Luswishi Farmblock Land Classifciation Chisanga

Free Open Source Software (FOSS) Geographic Resource Analysis

Support System (GRASS GIS 6.4) for Mapping Land Use, Land Cover

dynamics, Luswishi Farm Block, Lufwanyama District

By Charles Bwalya Chisanga

Certificate in Specialized Application Development using GIS and Remote Sensing

Bachelor of Science Natural Resources

July 2012

ii

Table of Contents

List of Figures ..................................................................................................................................... iii List of Tables ....................................................................................................................................... iv

Abstract ................................................................................................................................................ v 1.0 Introduction ................................................................................................................................ 1

1.1 Goal ........................................................................................................................................ 2 1.2 Purpose ................................................................................................................................... 2 1.3 Objectives .............................................................................................................................. 2

2.0 Description of the Study Site ...................................................................................................... 2 3.0 Data Collection ........................................................................................................................... 4 3.1 Satellite images data acquisition ................................................................................................. 4

4.0 Methodology ............................................................................................................................... 4 5.1 RESULTS AND DISCUSSIONS ............................................................................................... 5 5.1 Change detection techniques ...................................................................................................... 5 5.2 Image Processing and Classification .......................................................................................... 6

5.3 Unsupervised classification ........................................................................................................ 7 5.3.1 Set region setting to red image: ......................................................................................... 7 5.3.2 Creation of group and subgroup ........................................................................................ 7 5.3.3 Clustering of image data and Generation of unsupervised statistics ................................. 7

5.3.3 Unsupervised classification ............................................................................................... 8 5.4.1 Normalized Difference Vegetative Index (NDVI) .......................................................... 10 5.4.2 Image Ratioing ................................................................................................................ 11

5.4.3 Ratio transformations ...................................................................................................... 12

5.5 Tasseled Cap transformation (TC) ............................................................................................ 18 5.7 Principal Component Analysis (PCA) ...................................................................................... 20 5.8 Image Fusion ............................................................................................................................ 24

5.8.1 Image fusion with HSI transformation ............................................................................ 24 5.8.2 Brovey transformation methodology .............................................................................. 26

6.0 Conclusions .............................................................................................................................. 27

References ............................................................................................................................................. i

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List of Figures

Figure 1: Location of the Luswishi Farm Block .................................................................................. 3 Figure 2: Copperbelt Province Boundaries .......................................................................................... 3 Figure 3: False Colour Composite for 2001 and 2005 ......................................................................... 9

Figure 4: 2001 land cover classification for 2001 (left) and 2005 (right) ............................................ 9 Figure 5: NDVI map for 2001 and 2005 ............................................................................................ 11 Figure 6: Spectral Ratioing for NIR and Red for 2001 (Left) and 2005 (Right) ............................... 12 Figure 7: Image Ratio TM3/TM4 for 2001 and 2005 ........................................................................ 13

Figure 8: Image Ratio TM4/TM3 for 2001 and 2005 ........................................................................ 13 Figure 9: Image Ratio Histograms for 2001 and 2005 ....................................................................... 14 Figure 12: Image Ratio TM2/TM3 for 2001 and 2005 ...................................................................... 14 Figure 10: Image Ratio TM3/TM2 for 2001 and 2005 ...................................................................... 15

Figure 11: Image Ratio TM4/TM5 for 2001 and 2005 ...................................................................... 15 Figure 12: Image Ratio TM5/TM4 for 2001 and 2005 ...................................................................... 16 Figure 13: Image Ratio TM5/TM7 for 2001 and 2005 ...................................................................... 16 Figure 14: Image Ratio TM3/TM5 for 2001 and 2005 ...................................................................... 17

Figure 15: Image Ratio TM7/TM2 for 2001 and 2005 ...................................................................... 17 Figure 16: Image Ratio Histograms for 2001 and 2005 ..................................................................... 18

Figure 17: Tasseled Cap transformation for 2001 .............................................................................. 19 Figure 18: Tasseled Cap transformation for 2001 .............................................................................. 20

Figure 19: Change in Luswishi Farm Block from 2001 to 2005 overlaid with roads and rivers ...... 23 Figure 20: Major changes (2001 to 2005) overlaid with roads and rivers ......................................... 24

Figure 21: Image fusion with HSI transformation for Luswishi Farm Block 2001 (left); 2005 (right)

at 14.25m ............................................................................................................................................ 26 Figure 22: Image fusion with Brovey transform 2001 (left) and 2005 (right) ................................... 27

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List of Tables

Table 1: TM Band and their spanned wavelength ................................................................................ 4 Table 2: RASTER MAP CATEGORY REPORT for 2001 and 2005 ................................................ 10 Table 3: NDVI for typical cover types ............................................................................................... 10

Table 4: NDVI for 2001 and 2005 ..................................................................................................... 11

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Abstract

The world of GIS is always in development, and this fact is particularly true for Open Source GIS.

Among the Geographic Information System (GIS) software, in the last years Geographic Resource

Analysis Support System (GRASS) GIS has known wide popularity as it is well documented.

GRASS GIS was developed by the United States Army Construction Research Engineering

Laboratories (USA CERL) as a tool for military land management and environmental planning. In

the early nineties its licenses switched to GPL and by 1998 a GNU/Linux release was freely

available. GRASS has evolved into one of the most comprehensive, general purpose open source

geoinformation systems since its original design as a land management software tool for military

installations. Support for environmental applications has been an integral part of its 20+ years of

development. Current stable release includes, among other features, a 2D/3D vectorial engine, SQL

based database systems and a rich variety of raster and vector graphic formats, allowing

applications in the areas such as geospatial data management and analysis, image processing,

graphics/maps production, spatial modeling and visualization, both in academic and commercial

fields as well as by many governmental agencies and environmental consulting companies.

Change detection is the measure of the distinct data framework and thematic change information

that can guide to more tangible insights into underlying process involving land cover and land use

changes than the information obtained from continuous change. Digital change detection is the

process that helps in determining the changes associated with land use and land cover properties

with reference to geo-registered multitemporal remote sensing data. It helps in identifying change

between two or more dates that is uncharacterized of normal variation. Change detection is useful in

many applications such as land use changes, habitat fragmentation, rate of deforestation, urban

sprawl, and other cumulative changes through spatial and temporal analysis techniques such as GIS

and Remote Sensing along with digital image processing techniques. Vegetation indices (VIs) are

among the oldest tools in remote sensing studies. Although many variations exist most of them ratio

the reflection of light in the red and NIR sections of the spectrum to separate the landscape into

water, soil, and vegetation.

Normalized Difference Vegetation Index (NDVI), Principal Component Analysis (PCA), Tasseled

Cap Transformation (TCT) images were derived from the satellite data analysis. As for land use

change detection technique, PCA and unsupervised were used. Land use change detection was

based on the difference of multi-date of NDVI, PCA and panchromatic images.

Key words: GRASS GIS, GIS, NDVI, PCA, Remote Sensing, classification, Landsat7 EMT, Image

differencing, change detection and Image Ratios.

1

1.0 Introduction

A Geographic Information System (GIS) is software for gathering, storing, managing, and

presenting data that are geographically referenced. The characteristic of a GIS is the capability of

examining different sets of geo-referenced data and reaching a conclusion about the existing

relationship among the data. In other words, a GIS can link information that is difficult to associate

by other means (Harmon and Shapiro, 2008).

According to Harmon and Shapiro (2008) and Buckley (1997) a GIS has four components: a data

input subsystem, a data storage and retrieval subsystem, a data manipulation and analysis

subsystem, and a data reporting subsystem (or a data output and display subsystem). Furthermore

Harmon and Shapiro (2008) indicated that the data inputs include spatial and thematic data derived

from a combination of existing maps, aerial photographs, and manual interpretations of remotely

sensed imagery. Remotely sensed imagery is imagery acquired at some distance using a camera or a

sensor. With a GIS an analyst can define spatial procedures to generate new information, such as the

best location to build a road, preserve a wildlife habitat, or harvest timber. Remote sensing data are

an essential and cost-effective means to update the spatial and thematic information in a GIS.

Remote sensing products are also valuable in themselves, as a cost effective source of geographic

information such as vegetation type, vegetation productivity, water quality, and land use change.

Harmon and Shapiro (2008) reported that an image-processing system has at least five elements:

image input, image storage, image analysis, accuracy assessment, and information reporting. The

two systems (GIS and Image-processing) are similar. Only the input and analysis elements require

different functions for each system. If the GIS is a raster based system, the storage and reporting

functions are identical. Some of the analysis functions may overlap. For example, the image

accuracy assessment programmes are actually standard GIS analysis routines. It seems natural then,

that these two systems should be integrated. When GIS and image-processing systems are

integrated, not only can the remote sensing data be used to update the GIS, but the GIS thematic

data and attributes can be used to guide image classification. By integrating GIS and image-

processing capabilities, GRASS GIS offers the analyst these advantages.

A GIS can be divided into five physical components: People (GIS specialists and end users), Data

(vector and raster), Hardware (computer servers, desktops, Local Area Network), Software (GRASS

GIS, Saga GIS, ArcGIS, Idrisi Andes, ERDAS Imagine), and Procedures (well-designed

implementation plan and business rules). All of these components need to be in balance for the

system to be successful. No one part can run without the other.

Mahmoodzadeh (2007) define changes detection as a technique used in remote sensing to determine

the changes in a particular object of study between two or more time periods. According to

Ramachandra and Kumar (2004) change detection is the measure of the distinct data framework and

thematic change information that can guide to more tangible insights into underlying process

involving land cover and land use changes than the information obtained from continuous change.

Digital change detection is the process that helps in determining the changes associated with land

use and land cover (LULC) properties with reference to geo-registered multitemporal remote

sensing data. It helps in identifying change between two or more dates that is uncharacterized of

normal variation. Change detection is useful in many applications such as LULC changes, habitat

fragmentation, and rate of deforestation, coastal change, urban sprawl, and other cumulative

changes through spatial and temporal analysis techniques such as GIS and Remote Sensing along

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with digital image processing techniques. Change detection is an important process for monitoring

and managing natural resources and urban development because it provides quantitative analysis of

spatial distribution in the area of interest (Mahmoodzadeh, 2007).

In this regard Open Source GIS such as GRASS GIS helps in LULC analysis in a cost-effective

way. GRASS GIS is being developed for the GNU/Linux, Mac OS/X, and Windows operating

systems. It is a GIS used for geospatial data management and analysis, image processing,

graphics/maps production, spatial modeling, and visualization. Most of the commands in GRASS

GIS are command line arguments and requires a user friendly and cost-effective graphical user

interface (GUI). This paper discusses different type of satellite image analysis such as image

unsupervised classification, image ratioing, Principal Component Analysis (PCA), Normalized

Difference Vegetation Index (NDVI) and Tasseled Cap Transformation (TCT).

1.1 Goal

The goal of this paper was to undertake change detection, construct a Landsat7 ETM+ Image

Database and LULC Maps of the Luswishi Farm Block using GRASS GIS.

1.2 Purpose

The purpose of the analysis is to undertake change detection of the Luswishi Farm Block.

1.3 Objectives

To carry out the LULC and temporal change analysis for Luswishi Farm Block,

Lufwanyama

To construct a TM Image Database and LULC Maps of the Luswishi Farm Block

2.0 Description of the Study Site

Luswishi Farm Block area is located on the Copperbelt Province of Zambia in Lufwanyama district

between 100 kilometers to 132 kilometers west of Kalulushi. An M18 Kalengwa road runs centrally

through the Farm Block. The area is between Luswishi River in the east and Mushingashi River to

the west. In the north it lies between Mushingashi-Kambilombilo Resettlement Scheme and

Mirumbi river at latitudes 12o 45

’ S and 13

o 05

’ S and longitudes 27

o 05

’ E and 27

o 15

’ E.

Mushingashi River also marks the boundary between the Kambilombilo Resettlement and the Farm

Block. The northern part of the farm block is not easily accessible as it is served by tracks and so is

the southern half. Present settlements are randomly scattered within the area. Few farming families

own livestock. Active or sizable settlements are along the Kalengwa road and the state of this road

is poor to very poor.

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Figure 1: Location of the Luswishi Farm Block

Figure 2: Luswishi Farm Block Map overlayed with Roads and Rivers

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3.0 Data Collection

3.1 Satellite images data acquisition

Satellite images of LANDSAT 7 ETM+ (Enhanced Thematic Mapper plus) were downloaded from

the Global Land Cover Change Facility [http://glcf.umiacs.umd.edu/data/] through the web

interface and imported to GRASS GIS database using the GEOTIFF/TIFF (r.in.gdal) conversion

module. The images were acquired in one scene (with eight bands) (path 173, row 069) and

covering the whole area of the Luswishi Farm Block. The pixel sizes are 30.0m x 30.0m for bands 1

(blue), 2 (green), 3 (red), 4 (Near Infrared), 5 (mid infrared) and 7 (mid infrared), 57m x 57m for

thermal band 6 and 14.25m x 14.25m for panchromatic band 8. The ETM+ data included one scene

(path 173, row 069) from 1st May 2001 and 12

th May 2005. A list of the bands with their spanned

wavelength (in micrometers) is given in Table 1 below.

Table 1: TM Band and their spanned wavelength Landsat 5 (TM sensor) Wavelength

(micrometers)

Resolution

(meters)

Application

Band 1 (Blue) 0.45 - 0.52 30 Soil/vegetation discrimination; bathymetry/coastal mapping;

cultural/urban feature identification; forest type mapping

Band 2 (Green) 0.52 - 0.60 30 Green vegetation mapping (measures reflectance peak);

cultural/urban feature extraction

Band 3 (Red) 0.63 - 0.69 30 Vegetation vs. non-vegetated and aiding in plant species

discrimination (plant chlorophyll absorption); cultural/urban

feature identification

Band 4 (Near Infrared) 0.76 - 0.90 30 Identification of plant/vegetation types, health, and biomass

content; water body delineation; soil moisture discrimination

Band 5 (Mid Infrared) 1.55 - 1.75 30 Sensitive to moisture in soil and vegetation; discriminating

snow and cloud covered areas

Band 6 (Thermal

Infrared)

10.40 - 12.50 120 Useful in vegetation stress analysis, soil moisture

discrimination related to thermal radiation; thermal mapping

(urban, water)

Band 7 (Mid Infrared) 2.08 - 2.35 30 Discrimination of mineral and rock types; sensitive to

vegetation moisture content

4.0 Methodology Basically, all satellite image-processing/analysis activities/operations can be grouped into three

categories: Image Rectification and Restoration (atmospheric effects, radiometric or geometric

distortion etc); Image Enhancement (contrast, edge enhancement, colour composites or ratios); and

Information Extraction (supervised and unsupervised classification). The former deals with initial

processing of raw image data to correct for geometric distortion, to calibrate the data

radiometrically and to eliminate noise present in the data. The enhancement procedures are applied

to image data in order to effectively display the data for subsequent visual interpretation. It involves

techniques for increasing the visual distinction between features in a scene.

Data from Landsat7 ETM+ imageries were processed using GRASS GIS software and spatial

analysis, interpolation and other calculations. There are many change detection techniques from

visual comparison to detailed quantitative approaches (Wickware and Howarth, 1981). In this paper

techniques evaluated are: PCA, NDVI, image ratios, unsupervised classification and image fusion.

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The methodology of the study involved -

1. Reviewing of topological map sheets and literature

2. Creation of base layers like district boundary, farm block boundaries, road network,

mapping of water bodies, etc. from the topographic sheets of scale 1:250,000 and

1:50,000.

3. Extraction of bands from the data respectively downloaded from Global Land Cover

Change Facility

4. Identification of ground control points (GCP’s) and geo-correction of bands through

resampling.

5. Image restoration (geometric correction (Geometric correction is the process of

assigning real-world co-ordinates to an image) and Radiometric restoration), Cropping

(subsetting or clipping) of data corresponding to the study area.

6. Fusion of data using RGB (Red, Green, Blue) to HIS (Hue, Intensity, Saturation) and

HIS to RGB conversion technique.

7. Histogram generation, Bi-spectral plots, Regression analysis, Principal Component

Analysis (PCA), accuracy assessment, computation of the Root Mean Square Error

(RMSE) for the images.

8. Computation and analysis of various vegetation indices (NDVI).

9. Generation of TCC (True Colour Composites) and FCC (False Colour Composite) and

identification of training sites on FCC.

10. Collection of attribute information from field corresponding to the chosen training sites

using Geographical Positioning System (GPS).

11. Classification of remote sensing data. LULC analyses in the farm block.

12. Change detection analysis using different techniques (Image differencing, Image

ratioing, etc.).

13. Detection, visualization and assessment of change analysis.

14. Statistical analysis and report generation.

5.1 RESULTS AND DISCUSSIONS

5.1 Change detection techniques

Different change detection techniques such as composite analysis, normalized vegetation index,

image ratioing, Principal Component Analysis and image fusion were used to attempt to assess the

amount of change in the study area. Before the analysis could be performed the raster images were

clipped by masking. Raster MASK allows the user to block out certain areas of a map from analysis

by hiding them from other GRASS raster modules. MASK is a raster map which contains the values

1 and NULL. Those cells where the MASK map shows value 1 are available for display and

processing while those assigned NULL are hidden. The map name MASK is a reserved filename for

raster maps. The presence of a MASK map in a MAPSET entails that it will be used as for all raster

operations when reading raster data. Any raster data falling outside of the MASK are treated as if its

value were NULL. To create a MASK, a base map should be available that is used to select which

values will represent the hidden and the active areas (Neteler and Mitasova, 2008). The procedure

undertaken to mask the boundary of Luswishi Farm Block are as follows: v.to.rast – convert vector

to rast; r.mask – create a mask from the raster; and r.resample – to create a new raster clipped by

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the mask. Both images were resampled using the nearest neighbor algorithm. All the satellite bands for

2001 and 2005 time periods were clipped within the GRASS GIS database.

5.2 Image Processing and Classification

In general there are two methods for classification of remotely-sensed data; unsupervised and

supervised classification. For both methods the classification process required two major steps. The

first step in an unsupervised image classification is performed by i.cluster; the first step in a

supervised classification is executed by the GRASS programme i.class. In both cases, the second

step in the image classification procedure is performed by i.maxlik. The data have to be analyzed for

similarities in their spectral responses, and then the pixels have to be assigned to classes. The

unsupervised method is fully automated based on the image statistics but it delivers only abstract

class numbers. Then the main task is to find the reasonable number of clusters/classes and assign

ground truth information to these classes. The supervised classification on the other hand requires

user interaction and here training areas covering known land use have to be digitized. Image

statistics are automatically derived from these training areas and used for classification.

Common classification methods such as the Maximum Likelihood Classifier (MLC) are pixel

based. GRASS additionally provides a different method called Sequential Maximum A Posteriori

(SMAP) classifier which takes into account that neighborhood pixels may be similar. The fact that a

neighborhood of similar pixels will lead to spatial auto-correlation is used to improve the results.

Figure 3: Unsupervised and Supervised classification procedure for multispectral data (Eastman,

2006) (Adapted from Neteler and Mitasova, 2008)

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5.3 Unsupervised classification

In an unsupervised classification, the maximum-likelihood classifier uses the cluster means and

covariance matrices from the i.cluster signature file to determine to which category (spectral class)

each cell in the image has the highest probability of belonging. In a supervised image classification,

the maximum-likelihood classifier uses the region means and covariance matrices from the spectral

signature file generated by i.class, based on regions (groups of image pixels) chosen by the user, to

determine to which category each cell in the image has the highest probability of belonging. In

either case, the raster map layer output by i.maxlik is a classified image in which each cell has been

assigned to a spectral class (i.e., a category). The spectral classes (categories) can be related to

specific land cover types on the ground.

The programme will run non-interactively if the user specifies the names of raster map layers, i.e.,

group and subgroup names, seed signature file name, result classification file name, and any

combination of non-required options in the command line, using the form.

5.3.1 Set region setting to red image:

g.region rast=2001_lsat7_2001.3 -p

5.3.2 Creation of group and subgroup

i.group allows the user to collect raster map layers in an imagery group by assigning them to user-

named subgroups or other groups. This enables the user to run analysis on any combination of the

raster map layers in a group. The user creates the groups and subgroups and selects the raster map

layers that are to reside in them. Imagery analysis programmes like i.point, i.rectify, i.ortho.photo

and others ask the user for the name of an imagery group whose data are to be analyzed. Imagery

analysis programmes like i.cluster and i.maxlik ask the user for the imagery group and imagery

subgroup whose data are to be analyzed.

Command: i.group [-rlg] group=name [subgroup=string] [input=name[,name,...]] [--verbose] [--quiet] i.group group=lsat7_20010 subgroup=lsat7_20010 input=lsat7_2001.1,lsat7_2001.2,lsat7_2001.3,lsat7_2001.4,lsat7_2001.5,lsat7_2001.7 i.group group=lsat7_20050 subgroup=lsat7_20050 input=lsat7_2005.1,lsat7_2005.2,lsat7_2005.3,lsat7_2005.4,lsat7_2005.5,lsat7_2005.7

5.3.3 Clustering of image data and Generation of unsupervised statistics

i.cluster performs the first pass in the GRASS two-pass unsupervised classification of imagery,

while the GRASS programme i.maxlik executes the second pass. Both programmes must be run to

complete the unsupervised classification.

i.cluster is a clustering algorithm that reads through the (raster) imagery data and builds pixel

clusters based on the spectral reflectance of the pixels. The pixel clusters are imagery categories that

can be related to land cover types on the ground. The spectral distributions of the clusters (which

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will be the land cover spectral signatures) are influenced by six parameters set by the user. The first

parameter set by the user is the initial number of clusters to be discriminated.

i.cluster starts by generating spectral signatures for this number of clusters and "attempts" to end up

with this number of clusters during the clustering process. The resulting number of clusters and

their spectral distributions, however, are also influenced by the range of the spectral values

(category values) in the image files and the other parameters set by the user. These parameters are:

the minimum cluster size, minimum cluster separation, the percent convergence, the maximum

number of iterations, and the row and column sampling intervals.

Command: i.cluster [-q] group=name subgroup=name sigfile=name classes=integer [seed=name] [sample=row_interval,col_interval] [iterations=integer] [convergence=float] [separation=float] [min_size=integer] [reportfile=name] [--verbose] [--quiet] i.cluster group=lsat7_20010 subgroup=lsat7_20010 sigfile=lsat7_20010_sig classes=8 reportfile=lsat7_20010.txt i.cluster group=lsat7_20050 subgroup=lsat7_20050 sigfile=lsat7_20050_sig classes=8 reportfile=lsat7_20050.txt # look at report file gedit lsat72001.txt gedit lsat72005.txt

5.3.3 Unsupervised classification

i.maxlik is a maximum-likelihood discriminate analysis classifier. It can be used to perform the

second step in either an unsupervised or a supervised image classification.

Command: i.maxlik [-q] group=name subgroup=name sigfile=name class=name [reject=name] [--overwrite] [--verbose] [--quiet]

i.maxlik group=lsat7_2001 subgroup=lsat7_2001 sigfile=lsat7_2001_sig class=lsat7_2001_class reject=lsat7_2001_reject i.maxlik group=lsat7_2005 subgroup=lsat7_2005 sigfile=lsat7_2005_sig class=lsat7_2005_class reject=lsat7_2005_reject # false color composite d.mon x0 ; rgb b=2001_lsat7.7 g=2001_lsat7.5 r=2001_lsat7.1 d.rgb b=2005_lsat7.7 g=2005_lsat7.5 r=2005_lsat7.1

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Figure 3: False Colour Composite for 2001 and 2005

#classification result d.mon x1 d.rast.leg lsat7_2001_class d.rast.leg lsat7_2005_class

Figure 4: 2001 land cover classification for 2001 (left) and 2005 (right)

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Table 2: RASTER MAP CATEGORY REPORT for 2001 and 2005

Categories (unsupervised classification) 2001 2005

1 Mixed vegetation 23,323 25,393

2 Health vegetation 21,577 19,962

3 Sparse vegetation 24,432 21,706

4 Water 16,587 15,637

5 Bare land/agricultural land 9,769 10,850

6 Human settlements 4,391 6,530

Total Hectare 100,079 100,079

5.4 Vegetation Indices

5.4.1 Normalized Difference Vegetative Index (NDVI)

Landsat7 ETM+ data can be used to assess the type, extent, and condition of vegetation over a

region, or vegetative change over time. These studies require that a vegetative index be calculated

from sets of remotely-sensed data. Vegetation indices are commonly used to monitor vegetation at

various spatial and temporal scales. Vegetation indices are based on vegetation spectral properties in

the red and the near-infra red part of the spectrum. The most widely used index is the Normalized

Difference Vegetative Index (NDVI). The NDVI is calculated as a ratio between measured

reflectivity in the red (band 3) and near-infrared (band 4) portions of the electromagnetic spectrum.

These two spectral bands are chosen because they are affected by the absorption of chlorophyll in

leafy green vegetation and by the density of the green vegetation on the surface. Moreover, in red

and near-infrared bands, the contrast between vegetation and soil is at a maximum.

To study the vegetation status with NDVI, the Red and the Near Infrared channels (NIR) are taken

and used as input for simple map algebra in the GRASS GIS command r.mapcalc (ndvi = 1.0 * (nir - red)/(nir + red). With r.colors an optimized "ndvi" color table can be assigned

afterward. By normalizing the difference in this way, the values can be scaled between a value of -1

to +1. This also reduces the influence of atmospheric absorption. The table below shows typical

reflectance values in the red and infrared channels, and the NDVI for typical cover types (Holben,

1986). Water typically has an NDVI value less than 0, bare soils between 0 and 0.1 and vegetation

over 0.1. Negative values of NDVI (values approaching -1) correspond to water. Values close to

zero (-0.1 to 0.1) generally correspond to barren areas of rock, sand, or snow. Lastly, low, positive

values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate

temperate and tropical rainforests (values approaching 1). Figure 4 and table 4 shows the NDVI

map and computed statistics for 2001 and 2005 respectively.

Table 3: NDVI for typical cover types

COVER TYPE RED NIR NDVI

Dense vegetation 0.1 0.5 0.7

Dry Bare soil 0.269 0.283 0.025

Clouds 0.227 0.228 0.002

Snow and ice 0.375 0.342 -0.046

Water 0.022 0.013 -0.257

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Figure 5: NDVI map for 2001 and 2005

Table 4: NDVI for 2001 and 2005 2001 Ndvi ranges Hectares 2005 Ndvi ranges Hectares

-0.470588--0.300916 5 -0.301587--0.163228 59

-0.300916--0.131244 16 -0.163228--0.024868 1,038

-0.131244-0.038429 722 -0.024868-0.113492 20,713

0.038429-0.208101 49,295 0.113492-0.251852 74,952

0.208101-0.377773 49,947 0.251852-0.390212 3,310

0.377773-0.547445 95 0.390212-0.528571 7

Total hectare 100,079 Total hectare 100,079

NDVI is usually used for observing the spatial distribution of vegetated area along with the health

status. It is the most commonly used VI as it retains the ability to minimize topological effects while

producing a linear measurement scale. In additional, divisions by zero errors are significantly

reduced. Furthermore, the measurement scale has the desirable property of ranging from -1 to 1

with 0 representing the approximate value of no vegetation. Thus negative values represent non-

vegetated surfaces (Eastman, 2006).

5.4.2 Image Ratioing

Spectral Ratioing is used to highlight differences between features and often reduces the effect of

different light reflectance intensities. Spectral ratioing can be used to detect stressed or diseased

crops. One common ratio, called Normalized Difference Vegetation Index (NDVI) is: (NIR-RED) /

(NIR+RED) or simplified as below to (NIR /RED). To perform this spectral ratio:

STEPS 1. Open the GRASS shell and type in:

r.mapcalc etm_ratio="etm40/etm30"

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Confirm with Enter 2. Go to the Browser and click the Refresh button 3. Load etm_ratio to the map canvas. 4. Change raster properties to Pseudo color and activate the option “Invert Color map”.

Figure 6: Spectral Ratioing for NIR and Red for 2001 (Left) and 2005 (Right)

The result clearly captures the contrast between the red and infrared bands for vegetated pixels, with

high index values being produced by combination of low red and high infrared reflectance. The

index is susceptible to division by zero errors and the resulting measurement scale is not linear. As a

result, RATIO Vegetative Indices (VI) images do not have normal distribution making it difficult to

apply some statistical procedures (Eastman, 2006).

5.4.3 Ratio transformations

Ratio transformations of the remotely sensed data can be applied to reduce the effects of the

environment. Ratios also provide unique information and subtle spectral-reflectance or color

differences between surface materials that are often difficult to detect in a standard image. It is also

useful for discriminating between soils and vegetation. The number of possible ratio combinations

for a multispectral sensor with P bands is n= P(P-1). Thus for the TM's six reflectance bands there

are thirty different ratio combinations - 15 original and 15 reciprocal. For the purpose of this study

ten band ratios were examined using GRASS GIS to identify the Land Use – Land Cover (LULC)

features.

TM3/TM4: This ratio has defined barren lands and urban area uniquely. But it could not define

water body, forests and croplands.

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Figure 7: Image Ratio TM3/TM4 for 2001 and 2005

TM4/TM3: This ratio distinguished vegetation, water and croplands. It has enhanced forests,

barren lands. Because forests or vegetation exhibits higher reflectance in near IR region (0.76 -

0.90u m) and strong absorption in red region (0.63-0.69u m) region. This ratio uniquely defines the

distribution of vegetation. The lighter the tone, the greater the amount of vegetation present.


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Figure 9: Image Ratio Histograms for 2001 and 2005

TM2/TM3: this ratio has distinguished croplands, barren lands sharply. But it hasn't separated

croplands, forests and water body. Both forests and water body has appeared as lighter tone and

barren land appeared has dark tone. It did not enhance urban area. Chlorophyll has strong

reflectance in the band 2 (0.52 -0.60u m) region and strong absorption in the band 3(0.63 -0.69u m)

region, vegetation has appeared as higher tone.


TM3/TM2: This ratio has separated forests and croplands. Because band 3 (0.63-0.69m m) is the

red chlorophyll absorption band of healthy green vegetation and band 2 (0.52-0.69m m) is the

reflectance band from leaf surfaces. This ratio can be useful to discriminate broad classes of

vegetation. Croplands have appeared as lighter (brighter) tone and forests appeared as dark tone.

15


TM4/TM5: It enhances the water body, vegetation and presence of moisture content in the

croplands. Water body has appeared as dark tone and vegetation as lighter tone. Because water is a

strong absorber in near IR region( band4) and higher reflectance in band 5 region. It can be useful

for discriminating water bodies from land.


TM5/TM4: It has separated water body from forest, barren lands and vegetation. In this ratio water

has appeared as dark tone and forest, barren lands, bare croplands all have exhibited brighter tone.

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TM5/TM7: It has separated water body from lands (soils). It has also enhanced presence of

moisture in croplands. All water bodies appeared as dark tone. Both band 5 and band 7 are sensitive

to moisture content variation in soils and vegetation. This ratio is useful for crop-drought studies

and plant vigor investigations. This ratio separates land and water uniquely. Since soils exhibit

strong absorption in the band 7 (2.08 -2.35u m) and high reflectance in band 5 (1.55 - 1.75u m), soil

has been enhanced in this ratio. Land has appeared as lighter tone and water appeared as dark tone.


TM3/TM5: This ratio enhances barren lands, highways, street patterns within the urban areas and

urban built-up or cemented areas. It could not enhance the clear water but it enhanced turbid water.

This ratio is useful for observing differences in water turbidity. Barren lands, highways, urban and

built-up areas have appeared as lighter tone and forests, water body and croplands appeared as dark

tone.

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TM7/TM2: This ratio has separated forests and croplands. But it could not separated forests from

water body; both features have appeared as dark tone. It enhances highways, urban and built-up

areas and croplands and all of them have appeared as lighter tone.


Ramachandra and Kumar (2004) indicated that geocorrected images (Green, Red and Near Infra

Red bands) of different dates were ratioed pixel by pixel (band by band) basis. Areas of change are

represented by pixel values that differ from 1. The ratio value greater than 1 or less than 1

represents a change depending upon the nature of changes occurred between the two dates. Figure

16 below shows the histogram obtained for the near infrared band after performing the image

ratioing. Image ratios are the basis of a simple algebraic method used for feature extraction,

reduction of terrain illumination effects, image enhancement, computation of vegetation indices and

more. Image ratios are the basis of a simple algebraic method used for feature extraction, reduction

of terrain illumination effects, image enhancement, computation of vegetation indices and more. In

general, the ratio result for pixels with very different values for the input channels is larger

(brighter) than for pixels with similar values. The image ratio equations can be computed with

r.mapcalc. It is important to include a multiplier of 1.0 at the beginning of the map algebra

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expression because integer values are being divided. Otherwise the results would be zero and not

the expected floating point numbers (Neteler and Mitasova, 2008).

Band 7 (Medium infrared) and 4 (Near infrared)

g.region rast=2005_lsat7.1 -p r.mapcalc "ratio7_4_2005=1.0 * 2005_lsat7.7/2005_lsat7.4" d.erase d.rast ratio7_4

Figure 16: Image Ratio Histograms for 2001 and 2005

5.5 Tasseled Cap transformation (TC)

The TC transformation optimizes data viewing for vegetation studies as one of the available

methods for enhancing spectral information content of Landsat7 TM. Four bands for TC

components are generated:

tasscap.1: corresponds to brightness, measure of soil

tasscap.2: corresponds to greenness, measure of vegetation

tasscap.3: corresponds to wetness, interrelationship of soil and canopy moisture

tasscap.4: corresponds to atmospheric haze.

The Tasseled Cap Transformation in remote sensing is the conversion of the readings in a set of

channels into composite values; i.e., the weighted sums of separate channel readings. One of these

weighted sums measures roughly the brightness of each pixel in the scene. The other composite

values are linear combinations of the values of the separate channels, but some of the weights are

negative and others positive. One of these other composite values represents the degree of

greenness of the pixels and another might represent the degree of yellowness of vegetation or

perhaps the wetness of the soil. Usually there are just three composite variables (Watkins, 2006).

i.tasscap calculates Tasseled Cap (Kauth Thomas, TC) transformation for LANDSAT-TM data

(TM4, TM5, TM7). The TC transformation maps for 2001 and 2005 are shown in figures 6 and 7

below.

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Tasseled Cap transformation 2001: Brightness Tasseled Cap transformation 2001: greenness

Tasseled Cap transformation 2001: Wetness Tasseled Cap transformation 2001: Atmospheric haze

Figure 17: Tasseled Cap transformation for 2001

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Tasseled Cap transformation 2005: Brightness Tasseled Cap transformation 2005: greenness

Tasseled Cap transformation 2005: Wetness Tasseled Cap transformation 2005: Atmospheric haze

Figure 18: Tasseled Cap transformation for 2001

5.7 Principal Component Analysis (PCA)

Principal Component Analysis (PCA) is a linear transformation technique related to Factor

Analysis. Given a set of image bands, PCA produces new set of images, known as components that

are uncorrelated with one another and are ordered in terms of the amount of variance they explain

from the original band set (Eastman, 2006). Furthermore, Basith et al., (2010) indicated that PCA is

a mathematical transformation that converts original data into new data channels that are

uncorrelated and minimize data redundancy. This technique is usually used to reduce the number of

spectral components (spectral bands) to fewer principal components accounting for the most

variance in the original multispectral images. Image spectral bands of two or more dates are treated

as a single data set. The current implementation of PCA in GRASS GIS uses an eigenvalue

decomposition (EVD) based algorithm (Richards et al., 2006). This algorithm features covariance

matrix computation and matrix multiplication based data re-projection whose performance

degradation becomes prohibitive for large data sets.

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A characteristic of PCA is that the piece of information common to all input bands (high correlation

between bands) is mapped to the first Principal Component (PC) whilst subsequent PC accounts for

progressively less of the total scene variance. This principle can be applied to multitemporal

datasets. If two images covering the same ground area but taken at different times of the year are

subjected to PCA, the first PC will contain all of the information that has not changed between the

two dates whilst the second PC will contain all the changed information. According to Basith et al.,

(2010) when PCA is applied to data embracing several spectral bands, data is concentrated in the

first two or three components. The other components generally contain only noise. The areas of

greatest change are found in the tails of the image histogram (Mahmoodzadeh, 2007).

In GRASS, the Principal Component Transformation is implemented in i.pca. The module requires

the input channel names (at least two images) and a prefix for the transformed PC image files,

which will be enumerated incrementally. Optionally, the data can be rescaled to a range different

from the default range of 0-255. i.pca is an image processing programme based on the algorithm

provided by Vali (1990), that processes n (2 >= n) input raster map layers and produces n output

raster map layers containing the PC of the input data in decreasing order of variance ("contrast").

The output raster map layers are assigned names with .1, .2, ... .n suffixes. The current geographic

region definition and mask settings are respected when reading the input raster map layers. When

the rescale option is used, the output files are rescaled to fit the min-max range.

Step 1

g.region rast=lsat7_2001.10 -p

GRASS 6.4.0+42329 (luswishifb):~ > i.pca

in=lsat7_2005.10,lsat7_2005.20,lsat7_2005.30,lsat7_2005.10,lsat7_2005.20,lsat7_2005.30

out=pca_01_05

Eigen values, (vectors), and [percent importance]:

PC1 1940.11 (-0.4877,-0.3785,-0.3448,-0.4877,-0.3785,-0.3448) [99.16%]

PC2 15.58 (-0.3641,-0.0784, 0.6011,-0.3641,-0.0784, 0.6011) [0.80%]

PC3 0.85 (-0.3600, 0.5921,-0.1408,-0.3600, 0.5921,-0.1408) [0.04%]

PC4 0.00 (0.1640,-0.6878,-0.0002,-0.1640, 0.6878, 0.0002) [0.00%]

PC5 0.00 (-0.6176,-0.1474, 0.3113, 0.6176, 0.1474,-0.3113) [0.00%]

PC6 -0.00 (0.3028, 0.0721, 0.6349,-0.3028,-0.0721,-0.6349) [-0.00%]

GRASS 6.4.0+42329 (luswishifb):~ > r.univar -e pca_01_05.3

100%

total null and non-null cells: 3085120

total null cells: 1852714

Of the non-null cells:

----------------------

n: 1232406

minimum: 196

maximum: 216

range: 20

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mean: 204.607

mean of absolute values: 204.607

standard deviation: 1.29921

variance: 1.68793

variation coefficient: 0.634976 %

sum: 252158813

1st quartile: 204

median (even number of cells): 205

3rd quartile: 205

90th percentile: 206

GRASS 6.4.0+42329 (luswishifb):~ >

Step 3: PCA can be used to perform a simple change detection analysis

d.rgb b=pca_01_05.1 g=pca_01_05.2 r=pca_01_05.3

d.rgb b=lsat7_2001.10 g=lsat7_2001.20 r=lsat7_2001.30


r.univar -e pca_01_05.3

# Simple change detection:

# consider high PCA values in high PCA component as change

r.mapcalc "changes_01_05=if(pca_01_05.3 > 205,1,null())"

# vectorize and remove small areas

# (3 pixel * 28.5m)^2 = 7310.2m^2

r.to.vect -s changes_01_05 out=changes_01_05 feat=area

v.clean changes_01_05 out=major_changes_01_05 tool=rmarea thresh=7300

# overlay to Oct/1987 map


d.vect major_changes type=boundary col=red

d.vect lakes fcol=blue type=area

# overlay to May/2002 map


d.vect major_changes type=boundary col=red

d.vect rivers fcol=blue type=area

The resulting vector polygons captured significant land use changes, especially conversion from

vegetated area to developed areas. Changes (Figure 19) and major changes (Figure 20) where

forests were being converted to agricultural land are 22, 978.39 ha and 9, 204.5 ha respectively.

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Figure 19: Change in Luswishi Farm Block from 2001 to 2005 overlaid with roads and rivers

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Figure 20: Major changes (2001 to 2005) overlaid with roads and rivers

5.8 Image Fusion

According to Neteler and Mitasova (2008) satellite data sets with high radiometric resolution

(multispectral channels) lack high geometric resolution and vice versa. However, for an accurate

image interpretation, both radiometric and geometric resolution should be high. Image fusion is a

method used to geometrically enhance images with high radiometric resolution by merging the

multispectral channels with a panchromatic image. GRASS provides the HIS (i.rgb.his, ihis.grb)

and the Brovey transform methods. GRASS provides two color conversion modules, the i.rgb.his to

convert an image from RGB to HSI and i.his.rgb to convert back from HSI to RGB. The Brovey

transform method is implemented in GRASS as i.fusion.brovey. The formula was originally

developed for LANDSAT-TM5 and SPOT, but it also works well with LANDSAT7 TM.

i.fusion.brovey - Brovey transform to merge multispectral and high-resolution panchromatic

channels.

5.8.1 Image fusion with HSI transformation Two data sets were used whose acquisition time was 2001 and 2005 to avoid land use change which

may modify the result. For an HSI fusion the three RGB channels were transformed to HSI color

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model. The general idea of HSI is replacement of the intensity channel with a high resolution

panchromatic channel for the back transformation from HSI to RGB color model. The effect is that

the color information in lower resolution is merged with the high spatial resolution of the

panchromatic channel. In term of GIS a resolution change is required before back- transforming the

image to archive the highest spatial resolution in the output.

The Landsat7 TM images for Luswishi Farm Block were enhanced each at 28.5m resolution with

the panchromatic channel of 14.25m resolution of the same satellite acquired at the same time.

Before starting the procedure as outlined below, the input channels contrast were enhanced with

r.colors. Then the input channels were converted to HSI color model with i.rgb.his at 28.5m

resolution. The higher resolution was set with g.region as defined by the panchromatic channel. To

improve the geometrical resolution, the original intensity image which resulted from the RGB to

HIS transformation was replaced by the panchromatic channel for the back- transformation to the

RGB color model. Finally three new RGB channels at 14.25m resolution containing the multi-

spectral information from the input channels were generated.

STEP 1: Applying a contrast stretch (histogram equal.) g.region rast=lsat7_2001.10 -p r.colors lsat7_2001.10 color=grey.eq r.colors lsat7_2001.20 color=grey.eq r.colors lsat7_2001.30 color=grey.eq r.colors lsat7_2001.40 color=grey.eq STEP2: RGB image fusion at 28.5m resolution of RGB channels d.erase d.rgb b=lsat7_2001.10 g=lsat7_2001.20 r=lsat7_2001.30 STEP 3: RGB/IHS conversion i.rgb.his blue=lsat7_2001.10 green=lsat7_2001.20 \ red=lsat7_2001.30 hue=hue intensity=int saturation=sat STEP 4: IHS/RGB back conversion. With ETMPAN replacing old intensity Image g.region rast=lsat7_2001.80 -p i.his.rgb hue=hue intensity=lsat7_2001.80 \ saturation=sat blue=etm2001.10_15 green=etm2001.20_15 red=etm2001.30_15 STEP 5: color contrast enhancement r.colors etm2001.10_15 color=grey.eq r.colors etm2001.20_15 color=grey.eq r.colors etm2001.30_15 color=grey.eq STEP 6: Visualization of higher resolution color composite d.erase d.rgb b=etm2001.10_15 g=etm2001.20_15 r=etm2001.30_15

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Figure 21: Image fusion with HSI transformation for Luswishi Farm Block 2001 (left); 2005

(right) at 14.25m

5.8.2 Brovey transformation methodology

An alternative method for image fusion is Brovey transformation. This method can easily be

implemented in GRASS GIS. The formula for Brovey transformation is shown as below.

i.fusion.brovey performs a Brovey transformation using three multispectral and the panchromatic

satellite image scene channels. Three new channels are calculated according to the formula and

steps below:

DN_b1 DN_fused = ------------------------------- * DN_pan DN_b1 + DN_b2 + DN_b3

STEP 1:if not done yet, apply a contrast stretch (histogram equal.) g.region rast=lsat7_2001.1 -p d.erase i.fusion.brovey -l ms1=lsat7_2001.1 ms2=lsat7_2001.2 \ ms3=lsat7_2001.3 pan=lsat7_2001.8 out=brov_2001 STEP 2: it temporarily sets raster resolution to PAN resolution: 14.25 # Original RGB d.erase d.rgb b=lsat7_2001.1 g=lsat7_2001.2 r=lsat7_2001.3 STEP 3: Brovey transformed RGB g.region -p rast=brov_2001.red d.erase d.rgb b=brov_2001.blue g=brov_2001.green r=brov_2001.red d.vect luswishifarmb col=red

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i.landsat.rgb b=brov_2001.blue g=brov_2001.green r=brov_2001.red STEP 4: Reversed B and G channel assignment d.rgb g=brov_2001.blue b=brov_2001.green r=brov_2001.red

Calculation of Brovey fusion map from Luswisi Farm Block Landsat scene: g.region rast=lsat7_2001.1 -p STEP 5: R, G, B composite at 28.5m d.rgb b=lsat7_2001.1_sub g=lsat7_2001.2_sub r=lsat7_2002_1.3_sub STEP 6: Brovey fusion i.fusion.brovey -l ms1=lsat7_2001.2_sub ms2=lsat7_2001.4_sub \ ms3=lsat7_2001.5_sub pan=lsat7_2001.8_sub \ outputprefix=brovey_2001_sub STEP 7: Display at 14.25m g.region rast=brovey_2001_sub.blue -p d.rgb b=brovey_2001_sub.blue g=brovey_2001_sub.green r=brovey_2001_sub.red

Figure 22: Image fusion with Brovey transform 2001 (left) and 2005 (right)

6.0 Conclusions

Holistic decisions and scientific approaches are required for sustainable development of the farm

block. Change detection techniques using temporal remote sensing data provide detailed

information for detecting and assessing land cover and land use dynamics. Different change

detection techniques were applied to monitor the changes. The change analysis based on two dates,

spanning over a period of four years using unsupervised classification, showed an increasing trend

in human settlement and bareland and decline in spatial extent of health vegetated areas. Depletion

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of water bodies and large extent of bareland in the farm block is mainly due to lack of integrated

land use planning and watershed approaches and mismanagement of natural resources.

Image Ratio transformations of the remotely sensed data can be applied to reduce the effects of the

environment. Ratios also provide unique information and subtle spectral-reflectance or color

differences between surface materials that are often difficult to detect in a standard image. It is also

useful for discriminating between soils and vegetation.

NDVI calculations are based on the principle that actively growing green plants strongly absorb

radiation in the visible region of the spectrum while strongly reflecting radiation in the Near

Infrared regionThe NDVI has been most commonly used to map spatial and temporal variation in

vegetation and the calculation of the NDVI value turns out to be sensitive to a number of perturbing

factors including: atmospheric effects, clouds, soil effects, anisotropic effects and spectral effects.

The limitation of the study is that no identification of ground control points (GCP’s) and collection

of attribute information from field corresponding to the chosen training sites using Geographical

Positioning System (GPS) was done. Bi-spectral plots, Regression analysis, accuracy assessment

and computation of the Root Mean Square Error (RMSE) for the images were not undertaken and

no supervised classification was performed on the satellite imagery.

i

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