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

Abstract

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.

Estimating tree species diversity from space in an alpine conifer forest: The Rao's Q diversity index meets the spectral variation hypothesis

“…The SVH has been tested in different ecosystems including wetlands , prairie vegetation (Palmer et al, 2002), tropical forests (Féret and Asner, 2014), grasslands (Lopes et al, 2017) and Mediterranean vegetation (Levin et al, 2007). Recently Schmidtlein and Fassnach (Schmidtlein and Fassnacht, 2017) tested the SVH across different habitats observing that it does not hold across different ecosystems, stressing its ecosystem dependency.…”

“…Recently Schmidtlein and Fassnach (Schmidtlein and Fassnacht, 2017) tested the SVH across different habitats observing that it does not hold across different ecosystems, stressing its ecosystem dependency. The SVH has been tested using data from airborne hyperspectral sensors (Oldeland et al, 2010), (Gholizadeh et al, 2018), multi-spectral satellite such as MODIS (Schmidtlein and Fassnacht, 2017), Landsat (Rocchini, 2007), (Levin et al, 2007), QuickBird (Hall et al, 2010), ASTER (Levin et al, 2007) and SPOT (Lopes et al, 2017). These studies showed the strong sensor dependency of SVH resulting from different spatial scales (spatial resolution and image extent) and spectral scales (number of bands, radiometric resolution, band width and spectral range covered).…”

Identifying Urine Patches on Intensively Managed Grassland Using Aerial Imagery Captured From Remotely Piloted Aircraft Systems

“…To automate the patch detection, the K-means method (Hartigan and Wong, 1979), commonly used for image segmentation (Lopes et al, 2017;Singh and Misra, 2017), was implemented in the R script. The advantage of this algorithm is that it has a low computational complexity, it is an unsupervised learning mechanism and the resulted clusters of this method are not overlapping.…”

“…The effort required to deploy an RPAS platform has greatly reduced in recent years, contributing in some cases to more flexible and affordable experimentation than with other aerial image acquisition systems (Zhang and Kovacs, 2012). Use of other remote sensing techniques (e.g., piloted aircraft, helicopter, satellite platforms) can be limited in its ability to provide adequate field-scale image acquisition, image quality, and spatial and temporal resolutions partly due to cost and sensitivity to weather conditions (Dennis et al, 2013;Ali et al, 2016;Lopes et al, 2017).…”

Remote Sensing and Cropping Practices: A Review

“…In a study performed in Southwest France, Lopes et al [169] were able to distinguish mowing, grazing, and mixed practices, using an object-based classification of grasslands from high-resolution satellite image time series (Formosat-2) and Gaussian mean map kernels. However, they underlined that the spectro-temporal response of the grassland does not only depend on the practice itself, but also on the phenological stage when the practice occurs, the weather conditions, and the topography [170]. Notably, the identification of mowing events or gradual grazing management can also be derived from the numerous approaches initially used for the biomass production amount and biomass quality [171,172].…”