To assess the status and change trend of forest in China,an indicator framework was developed using SDG sub-indicators.In this paper,we propose an improved methodology and a set of workflows for calculating SDG indica...To assess the status and change trend of forest in China,an indicator framework was developed using SDG sub-indicators.In this paper,we propose an improved methodology and a set of workflows for calculating SDG indicators.The main modification include the use of moderate and high spatial resolution satellite data,as well as state-of-the-art machine learning techniques for forest cover classification and estimation of forest above-ground biomass(AGB).This research employs GF-1 and GF-2 data with enhanced texture information to map forest cover,while time series Landsat data is used to estimate forest AGB across the whole territory of China.The study calculate two SDG sub-indicators:SDG_(15.1.1) for forest area and SDG_(15.2.1) for sustainable forest management.The evaluation results showed that the total forest area in China was approximately 219 million hectares at the end of 2021,accounting for about 23.51%of the land area.The average annual forest AGB from 2015 to 2021 was estimated to be 105.01Mg/ha,and the overall trend of forest AGB change in China was positive,albeit with some spatial differences.展开更多
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA 19090300,XDA 19090124)the National Natural Science Foundation of China(61731022)+1 种基金the Second Tibetan Plateau Scientific Expedition and Research Program(2019QZKK030701)Chinese Academy of Sciences Net-work Security and Informatization Special Project(CAS-WX2021PY-0107-01).
文摘To assess the status and change trend of forest in China,an indicator framework was developed using SDG sub-indicators.In this paper,we propose an improved methodology and a set of workflows for calculating SDG indicators.The main modification include the use of moderate and high spatial resolution satellite data,as well as state-of-the-art machine learning techniques for forest cover classification and estimation of forest above-ground biomass(AGB).This research employs GF-1 and GF-2 data with enhanced texture information to map forest cover,while time series Landsat data is used to estimate forest AGB across the whole territory of China.The study calculate two SDG sub-indicators:SDG_(15.1.1) for forest area and SDG_(15.2.1) for sustainable forest management.The evaluation results showed that the total forest area in China was approximately 219 million hectares at the end of 2021,accounting for about 23.51%of the land area.The average annual forest AGB from 2015 to 2021 was estimated to be 105.01Mg/ha,and the overall trend of forest AGB change in China was positive,albeit with some spatial differences.
