Discriminating treed and non-treed wetlands in boreal ecosystems using time series Sentinel-1 data
Wetlands are recognized for their importance to a range of ecosystem goods and services; however, detailed information on wetland presence, type, extent, and persistence is challenging to attain over large areas and/or long time periods due to the spatial complexity and temporal dynamism of wetlands...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-03-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0303243419308815 |
| _version_ | 1811228410342539264 |
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| author | Zhan Li Hao Chen Joanne C. White Michael A. Wulder Txomin Hermosilla |
| author_facet | Zhan Li Hao Chen Joanne C. White Michael A. Wulder Txomin Hermosilla |
| author_sort | Zhan Li |
| collection | DOAJ |
| description | Wetlands are recognized for their importance to a range of ecosystem goods and services; however, detailed information on wetland presence, type, extent, and persistence is challenging to attain over large areas and/or long time periods due to the spatial complexity and temporal dynamism of wetlands. In this study we explored the potential for within-year time series of C-band Synthetic Aperture Radar (SAR) observations from the free and open Sentinel-1 data archive to improve discrimination of treed and non-treed wetlands and non-wetlands in a boreal forest environment. Through a set of 3843 classification experiments for the year 2017, we tested the influence of three factors on classification accuracy: (i) input features (two backscatter coefficients in VV and VH polarization (σVV and σVH) and four quantitative measures derived from the Stokes vector); (ii) the temporal form of features (i.e. using all within-year observations versus generalized measures such as monthly/seasonal means or annualized statistics); and (iii) missing observations in Sentinel-1 time series due to varying observation availability across space. Among the tested features, we found the greatest utility in σVV and σVH. Directly using all within-year observations yielded higher accuracy than using generalized temporal forms. Moreover, the temporal form of the features had a greater impact on classification accuracy than the features themselves. The highest overall accuracy (0.860 ± 0.002) was achieved using σVV and σVH from all within-year observations. The majority of class confusion occurred between treed wetlands and non-wetlands. We found no significant reduction in the overall accuracy by simulated missing observations in time series when using all within-year observations. With the increasing availability of free and open data from the Sentinel-1 archive, new opportunities are emerging to readily integrate within-year time series into large-area land cover mapping, particularly if analysis-ready SAR data products further reduce preprocessing requirements for end users. |
| first_indexed | 2024-04-12T09:57:28Z |
| format | Article |
| id | doaj.art-6553054c5d7d4ae29ee9cf5907411519 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-12T09:57:28Z |
| publishDate | 2020-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-6553054c5d7d4ae29ee9cf59074115192022-12-22T03:37:38ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322020-03-0185102007Discriminating treed and non-treed wetlands in boreal ecosystems using time series Sentinel-1 dataZhan Li0Hao Chen1Joanne C. White2Michael A. Wulder3Txomin Hermosilla4Corresponding author.; Canadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, 506 West Burnside Road, Victoria, British Columbia, V8Z 1M5, CanadaCanadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, 506 West Burnside Road, Victoria, British Columbia, V8Z 1M5, CanadaCanadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, 506 West Burnside Road, Victoria, British Columbia, V8Z 1M5, CanadaCanadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, 506 West Burnside Road, Victoria, British Columbia, V8Z 1M5, CanadaCanadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, 506 West Burnside Road, Victoria, British Columbia, V8Z 1M5, CanadaWetlands are recognized for their importance to a range of ecosystem goods and services; however, detailed information on wetland presence, type, extent, and persistence is challenging to attain over large areas and/or long time periods due to the spatial complexity and temporal dynamism of wetlands. In this study we explored the potential for within-year time series of C-band Synthetic Aperture Radar (SAR) observations from the free and open Sentinel-1 data archive to improve discrimination of treed and non-treed wetlands and non-wetlands in a boreal forest environment. Through a set of 3843 classification experiments for the year 2017, we tested the influence of three factors on classification accuracy: (i) input features (two backscatter coefficients in VV and VH polarization (σVV and σVH) and four quantitative measures derived from the Stokes vector); (ii) the temporal form of features (i.e. using all within-year observations versus generalized measures such as monthly/seasonal means or annualized statistics); and (iii) missing observations in Sentinel-1 time series due to varying observation availability across space. Among the tested features, we found the greatest utility in σVV and σVH. Directly using all within-year observations yielded higher accuracy than using generalized temporal forms. Moreover, the temporal form of the features had a greater impact on classification accuracy than the features themselves. The highest overall accuracy (0.860 ± 0.002) was achieved using σVV and σVH from all within-year observations. The majority of class confusion occurred between treed wetlands and non-wetlands. We found no significant reduction in the overall accuracy by simulated missing observations in time series when using all within-year observations. With the increasing availability of free and open data from the Sentinel-1 archive, new opportunities are emerging to readily integrate within-year time series into large-area land cover mapping, particularly if analysis-ready SAR data products further reduce preprocessing requirements for end users.http://www.sciencedirect.com/science/article/pii/S0303243419308815Land coverMonitoringSynthetic aperture radar |
| spellingShingle | Zhan Li Hao Chen Joanne C. White Michael A. Wulder Txomin Hermosilla Discriminating treed and non-treed wetlands in boreal ecosystems using time series Sentinel-1 data International Journal of Applied Earth Observations and Geoinformation Land cover Monitoring Synthetic aperture radar |
| title | Discriminating treed and non-treed wetlands in boreal ecosystems using time series Sentinel-1 data |
| title_full | Discriminating treed and non-treed wetlands in boreal ecosystems using time series Sentinel-1 data |
| title_fullStr | Discriminating treed and non-treed wetlands in boreal ecosystems using time series Sentinel-1 data |
| title_full_unstemmed | Discriminating treed and non-treed wetlands in boreal ecosystems using time series Sentinel-1 data |
| title_short | Discriminating treed and non-treed wetlands in boreal ecosystems using time series Sentinel-1 data |
| title_sort | discriminating treed and non treed wetlands in boreal ecosystems using time series sentinel 1 data |
| topic | Land cover Monitoring Synthetic aperture radar |
| url | http://www.sciencedirect.com/science/article/pii/S0303243419308815 |
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