Large‐scale and fine‐grained mapping of heathland habitats using open‐source remote sensing data - Institut Brestois des Sciences de l’Homme et de la Société Access content directly
Journal Articles Remote Sensing in Ecology and Conservation Year : 2022

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

Abstract

Grassland habitats provide many ecosystem services but are threatened by agricultural intensification and urbanization. While the lack of accurate and comprehensive inventories at the national scale makes them difficult to manage, advances in spatial modeling using open remote sensing data and open-source software, as well as the increasing use of ecological archives, provide new perspectives for mapping natural habitats. In this context, this study evaluated the contribution of spectral and environmental variables to discriminate and then map grassland habitats throughout France. To this end, 92 spectral variables derived from moderate-resolution imaging spectroradiometer data, 19 bioclimatic variables derived from WorldClim data, 4 topographic variables derived from the European Union Digital Elevation Model (DEM), and 8 soil variables derived from SoilGrids data were combined at a spatial resolution of 250 m. Reference plots that characterized 6 and 21 grassland ecosystems at European Nature Information System (EUNIS) levels 2 (broad habitats) and 3 (habitats), respectively, were collected from vegetation archives. We first performed descriptive analysis that included habitat description, ordination, and pairwise separability. We then performed predictive analysis of grassland habitats using a cross-validated random forest model that included a spatial constraint. While environmental and spectral variables characterized most grassland habitats well and consistently, some confusion occurred between habitats with similar abiotic conditions. The main grassland habitat types were correctly mapped at EUNIS level 2 ( F1 score = 0.68), but not at EUNIS level 3 ( F1 score = 0.52). In addition, the two variables that contributed most to the model were the near-infrared spectral band in spring and the minimum temperature of the coldest month. The model’s prediction at EUNIS level 2 for mainland France provides the map of grassland habitats at a new spatial scale.
Fichier principal
Vignette du fichier
Remote Sens Ecol Conserv - 2022 - Hubert‐Moy - Large‐scale and fine‐grained mapping of heathland habitats using open‐source.pdf (2.44 Mo) Télécharger le fichier
Origin Publication funded by an institution
licence

Dates and versions

halshs-03791570 , version 1 (24-05-2024)

Licence

Identifiers

Cite

Laurence Hubert-Moy, Clémence Rozo, Gwenhael Perrin, Frédéric Bioret, Sébastien Rapinel. Large‐scale and fine‐grained mapping of heathland habitats using open‐source remote sensing data. Remote Sensing in Ecology and Conservation, 2022, 8 (4), pp.448-463. ⟨10.1002/rse2.253⟩. ⟨halshs-03791570⟩
26 View
0 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More