Seasonal evaluation and mapping of aboveground biomass in natural rangelands using Sentinel-1 and Sentinel-2 data

Context The invasion of annual grasses in western U.S. rangelands promotes high litter accumulation throughout the landscape that perpetuates a grass-fire cycle threatening biodiversity. Objectives To provide novel evidence on the potential of fine spatial and structural resolution remote sensing data derived from Unmanned Aerial Vehicles (UAVs) to separately estimate the biomass of vegetation and litter fractions in sagebrush ecosystems. Methods We calculated several plot-level metrics with ecological relevance and representative of the biomass fraction distribution by strata from UAV Light Detection and Ranging (LiDAR) and Structure-from-Motion (SfM) datasets and regressed those predictors against vegetation, litter, and total biomass fractions harvested in the field. We also tested a hybrid approach in which we used digital terrain models (DTMs) computed from UAV LiDAR data to height-normalize SfM-derived point clouds (UAV SfM-LiDAR). Results The metrics derived from UAV LiDAR data had the highest predictive ability in terms of total (R2 = 0.74) and litter (R2 = 0.59) biomass, while those from the UAV SfM-LiDAR provided the highest predictive performance for vegetation biomass (R2 = 0.77 versus R2 = 0.72 for UAV LiDAR). In turn, SfM and SfM-LiDAR point clouds indicated a pronounced decrease in the estimation performance of litter and total biomass. Conclusions Our results demonstrate that high-density UAV LiDAR datasets are essential for consistently estimating all biomass fractions through more accurate characterization of (i) the vertical structure of the plant community beneath top-of-canopy surface and (ii) the terrain microtopography through thick and dense litter layers than achieved with SfM-derived products.

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Determining Optimal SAR Parameters for Quantifying Above-Ground Grass Carbon Stock in Savannah Ecosystems Using a Tree-Based Algorithm

Maake,

Mutanga,

Chirima

et al. 2024

Remote Sens Earth Syst Sci

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The quantification and monitoring of above-ground grass carbon stock (AGGCS) will inform emission reduction policies and aid in minimising the risks associated with future climate change. This study investigated the sensitivity of Synthetic Aperture Radar (SAR)-derived parameters to predict AGGCS in a savannah ecosystem in Kruger National Park, South Africa. Particularly, we investigated the capabilities of Sentinel-1 derived parameters, including backscatter coefficients, intensity ratios, normalised radar backscatter, arithmetic computations, and the XGBoost tree-based algorithm, to predict the AGGCS. We further tested if incorporating texture matrices (i.e. Gray Level Co-Occurrence Matrix) can enhance the predictive capability of the models. We found that the linear polarisation (i.e. VV) and the intensity ratio (i.e. VH/VV) achieved similar results (R2 = 0.38, RMSE% = 31%, MAE = 6.87) and (R2 = 0.37, RMSE = 31%, MAE = 8.80) respectively. The Radar Vegetation Index (RVI) performed marginally (1%) better (R2 = 0.39, RMSE = 30% and MAE = 6.77) compared to the other variables. Nevertheless, the incorporation texture matrix into the model enhanced prediction capability by approximately 20% (R2 = 0.60, RMSE% = 20%, MAE = 3.91). Furthermore, the most influential predictors for AGGCS estimation were RVI, VHcor and VVcor order of importance. These findings (R2 values of 0.35–0.39) suggest that SAR data alone does not fully capture the variability in above-ground grass carbon stock, particularly in the complexly configured savannah ecosystems. Nevertheless, the results further suggest that the prediction accuracy of SAR-based above-ground grass carbon stock models can be enhanced with the incorporation of texture matrices.

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Seasonal evaluation and mapping of aboveground biomass in natural rangelands using Sentinel-1 and Sentinel-2 data

Cited by 6 publications

References 78 publications

Southern African Savannas and Dry Forests

Southern African Savannas and Dry Forests

Estimating vegetation and litter biomass fractions in rangelands using structure-from-motion and LiDAR datasets from unmanned aerial vehicles

Determining Optimal SAR Parameters for Quantifying Above-Ground Grass Carbon Stock in Savannah Ecosystems Using a Tree-Based Algorithm

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