Abstract
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.
Highlights
Forest ecosystems provide essential services to human society
The results revealed a very high potential of Formosat-2 image time series to be used to discriminate forest tree species based on phenological differences
Our study demonstrates the high suitability of dense optical high spatial resolution satellite image time-series (SITS) for mapping dominant tree species in temperate woodlands
Summary
Forest ecosystems provide essential services to human society. Beyond the production of multiple resources (timber, energy, foods), these ecosystems play a major role in carbon sequestration, regulating biogeochemical cycles and climate [1]. The provision of such ecosystem services may depends on tree species diversity [2]. The complementarity among species can sustain multiple services simultaneously. Mapping tree species is crucial to assess forest ecosystems and their services. Under the current context of climate change, it is essential to build more accurate models predicting future tree habitat shifts [3]. Information about tree species diversity is required to assess forest resilience and vulnerability to drought and pathogens [4,5]
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