Traditional spectral-based methods of extracting urban land cover and land use information from remote sensing imagery have proven to be unsuitable for high spatial resolution images. Texture has been widely investigated as a supplement to spectral data for the analysis of complex urban scenes. This research evaluates the grey level co-occurrence matrix (GLCM) texture analysis technique and the maximum likelihood classification approach for the extraction of texture features to be combined with spectral data, as a method for obtaining more accurate urban land cover and land use information from high spatial resolution images. Classifications were performed on IKONOS imagery using three datasets: a spatial dataset consisting of three texture images (mean, homogeneity and dissimilarity), a spectral dataset consisting of four spectral images (red, green, blue and NIR) and a combination dataset (spatial and spectral). Results show that the combination dataset produced the highest overall classification accuracy of 86.1%, an improvement of 7.2% over the spectral dataset.
This study advocates the use of GIS and remote sensing technologies to establish urban evolution maps and assess the impact of urbanization on agricultural areas over the last three decades. The target area is the city of Béni-Mellal, located in central Morocco. The methodology adopted makes use of panchromatic SPOT images to survey the urban areas during the 1980s and 1990s. Available topographic maps provided the information for the 1970s. Maps and statistics of land use and urban growth for Béni Mellal were established after manually classifying images on a perpolygon basis and digitizing topographic maps using GIS capabilities. The results show an increase in dense urban area by 980.7 ha from the 1970s to the 1990s. This increase occurred at the expense of forests (24.7 ha), plantations (752.3 ha), rangeland (113.4 ha), non-irrigated land (69.7 ha), and irrigated land (20.6 ha). During this period, scattered urban areas, predominantly suburbs, increased by 755.9 ha to the detriment of forests (14.9 ha), plantations (109.8 ha), rangeland (138.9 ha), non-irrigated land(400.5 ha), and irrigated land (91.9 ha). These cartographic and statistic results are efficient decision-making tools for protecting agricultural land and planning urban and suburban areas.
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