Chinese Geographical Science ›› 2021, Vol. 31 ›› Issue (5): 782-794.doi: 10.1007/s11769-021-1226-4

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A Modified Self-adaptive Method for Mapping Annual 30-m Land Use/Land Cover Using Google Earth Engine: A Case Study of Yangtze River Delta

QU Le'an1,2,3, LI Manchun1,3, CHEN Zhenjie1,3, ZHI Junjun2   

  1. 1. School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China;
    2. School of Geography and Tourism, Anhui Normal University, Anhui Province, Wuhu 241002, China;
    3. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China
  • Received:2021-01-18 Online:2021-09-20 Published:2021-09-02
  • Contact: LI Manchun;
  • Supported by:
    Under the auspices of the National Key Research and Development Program of China (No. 2017YFB0504205), National Natural Science Foundation of China (No. 41571378), Natural Science Research Project of Higher Education in Anhui Provence (No. KJ2020A0089)

Abstract: Annual Land Use/Land Cover (LULC) change information at medium spatial resolution (i.e., at 30 m) is used in applications ranging from land management to achieving sustainable development goals related to food security. However, obtaining annual LULC information over large areas and long periods is challenging due to limitations on computational capabilities, training data, and workflow design. Using the Google Earth Engine (GEE), which provides a catalog of multi-source data and a cloud-based environment, we developed a novel methodology to generate a high accuracy 30-m LULC cover map collection of the Yangtze River Delta by integrating free and public LULC products with Landsat imagery. Our major contribution is a hybrid approach that includes three major components: 1) a high-quality training dataset derived from multi-source LULC products, filtered by k-means clustering analysis; 2) a yearly 39-band stack feature space, utilizing all available Landsat data and DEM data; and 3) a self-adaptive Random Forest (RF) method, introduced for LULC classification. Experimental results show that our proposed workflow achieves an average classification accuracy of 86.33% in the entire Delta. The results demonstrate the great potential of integrating multi-source LULC products for producing LULC maps of increased reliability. In addition, as the proposed workflow is based on open source data and the GEE cloud platform, it can be used anywhere by anyone in the world.

Key words: Land Use/Land Cover (LULC), self-adaptive Random Forest (RF), Google Earth Engine (GEE), Yangtze River Delta