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

Ecological Indicators

Sergio Rossi¹  Hooman Latifi²  Siddhartha Khare³ 

[1] Corresponding author.;Department of Biology, McGill University, Montreal, QC H3A 1B1, Canada;Département des Sciences Fondamentales, Université du Québec à Chicoutimi, Canada;

关键词: β-diversity;    Rao’s Q index;    Time series;    NDVI;    MSAVI;    Google Earth Engine;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown