Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Yuling Chen
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Tianyu Xiang
College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
Haitao Yang
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Weiyan Liu
State Forestry and Grassland Administration Key Laboratory of Forest Resources & Environmental Management, Beijing Forestry University, Beijing 100083, China
Yu Ren
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Institute of Ecology, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
Hongcan Guan
School of Tropical Agriculture and Forestry, Hainan University, Haikou 570100, China
Tianyu Hu
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
Qin Ma
School of Geography, Nanjing Normal University, Nanjing 210023, China
To quantify forest carbon stock and its future potential accurately, we generated a 30 m resolution forest age map for China in 2020 using multisource remote sensing datasets based on machine learning and time series analysis approaches. Validation with independent field samples indicated that the mapped forest age had an R2 of 0.51--0.63. Nationally, the average forest age is 56.1 years (standard deviation of 32.7 years).
To quantify forest carbon stock and its future potential accurately, we generated a 30 m...