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Article

Estimating Biomass of Native Grass Grown under Complex Management Treatments Using WorldView-3 Spectral Derivatives

1
School of Agriculture, Earth and Environmental Science, University of KwaZulu-Natal, P. Bag X01, Scottsville, Pietermaritzburg 3209, South Africa
2
School of Environmental & Rural Science, University of New England, Armidale NSW 2351, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Lenio Soares Galvao, Xiaofeng Li and Prasad S. Thenkabail
Remote Sens. 2017, 9(1), 55; https://doi.org/10.3390/rs9010055
Received: 20 September 2016 / Revised: 3 January 2017 / Accepted: 4 January 2017 / Published: 11 January 2017
(This article belongs to the Special Issue Remote Sensing of Above Ground Biomass)
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. View Full-Text
Keywords: grass biomass; SPLSR; vegetation indices; estimation accuracy grass biomass; SPLSR; vegetation indices; estimation accuracy
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MDPI and ACS Style

Sibanda, M.; Mutanga, O.; Rouget, M.; Kumar, L. Estimating Biomass of Native Grass Grown under Complex Management Treatments Using WorldView-3 Spectral Derivatives. Remote Sens. 2017, 9, 55. https://doi.org/10.3390/rs9010055

AMA Style

Sibanda M, Mutanga O, Rouget M, Kumar L. Estimating Biomass of Native Grass Grown under Complex Management Treatments Using WorldView-3 Spectral Derivatives. Remote Sensing. 2017; 9(1):55. https://doi.org/10.3390/rs9010055

Chicago/Turabian Style

Sibanda, Mbulisi, Onisimo Mutanga, Mathieu Rouget, and Lalit Kumar. 2017. "Estimating Biomass of Native Grass Grown under Complex Management Treatments Using WorldView-3 Spectral Derivatives" Remote Sensing 9, no. 1: 55. https://doi.org/10.3390/rs9010055

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