Testing the Height Variation Hypothesis with the R rasterdiv Package for Tree Species Diversity Estimation

Abstract

Forest biodiversity is a key element to support ecosystem functions. Measuring biodiversity is a necessary step to identify critical issues and to choose interventions to be applied in order to protect it. Remote sensing provides consistent quality and standardized data, which can be used to estimate different aspects of biodiversity. The Height Variation Hypothesis (HVH) represents an indirect method for estimating species diversity in forest ecosystems from the LiDAR data, and it assumes that the higher the variation in tree height (height heterogeneity, HH), calculated through the ’Canopy Height Model’ (CHM) metric, the more complex the overall structure of the forest and the higher the tree species diversity. To date, the HVH has been tested exclusively with CHM data, assessing the HH only with a single heterogeneity index (the Rao’s Q index) without making use of any moving windows (MW) approach. In this study, the HVH has been tested in an alpine coniferous forest situated in the municipality of San Genesio/Jenesien (eastern Italian Alps) at 1100 m, characterized by the presence of 11 different tree species (mainly Pinus sylvestris, Larix decidua, Picea abies followed by Betula alba and Corylus avellana). The HH has been estimated through different heterogeneity measures described in the new R rasterdiv package using, besides the CHM, also other LiDAR metrics (as the percentile or the standard deviation of the height distribution) at various spatial resolutions and MWs (ALS LiDAR data with mean point cloud density of 2.9 point/m2). For each combination of parameters, and for all the considered plots, linear regressions between the Shannon’s H’ (used as tree species diversity index based on field data) and the HH have been derived. The results showed that the Rao’s Q index (singularly and through a multidimensional approach) performed generally better than the other heterogeneity indices in the assessment of the HH. The CHM and the LiDAR metrics related to the upper quantile point cloud distribution at fine resolution (2.5 m, 5 m) have shown the most important results for the assessment of the HH. The size of the used MW did not influence the general outcomes but instead, it increased when compared to the results found in the literature, where the HVH was tested without MW approach. The outcomes of this study underline that the HVH, calculated with certain heterogeneity indices and LiDAR data, can be considered a useful tool for assessing tree species diversity in considered forest ecosystems. The general results highlight the strength and importance of LiDAR data in assessing the height heterogeneity and the related biodiversity in forest ecosystems.

Grassland vertical height heterogeneity predicts flower and bee diversity: an UAV photogrammetric approach

“…Firstly, the primary objective of this study was to investigate the feasibility of utilizing RGB UAV images to assess vegetation structure complexity for estimating HH and flower and bee diversity and bee abundance. Secondly, choice was guided by the findings of Tamburlin et al 72 , who, testing the HVH with LiDAR data, evaluated various LiDAR metrics (such as entropy and standard deviation of point cloud distribution, percentage of returns above mean height) for HH estimation and demonstrated that the CHM was the most effective metric to characterize vegetation HH.…”

“…Torresani et al 79 , 80 tested this approach positively in different forested areas using both Airborne Laser Scanning (ALS, where the LiDAR sensor is mounted on an aircraft) and space-borne GEDI (Global Ecosystem Dynamics Investigation) LiDAR data 14 , 16 , 34 , 53 for the assessment of tree species diversity. Tamburlin et al 72 also tested the methodology in forested areas using ALS data, showing that the Canopy Height Model (CHM) is the most appropriate LiDAR metric for an accurate estimation of vegetation height heterogeneity and inference of species diversity. The approach has been used not only to assess vegetation diversity but also to estimate animal diversity, different studies showed that the variability in habitat structure has a significant effect on the bird diversity in both agricultural and forest ecosystems 2 , 43 .…”

Remote sensing of savanna woody species diversity: A systematic review of data types and assessment methods

“…Although overstorey and understorey LiDAR returns can be successfully separated in sparsely vegetated savanna environments, the separation capabilities becomes less effective in canopies with dense and continuous architecture especially when LiDAR point density is low [ 160 , 162 ]. Canopy height models (CHM), which simulate continuous surfaces (grids) of canopy tops, have been used to solve such problem instead of directly relying on point cloud height information.…”

“…Canopy height models (CHM), which simulate continuous surfaces (grids) of canopy tops, have been used to solve such problem instead of directly relying on point cloud height information. For example, [ 162 ] used LiDAR metrics generated from CHM to correlate Rao’s Q and Shannon diversity indices extracted from 100-m 2 plots in a Mediterranean savanna of 270 ha. Using simple linear regression, the authors reported R 2 of 0.73 for Shannon diversity index and R 2 of 0.75 for Rao’s Q.…”

A novel approach for surveying flowers as a proxy for bee pollinators using drone images

“…Recently, remote sensing and Earth observation has made significant progress with estimating various aspects of biodiversity in a standardized way at large spatial scales (Torresani et al, 2020;Tamburlin et al, 2021;Michele et al, 2018;Rocchini et al, 2022). Satellite remote sensing (hereafter SRS) has the advantage that it covers vast spatial scales, it can provide information on a variety of ecological characteristics such as vegetation distribution (e.g.…”