## 2 Remote Sensing Technologies for the Monitoring of Coastal and River Delta Regions
### Remote Sensing for Coastline Change Extraction  
Optical RS technology can help efficiently extract and measure coastline changes with high accuracy over large areas [161; 162; 163; 164; 165]. These changes can be stimulated by natural phenomena, such as SLR, the effects of hurricanes and possibly, in case of large ruptures, the impacts of coastal earthquakes. Coastline changes can also be due to the direct involvement of humans. With RS techniques, the change detection of the coastline's orientation,position, and shape [166] can retrieve valuable information on the inter-relationships between SLR and tidal currents, wave energy, land subsidence, and human intervention. Thus, the mapping of coastline changes is fundamental for coastal management, land use land change investigations, autonomous navigation, coastal erosion study, the planning of protection infrastructures, and an overall assessment of the increased risk of inundation at the coast. Coastline extraction is related to boundary extraction and image segmentation and belongs to the class of machine vision approaches [166; 167]. Coastline changes have manually been outlined by expert photo interpreters who have visually inspected aerial photographs for many decades. However, these operations were costly and time-consuming. The frequently used automatic interpretation-based methods are mainly based on edge detection, index analysis, threshold segmentation, region growth, neural network, and sub-pixel [168; 169; 170; 171; 172; 173; 174]. Landsat imagery has efficiently been applied to monitor coastline evolution using dozens of years of data [175; 176]. Long-term coastal changes of the Yellow River Delta have been determined based on decades of Landsat imagery. Sediment siltation, erosion, and human activities drive the dramatic deltaic coastline change [176].  
Detecting coastal erosion with optical remote sensing imagery strongly depends on the accuracy and frequency of coastline extraction. High resolution optical remote sensing imaging methods, such as IKONOS, WorldView-2, WorldView-3, QuickBird, and GF-I, have been applied to extract coastline changes and geomorphology changes and map coastal erosion at a fine resolution [177; 178; 135; 179]. Twenty-nine deltas of the world are in a state of overall erosion, determined by complementary analysis and quantification of coastline changes using satellite images of Landsat, SPOT-5, and SPOT-6 taken from 1972 to 2015 [131].  
Although many coastline extraction methods using optical remote sensing imagery have been developed over the decades, there is no universally valid coastline extraction technique. All existing algorithms are applied to images of individual satellites or for applications. It is also challenging to choose an algorithm for an application, because there is still no established theory for this purpose and no index for comparing and evaluating the performance of different methods in other specific cases. Complete automatic coastline extraction methods and algorithms using high-resolution optical remote sensing imagery still need to be developed, and time series images of \"30 m MODIS\" and virtual satellite constellations promise future research directions. Despite the advantages of optical satellite remote sensing, there are also some obvious limitations due to cloud coverage on rainy days, solar illumination, and natural meteorological conditions. Microwave data can circumvent these problems, because they are not significantly affected by these factors. As clarified below, the exploitation of independent information from sets of radar images helps obtain accurate coastline change maps. Some approaches also use coherence information between couples of complex-valued synthetic aperture radar (SAR) images to discriminate the land and sea boundaries [180].