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Characterizing Crop Distribution and the Impact on Forest Conservation in Central Africa

Ozigis, Mohammed S., Wich, Serge, Abdolshahnejad, Mahsa, Descals, Adrià, Szantoi, Zoltan, Sheil, Douglas, Meijaard, Erik (2025) Characterizing Crop Distribution and the Impact on Forest Conservation in Central Africa. Remote Sensing, 17 (11). Article Number 1958. ISSN 2072-4292. (doi:10.3390/rs17111958) (KAR id:110583)

Abstract

While the role of expanding agriculture in deforestation and the loss of other natural ecosystems is well known, the specific drivers in the context of small- and large-scale agriculture remain poorly understood. In this study, we employed satellite data and a deep learning algorithm to map the agricultural landscape of Central Africa (Cameroon, Central Africa Republic, Congo, Democratic Republic of Congo, Equatorial Guinea, and Gabon) into large- (including for plantations and intensively cultivated areas) and small-scale tree crops and non-tree crop cover. This permits the assessment of forest loss between the years 2000 and 2022 as a result of small- and large-scale agriculture. Thematic [user’s] accuracy ranged between 91.2 ± 2.5 percent (large-scale oil palm) and 17.8 ± 3.9 percent (large-scale non-tree crops). Small-scale tree crops achieved relatively low accuracy (63.5 ± 5.9 percent), highlighting the difficulties of reliably mapping crop types at a regional scale. In general, we observed that small-scale agriculture is fifteen times the size of large-scale agriculture, as area estimates of small-scale non-tree crops and small-scale tree crops ranged between 164,823 ± 4224 km2 and 293,249 ± 12,695 km2, respectively. Large-scale non-tree crops and large-scale tree crops ranged between 20,153 ± 1195 km2 and 7436 ± 280 km2, respectively. Small-scale cropping activities represent 12 percent of the total land cover and have led to dramatic encroachment into tropical moist forests in the past two decades in all six countries. We summarized key recommendations to help the forest conservation effort of existing policy frameworks.

Item Type: Article
DOI/Identification number: 10.3390/rs17111958
Uncontrolled keywords: deep learning, agriculture, multi-source remote sensing data, forest conservation, deforestation
Subjects: Q Science > QH Natural history > QH75 Conservation (Biology)
Institutional Unit: Institutes > Durrell Institute of Conservation and Ecology
Former Institutional Unit:
There are no former institutional units.
SWORD Depositor: JISC Publications Router
Depositing User: JISC Publications Router
Date Deposited: 12 Sep 2025 13:07 UTC
Last Modified: 15 Sep 2025 11:36 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/110583 (The current URI for this page, for reference purposes)

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