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Accuracy of Land Suitability Modeling using Spectral Characterization
4. Spatial analysis for land evaluation
In this step, the 7 important factors were mapped and classified into 3 classes (R1, R2 and R3 for rubber and P1, P2 and P3 for oil palm) namely; growing period map or water deficit map, soil depth map, water table map, slope map, soil drainage map, soil texture map, and organic carbon map. Ranging of these R1, R2, R3, P1, P2 and P3 of each factors were done according to Somyot (1992), Nakorn et al. (1998) and Sutat et al. (1999). The relative weight of factor obtained from Table 4 was used for maps (factors) weighting. Then rubber suitability map and oil palm suitability map were generated. (See Figure 1 and 2)  Fig. 1 Rubber suitability map after weighted evaluation factor  Fig. 2 Oil Palm suitability map after weighted evaluation factor 5. Classification accuracy assessment This step the satellite image, NDVI image and soil map of the study area are linked together with same coordinate point (x,y) or same latitude – longitude by computer software. This satellite image is the false color composite of Landsat 5 TM image using band 4 red color, band 5 green color and band 3 blue color respectively. The NDVI* image was calculated by using landsat 5 TM satellite image band 3 and band 4. Soil map of the study area in digital format and soil profile description of soil types in this study area refer to soil survey of Krabi province that was done by soil survey staff, department of soil development Thailand, reported in year 1986. These 2 images and 1 map are taken into account for sampling regions of interest (ROIs) at existing crop and transferred to new image (ground truth image) by digitizing at the same coordinate. Now all information that need for accuracy assessment is linked together. The classification accuracy assessment was done by compare those rubber and oil palm suitability classified image and their ground truth image. Table 2 the ground truth (percent) shows the suitability classes for rubber distribution in percent for each ground truth class. The producer accuracy indicated that in R1, R2 and R3 classes the percent classified correctly are 91.57, 100 and 50.0 % respectively. Mean while the user accuracy indicated that the R1, R2 and R3 classes, pixel labeled by classifier are classified correctly 96.2, 74.36 and 100 % respectively. The overall accuracy is 89.5161 %. Kappa Coefficient with value 0.7883 is interpreted that a classified image achieves an accuracy that is 78.83 percent better than the chance assignment of pixels to categories. The ground truth (percent) table 3 shows the suitability classes for oil palm distribution in percent for each ground truth class. The producer accuracy indicated that in P1, P2 and P3 classes the percent classified correctly are 95.4, 67.8 and 60.0 % respectively. Mean while the user accuracy indicated that the P1, P2 and P3 classes, pixel labeled by classifier are classified correctly 80.5, 93.02 and 60 % respectively. The overall accuracy is 83.4437 %. Kappa Coefficient with value 0.6653 is interpreted that a classified image achieves an accuracy that is 66.53 percent better than the chance assignment of pixels to categories. Due to ground truth classification of land suitability was based on existing crops. The planting of both rubber and oil palm on unsuitable condition in this study area are rare. A very few number of pixel of unsuitable R3 and P3 can be labeled, this may cause the producer accuracy of R3 and P3 seem rather low. However, if consider the overall accuracy, this new methodology can be accepted. Ground Truth (%)
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