Potential and Limitations of Grasslands alpha-Diversity Prediction Using Fine-Scale Hyperspectral Imagery
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F21%3A87026" target="_blank" >RIV/60460709:41330/21:87026 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/2072-4292/13/14/2649" target="_blank" >https://www.mdpi.com/2072-4292/13/14/2649</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/rs13142649" target="_blank" >10.3390/rs13142649</a>
Alternative languages
Result language
angličtina
Original language name
Potential and Limitations of Grasslands alpha-Diversity Prediction Using Fine-Scale Hyperspectral Imagery
Original language description
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 alpha-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, Shannons, species evenness, and Simpsons indices) and optical diversity me
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10618 - Ecology
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Remote Sensing
ISSN
2072-4292
e-ISSN
2072-4292
Volume of the periodical
14
Issue of the periodical within the volume
13
Country of publishing house
CH - SWITZERLAND
Number of pages
23
Pages from-to
1-23
UT code for WoS article
000676960800001
EID of the result in the Scopus database
2-s2.0-85110773774