Abstract
Urban Land Use/Land Cover (LULC) information is essential for urban and environmental management. It is, however, very difficult to automatically extract detailed urban LULC information from remote sensing imagery, especially for a large urban area. Medium resolution imagery, such as Landsat Thematic Mapper (TM) data, cannot uncover detailed LULC information. Further, very high resolution (VHR) satellite imagery, such as IKONOS and QuickBird data, can only be applied to a small area, largely due to the data unavailability and high computation cost. As a result, little research has been conducted to extract detailed urban LULC information for a large urban area. This study, therefore, developed a three-layer classification scheme for deriving detailedurban LULC information by integrating newly launched Chinese GF-1 (medium resolution) and GF-2 (very high resolution) satellite imagery and synthetically incorporating geometry, texture, and spectral information through multi-resolution image segmentation and object-based image classification (OBIA). Homogeneous urban LULC types such as water bodies or large areas of vegetation could be derived from GF-1 imagery with 16 m and 8 m spatial resolutions, while heterogeneous urban LULC types such as industrial buildings, residential buildings, and roads could be extracted from GF-2 imagery with 3.2 m and 0.8 m spatial resolutions. The multi-resolution segmentation method and a random forest algorithm were employed to perform image segmentation and object-based image classification, respectively. An analysis of the results suggests an overall accuracy of 0.89 and 0.87 were achieved for the second and third level urban LULC classification maps, respectively. Therefore, the three-layer classification scheme has the potential to derive high accuracy urban LULC information through integrating medium and high-resolution remote sensing imagery.
Highlights
Land Use/Land Cover (LULC) is defined as the physical composition and characteristics or human-related activities of land elements on the Earth’s surface [1]
Based on the visual analysis and interpretation of GF-2 and GF-1 false color images and the prior knowledge of this region, we identified and labelled 21 urban LULC types (Table 3), including two types of water bodies, two types of vegetation, one type of farmland, two types of bare lands, two types of roads and squares, five types of industrial buildings, and seven types of residential buildings, as well as shadow
A portion of third layer of the urban LULC map was zoomed in, and visualized compared with the GF-2 image (Figure 10). It indicates a good consistency exists between the classified urban LULC types and the GF-2 image
Summary
Land Use/Land Cover (LULC) is defined as the physical composition and characteristics (e.g., grass, forest, and impervious surfaces) or human-related activities (e.g., residential, commercial, and Sensors 2019, 19, 3120; doi:10.3390/s19143120 www.mdpi.com/journal/sensorsSensors 2019, 19, 3120 transportation) of land elements on the Earth’s surface [1]. The Climate Research Committee of the National Council stressed that the distribution of LULCs has a pronounced impact on Earth’s radiation balancing, since any changes in LULC would affect evaporation, transpiration, and heat flux on the ground surface [2]. It is important for scientists and practitioners to understand. National and international agencies have successfully created no less than ten global scale LULC datasets with spatial resolutions of 1 km, 500 m, 300 m, 30 m, and 12 m These existing LULC datasets provide basic geographic information for studying climate, hydrology, environment, ecology, and urban regions [5,6]. Four major urban LULC classes in the National Land Cover Database (NLCD)
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