Evaluation of sentinel-1 and 2 time series for land cover classification of forest–agriculture mosaics in temperate and tropical landscapes

Mercier Audrey, Betbeder Julie, Rumiano Florent, Baudry Jacques, Gond Valéry, Blanc Lilian, Bourgoin Clément, Cornu Guillaume, Ciudad Carlos, Marchamalo Miguel, Poccard-Chapuis René, Hubert-Moy Laurence. 2019. Evaluation of sentinel-1 and 2 time series for land cover classification of forest–agriculture mosaics in temperate and tropical landscapes. Remote Sensing, 11:979, 20 p.

Quartile : Q2, Sujet : REMOTE SENSING

Résumé : Monitoring forest–agriculture mosaics is crucial for understanding landscape heterogeneity and managing biodiversity. Mapping these mosaics from remotely sensed imagery remains challenging, since ecological gradients from forested to agricultural areas make characterizing vegetation more difficult. The recent synthetic aperture radar (SAR) Sentinel-1 (S-1) and optical Sentinel-2 (S-2) time series provide a great opportunity to monitor forest–agriculture mosaics due to their high spatial and temporal resolutions. However, while a few studies have used the temporal resolution of S-2 time series alone to map land cover and land use in cropland and/or forested areas, S-1 time series have not yet been investigated alone for this purpose. The combined use of S-1 & S-2 time series has been assessed for only one or a few land cover classes. In this study, we assessed the potential of S-1 data alone, S-2 data alone, and their combined use for mapping forest–agriculture mosaics over two study areas: a temperate mountainous landscape in the Cantabrian Range (Spain) and a tropical forested landscape in Paragominas (Brazil). Satellite images were classified using an incremental procedure based on an importance rank of the input features. The classifications obtained with S-2 data alone (mean kappa index = 0.59–0.83) were more accurate than those obtained with S-1 data alone (mean kappa index = 0.28–0.72). Accuracy increased when combining S-1 and 2 data (mean kappa index = 0.55–0.85). The method enables defining the number and type of features that discriminate land cover classes in an optimal manner according to the type of landscape considered. The best configuration for the Spanish and Brazilian study areas included 5 and 10 features, respectively, for S-2 data alone and 10 and 20 features, respectively, for S-1 data alone. Short-wave infrared and VV and VH polarizations were key features of S-2 and S-1 data, respectively. In addition, the method enables defining key periods that discriminate land cover classes according to the type of images used. For example, in the Cantabrian Range, winter and summer were key for S-2 time series, while spring and winter were key for S-1 time series.

Mots-clés Agrovoc : télédétection, cartographie de l'utilisation des terres, forêt, terre agricole, utilisation des terres, biodiversité

Mots-clés géographiques Agrovoc : Espagne, Brésil

Mots-clés libres : Remote sensing, Optical and SAR data, Feature selection, Decision tree, Random forest

Classification Agris : U30 - Méthodes de recherche
K01 - Foresterie - Considérations générales
P01 - Conservation de la nature et ressources foncières

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

Auteurs et affiliations

• Mercier Audrey, CIRAD-ES-UPR Forêts et sociétés (FRA) - auteur correspondant
• Betbeder Julie, CIRAD-ES-UPR Forêts et sociétés (CRI)
• Rumiano Florent, CIRAD-ES-UMR TETIS (FRA)
• Baudry Jacques, INRA (FRA)
• Gond Valéry, CIRAD-ES-UPR Forêts et sociétés (FRA) ORCID: 0000-0002-0080-3140
• Blanc Lilian, CIRAD-ES-UPR Forêts et sociétés (FRA) ORCID: 0000-0003-3605-4230
• Bourgoin Clément, CIRAD-ES-UPR Forêts et sociétés (FRA)
• Cornu Guillaume, CIRAD-ES-UPR Forêts et sociétés (FRA) ORCID: 0000-0002-7523-5176
• Ciudad Carlos, Madrid University (ESP)
• Marchamalo Miguel, UPM (ESP)
• Poccard-Chapuis René, CIRAD-ES-UMR SELMET (BRA) ORCID: 0000-0003-2200-0637
• Hubert-Moy Laurence, Université de Haute-Bretagne (FRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/592787/)

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