Determination of Land Use Change Categories Using Classification of Multitemporal Satellite Image Data
Classification
Each of there data sets was classified separately to overcome the discrepancy of spectral variation by the data acquisition time. Since the vegetation condition at three different times (April, October, and May) might be different, it was difficult to apply an identical classification scheme to all there data sets. Each data sets was classified using an unsupervised clustering algorithm. Because there were lack of reference data to be used for delineating training fields, it was difficult to apply supervised classification procedure. By careful examination of the spectral and spatial pattern of each cluster, every cluster was assigned into on of six land cover classes (water, forest, rice, field, non-irrigated crop, urban and bare soil). The accuracy of each classified map was separately determine by using test pixels that were carefully selected throughout the study area and evenly distributed for each of six cover types. For each data set, about 1,700 test pixels were used to determine the classification accuracy.
Determination of change categories
After the classification of there data sets, each pixel unit has three lands cover class values for the year of 1973,1984 and 1993. Combing these three classified land cover maps, the temporal variation of change and no-change of land cover could be described. Each pixel can have 216 possible combinations of change and no-change categories. There would be no change where the class values in all three maps were identical. Except those six cases where the class value were the same, all other combinations of three class values represent land cover changes from 1973 to 1993. However, such assumption is only valid for the case where the classification of each data set is entirely correct, which is certainly impossible in real situations.
Determination of meaningful change categories was conducted by the evaluating the three time-sequential cover types and classification accuracy (figure1). Every classified pixel has accuracy for a particular cover type at one three years. Since the classification accuracy of 1973 is statistically independent from the ones of 1984 and 1994, the product of all three accuracies can b considered as accuracy of the change category C
ijk.
P(Cijk)=P(C1iÇC2iÇC3k) = P(C1i). P(C2j). P(C3k),
Where C
ijk is a change category where the cover type was I, j, and k for the years of 1973, 1994, and 193, respectively. The change category C
ijk would be considered and re-examined by visual analysis if P(C
ijk ) value was too low.
Figure 1. Data analysis procedure to determine change categories.
Some of the change categories, such as 'rice -forest-crop' in three years, are undoubtedly unreasonable in ordinary land use practice. Once each one of 216 change and no-change categories was carefully analyzed by using the classification accuracy and the visual interpretation on image display, primary change categories were determined. Major change categories include the conversions of forest to agriculture, bare soil, and urban, the conversion of agriculture to bare soil and urban, the reclamation of tidal flat to cropland, and the inundation by newly built reservoir.
