Land-use classification applicable to rural landscapes of Southeast Asia using very high spatial resolution satellite imagery

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Monitoring land use/cover changes in humid tropical agricultural landscapes is crucial to establish sustainable rural developments. However, the characteristic spatio-temporal complexity of mosaic landscapes makes it difficult to obtain accurate land use/cover maps using single-dated and moderate-resolution remotely sensed images. In this study, we attempted to classify land use/cover by utilizing texture measures to improve object-oriented classification based on a single-dated QuickBird image. GLCM texture measures, especially entropy, improved classification accuracy in delineating paddy rice fields irrespective of the stage of rice growth. The classification rule set derived from the CART modelling was intuitively understandable: a whole image was divided into green or non-green texturally homogeneous or heterogeneous land use/cover classes, which seems to describe the fundamental nature of the characteristics of various land use/covers of agricultural landscapes.