Mapping Surficial Materials in Nunavut using RADARSAT-2 C-HH and C-HV, Landsat-8 OLI, DEM and Slope Data
The Canadian Arctic is currently subject to increased mapping activities for providing better knowledge to assist in making informed decisions for sustainable development. Surficial material maps are one of the required maps. For an area located in Nunavut, we produced a map with 21 surficial materi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2018-09-01
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| Series: | Canadian Journal of Remote Sensing |
| Online Access: | http://dx.doi.org/10.1080/07038992.2018.1545566 |
| _version_ | 1797661114997145600 |
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| author | Justin Byatt Armand LaRocque Brigitte Leblon Jeff Harris Isabelle McMartin |
| author_facet | Justin Byatt Armand LaRocque Brigitte Leblon Jeff Harris Isabelle McMartin |
| author_sort | Justin Byatt |
| collection | DOAJ |
| description | The Canadian Arctic is currently subject to increased mapping activities for providing better knowledge to assist in making informed decisions for sustainable development. Surficial material maps are one of the required maps. For an area located in Nunavut, we produced a map with 21 surficial material classes by applying a non-parametric classifier, Random Forests (RF), to a combination of RADARSAT-2 C-HH and C-HV with Landsat-8 OLI, digital elevation model, and slope data. We also tested the All-polygon and Sub-polygon scripts of RF. Validation accuracies were determined by comparing the resulting maps to more than 1000 field sites. By adding RADARSAT-2 dual-polarized images, the classification overall accuracy increases from 90.6% to 96.4% with the Sub-polygon script and from 92.8% to 98.1% with the All-polygon script. The overall validation accuracy increases from 76.3% to 88.9% with the Sub-polygon script and from 76.4% to 93.3% with the All-polygon script. With the All-polygon script, the validation accuracies are above 85% for all classes, except the user’s accuracy of gravelly till (76.7%) and the producer’s accuracy of sand and gravel with vegetation (70%), both classes being confused with thin till over bedrock. |
| first_indexed | 2024-03-11T18:41:01Z |
| format | Article |
| id | doaj.art-636ad27085b5412eb37ac092dc230bfe |
| institution | Directory Open Access Journal |
| issn | 1712-7971 |
| language | English |
| last_indexed | 2024-03-11T18:41:01Z |
| publishDate | 2018-09-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Canadian Journal of Remote Sensing |
| spelling | doaj.art-636ad27085b5412eb37ac092dc230bfe2023-10-12T13:36:22ZengTaylor & Francis GroupCanadian Journal of Remote Sensing1712-79712018-09-0144549151210.1080/07038992.2018.15455661545566Mapping Surficial Materials in Nunavut using RADARSAT-2 C-HH and C-HV, Landsat-8 OLI, DEM and Slope DataJustin Byatt0Armand LaRocque1Brigitte Leblon2Jeff Harris3Isabelle McMartin4Faculty of Forestry and Environmental Management, University of New BrunswickFaculty of Forestry and Environmental Management, University of New BrunswickFaculty of Forestry and Environmental Management, University of New BrunswickPrivate Consultant – 6 Sixth St.Geological Survey of CanadaThe Canadian Arctic is currently subject to increased mapping activities for providing better knowledge to assist in making informed decisions for sustainable development. Surficial material maps are one of the required maps. For an area located in Nunavut, we produced a map with 21 surficial material classes by applying a non-parametric classifier, Random Forests (RF), to a combination of RADARSAT-2 C-HH and C-HV with Landsat-8 OLI, digital elevation model, and slope data. We also tested the All-polygon and Sub-polygon scripts of RF. Validation accuracies were determined by comparing the resulting maps to more than 1000 field sites. By adding RADARSAT-2 dual-polarized images, the classification overall accuracy increases from 90.6% to 96.4% with the Sub-polygon script and from 92.8% to 98.1% with the All-polygon script. The overall validation accuracy increases from 76.3% to 88.9% with the Sub-polygon script and from 76.4% to 93.3% with the All-polygon script. With the All-polygon script, the validation accuracies are above 85% for all classes, except the user’s accuracy of gravelly till (76.7%) and the producer’s accuracy of sand and gravel with vegetation (70%), both classes being confused with thin till over bedrock.http://dx.doi.org/10.1080/07038992.2018.1545566 |
| spellingShingle | Justin Byatt Armand LaRocque Brigitte Leblon Jeff Harris Isabelle McMartin Mapping Surficial Materials in Nunavut using RADARSAT-2 C-HH and C-HV, Landsat-8 OLI, DEM and Slope Data Canadian Journal of Remote Sensing |
| title | Mapping Surficial Materials in Nunavut using RADARSAT-2 C-HH and C-HV, Landsat-8 OLI, DEM and Slope Data |
| title_full | Mapping Surficial Materials in Nunavut using RADARSAT-2 C-HH and C-HV, Landsat-8 OLI, DEM and Slope Data |
| title_fullStr | Mapping Surficial Materials in Nunavut using RADARSAT-2 C-HH and C-HV, Landsat-8 OLI, DEM and Slope Data |
| title_full_unstemmed | Mapping Surficial Materials in Nunavut using RADARSAT-2 C-HH and C-HV, Landsat-8 OLI, DEM and Slope Data |
| title_short | Mapping Surficial Materials in Nunavut using RADARSAT-2 C-HH and C-HV, Landsat-8 OLI, DEM and Slope Data |
| title_sort | mapping surficial materials in nunavut using radarsat 2 c hh and c hv landsat 8 oli dem and slope data |
| url | http://dx.doi.org/10.1080/07038992.2018.1545566 |
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