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1Rapid Urbanization Induced Extensive Forest Loss to Urban Land in the Guangdong-Hong Kong-Macao Greater Bay Area, China显示文摘China has experienced rapid urbanizations with dramatic land cover changes since 1978. Forest loss is one of land cover changes, and it induces various eco-environmental degradation issues. As one of China’s hotspot regions, the Guangdong-Hong KongMacao Greater Bay Area(GBA) has undergone a dramatic urban expansion. To better understand forest dynamics and protect forest ecosystem, revealing the processes, patterns and underlying drivers of forest loss is essential. This study focused on the spatiotemporal evolution and potential driving factors of forest loss in the GBA at regional and city level. The Landsat time-series images from 1987 to2017 were used to derive forest, and landscape metrics and geographic information system(GIS) were applied to implement further spatial analysis. The results showed that: 1) 14.86% of the total urban growth area of the GBA was obtained from the forest loss in1987–2017;meanwhile, the forest loss area of the GBA reached 4040.6 km2, of which 25.60%(1034.42 km2) was converted to urban land;2) the percentages of forest loss to urban land in Dongguan(19.14%), Guangzhou(18.35%) and Shenzhen(15.81%) were higher than those in other cities;3) the forest became increasingly fragmented from 1987–2007, and then the fragmentation decreased from2007 to 2017);4) the landscape responses to forest changes varied with the scale;and 5) some forest loss to urban regions moved from low-elevation and gentle-slope terrains to higher-elevation and steep-slope terrains over time, especially in Shenzhen and Hong Kong.Urbanization and industrialization greatly drove forest loss and fragmentation, and, notably, hillside urban land expansion may have contributed to hillside forest loss. The findings will help policy makers in maintaining the stability of forest ecosystems, and provide some new insights into forest management and conservation.YANG Chao LIU Huizeng LI Qingquan CUI Aihong XIA Rongling SHI Tiezhu ZHANG Jie GAO Wenxiu ZHOU Xiang WU Guofeng 2021Chinese Geographical Science2021,31,1:3
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