Automatic Methodology to Detect the Coastline from Landsat Images with a New Water Index Assessed on Three Different Spanish Mediterranean Deltas

Sandra Viaña-Borja,Miguel Ortega-Sánchez

doi:10.3390/rs11182186

Sandra Viaña-Borja, Miguel Ortega-Sánchez

Open Access

https://doi.org/10.3390/rs11182186

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Abstract

Due to the importance of coastline detection in coastal studies, different methods have been developed in recent decades in accordance with the evolution of measuring techniques such as remote sensing. This work proposes an automatic methodology with new water indexes to detect the coastline from different multispectral Landsat images; the methodology is applied to three Spanish deltas in the Mediterranean Sea. The new water indexes use surface reflectance rather than top-of-atmosphere reflectance from blue and shortwave infrared (SWIR 2) Landsat bands. A total of 621 sets of images were analyzed from three different Landsat sensors with a moderate spatial resolution of 30 m. Our proposal, which was compared to the most commonly used water indexes, showed outstanding performance in automatic detection of the coastline in 96% of the data analyzed, which also reached the minimum value of bias of − 0.91 m and a standard deviation ranging from ±4.7 and ±7.29 m in some cases in contrast to the existing values. Bicubic interpolation was evaluated for a simple sub-pixel analysis to assess its capability in improving the accuracy of coastline extraction. Our methodology represents a step forward in automatic coastline detection that can be applied to micro-tidal coastal sites with different land covers using many multi-sensor satellite images.

Highlights

The coastal areas constitute one of the most productive yet vulnerable ecosystems in the world [1]
An example of the sequence of the methodology applied to every image can be seen in Figure 6 from an image obtained in the Guadalfeo delta on September 12, 2013 by the Operational Land Imager (OLI) sensor
It can be observed through a visual comparison that the methodology applied using the water indexes WI1 and WI2 were able to separate water from non-water pixels across the study areas covered

Summary

Introduction

The coastal areas constitute one of the most productive yet vulnerable ecosystems in the world [1]. Most of the world population is concentrated in the coastal area, with up to 60% living close to the sea [2,3]. Coastline monitoring must be an essential issue for public policies regarding coastal management. The coastal zone is a very dynamic environment affected by different factors, such as hydrography, geology, climate, and vegetation [4]. The increasing development of coastal areas causes persistent erosion and flooding problems [5]. Coastal observation is under permanent development to improve measuring capabilities and to characterize processes related to water quality, hydrodynamics, meteorology, and ecology, as well as submarine geomophology [7]

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