European Wide Forest Classification Based on Sentinel-1 Data Article Swipe
Alena Dostálová
,
Mait Lang
,
Jānis Ivanovs
,
Lars T. Waser
,
Wolfgang Wagner
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/rs13030337
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/rs13030337
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs13030337
- https://www.mdpi.com/2072-4292/13/3/337/pdf?version=1611550681
- OA Status
- gold
- Cited By
- 55
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3125019278
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W3125019278Canonical identifier for this work in OpenAlex
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https://doi.org/10.3390/rs13030337Digital Object Identifier
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European Wide Forest Classification Based on Sentinel-1 DataWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-01-20Full publication date if available
- Authors
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Alena Dostálová, Mait Lang, Jānis Ivanovs, Lars T. Waser, Wolfgang WagnerList of authors in order
- Landing page
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https://doi.org/10.3390/rs13030337Publisher landing page
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https://www.mdpi.com/2072-4292/13/3/337/pdf?version=1611550681Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2072-4292/13/3/337/pdf?version=1611550681Direct OA link when available
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Remote sensing, Synthetic aperture radar, Scale (ratio), Environmental science, Random forest, Geography, Physical geography, Cartography, Computer science, Machine learningTop concepts (fields/topics) attached by OpenAlex
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55Total citation count in OpenAlex
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2025: 13, 2024: 10, 2023: 13, 2022: 14, 2021: 5Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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