Assessment of landsat 7 scan line corrector-off data gap-filling methods for seagrass distribution mapping
Methods to predict and fill Landsat 7 Scan Line Corrector (SLC)-off data gaps are diverse and their usability is case specific. An appropriate gap-filling method that can be used for seagrass mapping applications has not been proposed previously. This study compared gap-filling methods for filling S...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis
2015
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/43860/ http://psasir.upm.edu.my/id/eprint/43860/1/Assessment%20of%20Landsat%207%20Scan%20Line%20Corrector-off%20data%20gap-filling%20methods%20for%20seagrass%20.pdf |
| _version_ | 1848850342739968000 |
|---|---|
| author | Hossain, Mohammad Shawkat Bujang, Japar Sidik Zakaria @ Ya, Muta Harah Hashim, Mazlan |
| author_facet | Hossain, Mohammad Shawkat Bujang, Japar Sidik Zakaria @ Ya, Muta Harah Hashim, Mazlan |
| author_sort | Hossain, Mohammad Shawkat |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Methods to predict and fill Landsat 7 Scan Line Corrector (SLC)-off data gaps are diverse and their usability is case specific. An appropriate gap-filling method that can be used for seagrass mapping applications has not been proposed previously. This study compared gap-filling methods for filling SLC-off data gaps with images acquired from different dates at similar mean sea-level tide heights, covering the Sungai Pulai estuary area inhabited by seagrass meadows in southern Peninsular Malaysia. To assess the geometric and radiometric fidelity of the recovered pixels, three potential gap-filling methods were examined: (a) geostatistical neighbourhood similar pixel interpolator (GNSPI); (b) weighted linear regression (WLR) algorithm integrated with the Laplacian prior regularization method; and (c) the local linear histogram matching method. These three methods were applied to simulated and original SLC-off images. Statistical measures for the recovered images showed that GNSPI can predict data gaps over the seagrass, non-seagrass/water body, and mudflat site classes with greater accuracy than the other two methods. For optimal performance of the GNSPI algorithm, cloud and shadow in the primary and auxiliary images had to be removed by cloud removal methods prior to filling data gaps. The gap-filled imagery assessed in this study produced reliable seagrass distribution maps and should help with the detection of spatiotemporal changes of seagrasses from multi-temporal Landsat imagery. The proposed gap-filling method can thus improve the usefulness of Landsat 7 ETM+ SLC-off images in seagrass applications. |
| first_indexed | 2025-11-15T10:04:46Z |
| format | Article |
| id | upm-43860 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:04:46Z |
| publishDate | 2015 |
| publisher | Taylor & Francis |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-438602016-10-27T05:12:03Z http://psasir.upm.edu.my/id/eprint/43860/ Assessment of landsat 7 scan line corrector-off data gap-filling methods for seagrass distribution mapping Hossain, Mohammad Shawkat Bujang, Japar Sidik Zakaria @ Ya, Muta Harah Hashim, Mazlan Methods to predict and fill Landsat 7 Scan Line Corrector (SLC)-off data gaps are diverse and their usability is case specific. An appropriate gap-filling method that can be used for seagrass mapping applications has not been proposed previously. This study compared gap-filling methods for filling SLC-off data gaps with images acquired from different dates at similar mean sea-level tide heights, covering the Sungai Pulai estuary area inhabited by seagrass meadows in southern Peninsular Malaysia. To assess the geometric and radiometric fidelity of the recovered pixels, three potential gap-filling methods were examined: (a) geostatistical neighbourhood similar pixel interpolator (GNSPI); (b) weighted linear regression (WLR) algorithm integrated with the Laplacian prior regularization method; and (c) the local linear histogram matching method. These three methods were applied to simulated and original SLC-off images. Statistical measures for the recovered images showed that GNSPI can predict data gaps over the seagrass, non-seagrass/water body, and mudflat site classes with greater accuracy than the other two methods. For optimal performance of the GNSPI algorithm, cloud and shadow in the primary and auxiliary images had to be removed by cloud removal methods prior to filling data gaps. The gap-filled imagery assessed in this study produced reliable seagrass distribution maps and should help with the detection of spatiotemporal changes of seagrasses from multi-temporal Landsat imagery. The proposed gap-filling method can thus improve the usefulness of Landsat 7 ETM+ SLC-off images in seagrass applications. Taylor & Francis 2015 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/43860/1/Assessment%20of%20Landsat%207%20Scan%20Line%20Corrector-off%20data%20gap-filling%20methods%20for%20seagrass%20.pdf Hossain, Mohammad Shawkat and Bujang, Japar Sidik and Zakaria @ Ya, Muta Harah and Hashim, Mazlan (2015) Assessment of landsat 7 scan line corrector-off data gap-filling methods for seagrass distribution mapping. International Journal of Remote Sensing, 36 (4). pp. 1188-1215. ISSN 0143-1161 10.1080/01431161.2015.1007257 |
| spellingShingle | Hossain, Mohammad Shawkat Bujang, Japar Sidik Zakaria @ Ya, Muta Harah Hashim, Mazlan Assessment of landsat 7 scan line corrector-off data gap-filling methods for seagrass distribution mapping |
| title | Assessment of landsat 7 scan line corrector-off data gap-filling methods for seagrass distribution mapping |
| title_full | Assessment of landsat 7 scan line corrector-off data gap-filling methods for seagrass distribution mapping |
| title_fullStr | Assessment of landsat 7 scan line corrector-off data gap-filling methods for seagrass distribution mapping |
| title_full_unstemmed | Assessment of landsat 7 scan line corrector-off data gap-filling methods for seagrass distribution mapping |
| title_short | Assessment of landsat 7 scan line corrector-off data gap-filling methods for seagrass distribution mapping |
| title_sort | assessment of landsat 7 scan line corrector-off data gap-filling methods for seagrass distribution mapping |
| url | http://psasir.upm.edu.my/id/eprint/43860/ http://psasir.upm.edu.my/id/eprint/43860/ http://psasir.upm.edu.my/id/eprint/43860/1/Assessment%20of%20Landsat%207%20Scan%20Line%20Corrector-off%20data%20gap-filling%20methods%20for%20seagrass%20.pdf |