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dc.contributor.author Ramoelo, Abel
dc.contributor.author Cho, Moses A
dc.date.accessioned 2015-02-09T07:21:15Z
dc.date.available 2015-02-09T07:21:15Z
dc.date.issued 2014-10
dc.identifier.citation Ramoelo, A. and Cho, M.A. 2014. Dry season biomass estimation as an indicator of rangeland quantity using multi-scale remote sensing data. In: 10th International Conference on African Association of Remote Sensing of Environment (AARSE) 2014, University of Johannesburg, 27-31 October 2014 en_US
dc.identifier.uri http://hdl.handle.net/10204/7852
dc.description 10th International Conference on African Association of Remote Sensing of Environment (AARSE) 2014, University of Johannesburg, 27-31 October 2014 en_US
dc.description.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. en_US
dc.language.iso en en_US
dc.publisher African Association of Remote Sensing of Environment (AARSE) en_US
dc.relation.ispartofseries Workflow;13813
dc.subject Biomass en_US
dc.subject RapidEye en_US
dc.subject Landsat en_US
dc.subject Remote sensing en_US
dc.subject Rangeland quantity en_US
dc.title Dry season biomass estimation as an indicator of rangeland quantity using multi-scale remote sensing data en_US
dc.type Presentation en_US


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