Detecting cocoa plantations in Cote d'Ivoire and Ghana and their implications on protected areas.

ABSTRACT: Côte d'Ivoire and Ghana are the largest producers of cocoa in the world. In recent decades the cultivation of this crop has led to the loss of vast tracts of forest areas in both countries. Efficient and accurate methods for remotely identifying cocoa plantations are essential to the implementation of sustainable cocoa practices and for the periodic and effective monitoring of forests. In this study, a method for cocoa plantation identification was developed based on a multi-temporal stack of Sentinel-1 and Sentinel-2 images and a multi-feature Random Forest (RF) algorithm. The Normalized Difference Vegetation Index (NDVI) and second-order texture features were assessed for their importance in an RF classification, and their optimal combination was used as input variables for the RF model

SUBMITTER: Abu IO 

PROVIDER: S-EPMC8329934 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature