Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/20166
Title: Estimating Biomass of Native Grass Grown under Complex Management Treatments Using WorldView-3 Spectral Derivatives
Contributor(s): Sibanda, Mbulisi (author); Mutanga, Onisimo (author); Rouget, Mathieu (author); Kumar, Lalit  (author)orcid 
Publication Date: 2017
Open Access: Yes
DOI: 10.3390/rs9010055Open Access Link
Handle Link: https://hdl.handle.net/1959.11/20166
Abstract: The ability of texture models and red-edge to facilitate the detection of subtle structural vegetation traits could aid in discriminating and mapping grass quantity, a challenge that has been longstanding in the management of grasslands in southern Africa. Subsequently, this work sought to explore the robustness of integrating texture metrics and red-edge in predicting the above-ground biomass of grass growing under different levels of mowing and burning in grassland management treatments. Based on the sparse partial least squares regression algorithm, the results of this study showed that red-edge vegetation indices improved above-ground grass biomass from a root mean square error of perdition (RMSEP) of 0.83 kg/m² to an RMSEP of 0.55 kg/m². Texture models further improved the accuracy of grass biomass estimation to an RMSEP of 0.35 kg/m². The combination of texture models and red-edge derivatives (red-edge-derived vegetation indices) resulted in an optimal prediction accuracy of RMSEP 0.2 kg/m² across all grassland management treatments. These results illustrate the prospect of combining texture metrics with the red-edge in predicting grass biomass across complex grassland management treatments. This offers the detailed spatial information required for grassland policy-making and sustainable grassland management in data-scarce regions such as southern Africa.
Publication Type: Journal Article
Source of Publication: Remote Sensing, 9(1), p. 1-21
Publisher: MDPIAG
Place of Publication: Switzerland
ISSN: 2072-4292
Field of Research (FOR): 050206 Environmental Monitoring
090903 Geospatial Information Systems
090905 Photogrammetry and Remote Sensing
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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