Large‐scale and fine‐grained mapping of heathland habitats using open‐source remote sensing data

• Published in: Remote sensing in ecology and conservation Vol. 8; no. 4; pp. 448 - 463
• Main Authors: Hubert‐Moy, Laurence, Rozo, Clémence, Perrin, Gwenhael, Bioret, Frédéric, Rapinel, Sébastien, Disney, Mat, Abdi, Abdulhakim
• Format: Journal Article
• Language: English
• Published: Oxford John Wiley & Sons, Inc 01-08-2022; Wiley
• Subjects: Accuracy; Bioclimatology; Classification; Conservation status; Emission analysis; Environmental Engineering; Environmental policy; Environmental Sciences; Geography; Habitats; heathland; Humanities and Social Sciences; Mapping; MaxEnt; natural vegetation; Open access; Open data; Phenology; Remote sensing; satellite imagery; Spatial discrimination; Spatial resolution; Spectroradiometers; Thermal emission; Time series; Topography; Variables; Vegetation; vegetation mapping; France; natural vegetation; vegetation mapping; MaxEnt; Conservation status; satellite imagery; heathland
• ISSN: 2056-3485; 2056-3485
• DOI: 10.1002/rse2.253

Mapping natural habitats remains challenging, especially at a national scale. Although new open‐access variables for vegetation and its environment and increased spatial resolution derived from satellite remote sensing data are available at the global scale, the relevance of these new variables for fine‐grained mapping of natural habitats at a national scale remains underexplored. This study aimed to map the fine‐grained pattern of four heathland habitats throughout France (550 000 km2). Environmental (bioclimatic, soil and topographic) and spectral (vegetation) variables derived from MODerate resolution Imaging Spectroradiometer, Advanced Spaceborne Thermal Emission and Reflection Radiometer, and Sentinel‐2 satellite data were analyzed using the MaxEnt classifier. Open‐access field databases were used to calibrate and validate the classification, based on the threshold‐independent area under the curve (AUC) index and the conventional F1‐score. For each heathland habitat, potential and actual areas were mapped using environmental and spectral variables, respectively. The results showed high classification accuracy for potential (AUC 0.92–0.99) and actual (AUC 0.88–0.99) suitability maps of the four heathland habitats. Visual interpretation of maps of the probability of occurrence indicated that the fine‐grained distribution of heathland habitat was detected satisfactorily. However, although the accuracy of the crisp map of combined classifications of actual heathland habitats was high (overall accuracy 0.72), estimated producer's accuracies in terms of proportion of area were low (<0.25). This study provides the first fine‐grained pattern maps of heathland habitats at a national scale, thus highlighting the value of combining environmental and spectral variables derived from open‐remote sensing data and open‐source field databases. These suitability maps could support the identification of heathland habitats in the framework of national conservation policies.
Mapping natural habitats remains challenging, especially at the national scale. Here, we mapped the fine‐grained pattern of four heathland habitats throughout France (550 000 km2) using open‐source remote sensing data, including Sentinel‐2 time series. This study provides the first fine‐grained map of heathland habitats at a national scale, highlighting the value of combining environmental and vegetation variables. This map will support the monitoring and evaluation of the conservation status of natural habitats.