Land Use Mapping of the Guangdong-Hong Kong Macao Greater Bay Area Based on a New Approach at 30 m Resolution for the Years 1976 to 2020

作     者：Gu, Yu Chen, Yangbo Liu, Jun 

作者机构：Sun Yat Sen Univ Sch Geog & Planning Guangzhou 510275 Peoples R China 

出 版 物：《IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING》 (IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.)

年 卷 期：2025年第18卷

页      面：3943-3958页

核心收录：

学科分类：0808[工学-电气工程] 1002[医学-临床医学] 08[工学] 0705[理学-地理学] 0816[工学-测绘科学与技术] 

基　　金：Natural Science Foundation of China [U2243227] Science and Technology Program of Guangdong Province [2020B1515120079] 

主　　题：Urban areas Remote sensing Landsat Land surface Spatial resolution Earth Sensors Classification algorithms Satellite images Planning 30-meter resolution annual land use change GBA landsat satellite imagery remote sensing 

摘      要：Multicategory land use data of high spatiotemporal resolution and large scale are crucial for studying regional ecological and environmental changes and urbanization impacts as well as for sustainable development planning. Currently available public data products include those of high spatial resolution global land use temporally limited to a single or short period, or global annual land cover products in which only a single land use type is depicted, such that regional characteristics are overlooked. In either case, fine-scale annual variation over longer time spans may not be reflected. In this study, the Google Earth Engine platform, Landsat satellite imagery, and a substantial number of manually interpreted samples were used to develop a dataset of annual land use changes in the Guangdong-Hong Kong Macao Greater Bay Area (GBA) at a 30 m resolution for the years 1976 to 2020. This dataset, termed Annual Land Use/Cover of the Greater Bay Area (LUC-GBA), was used to analyze the annual land use variation in 11 cities within the GBA. The high level of accuracy achieved with the LUC-GBA dataset was evidenced by an overall accuracy (OA) of 93.9% in 2020. The OA of interannual classification models ranged from 83.9% to 93.9%, and the kappa coefficients from 0.805 to 0.923. These results indicate that the LUC-GBA dataset effectively reflects the surface cover distribution and interannual dynamic evolution of the land area in the GBA at a 30 m spatial resolution, thus providing reliable data support for land surface process research and related applications.