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China's annual forest age dataset at a 30 m spatial resolution from 1986 to 2022

文献类型：期刊论文

作者Shang, Rong2,3; Lin, Xudong2; Chen, Jing M.2,4; Liang, Yunjian2; Fang, Keyan2; Xu, Mingzhu2; Yan, Yulin2; Ju, Weimin1; Yu, Guirui5; He, Nianpeng5
刊名EARTH SYSTEM SCIENCE DATA
出版日期2025-07-04
卷号17期号:7页码:3219-3241
ISSN号1866-3508
DOI10.5194/essd-17-3219-2025
产权排序5
文献子类Article
英文摘要Forest age is crucial for both carbon cycle modeling and effective forest management. Remote sensing provides crucial data for large-scale forest age mapping, but existing products often suffer from a low spatial resolution (typically 1000 m), making them unsuitable for most forest stands in China, which are generally smaller than this threshold. Recent studies have generated static forest age products for 2019 (CAFA V1.0) (Shang et al., 2023a) and 2020 (Cheng et al., 2024) at a 30 m spatial resolution. However, their low temporal resolution limits their applicability to track multiyear forest carbon changes. This study aims to generate China's annual forest age dataset (CAFA V2.0) at a 30 m resolution from 1986 to 2022, utilizing forest disturbance monitoring and machine learning techniques. Forest disturbance monitoring, which typically has lower uncertainty compared to machine learning approaches, is primarily employed to update annual forest age. The modified COLD (mCOLD) algorithm, which incorporates spatial information and bidirectional monitoring, was used for forest disturbance monitoring. For undisturbed forests, forest age was estimated using machine learning models trained separately for different regions and forest cover types, with inputs including forest height, vegetation indices, climate, terrain, and soil data. Additionally, adjustments were made for underestimations in the Northeastern and Southwestern regions of China identified in CAFA V1.0 using additional reference age samples and region-specific and forest-type-specific models. Validation, using a randomly selected 30 % of two reference datasets, indicated that the mapped age of disturbed forest exhibited a small error of +/- 2.48 years, while the mapped age of undisturbed forest from 1986 to 2022 had a larger error of +/- 7.91 years. The generated 30 m annual forest age dataset can facilitate forest carbon cycle modeling in China, offering valuable insights for national forest management practices.
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WOS关键词TIME-SERIES ; STAND AGE ; CLASSIFICATION ; RECONSTRUCTION ; BIOMASS ; MAP
WOS研究方向Geology ; Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:001522319100001
出版者COPERNICUS GESELLSCHAFT MBH
源URL[http://ir.igsnrr.ac.cn/handle/311030/215325]  
专题生态系统网络观测与模拟院重点实验室_外文论文
通讯作者Lin, Xudong; Chen, Jing M.
作者单位1.Nanjing Univ, Int Inst Earth Syst Sci, Nanjing 210023, Peoples R China;
2.Fujian Normal Univ, Sch Geog Sci, Key Lab Humid Subtrop Ecogeog Proc, Minist Educ, Fuzhou 350117, Peoples R China;
3.Fujian Normal Univ, Acad Carbon Neutral, Fuzhou 350117, Peoples R China;
4.Univ Toronto, Dept Geog & Planning, Toronto, ON M5S 3G3, Canada;
5.Chinese Acad Sci, Key Lab Ecosyst Network Observat & Modeling, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China;
6.Int Res Ctr Big Data Sustainable Dev Goals, Beijing 100094, Peoples R China;
7.Chinese Acad Sci, State Key Lab Remote Sensing Sci, Aerosp Informat Res Inst, Beijing 100101, Peoples R China;
8.Minist Ecol & Environm Peoples Republic China, Satellite Applicat Ctr Ecol & Environm, Beijing 100094, Peoples R China;
9.Hainan Univ, Coll Ecol & Environm, Haikou 570228, Peoples R China
推荐引用方式
GB/T 7714		 Shang, Rong,Lin, Xudong,Chen, Jing M.,et al. China's annual forest age dataset at a 30 m spatial resolution from 1986 to 2022[J]. EARTH SYSTEM SCIENCE DATA,2025,17(7):3219-3241.
APA Shang, Rong.,Lin, Xudong.,Chen, Jing M..,Liang, Yunjian.,Fang, Keyan.,...&Hu, Zhongmin.(2025).China's annual forest age dataset at a 30 m spatial resolution from 1986 to 2022.EARTH SYSTEM SCIENCE DATA,17(7),3219-3241.
MLA Shang, Rong,et al."China's annual forest age dataset at a 30 m spatial resolution from 1986 to 2022".EARTH SYSTEM SCIENCE DATA 17.7(2025):3219-3241.

入库方式： OAI收割

来源：地理科学与资源研究所

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