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.

The use of mobile laser scanning to survey forest ecosystems is a promising, scalable technology to describe the 3D structure of forests at a high resolution. We use a structurally complex, mixed-species Mediterranean forest to test the performance of a mobile Handheld Laser Scanning (HLS) system to estimate tree attributes within a forest patch in central Spain. We describe the different stages of the HLS approach: field position, ground data collection, scanning path design, point cloud processing, alignment between detected trees and measured reference trees, and finally, the assessment of main tree structural attributes diameter at breast height (DBH) and tree height considering species and tree size as control factors. We surveyed 418 reference trees to account for omission and commission error rates over a 1 ha plot divided into 16 sections and scanned using two different scanning paths. The HLS-based approach reached a high of 88 and 92% tree detection rate for the best combination of scanning path and point cloud processing modes for the HLS system. The root mean squared errors for DBH estimates varied between species: errors for Pinus pinaster were below 2 cm for Scan 02. Quercus pyrenaica, and Alnus glutinosa showed higher error rates. We observed good agreement between ALS and HLS estimates for tree height, highlighting differences to field measurements. Despite the complexity of the mixed forest area surveyed, our results show that HLS is highly efficient at detecting tree locations, estimating DBH, and supporting tree height measurements as confirmed with airborne laser data used for validation. This study is one of the first HLS-based studies conducted in the Mediterranean mixed forest region, where variability in tree allometries and spacing and the presence of natural regeneration pose challenges for the HLS approach. HLS is a feasible, time-efficient, scalable technology for tree mapping in mixed forests with potential to support forest monitoring programmes such as national forest inventories lacking three-dimensional, remote sensing data to support field measurements.

Background: Modern remote sensing methods enable the prediction of tree-level forest resource data. However, the benefits of using tree-level data in forest or harvest planning is not clear given a relative paucity of research. In particular, there is a need for tree-level methods that simultaneously account for the spatial distribution of trees and other objectives. In this study, we developed a spatial tree selection method that considers tree-level (relative value increment), neighborhood related (proximity of cut trees) and global objectives (total harvest).Methods: We partitioned the whole surface area of the stand to trees, with the assumption that a large tree occupies a larger area than a small tree. This was implemented using a power diagram. We also utilized spatially explicit tree-level growth models that accounted for competition by neighboring trees. Optimization was conducted with a variant of cellular automata. The proposed method was tested in stone pine (Pinus pineaL.) stands in Spain where we implemented basic individual tree detection with airborne laser scanning data.Results: We showed how to mimic four different spatial distributions of cut trees using alternative weightings of objective variables. The Non-spatial selection did not aim at a particular spatial layout, the Single-tree selection dispersed the trees to be cut, and the Tree group and Clearcut selections clustered harvested trees at different magnitudes.Conclusions: The proposed method can be used to control the spatial layout of trees while extracting trees that are the most economically mature.