LiDAR GEDI derived tree canopy height heterogeneity reveals patterns of biodiversity in forest ecosystems

Michele Torresani; Duccio Rocchini; Alessandro Alberti; Vítězslav Moudrý; Michael Heym; Elisa Thouverai; Patrick Kacic; Enrico Tomelleri

doi:10.1016/j.ecoinf.2023.102082

LiDAR GEDI derived tree canopy height heterogeneity reveals patterns of biodiversity in forest ecosystems

Ecol Inform. 2023 Sep:76:102082. doi: 10.1016/j.ecoinf.2023.102082.

Authors

Michele Torresani¹, Duccio Rocchini^{2

3}, Alessandro Alberti¹, Vítězslav Moudrý³, Michael Heym⁴, Elisa Thouverai², Patrick Kacic⁵, Enrico Tomelleri¹

Affiliations

¹ Free University of Bolzano/Bozen, Faculty of Agricultural, Environmental and Food Sciences, Piazza Universitá/Universitätsplatz 1, 39100 Bolzano/Bozen, Italy.
² BIOME Lab, Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, via Irnerio 42, 40126, Bologna, Italy.
³ Czech University of Life Sciences Prague, Faculty of Environmental Sciences, Department of Spatial Sciences, Kamýcka 129, Praha - Suchdol 16500, Czech Republic.
⁴ Bavarian State Institute of Forestry (LWF), Hans-Carl-von-Carlowitz-Platz-1, 85354 Freising, Germany.
⁵ Department of Remote Sensing, Institute of Geography and Geology, University of Würzburg, Würzburg, Germany.

Abstract

The "Height Variation Hypothesis" is an indirect approach used to estimate forest biodiversity through remote sensing data, stating that greater tree height heterogeneity (HH) measured by CHM LiDAR data indicates higher forest structure complexity and tree species diversity. This approach has traditionally been analyzed using only airborne LiDAR data, which limits its application to the availability of the dedicated flight campaigns. In this study we analyzed the relationship between tree species diversity and HH, calculated with four different heterogeneity indices using two freely available CHMs derived from the new space-borne GEDI LiDAR data. The first, with a spatial resolution of 30 m, was produced through a regression tree machine learning algorithm integrating GEDI LiDAR data and Landsat optical information. The second, with a spatial resolution of 10 m, was created using Sentinel-2 images and a deep learning convolutional neural network. We tested this approach separately in 30 forest plots situated in the northern Italian Alps, in 100 plots in the forested area of Traunstein (Germany) and successively in all the 130 plots through a cross-validation analysis. Forest density information was also included as influencing factor in a multiple regression analysis. Our results show that the GEDI CHMs can be used to assess biodiversity patterns in forest ecosystems through the estimation of the HH that is correlated to the tree species diversity. However, the results also indicate that this method is influenced by different factors including the GEDI CHMs dataset of choice and their related spatial resolution, the heterogeneity indices used to calculate the HH and the forest density. Our finding suggest that GEDI LIDAR data can be a valuable tool in the estimation of forest tree heterogeneity and related tree species diversity in forest ecosystems, which can aid in global biodiversity estimation.

Keywords: Canopy height model; GEDI; Height heterogeneity; Rao’s Q index; Remote sensing; Species diversity.