A 15-year spatio-temporal analysis of plant β-diversity using Landsat time series derived Rao’s Q index

Understanding temporal dynamics of plant biodiversity is crucial for conservation strategies at regional and local levels. The mostly applied hitherto methods are based on field observations of the plant communities and the related taxa. Satellite earth observation time series offer continuous and w...

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Autores principales:	Siddhartha Khare, Hooman Latifi, Sergio Rossi
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	β-diversity Rao’s Q index Time series NDVI MSAVI Google Earth Engine Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/bd0ad532fc3f420d938b2653437302d0
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spelling	oai:doaj.org-article:bd0ad532fc3f420d938b2653437302d02021-12-01T04:35:17ZA 15-year spatio-temporal analysis of plant β-diversity using Landsat time series derived Rao’s Q index1470-160X10.1016/j.ecolind.2020.107105https://doaj.org/article/bd0ad532fc3f420d938b2653437302d02021-02-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X2031044Xhttps://doaj.org/toc/1470-160XUnderstanding temporal dynamics of plant biodiversity is crucial for conservation strategies at regional and local levels. The mostly applied hitherto methods are based on field observations of the plant communities and the related taxa. Satellite earth observation time series offer continuous and wider coverage for the assessment of plant diversity, especially in remote areas. Theoretical basis and large-scale solutions for assessing beta-diversity have been recently presented. Yet landscape-scale and context-based analysis are missing. We assessed temporal β-diversity using Raós Q diversity derived from Landsat-based vegetation indices by considering the effect of ERA-5 monthly aggregates environmental factors (temperature and precipitation) extracted using Google Earth Engine (GEE), land use classes, and two common vegetation indices. We derived 15-year Rao’s Q diversity using Landsat-7 based normalized difference vegetation index (NDVI) and modified soil-adjusted vegetation index (MSAVI). We evaluated the temporal turnover in Rao’s Q on multiple land use classes, including agriculture, intact forest and areas affected by and invasive species. Vegetation index and Rao’s Q diverged between pre- and post- monsoon seasons. Rao’s Q had higher temporal turnover with NDVI than MSAVI for all vegetation classes, however the latter showed higher sensitivity towards temperature and precipitation. Moreover, agriculture generally showed higher variability than forest and invasive species. The temporal turnover was correlated between NDVI and MSAVI for all vegetation classes, which indicated that the variability among vegetation types was directly related to spectral heterogeneity. Furthermore, MSAVI was less sensitive to the effect of soil in assessing the vegetation indices, which resulted in higher global sensitivity of QMSAVI. Near infrared and red spectra used in vegetation indices are able to capture a small variation in leaf traits reflectance for vegetation types. Here, the β-diversities and their temporal dynamics derived from the vegetation indices differed based on their sensitivity to soil, vegetation density and seasonality. This approach and its open source implementation can be tested for different forest ecosystems at varying spatial scales.Siddhartha KhareHooman LatifiSergio RossiElsevierarticleβ-diversityRao’s Q indexTime seriesNDVIMSAVIGoogle Earth EngineEcologyQH540-549.5ENEcological Indicators, Vol 121, Iss , Pp 107105- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	β-diversity Rao’s Q index Time series NDVI MSAVI Google Earth Engine Ecology QH540-549.5
spellingShingle	β-diversity Rao’s Q index Time series NDVI MSAVI Google Earth Engine Ecology QH540-549.5 Siddhartha Khare Hooman Latifi Sergio Rossi A 15-year spatio-temporal analysis of plant β-diversity using Landsat time series derived Rao’s Q index
description	Understanding temporal dynamics of plant biodiversity is crucial for conservation strategies at regional and local levels. The mostly applied hitherto methods are based on field observations of the plant communities and the related taxa. Satellite earth observation time series offer continuous and wider coverage for the assessment of plant diversity, especially in remote areas. Theoretical basis and large-scale solutions for assessing beta-diversity have been recently presented. Yet landscape-scale and context-based analysis are missing. We assessed temporal β-diversity using Raós Q diversity derived from Landsat-based vegetation indices by considering the effect of ERA-5 monthly aggregates environmental factors (temperature and precipitation) extracted using Google Earth Engine (GEE), land use classes, and two common vegetation indices. We derived 15-year Rao’s Q diversity using Landsat-7 based normalized difference vegetation index (NDVI) and modified soil-adjusted vegetation index (MSAVI). We evaluated the temporal turnover in Rao’s Q on multiple land use classes, including agriculture, intact forest and areas affected by and invasive species. Vegetation index and Rao’s Q diverged between pre- and post- monsoon seasons. Rao’s Q had higher temporal turnover with NDVI than MSAVI for all vegetation classes, however the latter showed higher sensitivity towards temperature and precipitation. Moreover, agriculture generally showed higher variability than forest and invasive species. The temporal turnover was correlated between NDVI and MSAVI for all vegetation classes, which indicated that the variability among vegetation types was directly related to spectral heterogeneity. Furthermore, MSAVI was less sensitive to the effect of soil in assessing the vegetation indices, which resulted in higher global sensitivity of QMSAVI. Near infrared and red spectra used in vegetation indices are able to capture a small variation in leaf traits reflectance for vegetation types. Here, the β-diversities and their temporal dynamics derived from the vegetation indices differed based on their sensitivity to soil, vegetation density and seasonality. This approach and its open source implementation can be tested for different forest ecosystems at varying spatial scales.
format	article
author	Siddhartha Khare Hooman Latifi Sergio Rossi
author_facet	Siddhartha Khare Hooman Latifi Sergio Rossi
author_sort	Siddhartha Khare
title	A 15-year spatio-temporal analysis of plant β-diversity using Landsat time series derived Rao’s Q index
title_short	A 15-year spatio-temporal analysis of plant β-diversity using Landsat time series derived Rao’s Q index
title_full	A 15-year spatio-temporal analysis of plant β-diversity using Landsat time series derived Rao’s Q index
title_fullStr	A 15-year spatio-temporal analysis of plant β-diversity using Landsat time series derived Rao’s Q index
title_full_unstemmed	A 15-year spatio-temporal analysis of plant β-diversity using Landsat time series derived Rao’s Q index
title_sort	15-year spatio-temporal analysis of plant β-diversity using landsat time series derived rao’s q index
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/bd0ad532fc3f420d938b2653437302d0
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A 15-year spatio-temporal analysis of plant β-diversity using Landsat time series derived Rao’s Q index

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