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Article

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

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BIOME Laboratory, Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, Via Irnerio 42, 40126 Bologna, Italy
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Faculty of Science and Technology, Free University of Bozen-Bolzano, Piazza Universitá/Universitätsplatz 1, 39100 Bolzano, Italy
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Department of Applied Geoinformatics and Spatial Planning, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcka 129, Suchdol, 16500 Praha, Czech Republic
*
Author to whom correspondence should be addressed.
Academic Editor: Alex Lechner
Remote Sens. 2021, 13(18), 3569; https://doi.org/10.3390/rs13183569
Received: 18 August 2021 / Revised: 2 September 2021 / Accepted: 6 September 2021 / Published: 8 September 2021
(This article belongs to the Special Issue Adding the Third Dimension to Biodiversity Mapping)
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. View Full-Text
Keywords: forest ecosystems; biodiversity; Rao’s Q index; height heterogeneity; remote sensing; LiDAR; rasterdiv forest ecosystems; biodiversity; Rao’s Q index; height heterogeneity; remote sensing; LiDAR; rasterdiv
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MDPI and ACS Style

Tamburlin, D.; Torresani, M.; Tomelleri, E.; Tonon, G.; Rocchini, D. Testing the Height Variation Hypothesis with the R rasterdiv Package for Tree Species Diversity Estimation. Remote Sens. 2021, 13, 3569. https://doi.org/10.3390/rs13183569

AMA Style

Tamburlin D, Torresani M, Tomelleri E, Tonon G, Rocchini D. Testing the Height Variation Hypothesis with the R rasterdiv Package for Tree Species Diversity Estimation. Remote Sensing. 2021; 13(18):3569. https://doi.org/10.3390/rs13183569

Chicago/Turabian Style

Tamburlin, Daniel, Michele Torresani, Enrico Tomelleri, Giustino Tonon, and Duccio Rocchini. 2021. "Testing the Height Variation Hypothesis with the R rasterdiv Package for Tree Species Diversity Estimation" Remote Sensing 13, no. 18: 3569. https://doi.org/10.3390/rs13183569

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