Characterizing the Spatial Distribution of Grazing and Browsing Resources in Africa Using Random Forest Classifier and Multi-Sensor Data

Abstract

African rangelands are threatened by anthropogenic land-use activities, adverse climate phenomena such as droughts, and poor land management. These undermine their capacity to support various fauna and flora, provide ecosystem services, and sustain livestock agriculture, i.e., a key economic activity in Africa. Therefore, preserving the integrity of African rangelands is critical for addressing African food security challenges. Using multi-sensor Earth observation data and Random Forest classifier, this study characterized the spatial distribution of African rangelands, to support grazing and browsing capacity modelling, assessment of rangeland changes, and rangeland management policy development and decision making. The results show that rangelands could be characterised with good accuracies exceeding 70% in most AfriCultuReS pilot countries using the high-resolution land cover map and MCD12Q1 products as training and validation data. The spatial distribution maps can be used as masks that would aid accurate monitoring of rangeland health, productivity, phenology and changes.