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Article

Potential and Limitations of Grasslands α-Diversity Prediction Using Fine-Scale Hyperspectral Imagery

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Research and Innovation Centre, Sustainable Ecosystems and Bioresources Department, Fondazione Edmund Mach, Via E. Mach 1, 38098 San Michele all’Adige, TN, Italy
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Department of Civil, Environmental and Mechanical Engineering, University of Trento, Via Mesiano 77, 38123 Trento, TN, Italy
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Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), Viale dell’Università 16, 35020 Legnaro, PD, Italy
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BIOME Lab, Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum, University of Bologna, Via Irnerio 42, 40126 Bologna, BO, Italy
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Department of Spatial Sciences, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcka 129, 16500 Prague-Suchdol, Czech Republic
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Institute of BioEconomy, National Research Council (IBE-CNR), Via Biasi 75, 38098 San Michele all’Adige, TN, Italy
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Foxlab Joint CNR-FEM Initiative, Via E. Mach 1, 38098 San Michele all’Adige, TN, Italy
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Author to whom correspondence should be addressed.
Academic Editor: Michael J. Hill
Remote Sens. 2021, 13(14), 2649; https://doi.org/10.3390/rs13142649
Received: 22 May 2021 / Revised: 17 June 2021 / Accepted: 30 June 2021 / Published: 6 July 2021
(This article belongs to the Special Issue Remote Sensing of Ecosystem Diversity)
Plant biodiversity is an important feature of grassland ecosystems, as it is related to the provision of many ecosystem services crucial for the human economy and well-being. Given the importance of grasslands, research has been carried out in recent years on the potential to monitor them with novel remote sensing techniques. In this study, the optical diversity (also called spectral diversity) approach was adopted to check the potential of using high-resolution hyperspectral images to estimate α-diversity in grassland ecosystems. In 2018 and 2019, grassland species composition was surveyed and canopy hyperspectral data were acquired at two grassland sites: Monte Bondone (IT-MBo; species-rich semi-natural grasslands) and an experimental farm of the University of Padova, Legnaro, Padua, Italy (IT-PD; artificially established grassland plots with a species-poor mixture). The relationship between biodiversity (species richness, Shannon’s, species evenness, and Simpson’s indices) and optical diversity metrics (coefficient of variation-CV and standard deviation-SD) was not consistent across the investigated grassland plant communities. Species richness could be estimated by optical diversity metrics with an R = 0.87 at the IT-PD species-poor site. In the more complex and species-rich grasslands at IT-MBo, the estimation of biodiversity indices was more difficult and the optical diversity metrics failed to estimate biodiversity as accurately as in IT-PD probably due to the higher number of species and the strong canopy spatial heterogeneity. Therefore, the results of the study confirmed the ability of spectral proxies to detect grassland α-diversity in man-made grassland ecosystems but highlighted the limitations of the spectral diversity approach to estimate biodiversity when natural grasslands are observed. Nevertheless, at IT-MBo, the optical diversity metric SD calculated from post-processed hyperspectral images and transformed spectra showed, in the red part of the spectrum, a significant correlation (up to R = 0.56, p = 0.004) with biodiversity indices. Spatial resampling highlighted that for the IT-PD sward the optimal optical pixel size was 1 cm, while for the IT-MBo natural grassland it was 1 mm. The random pixel extraction did not improve the performance of the optical diversity metrics at both study sites. Further research is needed to fully understand the links between α-diversity and spectral and biochemical heterogeneity in complex heterogeneous ecosystems, and to assess whether the optical diversity approach can be adopted at the spatial scale to detect β-diversity. Such insights will provide more robust information on the mechanisms linking grassland diversity and optical heterogeneity. View Full-Text
Keywords: biodiversity indices; coefficient of variation (CV); man-made grasslands; natural grasslands; optical diversity; standard deviation (SD) biodiversity indices; coefficient of variation (CV); man-made grasslands; natural grasslands; optical diversity; standard deviation (SD)
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MDPI and ACS Style

Imran, H.A.; Gianelle, D.; Scotton, M.; Rocchini, D.; Dalponte, M.; Macolino, S.; Sakowska, K.; Pornaro, C.; Vescovo, L. Potential and Limitations of Grasslands α-Diversity Prediction Using Fine-Scale Hyperspectral Imagery. Remote Sens. 2021, 13, 2649. https://doi.org/10.3390/rs13142649

AMA Style

Imran HA, Gianelle D, Scotton M, Rocchini D, Dalponte M, Macolino S, Sakowska K, Pornaro C, Vescovo L. Potential and Limitations of Grasslands α-Diversity Prediction Using Fine-Scale Hyperspectral Imagery. Remote Sensing. 2021; 13(14):2649. https://doi.org/10.3390/rs13142649

Chicago/Turabian Style

Imran, Hafiz A., Damiano Gianelle, Michele Scotton, Duccio Rocchini, Michele Dalponte, Stefano Macolino, Karolina Sakowska, Cristina Pornaro, and Loris Vescovo. 2021. "Potential and Limitations of Grasslands α-Diversity Prediction Using Fine-Scale Hyperspectral Imagery" Remote Sensing 13, no. 14: 2649. https://doi.org/10.3390/rs13142649

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