Agritrop
Home

Mapping land cover on Reunion Island in 2017 using satellite imagery and geospatial ground data

Dupuy Stéphane, Gaetano Raffaele, Le Mézo Lionel. 2019. Mapping land cover on Reunion Island in 2017 using satellite imagery and geospatial ground data. Data in Brief, 28:104934, 12 p.

Journal article ; Data paper ; Article de revue à comité de lecture Revue en libre accès total
[img]
Preview
Published version - Anglais
License Licence Creative Commons.
Mapping land cover on Reunion Island in 2017 using satellite imagery and geospatial ground data.pdf

Télécharger (2MB) | Preview

Url - jeu de données : https://doi.org/10.18167/DVN1/TOARDN / Url - jeu de données : https://doi.org/10.18167/DVN1/RTAEHK

Abstract : We here present a reference database and three land use maps produced in 2017 over the Reunion island using a machine learning based methodology. These maps are the result of a satellite image analysis performed using the Moringa land cover processing chain developed in our laboratory. The input dataset for map production consists of a single very high spatial resolution Pleiades images, a time series of Sentinel-2 and Landsat-8 images, a Digital Terrain Model (DTM) and the aforementioned reference database. The Moringa chain adopts an object based approach: the Pleiades image provides spatial accuracy with the delineation of land samples via a segmentation process, the time series provides information on landscape and vegetation dynamics, the DTM provides information on topography and the reference database provides annotated samples (6256 polygons) for the supervised classification process and the validation of the results. The three land use maps follow a hierarchical nomenclature ranging from 4 classes for the least detailed level to 34 classes for the most detailed one. The validation of these maps shows a good quality of the results with overall accuracy rates ranging from 86% to 97%. The maps are freely accessible and used by researchers, land managers (State services and local authorities) and also private companies.

Mots-clés Agrovoc : Télédétection, Imagerie par satellite, Cartographie de l'occupation du sol, Base de données spatiale, Landsat, Topographie

Mots-clés géographiques Agrovoc : Réunion

Mots-clés complémentaires : machine learning

Mots-clés libres : Remote sensing, Land cover map, Spatial database, Landsat-8, Sentinel-2, Pleiades

Classification Agris : P01 - Nature conservation and land resources
P30 - Soil science and management

Champ stratégique Cirad : CTS 5 (2019-) - Territoires

Agence(s) de financement européenne(s) : European Regional Development Fund

Auteurs et affiliations

  • Dupuy Stéphane, CIRAD-ES-UMR TETIS (REU) ORCID: 0000-0002-9710-5364 - auteur correspondant
  • Gaetano Raffaele, CIRAD-ES-UMR TETIS (FRA)
  • Le Mézo Lionel, CIRAD-PERSYST-UPR AIDA (REU)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/594548/)

View Item (staff only) View Item (staff only)

[ Page générée et mise en cache le 2020-11-02 ]