Mapping the Time-Series of Essential Urban Land Use Categories in China: A Multi-Source Data Integration Approach

被引:0
|
作者
Tian, Tian [1 ]
Yu, Le [1 ,2 ,3 ]
Tu, Ying [4 ]
Chen, Bin [5 ,6 ,7 ]
Gong, Peng [2 ,6 ,8 ]
机构
[1] Tsinghua Univ, Inst Global Change Studies, Dept Earth Syst Sci, Minist Educ Key Lab Earth Syst Modeling, Beijing 100084, Peoples R China
[2] Tsinghua Univ, Dept Earth Syst Sci, Minist Educ, Ecol Field Stn East Asian Migratory Birds, Beijing 100084, Peoples R China
[3] Tsinghua Univ, Xian Inst Surveying, Mapping Joint Res Ctr Next Generat Smart Mapping, Dept Earth Syst Sci, Beijing 100084, Peoples R China
[4] Cornell Univ, Dept Global Dev, Ithaca, NY 14850 USA
[5] Univ Hong Kong, Fac Architecture, Div Landscape Architecture, Future Urban & Sustainable Environm FUSE Lab, Hong Kong 999077, Peoples R China
[6] Univ Hong Kong, Urban Syst Inst, Hong Kong 999077, Peoples R China
[7] Univ Hong Kong, Musketeers Fdn Inst Data Sci, Hong Kong 999077, Peoples R China
[8] Univ Hong Kong, Dept Geog, Hong Kong 999077, Peoples R China
基金
中国国家自然科学基金;
关键词
urban land use; random forest; mapping; local climate zone; image segmentation; point of interest; LOCAL CLIMATE ZONES; POINTS-OF-INTEREST; COVER CLASSIFICATION; SPATIAL-RESOLUTION; NIGHTTIME LIGHT; REAL-TIME; FROM-GLC; CITY; SATELLITE; IMAGERY;
D O I
10.3390/rs16173125
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Accurate, detailed, and long-term urban land use mapping is crucial for urban planning, environmental assessment, and health evaluation. Despite previous efforts, mapping essential urban land use categories (EULUCs) across multiple periods remains challenging, primarily due to the scarcity of enduring consistent socio-geographical data, such as the widely used Point of Interest (POI) data. Addressing this issue, this study presents an experimental method for mapping the time-series of EULUCs in Dalian city, China, utilizing Local Climate Zone (LCZ) data as a substitute for POI data. Leveraging multi-source geospatial big data and the random forest classifier, we delineate urban land use distributions at the parcel level for the years 2000, 2005, 2010, 2015, 2018, and 2020. The results demonstrate that the generated EULUC maps achieve promising classification performance, with an overall accuracy of 78% for Level 1 and 71% for Level 2 categories. Features derived from nighttime light data, LCZ, Sentinel-2 satellite imagery, and topographic data play leading roles in our land use classification process. The importance of LCZ data is second only to nighttime light data, achieving comparable classification accuracy to that when using POI data. Our subsequent correlation analysis reveals a significant correlation between POI and LCZ data (p = 0.4), which validates the rationale of the proposed framework. These findings offer valuable insights for long-term urban land use mapping, which can facilitate effective urban planning and resource management in the near future.
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收藏
页数:23
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