Performance of ratio‐based, soil‐adjusted and atmospherically corrected multispectral vegetation indices in predicting herbaceous aboveground biomass in a Colophospermum mopane tree–shrub savanna

Accurate and near‐real‐time estimation of herbaceous aboveground biomass (AGB) at farm level is crucial for monitoring pasture production and proactive management of stock in semiarid rangelands. Despite its importance, remote sensing has been rarely used by range ecologists and managers in Zimbabwe...

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Bibliographic Details
Published in:Grass and forage science 2018-09, Vol.73 (3), p.727-739
Main Authors: Svinurai, Walter, Hassen, Abubeker, Tesfamariam, Eyob, Ramoelo, Abel
Format: Article
Language:English
Subjects:
aboveground biomass
Atmospheric correction
Biomass
Colophospermum mopane
Ecological monitoring
Estimation
farms
Landsat
Landsat satellites
monitoring
multispectral vegetation indices
Pasture
Pasture management
pastures
prediction
Rainy season
Range management
Rangelands
regression
Regression analysis
Remote sensing
savanna
Savannahs
savannas
Soils
spectral analysis
Spectral bands
Vegetation
vegetation index
wet season
Zimbabwe
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Summary:Accurate and near‐real‐time estimation of herbaceous aboveground biomass (AGB) at farm level is crucial for monitoring pasture production and proactive management of stock in semiarid rangelands. Despite its importance, remote sensing has been rarely used by range ecologists and managers in Zimbabwe. This study aimed at assessing the performance of classical multispectral vegetation indices (MVIs) when either singly regressed with measured herbaceous AGB or combined with other visible spectral bands in predicting herbaceous AGB in a Colophospermum mopane savannah. Field herbaceous AGB and corresponding Landsat 8 Operational Land Imager visible spectral data were collected during the 2016–2017 rainy season. Relationships between measured AGB and classical MVIs and extended models of MVIs combined with other visible bands were analysed using bootstrapped simple and stepwise multiple linear regression functions. When MVIs were singly regressed with measured AGB, ratio‐based indices yielded the highest r2 value of 0.64, followed by soil‐adjusted indices (r2 = 0.61), while atmospherically corrected MVIs showed the lowest r2 of 0.58 (p = 0.00). A significant improvement in herbaceous AGB estimation was obtained using a combination of MVIs and other visible bands. Soil‐adjusted MVIs showed the greatest increase (44–46%) in r2, while atmospherically corrected and ratio‐based MVIs poorly improved (
ISSN:0142-5242
1365-2494
DOI:10.1111/gfs.12367