Detecting and mapping the spatial distribution of Chromoleana odorata invasions in communal areas of South Africa using Sentinel-2 multispectral remotely sensed data

Abstract

Alien Invasive Plants (AIPs) have been proclaimed to be a significant non-climatic driver of global change. The Siam weed (Chromoleana odorata) is highly invasive in South African rangelands, with serious environmental, agricultural and socio-economic consequences. Therefore, a comprehensive analysis of C.odorata's spatial distribution is necessary for implementing relevant mitigation and management approaches to vulnerable and invaded landscapes. Remotely sensed data offer a viable opportunity for detecting and mapping AIPs spatial extents. Hence, this study sought to compare the value of algorithms (i.e. maximum likelihood and random forest) in detecting and mapping C.odorata's spatial distribution in relation to other land use land cover classes (LULC) using the freely available Sentinel-2 multispectral image data. The findings of the study revealed that the vegetation red edge and near-infrared spectral bands of Sentinel-2 multispectral imagery (MSI) sensor were the most important spectral variables for discriminating C.odorata from other land cover classes. The random forest algorithm yielded the highest overall classification accuracy of 83%, outperforming the traditional maximum likelihood classifier (75%) in classifying C.odorata's spatial distribution. Overall, results demonstrate that the combination of freely available Sentinel-2 MSI satellite data and machine learning random forest algorithm produces the highest accuracies for detecting the spatial distribution of C.odorata, particularly in heterogeneous environments. These results could be beneficial to sustainable rangeland management and the adoption of site-specific mitigation approaches in areas invaded by C.odorata.