Universiti Teknologi Malaysia Institutional Repository

Assessment of Landsat 7 Scan Line Corrector-off data gap-filling methods for seagrass distribution mapping

Hossain, M. S. and Bujang, J. S. and Zakaria, M. H. and Hashim, M. (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

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Official URL: http://dx.doi.org/10.1080/01431161.2015.1007257


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.

Item Type:Article
Uncontrolled Keywords:filling, image matching, mapping, pixels, sea level
Subjects:G Geography. Anthropology. Recreation > G Geography (General) > G109.5 Global Positioning System
Divisions:Geoinformation and Real Estate
ID Code:57912
Deposited By: Haliza Zainal
Deposited On:04 Dec 2016 04:07
Last Modified:07 Apr 2022 04:23

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