| Forest surface area is a fundamental input for forest-related research,such as carbon balance,biodiversity conservation,and ecosystem functioning and services.Forestland is not only a kind of carbon source but also carbon sink,its spatial changes,such as the increase and decrease of area and the change of types,will have a great impact on the ecosystem and human society.Therefore,it is of great practical and strategic significance to realize accurate statistics of forest land resources.However,an accurate assessment of the area of forestland in China is not available because the forested area is usually calculated as a 2D projected area rather than a 3D surface area,and the impact of changes in the surface terrain on the area is ignored.In this study,we attempted to solve these problems based on the above studies.Considering the complex terrain environment,forest fragmentation distribution characteristics,and objective realities of large amounts of data,we overlay DEM data with remote sensing data,use the mean change point and system clustering analysis methods to partition the national-scale region,identify suitable surface area algorithms for every type of geomorphic area,define geomorphic dominance and combine it with a surface area calculation algorithm,and select the appropriate combination for every region.The results show that 1)China’s surface area is 2,269,026.76 km~2,approximately5.48%larger than the conventional estimates;2)the integrated multiscale method exhibits good adaptability and high precision for large-scale surface area calculations;and 3)the calculation results of this method are superior to those of remote sensing data or single surface area algorithms,and the calculation efficiency is high.This study achieves highly efficient and accurate statistics for forestland areas in large areas,and provides a theoretical basis for more reasonable carbon emission statistics and climate change research. |
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