用1.5米分辨率卫星数据，精准捕捉单棵树级森林扰动，发现小规模破坏占主导。
FORMSpoT: Revealing Fine-Scale Forest Disturbances from Nation-Wide 1.5 m Forest Canopy Height Time Series
- 基于SPOT影像与深度模型，生成十年法国森林冠层高程图
- 小范围扰动占事件总数97%，远超现有卫星产品检出能力
- 可识别松树人工林砍伐与山地林区零星扰动的时空动态
当前大尺度卫星森林扰动监测系统分辨率在10-30米之间，过粗而无法捕捉单株树木尺度的变化，导致森林扰动被系统性低估。本文提出FORMSpoT（Forest Mapping with SPOT Time series），构建2014-2024年覆盖法国全境、分辨率为1.5米的森林冠层高度时序数据集，并基于该时序生成年度扰动多边形（FORMSpoT-$Δ$）。冠层高度由年度SPOT-6/7影像合成图通过在机载激光扫描（ALS）数据上训练的分层变压器模型（PVTv2）反演获得。为提升变化检测鲁棒性，开发了结合共配准与时空总变差去噪的后处理流程。结果表明：（1）法国扰动以小规模事件为主，面积小于100平方米的扰动占事件总数72%；低于0.1公顷的扰动占事件总数97%且贡献39%的扰动面积，这些均未被哨兵-1/2和陆地卫星产品有效捕捉。（2）在19个站点及5,087个国家森林清查（NFI）样点的重复观测验证下，FORMSpoT-$Δ$在100平方米以上区域实现超过0.8的F1分数，同时对更细尺度扰动保持敏感性，优于现有产品。（3）在全国尺度上，该方法揭示了不同扰动模式：滨海松林以皆伐为主，山地森林则呈现分散的小型扰动；并捕捉到2017-2022年东北部地区松材线虫危机后的采伐清理特征。
原文摘要 · Abstract (English)
Current large-scale satellite-based forest disturbance monitoring systems operate at 10-30~m resolution, too coarse to detect changes at the scale of individual trees and resulting in a systematic underestimation of forest disturbances. Here, we introduce FORMSpoT (Forest Mapping with SPOT Time series), a decade-long (2014-2024), country-scale mapping of forest canopy height at 1.5 m resolution over France, together with FORMSpoT-$Δ$, annual disturbance polygons derived from height differences in the FORMSpoT time series. Canopy heights were derived from annual SPOT-6/7 composites using a hierarchical transformer model (PVTv2) trained on high-resolution airborne laser scanning (ALS) data. To enable robust change detection, we developed a post-processing pipeline combining co-registration and spatio-temporal total variation denoising. We find that (1) the French disturbance regime is dominated by small events. Sub-100 m$^{2}$ disturbances alone represent 72% of all events, and disturbances below 0.1 ha account for 97% of events and 39% of the disturbed area. These events are largely missed by Sentinel-1/2 and Landsat-based products. (2) Validated against successive ALS revisits across 19 sites and 5,087 NFI plot revisits, FORMSpoT-$Δ$ provides reliable detection (F1>0.8) above 100 m$^{2}$ while retaining sensitivity to finer events that coarser products do not capture. (3) At the national scale, FORMSpoT-$Δ$ resolves contrasted disturbance regimes, from clear-cut-dominated dynamics in maritime pine plantations to diffuse, smaller disturbance events in mountain forests, and captures their temporal dynamics, including the salvage-logging signature of the 2017-2022 bark beetle crisis in northeastern France
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