森林资源清查
森林生态学
植被(病理学)
代理(统计)
森林经营
森林动态
每年落叶的
温带落叶林
温带森林
固碳
环境科学
林业
自然地理学
生态系统
地理
生态学
统计
病理
生物
医学
二氧化碳
数学
作者
Kai Cheng,Yuling Chen,Tianyu Xiang,Haitao Yang,Weiyan Liu,Yu Ren,Hongcan Guan,Tianyu Hu,Qin Ma,Qinghua Guo
标识
DOI:10.5194/essd-16-803-2024
摘要
Abstract. A high-resolution, spatially explicit forest age map is essential for quantifying forest carbon stocks and carbon sequestration potential. Prior attempts to estimate forest age on a national scale in China have been limited by sparse resolution and incomplete coverage of forest ecosystems, attributed to complex species composition, extensive forest areas, insufficient field measurements, and inadequate methods. To address these challenges, we developed a framework that combines machine learning algorithms (MLAs) and remote sensing time series analysis for estimating the age of China's forests. Initially, we identify and develop the optimal MLAs for forest age estimation across various vegetation divisions based on forest height, climate, terrain, soil, and forest-age field measurements, utilizing these MLAs to ascertain forest age information. Subsequently, we apply the LandTrendr time series analysis to detect forest disturbances from 1985 to 2020, with the time since the last disturbance serving as a proxy for forest age. Ultimately, the forest age data derived from LandTrendr are integrated with the result of MLAs to produce the 2020 forest age map of China. Validation against independent field plots yielded an R2 ranging from 0.51 to 0.63. On a national scale, the average forest age is 56.1 years (standard deviation of 32.7 years). The Qinghai–Tibet Plateau alpine vegetation zone possesses the oldest forest with an average of 138.0 years, whereas the forest in the warm temperate deciduous-broadleaf forest vegetation zone averages only 28.5 years. This 30 m-resolution forest age map offers crucial insights for comprehensively understanding the ecological benefits of China's forests and to sustainably manage China's forest resources. The map is available at https://doi.org/10.5281/zenodo.8354262 (Cheng et al., 2023a).
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