Prediction of plant diversity in grasslands using Sentinel-1 and-2 satellite image time series

REMOTE SENSING OF ENVIRONMENT

卷:237

Article

Fauvel, Mathieu¹  Lopes, Mailys²  Dubo, Titouan²  Rivers-Moore, Justine²  Frison, Pierre-Louis⁴  Gross, Nicolas³  Ouin, Annie² 

[1] Univ Toulouse, CESBIO, CNRS, CNES,INRA,IRD,UPS, Toulouse, France

[2] Univ Toulouse, INRA, DYNAFOR, Castanet Tolosan, France

[3] UCA, INRA, VetAgro Sup, UMR 0874,Ecosyst Prairial, F-63000 Clermont Ferrand, France

[4] Univ Paris Est, LaSTIG, IGN, 5 Bd Descartes, F-77455 Marne La Vallee, France

关键词: Satellite image time series;    Sentinel-1 &-2;    Statistical learning;    Grasslands;    taxonomic diversity;   

DOI  :  10.1016/j.rse.2019.111536

来源: Elsevier

【 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 x 10 m) over a large geographical extent (40,000 km(2)). 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.

【 授权许可】

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