Abstract
This paper presents algorithmic components and corresponding software routines for extracting shoreline features from remote sensing imagery and LiDAR data. Conceptually, shoreline features are treated as boundary lines between land objects and water objects. Numerical algorithms have been identified and de-vised to segment and classify remote sensing imagery and LiDAR data into land and water pixels, to form and enhance land and water objects, and to trace and vectorize the boundaries between land and water ob-jects as shoreline features. A contouring routine is developed as an alternative method for extracting shore-line features from LiDAR data. While most of numerical algorithms are implemented using C++ program-ming language, some algorithms use available functions of ArcObjects in ArcGIS. Based on VB .NET and ArcObjects programming, a graphical user’s interface has been developed to integrate and organize shoreline extraction routines into a software package. This product represents the first comprehensive software tool dedicated for extracting shorelines from remotely sensed data. Radarsat SAR image, QuickBird multispectral image, and airborne LiDAR data have been used to demonstrate how these software routines can be utilized and combined to extract shoreline features from different types of input data sources: panchromatic or single band imagery, color or multi-spectral image, and LiDAR elevation data. Our software package is freely available for the public through the internet.
Highlights
A shoreline is a spatially continuous line of contact between the land and a body of water
The algorithms and software routines presented in this paper are object-based in the sense that shoreline features are treated as boundary lines between land objects and water objects
With the object-based approach, algorithms and software routines used for processing image data and LiDAR data are almost the same, except for those used for land/water segmentation
Summary
A shoreline is a spatially continuous line of contact between the land and a body of water (sea or lake). Liu et al [22] developed a segmentation-based method for extracting tidal datum referenced shorelines from LiDAR data. The algorithms and software routines presented in this paper are object-based in the sense that shoreline features are treated as boundary lines between land objects and water objects. Radarsat SAR image, QuickBird multispectral image, and airborne LiDAR data have been used to illustrate how software routines can be utilized and combined to automate shoreline extraction from different data sources: panchromatic or single band imagery, color or multi-spectral image, and LiDAR elevation data. We believe that this paper and the corresponding software package will provide the geosciences and coastal research community with a powerful tool for efficiently processing high-resolution remote sensing imagery and LiDAR for frequent and timely shoreline measurements
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