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Tree species classification in temperate forests using formosat-2 satellite image time series

Authors :
Jean-François Dejoux
Veliborka Josipović
David Sheeren
Mailys Lopes
Carole Planque
Mathieu Fauvel
Jérôme Willm
Dynamiques Forestières dans l'Espace Rural (DYNAFOR)
Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP)
Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées
Dynamiques et écologie des paysages agriforestiers (DYNAFOR)
École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP)
Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Université Toulouse III - Paul Sabatier (UT3)
Université Fédérale Toulouse Midi-Pyrénées
Sheeren, David
Centre National d'Études Spatiales - CNES (FRANCE)
Centre National de la Recherche Scientifique - CNRS (FRANCE)
Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Institut National de la Recherche Agronomique - INRA (FRANCE)
Institut de Recherche pour le Développement - IRD (FRANCE)
Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Institut National Polytechnique de Toulouse - INPT (FRANCE)
Source :
Remote Sensing, Remote Sensing, MDPI, 2016, 8 (9), 29 p. ⟨10.3390/rs8090734⟩, Remote Sensing, Vol 8, Iss 9, p 734 (2016), Volume 8, Issue 9, Pages: 734, Remote Sensing 9 (8), 29 p.. (2016)
Publication Year :
2016
Publisher :
HAL CCSD, 2016.

Abstract

International audience; Mapping forest composition is a major concern for forest management, biodiversity assessment and for understanding the potential impacts of climate change on tree species distribution. In this study, the suitability of a dense high spatial resolution multispectral Formosat-2 satellite image time-series (SITS) to discriminate tree species in temperate forests is investigated. Based on a 17-date SITS acquired across one year, thirteen major tree species (8 broadleaves and 5 conifers) are classified in a study area of southwest France. The performance of parametric (GMM) and nonparametric (k-NN, RF, SVM) methods are compared at three class hierarchy levels for different versions of the SITS: (i) a smoothed noise-free version based on the Whittaker smoother; (ii) a non-smoothed cloudy version including all the dates; (iii) a non-smoothed noise-free version including only 14 dates. Noise refers to pixels contaminated by clouds and cloud shadows. The results of the 108 distinct classifications show a very high suitability of the SITS to identify the forest tree species based on phenological differences (average κ=0.93 estimated by cross-validation based on 1235 field-collected plots). SVM is found to be the best classifier with very close results from the other classifiers. No clear benefit of removing noise by smoothing can be observed. Classification accuracy is even improved using the non-smoothed cloudy version of the SITS compared to the 14 cloud-free image time series. However conclusions of the results need to be considered with caution because of possible overfitting. Disagreements also appear between the maps produced by the classifiers for complex mixed forests, suggesting a higher classification uncertainty in these contexts. Our findings suggest that time-series data can be a good alternative to hyperspectral data for mapping forest types. It also demonstrates the potential contribution of the recently launched Sentinel-2 satellite for studying forest ecosystems.

Details

Language :
English
ISSN :
20724292
Database :
OpenAIRE
Journal :
Remote Sensing, Remote Sensing, MDPI, 2016, 8 (9), 29 p. ⟨10.3390/rs8090734⟩, Remote Sensing, Vol 8, Iss 9, p 734 (2016), Volume 8, Issue 9, Pages: 734, Remote Sensing 9 (8), 29 p.. (2016)
Accession number :
edsair.doi.dedup.....29603c301f134f7e9d29d3681029c48e
Full Text :
https://doi.org/10.3390/rs8090734⟩