Advaned Remote Sensing Project Yoseph Alemayehu and Zemenu Mintesnot

8/8/2019 Advaned Remote Sensing Project Yoseph Alemayehu and Zemenu Mintesnot

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Faculty of science

Department of Earth Science

(Remote sensing and Geographical Information System Division)

Applied Remote Sensing (Ersc. 771)

Project on:

Evaluation of Bush Encroachment Mapping Using TraditionalSupervised Classification and Spectral Mixture Analysis, a Case for

Borena Rangelands

Submitted toDagnachew Legesse (PhD)

By:

Yoseph Alemayehu (GSR/2035/00)

Zemenu Mintesnot (GSR/ 2037/00)

July 2008


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Abstract

Bush encroachment is rated second to cause food insecurity in pastoral areas. Careful

management plan and practice requires knowledge of both the spatial and temporal

extent of the problem. This study was aimed at mapping the bush encroachment and

detecting the changes occur from 1986 through 2002. Landsat TM and ETM+ images,

p168r57, taken in January 1986 and February 2002 were used for this study. The

images were analyzed using supervised classification and spectral mixture analysis

(SMA). The mixture analysis was done based on endmembers spectral reflectance

derived from the scene. Change detection on out put images from SMA was conducted

by image differencing. The study showed that there exists substantial bush

encroachment expansion, about 14persent. This needs for immediate measure. Future

study needs to be conducted to evaluate the converted landcover type. Accuracy can be

achieved with utilization of reference data collected through field survey or available

reference data.

Key words: bush encroachment, change detection, supervised classification, spectral

mixture analysis.

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List of figures

Fig 1: General Methodology Flow chart

Fig 2: map showing major land covers in Borena range lands (1986)Fig. 3: map showing major land cover in Borena range lands (2002)

Figure 4: gray scale images from the spectral mixture analysis, for bush endmember.

The images are 2002(a) and 1986 (b).

Figure 5: map showing percentage categories of changes in bush encroachment

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List of tables

Table 1: major land covers and their respective area coverage

Table 2: areas changed in their percentage composition

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1. Introduction

1.1. Background

About 61-65 percent of the total area of Ethiopia is estimated to be occupied by pastoral

areas. These areas are home to 12-13 percent of the total population of the country. In

addition, out of the total estimated livestock population the pastoral areas constitute

approximately 30 percent of the cattle 52 percent of the sheep, 45 percent of the goats

and 100 percent of the camel (MOA, 2000). However the pastoral production system and

in particular the food security and lively hood situation is highly threatened because of

different man made and natural risks. Unwanted plant species encroachment is one of the

major challenges to dry land development among others.

Bush encroachment means the invasion of shrubs and bushes or trees in to former

grassy range lands. Currently this problem is highly pronounced in the Borena range

lands, which is a home for very large livestock population. Literatures showed that the

area under bush encroachment in the Borena range lands is about 40 percent

(Coppock1994, and Ahimed and Florian, 2000), and it is still progressing (Bruck Y.,

2003). The Oromiya Water Woks Design and Supervision Enterprise (OWWDSE) has

conducted a socio-economic analysis on the causes of food insecurity in the Borena zone

in 2007. And in its detailed analysis bush encroachment was ranked second next to

drought (OWWDSE, unpublished).

Coppock (1994) reported that about 15 woody plant species are considered to be

encroachers. Among these, according to Ahimed and Florian, 2000, Acacia brerispica,

Acacia bussei, Acacia drepanolobium, Acacia melifera, Acacia reficiens, Acacia seyaland commiphora africana are the major encroaching species in the Borena range land.

The magnitude of encroaching varies from locality to locality and from species to

species. A.drepanolobium, A. reicience, and A. bussei are major and common

encroaching species found across all the range lands.

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These encroachers have reduced the total area devoted to grazing for cattle. Their thorny

characteristics prohibit the access of livestock to areas where the stand is dense, suppress

grass growth and provide harbor for predators. The causes for this sort of deterioration

seem to be combination of over grazing, ban of range land burning during the former

regime (Ahimed and Florian, 2000). Indigenous knowledge and traditional systems were

neglected and considered backward by the decision makers. The Borena pastoralists had

traditionally used controlled fire as a range management tool. In the youth stage, the

Acacia species are susceptible for fire and can be killed with repetitive burning. Since

1970s the practice of burning had been banned. Hence unwanted (unpalatable)

herbs/shrubs/bush species got the chance to grow. As they were not eaten by most

livestock, they over took other palatable grass species and begin to dominate. Some of

these woody species are indicators of desertification. It could be attributed to the

repeatedly occurring drought in the area.

Remote sensing and satellites imageries with temporal and synoptic view play a major

role in developing a global and local operational capability for monitoring land

degradation and desertification in dry lands (Khiry, 2007). Therefore, this study is

intending to improve the monitoring capability afforded by Remote Sensing to analyze

and map the bush encroachment change in Borena rangelands.

1.2. Objectives

1.2.2. General Objective

The general objective of this project is to assess the expansion of bush land in the Borena

range lands between 1986 and 2002.

1.2.2. Specific objectives

To determine the total area converted to bush land between 1986 and 2002.

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To undertake classification of main land cover types in the study area usingsupervised classification.

To apply spectral mixture analysis To spatially locate bush encroached areas. To apply basic concepts of image classification and processing techniques those

were grasped during the theoretical sessions.

To be well acquainted with image processing software (ERDAS IMAGINE)

1.3. Problem statement

Bush encroachment is one of the most serious problems in the Borena range lands.

Magnitude and location of areas under bush encroachment are not properly determined.

Proper management of range lands require to locate and quantify areas under bush

encroachment. Mitigation and control measures should be based on informed decision

making.

1.4. Hypothesis

Multi temporal satellite images taken from the same scene can be used to detect bush

encroachment changes occurred with in significant period of time.

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2. Literature Review

2.1. The concept of spectral mixture analysis

The development in satellite technology as well as remotely sensed image acquisition and

analysis offer effective opportunities for monitoring land cover change in arid and semi

arid areas. Spatial resolution of an image is important in scaling observations. Each pixel

within an image provides only a single measurement of spectral response of an area. It is

however consisting of a multiple surface component. (Khiry, 2007) Usually mapping

land use and land cover has been accomplished using traditional (supervised and

unsupervised) techniques. However, it is difficult to find consistent classes with this

approach between images taken at different times (David etal, 2001). Sub pixel

classification interms of SMA is based on reflection proportion of the observed material.

SMA is a promising technique developed from the efforts of earth and land cover types

have shown characteristic patterns of reflectance within wave length across the EMS

(electro magnetic spectrum). In reality surfaces of land cover types are often composed of

a variety of mixtures of materials. For example a pixel composed of both soil and

vegetation will have a spectral response which depends on combination of the general

soil and vegetation spectra.(Borrison and Nicolav, no date) SMA provides a means for

determining the relative abundance of land cover materials present in any pixel based on

the spectral characteristics of the material. A combined spectrum thus can be decomposed

in to a linear mixture of its spectral and members that is the spectra of distinct material in

that IFOV (instantaneous field of view). (Leica Geosystems Geospatial Imaging, 2005)

SMA involves two steps. First defining the spectra for pure selected land cover type and

second each pixel is modeled as spatial mixture endmember spectra to determine the

physical abundance.

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The following equation is solved for each pixel

Fi = fractional abundance of particular endmember

DNi = intensity of the image endmember at particular band.

David et al, 2001

DN = brightness value of a given pixel for a specific wave length.

Where,DIV = Fi DNi

SMAs advantage can be summarized as follows (David et a,l 2001)

SMA endmember can be identified from image data, field data lab inventories or

from endmember fraction libraries. Therefore, time series of multiple geographic

location SMA fraction coverage is more readily comparable than the products from

classification based on DN.

It uses all the information in the multispectral band. It allows a more detailed analysis of pixel contents.

The traditional method for inferring characteristics about vegetation cover from satellite

data is to classify each pixel in to specific land cover classes based on predefined

classification scheme. The goal of linear mixture models is to estimate the fractional

cover of each major landscape unit of interest (endmember) within image pixels. The

inputs to mixture models are endmember reflectance and an image of observation vectors

(pixel reflectance), and the output is a fraction image for each endmember along with an

image containing an error of fit (Khiry, 2007). These fraction images can then be used to

constrain additional spectral analyses, as input to biophysical and biogeochemical

models, or simply as a measure of land cover used to analyze spatial and temporal

changes (David et. al, 2001).

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2.1.1. Endmember Selection

The selection of endmember is a critical component to successful application of mixture

modeling. Two general approaches exist for deriving endmembers, each with their ownadvantages and shortcomings. The first uses reflectance spectra measured in the field or

laboratory. This method allows great control over the selection of endmember spectra,

but requires that raw image data be correctly converted to reflectance, an often difficult

task in remote sensing. It is also often difficult to obtain reference endmember spectra for all

cover endmembers. The second approach derives endmember spectra directly from the

image by extracting reflectance from relatively pure pixels. However, isolating a pure

pixel is often impossible given the great surface heterogeneity at the scale of most remote sensors.

2.2. Change detection

Though it is an elusive task, since different techniques (different maps of change, post-

classification or pre-classification) often produce different results (Bonaue, no date),

Change detection is a very common and powerful application of satellite based remote

sensing (RS).

Image differencing is one of the techniques used in change detection. It is a pre classification

technique. In this method the after image is subtracted from the before image, in a pixel

by pixel format. Even though it was found to be effective so far , it needs accurate geometric

co registration .

2.3. Image classification

Multispectral classification is the process of sorting pixels into a finite number of

individual classes, or categories of data, based on their data file values (Leica

Geosystems Geospatial Imaging, 2005b). Commonly known are the supervised and

unsupervised classification techniques.

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In supervised classification partitioning of the feature space is realized by an ooperator

who defines the spectral characteristics of the classes by identifying sample areas

(training areas). There fore the operator needs to know where to find the classes of

interest in the area covered by the image.

In unsupervised classification clustering algorisms are used to partition the feature space

into a number of clusters. The choice of algorism depends on classification and the

characteristics of the images.

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3. Methodology

3.1. Location of the study area

The study area is located in Oromiya region Borena administrative zone a boarding zone

of Ethiopia with Kenya. It is found at a distance of about 540 Km south east from Addis

Ababa. Geographically it is bounded 416595.55-351971.74 (UTM) N and 457487.82-

454583.38 (UTM) E. It comprises whole of Dire woreda and part of Yabello and Teltele

woredas.

The climate of the Borena lowlands is semi arid. Annual mean temperatures vary from

18-25 degrees Celsius, with little seasonal variation. Annual rainfall varies from 440-

1100mm, the average is 600mm. rain fall is bimodal; 59 percent of the annual

precipitation occurs during March to May and 27 percent in September to November. The

landscape is in general undulating, with a few scattered volcanic cones and rock outcrop.

The Borena lowlands have some surface water. However, Dawa and Genale rivers rising

from the Borena highlands and from the mountains of Bale Zone turn both soon to the

east into Somalia.

3.2. General methodology

The Processing in this study composes a supervised classification and spectral mixture

analysis (SMA) technique. The general methodology flow chart is given in Figure 1. Two

year data obtained from satellite imagery of the study area were analyzed by classifying

the image in to major land cover types. In addition SMA technique was applied to

evaluate the percentage of bush (unpalatable vegetation) encroachment in the study area.

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3.2.1. Data acquisition and pre processing

Two cloud free Land Sat scene (p168r57) covering the study area were selected for

analysis. The TM and ETM+ respectively were taken in January 1986 and February

2002. This time is a dry season for the study area and it helps to discriminate the woody

ever green vegetation from dry grass or photosynthetically less active vegetation.

3.2.2. Image processing

The whole task of digital image processing revolves around increasingly spectral

separablity of the object features in the image. Accordingly the two images were

geometrically registered taking the ETM+ 2002 image as a reference. Subset of the study

area was selected using the ETHIO GIS vector layer as an AOI (Area of Interest) layer.

To apply the spectral mixture analysis atmospheric calibration was required to balance

the reflectance. This calibration was done by using the internal average relative

reflectance method.

3.2.3. Spectral mixture analysis (SMA)

In order to assess the vegetation composition of pixels multi temporal spectral mixture

analysis was done. This method involves image preprocessing, image endmembers

selection, image fraction production and classification of the fraction image. To

determine the endmembers in the image tasseled cap transformation and separability

merging was done to enhance the visual capability to select endmember from the images.

The endmembers used in this study are image endmembers, because they are easily

accessed and they are representatives of the spectra measured at the same scale as image

data. Three endmembers; vegetation (bush), bare ground and dry grass were defined.

The general mathematically model for linear mixing can expressed as

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f = fraction of the endmember in the specified pixel.

(David et. al, 2001)

EM = Relative Radiance of endmember

DN = Em * f,

f = 1Where,

DN = Relative radiance for each pixel

Generally Sf = 1, this means that the fraction of the endmember of each pixel in the

image sums to 100%. An endmember greater than 100% occurs as the pixel has spectra

similar to that of the particular endmember but with higher reflectance. A negative value

of fraction indicates that an endmember is unnecessary to model in that particular pixel.

Visual interpretation of the vegetation fraction was done to asses the land cover change.

Changes in vegetation fractions were visually interpreted by displaying fractions.

3.2.3.1. Signature derivation

Signature derivation and evaluation is an important part in the subpixel classification.

There are two ways to derive a signature, manual and automatic. Manual derivation is

used when whole pixel Material of interest can be used as training set. In this case, an

interactive method was applied. 30 signatures were taken from each scene. These pixels

were believed to represent pure bush reflectance. To minimize error these signatures were

merged to produce a single representative scene derived signature of pure pixel.

3.2.4. Change detection

The images from the spectral mixture analysis were in gray scale. The pixels of the image

represent the percentage composition of each end member. A visual interpretation was

done to evaluate the magnitude of encroachment in both images. Image differencing was

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conducted to locate the changes in bush composition. The out put image from Image

Differencing was classified using RGB slice to separate between the degrees of bush

encroachment.

3.2.5. Supervised Classification

Both images were classified separately in to major land cover types using supervised

classification. Classes of major land cover types were identified. These are bush, wood

land, farm land, bare land, grass (pasture land), water body and unknown cover.

Three steps are included in this process. These are the preprocessing and processing

stage, training and classification stage, and the interpretation stage.

3.2.5.1. The processing stage

Normalized difference vegetation index (NDVI), tasseled cap transformation and

principal component analysis were computed to aid the training stage. The greenness

band from the tasseled cap and the second principal component were used to carefully

locate and select representative AOI (training areas).

3.2.5.2. The training and classification stage

Intensive training was made to separate between the wood land and bush land relative to

the other land cover classes. Maximum likely hood classifier was used and AOIs are

given based on seed pixels. The training areas were assigned based on previous

knowledge of the area. No reference data/ image were used. This can be considered as a

limitation during the training stage.

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3.2.5.4. Interpretation

The change in land cover was visually interpreted. The total land converted to bush

(encroached land covers) were computed from the data found during classification.

TM (1986) and ETM+ (2002)

Imagery

Supervised Classification

Selection of Endmembers

Fraction Image of

Components

Change analysis For Bush

Image subsetting

Geo Rectification

Linear Mixture Model

Map of Change in Fraction Image

(Image Differencing)

Analyzing of Fraction Image

Stacking Separate Bands

Image Enhancement

Fig 1: General Methodology Flow chart

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4. Result and Discussion

In this study two separate out puts were found. These are from the supervised

classification of the images and the spectral mixture analysis.

4.1. Supervised classification

The image processing phase has helped the training phase for it has enhanced the visual

separablity of the images. The tasseled cap transformation has helped to distinguish

between the bare land and green features in the scene.

The two images were classified in two seven classes with over all accuracy of 95 and 94

percent for the 1986 TM image and 2002 ETM+ image respectively.(the out put images

are given in Fig. 2 and Fig. 3) The error matrix is given in the appendix. At the training

stage intensive training was given two discriminate between wood land and bush land

(lands encroached with bush). The intensive training has increased the accuracy of the

classification process.

Area coverage (in hectare)Major land cover types

1986 2002

Unknown feature 2678.88 2546

Bare Land 218516 124136

Grazing land 596178 903590

Water body 50.9281 29.4847

Farm land 239505 138379

Wood land 216926 167711

Bush land 603229 689083

Table 1 major land covers and their respective area coverage

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The result found shows there exists an increase in the magnitude of land encroached

with bush. In sixteen year (1986-2002) 14.23 percent increase was observed. This rate is

equivalent to about one percent annual increment.

Fig. 2 map showing major land covers in Borena range lands (1986)

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Fig. 3: map showing major land cover in Borena range lands (2002)

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4.2. Spectral Mixture Analysis

4.2.1. Mixture Analysis

Spectral mixture analysis unmixed pixel values in to their component end member.

Generally three end members were selected. Bush, grass (dry), and bare land were used

as major components of the scene. The analysis gave three end member composition gray

scale imagery for both images.

Only the image for the bush endmember was further analyzed for change detection. The

image values ranges -0.56 to 1.74 and -0.6 to 1.43 for the 1986 and 2002 images

respectively. Under ideal accuracy in spectral mixture analysis values should range from

0 to 1(David etal, 2001). Values below zero indicate that the pixel was unnecessary for

that specific end member. On the other hand values above one indicates that the pixel

contains material that has similar reflectance but with relatively higher values. Visual

examination showed that pixel values that fall below zero are derived from area that

were considered bare and unknown features during the supervised classification stage.

Similarly areas that were thought to be wood land showed a pixel value above one. The

output images from the SMA are shown in figure 4 (a) and (b). the gray scale images

helps to visualize the direction of change in bush density.

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(a) (b)Figure 4: gray scale images from the spectral mixture analysis, for bush endmember.

The images are 2002(a) and 1986 (b).

4.2.2. Image differencing

Image differencing was the technique applied to detect the change in bush encroachment.

The output of the process is gray scale image. The DN (pixel values) can be negative,

indicating decreasing pixel value, positive, indicating an increase in pixel value, or zero

indicating no change in the after image.

The out put form the image differencing was clustered in to groups using a level slicing

method. The slicing was done based on pixel values. The image developed after image

level slicing is given in figure 5. The pixel values indicate the percentage composition

change of bush in that specific pixel. The following table shows areas of land with

percentage increase categories.

0-5% 5-10% 10-15% 15-20% 20-25% 25-35% 35-40% 40-

50%

>50%

284946 291614 130328 68424 24731 32020 6545 13140 2918

Table 2 areas changed in their percentage composition

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From the table it can be seen that a large acreage of land is being encroached with bush.

Spectral Mixture analysis provides the data in more detail than the traditional supervised

classification. The method gives additional information other than merely indicating

areas of change. It showed the composition change in each pixel.

Figure 5 map showing percentage categories of changes in bush encroachment

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5 Conclusions

From this study the following conclusions have been made.

Bush encroachment (both conversion of land covers in to bushland andpercentage increase in an already encroached areas) are significant and calls for

measures.

The techniques used to enhance the image (the tasseled cap transformation andthe PCA) enhances the visual interpretability of the images.

It was possible to detect the bush encroachment with both supervisedclassification and spectral mixture analysis.

The results from the spectral mixture analysis provide more detail informationthan the ordinary supervised classification.

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6. Recommendations

Bush encroachment change detection requires well organized ground reference data in

addition to the utilized satellite images. Classification accuracy can be increased if

training areas are given based on reliable and on site reference data.

Further study needs to be undertaken to map the land cover conversion probabilities. In

addition the invasion can be separately mapped across location for different invading

species. This, however, requires satellite images with better spatial, temporal and spectral

resolution than utilized in this study.

The study assessed that the conversion of the land cover to a bush land in the study area

is significant. The highly noxious species has been invading a wide range of new areas.

This calls for a rapid and sound control and mitigation measures.

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References

Ababu, A. and Menberu, L. 2007 Awareness raising In drylands coordination group

Ahmad, J. and F. Menzel, 2000 Bush encroachment in the Borena Range lands and how

to turn harmful bush in to use full bush. BLPDP and ORBAD, Negelle Borena

Borrison D. and Nikolov B.(2001) Vegetation and soil spectral mixture analysis, new

south Wales university, Australia

Bruck, Y. 2003 Food security Situation in the pastoral areas of Ethiopia, Oxfam Addis

Coppock, D.L., 1994 The Borena Platue Of southern Ethiopia: Synthesis of

pastoral

David et al. (2001). Per Pixel Analysis of Tree Structure, Vegetation Indices, Spectral

Mixture Analysis and Canopy Reflectance Modelling. Stanford University,

Stanford.

Khiry M.A., (2007). Spectral Mixture Analysis for Monitoring and Mapping of

Desertification Process in Semi-arid Areas in North Korodofan state, Sudan. PhDThesis. Dresden

Leica Geosystems Geospatial Imaging, 2005, ERDAS IMAGINE field guide, Leica

Geosystems

Leica Geosystems Geospatial Imaging, 2005, ERDAS IMAGINE Spectral Analysis tour

guide, Leica Geosystems

MAO 1998, Natural resources and regulatory Agro-ecological zones of Ethiopia, Addis

OWWDSE, (Unpublished). Socioeconomic study of Borena Development

corridor proceedings No. 23 Norway Research, development and change, 1980-

1991 system study no 5 ILKA Addis Ababa, Ethiopia

Yusuf.A. and Virginia Atmopawiro (no date) subpixel and maximum likelihood

classification of land sat ETM+ images for detecting illegal logging and mapping

rainforest cover types in East Kalimantan, Indonesia.

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Appendix

ERROR MATRIX (1986)

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

Reference Data

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

Classified

Data Bare Land Pasture La Water Unknown Feature

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

Bare Land 5828 123 0 0

Pasture La 313 9331 0 0

Water 0 0 28 0

Unknown Fe 0 0 0 3287

Farm Land 13 0 0 0

Bush Land 0 0 0 0

Wood Land 0 0 0 3

Column Total 6154 9454 28 3290

Classified

Data farm Land Bush Land Wood Land Row Total

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

Bare Land 14 0 0 5965

Pasture La 1 0 0 9645

Water 0 0 0 28

Unknown Fe 0 0 0 3287

Farm Land 1229 0 0 1242

Bush Land 0 6912 492 7404

Wood Land 0 464 6923 7390

Column Total 1244 7376 7415 34961

ERROR MATRIX (2002)

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

Reference Data

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

Classified

Data Bush land Wood Land Bare Land Grazing La

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

Bush land 7620 115 0 0

Wood Land 4 4416 0 0

Bare Land 0 0 2858 0Grazing La 18 0 0 6670

Farm Land 0 0 0 0

Water Body 0 0 0 0

Unknown Fe 0 0 0 0

Column Total 7642 4531 2858 6670

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Classified

Data Farm Land Water Body Unknown Feature Row Total

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

Bush land 0 0 3 7738

Wood Land 0 0 0 4420

Bare Land 1 0 0 2859

Grazing La 0 0 0 6688

Farm Land 2075 0 0 2075

Water Body 0 218 0 218

Unknown Fe 0 0 4443 4443

Column Total 2076 218 4446 28441

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Advaned Remote Sensing Project Yoseph Alemayehu and Zemenu Mintesnot

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Transcript of Advaned Remote Sensing Project Yoseph Alemayehu and Zemenu Mintesnot