Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series
AbstractMapping 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
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Sheeren, D.; Fauvel, M.; Josipović, V.; Lopes, M.; Planque, C.; Willm, J.; Dejoux, J.-F. Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series. Remote Sens. 2016, 8, 734.
Sheeren D, Fauvel M, Josipović V, Lopes M, Planque C, Willm J, Dejoux J-F. Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series. Remote Sensing. 2016; 8(9):734.Chicago/Turabian Style
Sheeren, David; Fauvel, Mathieu; Josipović, Veliborka; Lopes, Maïlys; Planque, Carole; Willm, Jérôme; Dejoux, Jean-François. 2016. "Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series." Remote Sens. 8, no. 9: 734.
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