Potential of ALOS2 and NDVI to Estimate Forest Above-Ground Biomass, and Comparison with Lidar-Derived Estimates

Abstract

Remote sensing supports carbon estimation, allowing the upscaling of field measurements to large extents. Lidar is considered the premier instrument to estimate above ground biomass, but data are expensive and collected on-demand, with limited spatial and temporal coverage. The previous JERS and ALOS SAR satellites data were extensively employed to model forest biomass, with literature suggesting signal saturation at low-moderate biomass values, and an influence of plot size on estimates accuracy. The ALOS2 continuity mission since May 2014 produces data with improved features with respect to the former ALOS, such as increased spatial resolution and reduced revisit time. We used ALOS2 backscatter data, testing also the integration with additional features (SAR textures and NDVI from Landsat 8 data) together with ground truth, to model and map above ground biomass in two mixed forest sites: Tahoe (California) and Asiago (Alps). While texture was useful to improve the model performance, the best model was obtained using joined SAR and NDVI (R 2 equal to 0.66). In this model, only a slight saturation was observed, at higher levels than what usually reported in literature for SAR; the trend requires further investigation but the model confirmed the complementarity of optical and SAR datatypes. For comparison purposes, we also generated a biomass map for Asiago using lidar data, and considered a previous lidar-based study for Tahoe; in these areas, the observed R 2 were 0.92 for Tahoe and 0.75 for Asiago, respectively. The quantitative comparison of the carbon stocks obtained with the two methods allows discussion of sensor suitability. The range of local variation captured by lidar is higher than those by SAR and NDVI, with the latter showing overestimation. However, this overestimation is very limited for one of the study areas, suggesting that when the purpose is the overall quantification of the stored carbon, especially in areas with high carbon density, satellite data with lower cost and broad coverage can be as effective as lidar.

Retrieval of Tropical Peatland Forest Biomass From Polarimetric Features in Central Kalimantan, Indonesia

“…Among available variables, perplexity, Shannon index, and entropy were found most correlated with the referenced aboveground biomass with R 2 0.67, 0.67, and 0.66, respectively; these results are in line with the results obtained in published literature [31][32][33][34][35][36]. Detailed regression results are shown in Table 3.…”

Comparison of Machine Learning Methods for Estimating Mangrove Above-Ground Biomass Using Multiple Source Remote Sensing Data in the Red River Delta Biosphere Reserve, Vietnam

“…For instance, the ratio vegetation index (RVI) is effective for retrieval of the mangrove structure [48]. The normalized difference vegetation index (NDVI) has been widely used for mangrove AGB as it is strongly correlated with mangrove biophysical parameters [49]. Further, enhanced vegetation index-2 (EVI-2) is relatively sensitive to mangrove AGB [50,51].…”

Optimal Combination of Predictors and Algorithms for Forest Above-Ground Biomass Mapping from Sentinel and SRTM Data

“…For example, reported studies suggested that improved spatial resolution from the Sentinel series helped to reduce the uncertainty, and improved the accuracy of AGB mapping at finer scale [52,53]. Promising results demonstrated that texture measurement of SAR data with higher spatial resolution could improve biomass estimation [54,55]. However, uncertainties in implementing the texture measurement for biomass estimation were reported, due to the selection of window size [56,57].…”