rasterdiv—An Information Theory tailored R package for measuring ecosystem heterogeneity from space: To the origin and back

Rocchini, Duccio; Thouverai, Elisa; Marcantonio, Matteo; Iannacito, Martina; Re, Daniele Da; Torresani, Michele; Bacaro, Giovanni; Bazzichetto, Manuele; Bernardi, Alessandra; Foody, Giles M.; Furrer, Reinhard; Kleijn, David; Larsen, Stefano; Lenoir, Jonathan; Malavasi, Marco; Marchetto, Elisa; Messori, Filippo; Montaghi, Alessandro; Moudrý, Vítězslav; Naimi, Babak; Ricotta, Carlo; Rossini, Micol; Santi, Francesco; Santos, Maria J.; Schaepman, Michael E.; Schneider, Fabian; Schuh, Leila; Silvestri, S.; Šímová, Petra; Skidmore, A.K.; Tattoni, Clara; Tordoni, Enrico; Vicario, Saverio; Zannini, Piero; Wegmann, Martin

doi:10.1111/2041-210x.13583

Cited by 51 publications

(37 citation statements)

References 48 publications

(53 reference statements)

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“…Algorithms that aim to measure environmental heterogeneity through remote sensing data can rely on the moving window technique, which divides remotely sensed imagery into user-defined squares (windows) to derive measures of heterogeneity. Examples are included in the rasterdiv R package (Rocchini et al, 2021b). One of the most used metrics included in the package is the Shannon entropy index H (Shannon, 1948):…”

Section: The Theorymentioning

confidence: 99%

“…Therefore, the use of generalized entropies, where one single formula represents a parameterized version of an index, provides a continuum of heterogeneity metrics reflecting all the characteristics of the heterogeneity spectrum. Rocchini et al (2021b) presented a parametric version of Rao's Q allowing the characterization of the dimensionality of heterogeneity in different ecosystems:…”

Section: The Theorymentioning

confidence: 99%

“…In this context, the R platform is one of the most used statistical and computational environment in ecology, partially thanks to the continuous development of relevant packages. In particular, the rasterdiv package (Marcantonio et al, 2021;Rocchini et al, 2021b;Thouverai et al, 2021) allows to calculate a plethora of different indices to measure spatio-ecological heterogeneity from space.…”

Section: Introductionmentioning

confidence: 99%

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Integrals of life: Tracking ecosystem spatial heterogeneity from space through the area under the curve of the parametric Rao’s Q index

Thouverai

Marcantonio

Lenoir

et al. 2022

Ecological Complexity

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Section: The Theorymentioning

confidence: 99%

Section: The Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrals of life: Tracking ecosystem spatial heterogeneity from space through the area under the curve of the parametric Rao’s Q index

Thouverai

Marcantonio

Lenoir

et al. 2022

Ecological Complexity

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“…There was a higher positive correlation between the entropy, dissimilarity, and contrast metrics, which were negatively correlated with homogeneity and second moment [82]. We calculated several diversity indices from the land-cover classifications using the 'rasterDiv' package [65]; namely, Shannon's Diversity [66], Pielou's Evenness [67], the Berger-Parker Index [68], Cumulative Residual Entropy [70], Rao's quadratic entropy [69], Hill's number [71], and Rényi's index [83]. The window size was also 10 m × 10 m (or 108 × 108 pixels), corresponding to the size of the plots.…”

Section: Vegetation Indicesmentioning

confidence: 99%

Area-Wide Prediction of Vertebrate and Invertebrate Hole Density and Depth across a Climate Gradient in Chile Based on UAV and Machine Learning

Grigusova

Larsen

Achilles

et al. 2021

Drones

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Burrowing animals are important ecosystem engineers affecting soil properties, as their burrowing activity leads to the redistribution of nutrients and soil carbon sequestration. The magnitude of these effects depends on the spatial density and depth of such burrows, but a method to derive this type of spatially explicit data is still lacking. In this study, we test the potential of using consumer-oriented UAV RGB imagery to determine the density and depth of holes created by burrowing animals at four study sites along a climate gradient in Chile, by combining UAV data with empirical field plot observations and machine learning techniques. To enhance the limited spectral information in RGB imagery, we derived spatial layers representing vegetation type and height and used landscape textures and diversity to predict hole parameters. Across-site models for hole density generally performed better than those for depth, where the best-performing model was for the invertebrate hole density (R2 = 0.62). The best models at individual study sites were obtained for hole density in the arid climate zone (R2 = 0.75 and 0.68 for invertebrates and vertebrates, respectively). Hole depth models only showed good to fair performance. Regarding predictor importance, the models heavily relied on vegetation height, texture metrics, and diversity indices.

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“…In particular, the main objective is to test different LiDAR datasets, metrics and heterogeneity indices at different spatial resolutions and MW, in order to understand which is the best combination of data that can define the above mentioned relationships. In order to test all these combinations, we decided to relay on the recent R rasterdiv package [42] developed by Rocchini et al [43]. It is a new R package that, to date, has never been tested with remote sensing data having field data as validation.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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rasterdiv—An Information Theory tailored R package for measuring ecosystem heterogeneity from space: To the origin and back

Abstract: This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

Cited by 51 publications

References 48 publications

Integrals of life: Tracking ecosystem spatial heterogeneity from space through the area under the curve of the parametric Rao’s Q index

Integrals of life: Tracking ecosystem spatial heterogeneity from space through the area under the curve of the parametric Rao’s Q index

Area-Wide Prediction of Vertebrate and Invertebrate Hole Density and Depth across a Climate Gradient in Chile Based on UAV and Machine Learning

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

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