Using multisource remotely sensed data and cloud computing approaches to map non-native species in the semi-arid savannah rangelands of Mpumalanga, South Africa

Abstract

Semi-arid savannah rangelands are diverse environments (in terms of species) that play an important role in sustaining biodiversity and providing ecosystem services. However, the emergence of non-native species, as well as bush encroachment, are currently threatening these (semi-arid rangeland and grassland) ecosystems. The purpose of this study was therefore to map and quantify the spatial extents of non-native woody vegetation in the Kruger National Park and surrounding communal areas in Mpumalanga, South Africa. To achieve the study's objectives, Sentinel-1 and Sentinel-2 remotely sensed data were combined and analysed using the random forest (RF) machine-learning algorithm in the Google Earth Engine (GEE) cloud computing platform. Specifically, spectral bands and selected spectral derivatives, e.g. enhanced vegetation index (EVI2), normalized difference moisture index (NDMI) and normalized difference phenology index (NDPI) were computed and used to map non-native woody vegetation. After optimizing the model combination, the classification outputs had an overall accuracy of 70%, with class accuracies such as producer's accuracy (PA) and user's accuracy (UA) ranging from 67% to 95%.It was shown in this study that using Sentinel-2 and Sentinel-1 data together led to better overall accuracy than using single sensor models when mapping semi-arid savannah rangelands. It was also found in this study that the overall classification accuracy of non-native (invasive) species using optical sensors was higher than in previous studies. On a free platform like GEE, it was possible to utilize advanced classification processes to fully exploit the informative content of Sentinel-1 and Sentinel-2 data.