Disturbance-Informed Annual Land Cover Classification Maps of Canada's Forested Ecosystems for a 29-Year Landsat Time Series
Land cover classification of large geographic areas over multiple decades at an annual time step is now possible based upon free and open access to the Landsat data archive. Annual gap-free, best-available-pixel, surface reflectance, image composites and annual forest change maps have been generated...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2018-01-01
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| Series: | Canadian Journal of Remote Sensing |
| Online Access: | http://dx.doi.org/10.1080/07038992.2018.1437719 |
| _version_ | 1797661148597714944 |
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| author | Txomin Hermosilla Michael A. Wulder Joanne C. White Nicholas C. Coops Geordie W. Hobart |
| author_facet | Txomin Hermosilla Michael A. Wulder Joanne C. White Nicholas C. Coops Geordie W. Hobart |
| author_sort | Txomin Hermosilla |
| collection | DOAJ |
| description | Land cover classification of large geographic areas over multiple decades at an annual time step is now possible based upon free and open access to the Landsat data archive. Annual gap-free, best-available-pixel, surface reflectance, image composites and annual forest change maps have been generated for Canada for the years 1984 to 2012. Using these data, we demonstrate the Virtual Land Cover Engine (VLCE), a framework for change-informed annual land cover mapping, over the 650 million ha forested ecosystems of Canada, to produce a 29-year data cube of land cover. Post-processing aimed to reduce spurious class transitions is undertaken integrating change information, land cover transition likelihoods, and year-on-year class membership likelihoods. Validation was assessed for a single year (2005) using independent data for an overall accuracy of 70.3% (± 2.5%). Key results are the detailed capture of trends in land cover, illustration of land cover links to disturbance processes, and insights related to the general stability of land cover over time with stand replacing disturbance followed by regeneration of forests. The portable mapping framework and resultant data products offer an integrated, long baseline, disturbance-informed and detailed depiction of land cover to meet science and program related information needs. |
| first_indexed | 2024-03-11T18:41:17Z |
| format | Article |
| id | doaj.art-dfe9d07faa4748b29ca8a647f396e55e |
| institution | Directory Open Access Journal |
| issn | 1712-7971 |
| language | English |
| last_indexed | 2024-03-11T18:41:17Z |
| publishDate | 2018-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Canadian Journal of Remote Sensing |
| spelling | doaj.art-dfe9d07faa4748b29ca8a647f396e55e2023-10-12T13:36:22ZengTaylor & Francis GroupCanadian Journal of Remote Sensing1712-79712018-01-01441678710.1080/07038992.2018.14377191437719Disturbance-Informed Annual Land Cover Classification Maps of Canada's Forested Ecosystems for a 29-Year Landsat Time SeriesTxomin Hermosilla0Michael A. Wulder1Joanne C. White2Nicholas C. Coops3Geordie W. Hobart4University of British ColumbiaNatural Resources CanadaNatural Resources CanadaUniversity of British ColumbiaNatural Resources CanadaLand cover classification of large geographic areas over multiple decades at an annual time step is now possible based upon free and open access to the Landsat data archive. Annual gap-free, best-available-pixel, surface reflectance, image composites and annual forest change maps have been generated for Canada for the years 1984 to 2012. Using these data, we demonstrate the Virtual Land Cover Engine (VLCE), a framework for change-informed annual land cover mapping, over the 650 million ha forested ecosystems of Canada, to produce a 29-year data cube of land cover. Post-processing aimed to reduce spurious class transitions is undertaken integrating change information, land cover transition likelihoods, and year-on-year class membership likelihoods. Validation was assessed for a single year (2005) using independent data for an overall accuracy of 70.3% (± 2.5%). Key results are the detailed capture of trends in land cover, illustration of land cover links to disturbance processes, and insights related to the general stability of land cover over time with stand replacing disturbance followed by regeneration of forests. The portable mapping framework and resultant data products offer an integrated, long baseline, disturbance-informed and detailed depiction of land cover to meet science and program related information needs.http://dx.doi.org/10.1080/07038992.2018.1437719 |
| spellingShingle | Txomin Hermosilla Michael A. Wulder Joanne C. White Nicholas C. Coops Geordie W. Hobart Disturbance-Informed Annual Land Cover Classification Maps of Canada's Forested Ecosystems for a 29-Year Landsat Time Series Canadian Journal of Remote Sensing |
| title | Disturbance-Informed Annual Land Cover Classification Maps of Canada's Forested Ecosystems for a 29-Year Landsat Time Series |
| title_full | Disturbance-Informed Annual Land Cover Classification Maps of Canada's Forested Ecosystems for a 29-Year Landsat Time Series |
| title_fullStr | Disturbance-Informed Annual Land Cover Classification Maps of Canada's Forested Ecosystems for a 29-Year Landsat Time Series |
| title_full_unstemmed | Disturbance-Informed Annual Land Cover Classification Maps of Canada's Forested Ecosystems for a 29-Year Landsat Time Series |
| title_short | Disturbance-Informed Annual Land Cover Classification Maps of Canada's Forested Ecosystems for a 29-Year Landsat Time Series |
| title_sort | disturbance informed annual land cover classification maps of canada s forested ecosystems for a 29 year landsat time series |
| url | http://dx.doi.org/10.1080/07038992.2018.1437719 |
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