At  the  University  of Karlsruhe  as  a  graduating student  I  could  join  to  a  project  created 
by  the  European  Community  for  supporting  the  developing  countries.  In  my  thesis  work 
I  classified  two  images  with  relatively  few  ground  data.  I  used  the  excellent  possibilities 
of  the  DIDIX  image  processing  package  and  passing-through  with  ARC/INFO.  The  de-
scribed  classification  will  be  followed  in  the future  by  careful  analysis  and  suggestion  for 
cultivation

Barsi , Árpád

English

Periodica Polytechnica (Budapest University of Technology and Economics)

PERIODICA POLYTECHNICA SER. CIVIL ENG. VOL. 39, NO. 2, PP. 127-133 (1995) THEMATIC MAPPING OF THE NAIVASHA-REGION (KENYA) FROM LANDSAT IMAGES Arpad BARSI Department of Photogrammetry Technical University of Budapest H-1521 Budapest, Hungary Tel: 463 1187 Received: 15 Nov. 1995 Abstract At the University of Karlsruhe as a graduating student I could join to a project created by the European Community for supporting the developing countries. In my thesis work I classified two images with relatively few ground data. I used the excellent possibilities of the DIDIX image processing package and passing-through with ARC/INFO. The de-scribed classification will be followed in the future by careful analysis and suggestion for cultivation. Keywords: thematic classification, LANDSAT. Some Words about the Working Area and the Data From the very dry desert to the afro-alpine swamp six ecologic zones can be differentiated in Kenya. Most of the area is savannah. The meaning of the indian-spanish word is all but a forest. The flora is classified into two categories: high value rangeland and low value rangeland. Vegetation types of the first category are bushland, woodland, grass-land. All these types have sub categories depending on the species, height and swamping rate. There are much mixed classes (e.g. bushed grassland, wooded grassland, etc.), what makes the uniquee classification very dif-ficult. Mostly the classification errors could be traced back to this fact. Vegetation categories of the low value rangeland are the forest, swamp and barren land. Lake Naivasha can be found in the Central Kenya Rift, to the N orth-west of Nairobi. The region has an area of 900 km2(30 X 30 km), that means 1000 x 1000 pixels in LANDSAT TM images. The average height above the sea level is 2010 lll, the highest point of the region is 2634 m. I have used two 'multitemporal' images: the first was made on 10 July 1987, the second on 1 March 1989. In order to get the best results I used all bands except the 6th one, the thermal infrared band. 128 A. BARSI Fig. 1. The image taken in March U sing the thematic classification I had to determine 38 categories. These categories were described in the previous phase of the project. I would like to show as an example the most analysed category: Bush- and shrubland b1 8 rift bush b2 9 lava bush b3 10 mountain bush b4 11 high mountain bush b5 12 clear mountain bush b6 13 clear dry cedar b7 14 clear moisture cedar b8 15 clear bamboo THEMATIC MAPPING OF THE NAIVASHA·REGION 129 The first column contains class codes, the second one shows the iden-tifiers for the later ARC jINFO analysis, and the last one includes class names. As this example shows, the classes were differentiated not only by species but also by geographic placement and cultivation. Digital Image Processing In the Institute for Photogrammetry and Remote Sensing of the Uni-versity Karlsruhe an image processing system was developed for remote sensing purposes running on UNIX. DIDIX (Digital Image Data Processing in UNIX) is a modular package mirroring the philosophy of the operation system: it contains special modules for displaying, classification, Fourier-transformation, etc. and the user should select the ones that are necessary for his job. After rectification and registration of the bands of the satellite images I have selected the training areas with my project adviser who knew this region well. During the selection it was very helpful that I could display both images simultaneously so the selections of the agricultural areas were simplified a bit. The selection of the training areas was followed by the thematic classi-fication. The mathematical formulas of the classification could be used only in that case when the distribution of the selected pixels by each training area was normal. Because of the inadequate ground information (obsolete topographic maps, quickly changing vegetation) I had to make an effort for using the training areas maximally but reliably. The normality analysis module not only decides with a very clever algorithm whether the pixels of a given training area show normal distri-bution, but makes recursive subclass building, too. By using some con-necting modules statistical values can be derived (Kernel hyperconfidence ellipsoid, Jeffrey-Matusita distance, transformed divergence) which are the basic data of the statistical analysis. Applying the values written in table form it can be examined to what extent the subclasses overlap, how many classification errors (a priori errors) can be expected, and whether new training areas should be created or some of them must be deleted. More-over, the locations of thematic classes in the intensity space are displayed as well. After the correction and modification of the training areas the new situation must be analysed again and these steps must be iterated until the estimated values of the classification errors or the related overlap values become acceptable. -~~-.----------130 C1 69 39 C5 146 94 A. BARSI g1 with 8 subclasses scale: 1cm - 10.60 grayvalues ;f 3 0 2 tJ 0 S 5 cj;P' 61 Jl 52 76 52 76 52 C4'6 0 0 5 3 \)07 7 s2 0 64 , " .. I" 54 89 54 89 54 C7 Fig. 2. A SYNCLASS detail 89 cll: '26 pixels cl2: 104 pixels c13: 126 pixels cl4: 125 pixels c15: 310 pixels c16: 30 pixels c17: 39 pixels cia: 57 pixels cll: 0.0 ott cl2: -1.0 1 il c13: -1.9 111 c14: ·0.4 1 I1 c15: -1.2 1 il c16: 0.0 0 tt c17: 0.0 0 k clS: -1.3 10 tt THEMATIC MAPPING OF THE NAIVASHA-REGION 131 Now I can complete the classification using the so optimized training areas. The classifier program differs from the conventional image process-ing packages. We can choose from several mathematical methods (e.g. maximum likelihood, Mahalanobis), but the most remarkable is that the program calculates the second or, when needed, the n-th probable classi-fication. This makes the creation of a more accurate mathematical model of the thematic mapping possible. Fig. 3. Last classification of the July image In the result image we can find rejected pixels that would be classified with very low probability in spite of the carefully selected training areas. In consequence of this fact we need to select further training areas for 132 A. BARS! repeating the analysis and the classification. For example, after the first classification I got 14.0% of rejected pixels from the image taken in March and 9.4% of the image from July. After the last (fourth) classification these data are the following: 6.1% and 2.3%. (From these 6% of the March image those pixels should be subtracted, on which there were no data originally!) Image analysis in ARC jINFO After the digital image processing I submitted the result images to further analysis by applying ARC/Info's GRID module. I simplified the technics of this analysis using the Map Algebra Language so I could concentrate on the theoretical solution. so 'julLhlst' -eo -70 60 50 --40 --r--30 - r-- -20 -10 r-- -- I n n-o o 5 10 15 20 25 Fig. 4. The main diagonal of the combination matrix After format conversion I produced all possible combinations of the pixels of both images with the combinatorial procedure called COMBINE. It was evident to collect these values into a matrix. Instead of absolute pixel values (frequencies) I used percentages. The meaning of a matrix element is: x percent of all pixels of a class (row) of the July image was classified as a class (column) in the March image. The elements of the main diagonal THEMATIC MAPPING OF THE NAIVASHA·REGION 133 are very interesting. They include the pixels which were classified into the same category in both images. A peak can be seen in the class 11 (lake deep) with the value of 80.5%, that means this group is stationary and can be classified reliably, whereas the agricultural categories have about 10% because of the changes. After the general analysis I examined the cultivated areas. Using an AML-routine I could distinguish the change of the cover of agricultural-areas and the possible, long-term change of cultivation. By introducing new identifiers these new thematics became selectable and displayable. As endproduct I finished three images. On the first one I put the pixels of agricultural cultivated areas (all possible combinations) on the satellite image of March. Thus, the distribution of agriculture is visible. -By producing other images I used a crosscombination to avoid disturbing effects. It means that instead of clouds, shadows and rejected pixels I inserted the corresponding pixels of the other image and in this way I got two filtered, aesthetic images. (Using this method from the nearly 61 thousand rejected pixels of the March image only 3016 remain!) References L ARCjINFO User's Guide, Cell· based Modelling with GRID Analysis, Display and Management ESRI 199L 2. DIDIX On-line Manual and Help, IPF Karlsruhe, 1994. 3. JAHNE, B.: Digitale Bildverarbeitung, Springer Verlag, 1991. 4. Manual of Remote Sensing, Ed·in·chief ColweIl, R. M, Sheridan Press, 1983. 

THEMATIC MAPPING OF THE NAIVASHA-REGION (KENYA) FROM LANDSAT IMAGES

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THEMATIC MAPPING OF THE NAIVASHA-REGION (KENYA) FROM LANDSAT IMAGES

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