Mapping oil palm plantations and their implications on forest and great ape habitat loss in Central Africa
Abstract Oil palm (Elaeis guineensis) cultivation in Central Africa (CA) has become important because of the increased global demand for vegetable oils. The region is highly suitable for the cultivation of oil palm and this increases pressure on forest biodiversity in the region. Accurate maps are t...
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| Format: | Article |
| Language: | English |
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Wiley
2025-06-01
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| Series: | Remote Sensing in Ecology and Conservation |
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| Online Access: | https://doi.org/10.1002/rse2.428 |
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| author | Mohammed S. Ozigis Serge Wich Adrià Descals Zoltan Szantoi Erik Meijaard |
| author_facet | Mohammed S. Ozigis Serge Wich Adrià Descals Zoltan Szantoi Erik Meijaard |
| author_sort | Mohammed S. Ozigis |
| collection | DOAJ |
| description | Abstract Oil palm (Elaeis guineensis) cultivation in Central Africa (CA) has become important because of the increased global demand for vegetable oils. The region is highly suitable for the cultivation of oil palm and this increases pressure on forest biodiversity in the region. Accurate maps are therefore needed to understand trends in oil palm expansion for landscape‐level planning, conservation management of endangered species, such as great apes, biodiversity appraisal and supply of ecosystem services. In this study, we demonstrate the utility of a U‐Net Deep Learning Model and product fusion for mapping the extent of oil palm plantations for six countries within CA, including Cameroon, Central African Republic, Democratic Republic of Congo (DRC), Equatorial Guinea, Gabon and Republic of Congo. Sentinel‐1 and Sentinel‐2 data for the year 2021 were classified using a U‐Net model. Overall classification accuracy for the final oil palm layer was 96.4 ± 1.1%. Producer Accuracy (PA) and User Accuracy (UA) for the industrial and smallholder oil palm classes were 91.6 ± 1.7% and 95.0 ± 1.3%, 67.7 ± 2.8% and 70.0 ± 2.8%. Post classification assessment of the transition from tropical moist forest (TMF) cover to oil palm within the six CA countries suggests that over 1000 Square Kilometer (km2) of forest within great ape ranges had so far been converted to oil palm between 2000 and 2021. Results from this study indicate a more extensive cover of smallholder oil palm than previously reported for the region. Our results also indicate that expansion of other agricultural activities may be an important driver of deforestation as nearly 170 000 km2 of forest loss was recorded within the IUCN ranges of the African great apes between 2000 and 2021. Output from this study represents the first oil palm map for the CA, with specific emphasis on the impact of its expansion on great ape ranges. This presents a dependable baseline through which future actions can be formulated in addressing conservation needs for the African Great Apes within the region. |
| format | Article |
| id | doaj-art-8a33d0e01d694ec1946b0b0793cc7d5a |
| institution | Matheson Library |
| issn | 2056-3485 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Remote Sensing in Ecology and Conservation |
| spelling | doaj-art-8a33d0e01d694ec1946b0b0793cc7d5a2025-06-28T18:00:06ZengWileyRemote Sensing in Ecology and Conservation2056-34852025-06-0111333935610.1002/rse2.428Mapping oil palm plantations and their implications on forest and great ape habitat loss in Central AfricaMohammed S. Ozigis0Serge Wich1Adrià Descals2Zoltan Szantoi3Erik Meijaard4School of Biological and Environmental Sciences Liverpool John Moores University James Parsons Building, 3 Byrom Street Liverpool L3 3AF UKSchool of Biological and Environmental Sciences Liverpool John Moores University James Parsons Building, 3 Byrom Street Liverpool L3 3AF UKCREAF Cerdanyola del Vallès 08193 Barcelona SpainScience, Applications and Climate Department European Space Agency Frascati 00044 ItalyDurrell Institute of Conservation and Ecology University of Kent Canterbury CT2 7NR UKAbstract Oil palm (Elaeis guineensis) cultivation in Central Africa (CA) has become important because of the increased global demand for vegetable oils. The region is highly suitable for the cultivation of oil palm and this increases pressure on forest biodiversity in the region. Accurate maps are therefore needed to understand trends in oil palm expansion for landscape‐level planning, conservation management of endangered species, such as great apes, biodiversity appraisal and supply of ecosystem services. In this study, we demonstrate the utility of a U‐Net Deep Learning Model and product fusion for mapping the extent of oil palm plantations for six countries within CA, including Cameroon, Central African Republic, Democratic Republic of Congo (DRC), Equatorial Guinea, Gabon and Republic of Congo. Sentinel‐1 and Sentinel‐2 data for the year 2021 were classified using a U‐Net model. Overall classification accuracy for the final oil palm layer was 96.4 ± 1.1%. Producer Accuracy (PA) and User Accuracy (UA) for the industrial and smallholder oil palm classes were 91.6 ± 1.7% and 95.0 ± 1.3%, 67.7 ± 2.8% and 70.0 ± 2.8%. Post classification assessment of the transition from tropical moist forest (TMF) cover to oil palm within the six CA countries suggests that over 1000 Square Kilometer (km2) of forest within great ape ranges had so far been converted to oil palm between 2000 and 2021. Results from this study indicate a more extensive cover of smallholder oil palm than previously reported for the region. Our results also indicate that expansion of other agricultural activities may be an important driver of deforestation as nearly 170 000 km2 of forest loss was recorded within the IUCN ranges of the African great apes between 2000 and 2021. Output from this study represents the first oil palm map for the CA, with specific emphasis on the impact of its expansion on great ape ranges. This presents a dependable baseline through which future actions can be formulated in addressing conservation needs for the African Great Apes within the region.https://doi.org/10.1002/rse2.428Agriculturedeep learningoil palmremote sensingU‐Net |
| spellingShingle | Mohammed S. Ozigis Serge Wich Adrià Descals Zoltan Szantoi Erik Meijaard Mapping oil palm plantations and their implications on forest and great ape habitat loss in Central Africa Remote Sensing in Ecology and Conservation Agriculture deep learning oil palm remote sensing U‐Net |
| title | Mapping oil palm plantations and their implications on forest and great ape habitat loss in Central Africa |
| title_full | Mapping oil palm plantations and their implications on forest and great ape habitat loss in Central Africa |
| title_fullStr | Mapping oil palm plantations and their implications on forest and great ape habitat loss in Central Africa |
| title_full_unstemmed | Mapping oil palm plantations and their implications on forest and great ape habitat loss in Central Africa |
| title_short | Mapping oil palm plantations and their implications on forest and great ape habitat loss in Central Africa |
| title_sort | mapping oil palm plantations and their implications on forest and great ape habitat loss in central africa |
| topic | Agriculture deep learning oil palm remote sensing U‐Net |
| url | https://doi.org/10.1002/rse2.428 |
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