Global Ecosystem Dynamics Investigation (GEDI) satellite mission is expanding the spatial bounds and temporal resolution of large-scale mapping applications. Integrating the recent GEDI data into Airborne Laser Scanning (ALS)-derived estimations represents a global opportunity to update and extend forest models based on area based approaches (ABA) considering temporal and spatial dynamics. This study evaluates the effect of combining ALS-based aboveground biomass (AGB) estimates with GEDI-derived models by using temporally coincident datasets. A gradient of forest ecosystems, distributed through 21,766 km² in the province of Badajoz (Spain), with different species and structural complexity, was used to: (i) assess the accuracy of GEDI canopy height in five Mediterranean Ecosystems and (ii) develop GEDI-based AGB models when using ALS-derived AGB estimates at GEDI footprint level. In terms of Pearson’s correlation (r) and rRMSE, the agreement between ALS and GEDI statistics on canopy height was stronger in the denser and homogeneous coniferous forest of P. pinaster and P. pinea than in sparse Quercus-dominated forests. The GEDI-derived AGB models using relative height and vertical canopy metrics yielded a model efficiency (Mef) ranging from 0.31 to 0.46, with a RMSE ranging from 14.13 to 32.16 Mg/ha and rRMSE from 38.17 to 84.74%, at GEDI footprint level by forest type. The impact of forest structure confirmed previous studies achievements, since GEDI data showed higher uncertainty in highly multilayered forests. In general, GEDI-derived models (GEDI-like Level4A) underestimated AGB over lower and higher ALS-derived AGB intervals. The proposed models could also be used to monitor biomass stocks at large-scale by using GEDI footprint level in Mediterranean areas, especially in remote and hard-to-reach areas for forest inventory. The findings from this study serve to provide an initial evaluation of GEDI data for estimating AGB in Mediterranean forest.

While sustainable forestry in Europe is characterized by the provision of a multitude of forest ecosystem services, there exists no comprehensive study that scrutinizes their sensitivity to forest management on a pan-European scale, so far. We compile scenario runs from regionally tailored forest growth models and Decision Support Systems (DSS) from 20 case studies throughout Europe and analyze whether the ecosystem service provision depends on management intensity and other co-variables, comprising regional affiliation, social environment, and tree species composition. The simulation runs provide information about the case-specifically most important ecosystem services in terms of appropriate indicators. We found a strong positive correlation between management intensity and wood production, but only weak correlation with protective and socioeconomic forest functions. Interestingly, depending on the forest region, we found that biodiversity can react in both ways, positively and negatively, to increased management intensity. Thus, it may be in tradeoff or in synergy with wood production and forest resource maintenance. The covariables species composition and social environment are of punctual interest only, while the affiliation to a certain region often makes an important difference in terms of an ecosystem service’s treatment sensitivity.

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 km² 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: R² 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 (R² = 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.

Europe’s forests provide vital habitat for biodiversity and essential ecosystem services whose provision must be sustained or enhanced over the coming century. However, the potential to secure or increase forest ecosystem services, while securing the habitat requirements of taxa remains unclear, especially within the context of uncertain climate and socio-economic developments. To tease out the associated trade-offs and synergies, we used ten case study landscapes within nine countries throughout Europe. Starting with the current status of the forests in the case study landscapes, we simulated forest development one hundred years into the future. Simulations were embedded in three combined climate and socio-economic frame scenarios based on global and European policies which varied in their climate change mitigation efficiency. Scenarios were translated into country specific projections of climate variables, and resultant demands for wood products. Forest management regimes were projected to vary in response to these scenarios at local scales. The specific combinations of alternative forest management practices were based on parallel research and input from local forest stakeholders. For each case study, a specific forest growth simulator was used. In general, the climate scenarios applied did not cause fundamentally different ecosystem service outputs at the case study level. Our results revealed almost no reduction in outcomes for biodiversity indicators with an increase in wood production, and in some cases synergistic results occurred when diversity was actively promoted as part of the management concept. Net carbon uptake was not strongly correlated with biodiversity, indicating that biodiversity-friendly forest management doesn’t need to curtail carbon sequestration. Notably, we obtained heterogeneous results for the relation between sustainable wood production and net carbon uptake. Most scenarios resulted in a more or less reduced net carbon uptake over the long term, often due to stand age class distribution shifts. Levels of sustainable wood production varied widely during the simulation period, from significant increases (Sweden, Lithuania) to minor changes (Slovakia, Turkey) and slight decreases (Ireland, Netherlands). We place our results within the larger context of European forest policy and the challenges of simulating and contrasting forest biodiversity and the ecosystem services that societies depend on.

Wildfires cause substantial environmental and socioeconomic impacts and threaten many Spanish forested landscapes. We describe how LiDAR-derived canopy fuel characteristics and spatial fire simulation can be integrated with stand metrics to derive models describing fire behaviour. We assessed the potential use of very-low-density airborne LiDAR (light detection and ranging) data to estimate canopy fuel characteristics in south-western Spain Mediterranean forests. Forest type-specific equations were used to estimate canopy fuel attributes, namely stand height, canopy base height, fuel load, bulk density and cover. Regressions explained 61–85, 70–85, 38–96 and 75–95% of the variability in field estimated stand height, canopy fuel load, crown bulk density and canopy base height, respectively. The weakest relationships were found for mixed forests, where fuel loading variability was highest. Potential fire behaviour for typical wildfire conditions was predicted with FlamMap using LiDAR-derived canopy fuel characteristics and custom fuel models. Classification tree analysis was used to identify stand structures in relation to crown fire likelihood and fire suppression difficulty levels. The results of the research are useful for integrating multi-objective fire management decisions and effective fire prevention strategies within forest ecosystem management planning.