Remote sensing and land resource surveys have been used in recent decades for land use/land cover(LULC)mapping;however,keeping the developed LULC up-to-date and consistent with land survey statistics remains challengi...Remote sensing and land resource surveys have been used in recent decades for land use/land cover(LULC)mapping;however,keeping the developed LULC up-to-date and consistent with land survey statistics remains challenging.This study developed a practical and effective framework to automatically update existing LULC products and bridge the gap between remote sensing classification results and land survey data.This study employed Landsat imagery time series,change detection algorithms,sample migration,and random forests to develop a framework for updating existing LULC products in China from 1980–2015 to 1980–2022.The updated LULC maps reflect the post-2015 LULC changes well and maintain continuity with the pre-2015 products.Additionally,a statistical space allocation method based on the minimum cross-entropy strategy was proposed to optimize the LULC maps,increasing the correlation coefficient(r)with China’s second and third national land survey statistics from 0.41–0.89 to 0.86–0.99.Thus,the framework and products developed in this study provide valuable tools for sustainable land use and policy planning.展开更多
基金supported by Fundamental National Key R&D Program of China(grant number 2019YFA0606601)Tsinghua University Initiative Scientific Research Program(grantnumber 20223080017)+1 种基金National Natural Science Foundation of China(grant number 42201367)Fundamental ResearchFunds for the Central Universities(grant number DUT23RC(3)064.
文摘Remote sensing and land resource surveys have been used in recent decades for land use/land cover(LULC)mapping;however,keeping the developed LULC up-to-date and consistent with land survey statistics remains challenging.This study developed a practical and effective framework to automatically update existing LULC products and bridge the gap between remote sensing classification results and land survey data.This study employed Landsat imagery time series,change detection algorithms,sample migration,and random forests to develop a framework for updating existing LULC products in China from 1980–2015 to 1980–2022.The updated LULC maps reflect the post-2015 LULC changes well and maintain continuity with the pre-2015 products.Additionally,a statistical space allocation method based on the minimum cross-entropy strategy was proposed to optimize the LULC maps,increasing the correlation coefficient(r)with China’s second and third national land survey statistics from 0.41–0.89 to 0.86–0.99.Thus,the framework and products developed in this study provide valuable tools for sustainable land use and policy planning.
