# Using Sentinel-1 satellite imagery to quantify oil palm cultivation: A case study from Cameroon

## TL;DR

This study uses satellite data and machine learning to track oil palm expansion in Cameroon, showing significant increases that threaten tropical forests.

## Contribution

The study introduces a method using Sentinel-1 SAR data and Random Forest to quantify oil palm cultivation changes in tropical regions.

## Key findings

- Oil palm cultivation increased by 13-55% in focus areas from 2015 to 2025.
- The South Region of Cameroon experienced the greatest forest cover loss due to oil palm expansion.
- Sentinel-1 data and machine learning proved effective for monitoring oil palm deforestation.

## Abstract

Tropical deforestation is increasing globally, putting forest ecosystems and the ecosystem services they provide at risk. The widespread use of palm oil in fast food, cosmetics, household cleaners, and other products has led to a high demand for oil palm trees, Elaeis guineensis, in tropical regions due to their favorable growth conditions and low production costs. Thus, rapid agricultural expansion poses a significant threat to regions reliant on palm expansion for economic growth, such as the African tropical nation of Cameroon. This study aims to quantify the change in oil palm extent from 2015 to 2025 in Cameroon, using Sentinel-1 SAR satellite data and the machine learning algorithm Random Forest. Our findings reveal an increase in oil palm cultivation ranging from 13-55% in the focus areas, with the South Region showing the greatest change in forest cover as a result of oil palm expansion. We also discuss the use of Sentinel-1 satellite data and machine learning for similar studies monitoring oil palm deforestation in tropical regions.