

73066

2514

242

239

164

6777

6862

04547

Good to Fair

26-50

64959

2235

328

331

253

7713

7644

0445



22

Poor

1-30

90677

3119

242

309

197

8140

6375

03701

Good to Fair

31-50

47349

1629

328

261

216

6585

8276

05939




Map

Class Name

Labeling Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics

ID

11

Impervious areas

1-40

127098

4372

258

216

210

8140

9722

09429

Open spaces

41-100

163581

5628

244

286

238

9754

8322

06734



12

Impervious areas

1-45

139227

4790

258

241

229

8876

9502

08976

Open spaces

46-100

151453

5210

244

261

232

9508

8889

07838



13

Impervious areas

1-50

152654

5252

258

261

247

9574

9464

08896

Open spaces

51-100

138025

4748

244

241

230

9426

9544

09112



14

Impervious areas

1-55

167757

5771

258

285

250

9690

8772

07473

Open spaces

56-100

122922

4229

244

217

209

8566

9631

09283



15

Impervious areas

1-60

184209

6337

258

306

251

9729

8203

06302

Open spaces

61-100

106470

3663

244

196

189

7746

9643

09305



Labels

Rules

Land Use Land Cover


Open Spaces








Labels

Rules

Land Use Land Cover


Impervious Areas








Open Spaces










ABSTRACT

METHODS

Study Area



REFERENCES












Eric Warmath





ABSTRACT


INTRODUCTION






METHODS



Study Area


(a) (b)







(a) (b)
















Where

















n = B 4b2

Where


































Description (avg)

B

3

Sample Area

4


Residential 18 acre

gt= 40468 and lt= 50585 sqm

2

14386463 sqm

2


2

)

123


Residential 14 acre

gt 50585 and lt= 101171 sqm

2

23333993 sqm

2

(025 acres 101171 sqm

2

)

1987


Residential 13 acre

gt 101171 and lt= 134759 sqm

2

40492530 sqm

2

(03330 acres 134759 sqm

2

)

345


Residential 12 acre

gt 134759 and lt= 202341 sqm

2

22059243 sqm

2


2

)

188



2

)gt 202341and lt= 404683 sqm

2

10562213 sqm

2

09



2

)gt 404683 and lt= 809365 sqm

2

1845352 sqm

2

016



gt= 202341 sqm

2

6054050 sqm

2

Institutions 052


and business

gt= 202341 sqm

2

25883493 sqm

2

22


Distribution and

gt= 202341 sqm

2

8109837 sqm

2

Wholesale 07

422489634 acres8572799881

362733112 sqm

2

3089

SCS CN


Total











CONCLUSIONS





References

























Digital Data13

darr13

Pre-Processing 13


darr13


darr13



darr13


darr13

Assess Accuracy13

darr13


darr13



darr13


13

13



_1186573192xls






FSD-AOI CN Result








Table1

Table2

Table3

Table 91


Cover
























Good 39 61 74 80






Fair 36 60 73 79

Good 25 55 70 77

Farmsteads ---- 59 74 82 86

---- 72 82 87 89

---- 74 84 90 92



Land Cover

A B C D

Woods 25 55 70 77




Water 98 98 98 98




A B C D







A B C D



Highly Impervious





ID Open Spaces

Map 21 21 Poor 1-25 73066 2514 242 239 164 6777 6862 04547

Good to Fair 26-50 64959 2235 328 331 253 7713 7644 0445

Map 22 22 Poor 1-30 90677 3119 242 309 197 8140 6375 03701

Good to Fair 31-50 47349 1629 328 261 216 6585 8276 05939




value)


ID


Open spaces 41-100 163581 5628 244 286 238 9754 8322 06734



Open spaces 46-100 151453 5210 244 261 232 9508 8889 07838



Open spaces 51-100 138025 4748 244 241 230 9426 9544 09112


Open spaces 56-100 122922 4229 244 217 209 8566 9631 09283



Open spaces 61-100 106470 3663 244 196 189 7746 9643 09305






Description (avg)


Residential 18 acre

(0125 acres or 50585 sqm2)


Residential 14 acre

(025 acres 101171 sqm2)


Residential 13 acre

(03330 acres 134759 sqm2)


Residential 12 acre

(05 acres 202341 sqm2)





Institutions


and business


Distribution and

Wholesale

Total 4224 89634 3089 8572 6577





ID and A B C D










(03330 acres 134759 sqm2) (CNadj=30)



(05 acres 202341 sqm2) (CNadj=25)





















ID A B C D


1 5040 3259 1694 9994 003 001 001 006

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


2 4087 3217 2694 9976 010 009 005 024

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557 257 169 008 443

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


6 2610 3882 1273 7774 051 1557 154 2226

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


7 7304 909 682 8903 869 144 085 1096

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


8 026 005 002 032 6454 1099 1349 8903 543 059 020 622 290 060 090 439



9 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)





ID C D


1 003 001 001 006

(CN=98) (CN=89) (CN=84)

(CNadj=65)

2 010 009 005 024

(CN=98) (CN=89) (CN=84)

(CNadj=38)

3

4

5 257 169 008 443

(CN=98) (CN=89) (CN=84)

(CNadj=20)

6 051 1557 154 2226 051 1557 154 2226

(CN=98) (CN=89) (CN=84)

(Cnadj=12)

7 869 144 085 1096

(CN=98) (CN=89) (CN=84)

(CNadj=85)

8 543 059 020 622 290 060 090 439

(CN = 98) (CN =86) (CN = 79) (CN=98) (CN=89) (CN=84)


9




ID A B


1 5040 3259 1694 9994

(CN=98) (CN=79) (CN=69)

(CNadj=65)

2 4087 3217 2694 9976

(CN=98) (CN=79) (CN=69)

(CNadj=38)

3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557

(CN=98) (CN=79) (CN=69)

(CNadj=20)

6 2610 3882 1273 7774 2610 3882 1273 7774

(CN=98) (CN=79) (CN=69)

(Cnadj=12)

7 7304 909 682 8903

(CN=98) (CN=79) (CN=69)

(CNadj=85)

8 026 005 002 032 6454 1099 1349 8903

(CN = 98) (CN =68) (CN = 49) (CN = 98) (CN=79) (CN=69)


9 459 048 503 8496 1146 357 9491 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)








Residential 18 acre

123


Residential 14 acre

1987


Residential 13 acre

345


Residential 12 acre

188


09


016



Institutions 052


and business

22


Distribution and

Wholesale 07

Total 4224 89634 acres 8572 79 9881 6577

3089
















and Wholesale










65 65 65 65

38 38 38 38

30 30 30 30

25 25 25 25

20 20 20 20

12 12 12 12



49 69 79 84








49 69 79 84



Labels Rules

Land Use Land Cover






Labels Rules

Land Use Land Cover









Land Use Land Cover

Curve numbers for



A2

B3

C4

D5

Impervious Areas

98

98

98

98


68

79

86

89


49

69

79

84


SCS CN Cover Type1

Rule by Area

11 Single family




gt= 40468 and lt= 50585 sqm2

11 Single family




gt 50585 and lt= 101171 sqm2

11 Single family




gt 101171 and lt= 134759 sqm2

11 Single family




gt 134759 and lt= 202341 sqm2

11 Single family




gt 202341and lt= 404683 sqm2

11 Single family




gt 404683 and lt= 809365 sqm2




33 Other offices






and Wholesale


Map

Class Name

Labeling Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics

ID

Open Spaces

21

Poor

1-25

73066

2514

242

239

164

6777

6862

04547

Good to Fair

26-50

64959

2235

328

331

253

7713

7644

0445



22

Poor

1-30

90677

3119

242

309

197

8140

6375

03701

Good to Fair

31-50

47349

1629

328

261

216

6585

8276

05939




Map

Class Name

Labeling Criteria

Areas (Acres)

Areas ()

Reference Totals

Classified Totals

Number Correct


Users Accuracy ()

Kappa Statistics

ID

11

Impervious areas

1-40

127098

4372

258

216

210

8140

9722

09429

Open spaces

41-100

163581

5628

244

286

238

9754

8322

06734



12

Impervious areas

1-45

139227

4790

258

241

229

8876

9502

08976

Open spaces

46-100

151453

5210

244

261

232

9508

8889

07838



13

Impervious areas

1-50

152654

5252

258

261

247

9574

9464

08896

Open spaces

51-100

138025

4748

244

241

230

9426

9544

09112



14

Impervious areas

1-55

167757

5771

258

285

250

9690

8772

07473

Open spaces

56-100

122922

4229

244

217

209

8566

9631

09283



15

Impervious areas

1-60

184209

6337

258

306

251

9729

8203

06302

Open spaces

61-100

106470

3663

244

196

189

7746

9643

09305



Labels

Rules

Land Use Land Cover


Open Spaces








Labels

Rules

Land Use Land Cover


Impervious Areas








Open Spaces












FSD-AOI CN Result








Table1

Table2

Table3

Table 91


Cover
























Good 39 61 74 80






Fair 36 60 73 79

Good 25 55 70 77

Farmsteads ---- 59 74 82 86

---- 72 82 87 89

---- 74 84 90 92



Land Cover

A B C D

Woods 25 55 70 77




Water 98 98 98 98




A B C D







A B C D



Highly Impervious





ID Open Spaces

Map 21 21 Poor 1-25 73066 2514 242 239 164 6777 6862 04547

Good to Fair 26-50 64959 2235 328 331 253 7713 7644 0445

Map 22 22 Poor 1-30 90677 3119 242 309 197 8140 6375 03701

Good to Fair 31-50 47349 1629 328 261 216 6585 8276 05939




value)


ID


Open spaces 41-100 163581 5628 244 286 238 9754 8322 06734



Open spaces 46-100 151453 5210 244 261 232 9508 8889 07838



Open spaces 51-100 138025 4748 244 241 230 9426 9544 09112


Open spaces 56-100 122922 4229 244 217 209 8566 9631 09283



Open spaces 61-100 106470 3663 244 196 189 7746 9643 09305






Description (avg)


Residential 18 acre

(0125 acres or 50585 sqm2)


Residential 14 acre

(025 acres 101171 sqm2)


Residential 13 acre

(03330 acres 134759 sqm2)


Residential 12 acre

(05 acres 202341 sqm2)





Institutions


and business


Distribution and

Wholesale

Total 4224 89634 3089 8572 6577





ID and A B C D










(03330 acres 134759 sqm2) (CNadj=30)



(05 acres 202341 sqm2) (CNadj=25)





















ID A B C D


1 5040 3259 1694 9994 003 001 001 006

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


2 4087 3217 2694 9976 010 009 005 024

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557 257 169 008 443

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


6 2610 3882 1273 7774 051 1557 154 2226

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


7 7304 909 682 8903 869 144 085 1096

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


8 026 005 002 032 6454 1099 1349 8903 543 059 020 622 290 060 090 439



9 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)





ID C D


1 003 001 001 006

(CN=98) (CN=89) (CN=84)

(CNadj=65)

2 010 009 005 024

(CN=98) (CN=89) (CN=84)

(CNadj=38)

3

4

5 257 169 008 443

(CN=98) (CN=89) (CN=84)

(CNadj=20)

6 051 1557 154 2226 051 1557 154 2226

(CN=98) (CN=89) (CN=84)

(Cnadj=12)

7 869 144 085 1096

(CN=98) (CN=89) (CN=84)

(CNadj=85)

8 543 059 020 622 290 060 090 439

(CN = 98) (CN =86) (CN = 79) (CN=98) (CN=89) (CN=84)


9




ID A B


1 5040 3259 1694 9994

(CN=98) (CN=79) (CN=69)

(CNadj=65)

2 4087 3217 2694 9976

(CN=98) (CN=79) (CN=69)

(CNadj=38)

3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557

(CN=98) (CN=79) (CN=69)

(CNadj=20)

6 2610 3882 1273 7774 2610 3882 1273 7774

(CN=98) (CN=79) (CN=69)

(Cnadj=12)

7 7304 909 682 8903

(CN=98) (CN=79) (CN=69)

(CNadj=85)

8 026 005 002 032 6454 1099 1349 8903

(CN = 98) (CN =68) (CN = 49) (CN = 98) (CN=79) (CN=69)


9 459 048 503 8496 1146 357 9491 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)








Residential 18 acre

123


Residential 14 acre

1987


Residential 13 acre

345


Residential 12 acre

188


09


016



Institutions 052


and business

22


Distribution and

Wholesale 07

Total 4224 89634 acres 8572 79 9881 6577

3089
















and Wholesale










65 65 65 65

38 38 38 38

30 30 30 30

25 25 25 25

20 20 20 20

12 12 12 12



49 69 79 84








49 69 79 84



Labels Rules

Land Use Land Cover






Labels Rules

Land Use Land Cover













FSD-AOI CN Result








Table1

Table2

Table3

Table 91


Cover
























Good 39 61 74 80






Fair 36 60 73 79

Good 25 55 70 77

Farmsteads ---- 59 74 82 86

---- 72 82 87 89

---- 74 84 90 92



Land Cover

A B C D

Woods 25 55 70 77




Water 98 98 98 98




A B C D







A B C D



Highly Impervious





ID Open Spaces

Map 21 21 Poor 1-25 73066 2514 242 239 164 6777 6862 04547

Good to Fair 26-50 64959 2235 328 331 253 7713 7644 0445

Map 22 22 Poor 1-30 90677 3119 242 309 197 8140 6375 03701

Good to Fair 31-50 47349 1629 328 261 216 6585 8276 05939




value)


ID


Open spaces 41-100 163581 5628 244 286 238 9754 8322 06734



Open spaces 46-100 151453 5210 244 261 232 9508 8889 07838



Open spaces 51-100 138025 4748 244 241 230 9426 9544 09112


Open spaces 56-100 122922 4229 244 217 209 8566 9631 09283



Open spaces 61-100 106470 3663 244 196 189 7746 9643 09305






Description (avg)


Residential 18 acre

(0125 acres or 50585 sqm2)


Residential 14 acre

(025 acres 101171 sqm2)


Residential 13 acre

(03330 acres 134759 sqm2)


Residential 12 acre

(05 acres 202341 sqm2)





Institutions


and business


Distribution and

Wholesale

Total 4224 89634 3089 8572 6577





ID and A B C D










(03330 acres 134759 sqm2) (CNadj=30)



(05 acres 202341 sqm2) (CNadj=25)





















ID A B C D


1 5040 3259 1694 9994 003 001 001 006

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


2 4087 3217 2694 9976 010 009 005 024

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557 257 169 008 443

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


6 2610 3882 1273 7774 051 1557 154 2226

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


7 7304 909 682 8903 869 144 085 1096

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


8 026 005 002 032 6454 1099 1349 8903 543 059 020 622 290 060 090 439



9 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)





ID C D


1 003 001 001 006

(CN=98) (CN=89) (CN=84)

(CNadj=65)

2 010 009 005 024

(CN=98) (CN=89) (CN=84)

(CNadj=38)

3

4

5 257 169 008 443

(CN=98) (CN=89) (CN=84)

(CNadj=20)

6 051 1557 154 2226 051 1557 154 2226

(CN=98) (CN=89) (CN=84)

(Cnadj=12)

7 869 144 085 1096

(CN=98) (CN=89) (CN=84)

(CNadj=85)

8 543 059 020 622 290 060 090 439

(CN = 98) (CN =86) (CN = 79) (CN=98) (CN=89) (CN=84)


9




ID A B


1 5040 3259 1694 9994

(CN=98) (CN=79) (CN=69)

(CNadj=65)

2 4087 3217 2694 9976

(CN=98) (CN=79) (CN=69)

(CNadj=38)

3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557

(CN=98) (CN=79) (CN=69)

(CNadj=20)

6 2610 3882 1273 7774 2610 3882 1273 7774

(CN=98) (CN=79) (CN=69)

(Cnadj=12)

7 7304 909 682 8903

(CN=98) (CN=79) (CN=69)

(CNadj=85)

8 026 005 002 032 6454 1099 1349 8903

(CN = 98) (CN =68) (CN = 49) (CN = 98) (CN=79) (CN=69)


9 459 048 503 8496 1146 357 9491 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)








Residential 18 acre

123


Residential 14 acre

1987


Residential 13 acre

345


Residential 12 acre

188


09


016



Institutions 052


and business

22


Distribution and

Wholesale 07

Total 4224 89634 acres 8572 79 9881 6577

3089
















and Wholesale










65 65 65 65

38 38 38 38

30 30 30 30

25 25 25 25

20 20 20 20

12 12 12 12



49 69 79 84








49 69 79 84



Labels Rules

Land Use Land Cover









FSD-AOI CN Result








Table1

Table2

Table3

Table 91


Cover
























Good 39 61 74 80






Fair 36 60 73 79

Good 25 55 70 77

Farmsteads ---- 59 74 82 86

---- 72 82 87 89

---- 74 84 90 92



Land Cover

A B C D

Woods 25 55 70 77




Water 98 98 98 98




A B C D







A B C D



Highly Impervious





ID Open Spaces

Map 21 21 Poor 1-25 73066 2514 242 239 164 6777 6862 04547

Good to Fair 26-50 64959 2235 328 331 253 7713 7644 0445

Map 22 22 Poor 1-30 90677 3119 242 309 197 8140 6375 03701

Good to Fair 31-50 47349 1629 328 261 216 6585 8276 05939




value)


ID


Open spaces 41-100 163581 5628 244 286 238 9754 8322 06734



Open spaces 46-100 151453 5210 244 261 232 9508 8889 07838



Open spaces 51-100 138025 4748 244 241 230 9426 9544 09112


Open spaces 56-100 122922 4229 244 217 209 8566 9631 09283



Open spaces 61-100 106470 3663 244 196 189 7746 9643 09305






Description (avg)


Residential 18 acre

(0125 acres or 50585 sqm2)


Residential 14 acre

(025 acres 101171 sqm2)


Residential 13 acre

(03330 acres 134759 sqm2)


Residential 12 acre

(05 acres 202341 sqm2)





Institutions


and business


Distribution and

Wholesale

Total 4224 89634 3089 8572 6577





ID and A B C D










(03330 acres 134759 sqm2) (CNadj=30)



(05 acres 202341 sqm2) (CNadj=25)





















ID A B C D


1 5040 3259 1694 9994 003 001 001 006

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


2 4087 3217 2694 9976 010 009 005 024

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557 257 169 008 443

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


6 2610 3882 1273 7774 051 1557 154 2226

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


7 7304 909 682 8903 869 144 085 1096

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


8 026 005 002 032 6454 1099 1349 8903 543 059 020 622 290 060 090 439



9 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)





ID C D


1 003 001 001 006

(CN=98) (CN=89) (CN=84)

(CNadj=65)

2 010 009 005 024

(CN=98) (CN=89) (CN=84)

(CNadj=38)

3

4

5 257 169 008 443

(CN=98) (CN=89) (CN=84)

(CNadj=20)

6 051 1557 154 2226 051 1557 154 2226

(CN=98) (CN=89) (CN=84)

(Cnadj=12)

7 869 144 085 1096

(CN=98) (CN=89) (CN=84)

(CNadj=85)

8 543 059 020 622 290 060 090 439

(CN = 98) (CN =86) (CN = 79) (CN=98) (CN=89) (CN=84)


9




ID A B


1 5040 3259 1694 9994

(CN=98) (CN=79) (CN=69)

(CNadj=65)

2 4087 3217 2694 9976

(CN=98) (CN=79) (CN=69)

(CNadj=38)

3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557

(CN=98) (CN=79) (CN=69)

(CNadj=20)

6 2610 3882 1273 7774 2610 3882 1273 7774

(CN=98) (CN=79) (CN=69)

(Cnadj=12)

7 7304 909 682 8903

(CN=98) (CN=79) (CN=69)

(CNadj=85)

8 026 005 002 032 6454 1099 1349 8903

(CN = 98) (CN =68) (CN = 49) (CN = 98) (CN=79) (CN=69)


9 459 048 503 8496 1146 357 9491 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)








Residential 18 acre

123


Residential 14 acre

1987


Residential 13 acre

345


Residential 12 acre

188


09


016



Institutions 052


and business

22


Distribution and

Wholesale 07

Total 4224 89634 acres 8572 79 9881 6577

3089
















and Wholesale










65 65 65 65

38 38 38 38

30 30 30 30

25 25 25 25

20 20 20 20

12 12 12 12



49 69 79 84








49 69 79 84




FSD-AOI CN Result








Table1

Table2

Table3

Table 91


Cover
























Good 39 61 74 80






Fair 36 60 73 79

Good 25 55 70 77

Farmsteads ---- 59 74 82 86

---- 72 82 87 89

---- 74 84 90 92



Land Cover

A B C D

Woods 25 55 70 77




Water 98 98 98 98




A B C D







A B C D



Highly Impervious





ID Open Spaces

Map 21 21 Poor 1-25 73066 2514 242 239 164 6777 6862 04547

Good to Fair 26-50 64959 2235 328 331 253 7713 7644 0445

Map 22 22 Poor 1-30 90677 3119 242 309 197 8140 6375 03701

Good to Fair 31-50 47349 1629 328 261 216 6585 8276 05939




value)


ID


Open spaces 41-100 163581 5628 244 286 238 9754 8322 06734



Open spaces 46-100 151453 5210 244 261 232 9508 8889 07838



Open spaces 51-100 138025 4748 244 241 230 9426 9544 09112


Open spaces 56-100 122922 4229 244 217 209 8566 9631 09283



Open spaces 61-100 106470 3663 244 196 189 7746 9643 09305






Description (avg)


Residential 18 acre

(0125 acres or 50585 sqm2)


Residential 14 acre

(025 acres 101171 sqm2)


Residential 13 acre

(03330 acres 134759 sqm2)


Residential 12 acre

(05 acres 202341 sqm2)





Institutions


and business


Distribution and

Wholesale

Total 4224 89634 3089 8572 6577





ID and A B C D










(03330 acres 134759 sqm2) (CNadj=30)



(05 acres 202341 sqm2) (CNadj=25)





















ID A B C D


1 5040 3259 1694 9994 003 001 001 006

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


2 4087 3217 2694 9976 010 009 005 024

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557 257 169 008 443

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


6 2610 3882 1273 7774 051 1557 154 2226

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


7 7304 909 682 8903 869 144 085 1096

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


8 026 005 002 032 6454 1099 1349 8903 543 059 020 622 290 060 090 439



9 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)





ID C D


1 003 001 001 006

(CN=98) (CN=89) (CN=84)

(CNadj=65)

2 010 009 005 024

(CN=98) (CN=89) (CN=84)

(CNadj=38)

3

4

5 257 169 008 443

(CN=98) (CN=89) (CN=84)

(CNadj=20)

6 051 1557 154 2226 051 1557 154 2226

(CN=98) (CN=89) (CN=84)

(Cnadj=12)

7 869 144 085 1096

(CN=98) (CN=89) (CN=84)

(CNadj=85)

8 543 059 020 622 290 060 090 439

(CN = 98) (CN =86) (CN = 79) (CN=98) (CN=89) (CN=84)


9




ID A B


1 5040 3259 1694 9994

(CN=98) (CN=79) (CN=69)

(CNadj=65)

2 4087 3217 2694 9976

(CN=98) (CN=79) (CN=69)

(CNadj=38)

3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557

(CN=98) (CN=79) (CN=69)

(CNadj=20)

6 2610 3882 1273 7774 2610 3882 1273 7774

(CN=98) (CN=79) (CN=69)

(Cnadj=12)

7 7304 909 682 8903

(CN=98) (CN=79) (CN=69)

(CNadj=85)

8 026 005 002 032 6454 1099 1349 8903

(CN = 98) (CN =68) (CN = 49) (CN = 98) (CN=79) (CN=69)


9 459 048 503 8496 1146 357 9491 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)








Residential 18 acre

123


Residential 14 acre

1987


Residential 13 acre

345


Residential 12 acre

188


09


016



Institutions 052


and business

22


Distribution and

Wholesale 07

Total 4224 89634 acres 8572 79 9881 6577

3089
















and Wholesale










65 65 65 65

38 38 38 38

30 30 30 30

25 25 25 25

20 20 20 20

12 12 12 12



49 69 79 84



FSD-AOI CN Result








Table1

Table2

Table3

Table 91


Cover
























Good 39 61 74 80






Fair 36 60 73 79

Good 25 55 70 77

Farmsteads ---- 59 74 82 86

---- 72 82 87 89

---- 74 84 90 92



Land Cover

A B C D

Woods 25 55 70 77




Water 98 98 98 98




A B C D







A B C D



Highly Impervious





ID Open Spaces

Map 21 21 Poor 1-25 73066 2514 242 239 164 6777 6862 04547

Good to Fair 26-50 64959 2235 328 331 253 7713 7644 0445

Map 22 22 Poor 1-30 90677 3119 242 309 197 8140 6375 03701

Good to Fair 31-50 47349 1629 328 261 216 6585 8276 05939




value)


ID


Open spaces 41-100 163581 5628 244 286 238 9754 8322 06734



Open spaces 46-100 151453 5210 244 261 232 9508 8889 07838



Open spaces 51-100 138025 4748 244 241 230 9426 9544 09112


Open spaces 56-100 122922 4229 244 217 209 8566 9631 09283



Open spaces 61-100 106470 3663 244 196 189 7746 9643 09305






Description (avg)


Residential 18 acre

(0125 acres or 50585 sqm2)


Residential 14 acre

(025 acres 101171 sqm2)


Residential 13 acre

(03330 acres 134759 sqm2)


Residential 12 acre

(05 acres 202341 sqm2)





Institutions


and business


Distribution and

Wholesale

Total 4224 89634 3089 8572 6577





ID and A B C D










(03330 acres 134759 sqm2) (CNadj=30)



(05 acres 202341 sqm2) (CNadj=25)





















ID A B C D


1 5040 3259 1694 9994 003 001 001 006

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


2 4087 3217 2694 9976 010 009 005 024

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557 257 169 008 443

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


6 2610 3882 1273 7774 051 1557 154 2226

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


7 7304 909 682 8903 869 144 085 1096

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


8 026 005 002 032 6454 1099 1349 8903 543 059 020 622 290 060 090 439



9 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)





ID C D


1 003 001 001 006

(CN=98) (CN=89) (CN=84)

(CNadj=65)

2 010 009 005 024

(CN=98) (CN=89) (CN=84)

(CNadj=38)

3

4

5 257 169 008 443

(CN=98) (CN=89) (CN=84)

(CNadj=20)

6 051 1557 154 2226 051 1557 154 2226

(CN=98) (CN=89) (CN=84)

(Cnadj=12)

7 869 144 085 1096

(CN=98) (CN=89) (CN=84)

(CNadj=85)

8 543 059 020 622 290 060 090 439

(CN = 98) (CN =86) (CN = 79) (CN=98) (CN=89) (CN=84)


9




ID A B


1 5040 3259 1694 9994

(CN=98) (CN=79) (CN=69)

(CNadj=65)

2 4087 3217 2694 9976

(CN=98) (CN=79) (CN=69)

(CNadj=38)

3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557

(CN=98) (CN=79) (CN=69)

(CNadj=20)

6 2610 3882 1273 7774 2610 3882 1273 7774

(CN=98) (CN=79) (CN=69)

(Cnadj=12)

7 7304 909 682 8903

(CN=98) (CN=79) (CN=69)

(CNadj=85)

8 026 005 002 032 6454 1099 1349 8903

(CN = 98) (CN =68) (CN = 49) (CN = 98) (CN=79) (CN=69)


9 459 048 503 8496 1146 357 9491 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)








Residential 18 acre

123


Residential 14 acre

1987


Residential 13 acre

345


Residential 12 acre

188


09


016



Institutions 052


and business

22


Distribution and

Wholesale 07

Total 4224 89634 acres 8572 79 9881 6577

3089
















and Wholesale



FSD-AOI CN Result








Table1

Table2

Table3

Table 91


Cover
























Good 39 61 74 80






Fair 36 60 73 79

Good 25 55 70 77

Farmsteads ---- 59 74 82 86

---- 72 82 87 89

---- 74 84 90 92



Land Cover

A B C D

Woods 25 55 70 77




Water 98 98 98 98




A B C D







A B C D



Highly Impervious





ID Open Spaces

Map 21 21 Poor 1-25 73066 2514 242 239 164 6777 6862 04547

Good to Fair 26-50 64959 2235 328 331 253 7713 7644 0445

Map 22 22 Poor 1-30 90677 3119 242 309 197 8140 6375 03701

Good to Fair 31-50 47349 1629 328 261 216 6585 8276 05939




value)


ID


Open spaces 41-100 163581 5628 244 286 238 9754 8322 06734



Open spaces 46-100 151453 5210 244 261 232 9508 8889 07838



Open spaces 51-100 138025 4748 244 241 230 9426 9544 09112


Open spaces 56-100 122922 4229 244 217 209 8566 9631 09283



Open spaces 61-100 106470 3663 244 196 189 7746 9643 09305






Description (avg)


Residential 18 acre

(0125 acres or 50585 sqm2)


Residential 14 acre

(025 acres 101171 sqm2)


Residential 13 acre

(03330 acres 134759 sqm2)


Residential 12 acre

(05 acres 202341 sqm2)





Institutions


and business


Distribution and

Wholesale

Total 4224 89634 3089 8572 6577





ID and A B C D










(03330 acres 134759 sqm2) (CNadj=30)



(05 acres 202341 sqm2) (CNadj=25)





















ID A B C D


1 5040 3259 1694 9994 003 001 001 006

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


2 4087 3217 2694 9976 010 009 005 024

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557 257 169 008 443

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


6 2610 3882 1273 7774 051 1557 154 2226

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


7 7304 909 682 8903 869 144 085 1096

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


8 026 005 002 032 6454 1099 1349 8903 543 059 020 622 290 060 090 439



9 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)





ID C D


1 003 001 001 006

(CN=98) (CN=89) (CN=84)

(CNadj=65)

2 010 009 005 024

(CN=98) (CN=89) (CN=84)

(CNadj=38)

3

4

5 257 169 008 443

(CN=98) (CN=89) (CN=84)

(CNadj=20)

6 051 1557 154 2226 051 1557 154 2226

(CN=98) (CN=89) (CN=84)

(Cnadj=12)

7 869 144 085 1096

(CN=98) (CN=89) (CN=84)

(CNadj=85)

8 543 059 020 622 290 060 090 439

(CN = 98) (CN =86) (CN = 79) (CN=98) (CN=89) (CN=84)


9




ID A B


1 5040 3259 1694 9994

(CN=98) (CN=79) (CN=69)

(CNadj=65)

2 4087 3217 2694 9976

(CN=98) (CN=79) (CN=69)

(CNadj=38)

3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557

(CN=98) (CN=79) (CN=69)

(CNadj=20)

6 2610 3882 1273 7774 2610 3882 1273 7774

(CN=98) (CN=79) (CN=69)

(Cnadj=12)

7 7304 909 682 8903

(CN=98) (CN=79) (CN=69)

(CNadj=85)

8 026 005 002 032 6454 1099 1349 8903

(CN = 98) (CN =68) (CN = 49) (CN = 98) (CN=79) (CN=69)


9 459 048 503 8496 1146 357 9491 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)








Residential 18 acre

123


Residential 14 acre

1987


Residential 13 acre

345


Residential 12 acre

188


09


016



Institutions 052


and business

22


Distribution and

Wholesale 07

Total 4224 89634 acres 8572 79 9881 6577

3089












Table1

Table2

Table3

Table 91


Cover
























Good 39 61 74 80






Fair 36 60 73 79

Good 25 55 70 77

Farmsteads ---- 59 74 82 86

---- 72 82 87 89

---- 74 84 90 92



Land Cover

A B C D

Woods 25 55 70 77




Water 98 98 98 98




A B C D







A B C D



Highly Impervious





ID Open Spaces

Map 21 21 Poor 1-25 73066 2514 242 239 164 6777 6862 04547

Good to Fair 26-50 64959 2235 328 331 253 7713 7644 0445

Map 22 22 Poor 1-30 90677 3119 242 309 197 8140 6375 03701

Good to Fair 31-50 47349 1629 328 261 216 6585 8276 05939




value)


ID


Open spaces 41-100 163581 5628 244 286 238 9754 8322 06734



Open spaces 46-100 151453 5210 244 261 232 9508 8889 07838



Open spaces 51-100 138025 4748 244 241 230 9426 9544 09112


Open spaces 56-100 122922 4229 244 217 209 8566 9631 09283



Open spaces 61-100 106470 3663 244 196 189 7746 9643 09305






Description (avg)


Residential 18 acre

(0125 acres or 50585 sqm2)


Residential 14 acre

(025 acres 101171 sqm2)


Residential 13 acre

(03330 acres 134759 sqm2)


Residential 12 acre

(05 acres 202341 sqm2)





Institutions


and business


Distribution and

Wholesale

Total 4224 89634 3089 8572 6577





ID and A B C D










(03330 acres 134759 sqm2) (CNadj=30)



(05 acres 202341 sqm2) (CNadj=25)





















ID A B C D


1 5040 3259 1694 9994 003 001 001 006

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


2 4087 3217 2694 9976 010 009 005 024

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557 257 169 008 443

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


6 2610 3882 1273 7774 051 1557 154 2226

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


7 7304 909 682 8903 869 144 085 1096

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


8 026 005 002 032 6454 1099 1349 8903 543 059 020 622 290 060 090 439



9 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)





ID C D


1 003 001 001 006

(CN=98) (CN=89) (CN=84)

(CNadj=65)

2 010 009 005 024

(CN=98) (CN=89) (CN=84)

(CNadj=38)

3

4

5 257 169 008 443

(CN=98) (CN=89) (CN=84)

(CNadj=20)

6 051 1557 154 2226 051 1557 154 2226

(CN=98) (CN=89) (CN=84)

(Cnadj=12)

7 869 144 085 1096

(CN=98) (CN=89) (CN=84)

(CNadj=85)

8 543 059 020 622 290 060 090 439

(CN = 98) (CN =86) (CN = 79) (CN=98) (CN=89) (CN=84)


9




ID A B


1 5040 3259 1694 9994

(CN=98) (CN=79) (CN=69)

(CNadj=65)

2 4087 3217 2694 9976

(CN=98) (CN=79) (CN=69)

(CNadj=38)

3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557

(CN=98) (CN=79) (CN=69)

(CNadj=20)

6 2610 3882 1273 7774 2610 3882 1273 7774

(CN=98) (CN=79) (CN=69)

(Cnadj=12)

7 7304 909 682 8903

(CN=98) (CN=79) (CN=69)

(CNadj=85)

8 026 005 002 032 6454 1099 1349 8903

(CN = 98) (CN =68) (CN = 49) (CN = 98) (CN=79) (CN=69)


9 459 048 503 8496 1146 357 9491 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)










Table1

Table2

Table3

Table 91


Cover
























Good 39 61 74 80






Fair 36 60 73 79

Good 25 55 70 77

Farmsteads ---- 59 74 82 86

---- 72 82 87 89

---- 74 84 90 92



Land Cover

A B C D

Woods 25 55 70 77




Water 98 98 98 98




A B C D







A B C D



Highly Impervious





ID Open Spaces

Map 21 21 Poor 1-25 73066 2514 242 239 164 6777 6862 04547

Good to Fair 26-50 64959 2235 328 331 253 7713 7644 0445

Map 22 22 Poor 1-30 90677 3119 242 309 197 8140 6375 03701

Good to Fair 31-50 47349 1629 328 261 216 6585 8276 05939




value)


ID


Open spaces 41-100 163581 5628 244 286 238 9754 8322 06734



Open spaces 46-100 151453 5210 244 261 232 9508 8889 07838



Open spaces 51-100 138025 4748 244 241 230 9426 9544 09112


Open spaces 56-100 122922 4229 244 217 209 8566 9631 09283



Open spaces 61-100 106470 3663 244 196 189 7746 9643 09305






Description (avg)


Residential 18 acre

(0125 acres or 50585 sqm2)


Residential 14 acre

(025 acres 101171 sqm2)


Residential 13 acre

(03330 acres 134759 sqm2)


Residential 12 acre

(05 acres 202341 sqm2)





Institutions


and business


Distribution and

Wholesale

Total 4224 89634 3089 8572 6577





ID and A B C D










(03330 acres 134759 sqm2) (CNadj=30)



(05 acres 202341 sqm2) (CNadj=25)





















ID A B C D


1 5040 3259 1694 9994 003 001 001 006

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


2 4087 3217 2694 9976 010 009 005 024

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557 257 169 008 443

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


6 2610 3882 1273 7774 051 1557 154 2226

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


7 7304 909 682 8903 869 144 085 1096

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


8 026 005 002 032 6454 1099 1349 8903 543 059 020 622 290 060 090 439



9 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)





ID C D


1 003 001 001 006

(CN=98) (CN=89) (CN=84)

(CNadj=65)

2 010 009 005 024

(CN=98) (CN=89) (CN=84)

(CNadj=38)

3

4

5 257 169 008 443

(CN=98) (CN=89) (CN=84)

(CNadj=20)

6 051 1557 154 2226 051 1557 154 2226

(CN=98) (CN=89) (CN=84)

(Cnadj=12)

7 869 144 085 1096

(CN=98) (CN=89) (CN=84)

(CNadj=85)

8 543 059 020 622 290 060 090 439

(CN = 98) (CN =86) (CN = 79) (CN=98) (CN=89) (CN=84)


9








Table1

Table2

Table3

Table 91


Cover
























Good 39 61 74 80






Fair 36 60 73 79

Good 25 55 70 77

Farmsteads ---- 59 74 82 86

---- 72 82 87 89

---- 74 84 90 92



Land Cover

A B C D

Woods 25 55 70 77




Water 98 98 98 98




A B C D







A B C D



Highly Impervious





ID Open Spaces

Map 21 21 Poor 1-25 73066 2514 242 239 164 6777 6862 04547

Good to Fair 26-50 64959 2235 328 331 253 7713 7644 0445

Map 22 22 Poor 1-30 90677 3119 242 309 197 8140 6375 03701

Good to Fair 31-50 47349 1629 328 261 216 6585 8276 05939




value)


ID


Open spaces 41-100 163581 5628 244 286 238 9754 8322 06734



Open spaces 46-100 151453 5210 244 261 232 9508 8889 07838



Open spaces 51-100 138025 4748 244 241 230 9426 9544 09112


Open spaces 56-100 122922 4229 244 217 209 8566 9631 09283



Open spaces 61-100 106470 3663 244 196 189 7746 9643 09305






Description (avg)


Residential 18 acre

(0125 acres or 50585 sqm2)


Residential 14 acre

(025 acres 101171 sqm2)


Residential 13 acre

(03330 acres 134759 sqm2)


Residential 12 acre

(05 acres 202341 sqm2)





Institutions


and business


Distribution and

Wholesale

Total 4224 89634 3089 8572 6577





ID and A B C D










(03330 acres 134759 sqm2) (CNadj=30)



(05 acres 202341 sqm2) (CNadj=25)





















ID A B C D


1 5040 3259 1694 9994 003 001 001 006

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


2 4087 3217 2694 9976 010 009 005 024

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


3 3533 3193 3271 10000

(CN=98) (CN=79) (CN=69)

(CNadj=30)

4 3400 3161 3436 10000

(CN=98) (CN=79) (CN=69)

(CNadj=25)

5 2614 3143 3798 9557 257 169 008 443

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


6 2610 3882 1273 7774 051 1557 154 2226

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


7 7304 909 682 8903 869 144 085 1096

(CN=98) (CN=79) (CN=69) (CN=98) (CN=89) (CN=84)


8 026 005 002 032 6454 1099 1349 8903 543 059 020 622 290 060 090 439



9 459 048 503 8496 1146 357 9491

(CN = 98) (CN =68) (CN=98) (CN = 79) (CN = 69)








Table1

Table2

Table3

Table 91


Cover
























Good 39 61 74 80






Fair 36 60 73 79

Good 25 55 70 77

Farmsteads ---- 59 74 82 86

---- 72 82 87 89

---- 74 84 90 92



Land Cover

A B C D

Woods 25 55 70 77




Water 98 98 98 98




A B C D







A B C D



Highly Impervious





ID Open Spaces

Map 21 21 Poor 1-25 73066 2514 242 239 164 6777 6862 04547

Good to Fair 26-50 64959 2235 328 331 253 7713 7644 0445

Map 22 22 Poor 1-30 90677 3119 242 309 197 8140 6375 03701

Good to Fair 31-50 47349 1629 328 261 216 6585 8276 05939




value)


ID


Open spaces 41-100 163581 5628 244 286 238 9754 8322 06734



Open spaces 46-100 151453 5210 244 261 232 9508 8889 07838



Open spaces 51-100 138025 4748 244 241 230 9426 9544 09112


Open spaces 56-100 122922 4229 244 217 209 8566 9631 09283



Open spaces 61-100 106470 3663 244 196 189 7746 9643 09305






Description (avg)


Residential 18 acre

(0125 acres or 50585 sqm2)


Residential 14 acre

(025 acres 101171 sqm2)


Residential 13 acre

(03330 acres 134759 sqm2)


Residential 12 acre

(05 acres 202341 sqm2)





Institutions


and business


Distribution and

Wholesale

Total 4224 89634 3089 8572 6577





ID and A B C D










(03330 acres 134759 sqm2) (CNadj=30)



(05 acres 202341 sqm2) (CNadj=25)























Table1

Table2

Table3

Table 91


Cover
























Good 39 61 74 80






Fair 36 60 73 79

Good 25 55 70 77

Farmsteads ---- 59 74 82 86

---- 72 82 87 89

---- 74 84 90 92



Land Cover

A B C D

Woods 25 55 70 77




Water 98 98 98 98




A B C D







A B C D



Highly Impervious





ID Open Spaces

Map 21 21 Poor 1-25 73066 2514 242 239 164 6777 6862 04547

Good to Fair 26-50 64959 2235 328 331 253 7713 7644 0445

Map 22 22 Poor 1-30 90677 3119 242 309 197 8140 6375 03701

Good to Fair 31-50 47349 1629 328 261 216 6585 8276 05939




value)


ID


Open spaces 41-100 163581 5628 244 286 238 9754 8322 06734



Open spaces 46-100 151453 5210 244 261 232 9508 8889 07838



Open spaces 51-100 138025 4748 244 241 230 9426 9544 09112


Open spaces 56-100 122922 4229 244 217 209 8566 9631 09283



Open spaces 61-100 106470 3663 244 196 189 7746 9643 09305






Description (avg)


Residential 18 acre

(0125 acres or 50585 sqm2)


Residential 14 acre

(025 acres 101171 sqm2)


Residential 13 acre

(03330 acres 134759 sqm2)


Residential 12 acre

(05 acres 202341 sqm2)





Institutions


and business


Distribution and

Wholesale

Total 4224 89634 3089 8572 6577






Table1

Table2

Table3

Table 91


Cover
























Good 39 61 74 80






Fair 36 60 73 79

Good 25 55 70 77

Farmsteads ---- 59 74 82 86

---- 72 82 87 89

---- 74 84 90 92



Land Cover

A B C D

Woods 25 55 70 77




Water 98 98 98 98




A B C D







A B C D



Highly Impervious





ID Open Spaces

Map 21 21 Poor 1-25 73066 2514 242 239 164 6777 6862 04547

Good to Fair 26-50 64959 2235 328 331 253 7713 7644 0445

Map 22 22 Poor 1-30 90677 3119 242 309 197 8140 6375 03701

Good to Fair 31-50 47349 1629 328 261 216 6585 8276 05939




value)


ID


Open spaces 41-100 163581 5628 244 286 238 9754 8322 06734



Open spaces 46-100 151453 5210 244 261 232 9508 8889 07838



Open spaces 51-100 138025 4748 244 241 230 9426 9544 09112


Open spaces 56-100 122922 4229 244 217 209 8566 9631 09283



Open spaces 61-100 106470 3663 244 196 189 7746 9643 09305





Table1

Table2

Table3

Table 91


Cover
























Good 39 61 74 80






Fair 36 60 73 79

Good 25 55 70 77

Farmsteads ---- 59 74 82 86

---- 72 82 87 89

---- 74 84 90 92



Land Cover

A B C D

Woods 25 55 70 77




Water 98 98 98 98




A B C D







A B C D



Highly Impervious





ID Open Spaces

Map 21 21 Poor 1-25 73066 2514 242 239 164 6777 6862 04547

Good to Fair 26-50 64959 2235 328 331 253 7713 7644 0445

Map 22 22 Poor 1-30 90677 3119 242 309 197 8140 6375 03701

Good to Fair 31-50 47349 1629 328 261 216 6585 8276 05939




value)

Table1

Table2

Table3

Table 91


Cover
























Good 39 61 74 80






Fair 36 60 73 79

Good 25 55 70 77

Farmsteads ---- 59 74 82 86

---- 72 82 87 89

---- 74 84 90 92



Land Cover

A B C D

Woods 25 55 70 77




Water 98 98 98 98




A B C D







A B C D



Highly Impervious




Table2

Table3

Table 91


Cover
























Good 39 61 74 80






Fair 36 60 73 79

Good 25 55 70 77

Farmsteads ---- 59 74 82 86

---- 72 82 87 89

---- 74 84 90 92



Land Cover

A B C D

Woods 25 55 70 77




Water 98 98 98 98




A B C D




Table3

Table 91


Cover
























Good 39 61 74 80






Fair 36 60 73 79

Good 25 55 70 77

Farmsteads ---- 59 74 82 86

---- 72 82 87 89

---- 74 84 90 92



Land Cover

A B C D

Woods 25 55 70 77




Water 98 98 98 98

Table 91


Cover
























Good 39 61 74 80






Fair 36 60 73 79

Good 25 55 70 77

Farmsteads ---- 59 74 82 86

---- 72 82 87 89

---- 74 84 90 92



Land Use Land Cover	Curve numbers for

	hydrologic Soil Group

Character of Surface	A2	B3	C4	D5

Impervious Areas	98	98	98	98

Open Spaces - Poor Condition	68	79	86	89

Open Spaces - Good to Fair Condition1	49	69	79	84



Activity Code and Description	SCS CN Cover Type1	Rule by Area

11 Single family	Residential districts by average lot size -	gt= 01 and lt= 0125 acre

	Residential 18 acre (0125 acres or 50585 sqm2)	gt= 40468 and lt= 50585 sqm2

11 Single family	Residential districts by average lot size -	gt 0125 and lt= 025 acre

	Residential 14 acre (025 acres or 101171 sqm2)	gt 50585 and lt= 101171 sqm2

11 Single family	Residential districts by average lot size -	gt 025 and lt= 03330 acre

	Residential 13 acre (03330 acres or 134759 sqm2)	gt 101171 and lt= 134759 sqm2

11 Single family	Residential districts by average lot size -	gt 03330 and lt= 05 acre

	Residential 12 acre (05 acres or 202341 sqm2)	gt 134759 and lt= 202341 sqm2

11 Single family	Residential districts by average lot size -	gt 05 and lt= 1 acre

	Residential 1 acre (404683 sqm2)	gt 202341and lt= 404683 sqm2

11 Single family	Residential districts by average lot size -	gt 1 and lt= 2 acres

	Residential 2 acres (809365 sqm2)	gt 404683 and lt= 809365 sqm2

31 Banks and Financial Institutions	Urban Districts - Commercial and business	gt= 05 acres or 202341 sqm2

33 Other offices	Urban Districts - Commercial and business	gt= 05 acres or 202341 sqm2

64 Warehousing Distribution	Urban Districts - Industrial	gt= 05 acres or 202341 sqm2

and Wholesale



Accuracy Assessment - Unsupervised Thematic Map2 (50 spectral clusters)

Map	Class Name	Labeling Criteria	Areas (Acres)	Areas ()	Reference Totals	Classified Totals	Number Correct	Producers Accuracy ()	Users Accuracy ()	Kappa Statistics

ID	Open Spaces

21	Poor	1-25	73066	2514	242	239	164	6777	6862	04547

	Good to Fair	26-50	64959	2235	328	331	253	7713	7644	0445

		Overall Classification Accuracy = 7316	Overall Kappa Statistics = 04498

22	Poor	1-30	90677	3119	242	309	197	8140	6375	03701

	Good to Fair	31-50	47349	1629	328	261	216	6585	8276	05939

		Overall Classification Accuracy = 7246	Overall Kappa Statistics = 0456



Labels		Rules

Land Use Land Cover

Character of Surface

Open Spaces		If land area = or lt 75 covered with vegetation naturally existing or planted

		(eg grass plants tree (leaf-on leaf-off) forest shrub and scrub etc)

		then Open Spaces - Poor condition (1)

		Else if land area gt 75 covered with vegetation naturally existing or planted

		(eg grass plants tree (leaf-on leaf-off) forest shrub and scrub etc)

		then Open Spaces - Good to fair condition (2)



		Else Open Space Poor condition (1)



Table 91	Runoff curve numbers for hydrologic soil-cover complexes

Cover

Land use		Treatment		Hydrologic			Hydrologic soil group

	or practice		condition			A		B		C		D

Fallow		Straight row		----			77		86		91		94

Row crops		Straight row		Poor			72		81		88		91

	Straight row		Good			67		78		85		89

	Contoured		Poor			70		79		84		88

	Contoured		Good			65		75		82		86

	Contoured and terraced		Poor			66		74		80		82

	Contoured and terraced		Good			62		71		78		81

Small grain		Straight row		Poor			65		76		84		88

	Straight row		Good			63		75		83		87

	Contoured		Poor			63		74		82		85

	Contoured		Good			61		73		81		84

	Contoured and terraced		Poor			61		72		79		82

	Contoured and terraced		Good			59		70		78		81

Close-seeded		Straight row		Poor			66		77		85		89

	Straight row		Good			58		72		81		85

or		Contoured		Poor			64		75		83		85

rotation		Contoured		Good			55		69		78		83

meadow		Contoured and terraced		Poor			63		73		80		83

	Contoured and terraced		Good			51		67		76		80

Pasture			Poor			68		79		86		89

or range			Fair			49		69		79		84

		Good			39		61		74		80

	Contoured		Poor			47		67		81		88

	Contoured		Fair			25		59		75		83

	Contoured		Good			6		35		70		79

Meadow			Good			30		58		71		78

Woods			Poor			45		66		77		83

		Fair			36		60		73		79

		Good			25		55		70		77

Farmsteads			----			59		74		82		86

		----			72		82		87		89

		----			74		84		90		92



Table 3	Pennsylvania Landsat Runoff Curve Numbers (from Bondelid et al 1980)

		Hydrologic Soil Group

Land Cover

		A		B		C		D

Woods			25		55		70		77

Agricultural			64		75		83		87

Residential			60		74		83		87

Highly Impervious			90		93		94		95

Water			98		98		98		98



Table 2	Runoff Curve Numbers for Landsat Rural Land Cover Delineations (from Slack and Welch 1980)

		Hydrologic Soil Group

Land Use Description

		A		B		C		D

Agricultural vegetated			52		68		79		84

Bare Ground			77		86		91		94

Forest Land			30		58		72		78



Table 1	Runoff Curve Numbers for Landsat Land Use Delineations (from Ragan and Jackson 1980)

		Hydrologic Soil Group

Land Use Description

		A		B		C		D

Forest Land			25		55		70		77

Grassed Open Space			36		60		73		78

Highly Impervious

(Commercial-Industrial-Parking Lot)			90		93		94		94

Residential			60		74		83		87

Bare Ground			72		82		88		90



GIS		Activity Code			Rule		Samples		Total		Total		CN		Adjusted

ID		and			by Area			Areas		Areas		(Avg)		CN

	Description								(avg)

1		11 Single family		Residential districts by average lot size -		gt= 01 and lt= 0125 acres		312		3555 acres		123		8688		7025

		Residential 18 acre

		(0125 acres or 50585 sqm2)

2		11 Single family		Residential districts by average lot size -		gt 0125 and lt= 025 acres		3284		5766 acres		1987		8430		5935

		Residential 14 acre

		(025 acres 101171 sqm2)

3		11 Single family		Residential districts by average lot size -		gt 025 and lt= 03330 acres		357		10006 acres		345		8244		5843

		Residential 13 acre

		(03330 acres 134759 sqm2)

4		11 Single family		Residential districts by average lot size -		gt 03330 and lt= 05 acres		140		5451 acres		188		8203		5721

		Residential 12 acre

		(05 acres 202341 sqm2)

5		11 Single family		Residential districts by average lot size -		gt 05 and lt= 1 acres		43		261 acres		090		7684		5600

6		11 Single family		Residential districts by average lot size -		gt 1 and lt= 2 acres		4		456 acres		016		8527		5824

7		31 Banks and		Urban Districts - Commercial		gt= 05 acres		17		1496 acres		052		9419		8351

	Financial		and business

	Institutions

8		33 Other offices		Urban Districts - Commercial		gt= 05 acres		56		6396 acres		220		9321		8303

		and business

9		64 Warehousing		Urban Districts - Industrial		gt= 05 acres		11		2004 acres		070		8631		6595

	Distribution and

	Wholesale

Total					4224		89634		3089		8572		6577

Note		1 The samples of activity codes are similar to the cover type of the urban land cover of the runoff curve number table in the TR-55 (NRCS 1986) publication [Figure 4]

	2 The study area is 290142 acres

	3 is Total areas of hydrologic soil group by character of surface and hydrologic condition by activity code in Percentage



GIS		Hydrologic Soil Group by Character of Surface () with CN amp Adjusted CN (CNadj)

ID		A						B						C					D

			Open		Total					Open		Total					Open	Total					Open		Total

1							5040		3259		1694		9994							003		001		001		006

						(CN=98)		(CN=79)		(CN=69)								(CN=98)		(CN=89)		(CN=84)

						(CNadj=65)											(CNadj=65)

2							4087		3217		2694		9976							010		009		005		024

						(CN=98)		(CN=79)		(CN=69)								(CN=98)		(CN=89)		(CN=84)

						(CNadj=38)											(CNadj=38)

3							3533		3193		3271		10000

						(CN=98)		(CN=79)		(CN=69)

						(CNadj=30)

4							3400		3161		3436		10000

						(CN=98)		(CN=79)		(CN=69)

						(CNadj=25)

5							2614		3143		3798		9557							257		169		008		443

						(CN=98)		(CN=79)		(CN=69)								(CN=98)		(CN=89)		(CN=84)

						(CNadj=20)											(CNadj=20)

6							2610		3882		1273		7774							051		1557		154		2226

						(CN=98)		(CN=79)		(CN=69)								(CN=98)		(CN=89)		(CN=84)

						(Cnadj=12)											(Cnadj=12)

7							7304		909		682		8903							869		144		085		1096

						(CN=98)		(CN=79)		(CN=69)								(CN=98)		(CN=89)		(CN=84)

						(CNadj=85)											(CNadj=85)

8		026		005		002		032			6454		1099		1349		8903		543		059		020		622		290	060	090	439

	(CN = 98)		(CN =68)		(CN = 49)				(CN = 98)		(CN=79)		(CN=69)				(CN = 98)	(CN =86)		(CN = 79)				(CN=98)		(CN=89)	(CN=84)

	(CNadj=85)						(CNadj=85)						(CNadj=85)					(CNadj=85)

9		459		048			503			8496		1146		357		9491

	(CN = 98)		(CN =68)					(CN=98)		(CN = 79)		(CN = 69)

	(CNadj=72)						(CNadj=72)

Note		1 is Total sample areas of hydrologic soil group by character of surface and hydrologic condition by activity code in Percentage

	2 is Total sample areas of hydrologic soil group by activity code in Percentage



GIS ID		Activity Code and Description		Rule by Area

1		11 Single family	Residential districts by average lot size -		gt= 01 and lt= 0125 acre

				Impervious areas of residential districts by average lot size

2		11 Single family	Residential districts by average lot size -		gt 0125 and lt= 025 acre

3		11 Single family	Residential districts by average lot size -		gt 025 and lt= 03330 acre

4		11 Single family	Residential districts by average lot size -		gt 03330 and lt= 05 acre

5		11 Single family	Residential districts by average lot size -		gt 05 and lt= 1 acre

6		11 Single family	Residential districts by average lot size -		gt 1 and lt= 2 acres

7		31 Banks and Financial Institutions	Urban Districts - Commercial and business

8		33 Other offices	Urban Districts - Commercial and business

9		64 Warehousing Distribution	Urban Districts - Industrial

	and Wholesale

	1 The samples of activity codes are similar to the cover type of the urban land cover of the runoff curve number table in the TR-55 (NRCS 1986) publication [Figure 4]

Note		1 acre = 40468 sqm frac12 acre = 20234 13 acre = 13355 frac14 acre = 10117 18 acre = 5059 sq m



Table 7 Runoff curve numbers in Percentage for the adjusted CN calculations defined from the TR-55 (NRCS 1986) Publication (Figure 4)

Land Use Land Cover		Curve numbers for hydrologic Soil Group

Description of Area		A		B		C		D

Impervious areas of Urban Districts

Commercial and business		85		85		85		85

Industrial		72		72		72		72

Impervious areas of residential districts by average lot size

	65		65		65		65

	38		38		38		38

	30		30		30		30

	25		25		25		25

	20		20		20		20

	12		12		12		12

Character of Surface		A		B		C		D

Open Spaces - Poor Condition		68		79		86		89

	49		69		79		84

Note 1 ndash CN of Fair condition are use for open spaces ndash good to fair condition



Table 6 Runoff curve numbers in Percentage for the CN and adjusted CN calculations defined from the TR-55 (NRCS 1986) Publication (Figure 4)

Land Use Land Cover		Curve numbers for

	hydrologic Soil Group

Character of Surface		A		B		C		D

Impervious Areas		98		98		98		98

Open Spaces - Poor Condition		68		79		86		89

	49		69		79		84

Note 1 ndash CN of Fair condition are use for open spaces ndash good to fair condition



Table 2 Classification Scheme for NDVI QuickBrid Thematic Map2

Labels		Rules

Land Use Land Cover

Character of Surface

Open Spaces		If land area = or lt 75 covered with vegetation naturally existing or planted

	(eg grass plants tree (leaf-on leaf-off) forest shrub and scrub etc)

	Else if land area gt 75 covered with vegetation naturally existing or planted

	(eg grass plants tree (leaf-on leaf-off) forest shrub and scrub etc)

MAPPING URBAN LAND USE / LAND COVER USING … · mapping urban land use / land cover using...

Documents

Transcript of MAPPING URBAN LAND USE / LAND COVER USING … · mapping urban land use / land cover using...

ABSTRACT

METHODS

Study Area

Digital Data amp Pre-Processing

Classification Approach

References

_1186573192xls

Classification Scheme - Map1

Classification Scheme - Map2

Defined SCS CN Table

Defined SCS adjusted CN Table

FSD-AOI Activity Codes

FSD-AOI CN Result

Area Covered by Soil Type 1of2

Area Covered by Soil Type 2of2

Area Covered by Soil Type

Area Covered by Soil Type_origi

FSD-AOI CN Result Original

Map1-Results-06-10-2005 - Table

Map2-Results-06-10-2005 - Table

Table1

Table2

Table3

Table 91

ABSTRACT

METHODS

Study Area

Digital Data amp Pre-Processing

Classification Approach

References

_1186573192xls

Classification Scheme - Map1

Classification Scheme - Map2

Defined SCS CN Table

Defined SCS adjusted CN Table

FSD-AOI Activity Codes

FSD-AOI CN Result

Area Covered by Soil Type 1of2

Area Covered by Soil Type 2of2

Area Covered by Soil Type

Area Covered by Soil Type_origi

FSD-AOI CN Result Original

Map1-Results-06-10-2005 - Table

Map2-Results-06-10-2005 - Table

Table1

Table2

Table3

Table 91

ABSTRACT

METHODS

Study Area

Digital Data amp Pre-Processing

Classification Approach

References

_1186573192xls

Classification Scheme - Map1

Classification Scheme - Map2

Defined SCS CN Table

Defined SCS adjusted CN Table

FSD-AOI Activity Codes

FSD-AOI CN Result

Area Covered by Soil Type 1of2

Area Covered by Soil Type 2of2

Area Covered by Soil Type

Area Covered by Soil Type_origi

FSD-AOI CN Result Original

Map1-Results-06-10-2005 - Table

Map2-Results-06-10-2005 - Table

Table1

Table2

Table3

Table 91

ABSTRACT

METHODS

Study Area

Digital Data amp Pre-Processing

Classification Approach