Improving Flood Detection and Monitoring through Remote Sensing

As climate-change- and human-induced floods inflict increasing costs upon the planet, both in terms of lives and environmental damage, flood monitoring tools derived from remote sensing platforms have undergone improvements in their performance and capabilities in terms of spectral, spatial and temporal extents and resolutions. Such improvements raise new challenges connected to data analysis and interpretation, in terms of, e.g., effectively discerning the presence of floodwaters in different land-cover types and environmental conditions or refining the accuracy of detection algorithms. In this sense, high expectations are placed on new methods that integrate information obtained from multiple techniques, platforms, sensors, bands and acquisition times. Moreover, the assessment of such techniques strongly benefits from collaboration with hydrological and/or hydraulic modeling of the evolution of flood events. The aim of this Special Issue is to provide an overview of recent advancements in the state of the art of flood monitoring methods and techniques derived from remotely sensed data

Between the tides: modelling the elevation of Australia’s exposed intertidal zone at continental scale

The intertidal zone represents a critical transition between marine and terrestrial ecosystems, supporting a complex mosaic of highly productive and biologically diverse habitats. However, our understanding of these important coastal environments is limited by a lack of spatially consistent topographic data, which can be extremely challenging and costly to obtain at continental-scale. Satellite remote sensing represents an important resource for monitoring extensive coastal zones. Previous approaches to modelling the elevation of the intertidal zone using earth observation (EO) data have been restricted to small study regions or have relied on manual image interpretation, thus limiting their ability to be applied consistently over large geographic extents. In this study, we present an automated open-source approach to generate satellite-derived elevation data for over 15,387 km2 of intertidal terrain across the entire Australian coastline. Our approach combines global tidal modelling with a 30-year time series archive of spatially and spectrally calibrated Landsat satellite data managed within the Digital Earth Australia (DEA) platform. The resulting National Intertidal Digital Elevation Model (NIDEM) dataset provides an unprecedented three-dimensional representation of Australia's vast exposed intertidal zone at 25 m spatial resolution. We validate our model against LiDAR, RTK GPS and multibeam bathymetry datasets, finding that modelled elevations are highly accurate across sandy beach (±0.41 m RMSE) and tidal flat environments (±0.39 m RMSE). Model performance was least accurate (±2.98 m RMSE) within rocky shores and reefs and other complex coastal environments with extreme and variable tidal regimes. We discuss key challenges associated with modelling intertidal elevation including tidal model performance and biased observations from sun-synchronous satellites, and suggest future directions to improve the accuracy and utility of continental-scale intertidal elevation modelling. Our model can be applied to tidally-influenced coastal environments globally, addressing a key gap between the availability of sub-tidal bathymetry and terrestrial elevation data

Tracking the rapid loss of tidal wetlands in the Yellow Sea

In the Yellow Sea region of East Asia, tidal wetlands are the frontline ecosystem protecting a coastal population of more than 60 million people from storms and sea-level rise. However, unprecedented coastal development has led to growing concern about the status of these ecosystems. We developed a remote-sensing method to assess change over ∼4000 km of the Yellow Sea coastline and discovered extensive losses of the region's principal coastal ecosystem - tidal flats - associated with urban, industrial, and agricultural land reclamations. Our analysis revealed that 28% of tidal flats existing in the 1980s had disappeared by the late 2000s (1.2% annually). Moreover, reference to historical maps suggests that up to 65% of tidal flats were lost over the past five decades. With the region forecast to be a global hotspot of urban expansion, development of the Yellow Sea coastline should pursue a course that minimizes the loss of remaining coastal ecosystems

An invasive species erodes the performance of coastal wetland protected areas

The world has increasingly relied on protected areas (PAs) to rescue highly valued ecosystems from human activities, but whether PAs will fare well with bioinvasions remains unknown. By analyzing three decades of seven of the largest coastal PAs in China, including World Natural Heritage and/or Wetlands of International Importance sites, we show that, although PAs are achieving success in rescuing iconic wetlands and critical shorebird habitats from once widespread reclamation, this success is counteracted by escalating plant invasions. Plant invasions were not only more extensive in PAs than non-PA controls but also undermined PA performance by, without human intervention, irreversibly replacing expansive native wetlands (primarily mudflats) and precluding successional formation of new native marshes. Exotic species are invading PAs globally. This study across large spatiotemporal scales highlights that the consequences of bioinvasions for humanity’s major conservation tool may be more profound, far reaching, and critical for management than currently recognized

Automatic extraction of shorelines from Landsat TM and ETM+ multi-temporal images with subpixel precision

A high precision geometric method for automated shoreline detection from Landsat TM and ETM+ imagery is presented. The methodology is based on the application of an algorithm that ensures accurate image geometric registration and the use of a new algorithm for sub-pixel shoreline extraction, both at the sub-pixel level. The analysis of the initial errors shows the influence that differences in reflectance of land cover types have over shoreline detection, allowing us to create a model to substantially reduce these errors. Three correction models were defined according to the type of gain used in the acquisition of the original Landsat images. Error assessment tests were applied on three artificially stabilised coastal segments that have a constant and well-defined land-water boundary. A testing set of 45 images (28 TM, 10 ETM high-gain and 7 ETM low-gain) was used. The mean error obtained in shoreline location ranges from 1.22 to 1.63. m, and the RMSE from 4.69 to 5.47. m. Since the errors follow a normal distribution, then the maximum error at a given probability can be estimated. The results confirm that the use of Landsat imagery for detection of instantaneous coastlines yields accuracy comparable to high-resolution techniques, showing the potential of Landsat TM and ETM images in those applications where the instantaneous lines are a good geomorphological descriptor. © 2012 Elsevier Inc.The authors appreciate the financial support provided by the Spanish Ministerio de Ciencia e Innovacion and the Spanish Plan E in the framework of the Projects CGL2009-14220-C02-01 and CGL2010-19591.Pardo Pascual, JE.; Almonacid Caballer, J.; Ruiz Fernández, LÁ.; Palomar-Vázquez, J. (2012). Automatic extraction of shorelines from Landsat TM and ETM+ multi-temporal images with subpixel precision. Remote Sensing of Environment. 123:1-11. doi:10.1016/j.rse.2012.02.024S11112

Topographic evolution of tidal flats based on remote sensing: an example in Jiangsu coast, Southern Yellow Sea

The topographic evolution of tidal flats is critical for local ecological conservation, coastal zone management, and physical oceanographic studies. However, obtaining this knowledge is often challenging due to the lack of frequently updated topographic data over large areas. With the explosion of remotely sensed data, the waterline method has become the most operational method for tidal flat topography acquisition. In this study, digital elevation models (DEMs) of the tidal flats around Tongzhou Bay on the Jiangsu coast were constructed using the waterline method for three periods (2013, 2015, and 2017) before and after the construction of phase I of the reclamation project. Furthermore, the topographic evolution characteristics were analyzed from four aspects: contours, area changes, erosion–deposition distribution, and typical cross-sections. The results showed that: 1) During the 5 years from 2013 to 2017, the overall tidal flat area (500 km2) of Tongzhou Bay on the Jiangsu coast had been in a state of deposition, with a total siltation thickness of 0.19 m. 2) The reclamation activities affected the topography of the tidal flats quickly, but the recovery was also rapid. During the implementation of the project (in 2015), the area of the tidal flats above the −2-m contour was rapidly reduced by 20 km2 but rapidly recovered to the pre-project level after the completion of the project (in 2017). 3) The reclamation project directly affected the distribution of erosion and siltation. Outside the seawall on the east side of the Yaosha sand ridge, the 0-m contour expanded rapidly to the outer sea, reaching more than 250 m/year. 4) The sandbars in Tongzhou Bay on the Jiangsu coast generally had a southward-moving trend. Over the past 40 years, the Yaosha sand ridge had shifted southward by 2,500 m and the Lengjiasha sand ridge by more than 5,000 m. This study provides a remote sensing solution for the topographic evolution of large tidal flats under the influence of human reclamation activities

Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land Applications: A Review

The coastal zone offers among the world’s most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of land‐ and water‐related applications in coastal zones. Compared to optical satellites, cloud‐cover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have all‐weather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloud‐prone tropical and sub‐tropical climates. The canopy penetration capability with long radar wavelength enables L‐band SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban sprawl and climate change‐induced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne L‐band SAR data for geoscientific analyses that are relevant for coastal land applications

Shoreline change and coastal vulnerability characterization with Landsat imagery : a case study in the Outer Hebrides, Scotland

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