期刊
COMPUTERS AND ELECTRONICS IN AGRICULTURE
卷 182, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2020.105980
关键词
Fractional vegetation cover; Grassland condition; Spectral mixture analysis; Rangeland management; Google Earth Engine
资金
- South African National Space Agency (SANSA)
- Red Meat Research Development-SA
This study evaluated the use of publicly available satellite imagery, spectral mixture analysis, and cloud geoprocessing technologies for dynamic, continuous, and accurate estimation of fractional vegetation cover. The research provides a robust approach to FVC estimation using limited field data and open-source remote sensing imagery, with the potential to improve grassland productivity modeling for sustainable environmental and economical rangeland planning and management.
Grasslands are under continuous threat of conversion and subsequent degradation, which has a detrimental impact on grassland productivity and grazing capacity, affecting the livestock industry. Fractional vegetation cover as indicator of grassland condition and productivity has been extensively researched, however, existing approaches and products are limited with respect to accessibility, affordability, applicability, and transferability. This study evaluated the use of publicly available satellite imagery, spectral mixture analysis and cloud geoprocessing technologies for dynamic, continuous, and accurate estimation of FVC for sustainable management. A linear spectral mixture model was developed, calibrated, and implemented in Google Earth Engine using Sentinel-2 and Landsat 8 imagery. Model accuracy and spatial and temporal transferability were evaluated using existing benchmark products and field data. It was found that Sentinel-2 performed the best using a feature combination of the SWIR2 band and the NDVI, EVI, MSAVI2 and DBSI indices. Accuracies were further improved by dividing the woody and bare endmembers into subclasses. The approach proved both spatially and temporally transferable, thus this research provides a robust approach to FVC estimation using limited field data and open source remote sensing imagery. The combination of this research with further grassland productivity modelling could prove valuable for sustainable environmental and economical rangeland planning and management.
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