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

Thouverai, Elisa; Marcantonio, Matteo; Lenoir, Jonathan; Galfré, Mariasole; Marchetto, Elisa; Bacaro, Giovanni; Gatti, Roberto Cazzolla; Re, Daniele Da; Musciano, Michele Di; Furrer, Reinhard; Malavasi, Marco; Moudrý, Vítězslav; Nowosad, Jakub; Pedrotti, Franco; Pelorosso, Raffaele; Pezzi, Giovanna; Šímová, Petra; Ricotta, Carlo; Silvestri, S.; Tordoni, Enrico; Torresani, Michele; Vacchiano, Giorgio; Zannini, Piero; Rocchini, Duccio

doi:10.1016/j.ecocom.2023.101029

Cited by 9 publications

(5 citation statements)

References 33 publications

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“…Regarding the evaluation of the use of different heterogeneity indices, our results demonstrated the usefulness of the Rao’s Q index in assessing the vegetation HH across areas of intensive and extensive grassland management. This heterogeneity index, widely used as a spectral heterogeneity index in studies on SVH 39 , 51 , 62 , 75 offers the advantage of coupling both the relative abundance and the pixel values (as quantified by the Euclidean distance between the pixel values) 78 , thus capturing the complete structural information derived from the heterogeneity of the photogrammetric outcomes. This index, when applied with a single layer or raster as in our study, can effectively serve as a proxy for heterogeneity by narrowing it down to variance using half of the squared Euclidean distance (1/2 ) (for further details on the mathematical characteristics of Rao’s Q, we refer to 57 – 59 , 62 ).…”

Section: Discussionmentioning

confidence: 99%

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

Torresani,

Rocchini,

Ceola

et al. 2024

Sci Rep

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The ecosystem services offered by pollinators are vital for supporting agriculture and ecosystem functioning, with bees standing out as especially valuable contributors among these insects. Threats such as habitat fragmentation, intensive agriculture, and climate change are contributing to the decline of natural bee populations. Remote sensing could be a useful tool to identify sites of high diversity before investing into more expensive field survey. In this study, the ability of Unoccupied Aerial Vehicles (UAV) images to estimate biodiversity at a local scale has been assessed while testing the concept of the Height Variation Hypothesis (HVH). This hypothesis states that the higher the vegetation height heterogeneity (HH) measured by remote sensing information, the higher the vegetation vertical complexity and the associated species diversity. In this study, the concept has been further developed to understand if vegetation HH can also be considered a proxy for bee diversity and abundance. We tested this approach in 30 grasslands in the South of the Netherlands, where an intensive field data campaign (collection of flower and bee diversity and abundance) was carried out in 2021, along with a UAV campaign (collection of true color-RGB-images at high spatial resolution). Canopy Height Models (CHM) of the grasslands were derived using the photogrammetry technique “Structure from Motion” (SfM) with horizontal resolution (spatial) of 10 cm, 25 cm, and 50 cm. The accuracy of the CHM derived from UAV photogrammetry was assessed by comparing them through linear regression against local CHM LiDAR (Light Detection and Ranging) data derived from an Airborne Laser Scanner campaign completed in 2020/2021, yielding an $$R^2$$ R 2 of 0.71. Subsequently, the HH assessed on the CHMs at the three spatial resolutions, using four different heterogeneity indices (Rao’s Q, Coefficient of Variation, Berger–Parker index, and Simpson’s D index), was correlated with the ground-based flower and bee diversity and bee abundance data. The Rao’s Q index was the most effective heterogeneity index, reaching high correlations with the ground-based data (0.44 for flower diversity, 0.47 for bee diversity, and 0.34 for bee abundance). Interestingly, the correlations were not significantly influenced by the spatial resolution of the CHM derived from UAV photogrammetry. Our results suggest that vegetation height heterogeneity can be used as a proxy for large-scale, standardized, and cost-effective inference of flower diversity and habitat quality for bees.

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Section: Discussionmentioning

confidence: 99%

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

Torresani,

Rocchini,

Ceola

et al. 2024

Sci Rep

Self Cite

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“…From a theoretical point of view, the CV considers only the pixels value (through mean and standard deviation) and not their relative abundance within the plots. On the other hand, the Rao’s Q index, that has shown excellent results as spectral heterogeneity index in different SVH studies ( Torresani et al, 2021 , Rocchini et al, 2017 , Thouverai et al, 2023 ), has the advantage to include both the relative abundance and the value of the pixels (through the Euclidean distance between the pixel values) ( Torresani et al, 2022 ) thus the whole structural information derived from the LiDAR data heterogeneity. This index, when used with a single layer/raster as in this study, succeeds in becoming a good proxy of heterogeneity by narrowing to variance using half the squared Euclidean distance (1/2

).…”

Section: Discussionmentioning

confidence: 99%

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

Torresani

Rocchini

Alberti

et al. 2023

Ecological Informatics

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“…This is especially valuable in exploratory data analysis where the choice of alpha for the Rao's index may not be immediately apparent. By assessing the AUC, researchers can gain insights into the overall trend of diversity across different scales of emphasis on spectral classes abundance and distance [6].…”

Section: Rao's Accumulation Functionmentioning

confidence: 99%

The rasterdiv package for measuring diversity from space

Marcantonio,

Thouverau,

Rocchini

2024

Preprint

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This book chapter provides an extensive overview of the rasterdiv package which provides a comprehensive suite of functions designed to calculate diversity indices from numerical matrices, including those derived from optical remote sens ing imagery. It facilitates a deeper analysis of spatial heterogeneity and ecosystem complexity by translating pixel values into ecological indicators. This package serves as a valuable tool for researchers and practitioners in the fields of landscape ecology, biodiversity monitoring, and environmental management, allowing for an enhanced understanding of landscape patterns and their ecological implications through advanced quantitative measures.

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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

Cited by 9 publications

References 33 publications

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

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

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

The rasterdiv package for measuring diversity from space

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