Differentiating oil palm plantations from natural forest to improve land cover mapping in Ghana
Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
Remote Sensing Applications: Society and Environment
Abstract
Tree crops like oil palm present a unique challenge in land cover mapping, as they are often
misclassified as natural forest. The area cultivated with oil palm in Ghana has rapidly expanded
since 2000, and production is expected to continue to increase. Sentinel-1 and Sentinel-2 satellite
data was used as inputs to a random forest classifier in Google Earth Engine to map mature,
closed-canopy oil palm extent in 2019 of a Ghana study area that includes both industrial
plantations and smallholders. The combination of Sentinel-1 and Sentinel-2 bands and derived
indices outperformed either satellite alone for mapping industrial oil palm (90.3% overall accuracy). A separate accuracy assessment for this combined input approach demonstrated high
accuracy mapping smallholders as well (80.4% overall accuracy), a key challenge in the West
African context. To validate these findings, results were compared with available production
information and a global oil palm remote sensing product. The resulting map can inform sustainable oil palm efforts in Ghana, which is understudied in current oil palm remote sensing
literature, and the methodology provides an example for future studies of oil palm sourcing areas
using only publicly available data.
Description
Research Article
Keywords
Oil palm, Ghana, Land cover