A Multi-Satellite Mapping Framework for Floating Kelp Forests
Kelp forests provide key habitat on the Pacific Coast of Canada; however, the long-term changes in their distribution and abundance remain poorly understood. With advances in satellite technology, floating kelp forests can now be monitored across large-scale areas. We present a methodological framew...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/5/1276 |
| _version_ | 1828715768309612544 |
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| author | Lianna Gendall Sarah B. Schroeder Peter Wills Margot Hessing-Lewis Maycira Costa |
| author_facet | Lianna Gendall Sarah B. Schroeder Peter Wills Margot Hessing-Lewis Maycira Costa |
| author_sort | Lianna Gendall |
| collection | DOAJ |
| description | Kelp forests provide key habitat on the Pacific Coast of Canada; however, the long-term changes in their distribution and abundance remain poorly understood. With advances in satellite technology, floating kelp forests can now be monitored across large-scale areas. We present a methodological framework using an object-based image analysis approach that enables the combination of imagery from multiple satellites at different spatial resolutions and temporal coverage, to map kelp forests with floating canopy through time. The framework comprises four steps: (1) compilation and quality assessment; (2) preprocessing; (3) an object-oriented classification; and (4) an accuracy assessment. Additionally, the impact of spatial resolution on the detectability of floating kelp forests is described. Overall, this workflow was successful in producing accurate maps of floating kelp forests, with global accuracy scores of between 88% and 94%. When comparing the impact of resolution on detectability, lower resolutions were less reliable at detecting small kelp forests in high slope areas. Based on the analysis, we suggest removing high slope areas (11.4%) from time series analyses using high- to medium-resolution satellite imagery and that error, in this case up to 7%, be considered when comparing imagery at different resolutions in low–mid slope areas through time. |
| first_indexed | 2024-03-11T07:12:19Z |
| format | Article |
| id | doaj.art-7497ba5a894349938382713b66f78a89 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T07:12:19Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-7497ba5a894349938382713b66f78a892023-11-17T08:30:53ZengMDPI AGRemote Sensing2072-42922023-02-01155127610.3390/rs15051276A Multi-Satellite Mapping Framework for Floating Kelp ForestsLianna Gendall0Sarah B. Schroeder1Peter Wills2Margot Hessing-Lewis3Maycira Costa4SPECTRAL Remote Sensing Laboratory, University of Victoria, 3800 Finnerty Road, Victoria, BC V8P 5C2, CanadaSPECTRAL Remote Sensing Laboratory, University of Victoria, 3800 Finnerty Road, Victoria, BC V8P 5C2, CanadaCanadian Hydrographic Service, Institute of Ocean Sciences, 9860 West Saanich Rd, Sidney, BC V8L 4B2, CanadaCanadian Hydrographic Service, Institute of Ocean Sciences, 9860 West Saanich Rd, Sidney, BC V8L 4B2, CanadaSPECTRAL Remote Sensing Laboratory, University of Victoria, 3800 Finnerty Road, Victoria, BC V8P 5C2, CanadaKelp forests provide key habitat on the Pacific Coast of Canada; however, the long-term changes in their distribution and abundance remain poorly understood. With advances in satellite technology, floating kelp forests can now be monitored across large-scale areas. We present a methodological framework using an object-based image analysis approach that enables the combination of imagery from multiple satellites at different spatial resolutions and temporal coverage, to map kelp forests with floating canopy through time. The framework comprises four steps: (1) compilation and quality assessment; (2) preprocessing; (3) an object-oriented classification; and (4) an accuracy assessment. Additionally, the impact of spatial resolution on the detectability of floating kelp forests is described. Overall, this workflow was successful in producing accurate maps of floating kelp forests, with global accuracy scores of between 88% and 94%. When comparing the impact of resolution on detectability, lower resolutions were less reliable at detecting small kelp forests in high slope areas. Based on the analysis, we suggest removing high slope areas (11.4%) from time series analyses using high- to medium-resolution satellite imagery and that error, in this case up to 7%, be considered when comparing imagery at different resolutions in low–mid slope areas through time.https://www.mdpi.com/2072-4292/15/5/1276kelp forestsmultispectralsatellitetime seriesspatial resolutionobject-based image analysis |
| spellingShingle | Lianna Gendall Sarah B. Schroeder Peter Wills Margot Hessing-Lewis Maycira Costa A Multi-Satellite Mapping Framework for Floating Kelp Forests Remote Sensing kelp forests multispectral satellite time series spatial resolution object-based image analysis |
| title | A Multi-Satellite Mapping Framework for Floating Kelp Forests |
| title_full | A Multi-Satellite Mapping Framework for Floating Kelp Forests |
| title_fullStr | A Multi-Satellite Mapping Framework for Floating Kelp Forests |
| title_full_unstemmed | A Multi-Satellite Mapping Framework for Floating Kelp Forests |
| title_short | A Multi-Satellite Mapping Framework for Floating Kelp Forests |
| title_sort | multi satellite mapping framework for floating kelp forests |
| topic | kelp forests multispectral satellite time series spatial resolution object-based image analysis |
| url | https://www.mdpi.com/2072-4292/15/5/1276 |
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