Dry season biomass estimation as an indicator of rangeland quantity using multi-scale remote sensing data

Abstract

For grazing, biomass is the main indicator of rangeland quantity, which is crucial to determine the amount of food available for animals (grazers), including livestock. Livestock production in the rural communities of the world, including Africa, is the main source of income and hence livelihood. Biomass information during dry season is not only important for grazing but also for determining the fuel load for fire risk. During dry season, grazers are mainly limited by grass quantity than quality. Therefore, it is important to quantify the variability of biomass during dry season to inform decision makers on planning and management of the grazing systems. Remote sensing provides opportunity to successfully estimate biomass in natural and agricultural areas. The conventional approach makes use of the vegetation indices such as the normalized difference vegetation index (NDVI), which is a measure of vegetation greenness. The use of vegetation indices has been successful during wet periods where vegetation is green and photosynthetic active. During dry season, biomass estimation is always not plausible using vegetation indices. The aim of this study is to estimate dry biomass using the multi-scale remote sensing data in the savanna ecosystem. Field data was collected in August 2013, and concerted to the acquisition of the satellite image from RapidEye and Landsat 8. Random forest algorithm (RF) was used to predict biomass using the band reflectance data, from RapidEye and Landsat 8 respectively. The results show that RF combined with RapidEye explained over 85% of biomass variation, as compared to 81% explained by RF with Landsat 8 data. For regional assessment of biomass as an indicator of rangeland quantity, high spatial resolution data can be used for calibration and validation. This study demonstrates that dry season biomass can be estimated using remote sensing, and it is important for understanding grazing and feeding patterns of animals, including livestock and wildlife.