RT info:eu-repo/semantics/article T1 Identifying forest structural types along an aridity gradient in peninsular Spain: Integrating low-density LiDAR, forest inventory, and aridity index A1 Tijerín Triviño, Julián A1 García Alonso, Mariano A1 Moreno Fernández, Daniel A1 Astigarraga Urcelay, Julen A1 Zavala Gironés, Miguel Ángel de K1 Aridity gradient K1 Forest structure K1 LiDAR K1 Low-density ALS K1 Random Forest K1 Regional scale K1 Medio Ambiente K1 Environmental science AB Forest structure is a key driver of forest functional processes. The characterization of forest structure across spatiotemporal scales is essential for forest monitoring and management. LiDAR data have proven particularly useful for cost-effectively estimating forest structural attributes. This paper evaluates the ability of combined forest inventory data and low-density discrete return airborne LiDAR data to discriminate main forest structural types in the Mediterranean-temperate transition ecotone. Firstly, we used six structural variables from the Spanish National Forest Inventory (SNFI) and an aridity index in a k-medoids algorithm to define the forest structural types. These variables were calculated for 2770 SNFI plots. We identified the main species for each structural type using the SNFI. Secondly, we developed a Random Forest model to predict the spatial distribution of structural types and create wall-to-wall maps from LiDAR data. The k-medoids clustering algorithm enabled the identification of four clusters of forest structures. A total of six out of forty-one potential LiDAR metrics were utilized in our Random Forest, after evaluating their importance in the Random Forest model. Selected metrics were, in decreasing order of importance, the percentage of all returns above 2 m, mean height of the canopy profile, the difference between the 90th and 50th height percentiles, the area under the canopy curve, and the 5th and the 95th percentile of the return heights. The model yielded an overall accuracy of 64.18%. The producer's accuracy ranged between 36.11% and 88.93%. Our results confirm the potential of this approximation for the continuous monitoring of forest structures, which is key to guiding forest management in this region. SN ISSN 2072-4292 YR 2022 FD 2022-01-05 LK http://hdl.handle.net/10017/50757 UL http://hdl.handle.net/10017/50757 LA eng NO Agencia Estatal de Investigación DS MINDS@UW RD 29-mar-2024