Urban land use classification based on remote sensing and multi-source geographic data
WU Linlin1,2(), LI Xiaoyan1(), MAO Dehua2, WANG Zongming2
1. College of Earth Sciences, Jilin University, Changchun 130012, China 2. Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Urban land use (ULU) reflects urban functions and structures, and the study of ULU classification can provide guidance for the sustainable development of cities. This study conducted the ULU classification of the main urban area of Harbin City using the object-oriented and random forest methods by integrating multi-source geospatial data including Sentinel-2A remote sensing images, OpenStreetMap (OSM) data, point of interest (POI) data, and nighttime light data from the Luojia-1 satellite. The results are as follows. The overall accuracy of the first-level land use type was 86.0%, with a Kappa coefficient of 0.75. The overall accuracy of the second-level land use types was 73.9%, with a Kappa coefficient of 0.69. The introduction of POI data can significantly improve the classification accuracy of residential land, industrial and mining storage land, and educational land. Meanwhile, night light data can effectively improve the classification accuracy of commercial office land and business land. This study shows that the combination of remote sensing images with multi-source geographic data is effective for ULU classification.
吴琳琳, 李晓燕, 毛德华, 王宗明. 基于遥感和多源地理数据的城市土地利用分类[J]. 自然资源遥感, 2022, 34(1): 127-134.
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2022, Vol. 34 
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