Together, these results suggest that MODIS data have a robust representation of environmental conditions in global coastal waters, at least when compared against gold-standard datasets of SST and Chlo-a. DE Africa Analysis Tools. 11, 17291740 (1990). DE Africa Analysis Tools | Digital Earth Africa Topographic and bathymetric data collection was also recently listed as a top priority by the Coastal Geosciences community (Power et al., 2021) due to the importance of the links between nearshore bathymetry and beach response, including sediment exchange as well as wave transformation processes. doi: 10.1016/j.jglr.2021.05.006, Dissanayake, P., Brown, J., Wisse, P., and Karunarathna, H. (2015). Remotely sensed environmental observations from the MODIS instrument, including SST and Chlo-a, have been validated profusely by the scientific community against a number of models and in situ measurements51,52,53,54,55,56,57,58 and used in a diverse set of studies13,14,19,59,60,61,62,63,64,65,66,67. Front. The constraint expression (or query) helped define the parameters, which correspond to the study period and spatial coverage. These particular models explicitly include the influence of time-varying water levels, which may be important along coastlines with large tidal variability, large storm surge, or at longer timescales where changes in the mean water level may be important. The primary forcing component that drives onshore/offshore shoreline movement is waves at the timescales of days to decades. Ocean Modell. Top. Earth Surface 116, 115. Effects of storm clustering on beach/dune evolution. J. Geophys. A push for more diverse groups to work together to advance the sciencethis may include engineers, geomorphologists, oceanographers, climatologists, and data scientists for example. Remote sensing satellite imagery has the potential to monitor and understand dynamic environmental phenomena by retrieving information about Earths surface. Bull. Sci. Google Scholar. Copyright 2021 Splinter and Coco. At night or when wind speeds are greater than ~6m/s, the relationship between the skin temperature and the subsurface are nearly equal. Res. From the available atmospheric and oceanic observations made available from NASAs Aqua Spacecraft, Sea Surface Temperature (SST) in C and Chlorophyll-a (Chlo-a) in mg*m3 were selected since they summarize major physical and biological phenomena. Fang, H., Wei, S. & Liang, S. Validation of MODIS and CYCLOPES LAI products using global field measurement data. An open source approach to improve both process-based models and reduced complexity models on the timescales of interest. C Oceans 120, 21592178. Remote Sens. Workflow diagram. Coastline - Datasets - Hawaii Open Data Google Scholar. doi: 10.1061/(ASCE)0733-950X(1991)117:6(624), Karunarathna, H., Pender, D., Ranasinghe, R., Short, A. D., and Reeve, D. E. (2014). Int. As a result predictions of shoreline change will need to be cast in a probabilistic framework and will require a joint community effort. Nevertheless, error could increase in optically complex waters like those present in coastal areas74,75. Watts, N. et al. 57, 620629. Ocean color chlorophyll algorithms for SeaWiFS. A., Mendez, F. J., and Allen, J. J. Geophys. Grimes, J. D. et al. 370, 6375. Recent open-source toolboxes, for example CoastSat (Vos et al., 2019b) or CASSIE (Almeida et al., 2021), allow for high resolution (order 10 m) bi-weekly shoreline measurements to be obtained at most sandy beaches around the world over the last 30 years. One-line models provide a simplified representation of the beachface and focus on shoreline change as a result of the gradients in alongshore transport resulting from oblique wave action () relative to the orientation of the coastline. Colwell, R. R. Global climate and infectious disease: The cholera paradigm. I. Remote Sens. 43, 21352143. doi: 10.1038/s41597-020-00750-5, Castelle, B., Marieu, V., Bujan, S., Splinter, K. D., Robinet, A., Snchal, N., et al. doi: 10.1029/2007JF000856, Yates, M. L., Guza, R. T., and O'Reilly, W. C. (2009). Geol. & Quan, W. An improved algorithm for retrieving chlorophyll-a from the Yellow River Estuary using MODIS imagery. Ocean Eng. Nurdin, S., Mustapha, M. A. Quantifying uncertainties of sandy shoreline change projections as sea level rises. United Nations Convention on the Law of the Sea. Policy 6, 441455 (2003). (2021) included upscaling, downscaling and direct forcing methods to link forcing to various timescales of shoreline evolution. Eng. To develop more accurate forecast tools for future coastal shoreline change, we recommend the following (also summarized in Figure 2): A continued push for real-time, high frequency and high quality in-situ coastal monitoring programs at relevant temporal and spatial scales to better understand the complex ocean/land boundary with a focus to capture the shoreline and nearshore bathymetry. Reliable and robust forecasts of future wave conditions, in the form of continuous timeseries mode, based on the best climate projections that account for changes in storminess and clustering of storms. GSHHG combines the older GSHHS shoreline database with WDBII rivers and borders, available in either ESRI shapefile format or in a native binary format. This may include the use of alternative approaches, such as surrogate models and emulators. Appl. These models will need to be open source to maximize uptake and development of the community-based efforts. Rosenqvist, ., Milne, A., Lucas, R., Imhoff, M. & Dobson, C. A review of remote sensing technology in support of the Kyoto Protocol. Shelf Res. Science 274, 20252031 (1996). 8:645797. doi: 10.3389/fmars.2021.645797, Ranasinghe, R. (2020). It is under these conditions that validation and uncertainty estimates relative to sub-surface in situ buoys are typically reported20,38. Introduction Eutrophication is a natural process characterizing excessive algal growth due to nutrient supply to the marine systems. Int. This perspectives article aims to summarize the current state of shoreline modeling at the sub-century timescale and provides an outlook on future challenges and opportunities ahead. 7:535. doi: 10.3389/fmars.2020.00535, Rosati, J. D., Dean, R. G., and Walton, T. L. (2013). SST and Chlo-a, among other environmental variables, can be accessed through National Oceanic and Atmospheric Administrations (NOAA) Coastal Watch Environmental Research Division (ERD) Environmental Research Division Data Access Protocol (ERDDAP) data server, also known as the NOAAs Coastal Watch. Benchmark data sets openly available for the community to test their models against. MODIS Chlo-a observations are derived from the OReilly OC3M algorithm and the Hu color index30,31. Remote Sens. DEA Coastlines combines satellite data with tidal modelling to map the typical location of the Australian coastline at mean sea level for every year since 1988 to 2021. Shoreline/Coastline Databases | NCEI - National Oceanic and Atmospheric Building off of established models as described above, a number of simple shoreline models have been proposed that encompass the impacts of cross-shore and alongshore processes (e.g., Vitousek et al., 2017b; Robinet et al., 2018; Antolnez et al., 2019; Roelvink et al., 2020; Alvarez-Cuesta et al., 2021). Wei, G. F., Tang, D. L. & Wang, S. Distribution of chlorophyll and harmful algal blooms (HABs): A review on space based studies in the coastal environments of Chinese marginal seas. (b) Economic Exclusive Zone (solid lines). We also wish to thank the reviewers for their insightful feedback that has improved the manuscript. (2013). Ocean Uses and Planning Areas. Res. Sci. (2020). Chlorophyll-a, SST and particulate organic carbon in response to the cyclone Amphan in the Bay of Bengal. Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. Eng. 1571, 466472 (2013). https://oceanservice.noaa.gov/facts/sea-surface-temperature.html (2020). Eng. https://modis.gsfc.nasa.gov/about/ (2021). Hou, W. W. & Arnone, R. There are large potential gains for data analysis if the Coast Guard invests in . Sci. 340, 7181. Hoge, F. E. et al. A., Sterling, E. J., Turner, W. & Spector, S. Remote sensing for ecology and conservation. Minnett, P. J. Application to wave-dominated coasts. temperature of sea surface chlorophyll a, Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.16638235. On complex coastlines, more refined models are needed to capture the complex wave transformation processes from offshore to nearshore in the absence of nearshore buoy networks. Data can be downloaded based on annually, monthly, or as summary composites of the 18-years period. Sp. Ensemble approaches that use advanced weighting techniques. Models that adapt in time to changes in forcing. Mar. J. Geophys. A significant benefit of satellite-derived information is the historical archives of data2,10,12. 117, 624640. Res., 101(B4), 87418743, doi:10.1029/96JB00104. Network Common Data Form (NetCDF). Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Data is fundamental for expanding our understanding of the coastline. Ranking uncertainty: wave climate variability versus model uncertainty in probabilistic assessment of coastline change. Coast. 253, 675683. Historically, a key challenge in our ability to fully understand coastal change has been the lack of long-term, large-scale, high-resolution (in time and space) coastal data sets (Turner et al., 2016; Ludka et al., 2019; Castelle et al., 2020). These models are well-suited for complex coastlines, where the influences of humans, sediment supply, and both alongshore and cross-shore processes contribute to the observed shoreline change. Environ. Sci. coastline data Pages tagged by "coastline data" DEA Coastlines. Remote Sens. (2020). (2017). Mar. Satellite infrared scanning radiometers AVHRR and ATSR/M. Considerations relevant to shoreline modeling at a variety of timescales of interest. Platforms, such as GitHub and the Community Surface Dynamics Modeling system (https://csdms.colorado.edu/) will become necessary for models to be integrated and developed with proper version control. (in press). Review article: Digital change detection techniques using remotely-sensed data. Res. To procure the inputs needed to assemble this database especially formed URLs were created through a programming algorithm in R (Auxiliary Materials44). Article Eng. The response of the ocean thermal skin layer to variations in incident infrared radiation. Marine ecosystems, however, have been studied with less intensity than terrestrial ecosystems due, in part, to data limitations. R: A Language and Environment for Statistical Computing. Mar. Coastline change analysis and erosion prediction using satellite images DOI: 10.1051/matecconf/201819713003 Authors: Universitas Warmadewa I Made Ardantha Windy Candrayana Universitas Warmadewa. 130, 1425. In contrast, Schepper et al. In the prevention of natural disasters, the use of data collected by marine sensors, meteorological satellites and other observation methods, through the analysis of machine learning algorithms, improves the level of forecasting and early warning of severe maritime weather in coastal areas and reduces the loss of life and property. Lancet 6736 (2020). Rapid adjustment of shoreline behavior to changing seasonality of storms: Observations and modelling at an open-coast beach. Liu, J. A decade of sea surface temperature from MODIS. Data was derived from the 1983 1:24,000 USGS Digital Line Graphs. J. Clim. doi: 10.1002/esp.4854, D'Anna, M., Idier, D., Castelle, B., Vitousek, S., and Le Cozannet, G. (2021b). J. Geophys. Lett. Many studies attempted to use remote sensing and geographic information systems as useful tools for monitoring coastline changes in terms of erosion and accretion, especially in . Use the Data Access Viewer. https://doi.org/10.6084/m9.figshare.16638235, https://coastwatch.pfeg.noaa.gov/erddap/griddap/documentation.html, https://coastwatch.pfeg.noaa.gov/erddap/griddap, https://appliedsciences.nasa.gov/our-impact/news/modis-viirs-making-switch-air-quality-professionals, https://oceanservice.noaa.gov/facts/sea-surface-temperature.html, https://doi.org/10.6084/m9.figshare.c.5660263.v1, https://coastwatch.pfeg.noaa.gov/erddapinfo/, https://doi.org/10.6084/m9.figshare.13708642.v4, https://library.stanford.edu/research/data-management-services/data-best-practices/best-practices-file-formats, https://www.unidata.ucar.edu/software/netcdf/, https://desktop.arcgis.com/en/arcmap/10.3/manage-data/raster-and-images/what-is-raster-data.htm, http://creativecommons.org/licenses/by/4.0/, http://creativecommons.org/publicdomain/zero/1.0/. Coast. (2009). Conceptualized the project: L.E.E., K.A.M., Created and contributed to the workflow for the project: M.C.G., L.E.E., Contributed to writing the R Scripts: M.C.G., G.M.S., R.S., Contributed to the writing and editing of the manuscript: M.C.G., L.E.E., K.A.M., G.M.S., R.S., Approved final version of the manuscript: L.E.E., K.A.M., M.C.G., G.M.S., R.S. Ocean. Also with the perspective of providing probabilistic estimates of long-term shoreline change, D'Anna et al. We compiled remotely sensed data of monthly SST and Chlo-a from the exclusive economic zone (EEZ) of coastal areas globally for a 18-year period (20032020). Extra-tropical and tropical events can cause widespread and rapid erosion over large areas of the coastline in a matter of hours to days (Castelle et al., 2015; Masselink et al., 2016b; Harley et al., 2017), whereas longer term climate variability (e.g., El-Nino Southern Oscillation, ENSO) can also cause enhanced erosion (or accretion) on time scales of 27 years (e.g., Barnard et al., 2015). Geography data are in five resolutions: crude(c), low(l), intermediate(i), high(h), and full(f). Escobar, L. E. et al. doi: 10.1002/2017JF004308, Vitousek, S., Barnard, P. L., Limber, P., Erikson, L., and Cole, B. Shoreline/Coastline Databases | NCEI - National Oceanic and Atmospheric A unifying framework for shoreline migration: 2. Rep. 8, 111. A multiscale approach to shoreline prediction. GSHHG is released under the Weather, Climate, and Hazards. Rev. MODIS SST observations represent the thermal skin layer of the ocean, which is <1mm thick and is cooler than the underlying water due to vertical heat flux68,69. (2021). Vezzulli, L. et al. J. Ocean Facts: Why do scientists measure sea surface temperature? 4, 6166 (2007). Golder, M. R. et al. However, this has been hindered by (1) a lack of long-term, large-scale coastal monitoring of sandy beaches and (2) a robust understanding of the key physical processes that drive shoreline change over multiple timescales. Due to potential atmospheric contamination some regions could have a limited number of observations from which to estimate the monthly values, which increases uncertainty. Data Analysis for Coastal Management - Field Studies Council At Coastline College we provide you the opportunity to jumpstart or advance your career with program pathways in data analytics or data science. The problem becomes insurmountable when looking at the future. Get the most important science stories of the day, free in your inbox. 3b). A. To represent the EEZ, a geospatial vector file in shapefile format was constructed by delimiting a buffer of ~200 miles off coastlines globally. Best practices for file formats. Correspondence to Digital Coast Data - National Oceanic and Atmospheric Administration A process-based boreal ecosystem productivity simulator using remote sensing inputs. Experimental data for mining is important in the law and . The program is designed to prepare students for entry-level jobs or to help them advance into careers, such as Business Analytics Specialist, Data Analyst, Data Visualization Developer, Operations Research Analyst, and . volume8, Articlenumber:304 (2021) (a) Remotely-sensed data were downloaded from the NASA ERDDAP server in the form of NetCDF files. (2017b). https://www.unidata.ucar.edu/software/netcdf/ (2021). Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG) is a high-resolution geography data set, amalgamated from two databases: World Vector Shorelines (WVS) and CIA World Data Bank II (WDBII). Res. A Bayesian network to predict coastal vulnerability to sea level rise. Initial airborne Lidar results. Geomorphology 238, 135148. 8, 666 (2016). 73, 191202. (2019) listed ensemble and probabilistic wave modeling and forecasting as one of the top 5 priorities for wind-wave research at present. Toward improved validation of satellite SST measurements for climate research. Int. & Nouri, N. Coastline change detection using remote sensing. in Microwave Remote Sensing for Oceanographic and Marine Weather-Forecast Models 141163 (Springer Netherlands, 1990). doi: 10.1029/2009JC005359, Keywords: shoreline, sandy coastline, prediction, waves, uncertainty, equilibrium models, ensemble modeling, Citation: Splinter KD and Coco G (2021) Challenges and Opportunities in Coastal Shoreline Prediction. Softw. OReilly, J. E. et al. IEEE J. Sel. CAS Carter, W. D. & Paulson, R. W. Introduction to monitoring dynamic environmental phenomena of the world using satellite data collection systems. doi: 10.1016/j.geomorph.2007.05.024, Bruun, P. (1962). J. Waterways Harbors Coast. Specifically, high quality temporal and spatial data of both the forcing (waves) and coastlines, enhanced quantification of shoreline change, and improved understanding of extreme events and the quantification of future impacts of climate change on our coasts were listed among the top priorities. doi: 10.1002/2014JC010329, Power, H. E., Pomeroy, A. W., Kinsela, M. A., and Murray, T. P. (2021). Ward, B. Near-surface ocean temperature. Eng. Enhanced coastal shoreline modeling using an ensemble Kalman filter to include nonstationarity in future wave climates. Article 1833 U.N.T.S. Mar. The result is that many studies on coastal shoreline dynamics have been based on a select few sites and span specific timeframes, thus limiting the universality of the findings. Castaneda-Guzman, M., Mantilla-Saltos, G., Murray, K. A., Settlage, R. & Escobar, L. E. Methods and code. Nat. Continent. Clin. Environ. ISSN 2052-4463 (online). Annual prediction of shoreline erosion and subsequent recovery. Forecasting coastal evolution on time-scales of days to decades. CAS Much like the advancements that have been made in climate modeling and wave forecasting on a global scale, workshops and working groups will help to foster the community spirit and rapidly advance the science. What is a raster data? 117, C01011 (2012). Modelling cross-shore shoreline change on multiple timescales and their interactions. Bull. By Mark Ford on May 4, 2021 Collaboration alongside the use of data analytics and digital processes have led to key successes in Singapore's anti-money laundering and counter financing of terrorism (AML/CFT) effectiveness according to assistant managing director of the Monetary Authority of Singapore (MAS). S1). Solve organizational problems by applying the methods, techniques, and tools relevant to systems analysis and design. Environ. We believe this is an area where much more work needs to be done to allow for robust future shoreline predictions in an uncertain future. Great Lakes Res. Method for Coastal Management - Field Studies Council GTSR covers the entire world's coastline and consists of time . Code in R language to recreate the database and the figures in the Usage Notes is available on Figshare41. Viewing marine bacteria, their activity and response to environmental drivers from orbit: Satellite remote sensing of bacteria. NASA Earth Science/Applied Science https://appliedsciences.nasa.gov/our-impact/news/modis-viirs-making-switch-air-quality-professionals (2020). Data 3:160024. doi: 10.1038/sdata.2016.24. Data may be of interest to researchers in the areas of ecology, oceanography, biogeography, fisheries, and global change. Deep Sea Res. 51, 531556. Eng. In this special issue, we introduce the Research Topic "Coastal Environmental and Ecological Data Analysis" to analyze the structure and functioning of diverse coastal ecosystems around global, adressing topics related with water quality, mangrove, coral reef and CO 2 emissions. Data can also be updated using the code included in the Auxiliary Material in Figshare44. Techniques in Ecology & Conservation Series (Oxford University Press, 2010). Am. Pages tagged by "coastline data" - Geoscience Australia 9:582. doi: 10.3390/jmse9060582, Smith, E. R., Wang, P., Ebersole, B. Sensors 12, 7778803 (2012). Remote Sens. After attaining better organization of its existing data and a workforce proficient with the basics of data science, the service can begin to seriously consider more advanced tools to improve its processes and data analysis. The overlay analysis has been completed with the incorporation of Google WMS in the current working model. Shoreline change at the timescales of hours to days is often quite noisy, such that models are often trained on the resulting time-integral of shoreline change [i.e., shoreline position x(t)]. This is particularly true for hybrid modeling approaches as described in section 3, that train specific predefined relationships to data. A., and Zhang, J. 114:F01009. Example of masking and cropping a raster. 3). & Clark, C. D. A review of remote sensing for the assessment and management of tropical coastal resources. 11, 110 (2020). Geol. (2020). Qin, H., Chen, G., Wang, W., Wang, D. & Zeng, L. Validation and application of MODIS-derived SST in the South China Sea. Figshare https://doi.org/10.6084/m9.figshare.13708642.v4 (2021). LEE was supported by a CeZAP 2020 Interdisciplinary Team-building Pilot Grant. J. Geophys. (2020). doi: 10.1029/2018JF004790, Antolinez, J. Geomorphology 204, 493501. A new global coastal database has been developed within the context of the DINAS-COAST project. The validation of sea surface temperature retrievals from spaceborne infrared radiometers. Sandy coastlines around the world are continuously adjusting in response to changing waves and water levels at both short (storm) and long (climate-driven, from El-Nino Southern Oscillation to sea level rise) timescales. J. Mar. Remote Sensing | Free Full-Text | Quantitative Analysis on Coastline The authors wish to thank Anna Blacka (UNSW) for her assistance with the graphics and many colleagues that have contributed to shoreline modeling over the past decade and continue to do so in an open and collaborative manner for the further advancement in our field. in SPIE 9111, Ocean Sensing and Monitoring VI (eds. Other researchers have proposed equilibrium approaches to model the planform of embayed beaches (e.g., Turki et al., 2013; Jaramillo et al., 2021). MODIS provides the longest standing observationalmarine time seriesdata, given that both the Aqua and Terra satellites have been in orbit since the early 2000s, and it provides a larger set of marine variables for potential evaluation at the same spatial and temporal scale18.
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