Access

Full dataset can be accessed and downloaded from the directory via: https://arcticdata.io/data/10.18739/A2610VT7V/.

Overview

This research is motivated by the limited resolution of existing global coastline datasets and the growing availability of high-resolution multispectral satellite imagery. We retrieve coastlines using a water probability algorithm, which stacks water masks generated from the Normalized Difference Water Index (NDWI) method. This dataset provides global coastlines, water probability maps, and intertidal zones derived from multispectral images captured by Maxar satellites (2009–2023) at a high spatial resolution of 2 meters (m). Coastlines represent the median tidal height of image acquisitions, with modeled tidal heights included. The intertidal zones derived from water probability maps represent dynamic regions sensitive to tidal variations. These high-resolution products support applications in coastal resource management, sea level rise analysis, and coastal habitat loss and migration.

This dataset provides a map of global intertidal habitats from European and Caribbean regions. The map is produced from Murray et al. (2022) High-resolution global maps of tidal flat ecosystems from 1984 to 2019 dataset, which provides the global distribution of intertidal habitats. We masked the Caribbean region based on Caribbean study area extent and European region based on EMODnet Bathymetry Coastline dataset, which was buffered by 30 kilometers in order to maintain the intertidal areas deeper inland.

The dataset contains global maps of tidal flat ecosystems produced via a supervised classification of 707,528 Landsat Archive images. The maps were created to identify the non-vegetated areas of Earth's coastline that undergo regular tidal inundation. In some areas, these occur as tidal flats up to 24- km wide, such as the tidal mudflats of western Europe and East Asia. Our analysis included 56 predictor layers, many of which were Landsat composite metrics designed to identify individual pixels that undergo frequent wetting and drying.

This dataset provides mapped tidal wetland gross primary production (GPP) estimates (g C/m2/day) derived from multiple wetland types at 250-m resolution across the conterminous United States at 16-day intervals from March 5, 2000, through November 17, 2019. GPP was derived with the spatially explicit Blue Carbon (BC) model, which combined tidal wetland cover and field-based eddy covariance (EC) tower GPP data into a single Bayesian framework along with Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) datasets. Tidal wetlands are a critical component of global climate regulation. Tidal wetland-based carbon, or "blue carbon," is a valued resource that is increasingly important for restoration and conservation purposes.

This raster dataset was developed for the Sea Grant South Coast MPA Baseline Program as part of the project “Nearshore Substrate Mapping and Change Analysis using Historical and Concurrent Multispectral Imagery” (#R/MPA 30 10-049). The study region is the South Coast Region (SCR). Imagery was acquired on June 25, 2012 at a spatial resolution of 0.3 meters using a Microsoft UltraCam-X digital camera acquiring in the red, green, blue and near-infrared bands. Information on the UltraCam-X camera system and wavelengths for each ban can be found in the file "The Microsoft Vexcel UltraCam X.pdf" included in the Support folder on the image data delivery media and on the OceanSpaces.org server. This image mosaic product is a result of the resampling of the 0.3 meter data to 1 meter GSD. Details on this system and the data processing are below in the Lineage section of this document. Individual UCX image tiles were mosaicked into sections based on the islands covered and local coastal regions as well as the SCR MPA zones in order to generate this multispectral image product. These imagery were subsequently used to generate habitat classification thematic maps of the SCR's intertidal region and kelp beds from Point Conception to Imperial Beach, CA. The imagery files deliverd are in GeoTIFF format. More information on the classes resolved and processing methods are in the Lineage section of this document. This raster dataset contains a habitat classification of either offshore giant kelp beds and/or the intertidal zone along the California South Coast Region (SCR) from from Point Conception, CA down to Imperial beach, CA. This specific raster classification includes the Tijuana River Mouth SMCA.

This raster dataset was developed for the Sea Grant South Coast MPA Baseline Program as part of the project “Nearshore Substrate Mapping and Change Analysis using This raster dataset was developed for the Sea Grant South Coast MPA Baseline Program as part of the project “Nearshore Substrate Mapping and Change Analysis using Historical and Concurrent Multispectral Imagery” (#R/MPA 30 10-049). The study region is the South Coast Region (SCR). Imagery was acquired on October 14, 2012 at a spatial resolution of 0.3 meters using a Microsoft UltraCam-X digital camera acquiring in the red, green, blue and near-infrared bands. Information on the UltraCam-X camera system and wavelengths for each ban can be found in the file "The Microsoft Vexcel UltraCam X.pdf" included in the Support folder on the image data delivery media and on the OceanSpaces.org server. This image mosaic product is a result of the resampling of the 0.3 meter data to 1 meter GSD. Details on this system and the data processing are below in the Lineage section of this document. Individual UCX image tiles were mosaicked into sections based on the islands covered and local coastal regions as well as the SCR MPA zones in order to generate this multispectral image product. These imagery were subsequently used to generate habitat classification thematic maps of the SCR's intertidal region and kelp beds from Point Conception to Imperial Beach, CA. The imagery files deliverd are in GeoTIFF format. This raster dataset contains a habitat classification of either offshore giant kelp beds and/or the intertidal zone along the California South Coast Region (SCR) from from Point Conception, CA down to Imperial beach, CA. This specific raster classification includes the Scorpion SMR.

This raster dataset was developed for the Sea Grant South Coast MPA Baseline Program as part of the project “Nearshore Substrate Mapping and Change Analysis using Historical and Concurrent Multispectral Imagery” (#R/MPA 30 10-049). The study region is the South Coast Region (SCR). Imagery was acquired on June 7, 2012 at a spatial resolution of 0.3 meters using a Microsoft UltraCam-X digital camera acquiring in the red, green, blue and near-infrared bands. Information on the UltraCam-X camera system and wavelengths for each ban can be found in the file "The Microsoft Vexcel UltraCam X.pdf" included in the Support folder on the image data delivery media and on the OceanSpaces.org server. This image mosaic product is a result of the resampling of the 0.3 meter data to 1 meter GSD. Details on this system and the data processing are below in the Lineage section of this document. Individual UCX image tiles were mosaicked into sections based on the islands covered and local coastal regions as well as the SCR MPA zones in order to generate this multispectral image product. These imagery were subsequently used to generate habitat classification thematic maps of the SCR's intertidal region and kelp beds from Point Conception to Imperial Beach, CA. The imagery files deliverd are in GeoTIFF format. More information on the classes resolved and processing methods are in the Lineage section of this document This raster dataset contains a habitat classification of either offshore giant kelp beds and/or the intertidal zone along the California South Coast Region (SCR) from from Point Conception, CA down to Imperial beach, CA. This specific raster classification includes the Santa Barbara Island SMR.

The primary purpose of the USGS National Assessment of Coastal Change Project is to provide accurate representations of pre-storm ground conditions for areas that are designated high priority because they have dense populations or valuable resources that are at risk from storm waves. A secondary purpose of the project is to develop a geomorphic (land feature) coastal classification that, with only minor modification, can be applied to most coastal regions in the United States.

A Coastal Classification Map describing local geomorphic features is the first step toward determining the hazard vulnerability of an area. The Coastal Classification Maps of the National Assessment of Coastal Change Project present ground conditions such as beach width, dune elevations, overwash potential, and density of development. In order to complete a hazard-vulnerability assessment, that information must be integrated with other information, such as prior storm impacts and beach stability. The Coastal Classification Maps provide much of the basic information for such an assessment and represent a critical component of a storm-impact forecasting capability.

The map above shows the areas covered by this web site. Click on any of the location names or outlines to view the Coastal Classification Map for that area.

[Summary provided by the USGS.]

Mangroves are a group of tree and bush species that thrive in coastal intertidal zones, or the area between high and low tide. Because they prefer warm-water ecosystems, they are most commonly found at tropical and subtropical latitudes around the world. They are halophytes, or salt tolerant, and adapted to live in low-oxygen saline or brackish water. They are easily recognizable due to their tangle of prop roots, which keep their leafy branches above the water as the cycle of tides changes the water level.

Though more than 50—and possibly up to 110—species are considered mangroves, not all are from the genus Rhizophora. Some mangrove species are the result of convergent evolution, when unrelated species adapt to a harsh environment (such as a high-salt, low-oxygen intertidal zone) by taking on similar characteristics (such as a tangled prop root system). For this reason, extensive mangrove forests may contain only a handful of unique mangrove species—and even within a single forest, different types of mangroves each occupy distinct niches.

Mangroves are crucial to the health of coastal ecosystems. Their root systems protect the shoreline against erosion by reducing wave intensity and trapping sediments against the land. Mangrove forests are also effective at carbon sequestration, the process of removing carbon dioxide (a harmful greenhouse gas) from the atmosphere. Their labyrinth of roots is a popular hiding spot for fish and other marine life evading predators. They also shield coastal communities from tsunamis and storm surge.

Today, the largest mangrove forest in the world is the Sundarbans forest along the coast of Bangladesh and India, spanning 10,000 square kilometers (almost 4,000 square miles). In the past 50 years, though, it is estimated that up to 35 percent of the world's mangrove forests have been lost. This is largely due to shrimp farming, in which mangrove forests are cleared and replaced with artificial ponds for aquaculture practices, as well as other growing threats: unsustainable tourism activities; agricultural practices that cause mangrove removal or harmful runoff and pollution; and mangrove deforestation for coastal development or to gather charcoal and timber. These risks are exacerbated by climate change causing sea levels to rise, effectively drowning mangrove forests, and alterations to water chemistry, temperature, and other conditions, which put stress on the mangroves’ preferred growing conditions.

As threats to mangroves become better understood, organizations around the world have been created and joined together to protect these important ecosystems. The data in this map layer is from the Global Mangrove Alliance, a collaboration between Conservation International, The International Union for the Conservation of Nature, The Nature Conservancy, Wetlands International, and the World Wildlife Fund. Supported by grants from the National Geographic Society, Explorers Ben Somerville and Margaret Owuor use research, education, and storytelling to protect mangrove forests near their homes in the Caribbean and southeastern Kenya. The map layer shows mangrove forest extent around the world in 2016.

About 40 percent of people live near the coast, and 90 percent of tropical storms form within 20 degrees of the Equator—the parts of the globe where mangrove forests flourish. You can use the Population Density and Longitudes and Latitudes layers to see how many people and mangroves live in these regions, and the Protected Areas layer to find the areas where special conservation plans exist to defend mangrove ecosystems.

To characterize spatial patterns of sedimentation and analyze the morphology of part of the modern tidal flats of northwest Andros Island in the Bahamas, this study integrated remote sensing, geographic information systems (GIS), and carbonate sedimentology. The fundamental data are a Landsat TM image that has been classified to create a thematic map of eight subfacies, interpreted to represent a distinct tidal-flat subenvironment such as adjacent marine, exposed levee-beach ridge, pond, and algal marsh. Spatial statistics of the thematic map characterize the patterns of sediment accumulation. Quantitative analysis highlights several interesting results concerning subfacies character and distribution: (1) of the eight mapped subfacies, low algal marsh is most widespread, representing 27.5% of the total area, whereas exposed levee-beach ridge is the least widespread, accounting for 10% of the area; (2) the patches of different subfacies have different shape complexities, with low algal marsh, high algal marsh, and mangrove ponds being the least complex and exposed levee-beach ridge being the most complex; (3) Markov chain analysis suggests that lateral transitions between different subfacies are highly ordered; (4) frequency distribution of subfacies patch area and lacunarity (gap size distribution) data exhibit power law relationships over several orders of magnitude, consistent with fractal characteristics; and (5) mean subfacies patch size is highly correlated with mean distance to a tidal channel.

The fractal nature of patch size and gaps between facies illustrate that on this tidal flat neither the size nor the spatial distribution of subfacies has a characteristic scale. This statistical behavior is consistent with the presence of self-organization, or emergence of pattern in the absence of a template or external forcing. The statistical self-organization on the tidal flat is the cumulative expression of local processes, but it becomes apparent only through analysis of the whole system. These results are inconsistent with models suggesting that tidal flats include a migrating complex of randomly distributed, randomly sized subenvironments. Ancient successions that include random patterns may reflect the more pronounced influence of forces external to the sedimentary system, instead of an absence of those forces. The purpose of this study is to characterize spatial patterns of sedimentation and analyze the morphology of part of the modern tidal flats of northwest Andros Island in the Bahamas, through the integration of remote sensing, geographic information systems (GIS), and carbonate sedimentology.

Spatial Data Organization Information -

Indirect Spatial Reference: All locations were determined using a UTM/ latitude-longitude system. Direct Spatial Reference: Point Spatial Reference Information - Horizontal Coordinate System Definition - Geodetic Model:

SPOT is a medium-resolution (for SPOT 1, 2, 3, 4) or high-resolution (for SPOT 5), optical imaging Earth observation satellite system operating from space. This programme is part of CNES's Earth observation strategy. Since 1986, the SPOT family of satellites has been viewing our planet and providing remarkably high-quality images. The system comprises a serie of spacecrafts plus ground facilities for satellite control and programming, image production and distribution. The SPOT satellites capture panchromatic and multispectral imagery in resolutions ranging from 2.5m to 20m. The satellites are fitted with two independent imaging instruments, containing detector arrays that operate using a "Push Broom" technique. This results in high geometric accuracy across the full 60km wide swath. Each instrument is also fitted with a steerable mirror that allows it to image areas up to 27 degrees east or west off the vertical, which increases the revisit capability and provides stereo imagery for digital elevation modelling. Importantly, the SPOT satellites can be programmed to target client specific areas of interest. SPOT imagery has a wide range of applications, including agriculture, environment, cartography and engineering. The satellite's unique features - variable viewing geometry, stereo imaging and high revisit capability - provide a flexible platform for capturing imagery on request.The level 0 data stored at CNES are those adressed in this description. Other levels are generated on command by Astrium GeoInformation. SPOT is a medium-resolution (for SPOT 1, 2, 3, 4) or high-resolution (for SPOT 5), optical imaging Earth observation satellite system operating from space. This programme is part of CNES's Earth observation strategy. Since 1986, the SPOT family of satellites has been viewing our planet and providing remarkably high-quality images. The system comprises a serie of spacecrafts plus ground facilities for satellite control and programming, image production and distribution. The SPOT satellites capture panchromatic and multispectral imagery in resolutions ranging from 2.5m to 20m. The satellites are fitted with two independent imaging instruments, containing detector arrays that operate using a "Push Broom" technique. This results in high geometric accuracy across the full 60km wide swath. Each instrument is also fitted with a steerable mirror that allows it to image areas up to 27 degrees east or west off the vertical, which increases the revisit capability and provides stereo imagery for digital elevation modelling. Importantly, the SPOT satellites can be programmed to target client specific areas of interest. SPOT imagery has a wide range of applications, including agriculture, environment, cartography and engineering. The satellite's unique features - variable viewing geometry, stereo imaging and high revisit capability - provide a flexible platform for capturing imagery on request.The level 0 data stored at CNES are those adressed in this description. Other levels are generated on command by Astrium GeoInformation. [https://spot.cnes.fr/en/SPOT/index.htm] [http://www.geoimage.com.au/geoweb/spot/spot_overview.htm] [http://www.cnes.fr/web/CNES-en/1415-spot.php] [http://www.geoimage.com.au/geoweb/spot/spot_overview.htm]

The Sample can drive classification algorithms, thus is a prerequisite for accurate classification. Coastal areas are located in the transitional zone between land and sea, requiring more samples to describe diverse land covers. However, there are scarce studies sharing their sample datasets, leading to a repeat of the time-consuming and laborious sampling procedure. To alleviate the problem, we share a sample set with a total of 16,444 sample points derived from a study of mapping mangroves of China. The sample set contains a total of 10 categories, which are described as follows. 1) The mangroves refer to “true mangroves” (excluding the associate mangrove species). In sampling mangroves, we used the data from the China Mangrove Conservation Network (CMCN, http://www.china-mangrove.org/), a non-governmental organization aiming to promote mangrove ecosystems. The CMCN provides an interactive map that can be annotated by volunteers with text or photos to record mangrove status at a location. Although the locations were shifted due to coordinate system differences and positioning errors, mangroves could be found around the mangrove locations depicted by the CMCN’s map on Google Earth images. There is a total of 1887 mangrove samples. 2) The cropland is dominated by paddy rice. We collected a total 1383 points according to its neat arrangement based on Google Earth images. 3) Coastal forests neighboring mangroves are mostly salt-tolerant, such as Cocos nucifera Linn., Hibiscus tiliaceus Linn., and Cerbera manghas Linn. We collected a total 1158 samples according to their distance to the shoreline based on Google Earth images. 4) Terrestrial forests are forests far from the shoreline, and are intolerant to salt. By visual inspection on Google Earth, we sampled 1269 points based on their appearances and distances to the shoreline. 5) For the grass category, we collected 1282 samples by visual judgement on Google Earth. 6) Saltmarsh, dominated by Spartina alterniflora, covering large areas of tidal flats in China. We collected 2065 samples according to Google Earth images. 7) The tidal flats category was represented by 1517 samples, which were sampled using the most recent global tidal flat map for 2014–2016 and were visually corrected. 8) The “sand or rock” category refers to sandy and pebble beaches or rocky coasts exposed to air, which are not habitats of mangroves. We collected 1622 samples on Google Earth based on visual inspection. 9) For the permanent water category, samples were first randomly sampled from a threshold result of NDWI (> 0.2), and then were visually corrected. A total of 2056 samples were obtained. 10) As to the artificial impervious surfaces category, we randomly sampled from a threshold result corresponding to normal difference built-up index (NDBI) (> 0.1), and corrected them based on Google Earth. The artificial impervious surface category was represented by 2205 samples. This sample dataset covers the low-altitude coastal area of five Provinces (Hainan, Guangdong, Fujian, Zhejiang, and Taiwan), one Autonomous region (Guangxi), and two Special Administrative Regions (Macau and Hong Kong) (see “study_area.shp” in the zip for details). It can be used to train models for coastal land cover classification, and to evaluate classification results. In addition to mangroves, it can also be used in identifying tidal flats, mapping salt marsh, extracting water bodies, and other related applications.Compared with the V1 version, we added a validation dataset for mangrove maps (Mangrove map validation dataset.rar), and thus can evaluate mangrove maps under the same dataset, which benefit the comparison of different mangrove maps. The validation dataset contains 10 shp files, in which each shp file contains 600 mangrove samples (cls_new field = 1) and 600 non-mangrove samples (cls_new field = 0).Compared with the V2 version, we added two classes of forest near water and grass near water, in addition to suppress the prevalent misclassified patches due to the spectral similarity between mangroves and those classes.

This raster dataset was developed for the Sea Grant South Coast MPA Baseline Program as part of the project “Nearshore Substrate Mapping and Change Analysis using Historical and Concurrent Multispectral Imagery” (#R/MPA 30 10-049). The study region is the South Coast Region (SCR). Imagery was acquired on June 7, 2012 and June 8, 2012 at a spatial resolution of 0.3 meters using a Microsoft UltraCam-X digital camera acquiring in the red, green, blue and near-infrared bands. Information on the UltraCam-X camera system and wavelengths for each ban can be found in the file "The Microsoft Vexcel UltraCam X.pdf" included in the Support folder on the image data delivery media and on the OceanSpaces.org server. This image mosaic product is a result of the resampling of the 0.3 meter data to 1 meter GSD. Details on this system and the data processing are below in the Lineage section of this document. Individual UCX image tiles were mosaicked into sections based on the islands covered and local coastal regions as well as the SCR MPA zones in order to generate this multispectral image product. These imagery were subsequently used to generate habitat classification thematic maps of the SCR's intertidal region and kelp beds from Point Conception to Imperial Beach, CA. The imagery files deliverd are in GeoTIFF format. More information on the classes resolved and processing methods are in the Lineage section of this document. This raster dataset contains a habitat classification of either offshore giant kelp beds and/or the intertidal zone along the California South Coast Region (SCR) from from Point Conception, CA down to Imperial beach, CA. This specific raster classification includes the Point Conception SMR and the Kashtayit SMCA.

LTER/VCR008: Data is a listing of available aerial photography of the Virginia Coast Reserve. Information included is location of photography now (i.e., where it is held), agency filmed for, data, scale, type of film, project number, roll number, frames, and additional comments. The photography can be used to identify changes and historical trends taking place on the Virginia barrier islands.

Information about LTER is also available at 'http://lternet.edu/'

Cross sectional data, all countries for the statistic Marine_Protected_Areas_(_Of_Territorial_Waters). Marine protected areas are areas of intertidal or subtidal terrain--and overlying water and associated flora and fauna and historical and cultural features--that have been reserved by law or other effective means to protect part or all of the enclosed environment.

Percent of coastline densely populated, by marine ecoregion.

The map shows the proportion of coastline (from the shore to within five kilometers of the coast) in each ecoregion where there are more than five hundred persons per square kilometer. By focusing attention on a narrow coastal strip, we believe that we are capturing areas with the highest likelihood of significant losses of intertidal and adjacent habitats as a result of building, dredging, land reclamation, and other forms of coastal engineering. It does not, of course, measure areas of coastal development per se and does not capture areas where aquaculture, agriculture, or low-density tourism have impacts.

These data were derived by The Nature Conservancy, and were displayed in a map published in The Atlas of Global Conservation (Hoekstra et al., University of California Press, 2010). More information at http://nature.org/atlas.

Data derived from:

Center for International Earth Science Information Network (CIESIN), Columbia University; and Centro Internacional de Agricultura Tropical (CIAT). 2005. Gridded Population of the World Version 3 (GPWv3), Socioeconomic Data and Applications Center (SEDAC), Columbia University Palisades, New York. Available at http://sedac.ciesin.columbia.edu/gpw. Digital media.

For more about The Atlas of Global Conservation check out the web map (which includes links to download spatial data and view metadata) at http://maps.tnc.org/globalmaps.html. You can also read more detail about the Atlas at http://www.nature.org/science-in-action/leading-with-science/conservation-atlas.xml, or buy the book at http://www.ucpress.edu/book.php?isbn=9780520262560

The primary purpose of the USGS National Assessment of Coastal Change Project is to provide accurate representations of pre-storm ground conditions for areas that are designated high priority because they have dense populations or valuable resources that are at risk from storm waves. A secondary purpose of the project is to develop a geomorphic (land feature) coastal classification that, with only minor modification, can be applied to most coastal regions in the United States.

A Coastal Classification Map describing local geomorphic features is the first step toward determining the hazard vulnerability of an area. The Coastal Classification Maps of the National Assessment of Coastal Change Project present ground conditions such as beach width, dune elevations, overwash potential, and density of development. In order to complete a hazard-vulnerability assessment, that information must be integrated with other information, such as prior storm impacts and beach stability. The Coastal Classification Maps provide much of the basic information for such an assessment and represent a critical component of a storm-impact forecasting capability.

[Summary provided by the USGS.]

This raster dataset was developed for the Sea Grant South Coast MPA Baseline Program as part of the project “Nearshore Substrate Mapping and Change Analysis using Historical and Concurrent Multispectral Imagery” (#R/MPA 30 10-049). The study region is the South Coast Region (SCR). Imagery was acquired in 2002 using the OI DMSC MKII multispectral instrument and in 2012 using a Microsoft UltraCam-X digital camera acquiring in the red, green, blue and near-infrared bands. Information on the UltraCam-X camera system and wavelengths for each ban can be found in the file "The Microsoft Vexcel UltraCam X.pdf" included in the Support folder on the image data delivery media and on the OceanSpaces.org server. The goal of this analysis was to characterize and analyze the decadal-long change in several sub/intertidal, general substrate/vegetation classes. The classes were identified in habitat classifications created as part of 2001-2002 work completed for the San Diego Association of Governments (SANDAG), and compared to the same classes identified and mapped during 2012 as part of this project. The imagery files deliverd are in GeoTIFF format. More information on the classes resolved and processing methods are in the Lineage section of this document. This raster dataset contains a change detection analysis of the intertidal zone along the California South Coast Region (SCR) from from Dana Point, CA down to Imperial beach, CA. This specific dataset covers the Tijuana River Mouth SMCA.

These GIS point show Chapter 91 (The Massachusetts Public Waterfront Act) non-water dependent sites along the coast of Massachusetts. All sites have some form of public access. Data were collected from Massachusetts Department of Environmental Protection (DEP) Waterways Program (http://www.mass.gov/dep/water/resources/waterway.htm). In addition to the physical location, all sites also have hyperlinks to photos and Ch. 91 licenses, as well as a list of amenities. Through Chapter 91, the Commonwealth seeks to preserve and protect the rights of the public, and to guarantee that private uses of tidelands and waterways serve a proper public purpose. Examples the Chapter 91 licensing process include: strolling rights in intertidal areas, pedestrian and waterfront walkways, dinghy docks, public boat landings, public restrooms, public meeting rooms, transient dockage, public water transportation facilities and services, creation of parkland, boat ramps, piers and floats for public recreational boarding facilities, fishing piers, public sailing programs, interpretive display, and interior facilities of public accommodation in private buildings, such as restaurants, museums and retail stores.