Assessing the utility of the spot 6 sensor in detecting and mapping Lantana camara for a community clearing project in KwaZulu-Natal, South Africa

Abstract

Lantana camara is a significant weed in South Africa which is causing severe impacts on agriculture by reducing grazing areas. This study assessed the potential of the SPOT 6 multispectral sensor and two broadband vegetation indices (NDVI and SR) for detecting and mapping Lantana camara in a community grazing land in KwaZulu-Natal, South Africa. The SPOT 6 bands and vegetation indices successfully classified Lantana camara with an overall accuracy of 75% on an independent test dataset using the random forest algorithm. Furthermore, it was tested if the random forest model based on variable importance (VIP) could improve the classification accuracy using the best subset of bands and indices. A backward feature elimination technique was used to select the best subset of VIP bands and indices to improve the classification. By eliminating SPOT bands 1 and 4 which yielded the lowest VIP scores the random forest model improved the classification accuracy to 83.33% on an independent test dataset. The study indicates the potential of satellite remote sensing in weed detection and mapping in South Africa using readily available multispectral data to assist poorer communities in grazing management.