Estimating above ground biomass as an indicator of carbon storage in vegetated wetlands of the grassland biome of South Africa

Naidoo, Laven; Deventer, Heidi van; Ramoelo, Abel; Mathieu, Renaud; Nondlazi, Basanda; Gangat, Ridhwannah

doi:10.1016/j.jag.2019.01.021

Cited by 44 publications

(50 citation statements)

References 39 publications

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“…The estimation accuracy of the multispectral satellite-based biomass retrieval ranged from 67% to 85%. In grasslands, vegetation indices offer the advantage of superseding the influences of soil background, atmospheric composition, and the viewing and zenith angle effects, while enhancing the vegetation signal, when estimating AGB [41]. The regression model with an input variable of VH using Sentinel-1 SAR data only does not perform very well.…”

Section: The Accuracy and Uncertainty Of The Agb Estimation Modelmentioning

confidence: 99%

Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery

Bao

et al. 2019

Remote Sensing

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The surface mining activities in grassland and rangeland zones directly affect the livestock production, forage quality, and regional grassland resources. Mine rehabilitation is necessary for accelerating the recovery of the grassland ecosystem. In this work, we investigate the integration of data obtained via a synthetic aperture radar (Sentinel-1 SAR) with data obtained by optical remote sensing (Worldview-3, WV-3) in order to monitor the conditions of a vegetation area rehabilitated after coal mining in North China. The above-ground biomass (AGB) is used as an indicator of the rehabilitated vegetation conditions and the success of mine rehabilitation. The wavelet principal component analysis is used for the fusion of the WV-3 and Sentinel-1 SAR images. Furthermore, a multiple linear regression model is applied based on the relationship between the remote sensing features and the AGB field measurements. Our results show that WV-3 enhanced vegetation indices (EVI), mean texture from band8 (near infrared band2, NIR2), the SAR vertical and horizon (VH) polarization, and band 8 (NIR2) from the fused image have higher correlation coefficient value with the field-measured AGB. The proposed AGB estimation model combining WV-3 and Sentinel 1A SAR imagery yields higher model accuracy (R 2 = 0.79 and RMSE = 22.82 g/m 2 ) compared to that obtained with any of the two datasets only. Besides improving AGB estimation, the proposed model can also reduce the uncertainty range by 7 g m −2 on average. These results demonstrate the potential of new multispectral high-resolution datasets, such as Sentinel-1 SAR and Worldview-3, in providing timely and accurate AGB estimation for mine rehabilitation planning and management.

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Section: The Accuracy and Uncertainty Of The Agb Estimation Modelmentioning

confidence: 99%

Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery

Bao

et al. 2019

Remote Sensing

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“…It is limited to understand the performance of S1 and the fusion of S1 and S2 to retrieve biomass in non-forest regions (Castillo et al, 2017). A recent publication on a grass-covered wetland reported that incorporating S1 and S2 yielded more accurate AGB estimates than did single sensors (Naidoo et al, 2019). In this study, the AGB models were built using field samples in 2015 and then assessed using the samples in 2016 (Figs.…”

Section: Estimates Of Grassland Agb From High Resolution Images (S1 mentioning

confidence: 99%

“…However, the SAR signals are affected by soil background and topography (Chang and Shoshany, 2016). Thus, integration of optical and SAR datasets would reduce the influences of soil background and weather conditions on image data analysis for grasslands (Naidoo et al, 2019). The advantages of integrated SAR and optical data were found to improve AGB estimation in non-grassland ecosystems by overcoming limitations of each sensor (Chang and Shoshany, 2016;Lu, 2006;Lu et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…To date, the existing LAI products (e.g. 500-m MODIS) and most herbaceous AGB estimates are in coarse or moderate spatial resolutions (Naidoo et al, 2019). The availability of Landsat-8 (LC8, 16day revisit), Sentinel-2 (S2, 10 or 5-day revisit), and Sentinel-1 (S1, 12 or 6-day revisit) together provides time series image data at finer spatial resolutions (10-m to 30-m) and at weekly intervals, which offers an unprecedented opportunity to study grassland LAI and AGB dynamics at the field scale.…”

Section: Introductionmentioning

confidence: 99%

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Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images

Wang

Xiao

Starks

et al. 2019

ISPRS Journal of Photogrammetry and Remote Sensing

233

126

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Grassland degradation has accelerated in recent decades in response to increased climate variability and human activity. Rangeland and grassland conditions directly affect forage quality, livestock production, and regional grassland resources. In this study, we examined the potential of integrating synthetic aperture radar (SAR, Sentinel-1) and optical remote sensing (Landsat-8 and Sentinel-2) data to monitor the conditions of a native pasture and an introduced pasture in Oklahoma, USA. Leaf area index (LAI) and aboveground biomass (AGB) were used as indicators of pasture conditions under varying climate and human activities. We estimated the seasonal dynamics of LAI and AGB using Sentinel-1 (S1), Landsat-8 (LC8), and Sentinel-2 (S2) data, both individually and integrally, applying three widely used algorithms: Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Random Forest (RF). Results indicated that integration of LC8 and S2 data provided sufficient data to capture the seasonal dynamics of grasslands at a 10-30-m spatial resolution and improved assessments of critical phenology stages in both pluvial and dry years. The satellite-based LAI and AGB models developed from ground measurements in 2015 reasonably predicted the seasonal dynamics and spatial heterogeneity of LAI and AGB in 2016. By comparison, the integration of S1, LC8, and S2 has the potential to improve the estimation of LAI and AGB more than 30% relative to the performance of S1 at low vegetation cover (LAI < 2 m 2 /m 2 , AGB < 500 g/m 2) and optical data of LC8 and S2 at high vegetation cover (LAI > 2 m 2 /m 2 , AGB > 500 g/m 2). These results demonstrate the potential of combining S1, LC8, and S2 monitoring grazing tallgrass prairie to provide timely and accurate data for grassland management.

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“…In general, the canopy height in sample plots of similar grassland palustrine wetland sites (Chrissiesmeer, Mpumalanga Province) was 1.5-2 m, with an estimated biomass ≤850 g/m². 50 The Normalised Difference Vegetation Index (NDVI) 51,52 is often used to compensate for the influence of vegetation in estimating SMC 24,49 . However, according to Hornacek et al 32 , vegetation and texture have very little impact on the %SMC modelling if grass vegetation is ≤1 kg/m 2 .…”

Section: In-situ Soil Moisture Collectionmentioning

confidence: 99%

Estimating soil moisture using Sentinel-1 and Sentinel-2 sensors for dryland and palustrine wetland areas

et al. 2020

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Soil moisture content (SMC) plays an important role in the hydrological functioning of wetlands. Remote sensing shows potential for the quantification and monitoring of the SMC of palustrine wetlands; however, this technique remains to be assessed across a wetland–terrestrial gradient in South Africa. The ability of the Sentinel Synthetic Aperture Radar (SAR) and optical sensors, which are freely available from the European Space Agency, were evaluated to predict SMC for a palustrine wetland and surrounding terrestrial areas in the grassland biome of South Africa. The percentage of volumetric water content (%VWC) was measured across the wetland and terrestrial areas of the Colbyn Wetland Nature Reserve, located in the City of Tshwane Metropolitan Municipality of the Gauteng Province, using a handheld SMT-100 soil moisture meter at a depth of 5 cm during the peak and end of the hydroperiod in 2018. The %VWC was regressed against the Sentinel imagery, using random forest, simple linear and support vector machine regression models. Random forest yielded the highest prediction accuracies in comparison to the other models. The results indicate that the Sentinel images have the potential to be used to predict SMC with a high coefficient of determination (Sentinel-1 SAR = R²>0.9; Sentinel-2 optical = R²>0.9) and a relatively low root mean square error (Sentinel-1 RMSE =<17%; Sentinel-2 optical = RMSE <21%). Predicted maps show higher ranges of SMC for wetlands (> 50%VWC; p<0.05) compared to terrestrial areas, and therefore SMC monitoring may benefit the inventorying of wetlands, as well as monitoring of their extent and ecological condition.

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Estimating above ground biomass as an indicator of carbon storage in vegetated wetlands of the grassland biome of South Africa

Cited by 44 publications

References 39 publications

Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery

Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery

Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images

Estimating soil moisture using Sentinel-1 and Sentinel-2 sensors for dryland and palustrine wetland areas

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