Predicting forest carbon stocks from high resolution satellite data in dry forests of Zimbabwe: exploring the effect of the red-edge band in forest carbon stocks estimation

Gara, Tawanda W.; Murwira, Amon; Ndaimani, Henry

doi:10.1080/10106049.2015.1041563

Cited by 21 publications

(13 citation statements)

References 27 publications

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“…Our results are consistent with those found in a growing body of contemporary literature [68][69][70][71]. For instance, Fernández-Manso et al [44] noted that red-edge derivatives detected the fire activities better and with higher accuracies (Modified Simple Ratio red-edge narrow R 2 : 0.69), when compared to single wave bands and broadband vegetation indices (Red band R 2 : 0.093, NIR R 2 : 0.63, and NDVI R 2 : 0.43) in Sierra de Gata (central-western Spain), based on Sentinel data.…”

Section: Combining Texture Models With Red-edge In Predicting Above-gsupporting

confidence: 83%

“…For instance, Fernández-Manso et al [44] noted that red-edge derivatives detected the fire activities better and with higher accuracies (Modified Simple Ratio red-edge narrow R 2 : 0.69), when compared to single wave bands and broadband vegetation indices (Red band R 2 : 0.093, NIR R 2 : 0.63, and NDVI R 2 : 0.43) in Sierra de Gata (central-western Spain), based on Sentinel data. Gara, et al [70] also noted that the inclusion of red-edge derivatives also improved the estimation of carbon stocks from an explained variance of 63%, based on NDVI, to 70% in the savanna dry forest of Zimbabwe.…”

Section: Combining Texture Models With Red-edge In Predicting Above-gmentioning

confidence: 99%

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

et al. 2017

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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/m 2 to an RMSEP of 0.55 kg/m 2 . Texture models further improved the accuracy of grass biomass estimation to an RMSEP of 0.35 kg/m 2 . 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/m 2 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.

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Section: Combining Texture Models With Red-edge In Predicting Above-gsupporting

confidence: 83%

Section: Combining Texture Models With Red-edge In Predicting Above-gmentioning

confidence: 99%

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

et al. 2017

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“…Gara et al. () made a similar observation for woody biomass in C. mopane savanna . A work based on HVIs in C. mopane savanna rangelands, Ramoelo et al.…”

Section: Discussionmentioning

confidence: 55%

“…(). Other studies focused on using measured AGB in predicting other vegetation attributes such as grass nutrient quality in communal lands (Zengeya et al., ) and forest carbon biomass (Gara et al., ) which have relatively little relevance to livestock production. The study also identified the Landsat 8 OLI green band as the prominent band that produces plausible MVI regression models, probably enhanced by a relatively narrow NIR spectral zone refined to avoid atmospheric absorption features and improve vegetation spectral response.…”

Section: Discussionmentioning

confidence: 99%

“…However, the most remotely sensed AGB literature in the south central of southern Africa is based on low‐spatial‐resolution satellite products that usually do not satisfactorily meet management objectives of herbaceous AGB at vegetation community or paddock level. Hyperspectral VIs (HVIs) have been considerably used to estimate AGB using high‐spatial‐resolution products with the emphasis on grass nutrient composition and total woody biomass (Gara, Murwira, & Ndaimani, ; Zengeya, Mutanga, & Murwira, ). These HVIs have been, however, used with little progress due to cost and limited availability of high‐resolution satellite products and have remained appropriate at a localized scale (Dube, Onisimo, & Riyad, ).…”

Section: Introductionmentioning

confidence: 99%

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

Svinurai

Hassen

Tesfamariam

et al. 2018

Grass and Forage Science

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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 (<5%). The findings demonstrate that combining MVIs with Landsat 8 optical bands, especially green band, provides the best models for estimating AGB in C. mopane savanna rangelands. These findings emphasize the importance of testing band‐MVI combinations when developing models for estimating herbaceous AGB.

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Quantifying Carbon Stock Variability of Species Within a Reforested Urban Landscape Using Texture Measures Derived from Remotely Sensed Imagery

Mngadi¹,

Odindi²,

Mutanga³

2022

Advances in Remote Sensing for Forest Monitoring

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Predicting forest carbon stocks from high resolution satellite data in dry forests of Zimbabwe: exploring the effect of the red-edge band in forest carbon stocks estimation

Cited by 21 publications

References 27 publications

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

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

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

Quantifying Carbon Stock Variability of Species Within a Reforested Urban Landscape Using Texture Measures Derived from Remotely Sensed Imagery

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