The over all accuracy is the most common and popular mean of indicating the accuracy. This measure indicates the correctly classified sample units in the entire matrix, and sim- ply given by the division of the sum of diagonal elements by the total number of sample units. However, it has to be borne in mind that, though the over all accuracy is the com- monly used measure, it does not explicitly indicate the accuracy of individual class clas- sification.
8.4.3.5 Kappa (K^) Statistics
“ Kappa analysis is used to describe the degree of agreement between two data sets, while allowing for agreement (i.e. correct classification) due to chance. It varies between +1.0 for perfect agreement down to 0.0” (Weir, 2001). Same as the Confusion matrix, Kappa also shows low classification accuracy for non-cultivated agricultural plots in year 2000/2001.
8.4.4 Accuracy of this Classification
Confusion matrix, percentage of accuracy and Kappa statistics obtained from this accu- racy assessment are shown respectively in Tables 8-1, 8-2 and 8-3. Although the overall accuracy of this classification is 81% the user accuracy of one agricultural class (Fallow plots) is very low i.e. 30%. This agricultural class is mixed with the class Savannah. It was very difficult to separate both Savannah and Fallow agricultural plots in year 2000/2001 by classification. Because when this Aster image acquired some agricultural plots, which are named, as Cultivated plots in map legend are cleaned prior to the next cultivation whereas some of them, which is named as Fallow plots, are dense with natu- ral vegetation. Therefore, this image is not good for identifying agricultural areas and es- timation of crop area in Serowe by classification based on reflectance values, as it was not acquired in the correct time when the agricultural crops are grown on the plots.
Improving Land Use Survey Method Using High Resolution Satellite Imagery Chapter-8
Table 8-1: Confusion matrix obtained from the classification
Table 8-2: Percentages of producer accuracy and user accuracy of each class and overall classification accuracy.
Conditional Kappa for Each Category Class Name Kappa
Water 1
Rock out crops 0.9
Savannah 0.9
Cultivated plots 0.6
Fallow plots 0.2
Bare land 0.7
Overall Kappa Statistics = 0.74
Table 8-3:Kappa Statistics resulted from classification. Reference Data Water Rock out crops Savannah Cultivated plots Fallow
plots Bare land Row Total
Water 5 0 0 0 0 0 5
Rock out crops 0 47 0 0 0 2 50
Savannah 0 0 116 1 2 0 120 Cultivated plots 0 5 0 31 0 9 45 Fallow plots 0 1 31 0 13 0 45 Bare land 0 0 1 6 2 41 50 C lassified D ata Column Total 5 53 148 38 17 52 315 Class Name Producers Accuracy Users Accuracy Water 100% 100%
Rock out crops 89% 94%
Savannah 79% 96%
Cultivated plots 82% 69%
Fallow plots 77% 30%
Bare Land 79% 82%
Improving Land Use Survey Method Using High Resolution Satellite Imagery Chapter-8
8.5 Evaluation between Visual Interpretation and
Supervised Classification for Identifying Agricultural
Plots.
8.5.1 Quantitative Evaluation
For quantitative evaluation between visual interpretation and supervised classification, classified map were compared with visually interpreted agricultural plot vector layer. The portion of visually interpreted agricultural plots vector layer was broken out from classi- fied map using subsetting operations for calculating the percentage of area of each classi- fied class within agricultural plots. In this process (Figure 8-7), first the visually inter- preted vector layer was copied to an AOI layer. Then, from the classified map the AOI layer was subsetted. Finally, using this subset map (see Figure 8-8), the area within agri- cultural plots of each classified class were estimated. Using raster attributes of the classi- fied map total area of each class were calculated. When the total area and the area within agricultural plots of each class are known area without plots can be calculated. The re- sults are shown as percentages in the Table 8-4.
These results show how much do the agriculture and non-agriculture areas mix with each other among the classified classes. As shown above the user accuracy of the class “Fal- low plots” is very low, the accuracy of this class is low too when comparing the classi- fied map with visually interpreted agric plot vector layer. Only 7% of area of this class is included within agricultural areas of visually interterpreted map. The user accuracy of the class “Cultivated plots” is about 69% according to the accuracy assessment of the classi- fication. Here also, the accuracy of this class is found to be higher than the accuracy of the class “Fallow plots” since cultivated plots show 52% of area within agric plots. If this classification is accurate, visually interpreted agricultural plot layer should contain only two agricultural classes but not the non-agricultural classes. However, according the figures shown in Table 8-5,62% of the visually interpreted agric plot area has been clas- sified as non-agriculture. Furthermore, it can be seen that only 5% of the area is classified as fallow plots while 33% of area is classified as cultivated plots. These results further confirm that fallow plots gains a very low accuracy.
Improving Land Use Survey Method Using High Resolution Satellite Imagery Chapter-8
Figure 8-6: The steps followed for qualitative evaluation of supervised classification and visual interpreta- tion
Aster Image
Visual interpretation Classification
Agric plot vec- tor layer
Classified Map
AIO layer
Raster attributes
Total area of each class
Subset
Subset Map
Area of each class within agric plots
Area of each class without agric
% Without % Within
Quantitative Evaluation
Improving Land Use Survey Method Using High Resolution Satellite Imagery Chapter-8
Improving Land Use Survey Method Using High Resolution Satellite Imagery Chapter-8
Figure 8-7: The map created by visual interpretation shows agricultural plots
Figure 8-8:The map subset from classified map based on visually interpreted agric plot layer, showing the different classes within agricultural plots
Improving Land Use Survey Method Using High Resolution Satellite Imagery Chapter-8
Table 8-4: Total area in hectare and percentage of area within agric plots and without agric plots of each classified class
Table 8-5: Quantitative evaluation of supervised classification based on visual interpretation
8.5.2 Qualitative Evaluation
Table 8-6: Qualitative evaluation of visual interpretation and supervised classification used in identifying agricultural areas
Land cover classes
Total area (Ha) %Area within agric plots %Area without agric plots Water 187 0 100
Rock out crops 23686 1 99
Savannah 128305 4 96
Cultivated plots 8830 52 48
Fallow plots 9920 7 93
Bare land 22651 14 86
Visually interpreted Agric Plot map (Ha) (%) Cultivated plots 4607 33 Fallow plots 676 5 Supe rv is e Cl ass i- fi ca ti on Non- Agricul- ture 8794 62
Visual Interpretation Supervised Classification
Time Time consuming
(One week time)
Less time consuming (5 hours)
Cost Need more cost due to
high labour consuming.
Less cost needed compared with visual Interpretation
Expert knowledge requirement
Same Same Accuracy In this case, Visual
Interpretation is more accurate
Less accurate due to difficulties in separation of agricultural classes from others
Quality Different classes can’t be identified among agricul- tural fields
Improving Land Use Survey Method Using High Resolution Satellite Imagery Chapter-8
8.6 Comparison between Farmer Reported and
Computer Measured Plot Size
During the fieldwork phase, data about the plot size, which was surveyed, were collected from farmers. Farmers reported the plot size that mentioned on their landowner ship document. During the interview, a transparent sheet on the enlarged block of satellite photograph was used to verify, the reported field. Such identified agricultural areas in each sample block later were measured using computerized measurement tool in Erdas Imagine.
Figure 8-9: The scatter plot of farmer reported and computer measured plot size
Table 8-7: Farmer reported and computer measured plot size
According to the above scatter plot drawn between farmer reported plot size and com- puter measured, there is positive linear correlation can be seen. As discussed earlier in some cases the areas reported by farmers are not reliable. However, in this case farmer reports can be consider as reliable information.
Field no Farmer reported Computer measured 1 10 9.2 2 20 18.9 3 25 25.2 4 9 9.8 5 10 10 6 24 24.8 7 10 10.8 8 8 7.6 9 15 15.6 10 7 6.4 11 8 8.5 12 7 6.7 13 5 4.8 14 5 5.44 15 5 5.9 16 5 4.9 17 5 4.2 18 6 6.8 19 17 16.4 20 14 14.8 21 3.5 3.8 22 3 2.96 23 10 10.4 24 5 4.88 25 16 16 26 20 20.8 27 20 20.4 28 6 6.8 29 15 15.5 30 20 21 31 10 10 32 9 9.2 33 10 10.4 34 25 26.2 35 15 14.8 36 15 15.7 37 9 9.6 38 8 8.4 39 3 3.8 40 10 10.2
The Relation Ship Between Farmer Reported And Computer Measured Ag:Plot Size
y = 1.0156x + 0.0779 R2 = 0.9927 0 5 10 15 20 25 30 0 5 10 15 20 25 30
Farmer Reported Plot Size
Co
mputer M
ea
sured Plo
Improving Land Use Survey Method Using High Resolution Satellite Imagery Chapter-8
Improving Land Use Survey Method Using High Resolution Satellite Imagery Chapter-9
9 CONCLUSIONS
In this case study, the Aster image used to develop an efficient area frame sampling sur- vey method saved cost of materials as it was downloaded from the web free of charge. Conventional method which, is manual and requiring up-to-date aerial photographs, sat- ellite images, current maps or combination of all is costly and involved laborious and meticulous work. Obtaining Aster images are much cheaper than obtaining other possible satellite images and updated aerial photographs.
Although the aerial photographs provide more details for frame construction, high- resolution (15m) Aster data also appropriate for visual interpretation. The method using Aster image in image processing software can most conveniently used to delineate frame limits, strata and Primary Survey Units (PSUs) since this procedure is completely com- puterised. It reduces much effort involved in different steps of frame construction. Strata and PSUs boundaries, and frame limits can easily be delineate by visual interpretation using vector tools. If the area needs to be surveyed is much larger than the area covered by single Aster image (60km x 60km), several images can be put together in one frame by using mosaic tools in image processing software with less effort than the effort in- volved in aerial photo mosaic. Subset techniques also can easily be used if needed to re- duce the size of survey area from large frame. In conventional method, strata and PSUs boundaries, and frame limits are manually transferred to map and measured. It needs much time and labour cost. Use Aster image consumes less time and labour cost because creating map in an image processing software like Erdas Imagine is much easier than do- ing the same thing manually. Even though the LANDSAT images also good for strata and PSUs delineation due to its large coverage (180km x 180km), Aster are more appro- priate for area frame construction since it provides more details than LANDSAT data due to its high resolution (15m). Important features for frame construction such as roads, riv- ers, water bodies, agricultural fields, urban areas, rocks etc can easily be recognised on Aster image.
Instead of using physical boundaries for identification and delineation of segments as used in conventional methods, use of square segments keeps the cost and effort of frame
Improving Land Use Survey Method Using High Resolution Satellite Imagery Chapter-9
construction at a low level without loosing the precision. Doing segmentation by means of UTM grids in image processing software is much easier than doing it manually. Aster image enlargement of segment for ground survey purposes can efficiently substi- tute the aerial photo enlargement with respect to reducing cost and effort. Although in conventional method, requiring scale of aerial photo enlargement is usually 1:5,000 Aster image enlargements in a 1:15,000 scale can be utilised for this situation as the agricul- tural plots are relatively large in Serowe area. Edge enhancement techniques are useful to sharpen the image enlargement. Agricultural plots and other important physical features like roads become more clear and visible than the clearness and visibility of the features on original image. LANDSAT image (30m) is not suitable for replacing the photo enlargement. In the case where the agricultural plots are relatively small, Aster image may not be able to be used.
Aster image at the scale of 1:100,000 is suitable for ground survey as a substitution for topographic maps when they are not available. Aster image provides more updated ground information than the information included in available topographic maps and road maps, in many developing countries. This helps to reduce material cost involved in area frame sampling surveys. LANDSAT image can’t be used this purpose since its low resolution.
When comparing the visual interpretation and supervised classification for identifying agricultural plots in Serowe area using single image, visual interpretation is more accu- rate than the classification. Since the time of image acquisition did not coincide with the crop-growing period, the reflectance in the image did not address the actual crop areas. Visual interpretation is more time consuming whereas supervised classification is less time consuming. Due to the high labour requirement visual interpretation is more costly than supervised classification. The necessity of expert knowledge is same for both tech- niques. When considering the quality of both techniques, supervised classification is more qualitative in terms of the quality of the output. Because by visual interpretation the agricultural plots are not easy to differentiate between each other. By supervised classifi- cation different classes can be identified among agricultural plots.
Even though farmer reports are not reliable in some cases, the field size reported by farm- ers tally with the area estimated by using the image software measuring tools in this case.
Improving Land Use Survey Method Using High Resolution Satellite Imagery Chapter-9
Considering above discussed factors, it can be concluded that high-resolution Aster im- ages have good potential for improving an area frame sampling survey method with re- spect to reduce cost and effort.
Improving Land Use Survey Method Using High Resolution Satellite Imagery Chapter-9
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APPEENDIX-1
Appendix 1: Climatic Data for Mahalapye
MONTHLY MAXIMUM TEMPERATURE FOR MAHALAPYE. FILE ANDREAS11
MONTHLY MINIMUM TEMPERATURE FOR MAHALAPYE. FILE ANDREAS.
MONTHS
YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1984 20.3 20.2 18.3 13.3 8.3 4.5 5.5 8.6 13.7 17.9 18.1 19.6 1985 20.0 19.6 18.6 13.2 9.4 6.4 5.6 9.2 13.8 17.4 18.3 19.7 1986 21.2 19.7 18.8 16.1 10.5 6.0 N/A 9.7 13.0 16.2 17.4 19.5 1987 19.7 21.5 19.6 16.7 10.8 5.9 4.8 8.6 14.2 16.6 20.7 20.2 1988 19.9 20.0 18.5 14.3 8.4 4.7 3.9 7.7 11.9 16.4 16.7 18.3 1989 18.7 19.1 16.7 13.2 10.3 7.9 5.4 9.8 11.7 N/A 17.2 18.5 1990 20.2 18.1 18.2 15.2 8.9 6.6 7.1 7.7 12.0 17.1 18.7 20.0 1991 19.8 19.4 17.3 11.3 8.4 6.7 5.0 8.1 15.1 17.1 19.2 19.4 1992 21.0 21.5 19.5 16.3 10.3 6.9 6.7 8.2 16.1 18.4 18.0 19.4 1993 19.2 19.5 17.7 15.3 11.6 6.6 9.2 9.2 13.9 18.7 18.7 19.3 1994 18.7 18.9 18.4 14.9 9.3 6.3 3.7 7.9 12.9 16.0 20.0 19.6 1995 20.5 19.9 18.3 14.3 10.9 5.3 6.7 9.8 15.2 18.6 19.5 17.6 1996 19.4 18.3 14.8 12.3 9.8 5.4 4.9 9.2 12.8 17.8 18.3 18.4 1997 19.3 17.9 17.2 11.7 8.0 6.1 8.7 8.3 13.7 13.8 18.0 19.6 1998 19.6 19.5 20.0 15.5 9.1 5.8 6.9 8.3 14.0 16.9 18.3 18.8 1999 19.1 19.3 18.2 15.3 12.0 7.5 7.7 9.6 12.6 15.6 19.5 19.3 2000 17.9 18.8 17.7 13.2 7.1 7.6 4.1 7.4 12.6 16.0 19.1 18.0 Mean 19.7 19.5 18.1 14.2 9.6 6.2 5.6 8.7 13.5 15.9 18.6 19.1 MONTHS
YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1984 33.7 33.1 29.6 26.7 25.7 21.5 21.6 25 29.9 31.8 29.5 32.4 1985 31.1 30.9 30.9 28.3 24.9 22.6 22.6 25.9 27.8 30.8 31.8 31.5 1986 32.6 31.7 31.3 26.4 25.8 22.5 N/A 27 27.9 28.4 29.7 31.7 1987 33.7 34 32.6 31 27.9 21.8 21.7 24.8 27.1 30.3 32.9 30.6 1988 33.1 28.8 27.9 26.2 23.9 22.4 23.1 25.6 27.9 30 30.5 29.6 1989 30.6 29.7 30.2 25.8 25.7 22.5 23.1 27.6 29.9 N/A 30.6 32.5 1990 32 30.3 30.5 28.4 25 23.9 25 25.1 28.7 31.2 33.1 32.3 1991 30.6 30.9 28.3 27.3 26.2 22.3 22.9 26.1 30.1 32.2 31.6 31.3 1992 34.6 35.0 32.5 30.8 26.6 23.6 22.8 23.7 31.4 31.7 29.9 31.5 1993 31.4 N/A 30.6 28.6 28.5 23.2 23 24.7 N/A 30.9 28.9 30.7 1994 29.6 29.6 32 29.1 26.2 21.9 20.8 24.7 29.7 29.2 32.1 32.1 1995 33.1 32 28.8 27 22.6 22 23 25.6 30.2 31.3 32.2 29.4 1996 29 25.1 28.9 26.7 24.5 23.4 22.3 25.4 30 32.7 31.2 31.2 1997 30.5 31.1 29 27.5 24.7 24.7 22.9 27.4 28.4 30.9 32.1 33.5 1998 31.3 33.4 33.8 31.2 27.4 25.5 24 24.8 29.6 29.8 30.7 29.5 1999 31.4 33 30.9 29.3 27 24.3 22.8 25.9 27.8 30.4 31.7 30.4 2000 28.1 27.5 27.9 25.6 23.9 21.6 21.9 25.8 29.3 31 31.2 31.8 Mean 31.5 31.0 30.3 27.9 25.6 22.9 22.7 25.5 29.1 30.7 31.1 31.2
APPEENDIX-1
MONTHLY RAINFALL TOTALS (mm) FOR MAHALAPYE. FILE MAHA DATA.
MONTHS
YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
1984 71.0 24.2 257.2 1.4 1.4 0.2 11.5 0 0.3 21.3 47.6 29.5 1985 79.8 32.6 36.3 0.6 0 0 0.1 1.9 0 46.6 15 31.3 1986 3.1 44.3 22.6 86.1 0 5.5 0 0 12.5 60.8 148.7 41.1 1987 32.4 11.4 17.6 1.6 0 0 0 0 21.6 1.0 57.3 83.3 1988 57.1 447 119.7 46.7 0 0 0 0 12.1 25.1 32.7 75.9 1989 51.3 149.1 37.9 40.9 0 28.5 0 0.6 0 24.3 63.4 74.2 1990 62.9 102.6 129.5 21.8 7.5 0 0 0 2.1 32.3 25.8 83.5 1991 173.4 112.1 107.2 0 2.5 22.6 0 0 4.7 23.8 26.0 26.5 1992 90.4 40.1 27.5 2.7 0 0 0 0 6.4 85.0 60.1 124.7 1993 96.8 56.0 38.4 8.3 0.2 0 7.2 0 19.8 32.4 67.5 72.0 1994 50.9 51.8 28.2 2.6 0 0 0 0 0 11.1 136.5 30.5 1995 49.0 161.3 67.4 30.3 16.8 0 0 0 1.3 7.9 52.6 327.4 1996 84.0 102.7 0.6 2.0 9.2 0 0 0 1.2 14.6 126.8 44.9 1997 245.8 7.2 70.7 5.6 31.6 0 0 0 8.1 17.8 99.9 108.4 1998 87.0 4.2 66.9 0 0 0 0 0 0 35.7 92.2 147.3 1999 18.9 5.7 14.8 0.3 7.8 0 0 0 0 1.3 51.0 146.6 2000 122.5 262.6 100.6 24.6 8.4 8.7 0 0 0 11 24.1 35.8
2001 8.5 127.3 36.5 15.9 6.6 5.2 NIL NIL NIL
Mean 76.9 96.8 65.5 16.2 5.1 3.9 1.1 0.1 5.3 26.6 66.3 87.2
MEAN MONTHLY RELATIVE HUMIDITY AT 1400hrs(%) FOR MAHALAPYE. FILE MAHA DATA.
MONTHS
YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
1984 32.3 34.4 46.4 44.6 31.7 34.6 36.5 31.0 29.1 30.2 43.1 34.7 1985 42.6 39.7 36.1 27.9 30.2 28.0 26.8 25.7 31.9 29.2 34.1 38.3 1986 33.5 37.2 30.5 45.7 31.0 28.1 26.4 19.1 27.7 38.0 38.5 41.3 1987 35.6 33.0 31.5 28.8 22.7 27.3 25.1 28.6 36.5 27.3 34.1 49.6 1988 36.7 57.0 56.2 47.4 37.9 28.7 23.3 24.5 26.8 38.7 35.3 45.3 1989 44.4 49.6 38.7 46.0 39.4 40.8 25.8 27.1 23.7 29.5 36.0 35.0 1991 48.9 46.1 55.7 31.2 28.9 34.8 24.4 22.0 29.9 26.3 34.8 37.4 1992 31.5 28.6 30.3 26.6 23.5 29.0 29.3 28.0 N/A 26.8 41.3 41.3 1993 40.6 45.3 38.3 34.3 24.6 31.0 36.1 29.2 26.3 36.7 46.2 45.0 1994 47.9 45.8 33.1 30.8 27.1 28.0 28.8 26.4 20.9 32.1 34.4 36.4 1995 38.2 40.4 50.5 43.5 51.4 35.0 32.0 27.6 23.1 24.1 36.2 48.9 1996 59.0 59.8 45.4 40.1 40.1 30.2 30.7 28.8 23.4 25.6 38.1 41.0 1997 52.6 43.3 48.3 40.2 33.8 27.6 39.2 24.4 38.8 33.0 36.8 39.0 1998 50.1 39.3 35.3 34.8 28.9 28.7 33.0 30.4 29.8 42.9 43.0 53.0 1999 45.1 38.8 40.7 21.2 34.6 31.8 42.0 27.0 30.0 29.8 39.0 47.0 Mean 42.6 42.6 41.1 36.2 32.4 30.9 30.6 26.7 28.4 31.3 38.1 42.2
APPEENDIX-1
MEAN MONTHLY RELATIVE HUMIDITY AT '0800hrs(%) FOR MAHALAPYE. FILE MAHA DATA.
MONTHS
YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
1984 55.4 59.4 71.0 73.3 63.4 69.4 67.8 62 56.7 54.0 63.8 59.2 1985 67.7 67.8 65.5 55.7 64.5 61.1 56.1 55.8 57.6 49.0 54.8 63.5 1986 56.5 60.5 56.8 72.3 64.2 59.3 59.5 44.5 51.2 56.3 58.4 62.3 1987 62.7 55.4 62.6 56.5 50.2 55.8 58.2 56.5 55.9 48.3 52.4 71.2 1988 61.3 75.4 79.8 75.7 76.1 65.5 60.4 56.4 54.5 60.4 56.8 68 1989 69.7 75.3 70.1 71.2 76.3 79.4 64.4 57.9 49.0 48.5 60.3 61.7 1991 73.1 74.9 79.3 65.8 64.9 76.5 60.6 53.4 59.4 51.1 53.5 59.5 1992 56.7 50.9 56.4 53.4 50.0 59.3 60.8 53.9 N/A 50.2 63.2 60.1 1993 63.6 73.0 70.3 63.6 55.3 58.0 69.7 57.6 47.6 59.6 65.9 66.3 1994 69.1 68.9 60.8 57.7 54.8 55.1 59.0 58.3 48.0 53.2 55.2 58 1995 61.5 63.2 71.0 71.5 86.0 73.4 68.1 56.7 47.3 44.3 57.3 69 1996 76.5 81.9 75.8 74.1 74.1 67.9 61.8 60.9 50.9 48.4 58.9 67.1 1997 76.1 72.0 80.4 73.1 70.1 64.8 76.7 52.2 64.7 56.9 56.1 64 1998 75.5 67.3 63.9 65.2 57.4 61.0 63.2 60.9 53.1 64.4 63.7 72.3 1999 70.6 70.3 73.7 46.6 69.1 60.5 68.5 54.5 52.7 56.2 58.3 71.1 Mean 66.4 67.7 69.2 65.0 65.1 64.5 63.7 56.1 53.5 53.4 58.6 64.9
MEAN MONTHLY SUNSHINE DURATION (hours) FOR MAHALAPYE. FILE MAHA DATA.
MONTHS
YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
1984 10.0 10.0 7.8 8.9 9.9 8.8 8.1 9.8 9.0 8.3 8.0 9.1 1985 8.1 10.3 9.2 10.0 8.7 7.1 9.1 9.5 8.5 9.9 10.6 6.3 1986 8.9 9.2 8.9 6.0 9.3 9.5 N/A 9.5 8.7 7.7 8.7 7.4 1987 8.9 9.6 8.2 8.9 9.4 9.1 N/A N/A 7.4 9.2 7.2 5.0 1988 9.2 6.9 7.7 7.5 8.6 8.6 9.4 N/A 8.9 7.1 9.1 4.5 1990 N/A 8.9 6.7 8.5 8.6 N/A 9.3 9.8 9.3 9.0 9.2 6.7 1991 7.8 8.7 N/A 9.9 9.5 8.7 9.9 9.7 8.0 9.7 8.6 7.4 1992 9.5 10.5 9.0 8.9 9.9 9.3 9.3 9.9 8.6 8.7 8.0 8.0 1993 8.9 7.6 8.3 8.5 9.7 9.3 8.1 9.4 9.3 7.9 7.8 8.0 1994 7.4 8.2 9.6 9.6 10.0 9.1 10.2 10.3 10.3 9.5 8.3 8.9 1995 9.2 9.4 8.3 8.5 7.7 9.4 9.3 9.7 9.3 9.4 8.6 8.9 1996 6.9 7.6 9.3 9.0 7.8 9.4 8.7 9.2 9.0 10.0 7.4 9.1 1997 8.5 9.0 7.1 9.3 9.7 10.2 9.0 10.4 7.0 8.2 9.1 8.6 1998 7.7 8.8 8.7 10.2 10.6 10.2 9.4 10.2 8.8 6.5 7.6 7.3 1999 9.8 10.3 9.0 9.6 9.5 9.6 8.5 10.3 9.2 10.0 6.9 7.7 2000 7.0 4.9 7.2 7.9 9.9 6.9 9.6 10.6 9.2 8.6 9.5 8.5 2001 11.4 5.3 7.3 6.6 9.2 9.6 9.6 9.5 8.3 Mean 8.7 8.5 8.3 8.7 9.3 9.1 9.2 9.9 8.8 8.7 8.4 7.6
APPEENDIX-2
Appendix 2: Signature Separability
Distance measure: Transformed DivergenceUsing bands: 1 2 3 Taken 3 at a time Classes
1 Rock out crops
2 Cultivated plots
3 Fallow plots
4 Bare land
5 Savannah
6 Water
Best Minimum Separability Bands AVE MIN Class Pairs: 1: 2 1: 3 1: 4 1: 5 1: 6 2: 3 2: 4 2: 5 2: 6 3: 4 3: 5 3: 6 4: 5 4: 6 5: 6 1 2 3 1976 1847 1982 2000 1937 1942 2000 2000 2000 1998 2000 1977 1847 2000 1952 2000 2000
Best Average Separability Bands AVE MIN Class Pairs: 1: 2 1: 3 1: 4 1: 5 1: 6 2: 3 2: 4 2: 5 2: 6 3: 4 3: 5 3: 6 4: 5 4: 6 5: 6 1 2 3 1976 1847 1982 2000 1937 1942 2000 2000 2000 1998 2000 1977 1847 2000 1952 2000 2000
APPEENDIX-3