Application of DEM Data to Landsat Image Classification

Application of DEM Data to Landsat Image Classification: Evaluation in a Tropical

Wet-Dry Landscape of Thailand Aplslt Elumnoh and Rajendra P. Shrestha

Abstract Integration of ancillary data in digital image classification has been shown to improve land-use/land-cover discrimination and classification accuracy. Studies demonstrating such techniques in the context of the tropical landscape are lacking. Cloud related problems limiting data acquisition during the crop season are frequently encountered in the humid tropics. Generally, cloud-free data are obtained during the dry season. The reason for carrying out this study was to explore and evaluate the use of ancillary data, such as elevation, in Landsat thematic mapper classification to produce a land-use/land- cover map of the Sakae Krang watershed of Thailand. Alto- gether, 12 feature sets containing the TM original bands, ratios, normalized differential vegetation index, principal components, and a digital elevation model were tested using unsupervised ISODATA clustering. Incorporation of elevation data was found to improve land-cover discrimination with relatively higher classification accuracy (77.5 percent) compared to TM data alone (65.3 percent). Further improvement in the classification accuracy (84.3 percent) was obtained when using elevation data under a supervised technique. The study also indicated that the classification results can be further improved by incorporating other geomorphometric variables, such as slope and soil moisture regime.

Introduction Remotely sensed data are widely used in land-uselland-cover classification. Several techniques have been reported to improve classification results in terms of land-use discrimination and accuracy of resulting classes while processing high-resolution remotely sensed data. One of the techniques to achieve improvement in digital classification is the incorporation of ancillary data, such as a digital elevation model (Hutchinson, 1982). There are several approaches for incorporating ancillary data in the image classification process, and earlier studies have demonstrated the use of a digital elevation model (DEM) for a number of purposes. DEM integration in image classification helped to increase the classification accuracy of digital data (Bonner et al., 1982; Jones et al., 1988; Frank, 1988; Janssen et al., 1990; Gastellu-Etchegorry et al., 1993; Palacio-Prieto and Luna-GonzBlez, 1996). A DEM has also been used to describe the distribution of terrain components which contribute to spectral response (Strahler et al., 1978), identify sites for fieldwork (Walsh, 1987), geographically stratify training areas or homo- geneous regions (Franklin, 1990), and provide topographic normalization of Landsat TM digital imagery (Civco, 1989).

In areas of rugged terrain, integration of other ancillary information together with elevation data has improved classification accuracy. For example, Franklin (1987) reported that classification accuracy using Landsat MSS increased from 58 to 79 percent with the addition of elevation data. Furthermore, the addition of four other geomorphometric variables (relief, con- vexity, slope, and incidence) yielded an 87 percent classification accuracy. Cibula and Nyquist (1987) used elevation along with slope and aspect variables to improve MSS classification. A DEM has also been used with microwave data for surface pattern discrimination (Peddle and Franklin, 1991), forestry studies (Teillet et al., 1985), and snow monitoring (Guneriussen and Johnsen, 1996), among others.

Most of the previous studies were conducted in the moder- ate to high relief terrain of alpine to sub-arctic climatic condi- tions. The studies, which evaluated such techniques as integrating ancillary data into image classification in the context of agriculturally dominated mixed relief terrain condition of a tropical wet-dry climate, are not sufficient. Such studies have particular importance in the case of Thailand for two main reasons: digital image classification has not yet been widely applied for producing land-uselland-cover data and the image data (optical) acquired generally represent the dry season.

Thailand experiences three distinct seasons of the year (ESCAPINOAAICIAM, 1990): rainy (mid May to mid October), winter (mid October to mid February), and summer (mid Febru- ary to mid May). In the majority of cases, the agriculture is a rainfed type, confining agricultural cultivation to the rainy season andlor prolonged to the first half of the winter season. The second crop is grown during the dry or summer season in irrigated areas. Such areas account for only about 20 percent of the total cultivated area in Thailand, leaving rest of the area fallow in the dry season.

Agricultural applications of remote sensing are time critical, and, hence, careful selection of image dates is important because land-uselland-cover class separations are usually related to plant phenology (Cibula and Nyquist, 1987). Experi- ence shows that, in the tropics, high-resolution visible and infrared satellite sensors cannot always provide the desired information due to constraints related to cloud cover and revisit schedules (Aschbacher et al., 1995). Langford and Bell (1997) also identify the problem of frequent cloud cover, as well as topographic effects and mixed pixels, in the tropical environment of Colombia. Cloud-free data are generally acquired

Photogrammetric Engineering & Remote Sensing Vol. 66, No. 3, March 2000, pp. 297-304.

School of Environment, Resources and Development, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand (apisit8ait.ac.th).

0099-111210016503-297$3.0010 O 2000 American Society for Photogrammetry

and Remote Sensing

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING March 2000 297

Sakae Krang Watershed

Figure 1. Location of the study area.

during the dry season when most of the agricultural fields are fallow. Such a situation makes the process of discriminating land uselland cover a difficult task.

Franklin (1990) mentioned that topography exerts a pow- erful influence on the spectral response observed by polar- orbiting satellites. The lack of studies on digital image classification using ancillary information in Thailand provided the main impetus for this work. The objectives of the study were to evaluate the use of elevation data in Landsat TM classification, and to produce a thematic map of land use of the Sakae Krang watershed, Thailand.

The Study Area Remote-sensing-derived land-cover information is essential for effective resource management. Because a geographical unit is considered appropriate for implementing resource management activities, we have selected the Sakae Krang watershed of Thailand as the study area (Figure 1). The underlying reason for selecting this watershed was its very poor ecological condition. Tangtham (1996) reported that this watershed has the low- est water yield, with an annual runoff of only 0.19 million cubic meters (McM)/km2 (the national average is 0.39 MCM), among the 25 major watersheds of the country.

Physical Settlngs The study area is situated from 15" 03' to 16" 05' N. latitude and 99" 07' to 100' 04' E. longitude in the central region of Thailand covering about 5,034 sq. km. The annual mean minimum and maximum temperatures in the area range from 19.5" C to 33.6" C. The mean temperature in the eastern part remains higher throughout the year than in the western part of the watershed. The rainfall pattern is uni-modal and erratic in nature, which starts in the middle of May and lasts until middle of October (Figure 2). The eastern part of the watershed gets an annual average precipitation of 950 mm, whereas the western part gets as high as 1,500 mm. The flat to gently sloping topography

extends from the east towards the west and ends up as a mountainous part covering about 20 percent of the watershed area. The elevation of the area ranges from 15 to 1,671 meters.

Land Use/Land Cover The western part of the watershed is mountainous, with slope gradients of 50 percent and greater (Plate 1). Another mountainous part of the watershed area includes small hills (less than 500 meters in elevation) of rhyollite and andesite geologi- cal material, running north to south between lower and higher terraces. The types of forest found in the area are dry evergreen (DEF), mixed deciduous (MDF), and dry Dipterocarp (DDF) species depend-ing upon the altitude.

--Annual rai&ll-1,225 m @urn 1981-1995)

Figure 2. Cropping pattern of Sakae Krang watershed, Thailand.

298 March 2000 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

Plate 1. Landsat TM image over Sakae Krang watershed, Thailand (image acquired on 21 February 1995 and displayed as BGR-2,3,4).

About 70 percent of the area is under agricultural use. The major agricultural crops of the area include paddy, sugarcane, maize, orchards, and plantation crops. Paddy is the main crop on the flood plain, river levees, and lower alluvial terraces where the soils have an "aquic" moisture regime. Upland crops, such as sugarcane, maize, and cassava, are grown mainly on eroded surfaces and higher alluvial terraces where dominant soil moisture regimes are "ustic" and "udic." The agricultural practice in the area is the rainfed agriculture (Figure 2); however, about 20 percent of the watershed area in the flood plain is irrigated where a second crop of rice is grown in the dry season. Similarly, taking a second harvest from the ratoon crop of sugarcane is found in practice in some of the upland areas. Only one crop per year is taken from the majority of upland and lowland areas. Burning of harvested fields is the usual practice to keep the fields free of field rodents during the next crop season.

The data used in the study are presented in Table 1.

DEM Generation The topographic maps of the region have a contour interval of 20 m with 10-m supplementary contours. Depending upon the problem of separating contour lines visually while digitizing, the 20- to 40-m contour lines in the mountains and 10-m contour lines in the plains were digitized, including all available spot elevations, using PC ArcIInfo. The digitization was done in point mode for two reasons: the data volume in point mode is less than in stream mode, and elevation points are used for interpolation in PC ERDAS software. In flat areas, the contour lines are separated by relatively larger distances. Hence, the areas having no contour lines in the topographic map were filled with enough digitized contours based on the adjacent contour elevations so that every grid contains enough data points, which is necessary for a regular gridding operation. The digitized coverage was imported to ERDAS for surface generation.

The regular ERDAS routines SORT and SURFACE were used to generate the surface. The first routine divides the data points into a user-defined grid of blocks, whereas the second routine processes groups of locations as blocks within a specified search radius by employing a 3 by 3 weighted moving average


Data Source Date

1. Landsat TM digital data (acquired in CCTs for Path 130 and Row 49; Thailand Remote Sensing Center, NRCT 21 Feb 1995 sun elevation and azimuth of 42.3' and 132.3', respectively)

2. Topographic maps (12 mapsheets of 1:50,000 scale, compiled by the Royal Thai Survey Dept. 1983 U.S. Army Topographic Command)

3. Soil maps (scale 1:100,000, soil series as mapping units) Land Development Dept., Thailand 1985 4. Land-use map (scale 1:250,000; visual interpretation of Landsat TM Office of Agricultural Economics, Thailand 1993

imagery) 5. Collateral information Fieldwork 1996

window. According to Burrough (1986, p. 153), for regularly spaced data, the weighted moving averages (local interpolator) in the symmetrical window are computed as

where the weights Ai are given by dd(x, xi)). A requirement is that &d) + a as d + 0, which is given by

the common1 used reciprocal or negative exponential func- Y tions d(-'), e- and e-(adzl. Perhaps the most common form of # d ) is the inverse squared distance weighting

where the xj are the points at which the surface is to be interpolated. Thus, the interpolated value at any point is weighted by a function of the distance between the available data points within the specified search radius and the value being interpolated. There are 11 different types of weighting functions available in ERDAS, each of which is suitable for different terrain situations (ERDAS, 1991). In our case, after testing several weighting functions, a smooth and noiseless 30-m grid DEM was obtained with one km of both blocking factor and search radius, and the following weighting function:

calculated distance where Q = search radius

The output DEM was evaluated by comparing the interpolated values from 107 randomly selected locations at a 1-km distance along the cross transect of the study area with the elevation values from the topographic map. A linear association was observed between the interpolated and real topographic elevation values with a multiple R2 of 0.97 and F-statistics of 9241.7 (p < 0,001).

Prefmcedng and CbMcatlon of Landsat TM The bulk format of Landsat TM data was acquired on two different computer compatible tapes ( c c ~ ) , which were down- loaded as two different scenes. Image restoration involved the correction of distortion, degradation, and noise introduced during the imaging process (Jensen, 1996). Upon visual inspection of both images, two bad lines found in the second scene were replaced by the average spectral values of the adjacent pixels. The geometric correction of individual images to the 1:50,000-scale topographic map was performed by selecting 45 ground control points (GCPS) for each image. The root-mean- square (RMS) error accepted was less than 0.65 for the first-order

and nearest-neighborhood transformations at a pixel resolution of 30 m. Then, two geometrically corrected scenes were stitched together. Satisfactory results were observed when a digitized vector boundary of the study area was superimposed on the geometrically corrected TM image.

The topographic effect is known as the spectral variation between the similar land-cover types due to solar inclination and azimuth and the terrain's degree of slope and orientation. There are several techniques suggested for topographic normalization. Of these, the band ratio transformation is the common method (Jensen, 1996). Colby (1991) compared the TM band 514 ratio with the backwards radiance correction transformation (BRCT) and reported that the latter was superior. Model- ing instantaneous solar radiation using a DEM is another common technique. This topographic normalization method can reduce radiance variance introduced by the topographic effect by as much as 69 percent (Civco, 1989).

Band ratios, NDVI, and PCA are among the standard image processing techniques (Matheson and Ringrose, 1994). In this study, 12 different feature sets, each containing original TM bands, band ratios, NDVI, PCA, and DEM (elevation), were tested with the ISODATA technique of unsupervised image classification (Table 2). The number of sets yielded by combining all the features was very large, and running the classification on all of them was not feasible. Consideration was given to selecting the feature sets which included the major combinations. For exarn- ple, the first set includes only TM original bands (2,3,4), which is the standard technique used in Thailand for visual interpretation of Landsat TM imagery for forest classification (Charup- pat, 1997). The DEM was arranged to be in variety of situations, such as with only original bands, ratio bands, and NDVI.

The ISODATA technique repeatedly performs an entire classification and recalculates statistics with minimum user inputs for locating the clusters and also is relatively simple (Tou and Gonzalez, 1974). The parameters assigned for each

TABLE 2. FEATURE SETS AND OVERALL CLASSIFICATION ACCURACY OBTAINED FROM UNSUPERVISED ISODATA CLUSTERING

Overall classification Feature Set (FS) Bands Accuracy (%)

FS-1 B2, B3, B4 58.1 2 B1, B4, B5 60.7 3 B2, B4, NDVI 60.2 4 B4, B7/B5, NDVI 64.2 5 B2/B1, B71B5, NDVI 65.3 6 B2, B4, DEM 67.1 7 B2/B1, B7/B5, DEM 66.3 8 B2, NDVI, DEM 70.4 9 B4, NDVI, DEM 71.9 10 B2/B1, B7/B5, NDVI, DEM 72.4 11 PC1, NDVI, DEM 77.5 12 B7/B5, NDVI, PC1, DEM 76.1

Note: B = Band; NDVI = (Band 4-Band 3)/(Band 4 + Band 3); PC1 = Principal Component 1

300 March 2000 PHOTOaRAMMETRIC ENalNEERlNG & REMOTE SENSING

TABLE 3. LANDCOVER CLASSES AND THEIR DESCRIPTION

Class Description - - - - -

1. Dry evergreen forest (DEF) Exists at elevations above 800 meters; Dipterocarp sp. Are generally mixed with other species, such as Bombacaceae, Sterculiaceae, and Leguminosae.

2. Mixed deciduous forest rn Exists at lower elevations and rainfall than DEF; Verbanaceae, including teak, Leguminosae, and Lagerstroemia, are dominant species.

3. Dry dipterocarp forest w Found on acid infertile sandy and lateritic soils; Deciduous species but poor richness; Gluta usitata and Pinus merkussi are dominant species.

4. Irrigated paddy Paddy grown in the lowland areas with irrigation water during DecIJan-AprIMay. 5. Sugarcane Sugarcane crop in the upland, especially the ratoon crop 6. Orchard Fruit orchards (citrus). 7. Tree vegetationlplantation Commercial teak plantation; trees along the streams. 8. Paddy (harvested) Rainfed paddy in lowland grown during JunIJul-NovIDec 9. Paddy (field burning after harvest) rn Same as above but these are the burnt fields. Field burning is usual practice to get rid of field rodents.

10. Field crops (harvested) rn Field crops (mainly sugarcane and maize with some bean fields) grown in the upland. Most of them are harvested.

11. Field crops (field burning after w Same as above but these are the burnt fields. harvest)

12. Bareland Exposed surfaces, especially eroded foothills. 13. water rn Reservoirs and rivers

feature set were kept the same, including the maximum number of clusters (40 clusters) to be formed. The clusters formed were regrouped with Ward's method of the hierarchical clustering technique designed to optimize the minimum variance within clusters (Ward, 1963). This technique calculates means for each variable within each cluster and squared Euclidean distance to the cluster means for each case (Aldenderfer and Blashfield, 1984). Resulting classes were identified as different land-cover classes on the basis of knowledge drawn from the field survey and previous land-use map.

One of the most common means of expressing classification accuracy is the construction of a confusion or error matrix (Lillesand and Kiefer, 1994), in which the relationship between the classified map and the reference test information is sum- marized (Jensen, 1996). According to the binomial probability theory, the required sample size for the classification accuracy test was 204, with an expected map accuracy of 85 percent and acceptable error of 5 percent at the probability level of 95 percent. It was not possible to verify all these test sites by conduct- ing fieldwork within limited resources. Hence, 51 samples (at 10 percent error] were again randomly selected from 204 test sites for the purpose of field verification. The rest of the sarn- ples (153) were compared individually with the existing land- use map of 1993. The classification accuracies for all the feature sets were calculated by constructing a confusion matrix based on 51 samples.

Finally, out of 1 2 feature sets tested, only one set that showed superior results in terms of land-uselland-cover discrimination with relatively higher accuracy during unsupervised classification was selected to test under supervised classification. Altogether, 13 representative land-cover classes were established from the visual inspection of the image (Table 3). At this stage, a geographical stratification approach was used to further select three to four sub-regions (group of pixels) from each class. Among the ancillary information, in addition to elevation which was used as an additional band of image. ., . soil information such as moisture regime and slope was uti- lized for selecting the training areas (Table 4). This was done by superimposi@ij the vectormaps of moisture regime [ex- tracted from the soil map) and the slope map (prepared from the DEM) onto the image. For example, the mixed class situation observed for dry evergreen forest and irrigated paddy under unsupervised classification was discriminated by training the signatures differently on the basis of elevation and moisture regime. The training areas for the irrigated rice were taken in those areas with an "aquic" moisture regime and an elevation less than 100 meters, whereas the training areas for the dry ever-

green forest were limited to areas greater than 800 meters in elevation (Santisuk et al., 1991). A similar approach was fol- lowed to select the training areas for other land-cover classes. As noted earlier, the cropping pattern of the area is basically rainfed and mono-cropping; therefore, the differentiation of agricultural land classes based solely on a crop's spectral response was not possible with the given image.

The individual classes including sub-regions, as training sites, were located on the ground with the help of a GPS during the fieldwork. It is important to note here that the fieldwork was conducted a year after data acquisition; hence, the local people were consulted for the crop's type in some of the training sites that were confusing to the authors. The training sites were re- evaluated incorporating the field information. The histograms of all the bands, except band 6 which was not included in the study, were uni-modal and almost normally distributed. The image was classified using a maximum-likelihood classifier (MLC) under supervised classification.

Results and Discussion Results of Unsupervised Classification Overall classification accuracies for the 1 2 feature sets tested with the unsupervised classification are given in Table 2. Over- all classification accuracies (error matrices not presented) for these sets ranged from 58 to 77 percent.

TABLE 4. CRITERIA OF ANCILLARY DATA FOR SELECTING TRAINING AREAS FOR TM Cln~slFlCATl0N IN THE SAKAE KRANG WATERSHED, THAILAND

Soil Elevation Slope Moisture

Class (4 (%) Regime

Dry evergreen forest Mixed deciduos forest Dry Dipterocarp Irrigated paddy Sugarcane Orchard Paddy (harvested) Paddy (burnt after harvest) Field crops (harvested) Field crops (burnt after harvest)

- - -

Aquic UsticIUdic UsticlUdic Aquic Aquic UsticlUdic UsticlUdic

Note: In Thailand, the mountainous areas are classified as slope com- plex, and no information on soil type was available for these areas in the study area.


In Feature Set 1 (FS-I), basically non-vegetated areas, such as harvested agriculture areas and mixed deciduous forests were discriminated. A mixed class situation was observed between rainfed agriculture, forest, and irrigated paddy. Band 2 was not very useful, having similar responses for all classes. Band 3 responded higher for harvested agricultural areas. Band 4, which is the near-infrared band, responded highest for vegetation, i.e., dry evergreen forest in the mountainous area and irrigated paddy in the lowland plain. In FS-2, parts of the harvested agricultural area were clearly discriminated because of high response from Band 5 , but the rest of the classes still could not be differentiated.

In FS-3, some of the harvested agricultural area was clearly discriminated but some of them were found mixed with the forest. Discrimination between dry evergreen forest, irrigated paddy, and other lowland vegetation was not possible due to similar spectral responses. In FS-4, seven land-cover classes were discriminated, unlike the five classes in the earlier sets. Waterbodies and burnt fields could be differentiated due to a higher response from the burnt areas in ratio band of 7 and 5 . But no discrimination between burnt fields from lowlands and uplands was observed. FS-5, however, gave a higher classification accuracy, but was not superior to FS-4 in class discrimination. In FS-6, the DEM significantly added to the differentiation of the forest class from other lowland vegetation; however, the other land-cover classes were found in mixed condition due to the low discrimination power of bands 2 and 4.

In FS-7, the ratio band of 7 and 5 in conjunction with the DEM helped to differentiate waterbodies, burnt areas, and harvested agricultural areas. However, there still remained some confusion between vegetation, particularly forest types, other vegetation (orchard, lowland natural vegetation), and irrigated paddy. In FS-8 and FS-9, two unique classes were discriminated due to the NDVI and DEM bands. These were scrubland with relatively no big trees and sparse forest, both in the foothills. The forest types were easily distinguishable, unlike the earlier mixed situation between dry evergreen forest and irrigated paddy. However, irrigated paddy was observed to be mixed with lowland tree vegetation, and burnt areas with the waterbody. In FS-10, the former mixed-class situation of irrigated paddy with other classes was improved due to the ratio 715. Similarly, the forest-cover types and agricultural areas, into lowland and upland, could also be easily discriminated.

In FS-11, seven land-cover classes were differentiated, including three types of forest cover; however, irrigated paddy was still found to be mixed with other lowland vegetation. No significant improvement in the classification accuracy was

observed in FS-12, except for the one additional land-cover class, than in FS-11. Some of the mixed deciduous forest was separated clearly but some of them were mixed with irrigated paddy and other lowland vegetation.

Overall, inclusion of elevation data as a feature of the set showed very promising results for satisfactorily discriminating the land covers, with relatively higher accuracy than with the TM data alone.

Results of Supervised Clascriflcation The supervised classification was run on the FS-12 containing the ratio of band 7 and 5 , NDVI, pCl, and DEM. Trisurat (1997) , in his study conducted in the Khao Yai National Park of Thailand having bio-climates similar to those of our study area, reported that the ratio 715 was useful for discriminating the forest classes. The reason for selecting the ratio of 7 and 5 was that, besides removing the topographic effect (shadowing), it also showed the discrimination between mountain vegetation and water on the visual display, which otherwise were not discriminated in both ratios 415 and 517.

Because this set yielded more classes than did the other sets during unsupervised classification, it was selected for testing in the supervised classification; however, the classification accuracy was slightly lower than in FS-11. The mixed deciduous forests, which were mis-classified either with irrigated paddy or other vegetation in the unsupervised classification, could be discriminated using ancillary information. This is because the forest occurs in the higher elevations and slopes than do the paddy and other vegetation, which occur in the plains.

All 13 classes were discriminated under the supervised condition with an overall classification accuracy of 84 percent and Khat statistics of 83 percent for the classified map (Table 5) . The higher accuracy obtained than in the unsupervised condition may be due to the increased control in defining signatures for the classification (Joria and Jorgenson, 1996). The higher confusion for class discrimination was between fruit orchards and other plantation crops, including patches of commercial purpose teak (Tectona grandis) plantations with low producer's (50 percent) and user's (33.3 percent) accuracy. Bareland and waterbodies were well discriminated. There was some confusion between the harvested and burnt fields of lowland paddy and upland field crops. The error chance of 25 percent was higher in the case of paddy fields because it was possible that these fields responded in a manner similar to that of the wet fields. Similarly, there was also confusion between the harvested fields of paddy and upland crops, but the error chance of

Reference Data User's

Land-Cover Class 1 2 3 4 5 6 7 8 9 10 11 12 13 Accuracy -

1 Dry evergreen forest 75.0 2 Mixed deciduos forest 100.0 3 Dry Dipterocarp 66.7 4 Irrigated paddy 75.0 5 Sugarcane 100.0 6 Orchard 33.3 7 Plantation/tree crops 0.0 8 Paddy (harvested) 100.0 9 Paddy (burnt after harvest) I 75.0

10 Field crops (harvested) 88.9 11 Field crops (burnt after harvest) 5 100.0 12 Bareland 3 100.0 13 Water 1 100.0

Producer's accuracy 100.0 60.0 100.0 100.0 75.0 50.0 0.0 88.9 100.0 100.0 83.3 100.0 100.0 84.31

Overall accuracy = 84.31% Khat = 82.6%

302 March ZOO0 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

1 2 percent was higher with upland fields in this case because ERDAS, 1991. ERDAS Field Guide, Second Edition, Version 7.5, of the similar responses of the harvested fields and natural ERDAS, Inc., Atlanta, Georgia, 394 p. exposed surfaces. Sugarcane and irrigated paddy were well ESCAPINOAAICIAM, 1990. Satellite Crop Monitoring: Test Program discriminated. The correctly classified pixels of the dry ever- in Thailand, Report of UNDPIUSAID-OFDA funded project, green forest and the dry dipterocarp forest were 75 and 67 per- Bangkok. cent. res~ectivelv. The reason was that the mixed deciduous Frank, T.D., 1988. Mapping Dominant Vegetation Communities in the species 6verlap h e ecological domains of both those forest Colorado Rocky ~ G u G a i n Front ~ a n g e with LANDSAT Thematic types and, hence, there is a higher chance of confusion. Mapper and Digital Terrain Data, Photograpmmetric Engineering b

Remote Sensing, 54(12):1727-1734.

Conclusions and Further Work Several factors influence the results of digital image classification. The superiority of any classification technique with respect to land-uselland-cover discrimination and classification accuracy depends upon the biophysical condition of the area being studied. This study carried out in the Sakae Water- shed area of Thailand evaluated the use of a DEM in land-use/ land-cover classification from Landsat TM data. The study has shown that spectral information alone is not sufficient for clas- sifying a TM image in the agricultural dominated landscape of Thailand. The proportion of errors in classification accuracy is not only accounted for by the discriminating capabilities of different feature sets but is also due to the scale and time of the reference information. Inclusion of elevation data as one of the bands of the image helped improve the classification results with higher accuracy. The analysis also indicated that the supervised technique is superior to the unsupervised technique. In this study, due to the unavailability of aerial photos for cross-reference, the validation of classification accuracy was constrained by limited resources for the field work; hence, a relatively higher error was accepted with few reference sites. Finally, it is concluded that there is scope to obtain better classification results by including other ancillary information and decision rules in the image classification of a tropical wet-dry landscape.

Acknowledgments We thank the anonymous referees for the critical comments on the manuscript together with appropriate suggestions which were very helpful in improving the quality of the paper. NRCT is acknowledged for providing TM data.

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