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Hyperspectral Image Classification Based on Multi-Scale Residual Network with Attention Mechanism

European Wide Forest Classification Based on Sentinel-1 Data

Department of Geodesy and Geoinformation, Vienna University of Technology, 1040 Vienna, Austria
Tartu Observatory, University of Tartu, 61602 Tõravere, Estonia
Institute of Forestry and Rural Engineering, Estonian University of Life Sciences, 51006 Tartu, Estonia
Latvian State Forest Research Institute ‘Silava’, 2169 Salaspils, Latvia
Swiss Federal Institute for Forest, Snow and Landscape Research, 8903 Birmensdorf, Switzerland
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(3), 337;
Received: 9 December 2020 / Revised: 11 January 2021 / Accepted: 18 January 2021 / Published: 20 January 2021
(This article belongs to the Section Forest Remote Sensing)
The constellation of two Sentinel-1 satellites provides an unprecedented coverage of Synthetic Aperture Radar (SAR) data at high spatial (20 m) and temporal (2 to 6 days over Europe) resolution. The availability of dense time series enables the analysis of the SAR temporal signatures and exploitation of these signatures for classification purposes. Frequent backscatter observations allow derivation of temporally filtered time series that reinforce the effect of changes in vegetation phenology by limiting the influence of short-term changes related to environmental conditions. Recent studies have already shown the potential of multitemporal Sentinel-1 data for forest mapping, forest type classification (coniferous or broadleaved forest) as well as for derivation of phenological variables at local to national scales. In the present study, we tested the viability of a recently published multi-temporal SAR classification method for continental scale forest mapping by applying it over Europe and evaluating the derived forest type and tree cover density maps against the European-wide Copernicus High Resolution Layers (HRL) forest datasets and national-scale forest maps from twelve countries. The comparison with the Copernicus HRL datasets revealed high correspondence over the majority of the European continent with overall accuracies of 86.1% and 73.2% for the forest/non-forest and forest type maps, respectively, and a Pearson correlation coefficient of 0.83 for tree cover density map. Moreover, the evaluation of both datasets against the national forest maps showed that the obtained accuracies of Sentinel-1 forest maps are almost within range of the HRL datasets. The Sentinel-1 forest/non-forest and forest type maps obtained average overall accuracies of 88.2% and 82.7%, respectively, as compared to 90.0% and 87.2% obtained by the Copernicus HRL datasets. This result is especially promising due to the facts that these maps can be produced with a high degree of automation and that only a single year of Sentinel-1 data is required as opposed to the Copernicus HRL forest datasets that are updated every three years. View Full-Text
Keywords: forest mapping; SAR; Sentinel-1; forest classification forest mapping; SAR; Sentinel-1; forest classification
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MDPI and ACS Style

Dostálová, A.; Lang, M.; Ivanovs, J.; Waser, L.T.; Wagner, W. European Wide Forest Classification Based on Sentinel-1 Data. Remote Sens. 2021, 13, 337.

AMA Style

Dostálová A, Lang M, Ivanovs J, Waser LT, Wagner W. European Wide Forest Classification Based on Sentinel-1 Data. Remote Sensing. 2021; 13(3):337.

Chicago/Turabian Style

Dostálová, Alena, Mait Lang, Janis Ivanovs, Lars T. Waser, and Wolfgang Wagner. 2021. "European Wide Forest Classification Based on Sentinel-1 Data" Remote Sensing 13, no. 3: 337.

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