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Land Use Dynamics from Multi-temporal Remotely Sensed Data: A Case Study Northern Thailand
2) Visual interpretation of the rectified aerial photos
The main disadvantage of relying on aerial photography in Thailand is the very low time resolution as sometimes there may be a gap of over five years between photos. For northern Thailand the most recent data set is from 1996, and the previous data set was from 1989. Thus rapid changes in land use are very difficult to study using this method. However, 8 similar land use types: paddy, orchard, field crop, disturbed forest, dense forest, village, construction area of Song reservoir and water body have been categorised for the photographs from the three dates. The aerial photographs were interpreted using three main criteria, grey scale, textural and proximity factors, grey scale relates to colour variations. In the upland areas dark grey areas with a smooth texture that tended to be located far from cultivated areas were classified as dense forest. Lighter grey areas with a rough texture that tended to be located close to cultivated areas were classified as disturbed forest. Light grey areas with a smooth texture that tended to be situated along the valleys were classified as field crops. All three classified data are converted to digital data by a scanner and re-rectified to UTM coordinates. The results were verified during the second ground truth campaign in May 2001 (figure 2)  Figure 2 Aerial photo interpretation in 1989, 1991 and 1996 3) Satellite image classification The maximum likelihood classification is selected to distinguish land cover/land use. As the satellite data were acquired within different seasons and from different platforms and having different resolution. The Landsat MSS, 80 meter resolution, was acquired in on February 1, 1977, the Landsat TM, 30 meter resolution, on January 11, 1989 and the Landsat ETM+, 30 meter resolution, on March 14, 2000. These three months have different vegetation conditions according to the climate. In Northern Thailand, January is the month of cool season where moisture in soil and forest still kept after rainy season. February is an intermediate month between the cool season and summer where moisture becomes to decrease. And March is the beginning of summer where moisture is lesser than the two previous months. In addition, according to farmers’ practice, since the end of February, they start to prepare their fields by burning residual vegetation, and some collect wild vegetation that give new leaves after being burnt. As consequence, there is a lot of haze in the 2000 Landsat ETM+ from field burning and forest fires. It is difficult to distinguish burnt agricultural fields from burnt forestlands as they become similarly burnt bare soil. These factors give effects on vegetation status causing reflectance values of land use quite difficult to compare. However possible similar nomenclatures are set up based on physical characteristics of land use despite different number of land use types. There are 13 classes for MSS, 15 classes for TM-5 and 17 classes for ETM. The classified images were regrouped into 6 major classes for Landsat MSS and Landsat TM-5 and into 7 major classes for Landsat ETM+. (figure 3) Despite the different vegetation status among the three satellite images and a lot of confusion among several land use types from one image to the others, we can identify land use dynamics of the study area well comparable with the results from the interpretation of aerial photos  Figure 3 The results of image classification |