Abstract
Heterogeneous and patchy landscapes where vegetation and abiotic factors vary at small spatial scale (fine-grained landscapes) represent a challenge for habitat diversity mapping using remote sensing imagery. In this context, techniques of spectral mixture analysis may have an advantage over traditional methods of land cover classification because they allow to decompose the spectral signature of a mixed pixel into several endmembers and their respective abundances. In this work, we present the application of Multiple Endmember Spectral Mixture Analysis (MESMA) to quantify habitat diversity and assess the compositional turnover at different spatial scales in the fine-grained landscapes of the Cantabrian Mountains (northwestern Iberian Peninsula). A Landsat-8 OLI scene and high-resolution orthophotographs (25 cm) were used to build a region-specific spectral library of the main types of habitats in this region (arboreal vegetation; shrubby vegetation; herbaceous vegetation; rocks–soil and water bodies). We optimized the spectral library with the Iterative Endmember Selection (IES) method and we applied MESMA to unmix the Landsat scene into five fraction images representing the five defined habitats (root mean square error, RMSE ≤ 0.025 in 99.45% of the pixels). The fraction images were validated by linear regressions using 250 reference plots from the orthophotographs and then used to calculate habitat diversity at the pixel (α-diversity: 30 × 30 m), landscape (γ-diversity: 1 × 1 km) and regional (ε-diversity: 110 × 33 km) scales and the compositional turnover (β- and δ-diversity) according to Simpson’s diversity index. Richness and evenness were also computed. Results showed that fraction images were highly related to reference data (R2 ≥ 0.73 and RMSE ≤ 0.18). In general, our findings indicated that habitat diversity was highly dependent on the spatial scale, with values for the Simpson index ranging from 0.20 ± 0.22 for α-diversity to 0.60 ± 0.09 for γ-diversity and 0.72 ± 0.11 for ε-diversity. Accordingly, we found β-diversity to be higher than δ-diversity. This work contributes to advance in the estimation of ecological diversity in complex landscapes, showing the potential of MESMA to quantify habitat diversity in a comprehensive way using Landsat imagery.
Highlights
A central point of natural sciences research is to identify, describe and understand biodiversity patterns for conservation purposes and natural resource management
Given the current challenges in habitat diversity monitoring and the potential benefits of spectral unmixing techniques, our study aims to use, for the first time, Multiple Endmember Spectral Mixture Analysis (MESMA) to accomplish a comprehensive analysis of habitat diversity in fine-grained landscapes, using the Cantabrian Mountains as a study case
The final Iterative Endmember Selection (IES) library consisted of 31 endmembers (Figure 3), which were unequally distributed among habitat classes: arboreal vegetation—four spectra; shrubby vegetation—10 spectra; herbaceous vegetation—seven spectra; rock and bare soil—nine spectra; water—one spectrum
Summary
A central point of natural sciences research is to identify, describe and understand biodiversity patterns for conservation purposes and natural resource management. Habitats, which are defined as the type of site where an organism or population naturally occurs, are of high interest because they can be used as a proxy of diversity in different hierarchical levels. In this sense, the habitat heterogeneity hypothesis [3] states that increases in habitat diversity leads to increases in the variety of ways to exploit resources, increasing the complexity of ecosystems, species diversity [4,5] and, genetic diversity [6]. Several studies suggest that the fulfillment of this hypothesis depends on the spatial scale of analysis and the target community [7,8]
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