Comparative Study of Change Detection and Urban Expansion ... · analyst 8.4, GeoMedia professional...

JKAU: Eng. Sci., Vol. 23 No. 2, pp: 139-172 (2012 A.D. /1433 A.H.)

DOI: 10.4197 / Eng. 23-2.8

139

Comparative Study of Change Detection and Urban

Expansion Using Multi-Date Multi-Source Data: A Case

Study of Greater Khartoum

Abdullah Salman Alsalman, Hamid Elmelaih Mattar1

and Abdullah Elsadeg Ali 1

College of Engineering, King Saud University, Box 800, Riyadh, Saudi

Arabia, and 1Sudan National Survey Authority (SNSA) Khartoum, Sudan

[email protected]

Abstract. Noting that Khartoum represents the most rapidly expanding

city in the Sudan and taking into account that change detection

operations are seldom , the present study has been initiated to

attempt to produce work that synthesizes land use/land cover (LULC)

to investigate change detection using GIS, remote sensing data and

digital image processing techniques; estimate, evaluate and map

changes that took place in the city from 1975 to 2003. The experiment

used the techniques of visual inspection, write-function-memory-

insertion, image differencing, image transformation i.e. normalized

difference vegetation index (NDVI), tasseled cap, principal

component analysis (PCA), post-classification comparison and GIS.

The results of all these various techniques were used by the authors to

study change detection of the geographic locale of the test area. Image

processing and GIS techniques were performed using Intergraph

Image analyst 8.4 and GeoMedia professional version 6, ERDAS

Imagine 8.7, and ArcGIS 9.2. Results obtained were discussed and

analyzed in a comparative manner and a conclusion regarding the best

method for change detection of the test area was derived.

Keywords: Change detection, urban expansion, multi-date, multi-

sensor, Greater Khartoum

1. Introduction

As is the case in many developing countries, Khartoum is facing many

challenges and complications in planning municipal services and

140 Abdullah Salman Alsalman et al.

managing available resources. The importance of this research lies in its

potential ability to address the following key issues:

- Study and monitor the fast expansion of the city and the impact

of this expansion on services and environment;

- Study of urban fringe area transformation and evaluation of the

transformation thus giving an insight about rate, type and change

direction;

- Determine and establish the appropriate change detection

methodology for the test area and as a consequence derive change indices

that can be used for prediction and forecast;

- Establish change inventory and geo-spatial temporal multi-

spectral/multi-sensor database system;

- Provide useful reference data for the city government about the

dynamic nature of LULC; and turn these into precise vector maps for

further analysis, distribution and presentation.

The digital image processing used to extract meaningful

information from remote sensing data consists of four major areas of

computer operations, namely image preprocessing (restoration), image

enhancement, image classification and change detection [1,2]. Removal of

errors due to platform motion, atmosphere, radiometric and geometric

imperfections is customarily known as “image preprocessing“ [1,2].

Conversely, image enhancement refers to the process used to improve

image appearance. The process includes contrast stretching, spatial

filtering and edge enhancement. Whilst classification and change

detection are computer algorithms for information extraction. Image

classification, on the other hand, is a form of pattern recognition, or

identification of the pattern associated with each pixel position in an

image in terms of geographic locale. Change detection is a process used

to identify change over a period of time. The basic technique of change

detection based on remotely sensed data depends on examining two

digital images of the same area acquired on two different dates. Overlay

or subtraction of these two images indicates areas experiencing change [2, 3].

The results can be assessed with supporting sources such as fieldwork or

ancillary data.

Comparative Study of Change Detection and Urban Expansion … 141

Several algorithms have been developed to extract change with remote sensor data. These include write-function-memory insertion (WFMI), multi-date composite image comparison, image algebra, post-classification analysis, spectral change vector and principal component (PCA) analyses

[1, 4-10]. Since most readers are familiar about other

techniques, a brief account on WFMI, image algebra (image differencing) and PCA follow.

WFMI involves the use of the three computer screen channels (RGB) to display two or three individual bands of multiple dates. Change between dates can be visually identified. For example, all areas that did not change between the two dates are depicted in a shade of yellow according to additive color theory

[2]. Areas that experienced change are

shown in either red or green. Image differencing, on the other hand, is a change detection technique in which digital numbers of one band, i.e. from the “after” image are subtracted from digital numbers of the same band from the “before” image. The difference in areas of no change will be very small or almost zero. Areas that experienced some changes will appear negatively or positively different. Usually some constant is added to keep the result in the range between 0 and 255. However, the most critical element of the image differencing method is deciding where to place the threshold boundaries between change and no-change. PCA is a transformation technique used to compress the information content of a number of bands into few components. Multispectral remotely sensed images are generally correlated. To work out this correlation, PCA

transformation is used to transform the correlated data sets to result in other uncorrelated multispectral data sets that have certain variance properties. The transformation recalculates digital number values (DNs)

along principal components (also known as eigenvectors) using variance

and covariance matrixes to produce eigenvalues. The eigenvalues and eigenvectors describe the lengths and directions of the principal axes respectively. Details can be found in [1, 11].

2. Study Area

Greater Khartoum comprises three main towns. These are

Khartoum, Khartoum North (Bahri) and Omdurman. Khartoum town lies at the confluence of the two Niles, the White Nile on its west side and the Blue Nile on its east. The shape of the meeting point of the two Niles


resembles elephant trunk and that is called in Arabic" Khartoum". This fact can be clearly seen on satellite imagery. Therefore, Khartoum, together with the two cities, Omdurman and Bahri, are jointly called The Tri-capital (or the Three Towns). Geographically the study area is situated at latitude 15° 12' 00" N to 15° 51' 00"N, and from longitude 32° 18' 00"E to 32° 46' 00"E. World Reference System refers to row=49 and path=173 (Fig. 1) and, on average, it is about 400m above mean sea level.

The present population of the three towns is about 5 million (with an annual growth of 4.04%) which represents 16% of the total urban population of Sudan. On the other hand, the size of Khartoum State administrative limits, which includes the three towns and environs, is about 4,168.7km² and it is believed to be increasing at a rate of 3% annually, (National Statistical Center, Khartoum 2001). The climate of

Khartoum area is poor savanna with two temperature maximums in May

and October. A short rainy season from June to September gives about 164mm of rain. August is the wettest month of the year in which the relative humidity rises to 70 per cent at 6.00 a.m. Most of the rainfall

occurs at night and to a lesser degree in the mornings and afternoons. The

precipitation is lost through evapotranspiration due to excessive heat, percolation and gradient runoff. April has the highest rate of evapotranspiration [12, 13].

3. Satellite and Map Data

Paper maps compiled by Sudan National Survey Authority (SNSA (used as collateral information), Landsat-5 Thematic Mapper (TM),

Fig.1. Location of the study area.


Landsat-7 ETM+, SPOT-2 HRV and SPOT-4 HRV2 were available for the test (Table 1). All test images were made to undergo a geometric registration operation. Simple geometric model affine transformation was used. The digital image processing system Intergraph Image Analyst (IA) was used to achieve the geometric correction. Contrast enhancement was done for all the data set after registration. It used the simple contrast/brightness approach in the image analyst and histogram equalization in ERDAS imagine. Then, the resulting imagery was saved as standard GeoTIFF file format. The geometric accuracy achieved is better than 0.5 pixel.

Table 1. "From ... to" temporal interval design of change detection data extraction. No. Data "From to" Date Period

1 L5 TM, ancillary data ( paper map- 1980) 1975-1984 10 years

2 L5 TM, L7 ETM+ 1984-2000 16 years

3 L4 TM, L7 ETM+ 1989-2000 11 years

4 L4 TM, L5 TM 1989-1996 7 years

5 L4 TM, L5 TM 1984-1989 5 years

6 L5 TM, SPOT4 HRVIR2 1996-2003 7 years

7 L7 ETM+, SPOT4 HRVIR2 2000-2003 3 years

4. Methodology Adopted

In this work, a comprehensive study, investigation and analysis were carried out using remotely sensed data and ancillary information to come up with, hopefully, good change detection results. Image processing and GIS techniques were performed using Intergraph Image

analyst 8.4, GeoMedia professional version 6, ArcGIS 9.2, and ERDAS Imagine. Building on experience gained elsewhere, the following change detection procedures were established by the present authors:

- Image processing (visual evaluation) and extraction of the area of interest;

- Geometric and radiometric correction and image enhancement of the test data;

- Design of change detection methodology;

- Implementation of the methodology;

- Results of the test ;

- Accuracy assessment of the experiment; and

- Mapping of all areas of change.


Detection of land-cover changes by analysis of satellite imagery requires a reliable change detection technique. Since the literature does not elaborate on uniform change detection techniques, it is necessary to investigate different methods with different areas and/or different projects

[14]. Therefore, it is both necessary and important for a specific

area, a method be developed, tested and evaluated. In the present work, the methods adopted and implemented to extract the changes that took place in the study area are; (i) change detection based on visual inspection using the test data, (ii) write-function memory insertion (WFMI), (iii) image differencing, image transformation (i.e. NDVI, Tasselled Cap and PCA), (iv) Post-classification comparison and (v) GIS spatial analysis.

The findings of these experiments are used as temporal land-use change model, to quantify the extent and nature of the change, determine the impact of the surrounding environment and assist planning agencies in developing sound sustainable land-use practices. In this study, the "from", "to" temporal interval is designed as shown in Table 1.

4.1. Change Detection Based on Visual Inspection of Satellite

Imageries

It is important to have an idea of where we expect to see possible change. To inspect possible change, visual inspection techniques were carried out as an initial step. The method goes as follows:

- Band selection methods were developed to select suitable band combinations (e.g. RGB = bands 4, 3, 2 is the standard false combination

in which vegetation is depicted in red);

- Color composite images were taken in pairs according to "before", "after" (Table 1) and displayed on the computer screen side by side (called view1 and view 2).

- "view1" and "view 2" are synchronized using "link/unlink

viewer geographical tool" available in ERDAS Imagine.

- Using mouse pan/locater indicator, the images are explored in order to detect visually where differences in the same geographic location appear.

- The captured areas are then marked.


4.2. Write Function Memory Insertion (WFMI)

WFMI was used to extract changes that took place in the test area between the selected dates (Table 1). It was carried out in ERDAS Imagine, because this software provides tools such as band combination, which allows image composite displays in RGB channels with a capability to use only two channels. The workflow of the process can be described as follows:

- Interpreter -> Utilities -> Layer Stack: to stack layers from different images. The stacked layers are saved to an *.img file format. For example (L51984b4_L72000b4.img) encompasses gray image file of band 4, Landsat-5 TM acquired in 1984 as "before or time 1" data and band4, Landsat-7 ETM+ acquired in 2000 as "after or time 2" data.

- Assign layer1 to red, layer2 to green, and blue to either layer1 or layer2 (Note; in the present experiment using RGB 1:2:2 seems to provide the most clearly defined change areas).

- After the image is displayed: the Raster - Band Combination tool is used to turn off the blue gun. This combination leaves just red and green e.g. (L5 TM b4, 1984) red and (L7 ETM+ b4, 2000) green.

All areas that did not change between the two dates are depicted in shades of yellow. Areas mapped in pink and red show a decrease in radiance while areas appearing in green or light blue show an increase in radiance over the time period concerned (Fig. 2).

Fig. 2. WFMI result with landsat imagery (Al-Shagara Area).

4.3. Band Image Differencing

In this method, registered images acquired at different times are subtracted from each other to produce a residual or change image. For example, band 4 (infrared) from the Landsat-7 ETM+ April 28, 2000


scene was subtracted from Landsat-5 TM April 8, 1984 band 4 on a pixel-by-pixel basis which produced a new image that represents the change between these two dates (Fig. 3).

a). Difference image b). Difference image (density slicing)

Fig. 3. Result of image differencing technique.

4.4 Image Differencing: Threshold Selection of Change Detection

As mentioned in the definition of image differencing, the threshold of change/no change needs to be considered. The resultant differencing image tends to have a histogram that is normal in distribution with peak at 128 (if standard scaling is used) and tails off rapidly in both directions. The degree and direction of change are therefore measurable. The peak at 128 represents pixels that have not changed very much while pixels in histogram tails have changed substantially. This can be illustrated in Fig.

4. In this work, two approaches i.e. “resolve change” or “no change”

were designed and tested. The first approach is the common method described by [3, 15]

. The workflow is expressed by the following points:

1. The range of the digital numbers (DNs) of a differenced image lies from -255 to +255. The statistical results of a differenced image of

interest are the mean (µ) and the standard deviation (σ). Then a test to get

the thresholds can be unfurled as follows:

2. To find out the best value to be used as boundary of the positive

change, it is possible to try µ + aσ. For example, µ + 2σ, µ+2.25σ,

µ+2.5σ, µ+2.75σ, µ+3σ......etc. The coefficient (a) of σ can start with 2

and is gradually increased by 0.25.The result of each (µ + aσ) needs to be

checked. The output is used as a mask in the image by resetting all the


grey values less than that number to 0. Therefore, the output image needs to be visually verified to see if the unmasked areas denote real change. This process is repeated each time with a new coefficient until the best number is reached.

Fig. 4. Threshold boundary of change/no change.

3. For the negative change, we can do the masks at µ – 2σ, µ-

2.25σ, µ-2.5σ, µ-2.75σ, µ-3σ......etc, to get the threshold in the same way

as for the positive change.

4. Therefore, a change image map including three levels of information: positive change, no-change and negative change are determined by the following formulas:

• µ- aσ = upper boundary of negative change; and

• µ+ aσ = lower boundary of positive change.

The second way to extract the changes is to make the mask at a

certain DN value, regardless of the statistical results (for example, 70) to

see whether it can highlight the positive change. If not, the mask is done in an empirical way by incrementing the DN values i.e. 72, 74, 76, 78… etc. Then the same can be done for the negative change. The thresholded images are then subjected to accuracy assessment.

4.5. Change Detection Using Image Transformation:

Normalized Difference Vegetation Indices (NDVI)

For forest canopy and vegetation monitoring, change detection

based on NDVI is generally recommended[16]. Vegetation index serves to


discriminate between vegetation and soil background. The most import

vegetation index that is used to assess vegetation land-cover in terms of

vegetative amount and condition based on an analysis of remotely sensed

data is normalized difference vegetation index (NDVI). The NDVI is

adopted in this research work to test its efficiency in detecting the general

change in land-use/land cover as well as identifying change within

vegetation type. The NDVI was generated from Landsat-4 and 5 TM,

Landsat-7 ETM+ and SPOT HRV data according to equations 1, 2 and 3

below. Then Write Function Memory Insertion and “image differencing”

change detection methodologies were performed.

NDVI= (TM4 – TM3)/ (TM4 + TM3) (1)

In this equation, TM4 is the near-infrared band and TM3 is the red

band. This equation usually returns a value of –1 to 1. To scale the results

(i.e. made them fall in the range 0 – 255 ranges), the above equation is

modified as follow:

NDVI= 127 x ((TM4 – TM3)/(TM4 + TM3)+1) (2)

The computation of the NDVI for SPOT scenes is similar to the

NDVI of Landsat. It uses band 3 and band 2 according to this equation:

NDVI= (XS3 –XS2) /(XS3 + XS2) (3)

4.6. Write Function Memory Insertion (WFMI) Change Detection

Using NDVI Results

For time series change detection analysis, the NDVI of different

years can be compared to highlight change in vegetation biomass. For

instance, the identification of changes within the vegetation land-cover

can be carried-out using the WFMI algorithm. The NDVI image

generated from the “before” data "time1" is rendered to , for example, the

red gun and the NDVI image generated from the “after” data "time2" is

applied to, for example, the green gun while no image in, for example,

the blue gun. The changes occurring between the two dates are shown in

red when the vegetation land-cover decreases and in green when it

increases (Fig. 5).


Fig. 5. WFMI of NDVIs produced from L5 TM and L7 ETM+.

4.7. Image Differencing Change Detection Using the NDVI Results

The image-differencing algorithm was applied to the NDVI images to generate the NDVI change images. The equation of the NDVI image-differencing algorithm is as follows:

ΔNDVI= (NDVI (time1)-NDVI (time2) +128 (4)

After image differencing was performed, the “change” and “no change” threshold boundary in the resultant image was considered. The change/no change threshold values were selected based on the empirical method discussed previously in the present paper. The NDVI image differencing proved to be a good technique in analyzing change in

vegetation. The NDVI not only measures change in vegetation cover, but also helps to estimate the green vigor condition of it.

4.8. Use of Tasselled Cap Transformation in Change Detection

Tasselled cap is an alternative image transformation algorithm in

which the process is intended to define a new pixel coordinate system in terms of which the soil boundary line and the region of vegetation are more clearly represented. The three axes of this new coordinate system are termed “brightness”, “greenness” and “yellowness or wetness” [11]. The Tasselled cap image transformation of the six bands (except the

thermal band) of Landsat data sets of the study area was achieved by Intergraph Image Analyst. The six bands of Landsat were used as input to tasselled cap command; the bands being selected in the order specified by the transformation. Then, the TM coefficient and scaling parameters were


selected. There are three different tasselled cap coefficient files associated with Landsat-4 TM, Landsat-5 TM, and Landsat-7 ETM+. For each specific Landsat sensor data, an appropriate coefficient file was used. Three files were produced, i.e. brightness, greenness, and wetness (Fig. 6).

Fig. 6. Tasselled cap transformation results generated with image analyst from Landsat-7

ETM+.

For change detection purposes, the tasselled cap transformation results of the three Landsat data sets were utilized using three techniques. These are the WFMI, the image differencing and the principal component analysis (PCA) transformation (Fig. 7).

4.9. Change Detection Based on Post-Classification Comparison

In a variety of studies, the post-classification change-detection method was claimed to be effective for detecting land cover changes [1, 2, 10, 16]. Post-Classification comparison involves classification of each of the temporal

Fig. 7. Change detection based on tasselled cap results of (L5 TM, 1984 and L7ETM+).

a) WFMI of greenness b). Image differencing of c) PCA transformation of 12 layers- pc2

b) Image differencing of greenness.

a). Brightness layer ( soil reflectance) b). Greenness layer c). Wetness layer (wet lands are emphasized)


(before and after) images independently, followed by a comparison of the corresponding pixel (thematic) labels to identify areas where change has occurred. Supervised and unsupervised classifications were utilized to produce thematic images for change detection comparison. The steps carried out to perform the work are summarized as follows:

- Landsat color composite b234 and b451 of “before” and “after” images were classified independently;

- The classified images were combined to create a new thematic image which indicates the changes (from- to) that took place;

- Visual inspection is undertaken to interpret the changes; and

- The confusion matrix between the two images is generated and analyzed.

Each pair of images (e.g., b451 of time1 and b451 of time 2 images) was classified separately to produce a thematic image of 15 classes (Fig. 8) by adopting ISODATA unsupervised training method. Then the change matrix was generated from the two thematic images using the ERDAS “summary model” module. For example, the thematic image of L5 TM, 1984 was exported to the summary model as an input zone file. Also, the L4 TM, 1989 thematic image was exported as an input class file. The resultant report then is "from to" change matrix such as the one shown in Table 2.

Fig. 8. Unsupervised classification of Landsat data generated with ERDAS.

a). L5TM b234 b). L7 ETM+ b234


Table 2. Change detection matrix produced from b234 (L5 TM, 1984 and L7 ETM+, 2000).

The computation of the change matrix in ERDAS is not straightforward. The following steps were used to construct the change matrix in standard format using the summary function:

- Using one image as the zone raster and the other as the class raster. Either image can be designated as the zone raster;

- The designation of a zone raster allows the calculation of percent change per "zone". For example, let us assume that time1 (T1) is the "classified "before" image” and time2 (T2) is the “classified "after" image", each having classes 1, 2, and 3. If the T1 image is the zone raster, the summary function can be used to calculate the percentage of class1 at T1 that stayed class1 in T2, changed to class 2, or changed to class 3;

- If the T2 image is the zone raster, the summary function can be used to calculate the percent of class1 at T2 that was class1 at T1, changed from class2 T1 or class3 T1.

To identify the type of change based on comparison of two thematic

images generated by supervised classification, it is important to determine the classification scheme before performing the supervised classification. Therefore, the objective here is to determine the "from/to" conversion of the

classes by comparing the features extracted from the two images under

consideration. For this test area, the classification scheme has been defined to incorporate 21 LULC classes as illustrated in Fig. 9.

Three supervised classification algorithms were used. These are the

maximum likelihood (ML), the minimum distance (MD) and the parallelepiped (PP). Three thematic image maps were produced from b234 combination for each of the Landsat data. Then, the efficiency of these three algorithms was evaluated based on statistics and visual


comparison. The best result was then selected for the change detection job and the change matrix was generated. In this study, the ML classifier produced the best results. Figure 10 depicts the resulting thematic images of the ML supervised classification.

Fig. 9. Supervised classification signature classes.

Fig. 10. Results of the ML classifier generated with ERDAS.

a). L5TM: b234 b).L7 ETM+: b234


4.10. Change Detection Based on GIS (GIS Comparison Method)

Head-up digitizing method of geographic features was used to extract changes manually by comparing the vector data digitized from two images "before” and “after". The aim of this work is to appraise various techniques resident in GIS systems. Two techniques were adopted; namely “overlay” and “spatial query”. Overlay is tested in two ways. In case1, the vector themes derived from image “time1” were superimposed with the image “time2” in order to discover the new elements. Therefore, the new segments can be captured by digitizing these changes as shown in Fig. 11(a).

In case2, vector themes derived from image “time2” were overlaid on image “time1” to find out the missing elements. Figure 11(b) demonstrates a small segment in the airport area that had been removed i.e. the road was demolished between the two dates. Spatial difference query provided in Intergraph GeoMedia is used to sense the change between the two layers.

Fig. 11. Change detected in the road network with the GIS method.

5. Analysis of the Results

The results obtained using the different change detection techniques are herewith, evaluated, analyzed and discussed. Moreover, attempts were made to answer some major questions associated with LULC change in the study area e.g.: (i) What are the geographic

distributions of change? (ii) what are the types of change in the area of interest (AOI)? (iii) what are the overall rates of change? and (iv) what

a) New Road Digitized From L7 ETM+ Band 8, 2000 b) Road Network Coverage of 2000 Overlaid on L5TM, 1984


are the driving agents, causes and consequences of these changes? These questions are answered according to the study and analysis of the previous and current status of the three towns. The analysis takes into account the main geographic features (viz. major road network, urban and built-up areas, vegetation land-cover, wetland areas, water land cover, bare soil /vacant areas, mountains and fringe areas).The visual interpretation approach was first followed to identify the type of change. As a result, possible types of LULC change derived from remotely sensed data of the study area can be derived as presented in Tables 3 and 4.

Table 3. Possible type of change in the study area. No. From To No. From To

1. Bare Soil New road 18. Bare-Soil Built-up area

2. Roads Bare Soil 19. Bare-Soil Vegetation 3. Road Time 1 Road Time 2 20. Bare-Soil Water 4. No Bridge New Bridge 21. Bare-Soil Wetland 5. Bridge No Bridge 22. Bare-Soil Time1 Bare-Soil Time 6. Bare Soil New Building 23. Bare-Soil Unknown 7. Building Unknown 24. Wetland Vegetation 8. Vegetation Built-up area 25. Wetland Water 9. Vegetation Water 26. Wetland Bare-Soil/dry 10. Vegetation Time1 Vegetation Time 2 27. Mountain Bare-Soil 11. Vegetation Wet Land 28. Mountain Vegetation/land 12. Vegetation Bare-Soil 29. Urban Low-density Urban High-13. Vegetation Unknown 30. Unplanned built-up urban planned urban 14. Water Vegetation 31. Temporal land-cover change from water-to-

15. Water Bare-Soil/land 32.Change in agricultural areas

due to phonological cycle

cultivation and

no cultivation

16. Water Wetland33. Unknown Unknown

17. Water Time1 Water Time2

5.1 Main Characteristics of Change Detection Techniques

The results obtained by each technique were visually examined, evaluated and qualitatively compared based on the LULC change

detection scheme shown in Table 1. The comparison was carried-out as

follows.

5.2 Change Detection Based on Visual Comparison

The method is simple and useful when the change is obvious or when investigating the degree of change highlighted by other methods. Although, the satellite data used in the present work was coarse and the study area is

heterogeneous and most of the objects were confused with background materials, this method was found to be useful especially in the early stage of the work. The main disadvantages of this are that it relies heavily on the


ability of the operator, it causes fatigue on the part of the operator and it does not produce an output unless the operator creates a vector layer.

Table 4. Tabular comparison of change detection techniques.

No. From To A B C D E F G H mode

1. Bare Soil New Road 75 75 30 75 60 30 10 60 75

2. Roads Bare Soil 30 60 10 60 30 30 30 30 30

3. Water New Bridge 10 75 30 60 10 30 60 10 10

4. Bridge No Bridge X X X X X X X X X

5. Bare Soil New

Building 75 75 75 60 30 30 75 60 75

6. Building Unknown 60 60 75 75 30 10 60 30 60

7. Vegetation Built-up area 60 75 75 75 60 10 30 60 75

8. Vegetation Water 30 75 10 75 10 30 10 10 10

9. Vegetation T1 Vegetation

T2 60 30 30 60 30 60 30 60 60

10. Vegetation Wet Land 60 60 75 60 30 30 30 30 30

11. Vegetation Bare-Soil 60 75 75 75 30 30 10 60 75

12. Vegetation Unknown X X X X X X X X X

13. Water Vegetation 30 75 30 75 10 30 60 60 30

14. Water Bare-Soil 75 75 60 75 30 30 75 75 75

15. Water Wetland 30 75 30 75 10 10 75 30 75

16. Water T1 Water T2 10 30 30 30 30 30 30 30 30

18. Bare-Soil Vegetation 60 60 75 75 60 30 10 60 60

19. Bare-Soil Water 75 75 60 75 30 10 10 60 75

20. Bare-Soil Wetland 60 75 75 75 30 10 30 60 75

21. Bare-Soil T1 Bare-Soil T2 60 75 60 75 60 60 10 75 60

22. Bare-Soil Unknown 60 60 30 60 30 10 60 60 60

23. Wetland Vegetation 60 75 75 75 75 30 30 60 75

24. Wetland Water 75 75 60 75 10 60 30 60 75

25. Wetland Bare-Soil/dry 60 75 30 75 75 30 60 60 75

26. Mountain Bare-Soil 75 75 10 75 60 30 60 30 75

27. Urban L-

density

Urban H-

density 60 75 60 60 60 30 30 60 60

28. Unknown Unknown X X X X X X X X X

Total 1310 1635 1170 1650 890 690 915 1190

Remarks:

- Change detection based on Tasselled cap used in this evaluation is WFMI of

greenness layer.

- Change detection based on PCA used in this evaluation is the

transformation of the 12 bands of L5TM, 1984 with L7ETM+ pan, 2000.

- Change detection based on image fusion used in the evaluation is IHS.

- “X” means not considered.

Legend:

No. Abbreviation Description No. Abbreviation Description

1 75 Very Good 7 C Change detection based on Tasseled Cap

2 60 Good 8 D Change detection based on PCA

3 30 Not Clear 9 E Post-Classification-Unsupervised

4 10 Poor 10 F Post-Classification-Supervised

5 A WFMI 11 G Change detection based on image fusion

6 B Image Differencing 12 H ERDAS change detection utility


5.3 Write Function Memory Insertion

The technique was found to be useful. For example, it highlights

the major change in two color-coded pixels red or green. Even though,

this procedure is easy and fast in highlighting changes between two

dates. It does not provide quantitative information on the amount of

hectares changing from one land-cover category to another.

5.4 Band Image Differencing

This technique is simple as the two previous methods. However, the

challenge is how to determine the change threshold. It is, therefore, an

iterative process and it takes long time to reach a result. Nevertheless, in

this test, the thresholds followed to determine the change/no change areas

are found to be useful for change detection. In general, it seems to be

more effective in terms of quantitative analysis. However, extra work in

terms of image enhancement such as reliable calibration and radiometric

normalization, spatial filtering and edge enhancement may improve the

accuracy of this method.

5.5 Change Detection Based on NDVI Index

Based on this technique, it is observed that the year 2000 exhibited

a large percentage in vegetation cover. The cultivated areas east of the

Blue Nile and the main River Nile increased in the years 1996, 2000 and

2003. Examples of impact of urbanization on vegetation include the

disappearance of the scattered vegetation west of Jabra road near

Khartoum Race Course. However, expectedly, change detection based on

the NDVI technique is poor in detecting changes in built-up areas.

5.6 Change Detection Based on Tasselled Cap

Compared to the NDVI technique, the overall results of this method

showed good ability in identifying changes in urban areas especially as

regards emergence of new buildings and vegetation. However, this

technique showed poor ability to detect changes that took place in soil

and rock areas. This is demonstrated by the poor quality of information

provided by this technique at the borders of Al-Murkhiyat mountains


range where it was difficult to detect the huge amount of change that took

place in that area as years dragged on.

5.7 Change Detection Based on PCA

With Landsat data, the first component (pc1) was good in detecting

new roads. The second component (pc2) was somewhat good for the

detection of change in built-up areas but not new roads. The third

component (pc3) was poor in detecting types of change. With SPOT data

when visual comparison was carried out, change detection based on PCA

transformation showed almost the same results as in the WFMI and

image differencing techniques. However, the procedure is believed to be

less sensitive to background content. As comparison, e.g. [16] found the

image differencing technique more effective than the PCA for tropical

deforestation monitoring. Muchoney and Haak [17] tested four methods

using SPOT multi-temporal data; namely PCA, image differencing,

composite analysis and post-classification techniques in order to identify

changes in hardwood defoliation. They concluded that the changes were

most accurately detected by image differencing.

5.8 Change Detection with Post-Classification Method

Contrary to some experiences gained elsewhere (see section 4.9),

the results obtained by this procedure are generally unsatisfactory.

Therefore, the main disadvantage of post classification change detection

is that it requires two separate classification steps and errors in each

classification will be brought forward to the final result. As an exception, [14] obtained the highest accuracy in land-cover change detection in a

coastal zone of Mexico using post-classification techniques. Advanced

image classification techniques such as “fuzzy classification” or

“complex hybrid classification” is needed to improve the results.

5.9 Change Detection Using GIS

A head-up digitizing method was utilized through GIS overlay and

spatial query. The method allowed good identification of new and

existing roads. However, the process is very time-consuming. It requires

a lot of simultaneous browsing, exhaustive analysis of the two temporal

images and monotonous vectorization of elements in both images.


6. Analysis of Change in the Three Towns

6.1 Changes That Took Place in Khartoum Town

In Khartoum town, about 37 sample sites were recognized and located and the general types of change were estimated. The analysis of various changes that took place in the town is carried out through examining and evaluating changes occurring in the main geographic features (i.e. road networks, residential areas, industrial areas, general stores, open spaces, fringe and isolated areas, vegetation areas and mountains). Brief comments on this follow.

6.1.1 Road Network

Road networks (new and existing) are mostly recognizable on most images used in the test.

6.1.2 Residential Areas

The change detection results showed that both metropolitan Khartoum and fringe areas experienced explosive growth in recent years, (from 2000 to 2003). The most significant land-use changes that occurred in the area over the last 15 years were mainly due to residential development. Four types of change are recognized in this land use class. These are bare soil changing to built-up area, vegetation to built-up area, low to high-density conversion and unplanned to planned sites. The first type is caused by building of new houses in vacant parcels or development of new districts in the outskirts of the city. The second type of change happened as a result of transfer of vegetation to land use such as the development that took place in the Green Belt area, which resulted

in new districts such as Al-Engaz and Al-Azhari quarters. The third type

of change is very remarkable in many districts, and is believed to be a normal result of rapid development. The configurations or layouts of houses and buildings in square kilometers is believed to have almost

doubled in the last two decades. A good example of the fourth type is the

organization and the transformation that took place in the previous Eshash Falata district near Khartoum racecourse.

6.1.3 Industrial Areas

The change in industrial areas can be expressed by four types

(increase in area size, increase in the intensity (i.e. building of new factories), surfacing of the inner roads and/or construction of new local roads and emergence of new industrial areas). Based on the present


change study, the situation of industrial areas did not change that much between 1975 and 1989. Only one industrial area appeared. The change is observed afterwards, i.e. from 1996 to 2003.

6.1.4 General Stores

The only known general stores are the fuel tanks “Al-Mustawdaat” situated in Alshagara. The area accommodates the main petrol supplies from Petronas, Nile ,Shell, Mobil, Total and Agip Oil companies. The area also experienced some development. For example, only few stores could be seen in the 1984 image. Conversely, many constructions and new roads were identified in the image.

6.1.5 Open Spaces Change in open spaces is another type of indication of urban

growth. The present experiment demonstrated differences in spectral reflectance of landscape between earlier images (i.e. those acquired in 1984 and 1989) and later images (i.e. images acquired in 1996 and 2000). The conversion of open spaces to built-up areas is evident in several places in the town.

6.1.6 Fringe and Isolated Areas

Multi-temporal analysis with remotely sensed data gives a unique perspective of how a city evolves. With reference to the paper map, Al-lamab Bahr Abyad, Al-Shagara, Al-Hamadab, Wad-Agieb, Al-Uzozab, El-Kalakla, Soba, Jabal Aulia and Esalama constituted isolated fringe areas in the town. In 1984, a lot of change in these areas took place. Furthermore, areas from Al-Hamadab to El-Kalakla became urban;

especially towards the White Nile. This transition in that short time

interval reflects a big change in terms of expansion. Between 1996 and 2003, the change is even bigger. Thus areas treated as fringes in the year 1975, 1984 and 1996 coalesced with the city metropolitan frame in all

directions in the early 2000’s (Fig. 12).

6.1.7 Vegetation Areas

In general, distribution of vegetation in Khartoum area is poor. The dominant type of vegetation is agricultural fields, orchards, green fields on the banks of the three rivers and scattered shrubs in the prairies and

fringe areas. Overall, there are three major forms of vegetation change. These are; (i) conversion from vegetation to another LULC type (e.g. The Green Belt Area south of Khartoum which changed to residential). (ii) Conversion from another LULC type to vegetation as in Sundus


Agricultural scheme. (iii) Modification to different vegetation type (growth cycles) as in Soba Garb Agricultural scheme.

6.1.8 Mountains

The topography of the test area is generally flat. However, some high ground, outcrops and isolated rocks exist. For example, Jabal Aulia experienced changes due to excavations for building materials and road construction works.

6.2 Changes That Took Place in Omdurman Town

Fifteen sample sites were identified and marked for change detection analysis. Various changes were noted. Urban set-ups of the town are predominated by residential areas. Urban expansion occurred mainly due to availability of open spaces. The analysis of the change will be illustrated

along the general feature class defined in the Khartoum town case.

6.2.1 Road Network

The road network experienced substantial changes in the period

1975-1989. In the period 1989-1996, the change was more extensive (e.g.

Fig. 13) and in 2003 the road network became resemblant to its modern counterparts in western cities. Remote sensing techniques, therefore, can provide very accurate information regarding this matter of urban

development.


The natural increase in population along with the high rate of migration to the capital are among the most important factors that influenced urbanization in the three towns. This is particularly clear in

Omdurman. In the present study, the major residential development and urban sprawl were detected between 1996 and 2003, then between 1975

Fig. 12. Transition of fringe zones in Khartoum Town.

a). Paper map1980 compiled map c). SPOT-4, 2003 b). Landsat-5, 1984


and 1989. As a result, several new districts appeared. Generally, most of the urban areas were a conversion from low-density to high-density and from high to very high-density housing. Moreover, the western side of the town experienced the highest rate of residential area increase.

a) Landsat-4 TM, b234-1989 b) Change image between 1989 and 1996 c) Landsat-5, 1996

Fig. 13. Changes That Took Place Due to Construction of New Roads (Al-Murkhiyat area of

Omdurman).


There is only one major industrial area in Omdurman. The nature of change in this area is characterized by low to high-density factory buildings and conversion of open spaces to built-up areas. Change detection techniques used in the present work highlighted many new buildings between 1984 and 2003.

6.2.4 General Stores

No general store areas were detected in Omdurman area.

6.2.5 Open Space

Change in open space is not very clear. This is due to lack of contrast

between soil and buildings since many of the latter are made of local material. Clear changes are shown in the south west part of the town, mostly due to conversion from low-density to high-density urban areas.


In the 1980 map of Omdurman, Zagalona and Al-Fittaihab were isolated quarters. Harat (districts) number 18, 20, 21, and the southern part of Umm-Baddah Al-Sabael were very low-density residential areas

in the town (Fig.14a). Similar to Khartoum, the characteristics of these areas changed dramatically in 1984 and forward. For example, the Harats were converted to high-density areas and became part of the main city while Zagalona was completely absorbed in the city boundaries.


Temporal analysis of the remote sensing data showed that this trend continued in the years 1989, 1996, 2000 and 2003 (Fig. 14 b,c).

a) 1980 map b) Landsat-5, 1989 c) SPOT-4, 2003

Fig. 14. Transition of fringe zones into town boundaries.


Because of the desert nature of the area, vegetation land cover in Omdurman is generally poor. Only the narrow vegetation strip parallel to the west bank of the two rivers is evident. Some scattered trees along the streets and houses exist. Vegetation cover and small shrubs exist in the fringe areas of the town, which are quickly absorbed by urban and sub-urban development.

6.2.8 Mountains

There are about six high grounds in the area. These are (Jabal Maqrun, Jabal Abu Wuleida, Jabal Dehm, Jabal Surkab, Al-Murkhiyat

and Jabal Toriyah). Al-Murkhiyat Mountain Series experienced the major changes (Fig. 13). They were used as a major source of building stones and gravel.

6.2.9 Changes That Took Place in Khartoum North Town

Sixteen random sites were selected and labeled as sample areas for change detection analysis. The visualized change in the town is expressed by variations in the following features.

6.2.10 Road Network

Examination of the test results indicated that several changes in terms of bare soil to new road occurred between 1989 and 2003. The obvious cases are those found in sample areas (Colonel Gadafi Road). New road and road mentainance type of change are detected in Shambat,

Al-Taiyar Abd Gadir Al-Kadaro and El-Engaz roads.



Like the case in Omdurman, the most dominant type of change is due to the increase in residential areas. This resulted in emergence of low to high-density areas and conversion of open space to built-up areas.


The Industrial Area of Khartoum North is the biggest in Sudan. The area experienced remarkable changes in almost all directions, but particularly in the north part of it. This is particularly true in the period from 1989 to 1996. Fig. 15 shows this quite well.

6.2.13 Open Spaces

Similar to Khartoum and Omdurman, Khartoum North also witnessed expansion of urbanization on the expense of open spaces.

a) L4TM, 1989 b234 b) i.e. Shifa pharmaceutical plant shown green c) L5TM, 1996 b234

Fig. 15. Changes detected by WFMI technique in Khartoum North Industrial Area.


In the period 1975-1984, Hillat Koko and Halfayet Al-Mulok were isolated districts. Shambat Extension, Dar Al-Salam Al-Magarbah and

Gerief Sharq were just fringe areas. In the period, 1984-2003, all these

outskirts were converted into high-density urban areas.


Vegetational areas in this part of the test area include the dense

fields along the River Nile, the Kafoori farm near the industrial area, El-Seleit Agriculture Scheme and the Arabian Company for Agricultural Products close to Umm Dom Village. Changes in vegetation of this area are divided into three types, namely conversion from (i) bare soil to vegetation, (ii) vegetation to bare soil and (iii) vegetation to built-up area.

This means urbanization is in most cases advancing at the expense of


vegetation which is a dangerous phenomenon that needs to be carefully monitored by the local authorities.

6.3 From Change Maps to GIS Database

It will be more useful to convert changes detected in this study to GIS database. To record time series changes, digital vector maps of major geographic features were compiled. The maps were produced by digitizing change locations of the change images. Attribute tables were then designed for change features in the GIS database. The aim of the change detection geo-spatial database is to serve some GIS applications including e.g.: (using GIS capabilities to analyze LULC changes, study of the effects of urbanization on the ecosystem of the test area, use of resulting change maps for decision making, the elements of change in land use can be used as a measure, indicator or predictor for increase in population, water consumption, health facilities, education, transportation, solid waste management, etc. , using change detection maps for map revision and determination of illegal land use (land piracy), change maps could be used for assessment and analysis of damage caused by natural disasters (such as floods, dust storms, fires,… etc.).

7. Methodology Used in Creating Change Detection GIS Database

GeoMedia Pro with Microsoft Access, SQL Server2000 and then Oracle 10g DBMS are used for the purpose. The derived change maps

include:

- Land-use change maps comprising road network, industrial areas, airport area limits, general urban areas (i.e. residential,

commercial, educational and recreational), general stores and isolated

quarters.

- Fringe areas and isolated villages.

- Land-cover change maps, namely; vegetation areas (forest,

farms, orchards, gardens, green yards, etc.), open spaces, water, bare soil, mountains and islands.

Figure 16 shows two thematic maps produced from time series geographic data. Some measurements were made to reflect the increase or decrease of the changes (Fig. 17). The most remarkable change that took place in the road network can be summarized as follows :


- Between 1975 and 1984 about 137 km of roads were built;

- Between 1989 and 1996 more than 192 km were constructed;

- From 1996 to 2000 , 73 km of road were added, and

- In the period 2000 to 2003, more than 200 km were constructed.

The effect of oil production in Sudan is, therefore, clear in this respect. Thus, a total of 602 km of roads were built between 1975 and 2003 most of it being after 1989. This is worthwhile information provided by remote sensing for decision makers and planners to count on and design their future strategies and plans accordingly. In addition, some interesting findings are:

- The increase of the road network was most noticeable between 1996 and 2003 (see Fig. 18).

- The areas occupied by mountains and outcrops decreased from 18.1 km² in 1984 to almost 16.7 km² in the year 2003. As an example, Al-Murkhiyat series of mountains decreased from about 13.66 km² in 1984 to only 12.74 km² in 2003 i.e. a decrease of 7% in less than 20 years.

a). Major LULC, Isolated and Fringe Areas in 1975 b). Major LULC, Isolated and Fringe Areas in 2003


Fig. 16. Major LULC, isolated and fringe areas.

Fig. 17. Change that took place in area/length of some features.

a). Road Network, 1984 b). Road network, 2003

Fig. 18. Change map of road network from 1984 to 2003.


8. Accuracy Assessment

To estimate accuracy and quantify effects of errors, an “Accuracy Assessment” task needs to be carried out. This is defined as a method of determining the quality of the information derived from remotely sensed data. Several methods are customarily used for accuracy assessment [18-25]

.

In the present work, a preliminary accuracy assessment was performed visually using aerial photographs flown in 1996. A few sites were inspected to verify the existence of the new detected buildings and roads in the time interval 1984-1996 (Fig. 19 and 20). Most of the sites visited agreed with the change detection results obtained in this experiment. From manual measurements and the set of collateral information available to the authors and using planimeter observations made on the features that experienced some changes, initially we can judge that the overall accuracy with which change detection was carried out in this study is about 70 to 85%.

Land-cover changes detected in the present work are large and obvious. Two to three point samples were visited to verify this fact. These are Allufah (Fig. 21a) and Jabal Aulia Hill area (Fig. 21b) to verify the changes that took place in the test area since 1984. The authors roamed on foot and by car. The preliminary accuracy assessment value of 70-85% seemed to be acceptable. However, change verification occurring in the land-use, required exhaustive efforts Because of time limitation, no further field evaluation was pursued.

a) WFMI of L5TM 1984 b234 with L7 ETM+,2000 b) Aerial photograph acquired in 1996 (new building and road)

Fig. 19. The Turkish Hospital near El-Kalakla Al-lufah Intersection.


a) PC substitution of L5TM 1984 b234 with SPOT2 1999 b) Aerial photograph acquired 1996

Fig. 20. Khartoum Dry Port near the Local Market.

a) sample 1: Turkish Hospital b) sample 2: Jabal Aulia Hill

Fig. 21. Some snapshots of the visited sites.

9. Conclusions

Land-use patterns are different in developing countries from those

in developed courtiers due to many factors such as different social set-ups, economy, climatical factors, topographic forms, etc. Ironically, most information extraction methods and technology used with remote sensors

data are meant for developed countries locales with often little attention being given developing countries who really need the technology. Therefore, the main aim of this research is to implement techniques to extract useful information from raw remotely sensed data for different

urban GIS applications in developing countries. The test utilized remotely sensed data of the area and a variety of techniques and produced results with an estimated accuracy level ranging from 70 to 85%. This is a worthwhile conclusion for developing countries who seek to implement remote sensing data for mapping their natural resources,


improve development plans and maximize use of the already scant resources allotted for mapping and charting tasks.

Visual comparison of multi-temporal satellite images and the results obtained with change detection techniques employed in this study, revealed that extensive changes took place in the test area in the period 1984-2003. Horizontal changes occurred in fringe zones, prairies and agricultural areas. Conversely, vertical changes were observed in inner-city areas and open spaces resulting in conversion of low -density built-up areas into high density ones. The occurrence of this massive change is due to the ongoing demand for spaces for commercial use, industrial development and housing to accommodate the vastly- increasing population. Generally speaking, Khartoum town witnessed greater change compared to the other two towns, which reflects its political, social and economic importance. In recent years, the town expanded greatly in terms of new roads, buildings and other aspect of land planning.

To the best knowledge of the present authors, the work undertaken in this study and reported in this paper constitutes good attempt to document, determine, map and analyze these changes in the test area. The authors, therefore, feel they have paved the way to decision-makers, planners and directors in both federal and private sectors to make full use of the remote sensing technology in their respective everyday works. By so doing, lots of efforts could be saved and sizeable budget savings could be gained and allotted to other deeds.

References

[1] Jensen, J.R., Introductory Digital Image Processing: A Remote Sensing Perspective, Second

Edition. Prentice-Hall, New Jersey, U.S.A. (1996).

[2] Jensen, J.R., Introductory Digital Image Processing: A Remote Sensing Perspective, Third

Edition. Prentice-Hall, New Jersey, U.S.A. (2005).

[3] Fung, T., "An Assessment of TM Imagery for Land-Cover Change Detection". IEEE

Transactions on Geo-science and Remote Sensing, 28(4): 681-684 (1990).

[4] Fung, T. and LeDrew, E., "Application of Principal Component Analysis to Change

Detection". Photogrammetric Engineering and Remote Sensing, 53(12): 1649-1658 (1987).

[5] Kwarteng, A.Y. and Chavez, P.S., "Change Detection Study of Kuwait City and Environs

Using Multitemporal Landsat Thematic Mapper Data". Int. J. of Remote Sensing, 19, (9):

1651-1662 (1998).

[6] Lillesand, T.M., Kiefer, R.W. and Chipman, J.W., Remote Sensing and Image

Interpretation, Fifth Edition. John Wiley and Sons, New York, U.S.A. (2004).


[7] Mather, P.M., Computer Processing of Remotely Sensed Images: An Introduction. John

Wiley and Sons, New York, U.S.A. (1989).

[8] Moller, M.S. and Blaschke, T., “Monitoring LULC Dynamics in the Urban - Rural Fringe”.

Anais XII Simpósio Brasileiro de Sensoriamento Remoto, Goiânia, Brasil, 16-21, INPE, pp:

3821-3828 (2005).

[9] Regnote, E.J. and Van Zyl, J.J., “Change Detection Techniques for ERS-1 SAR Data”.

IEEE Transaction on Geoscience and Remote Sensing, 31(4): 896-906 (1993).

[10] Tardie, P.S. and Congalton, R. G., "A Change-Detection Analysis: Using Remotely Sensed

Data to Assess the Progression of Development in Essex County, Massachusetts from 1990

to 2001". ACSM/ASPRS Annual Conference (2002).

[11] Richards, J.A., Remote Sensing Digital Image Analysis: An Introduction. Springer-Verlag

Berlin Heidelberg New York, London, Paris and Tokyo (1986).

[12] Ali, A.E., “Assessment of Visual Interpretation of Thematic Mapper (TM) Images for Land

Use and Land Cover in Sudan”. Earth Surface Process and Landforms, 14:399-405(1988).

[13] Ibrahim, I.M., “The Social and Environmental Impact of Industrial Wastes on the Waste

Workers in Khartoum North”. A monograph Published by the Institute of Environmental

Studies, University of Khartoum, 126 p. (1984).

[14] Mas, J.F., "Monitoring Land-cover Changes: A comparison of Change Detection

Techniques". Int. J. Remote Sensing, 20(1):139-152 (1999).

[15] Rogerson, P.A., “Change Detection Thresholds for Remotely Sensed Images". J.

Geographic System, 4: 85–97(2002).

[16] Singh, A., “Digital Change Detection Techniques Using Remotely-Sensed Data”. Int. J. of

Remote Sensing, 10: 989–1003 (1989).

[17] Muchoney, D.M. and Haack, B.N., "Change Detection for Monitoring Forest Defoliation".

Photogrammetric Engineering and Remote Sensing, 60(10) :1243-1251(1994).

[18] Aronoff, S., “The Minimum Accuracy Value as an Index of Classification Accuracy”.

Photogrammetric Engineering and Remote Sensing, 51(1): 99-111(1985).

[19] Congalton, R.G. and Green, K., Assessing the Accuracy of Remotely Sensed Data:

Principles and Practice. CRC Press, Inc. U.S.A. (1999).

[20] Congalton, R.G., "A Review on Assessing the Accuracy of Classification of Remotely

Sensed Data". Remote Sensing of Environment. 37: 35-46 (1991).

[21] Curran, P.J. and Williamson, H.D., "Sample Size for Ground and Remotely Sensed Data".

Remote Sensing of Environment, 20: 31-41(1986).

[22] Hay, A.M., "Sampling Designs to Test Land-Use Map Accuracy". Photogrammetric

Engineering and Remote Sensing, 45(4):529-533(1979).

[23] Rosenfield, G.H., "Sample Design for Estimating Change in Land Use and Land Cover".

Photogrammetric Engineering and Remote Sensing, 48(5): 793-801(1982).

[24] Newcomer, J.A. and Szajgin, J., “Accumulation of Thematic Map Error in Digital Overlay

Analysis”. The American Cartographer, 11(1): 58-62(1984).

[25] Skidmore, A. and Turner, B., “Assessing the Accuracy of Resource Inventory Maps, in

Proceedings of Global Natural Resource Monitoring and Assessments: Preparing for the

21st Century”. Venice, Italy, 2: 524-535 (1989).


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