Tree crop mapping of South America reveals links to deforestation and conservation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910031022129152 |
|---|---|
| author | Jiang, Yuchang Raichuk, Anton Tong, Xiaoye Garnot, Vivien Sainte Fare Ortiz-Gonzalo, Daniel Morris, Dan Schindler, Konrad Wegner, Jan Dirk Neumann, Maxim |
| author_facet | Jiang, Yuchang Raichuk, Anton Tong, Xiaoye Garnot, Vivien Sainte Fare Ortiz-Gonzalo, Daniel Morris, Dan Schindler, Konrad Wegner, Jan Dirk Neumann, Maxim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_17372 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Tree crop mapping of South America reveals links to deforestation and conservation Jiang, Yuchang Raichuk, Anton Tong, Xiaoye Garnot, Vivien Sainte Fare Ortiz-Gonzalo, Daniel Morris, Dan Schindler, Konrad Wegner, Jan Dirk Neumann, Maxim Computer Vision and Pattern Recognition 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. |
| title | Tree crop mapping of South America reveals links to deforestation and conservation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.17372 |