Mathieu Fauvel

Spectro-Temporal Heterogeneity Measures from Dense High Spatial Resolution Satellite Image Time Series: Application to Grassland Species Diversity Estimation

Grasslands represent a significant source of biodiversity that is important to monitor over large extents. The Spectral Variation Hypothesis (SVH) assumes that the Spectral Heterogeneity (SH) measured from remote sensing data can be used as a proxy for species diversity. Here, we argue the hypothesis that the grassland’s species differ in their phenology and, hence, that the temporal variations can be used in addition to the spectral variations. The purpose of this study is to attempt verifying the SVH in grasslands using the temporal information provided by dense Satellite Image Time Series (SITS) with a high spatial resolution. Our method to assess the spectro-temporal heterogeneity is based on a clustering of grasslands using a robust technique for high dimensional data. We propose new SH measures derived from this clustering and computed at the grassland level. We compare them to the Mean Distance to Centroid (MDC). The method is experimented on 192 grasslands from southwest France using an intra-annual multispectral SPOT5 SITS comprising 18 images and using single images from this SITS. The combination of two of the proposed SH measures—the within-class variability and the entropy—in a multivariate linear model explained the variance of the grasslands’ Shannon index more than the MDC. However, there were no significant differences between the predicted values issued from the best models using multitemporal and monotemporal imagery. We conclude that multitemporal data at a spatial resolution of 10 m do not contribute to estimating the species diversity. The temporal variations may be more related to the effect of management practices.

Potential of Sentinel-2 and SPOT5 (Take5) time series for the estimation of grasslands biodiversity indices

The aim of this study is to assess the potential of satellite image time series with high spatial and high temporal resolutions for the prediction of grasslands plant biodiversity. The grasslands are modeled at the object scale to be consistent with ecological measurements (one biodiversity index per grassland). A kernel regression is used to predict the biodiversity index of a grassland from its spectro-temporal reflectance. The method is applied using two intra-annual multispectral or NDVI time series of SPOT5 Take5 (18 dates) and Sentinel-2 (7 dates) to predict the Shannon and the Simpson indices of about 200 grasslands in south-west France. The best coefficient of determination for the prediction of the Shannon index is 0.13 and it is 0.17 for the Simpson index prediction. The unsatisfactory results suggest that a high temporal resolution combined with a high spatial resolution and multispectral bands are not sufficient to estimate grassland biodiversity at the grassland scale.