首张南美10米分辨率树作物地图，助力零毁林政策精准执行
Tree crop mapping of South America reveals links to deforestation and conservation
- 融合哨兵1/2时序影像，用深度学习生成高分辨率树作物图
- 发现1100万公顷树作物，其中23%与2000-2020年森林损失相关
- 揭示监管地图误判小农林农为森林，易导致误罚
监测树作物扩张对实施欧盟《无毁林产品法规》（EUDR）等零毁林政策至关重要。然而，现有工作受限于缺乏能区分农业系统与森林的高分辨率数据。本文首次基于哨兵1/2卫星时序影像，采用多模态时空深度学习模型，生成南美洲10米分辨率树作物地图。该地图识别出约1100万公顷树作物，其中23%与2000-2020年森林覆盖损失相关。关键发现是：现有支持EUDR的监管地图常将已建立的农业系统，特别是小农户林农混合系统，误标为“森林”。这一偏差可能导致虚假毁林警报，并对小规模农户造成不公平惩罚。本研究通过提供高分辨率基线数据，支持更有效、包容且公平的保护政策。
原文摘要 · Abstract (English)
Monitoring tree crop expansion is vital for zero-deforestation policies like the European Union's Regulation on Deforestation-free Products (EUDR). However, these efforts are hindered by a lack of highresolution data distinguishing diverse agricultural systems from forests. Here, we present the first 10m-resolution tree crop map for South America, generated using a multi-modal, spatio-temporal deep learning model trained on Sentinel-1 and Sentinel-2 satellite imagery time series. The map identifies approximately 11 million hectares of tree crops, 23% of which is linked to 2000-2020 forest cover loss. Critically, our analysis reveals that existing regulatory maps supporting the EUDR often classify established agriculture, particularly smallholder agroforestry, as "forest". This discrepancy risks false deforestation alerts and unfair penalties for small-scale farmers. Our work mitigates this risk by providing a high-resolution baseline, supporting conservation policies that are effective, inclusive, and equitable.
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