Annual Grass Biomass Mapping with Landsat-8 and Sentinel-2 Data over Kruger National Park, South Africa

Abstract

This study explores the potential of Landsat-8 and Sentinel-2 imagery for annual grass biomass mapping in savannas. To this end, three wet season image mosaics based on Landsat-8 and Sentinel-2 were created for 2016, 2017 and 2018 over Kruger National Park (KNP), South Africa. For the purpose of calibration and validation, use was made of in situ fuel biomass values measured as part of the yearly veld condition assessment (VCA) in KNP. The satellite and reference data were fed into a random forests machine learning approach to make park-wide predictions of grass biomass and to assess the performance of Landsat-8 and Sentinel-2 predictors (i.e., surface reflectance and the normalized difference vegetation index, NDVI). Examples of the data sets used and biomass maps produced are provided together with the obtained error statistics. The latter suggest that wet season NDVI mosaics from Landsat-8 and Sentinel-2 data enable the creation of fairly reliable, annual maps of fuel biomass for KNP. These new biomass estimates represent a slight improvement over recent mapping efforts based on Sentinel-1 data.