Editorial for the Special Issue “Remote Sensing in Coastal Zone Monitoring and Management—How Can Remote Sensing Challenge the Broad Spectrum of Temporal and Spatial Scales in Coastal Zone Dynamic?”

Doxaran,; Bustamante,; Dogliotti,; Malthus,; Senechal,

doi:10.3390/rs11091028

Cited by 4 publications

(3 citation statements)

References 14 publications

(24 reference statements)

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“…Its archives facilitate long-term time series analysis without the laborious stage of compiling data from multiple EO sources. Yet it is a demanding task to transform remote sensing and in situ observational data into useful forms supporting practical decision-making like ecosystem-based management or marine spatial planning [59,60].…”

Section: R Rs (λ) Time Seriesmentioning

confidence: 99%

Partition of Marine Environment Dynamics According to Remote Sensing Reflectance and Relations of Dynamics to Physical Factors

et al. 2021

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Seawaters exhibit various types of cyclic and trend-like temporal alterations in their biological, physical, and chemical processes. Surface water dynamics may vary, for instance, when the timings, durations, or amplitudes of seasonal developments of water properties alter between years and locations. We introduce a workflow using remote sensing to identify surface waters undergoing similar dynamics. The method, called ocean surface dynamics partitioning, classifies pixels based on their temporal change patterns instead of their properties at successive time snapshots. We apply an efficient parallel computing method to calculate Dynamic Time Warping (DTW) time series distances of large datasets of Earth Observation MERIS-instrument reflectance data Rrs(510 nm) and Rrs(620 nm), and produce a matrix of time series distances between 12,252 locations/time series in the Baltic Sea, for both wavelengths. We define cluster prototypes by hierarchical clustering of distance matrices and use them as initial prototypes for an iterative process of partitional clustering in order to identify areas that have similar reflectance dynamics. Lastly, we compute distances from the time series of the reflectance data to selected physical factors (wind, precipitation, and changes in sea surface temperature) obtained from Copernicus data archives. The workflow is reproducible and capable of managing large datasets in reasonable computation times and identifying areas of distinctive dynamics. The results show spatially coherent and logical areas without a priori information about the locations of the satellite image time series. The alignments of the reflectance time series vs. the observational time series of the physical environment clarify the causalities behind the cluster formation. We conclude that following the changes in an aquatic realm by biogeochemical observations at certain temporal intervals alone is not sufficient to identify environmental shifts. We foresee that the changes in dynamics are a sensitive measure of environmental threats and therefore they will be important to follow in the future.

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Section: R Rs (λ) Time Seriesmentioning

confidence: 99%

Partition of Marine Environment Dynamics According to Remote Sensing Reflectance and Relations of Dynamics to Physical Factors

et al. 2021

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“…Pressures from land reclamation activities lead to environmental change and degradation of coastal natural habitats, such as wetlands and dunes. RS can be used to measure changes and monitor reclaimed land and coastline length [172][173][174]. Erten and Rossi, 2019 [130] used SRS from Sentinel-1 images to detect and assess subtle land displacements to monitor small areas with high risk.…”

Section: Land Reclamationmentioning

confidence: 99%

Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review

et al. 2020

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Coastal and marine management require the evaluation of multiple environmental threats and issues. However, there are gaps in the necessary data and poor access or dissemination of existing data in many countries around the world. This research identifies how remote sensing can contribute to filling these gaps so that environmental agencies, such as the United Nations Environmental Programme, European Environmental Agency, and International Union for Conservation of Nature, can better implement environmental directives in a cost-effective manner. Remote sensing (RS) techniques generally allow for uniform data collection, with common acquisition and reporting methods, across large areas. Furthermore, these datasets are sometimes open-source, mainly when governments finance satellite missions. Some of these data can be used in holistic, coastal and marine environmental management frameworks, such as the DAPSI(W)R(M) framework (Drivers–Activities–Pressures–State changes–Impacts (on Welfare)–Responses (as Measures), an updated version of Drivers–Pressures–State–Impact–Responses. The framework is a useful and holistic problem-structuring framework that can be used to assess the causes, consequences, and responses to change in the marine environment. Six broad classifications of remote data collection technologies are reviewed for their potential contribution to integrated marine management, including Satellite-based Remote Sensing, Aerial Remote Sensing, Unmanned Aerial Vehicles, Unmanned Surface Vehicles, Unmanned Underwater Vehicles, and Static Sensors. A significant outcome of this study is practical inputs into each component of the DAPSI(W)R(M) framework. The RS applications are not expected to be all-inclusive; rather, they provide insight into the current use of the framework as a foundation for developing further holistic resource technologies for management strategies in the future. A significant outcome of this research will deliver practical insights for integrated coastal and marine management and demonstrate the usefulness of RS to support the implementation of environmental goals, descriptors, targets, and policies, such as the Water Framework Directive, Marine Strategy Framework Directive, Ocean Health Index, and United Nations Sustainable Development Goals. Additionally, the opportunities and challenges of these technologies are discussed.

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“…Several key land and water quality (vegetation, temperature, concentrations of suspended particulate matter and polychlorinated biphenyl, aquatic plants) and morphological (shorelines, mudbanks, wetlands) parameters, in different types of coastal environments (bays, estuaries, sandy and muddy systems), can be remotely sensed at various spatial and temporal scales, using innovative methods and providing validated products [31]. The availability of repetitive, synoptic and multi-spectral data from various satellite platforms, viz.…”

Section: Introductionmentioning

confidence: 99%

Mid-Term Monitoring of Suspended Sediment Plumes of Greek Rivers Using Moderate Resolution Imaging Spectroradiometer (MODIS) Imagery

Karalis,

Karymbalis,

Tsanakas

2023

Remote Sensing

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This study focuses on the suspended sediment delivery of 17 rivers and streams of various sizes to the sea over a wide geographical area covering most of the Greek peninsula, utilizing two Moderate Resolution Imaging Spectroradiometer (MODIS) products. Equal-area polygons (“plume” polygons), were delineated at the mouths of each selected river. These polygons were utilized to estimate the suspended sediment load of each river through the application of suspended sediment indices, ratios, and masks. To achieve this, 669 Level 1B MODIS images (MOD02) and their corresponding MODIS cloud products (MOD35) were downloaded and processed for a 10-water-year period (2004–2014). During this period of 669 days, there were 58 flood events (episodes) ranging in duration from 5 to 45 days. Relative atmospheric correction was applied to the images based on four selected bright invariant areas (PIFs) scattered along mainland Greece. The second product used in this study was MOD09Q1, an atmospherically corrected 8-day composite processed for the entire record period (2000–2019). Suspended sediment indices, ratios, and masks were developed using all three visible channels and near-infrared (NIR) for the MOD02 dataset, while only Red and Near-InfraRed (NIR) channels were available from the MOD09Q dataset. The resulting rankings from the remote sensing analysis were compared with the predictions of soil loss models, and the outcomes were largely consistent. While the remote sensing results can be considered as a type of experimental data or measurements, they come with inherent limitations. These include infrequent access to cloud-free data on stormy days, the influence of wind and currents, and the potential impact of dust storms originating from Africa, among others. On the other hand, soil loss models are sensitive to the parameter values used, and in some cases, the uncertainties are significant. Hence, the ranking derived from remote sensing can serve as a calibration of the models, particularly for the BQART model, which provides information on the catchment’s sink capacity. An index of “sediment productivity per square kilometer and mm of rainfall” was developed. This index can be considered a “sediment delivery ratio” and is crucial for accurately quantifying the phenomenon.

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Editorial for the Special Issue “Remote Sensing in Coastal Zone Monitoring and Management—How Can Remote Sensing Challenge the Broad Spectrum of Temporal and Spatial Scales in Coastal Zone Dynamic?”

Abstract: Coastal zones are sensitive areas responding at various scales (events to long-term trends) where the monitoring and management of physico-chemical, biological, morphological processes, and fluxes are highly challenging [...]

Cited by 4 publications

References 14 publications

Partition of Marine Environment Dynamics According to Remote Sensing Reflectance and Relations of Dynamics to Physical Factors

Partition of Marine Environment Dynamics According to Remote Sensing Reflectance and Relations of Dynamics to Physical Factors

Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review

Mid-Term Monitoring of Suspended Sediment Plumes of Greek Rivers Using Moderate Resolution Imaging Spectroradiometer (MODIS) Imagery

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