Evaluation of Sentinel-1 and 2 Time Series for Land Cover Classification of Forest–Agriculture Mosaics in Temperate and Tropical Landscapes

• Published in: Remote sensing (Basel, Switzerland) Vol. 11; no. 8; p. 979
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 24.04.2019; MDPI
• Subjects: Agricultural land; Agricultural management; Agriculture; Biodiversity; Brazilian Amazon; cantabrian range; Classification; Climate change; decision trees; Environmental Sciences; feature selection; Forests; Geography; Habitats; Heterogeneity; Humanities and Social Sciences; Land cover; Land use; Landscape; Mapping; Mosaics; optical and SAR satellite images; random forests; Remote sensing; Satellite imagery; Short wave radiation; Studies; Synthetic aperture radar; Temporal resolution; Time series; Tropical forests; Vegetation mapping; Winter; Brazil; France; Spain; brazilian amazon; decision trees; optical and SAR satellite images; remote sensing; random forests; feature selection; cantabrian range
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs11080979

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.