coastal tourism data

Office for Coastal Management

Tourism and recreation.

Fast Facts / Tourism and Recreation

Tourism and recreation support a wide range of businesses in the nation’s shore-adjacent zip codes, from restaurants, hotels, aquariums, and marinas to boat manufacturers and sporting goods stores. The following facts are based on the latest available economic data 1 (2018).

2.5 Million People

Almost 2.5 million people are employed by ocean-based tourism and recreation.

$66 Billion Earnings

Workers in the ocean-based tourism and recreation sector earned about $65.6 billion in annual wages.

$143 Billion GDP

Ocean-based tourism and recreation contributes approximately $143 billion in gross domestic product to the national economy each year.

An Industry Leader

The ocean-based tourism and recreation sector employs more Americans than the entire real estate industry, as well as more people than building construction and telecommunications combined.

Steady Growth

The industry added 54,000 jobs (2.3 percent growth) from 2017 to 2018—adding jobs at a slightly higher rate than the overall U.S. economy (1.6 percent growth).

In terms of employment, California, Florida, New York, Hawaii, and New Jersey are the five largest contributors to marine-based tourism and recreation, accounting for more than half of this sector’s total employment in 2018. In terms of gross domestic product, New York, California, Florida, Hawaii, and Washington State were the five largest contributors, accounting for more than half the national gross domestic product of this sector in 2018.

Leisure and Hospitality in Coastal Counties

In the larger economic sector of leisure and hospitality*, which includes all hotels and restaurants located in shoreline-adjacent counties, the economic contributions are also substantial. Statistics 2 include the following:

• Employs almost 6.9 million people
• Generates over $196 billion in earnings
• Contributes $427 billion to the gross domestic product

*Note: Economics: National Ocean Watch (ENOW) is a NOAA economics database on the Digital Coast that features time-series data focused on the six economic sectors that are dependent on the oceans and Great Lakes. ENOW's tourism and recreation statistics are good indicators of coastal and ocean tourism because they estimate the ocean-dependent portion of business for hotels and restaurants by including only those establishments located in shore-adjacent zip code areas, and they exclude all forms of sports and entertainment that are not ocean-related.

Statistics for the leisure and hospitality sector, on the other hand, include all hotels and restaurants located anywhere in a shore-adjacent county, many of which are far from the coast and have very little business connected to ocean tourism. These statistics also include activities associated with all forms of sports and entertainment, including sports arenas (e.g., professional baseball and football stadiums), Broadway plays, casinos, and racetracks.

Source: NOAA Office for Coastal Management

1: Economics: National Ocean Watch

2: Total Economy for US Coastal Shoreline Counties

Coastal tourism

EU coastal areas are amongst the most preferred touristic destinations for European and international travellers, making coastal and maritime tourism the biggest, growing sector of the EU Blue Economy in terms of GVA and employment. Coastal tourism comprises recreational activities taking place in the proximity of the sea (such as swimming, sunbathing, coastal walks, and wildlife watching) as well as those taking place in the maritime area, including nautical sports (e.g. sailing, scuba diving, cruising, etc.). Over half of the EU bed capacity is concentrated in regions with a sea border. For the economy of many non-landlocked EU Member States, tourism generates a significant portion of the national revenue. It has a wide-ranging impact on economic growth, employment and social development. Tourism is particularly significant for Southern European countries, such as Spain, Portugal, Italy, Malta and Greece. At the same time, coastal tourism is characterised by high seasonality , with demand concentrated in a limited number of months, usually July and August.

In 2020, Coastal tourism kept generating the largest share of employment and GVA in the EU Blue Economy, with 51% and 26%, respectively. EU Blue Economy Report 2023

The statistics presented in this section originate from the following expenditures by tourists in EU coastal areas: •    Accommodation •    Transport •    Other expenditures (e.g. food, culture, recreational activities, water-sport equipment and clothing).

The sector was hit hard by the COVID-19 pandemic. Because of the COVID-19 pandemic, millions of European citizens suddenly could not travel.

In March 2020, tourism came to a grinding halt, and its turnover did not fully recover until 2022 when the total number of nights spent in EU tourist accommodation reached 2.73 billion (against the pre-pandemic level of 2.88 billion nights in 2019).

In 2022, tourism figures in all months were higher than in 2021, with the fourth quarter of 2022 recording 472 million nights. In total, nights spent went up by 49% compared to 2021 (1.83 billion nights).

In 2020, the GVA generated by the sector amounted to €33.9 billion, down from €81.5 billion registered in 2019, i.e., a year-on-year 58% contraction.

Gross profits, at €3.9 billion, shrunk by more than 85% in 2019. 

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While recovering from the Covid-19 pandemic, the Coastal tourism sector has engaged in the sustainability transition with the aim to green its operations, reduce its negative impacts on the marine environment, and strengthen its  resilience to exogenous shocks such as commodity and fuel price inflation.

For this purpose, the European Commission has published the Tourism Transition Pathway involving all tourism stakeholders. The updated Industrial Strategy (2021) calls for the acceleration of green and digital transformation to increase the European economy's resilience.

The strategy also identifies 27 initiative areas for the green and digital transition to improve tourism in the EU. These include:

•     Regulation and public governance , which includes; improving statistics and indicators for tourism and comprehensive tourism strategies.

•     Green and digital transition , which includes; research and innovation projects on circular and climate-friendly tourism and support for digitalisation of tourism SMEs and destinations.

•     Resilience , which includes; seamless cross-border travelling, fostering skills in tourism, promoting fairness and equality in tourism jobs, and accessibility.

As part of the EU Sustainable Blue Economy , the coastal and maritime tourism sector has been identified as an area with potential to foster a smart, sustainable and inclusive Europe. The EU's tourism policy aims to keep Europe's position as a leading tourist destination while also maximising the industry's contribution to economic growth and jobs.

Interaction with other sectors

While European destinations are welcoming tourists at pre-pandemic levels, the rise in tourist numbers and their concentration in attractive touristic destinations may generate issues of over tourism, particularly in  coastal areas and small island. Increasing tourism intensity requires long-term solutions .

At the same time, Covid-19 will likely have long-term impacts on EU citizens’ travelling behavior. A recent Eurobarometer survey showed that 82% of EU citizens are ready to change their travel and tourism habits to be more sustainable, for example, by consuming local products (55%), choosing ecological means of transport (36%) or by paying more to protect the natural environment (35%) or to benefit the local community (33%). The survey also found out that 38% of European respondents are expecting more domestic travelling in the future .

One example of a sustainable approach to coastal and maritime tourism is the ECO-CRUISING FU_TOUR .

For many experts in the business, this is a game-changing opportunity that will lead to greater and faster adoption of more sustainable environmental solutions and greater respect for coastal natural and cultural qualities of coastal areas. The European Green Deal and the new EU Sustainable Blue Economy can help in such green transitions, thanks to policy reforms, specific financial mechanism, as well as innovation, digitalisation, education and training.

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Building a Sustainable Coastal and Marine Tourism Economy

The tourism industry was estimated to become the largest ocean economy by 2030 (OECD), but COVID-19 severely impacted all parts of the tourism value chain. Oceans and coasts are at the heart of tourism, as 80% of all tourism takes place in coastal areas. 183 countries have coastlines, 37% of the world’s population live in coastal communities and most commodities extracted from the ocean come from coastal regions (UNWTO, 2014). The new realities have awakened a new approach to tourism, particularly from island and coastal nations.

In 2020 the High Level Panel for a Sustainable Ocean Economy (Ocean Panel) set a goal of a sustainable tourism economy by 2030: “Coastal and ocean-based tourism is sustainable, resilient, addresses climate change, reduces pollution, supports ecosystem regeneration and biodiversity conservation and invests in local jobs and communities.” This guidance has been taken by government and industry in the approach to a recovery of the tourism sector. The Tourism Action Coalition for a Sustainable Ocean was created in response to the Ocean Panel's call to action and will serve as a knowledge hub and action platform to drive sustainable coastal and ocean-based tourism.

This session will bring together representatives of the Ocean Panel countries, alongside high-level representatives from the Tourism Action Coalition for a Sustainable Ocean to address how the public and private sectors are moving toward a sustainable blue tourism economy that builds a resilient future for tourism destinations and improves the well-being of local communities.

We invite tourism stakeholders and coastal and ocean communities to join this webinar. The event promises to showcase the actions that are being taken by countries and the tourism industry to recover and rebuild after the pandemic. These actions can provide guidance in building resilience to face other health and environmental crises across sectors.

• Jamie Sweeting , CEO , Planeterra (moderator)
• Gloria Fluxa Thienemann , Vice-Chairman and Chief Sustainability Officer , Iberostar Hotels & Resorts
• Daniel Skjeldam,  CEO , Hurtigruten Group
• Cesar Daniel Gonzalez Madruga,  General Director , SECTUR Mexico
• Louise Twining-Ward , Senior Private Sector Development Specialist , The World Bank
• Rita Marques , Secretary of State of Tourism, Portugal (keynote)

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UN Tourism Data Dashboard

The UN Tourism Data Dashboard – provides statistics and insights on key indicators for inbound and outbound tourism at the global, regional and national levels. Data covers tourist arrivals, tourism share of exports and contribution to GDP, source markets, seasonality and accommodation (data on number of rooms, guest and nights)

Two special modules present data on the impact of COVID 19 on tourism as well as a Policy Tracker on Measures to Support Tourism

The UN Tourism/IATA Destination Tracker

UN Tourism Recovery Tracker

• International tourist arrivals and receipts and export revenues
• International tourism expenditure and departures
• Seasonality
• Tourism Flows
• Accommodation
• Tourism GDP and Employment
• Domestic Tourism

International Tourism and COVID-19

• The pandemic generated a loss of 2.6 billion international arrivals in 2020, 2021 and 2022 combined
• Export revenues from international tourism dropped 62% in 2020 and 59% in 2021, versus 2019 (real terms) and then rebounded in 2022, remaining 34% below pre-pandemic levels.
• The total loss in export revenues from tourism amounts to USD 2.6 trillion for that three-year period.
• International tourist arrivals reached 88% of pre-pandemic levels in January-December 2023

COVID-19: Measures to Support Travel and Tourism

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Opportunities for Transforming Coastal and Marine Tourism: Towards Sustainability, Regeneration and Resilience

While the inherent balancing act of nature-based tourism has always been apparent, the unprecedented pause in global tourism induced by the pandemic has provided a unique opportunity to reassess and reset.

In December 2020, the High Level Panel for a Sustainable Ocean Economy (Ocean Panel) released its ‘Transformations for a Sustainable Ocean Economy: A Vision for Protection, Production and Prosperity’ .

One of the pillars of action within this ambitious roadmap for a sustainable ocean economy was for sustainable ocean-based tourism, establishing the goal that by 2030 “Coastal and ocean-based tourism is sustainable, resilient, addresses climate change, reduces pollution, supports ecosystem regeneration and biodiversity conservation and invests in local jobs and communities.”

To support efforts to deliver on this goal, the Ocean Panel commissioned a body of knowledge on coastal and marine tourism, including this special report.

We’ve seen the devastating impacts that the global pandemic has had on tourism, particularly for small island and coastal communities. We’ve also seen cases where reduced pressure from mass tourism has resulted in recovery of coastal and marine ecosystems. The past few years has made a strong case for change. There is an urgent need to ensure the continued viability of this important sector while at the same time building its resilience to climate change, disasters, pollution, urbanisation and ecosystem degradation. We know that sustainable ocean-based tourism can restore and protect the ocean while delivering jobs and prosperity. However, achieving this ambitious vision requires strategic public and private investments.

This special report outlines an approach for sustainable coastal and marine tourism that increases the focus on regeneration and resilience. It contains inspiring examples of destinations and individual businesses shifting towards a more sustainable approach that helps to restore the local environment on which it depends, supports local economic prosperity, and protects and even revitalises local traditional and heritage.

The coordinating lead author of this report is Eliza Northrop. Contributing authors include Peter Schuhmann, Lauretta Burke, Alan Fyall, Sergio Alvarez, Anna Spenceley, Susanne Becken, Kumi Kato, Joyashree Roy, Shreya Some, Joeli Veitayaki, Anil Markandya, Ibon Galarraga, Patxi Greño, Itziar Ruiz-Gauna, Matt Curnock, Megan Epler Wood, Melody Yue Yin, Sibylle Riedmiller, Eleanor Carter, Rizky Haryanto, Elizabeth Holloway, Robertico Croes, Jorge Ridderstaat and Maksim Godovykh.

Explore the accompanying Sustainable Coastal and Marine Tourism Expert Perspectives .

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• Data Descriptor
• Open access
• Published: 26 November 2021

A database of global coastal conditions

• Mariana Castaneda-Guzman   ORCID: orcid.org/0000-0001-6106-4284 1 ,
• Gabriel Mantilla-Saltos 2 ,
• Kris A. Murray 3 , 4 ,
• Robert Settlage   ORCID: orcid.org/0000-0002-1354-7609 5 &
• Luis E. Escobar   ORCID: orcid.org/0000-0001-5735-2750 1 , 6 , 7  

Scientific Data volume  8 , Article number:  304 ( 2021 ) Cite this article

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• Biogeography
• Ecological modelling
• Environmental impact
• Physical oceanography

Remote sensing satellite imagery has the potential to monitor and understand dynamic environmental phenomena by retrieving information about Earth’s surface. Marine ecosystems, however, have been studied with less intensity than terrestrial ecosystems due, in part, to data limitations. Data on sea surface temperature (SST) and Chlorophyll- a (Chlo- a ) can provide quantitative information of environmental conditions in coastal regions at a high spatial and temporal resolutions. Using the exclusive economic zone of coastal regions as the study area, we compiled monthly and annual statistics of SST and Chlo- a globally for 2003 to 2020. This ready-to-use dataset aims to reduce the computational time and costs for local-, regional-, continental-, and global-level studies of coastal areas. Data may be of interest to researchers in the areas of ecology, oceanography, biogeography, fisheries, and global change. Target applications of the database include environmental monitoring of biodiversity and marine microorganisms, and environmental anomalies.

Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.16638235

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Background & summary.

Remote sensing, referring to the acquisition of information about the Earth’s surface through satellite imagery, has become a powerful tool for monitoring the environment and predicting risks associated with environmental changes 1 , 2 , 3 , 4 . From a plethora of applications, remotely sensed data have been used to detect landscape change 5 , 6 , 7 , 8 , assess biodiversity 9 , monitor carbon emissions 10 , 11 , predict infectious diseases 12 , 13 , 14 , and track marine coasts 15 . Marine ecosystems, however, have been studied with less intensity than terrestrial ecosystems due, in part, to data limitations. A limitation in the use of global-level remotely sensed data is how time-consuming it proves to be, given that sometimes complex data compilation, curation, standardization, and storage may require high-performance computational facilities 16 , 17 . An open-access, free-of-cost database of global ocean conditions is instrumental in advancing our understanding of coastal phenomena 12 , 15 .

A significant benefit of satellite-derived information is the historical archives of data 2 , 10 , 12 . Technological advances and innovative design have resulted in new generations of satellite sensors that monitor marine environments, such as the Moderate Resolution Imaging Spectroradiometer (MODIS). MODIS sensors are part of the National Aeronautics and Space Administration’s Earth Observing System onboard the Terra and Aqua satellites and were designed to provide measurements of global dynamics of terrestrial, freshwater, and marine ecosystems 18 , 19 , 20 . MODIS provides the longest standing observational marine time series data, 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 scale 18 . Nevertheless, there are other enhanced satellite instruments 21 , such as the Along Track Scanning Radiometer 22 , Suomi National Polar-orbiting Partnership 23 , Visible Infrared Imaging Radiometer Suite 24 , 25 , and Sentinel 26 , which offer opportunities for future multi-sensor marine variables.

Out of the possible marine variables derived from observations of MODIS, sea surface temperature (SST) and Chlorophyll- a (Chlo- a ) have the potential to increase our understanding of abiotic (e.g., temperature) and biotic (e.g., primary productivity) ocean conditions 4 , 20 . SST measured by MODIS infrared radiometers is also referred to as the skin temperature of the ocean. This is because the radiance measured by infrared radiometers originates in the surface thermal skin layer of the ocean and not the water below as measured by in situ thermometers 27 . SST provides fundamental information on the global climate systems, and it is an essential parameter in weather prediction 28 . Chlo- a is a proxy for understanding fluctuations in algae and pigmented bacteria as it can elucidate photosynthetic activity in coastal systems 4 , 20 , 29 . The near-surface concentration of Chlo- a is calculated using an empirical relationship derived from in situ measurements, and the implementation of the standard O’Reilly band ratio OCx (e.g., OC3M, for the MODIS sensor) algorithm merged with the color index algorithm of Hu et al . 30 , 31 . SST and Chlo- a have been crucial in studies to reconstruct environmental phenomena, such as Vibrio cholerae emergence 13 , 32 , 33 , algae blooms 29 , 34 , 35 , El Niño and La Niña dynamics 36 , and coral bleaching 37 .

Satellite-derive data have many limitations given their sensitivity to absorption of solar isolation, heat exchange with the atmosphere, and sub-surface turbulence. Nevertheless, since these conditions are known and common, validation and uncertainty are estimated relative to in situ buoys to correct final datasets 38 , 39 , 40 . Satellite-derived data provide an opportunity to analyze large study areas during extended periods, at the cost of limiting the information to surface level. Complementary approaches may include the addition of more oceanic and atmospheric observations like bathymetry, wind direction, and wind speed 1 . 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 (2003–2020). Data were used to generate summary statistics at yearly and monthly composites. Code is included to update the database as data are released. This database can be downloaded freely through Figshare 41 .

This section describes the procedures used to generate the individual data records that comprise the SST and Chlo- a databases. Data retrieval and analysis performed during the development of the database were executed using the statistical software R 42 . The SST and Chlo- a databases were developed in four stages: (a) data procurement, (b) preparation, (c) processing, and (d) analysis. The first two stages were associated with input data, while the third stage was applied specific methods to construct the core of each database. The fourth stage included the statistical analyses of the data. The methodological stages are summarized in Fig.  1 and described in detail below.

Workflow diagram. ( a ) Remotely-sensed data were downloaded from the NASA ERDDAP server in the form of NetCDF files. ( b ) Data were then transformed into a raster object. ( c ) Data were then cropped and masked to the exclusive economic zone and imported as GeoTIFF. ( d ) Data were analyzed to include statistical analyses and exported as raster files.

Data procurement

The database is based on satellite observations derived from the MODIS satellite. The Terra and Aqua satellites have been orbiting around the Earth since their launch in 1999 and 2002, respectively, obtaining data of Earth’s surface every one to two days at three spatial resolutions (250, 500, 1000 m) and 36 spectral bands (from 0.405 to 14.385 µm). From the available atmospheric and oceanic observations made available from NASA’s Aqua Spacecraft, Sea Surface Temperature (SST) in °C and Chlorophyll- a (Chlo- a ) in mg*m −3 were selected since they summarize major physical and biological phenomena. SST and Chlo- a are available at a temporal resolution of 1-day, 8-day, and monthly composites and a spatial resolution of ~4 km (Table  1 ).

SST and Chlo- a , among other environmental variables, can be accessed through National Oceanic and Atmospheric Administration’s (NOAA) Coastal Watch Environmental Research Division (ERD) Environmental Research Division Data Access Protocol (ERDDAP) data server, also known as the NOAA’s Coastal Watch. NOAA’s Coastal Watch is a program that provides timely access to near-real-time satellite data to monitor, restore, and manage coastal ocean resources, and the ERDDAP Data Server supports manual downloads through a web application and remote downloads from any computer program (e.g., MATLAB, R, JSONP, Python) of both gridded and tabular data 43 .

Data downloading

The remote request to the ERDDAP Data Server relies on the creation of specially formed URLs to query the server for a specific database. A URL consists of a root, a target, and a constraint expression 43 . To procure the inputs needed to assemble this database especially formed URLs were created through a programming algorithm in R (Auxiliary Materials 44 ).

The root or base URLs that provided the location of the gridded database were obtained from the ERDDAP griddap documentation webpage ( https://coastwatch.pfeg.noaa.gov/erddap/griddap/documentation.html ) and remained constant in all requests for a specific database.

The target is the equivalent to the unique identifier or data set ID previously assigned by the ERDDAP ( https://coastwatch.pfeg.noaa.gov/erddap/griddap ), in conjunction with a specific data file type extension, for this study  .nc was selected producing NetCDF-3 binary files with COARDS/CF/ACDD metadata. NetCDF, Network Common Data Form, files are recommended when using software tools to analyze geospatial data as they provide multidimensional scientific data in a standardized manner ( https://coastwatch.pfeg.noaa.gov/erddap/griddap/documentation.html ) 45 , 46 .

The constraint expression (or query) helped define the parameters, which correspond to the study period and spatial coverage. Regarding the first parameter, the study period comprised all available observations from the MODIS instrument aboard the Aqua satellite (i.e., monthly composites from 2003 to 2020). The spatial coverage was defined by the minimum and maximum latitude (i.e., 89.98°S to 89.98°N) and longitude (i.e., 179.98°W to 179.98°E) from the original satellite image for global coverage.

Data preparation

Data within the NetCDF files were imported into R using the RNetCDF package 47 . A NetCDF object contains a list of at least four attributes: time, longitude, latitude, and the values of the variable being measured (i.e., SST and Chlo- a ). The attribute corresponding to the specific variable being measured was extracted from the NetCDF object and transformed into a raster object using the RNetCDF and raster packages in R 48 . A raster object consists of a matrix of cells (i.e., pixels) organized into rows and columns where each cell contains a value representing information (i.e., temperature and pigmentation) and the metadata corresponding to spatial information of object 49 .

As the last piece of the data preparation process, the extent of the raster was verified to match that of the original satellite data. Extent was set to latitude and longitude of 89.98°S to 89.98°N and 179.98°W to 179.98°E, respectively. The coordinate reference system (CRS) was defined to be relative to the WGS84 datum for easy manipulation by the end user.

Data processing

A significant feature of the SST and Chlo- a databases is the addition of the segmentation by the world’s exclusive economic zone (EEZ). EEZ is a marine zone within 200 nautical miles from a country’s coastline where each country claims jurisdiction for economic activities 50 . Given the oceanographic nature of the data, focusing on the 200-mile buffer of EEZ provides a more comprehensive explanation of oceanic changes, with the potential to promote the development of ocean planning initiatives directly influencing human settlements on the coasts. To represent the EEZ, a geospatial vector file in shapefile format was constructed by delimiting a buffer of ~200 miles off coastlines globally.

The EEZ regions were defined using the functions crop and mask from the raster 48 package. The function mask allowed to place the area of interest (i.e., the EEZ) on top of each monthly raster, assigning no value to cells outside of the area of interest, while the function crop ensured that each raster matched the extent of that of the area of interest (Fig.  2 ). The core database included 408 individual rasters cropped and masked to the EEZ of each country.

Data masking and cropping. Example of masking and cropping a raster. ( a ) Raster from original NetCDF. ( b ) Economic Exclusive Zone (solid lines). ( c ) Raster after crop and mask.

Statistical analysis

Complementary to the core database, data were treated as an m by n matrix, where m represents the years and n represents the months and stacked in two distinct ways (1) in yearly composites and (2) monthly composites.

We created the annual and monthly stacks by using stack function in the raster package 48 . The mean, range, maximum, minimum, and standard deviation values were estimated for annual and monthly SST and Chlo- a . We obtained a total of 90 rasters for the yearly composites (18 years, five different statistics) and 60 rasters for the monthly summaries (12 months, five different statistics).

Data Records

Final data are provided in the form of GeoTIFFs for the EEZs boundaries and statistical analysis results 41 . Data can be downloaded based on annually, monthly, or as summary composites of the 18-years period. Data can also be updated using the code included in the Auxiliary Material in Figshare 44 .

Technical Validation

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 measurements 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 and used in a diverse set of studies 13 , 14 , 19 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 . For instance, validation of the SST observation uses accurate ship-based infrared radiometers and differing and moored buoys with thermometers a meter of depth 38 , 56 , 57 . NASA’s standard processing and distribution of the SST products are performed using software developed by the Ocean Biology Processing Group 18 . SST products are validated internally by NASA using a collocated matchup database of in situ observations that are collected within 30 minutes of an overpass and 10 km of a pixel. MODIS SST observations represent the thermal skin layer of the ocean, which is <1 mm thick and is cooler than the underlying water due to vertical heat flux 68 , 69 . At night or when wind speeds are greater than ~6 m/s, the relationship between the skin temperature and the subsurface are nearly equal. It is under these conditions that validation and uncertainty estimates relative to sub-surface in situ buoys are typically reported 20 , 38 . The estimation vs. observation relationship, however, can be very variable under conditions of low wind speeds and reduced sub-surface turbulence 21 , 70 . Furthermore, NASA MODIS uses a collection of cloud classification algorithms to indicate when a pixel corresponds to clear sky conditions (i.e., no cloud coverage). The most recent cloud-classification method is the Alternating Decision Tress 71 . Other SST observations validations tests include a regional ice test, where reflectance thresholds are determined using the Sentinel-2 MSI calibrated reflectance 72 and correction of dust contamination 73 .

MODIS Chlo- a observations are derived from the O’Reilly OC3M algorithm and the Hu color index 30 , 31 . The algorithm is calculated using an empirical relationship from in situ measurements and remote sensing reflectance in the blue-to-green region of the visible spectrum. Level 3 MODIS data may provide biased minima and maxima values during errors in the observation that, for example, has some cloud contamination or sunlight affecting the value captured by the sensor. Due to potential atmospheric contamination some regions could have a limited number of observations from which to estimate the monthly values, which increases uncertainty. There is an estimated ± 35% nominal uncertainty related to the OC3M algorithm used to derive the global Chlo- a product. Nevertheless, error could increase in optically complex waters like those present in coastal areas 74 , 75 .

We performed a data validation procedure comparing MODIS observation of SST and Chlo- a against gold-standard sensors. More specifically, we compared MODIS data against SST data from Sentinel-3 76 during the year 2020. We found that data from MODIS and Sentinel-3 were statistically indistinguishable with a Pearson correlation coefficient of r  = 0.99 for the annual mean, minimum, and maximum composites ( R 2  = 0.99, p  < 0.05; Supplementary Fig.  S1 ). Additionally, Chlo- a data were evaluated by comparing MODIS data against SeaWiFS 30 observations for the year 2010, when the SeaWiFS satellite ended operations. We found that MODIS Chlo- a data were significantly correlated with SeaWiFS Chlo- a data but with less strength than for SST evaluations. More specifically, correlation was r  = 0.83 ( R 2  = 0.67, p  < 0.05) for the mean, r  = 0.71 ( R 2  = 0.53, p  < 0.05) for the maximum, and r  = 0.76 ( R 2  = 0.52, p  < 0.05) for the minimum Chlo- a composites (Fig.  S2 ). 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 .

Usage Notes

The proposed use of this dataset is for coarse-scale, regional or global-level studies of coastal environmental conditions. Fine-scale assessments of SST and Chlo- a are warranted to improve accuracy and detail of these variables for local-level applications. The data can be used to identify anomalies for SST and Chlo- a at local, regional, and global levels. The example demonstrates SST and Chlo- a data explorations in tropical and temperate localities, identifying patterns along time (Fig.  3 ). Areas in the mid-Atlantic region of the United States show an increase in mean SST during the month of June to October (Fig.  3a ), while areas in the subtropics of the Americas (i.e., Ecuador and Colombia) reveal cooler temperatures during the same period (Fig.  3b ). Additional exploration of the data in tropical and subtropical zones of different latitude reveal that Chlo- a increases from September to December (Fig.  4b ). Contrarily, in the tropics, Chlo- a concentration increases between March and May (Fig.  4a ).

Sea surface temperature mean monthly values from 2003–2020. ( a ) Temperate zone monthly averages between the years 2003–2020 (east coast of the United States). ( b ) Subtropical zone monthly averages between the years 2003–2020 (coast of Chile).

Chlorophyll- a mean monthly values from 2003–2020. ( a ) Tropical zone monthly averages between the years 2003–2020 (coast of Ecuador and Colombia). ( b ) Subtropical zone monthly averages between the years 2003–2020 (coast of Chile).

Code availability

Code in R language to recreate the database and the figures in the Usage Notes is available on Figshare 41 .

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Acknowledgements

LEE was supported by a CeZAP 2020 Interdisciplinary Team-building Pilot Grant. This publication was prepared by Mariana Castaneda-Guzman using Federal funds under award (NOAA award #: NA18OAR4170083), Virginia Sea Grant (VASG) Professional Development Award (VASG project #: R/72152 J), from the National Oceanic and Atmospheric Administration’s (NOAA) National Sea Grant College Program, U.S. Department of Commerce. The statements, findings, conclusions, and recommendations are those of the authors and do not necessarily reflect the views of Virginia Sea Grant, NOAA, or the U.S. Department of Commerce. MCG and LEE were also supported by Virginia Tech Library.

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Gabriel Mantilla-Saltos

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Kris A. Murray

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Robert Settlage

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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.

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Castaneda-Guzman, M., Mantilla-Saltos, G., Murray, K.A. et al. A database of global coastal conditions. Sci Data 8 , 304 (2021). https://doi.org/10.1038/s41597-021-01081-9

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Coastal Tourism

• Pre-covid-19

10% of all international visitors that arrive in UK go to coast

The Coast comprises a very high rate of SMEs (with less than 3% corporate brands represented)

Coastal Tourism in England is highly seasonal, but change was happening as a result of investment in research, product development and marketing:

Key challenges facing coastal communities:

Seasonality

• Productivity
• Perceptions (consumers, media and government)

Climate Change – coastal storms / flooding Business ownership and investment

• Large number of micro and small businesses – difficult to coordinate and deliver change
• Prior to covid, 28% of businesses said they were “planning to sell / retire in next 5 years”

High dependency on Tourism average 15-20% of employment - but 50%+ in places like St. Ives, Exmoor, Whitby and Newquay

Socio-economic pressures on Coastal Communities Skills Brexit

But there are opportunities:

Addressing seasonality and attracting off peak growth markets

• International visitors
• Domestic visitors - Wellness, Slow tourism, Business Events, Under 35s, Empty nesters (over 55s) and Active Experiences

Wider sector or coastal opportunities…

• England Coast Path

Impact of Covid-19

Pre-COVID spend £13.7bn in England

2020 England – coastal impact of COVID-19

Based on July re-opening, loss of international travel and reduced capacity due to social distancing, November lockdown

Verified with National Business Survey data on capacity and revenue

• 95m trips and day visits
• £7.64bn loss in tourism spend
• Equivalent of circa 131,000 jobs

2021 England – coastal impact of COVID-19

Based on closure Jan-Mar, partial April reopening, very limited international travel, reduced capacity and busy summer

• 44m trips and day visits (-23% on pre-Covid-19)
• £5.15bn loss in tourism spend
• -37% on pre-Covid spend / 41% increase on 2020

Hotel Solutions forecast 20-25% of accommodation in coastal communities will close

Institute of Fiscal Studies - there “is no longer a north-south, or urban-rural divide… coastal areas are notably vulnerable to both the health and economic impacts of the crisis” – especially Isle of Wight, Torquay, Blackpool, Dorset and Northumberland

Other reports highlighting impact on coast

Centre for Towns, The Effect of the COVID-19 Pandemic on our Towns and Cities report 2020 Institute for Employment Studies, Labour Market Statistics June 2020: IES Analysis Social Markets Foundation Report HOPE not hate Charitable Trust, Understanding Community Resilience in Our Towns report 2020 Social Investment Business Group: Covid-19 Coastal Communities The Place Bureau Report

Domestic tourism on the Coast

South west is the most popular region for seaside visits:

Visitor Profile demographics:

Source:GBTS 2019

Socio-economic group

International visitors to the coast

10% visit the English coast at some point during their stay in UK

88% are on break of 4+nights (non coastal visitors = 55% on 4+ night break)

210,000 jobs worth £3.6bn (Sheffield Hallam University 2014) + 1% growth

Higher than average concentration of SMEs in coastal visitor economy

<3% corporate brands on the coast (National Coastal Tourism Academy 2015)

31% of residents work part-time → Net outflow of commuters (ONS 2014)

Contact details

+44(0)1202 093 429 [email protected]

Coastal Tourism Resources

Sea Grant works with coastal communities across the country to promote the safe and sustainable use of ocean, coastal and Great Lakes resources.  In 2017, Sea Grant completed intensive thought exercises to focus work on several key topics, including coastal tourism. Check out the fact sheet below, or read the resulting action plan for Sea Grant’s work in coastal tourism.

• Coastal Tourism Fact Sheet (PDF)
• Coastal Tourism Vision Plan (PDF)

Mobile Apps & Websites

Big Bend Shellfish Trail

The  Big Bend Shellfish Trail  is where you can learn about Florida shellfish, aquaculture, sustainable environments that protect the waters, where to eat and where to buy (retail) Big Bend shellfish. This effort is sponsored in part by  Florida Sea Grant .

Blueways-Greenways in the Charleston, S.C. Region

South Carolina Sea Grant  worked with multiple partners to develop an interactive  web-mapping app  of blueways and greenways in Berkeley, Charleston, and Dorchester counties which allows users to explore features in the area such as land and water trails, water access points, green spaces and historic sites. You can also check the current weather, wind conditions, and stream gauges. For more information: “ How To Guide for Developing Blueways and Greenways in the State of South Carolina ."

Chicago Water Walk

Illinois-Indiana Sea Grant  developed the  Chicago Water Walk  website to help tourists explore the water front areas around Chicago. The website includes an online tour and mobile app download, and allows users to explore the city’s beautiful downtown lake and riverfronts. Along the way, users learn the importance of these resources to the region through fun facts, history, biology, and more.

Eat Oregon Seafood

Oregon Sea Grant  created this online resource to help people learn how and where to buy Oregon seafood. Find out about the types of seafood harvested in Oregon, see the seafood locator map and try out techniques for cooking and storing seafood  here .

Downeast Fisheries Trail

Maine Sea Grant  coordinates the  Downeast Fisheries Trail , which celebrates the fishing heritage of Downeast, Maine. The coastal trail is accessible by road, and each stop highlights a story related to fisheries heritage and history in the region. The project is also on  Facebook .

Dolphin and Whale 911

Mississippi-Alabama Sea Grant  and NOAA National Marine Fisheries Service partnered to develop  Dolphin and Whale 911 . The mobile app allows boaters and others to identify marine mammals and provides a feature to report injured or stranded marine mammals. In addition, the app promotes responsible wildlife viewing from a safe distance.

Guide to the Salt Marshes and Tidal Creeks of the Southeastern U.S.

This  comprehensive guide , produced in partnership by the South Carolina Department of Natural Resources,  South Carolina Sea Grant Consortium  and Clemson Extension, provides an overview of salt marsh and tidal creek ecosystems in the southeastern United States. The guide includes information on species identification and classification, ecosystem services and threats, cultural and historical highlights, and stewardship opportunities.

Eat Wisconsin Fish

Wisconsin Sea Grant 's  Eat Wisconsin Fish  is all about local fish that are available to purchase in Wisconsin. Learn about the different fish types, the people who grow and harvest them, where to purchase and recipes for cooking and preserving.

Great Lakes Angler Diary

Michigan Sea Grant  developed a mobile app that allows anglers to track their catch and share valuable information with fisheries biologists .  The site and app can be found  here .

Louisiana Direct Seafood

Louisiana Direct Seafood  is a   web-based resource to help coastal fishermen connect directly with consumers and build community support for a fresh, local, product ‘straight from the boat’. The site is a partnership effort between  Louisiana Sea Grant  and LSU Ag Center. In addition to connecting fishermen and consumers, this initiative is also focused on quality business practices, working with fishermen to deliver a superior, sustainable product that meets rigorous standards and preserves fisheries for generations to come.

North Carolina Oyster Trail

North Carolina Sea Grant 's  North Carolina Oyster Trail  offers unique tourism experiences centered on the state’s tasty oysters. It features shellfish farm tours, seafood markets and restaurants, and education and arts organizations, in addition to oyster-focused festivals and events.

Oyster Trail of Maine

Maine Sea Grant  assisted in creating the  Oyster Trail of Maine , a map of oyster farms and restaurants that highlights places to learn about and taste a variety of Maine oysters.

Seafood At Your Fingertips

Florida Sea Grant  helps consumers choose the best seafood. Market choices are more plentiful than ever, but so are questions. What’s imported and what’s local? Is my choice nutritious and safe to eat? How do I fix the fish I purchased at the grocery? The app is  available  for iPhone and Android.

Whale Alert

California Sea Grant  State Fellows based at the Channel Islands National Marine Sanctuary worked with partners to develop a whale sighting app to reduce ship strikes. Launched in 2013 and called Spotter, the app was merged with  Whale Alert . Follow the project  blog .

Louisiana Sea Grant  and partners developed  WAVE  to assist boaters with charts, emergency preparedness, current conditions and more. The purpose of the app is to improve emergency preparedness for commercial fishermen and other users of the waterway.

Wisconsin Fish ID

Wisconsin Sea Grant  developed a fish identification tool for species found in Wisconsin waters. Serach by name or appearance. Content includes images and descriptions. The  mobile app  available for both iPhone and Android.

Shoreline Rhode Island

Shoreline Rhode Island  provides over 300 public access points on the coast and bays of Rhode Island. The site allows users to search by activity and location.  Rhode Island Sea Grant  developed Shoreline Rhode Island as an update to a 2004 publication, Public Access to the Rhode Island Coast, which was published by Rhode Island Sea Grant and the URI Coastal Resources Center.

Rip Current Watch

Wisconsin Sea Grant  and partners maintain the  Rip Current Watch  site to provide real-time data and forecasts for rip currents at Port Washington, Wisconsin. The information is also available via a mobile app for Android devices.

Video Guides

Sea grant video guides playlist, sea talk - beach injuries, boat safety: survival kit, 15 second science - the power of waves, increasing safety at sea caves, boat safety: emergency beacons and flares, 15 second science - small item, big issue for our coasts, avoid the trap: what anglers should know about commercial fishing nets, sea talk - catch and release fishing, what is barotrauma, sea talk - proper fishing bait disposal, buying tuna directly from a commercial fisherman, retiree in training: new york sea grant’s dave white (october 2022), nature attracts tourism (part 1), oregon's working waterfront tour trailer - oregon seagrant.

Volusia tourism leaders celebrate 10.1 million visitors in destination 'reset' year

DAYTONA BEACH  ― Volusia County attracted 10.1 visitors in 2023, down from a record 10.6 million in 2022 , according to figures from the Daytona Beach Area Convention & Visitors Bureau.

The news was announced at a celebration event to mark National Travel & Tourism Week on Wednesday at Hard Rock Hotel.

Area tourism officials and other local leaders praised the strength of the area’s leading industry as they also acknowledged increasing competition for travelers as the impact of the COVID-19 pandemic continues fade into the past tense.

“That number reflects what we have been calling a ‘reset’ year, so we’re actually very pleased,” said Lori Campbell Baker, executive director of the Daytona Beach Area CVB. “The world has reopened. International travel is booming.”

Volusia's 2023 visitor total surpassed pre-pandemic 2019

As Florida emerged as one of the first states to re-open for tourists in the wake of pandemic-era restrictions, Volusia County’s record-setting 10.6 million visitor total in 2022 was the destination’s highest annual count since logging 10.2 million in 2018.

Although the 2023 visitor total failed to match that record high, Baker pointed out that it was better than the pre-COVID 2019 total of the 9.8 million.

“As always, we’re working to bring that total up,” she said. “Everything we do is geared toward attracting overnight visitors.”

In addition to a decline in visitors, Volusia County’s tourist development tax collections for the fiscal year that concluded on Sept. 30 finished 3.51% below the year-end total for 2021-22, according to figures from the Volusia County Revenue Division.

For the previous fiscal year that concluded on Sept. 30, 2022, record-high overall tourism bed-tax collections of $33.7 million countywide were more than 20% higher than the previous year, which also set a record.

The county collects a 6% tourism tax on hotels and lodges with half of the revenues going to fund the county-run Ocean Center convention complex in Daytona Beach. The other half goes to the county’s three tourism ad authorities to market their respective areas — the Daytona Beach/Halifax area, Southeast Volusia and West Volusia — as tourist and special event destinations.

Tourism trend also evident in statewide visitor total

The trend in Volusia County also is evident in statewide numbers.

In 2023, Florida welcomed an estimated 135 million tourists according to the Visit Florida tourism marketing agency. That's down from 137.4 million the year before, but up from 131.1 million in 2019, the last full year of data before the pandemic.

At Wednesday’s event, the contributions of the area’s tourism industry were heralded by host Bob Davis, president and CEO of the Lodging & Hospitality Association of Volusia County.

After nearly 60 years as a hotelier and tourism leader, Davis emphasized that connection among hotel owners, managers, small businesses, elected officials and educators was essential for success.

“It takes all of us together, working to make Volusia the No. 1 county in Florida,” Davis said. “We’re all in this together. We all have a part to play.”

That theme was echoed in presentations by leaders of the county’s three tourism advertising authorities representing the Halifax area, West Volusia and Southeast Volusia.

In case you missed it: After two record-setting years, Volusia tourism weathers downturn in 2023

There also were updates from Cyrus Callum, director of aviation and economic resources at Daytona Beach International Airport, Ocean Center Director Lynn Flanders and Volusia County Manager George Recktenwald.

Recktenwald offered thanks to county staff members and area businesses for contributing to the recovery from beach damage sustained during back-to-back tropical storms Ian and Nicole in fall 2022.

“The last 18 months touched us like never before,” said Recktenwald, adding that the future still holds the prospect of “significant investment” to ensure the condition of the area’s beaches. Yet he, too, was optimistic.

“The businesses, the hotels and the county’s environmental staff have acted swiftly to repair the damages,” he said. “Things are looking positive.”

10 things to know about tourism along North Carolina's coast

Whether you're looking for sandy beaches or cool mountain streams, North Carolina's got it. That's why the state has become a popular destination for those in the eastern United States.

Each year, Visit North Carolina, the unit of the Economic Development Partnership of North Carolina focused on travel and tourism, examines the data to determine just how much visitors contribute to the state and local economies. While the county breakdowns from 2023 won't be released until later this year, here's a look at data from 2022.

Summer bucket list: 24 things to do in 2024 in the Wilmington area

The state’s rank among U.S. states for domestic visitation.

New Hanover County's rank in visitor spending among North Carolina’s 100 counties.

11.2 million

The number of visitors who traveled to the state’s coastal region.

The number of those visitors who stayed overnight.

The most popular time for travelers to visit the coast.

Average party size for coastal visitors.

The percentage of travel parties that included children under 18.

The average expenditure for parties traveling to the coastal region in 2022.

Where did visitors travel from?

The top states of origin for overnight visitors to the coastal region in 2022 were: North Carolina, Virginia, Pennsylvania, South Carolina, Maryland, New York, New Jersey and Georgia.

The percentage of coastal region visitors in 2022 who were in-state residents.

Data Source: 2022 North Carolina Regional Visitor Profile, a publication of Visit North Carolina, a unit of the Economic Development Partnership of North Carolina. Research staff of Visit North Carolina created this report based on data from TravelTrakAmerica.

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Home » Home » Florida Remains No. 1 Destination for Domestic Tourism in the US, Data Shows

Florida Remains No. 1 Destination for Domestic Tourism in the US, Data Shows

By Space Coast Daily  //  May 7, 2024

Florida ranks No. 2 for international tourism

TALLAHASSEE, FLORIDA – Governor Ron DeSantis announced that VISIT FLORIDA data shows that Florida has maintained its position as the No. 1 domestic tourist destination in the United States and remained a top destination for international visitors (ranking #2 in the country).

In 2023, Florida’s market share of domestic tourists increased to 14.8%, up from 13.8% in 2022.

This surge in market share represents the largest increase of any state, underscoring Florida’s appeal to travelers from across the country.

“Florida’s world class attractions, hospitality, and unwavering commitment to freedom have solidified our position as the top vacation destination,” said Governor Ron DeSantis. “These figures show that Florida continues to be on the right path, as we have insisted on prioritizing public safety and common-sense leadership.”

While Florida’s domestic market share increased from 2022, other formerly popular destinations saw negative shifts in their shares. California experienced a notable decline, losing 1.2 percentage points, while New York saw a decrease of 0.8 percentage points. With these shifts, Florida now leads the U.S. by a substantial margin, boasting a 2.7 percentage point advantage over California, securing its position as the premier destination for American travelers.

“Florida’s continued ability to attract visitors speaks volumes about the state’s appeal, both domestically and internationally. From our pristine beaches and serene state parks to the thrills of our world-renowned theme parks and vibrant cities, Florida continues to captivate visitors with its unmatched experiences,” said Dana Young, President and CEO of VISIT FLORIDA.

“We look forward to continuing to surpass all expectations and welcoming visitors to our beautiful state.”

Florida saw a noticeable uptick in overseas tourism in 2023. As the recovery of international travel continued, Florida garnered 25.2% of the overseas market share of travelers to the U.S.

In March 2024, overseas visitation to Florida surpassed pre-pandemic levels for the first time, with a 1.7% increase compared to March 2019.

Notably, visitation from key markets such as the UK, Germany, and Mexico saw significant growth, with UK visitation up by 12%, German visitation up by 28%, and Mexican visitation up 61% compared to the same month in 2019.

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Assessment of land use and land cover change detection and prediction using deep learning techniques for the southwestern coastal region, Goa, India

• Published: 09 May 2024
• Volume 196 , article number  527 , ( 2024 )

Cite this article

• Nitesh Naik 1 ,
• Kandasamy Chandrasekaran 1 ,
• Venkatesan Meenakshi Sundaram 2 &
• Prabhavathy Panneer 3  

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Understanding the connections between human activities and the natural environment depends heavily on information about land use and land cover (LULC) in the form of accurate LULC maps. Environmental monitoring using deep learning (DL) is rapidly growing to preserve a sustainable environment in the long term. For establishing effective policies, regulations, and implementation, DL can be a valuable tool for assessing environmental conditions and natural resources that will positively impact the ecosystem. This paper presents the assessment of land use and land cover change detection (LULCCD) and prediction using DL techniques for the southwestern coastal region, Goa, also known as the tourist destination of India. It consists of three components: (i) change detection (CD), (ii) quantification of LULC changes, and (iii) prediction. A new CD assessment framework, Spatio-Temporal Encoder-Decoder Self Attention Network (STEDSAN), is proposed for the LULCCD process. A dual branch encoder-decoder network is constructed using strided convolution with downsampling for the encoder and transpose convolution with upsampling for the decoder to assess the bitemporal images spatially. The self-attention (SA) mechanism captures the complex global spatial-temporal (ST) interactions between individual pixels over space-time to produce more distinct features. Each branch accepts the LULC map of 2 years as one of its inputs to determine binary and multiclass changes among the bitemporal images. The STEDSAN model determines the patterns, trends, and conversion from one LULC type to another for the assessment period from 2005 to 2018. The binary change maps were also compared with the existing state of the art (SOTA) CD methods, with STEDSAN having an overall accuracy of 94.93%. The prediction was made using an recurrent neural network (RNN) known as long short term memory network (LSTM) for the year 2025. Experiments were conducted to determine area-wise changes in several LULC classes, such as built-up (BU), crops (kharif crop (KC), rabi crop (RC), zaid crop (ZC), double/triple (D/T C)), current fallow (CF), plantation (PL), forests (evergreen forest (EF), deciduous forest (DF), degraded/scurb forest (D/SF) ), littoral swamp (LS), grassland (GL), wasteland (WL), waterbodies max (Wmx), and waterbodies min (Wmn). As per the analysis, over the period of 13 years, there has been a net increase in the amount of BU (1.25%), RC (1.17%), and D/TC( 2.42%) and a net decrease in DF (3.29%) and WL(1.44%) being the most dominant classes being changed. These findings will offer a thorough description of identifying trends in coastal areas that may incorporate methodological hints for future studies. This study will also promote handling the spatial and temporal complexity of remotely sensed data employed in categorizing the coastal LULC of a heterogeneous landscape.

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Availability of data and materials

The data that support the findings of this study are available from the National Remote Sensing Centre, ISRO, Government of India, Hyderabad, India, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the National Remote Sensing Centre, ISRO, Government of India, Hyderabad, India.

Code Availability

Not applicable

Abbreviations

Advanced wide field sensor

Cellular automata

Change detection

Current fallow

Convolutional neural network

Degraded/scrub forest

Double/triple crop

Deciduous forest

Decision tree

Deep learning

Deep neural network

Encoder decoder LSTM

Evergreen forest

False negative

False positive

Fully convolutional neural network

Genetic algorithm

Generative adversarial networks

Geographical information systems

Indian Space Research Organization

Kharif crop

Littoral swamp

Long short term memory network

Land use and land cover

Land use and land cover change

Land use and land cover change detection

Markov chain model

Mean intersection over union

Machine learning

Multilayer perceptron

National Remote Sensing Centre

Normalized Difference Built-up Index

Overall accuracy

Producer accuracy

Principal component analysis

Rectified linear unit

Random forest

• Recurrent neural network

Remote sensing

• Self-attention

Satellite image time series

State of the art

Spatial-temporal

Spatio-Temporal Encoder-Decoder Self Attention Network

Support vector machine

True negative

True positive

User accuracy

Waterbodies min

Waterbodies max

Extreme gradient boosting

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Acknowledgements

The authors would like to thank the National Remote Sensing Centre, Indian Space Research Organization (ISRO), Government of India, Hyderabad, India, for providing time series data in form of LULC maps from 2005–06 to 2017–18 for the Goa Region.

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Department of Computer Science and Engineering, National Institute of Technology, Surathkal, Mangalore, 575025, Karnataka, India

Nitesh Naik & Kandasamy Chandrasekaran

Department of Computer Science and Engineering, National Institute of Technology, Puducherry, Karaikal, 609609, Puducherry, India

Venkatesan Meenakshi Sundaram

School of Information Technology and Engineering, Vellore Insitute of Technology, Katpadi, Vellore, 632014, TamilNadu, India

Prabhavathy Panneer

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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Nitesh Naik, Kandasamy Chandrasekaran, Meenakshi Venkatesan Sundaram, and Prabhavathy Panneer. The first draft of the manuscript was written by Nitesh Naik, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Naik, N., Chandrasekaran, K., Meenakshi Sundaram, V. et al. Assessment of land use and land cover change detection and prediction using deep learning techniques for the southwestern coastal region, Goa, India. Environ Monit Assess 196 , 527 (2024). https://doi.org/10.1007/s10661-024-12598-y

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Received : 30 March 2022

Accepted : 29 March 2024

Published : 09 May 2024

DOI : https://doi.org/10.1007/s10661-024-12598-y

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