Mapping global forest age from forest inventories, biomass and climate data

森林砍伐(计算机科学) 亚马逊雨林 环境科学 生物量(生态学) 森林动态 老林 气候变化 次生林 森林资源清查 高林 林地 自然地理学 地理 林业 农林复合经营 森林经营 生态学 生物 程序设计语言 生物化学 计算机科学 梅德林
作者
Simon Besnard,Sujan Koirala,Maurizio Santoro,Ulrich Weber,Jacob A. Nelson,Jonas Gütter,Bruno Hérault,Justin Kassi,Anny Estelle N’Guessan,C. S. R. Neigh,Benjamin Poulter,Tao Zhang,Nuno Carvalhais
出处
期刊:Earth System Science Data [Copernicus Publications]
卷期号:13 (10): 4881-4896 被引量:107
标识
DOI:10.5194/essd-13-4881-2021
摘要

Abstract. Forest age can determine the capacity of a forest to uptake carbon from the atmosphere. However, a lack of global diagnostics that reflect the forest stage and associated disturbance regimes hampers the quantification of age-related differences in forest carbon dynamics. This study provides a new global distribution of forest age circa 2010, estimated using a machine learning approach trained with more than 40 000 plots using forest inventory, biomass and climate data. First, an evaluation against the plot-level measurements of forest age reveals that the data-driven method has a relatively good predictive capacity of classifying old-growth vs. non-old-growth (precision = 0.81 and 0.99 for old-growth and non-old-growth, respectively) forests and estimating corresponding forest age estimates (NSE = 0.6 – Nash–Sutcliffe efficiency – and RMSE = 50 years – root-mean-square error). However, there are systematic biases of overestimation in young- and underestimation in old-forest stands, respectively. Globally, we find a large variability in forest age with the old-growth forests in the tropical regions of Amazon and Congo, young forests in China, and intermediate stands in Europe. Furthermore, we find that the regions with high rates of deforestation or forest degradation (e.g. the arc of deforestation in the Amazon) are composed mainly of younger stands. Assessment of forest age in the climate space shows that the old forests are either in cold and dry regions or warm and wet regions, while young–intermediate forests span a large climatic gradient. Finally, comparing the presented forest age estimates with a series of regional products reveals differences rooted in different approaches and different in situ observations and global-scale products. Despite showing robustness in cross-validation results, additional methodological insights on further developments should as much as possible harmonize data across the different approaches. The forest age dataset presented here provides additional insights into the global distribution of forest age to better understand the global dynamics in the forest water and carbon cycles. The forest age datasets are openly available at https://doi.org/10.17871/ForestAgeBGI.2021 (Besnard et al., 2021).
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