Airborne Hyperspectral Data Predict Fine-Scale Plant Species Diversity in Grazed Dry Grasslands

Abstract

Semi-natural grasslands with grazing management are characterized by high fine-scale species richness and have a high conservation value. The fact that fine-scale surveys of grassland plant communities are time-consuming may limit the spatial extent of ground-based diversity surveys. Remote sensing tools have the potential to support field-based sampling and, if remote sensing data are able to identify grassland sites that are likely to support relatively higher or lower levels of species diversity, then field sampling efforts could be directed towards sites that are of potential conservation interest. In the present study, we examined whether aerial hyperspectral (414-2501 nm) remote sensing can be used to predict fine-scale plant species diversity (characterized as species richness and Simpson's diversity) in dry grazed grasslands. Vascular plant species were recorded within 104 (4 mˆ4 m) plots on the island of Öland (Sweden) and each plot was characterized by a 245-waveband hyperspectral data set. We used two different modeling approaches to evaluate the ability of the airborne spectral measurements to predict within-plot species diversity: (1) a spectral response approach, based on reflectance information from (i) all wavebands, and (ii) a subset of wavebands, analyzed with a partial least squares regression model, and (2) a spectral heterogeneity approach, based on the mean distance to the spectral centroid in an ordinary least squares regression model. Species diversity was successfully predicted by the spectral response approach (with an error of ca. 20%) but not by the spectral heterogeneity approach. When using the spectral response approach, iterative selection of important wavebands for the prediction of the diversity measures simplified the model but did not improve its predictive quality (prediction error). Wavebands sensitive to plant pigment content (400-700 nm) and to vegetation structural properties, such as above-ground biomass (700-1300 nm), were identified as being the most important predictors of plant species diversity. We conclude that hyperspectral remote sensing technology is able to identify fine-scale variation in grassland diversity and has a potential use as a tool in surveys of grassland plant diversity.

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

“…To reduce the dimensional-space, some studies compute the MDC on the first few components of PCA performed on the spectral variables [18,19,22]. Theoretically, it is almost equivalent to the original MDC.…”

“…Abundance-based measures of species diversity, such as the Shannon index, give more weight to species with higher proportions. Therefore, these measures should be preferred to species richness in the context of SVH [18,19].…”

“…SH has been quantified with the standard variation or the coefficient of variation of the Normalized Difference Vegetation Index (NDVI) [14,20], using Principal Components Analysis (PCA) [18,21] and with the mean Euclidean distance to the spectral centroid [15,18,19,22]. However, these measures do not describe well the variability in the spectral space.…”

Habitat Mapping and Quality Assessment of NATURA 2000 Heathland Using Airborne Imaging Spectroscopy

“…IS can even be used to produce highly accurate species-level vegetation maps in highly complex grasslands with fine-scale mosaics of different vegetation types [14]. Imaging spectroscopy also provides opportunities for habitat quality and degradation assessment [15], e.g., by mapping of invasive species [16,17] or encroachment of undesired species [18], prediction of species richness measures [19], and mapping of plant functional types [20,21].…”

Preface: Remote Sensing of Biodiversity

“…Wang et al [19] followed seasonal changes measured with a field spectrometer for NDVI-species richness relationships at the Cedar Creek Prairie experiment and in grazed dry grasslands on the Baltic Island of Öland, Sweden. Möckel et al [20] flew an imaging spectrometer (414-2500 nm) and present best results for predicting species richness and Simpson's diversity using spectral responses from all wavebands analyzed with partial least squares regression (PLSR). Wang et al [21] also address grassland productivity in a Southern Alberta prairie using airborne imaging spectrometry combined with ground sampling and eddy covariance data, showing greater productivity in sites with higher biodiversity based on species richness and the Shannon Index.…”