Comprehension of vulnerability to coastal erosion in dynamic coastal environments strongly depends on accurate and frequent detection of shoreline position. The monitoring of such environments could benefit from the semi-automatic shoreline delineation method, especially in terms of time, cost, and labour-intensiveness. This article explores the potential of using a semi-automatic approach in delineating a proxy-based shoreline by processing high-resolution multispectral WorldView-2 satellite imagery. We studied the potential and differences of basic and easily accessible standard classification methods for shoreline detection. In particular we explored the use of high spatial and spectral resolution satellite imagery for shoreline extraction. The case study was carried out on a 40 km coastal stretch facing the Northern Adriatic Sea (Italy) and belonging to the Municipality of Ravenna. In this area a frequent monitoring of shoreline position is required because of the extreme vulnerability to erosion phenomena that have resulted in a general trend of coastal retreat over recent decades. The wet/dry shorelines were delineated between the classes of wet and dry sand, resulting from different supervised (Parallelepiped, Gaussian Maximum Likelihood, Minimum-Distance-to-Means, and Mahalanobis distance) image classification techniques and the unsupervised Iterative Self-Organizing Data Analysis Technique (ISODATA). In order to assign reliability to outcomes, the extrapolated shorelines were compared to reference shorelines visually identified by an expert, by assessing the average mean distance between them. In addition, the correlation between offset rates and different types of coast was investigated to examine the influence of specific coastal features on shoreline extraction capability. The results highlighted a high level of compatibility. The average median distance between reference shorelines and those resulting from the classification methods was less than 5.6 m (Maximum likelihood), whereas a valuable distance of just 2.2 m was detected from ISODATA and Mahalanobis. Heterogeneous coastal stretches exhibited a larger offset between extracted and reference shorelines than the homogeneous ones. To finally evaluate the coastal evolution of the area, results from Mahalanobis classification were compared to a shoreline derived from airborne light detection and ranging (lidar) data. The fine spatial resolution provided by both methodologies allowed a detailed Digital Shoreline Analysis System (DSAS) comparison, detecting an erosive trend within a wide portion of the study area.

Image classification methods applied to shoreline extraction on very high-resolution multispectral imagery / Sekovski, I; Stecchi, F; Mancini, Francesco; Del Rio, L.. - In: INTERNATIONAL JOURNAL OF REMOTE SENSING. - ISSN 0143-1161. - STAMPA. - 35:(2014), pp. 3556-3578. [10.1080/01431161.2014.907939]

Image classification methods applied to shoreline extraction on very high-resolution multispectral imagery

MANCINI, Francesco;
2014

Abstract

Comprehension of vulnerability to coastal erosion in dynamic coastal environments strongly depends on accurate and frequent detection of shoreline position. The monitoring of such environments could benefit from the semi-automatic shoreline delineation method, especially in terms of time, cost, and labour-intensiveness. This article explores the potential of using a semi-automatic approach in delineating a proxy-based shoreline by processing high-resolution multispectral WorldView-2 satellite imagery. We studied the potential and differences of basic and easily accessible standard classification methods for shoreline detection. In particular we explored the use of high spatial and spectral resolution satellite imagery for shoreline extraction. The case study was carried out on a 40 km coastal stretch facing the Northern Adriatic Sea (Italy) and belonging to the Municipality of Ravenna. In this area a frequent monitoring of shoreline position is required because of the extreme vulnerability to erosion phenomena that have resulted in a general trend of coastal retreat over recent decades. The wet/dry shorelines were delineated between the classes of wet and dry sand, resulting from different supervised (Parallelepiped, Gaussian Maximum Likelihood, Minimum-Distance-to-Means, and Mahalanobis distance) image classification techniques and the unsupervised Iterative Self-Organizing Data Analysis Technique (ISODATA). In order to assign reliability to outcomes, the extrapolated shorelines were compared to reference shorelines visually identified by an expert, by assessing the average mean distance between them. In addition, the correlation between offset rates and different types of coast was investigated to examine the influence of specific coastal features on shoreline extraction capability. The results highlighted a high level of compatibility. The average median distance between reference shorelines and those resulting from the classification methods was less than 5.6 m (Maximum likelihood), whereas a valuable distance of just 2.2 m was detected from ISODATA and Mahalanobis. Heterogeneous coastal stretches exhibited a larger offset between extracted and reference shorelines than the homogeneous ones. To finally evaluate the coastal evolution of the area, results from Mahalanobis classification were compared to a shoreline derived from airborne light detection and ranging (lidar) data. The fine spatial resolution provided by both methodologies allowed a detailed Digital Shoreline Analysis System (DSAS) comparison, detecting an erosive trend within a wide portion of the study area.
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Image classification methods applied to shoreline extraction on very high-resolution multispectral imagery / Sekovski, I; Stecchi, F; Mancini, Francesco; Del Rio, L.. - In: INTERNATIONAL JOURNAL OF REMOTE SENSING. - ISSN 0143-1161. - STAMPA. - 35:(2014), pp. 3556-3578. [10.1080/01431161.2014.907939]
Sekovski, I; Stecchi, F; Mancini, Francesco; Del Rio, L.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1055320
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