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Poster Session 1
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Land cover Mapping by Combining Satellite Estimated NDVI and Surface Temperature
Results and Discussion
The classification result ( fig. 1) based on NDVI shows some misidentification. Some agricultural area and orchard area were identified as forest with I Thailand. For example, in southern part of Thailand is partly cultivated with orchard according to land use map., but this intensive cropping is practiced but is misclassified as forest. Therefore, by using NDVI alone it is difficult to separate some active agricultural area from forest.
Fig 1. Classification results by NDVI
The classification result ( fig.2) obtained by using the ration of surface temperature to NDVI, which was used for African biome calcification ( Lambin , 19960, shows prominent separation of forest fro active agricultural area. Orchard area was separated from forest and some active agricultural area near Bangkok also separated from forest. But in Mekong delta , intensive agricultural area still shows some mis-classification. But the area misclassified as forests is smaller than the area misclassified as forest earlier by using NDVI alone.
Fig 2. Classification results by Ts/NDVI ratio
In order to reduce the misclassification we computed the squared value of surface temperature and the ration of squared surface temperature and NDVI was used as a feature for classification. The classification result ( Fig 3) obtained by using squared surface temperature and NDVI ratio shows improvement in separating forest form active agricultural are. In southern part of Thailand , forest is separated well than the other method . like wise most part of the Mekong delta agricultural area also separated from forest. But some misidentification is still observed in the this area. The misclassified area of agricultural area as forest in Mekong delta is small when compared to other methods. Thus this method shows a distinct improvement in differentiating forest from intensive agricultural area then the other methods employed in this study.
Fig 3. Classification results by Ts2/NDVI ratio
Reference:
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Goward, S.N., Cruickshanks, G.D., and Hope, A.S>, 1985. Observed relation between thermal emission and reflected spectal radiance of a complex vegetated landscape. Remote sensing of environment =, 18, 137-147.
-
Lambin, E.F., and STRAHLER, A.H., 1994a. Change-vector analysis; a tool to detect and categorize land-cover change processes using high temporal-resolution satellite data. Remote sensing Environment, 48, 231-244.
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Lambin, E.F., and Rhrlich, D., 1996. the surface temperature vegetation index space for land cover and land cover change analysis. Int .j.Remote sensing, 17, 463-487.
-
Price, j.C., 1990. using spatial context in satellite data to infer regional scale evapotranspiration. I.E.E.E Transaction in Geoscience and Remote sensing 28, 940-948.
-
Price, J.C., 1984. land surface temperature measurements from split window channels of NOAA-7 advance very high resolution radiometer. J. of Geophysical Res. 89, D5:7231-7237.
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Price, J.C.1983. Estimating surface temperature form satellite thermal infrared data-a simple formulation for atmospheric effect. Remote sensing of Environment 13:353 -361
-
Rosenberg, N.J., Blad, B.L., and Verma, S.B., 1983. Microclimate ; The biological environment ( New York ; John Wiley & sons. ).
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