Satellite surface reflectance improves habitat distribution mapping: a case study on heath and shrub formations in the Cantabrian Mountains (NW Spain)

Abstract

Aim We aim to map the distribution of four heath and shrub formations constituting habitats of high conservation priority in Europe, whose occurrence is strongly dependent on human activities. Specifically, we assess whether the use of LANDSAT data in habitat distribution modelling may account for land use management, allowing accurate mapping of real distribution patterns. In particular, we explore whether reflectance values may be a better alternative to other remote sensing data traditionally used in modelling approaches (i.e. spectral vegetation indices and classified land cover maps). Finally, we test whether modelling performance is affected by the ecological traits of the dominant species of the target formations. Location Cantabrian Mountains (NW Spain). Methods We generated maps for the four formations (two specialists vs. two generalists) using MaxEnt. First, we ran the models with environmental predictors only (topography, climate, lithology and human disturbances). Then, we compared the advantages of including, in turn, different data derived from LANDSAT imagery: reflectance values (corresponding to different wavelength channels of the multispectral image), a spectral index and a land cover map. We assessed changes in explanatory power and also in the formation’s predicted distribution patterns. Results Formations dominated by specialist species were accurately mapped on a base of environmental variables only, whereas those dominated by generalists were overpredicted. Average mean temperature, southness and distance to urban areas were the variables contributing most in predictions of environmental models. LANDSAT channels increased the accuracy of all models, but mainly those for formations dominated by generalist species. They showed advantages against other remote sensing data traditionally used in modelling approaches. Main conclusions Habitat distribution models allowed accurate mapping of heath and shrub formations. The use of reflectance values as predictors improved the accuracy of the models, particularly for formations dominated by generalist species, supplying environmental information that was otherwise unavailable.

Modelling the area of occupancy of habitat types with remote sensing

“…topography or climate) (Vanden Borre et al., ). This data combination provides valuable cues about vegetation attributes that are difficult to ascertain, increasing other than optical responses (Franklin, ; Morán‐Ordóñez, Suárez‐Seoane, Elith, Calvo, & de Luis, ). For example, LiDAR has proven useful for assessing woody vegetation structure characteristics, such as canopy height and above‐ground biomass (Rapinel, Hubert‐Moy, & Clément, ).…”

Can I Trust My One-Class Classification?

“…In contrast to the aforementioned approaches, Maxent has been used more frequently for one-class land cover classification in applied studies [19,[47][48][49][50].…”

“…This requires accurate estimates of the terms of Bayes' rule (see Equation (1)). In [19,[47][48][49][50]] P (y + ) has not been available neither has it been estimated from the data. Thus, the derived continuous output is not an a posteriori probability with an intuitive meaning.…”

Modeling Species Distribution Using Niche-Based Proxies Derived from Composite Bioclimatic Variables and MODIS NDVI

“…A few studies have shown that niche models developed by incorporating remotely-sensed predictors are more robust; in particular, these data can improve the prediction accuracy and tend to refine mapped distribution of species and habitats, compared with climatic/topographical variables-only models [27][28][29]. Remotely-sensed indices, such as NDVI, may hence provide the opportunity to complement or improve niche models based on climate data alone.…”