Question: The use of variations in the spectral responses of remotely sensed images was recently proposed as an indicator of plant species richness (Spectral Variation Hypothesis, SVH). In this paper we addressed the issue of the potential use of multispectral sensors by testing the hypothesis that only some of the bands recorded in a remotely sensed image contain information related to the variation in species richness.
Location: Montepulciano Lake, central Italy.
Methods: We assessed how data compression techniques, such as Principal Component Analysis (PCA), influence the relationship between spectral heterogeneity and species richness and evaluated which spectral interval is the most adequate for predicting species richness by means of linear regression analysis.
Results: The original multispectral data set and the first two non-standardized principal components can both be used as predictors of plant species richness (R2∼ 0.48; p < 0.001), confirming that PCA is an effective tool for compressing multi-spectral data without loss of information. Using single spectral bands, the near infrared band explained 41% of variance in species richness (p < 0.01), while the visible wavelengths had much lower prediction powers.
Conclusions: The potential of satellite data for estimating species richness is likely to be due to the near infrared bands, rather than to the visible bands, which share highly redundant information. Since optimal band selection for image processing is a crucial task and it will assume increasing importance with the growing availability of hyperspectral data, in this paper we suggest a ‘near infrared way’for assessing species richness directly from remotely sensed data.