Aboveground biomass mapping by integrating ICESat-2, SENTINEL-1, SENTINEL-2, ALOS2/PALSAR2, and topographic information in Mediterranean forests. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Guerra-Hernández, Juan; Narine, Lana L.; Pascual, Adrián; Gonzalez-Ferreiro, Eduardo; Botequim, Brigite; Malambo, Lonesome; Neuenschwander, Amy; Popescu, Sorin C.; Godinho, Sergio
• Is Part Of: GIScience & remote sensing. Volume 59:Issue 1(2022)
• Page Start: 1509
• Page End: 1533
• Publication Date: 2022-12-31
• Subjects: Spaceborne photon-count LiDAR -- ALS -- L-band SAR -- C-band SAR -- AGB -- forest monitoring; Geodesy -- Periodicals; Cartography -- Periodicals; Aerial photogrammetry -- Periodicals; Remote sensing -- Periodicals; 526.05
• DOI: 10.1080/15481603.2022.2115599
• Languages: English
• ISSNs: 1548-1603

Abstract

ABSTRACT: The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) provides an extraordinary opportunity to support global large-scale forest carbon mapping, but further research is needed in order to obtain wall-to-wall forest aboveground biomass (AGB) maps with this technology. The effects of vegetation structure on the performance of canopy height and AGB modeling using ICESat-2 photon-counting light detection and ranging (LiDAR) data in Mediterranean forest areas have not been previously studied in the literature. In this study, we combined recent ICESat-2 vegetation (ATL08) data, Airborne Laser Scanning (ALS)- and field-based estimates, and a multi-sensor earth observation composite for extrapolation of AGB estimates and AGB mapping. A diverse gradient of forest Mediterranean ecosystems, distributed over 19, 744.15 km 2 of forest area in the region of Extremadura (Spain), with different species and structural complexity forming 5 different forest types (3 Quercus spp. dominated and 2 Pinus spp. dominated forests), was used to (i) evaluate the precision of ICESat-2 canopy height estimations, (ii) develop ICESat-2-based AGB models, and (iii) generate a spatially continuous prediction of AGB by using data from the satellite missions Sentinel-1 (S1), Sentinel-2 (S2), Phased Array L-band Synthetic Aperture Radar (ALOS2/PALSAR2), and Shuttle Radar Topography Mission (SRTM). First, ALS- and ICESat-2-derived metrics that best described canopy height (p98 and rh98, respectively) were compared at the ATL08 segment level. Second, ALS-based AGB values were derived at the ATL08 segment scale. Third, ALS-based AGB estimates at the ICESat-2 segment level were used as dependent variables to fit ICESat-2-based AGB models. Fourth, a multi-sensor approach was then implemented to predict ICESat-2-derived AGB, by means of a Random Forest (RF) modeling technique, with predictors retrieved from S1, S2, ALOS2/PALSAR2, and SRTM. Finally, RF was used to generate wall-to-wall AGB maps that were compared with field-, ALS- and ICESat-2-based observations. The agreement between the ALS- and ICESat-2-derived metrics related to the canopy height distribution was higher for Pinus spp. forest than for the Quercus spp-dominated forests. The ICESat-2-based AGB models yielded model efficiency (Mef) values between 0.56 and 0.80, with a RMSE ranging from 7.76 to 17.71 Mg ha −1 and rRMSE from 19.04 to 55.21%. The multi-sensor RF models provided the following results when compared with the ICESat-2- and ALS-based AGB observations: R 2 values of 0.63 and 0.64, and RMSE values of 11.10 Mg ha −1 (rRMSE = 28.15%) and 12.28 Mg ha −1 (rRMSE = 31.45%), respectively, and an approximately unbiased result (0.03 Mg ha −1 and 0.09 Mg ha −1 ). When applied to the field-based validation data set (4th Spanish National Forest Inventory (SNFI-4) plots = 508), the RF-derived AGB model showed a relatively lower predictive capacity ( R 2 = 0.45), a higher RMSE value (25.88 Mg ha −1 ) and slightly biased results (−1.47 Mg ha −1 ), especially for larger field-derived AGB intervals. The results of this study serve to provide an initial quantitative assessment of the ICESat-2 ATL08 data for large-scale AGB estimation. The findings suggest that a multi-sensor approach may be feasible for extrapolating ICESat-2-derived AGB estimates over areas where field or ALS reference data are not available.