气候变化领域集成服务门户(CCPortal): Prediction of plant diversity in grasslands using Sentinel-1 and -2 satellite image time series

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DOI	10.1016/j.rse.2019.111536
	Prediction of plant diversity in grasslands using Sentinel-1 and -2 satellite image time series
	Fauvel M.; Lopes M.; Dubo T.; Rivers-Moore J.; Frison P.-L.; Gross N.; Ouin A.
发表日期	2020
ISSN	00344257
卷号	237
英文摘要	The prediction of grasslands plant diversity using satellite image time series is considered in this article. Fifteen months of freely available Sentinel optical and radar data were used to predict taxonomic and functional diversity at the pixel scale (10 m × 10 m) over a large geographical extent (40,000 km2). 415 field measurements were collected in 83 grasslands to train and validate several statistical learning methods. The objective was to link the satellite spectro-temporal data to the plant diversity indices. Among the several diversity indices tested, Simpson and Shannon indices were best predicted with a coefficient of determination around 0.4 using a Random Forest predictor and Sentinel-2 data. The use of Sentinel-1 data was not found to improve significantly the prediction accuracy. Using the Random Forest algorithm and the Sentinel-2 time series, the prediction of the Simpson index was performed. The resulting map highlights the intra-parcel variability and demonstrates the capacity of satellite image time series to monitor grasslands plant taxonomic diversity from an ecological viewpoint. © 2019 Elsevier Inc.
英文关键词	Grasslands; Satellite image time series; Sentinel-1 & -2; Statistical learning; taxonomic diversity
语种	英语
scopus关键词	Decision trees; Learning systems; Satellites; Time series; Grasslands; Satellite images; Sentinel-1; Statistical learning; Taxonomic diversity; Forecasting; algorithm; field margin; field method; grassland; plant; satellite data; satellite imagery; Sentinel; species diversity; taxonomy; time series analysis
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179540
作者单位	CESBIO, Université de Toulouse, CNES/CNRS/INRA/IRD/UPS, Toulouse, France; DYNAFOR, Université de Toulouse, INRA, Castanet-Tolosan, France; UCA, INRA, VetAgro Sup, UMR 0874 Ecosystème Prairial, Clermont-Ferrand, 63000, France; LaSTIG, Université Paris-Est, IGN, 5 Bd Descartes, Champs sur Marne, Marne la Vallée, 77455, France
推荐引用方式 GB/T 7714	Fauvel M.,Lopes M.,Dubo T.,et al. Prediction of plant diversity in grasslands using Sentinel-1 and -2 satellite image time series[J],2020,237.
APA	Fauvel M..,Lopes M..,Dubo T..,Rivers-Moore J..,Frison P.-L..,...&Ouin A..(2020).Prediction of plant diversity in grasslands using Sentinel-1 and -2 satellite image time series.Remote Sensing of Environment,237.
MLA	Fauvel M.,et al."Prediction of plant diversity in grasslands using Sentinel-1 and -2 satellite image time series".Remote Sensing of Environment 237(2020).