Improved assessment of post-fire recovery trajectory of forests in Amazon's protected areas

Abstract

Protected areas (PAs) in Amazon forests are vital in preserving tropical forest ecosystems and mitigating forest degradation. However, the increasing frequency and severity of fires in these regions necessitate a comprehensive understanding of post-fire vegetation recovery trajectories, which is essential to evaluate the effectiveness and resilience of PAs in the face of ongoing climate change. Recovery trajectories under natural conditions remain uncertain, as unregulated human settlements often interfere with or influence the recovery process, skewing the actual recovery rates detected by satellite remote sensing. To tackle this issue, we examined 2990 MODIS-derived fire events in eastern Amazon PAs from 2001 to 2020. We assessed the effectiveness of multi-source Earth observation data and the eXtreme Gradient Boost machine learning model to distinguish burned areas undergoing natural recovery (natural recovery areas) from areas that are permanently converted to other uses (permanently converted areas). We then analyzed greenness recovery rates and canopy structure recovery trajectories across all burned areas, natural recovery areas, and permanently converted areas. Greenness recovery rates were derived from Landsat data, while canopy structure recovery was assessed using GEDI lidar-derived metrics and the space-for-time substitution approach. Our model achieved an overall classification accuracy of 86.53 %, accurately differentiating natural recovery areas (n = 1942) from permanently converted areas (n = 1048). The differing patterns of post-fire greenness recovery rates and structure recovery trajectories highlight the importance of this distinction. In natural recovery areas, significant recovery of structural traits such as relative heights (RHs), canopy cover (CC), and plant area index (PAIs), was observed, returning to their pre-disturbance levels over a 20-year period. Notably, metrics related to understory recovery and plant vertical space use, such as PAI values across the entire vertical strata, exhibited stronger recovery rates than height-related metrics like RHs, highlighting their utility in characterizing complex ecosystem recovery processes. These findings demonstrate the potential and necessity of using multi-source Earth observation data to distinguish different post-fire vegetation recovery processes. This distinction improves our understanding of ecological recovery rates and the successional dynamics of post-fire forests under natural conditions, offering new opportunities to explore their biogeographical distribution, recovery rate variabilities, and impacts on carbon sequestration and ecosystem resilience.

Plant phenotyping relevance

衛星・LiDARデータと機械学習を統合し、森林キャノピーの構造形質や緑度の回復軌跡を大規模に推定・評価しており、植物状態の取得・抽出が研究の中心です。

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