Tree crop mapping of South America reveals links to deforestation and conservation

Abstract

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.

Figures (9)

• Figure 1: Tree crop map for South America. The main map displays a hexagon-based overview (80km per hexagon side), where brighter colors indicate higher tree crop density. Detailed maps a--d show zoomed-in views of the tree crop probability in the tree crop regions of (a) Argentina (mainly apple orchards), (b) Brazil (large-scale coffee plantations), (c) Peru (palm plantations), and (d) Colombia (smallholder coffee farms). (e) Tree crop area per country.
• Figure 2: Tree crops in the forest cover loss areas. a) Tree crop area within forest cover loss zones (left axis, ha) and its proportion relative to total tree crop area (right axis, %) by country. While higher tree crop area generally corresponds to greater overlap with forest cover loss, Peru stands out as an outlier with a high relative proportion despite a smaller absolute area. b) Temporal patterns of tree crops within forest cover loss in South America. The line chart shows the annual tree crop area inside forest cover loss per country. c) Example of large-scale oil palm expansion in Ucayali, Peru.
• Figure 3: Tree crops in the Protected Areas. a) Decrease in tree crop area with increasing distance from the boundary into Protected Areas. b) Increase in tree crop area with increasing distance outward from the boundary of the Protected Areas. c) Examples of selected Protected Areas and PA category names. The red rectangles are Protected Areas, white indicates the predicted tree crop areas while black indicates non-tree crop areas.
• Figure 4: Addressing EUDR in comparison of JRC maps. a) Comparison of tree crop representation in version 1 and version 2 of the JRC Global Forest Cover 2020 map. b) The bar chart shows the distribution of JRC’s Global Forest Type 2020 classes within our mapped tree crop areas. c) Zoomed-in examples from Colombia comparing our tree crop map with the JRC Global Forest Cover 2020 (v2). Orange indicates areas classified as non-forest by JRC, while white indicates those classified as forest. Large oil palm plantations are generally mapped as non-forest (windows 1–3 with latitude and longitude: [10.672, –74.240], [7.264, –73.889], [3.916, –73.385]), whereas smaller fields, such as coffee, are often classified as forest (windows 4–5 with latitude and longitude [5.321, –75.733], [2.499, –76.042]).
• Figure A1: Hexagon map of tree crop areas inside forest cover loss per country. Each hexagon covers $80 \times 80$$km^2$ area and darker hexagon indicate more tree crop areas inside forest cover loss. The line chart shows the annual tree crop area inside forest cover loss per country.