Near-shore reef boundaries mapped from available aerial photography over a range of years and scales. The mapped reef boundaries represent the greatest extent of reef observed over multiple years ie. mapped reef area includes reefs prone to intermittent sand inundation. Unrectified photos were used. Mapping has been conducted in several stages with the current version 5 being extended to include the coast between Port Jackson and Newcastle. Clarence River and Tweed Heads. Mapping is effectively complete with about 0.2% of the NSW coast remaining unmapped due to a number of reasons - unavailability of suitable aerial photos; poor visibility through the water column in deep nearshore zones (eg Sydney Heads south). This mapping was conducted by NSW National Parks and Wildlife Service, and is owed jointly by NPWS, NSW Fisheries, NSW Marine Parks Authority, NSW Department of Land and Water Conservation and Environment Australia. Aerial photos used in this process were provided by NSW Dept of Land and Water Conservation's Specialist Coastal and Floods Unit.

Rocky reef polygons were delineated from bathymetry data for the NSW seabed. Bathymetry data was sourced from marine lidar data, multibeam echosounder data and aerial imagery. Reef polygons were extracted from the seabed landforms polygons for the statewide marine lidar and multibeam surveys for Wollongong and Shellharbour (See below for associated SEED records). For the remaining areas, reef polygons were sourced from the ‘NSW subtidal marine habitat layer’ on SEED which included reef polygons manually digitised from multibeam surveys acquired from 2005-2017. Sources of reef polygons: https://datasets.seed.nsw.gov.au/dataset/nsw-seabed-landforms-derived-from-marine-lidar-data-2022 https://datasets.seed.nsw.gov.au/dataset/wollongong-seabed-landforms-derived-from-mbes-data-2022 https://datasets.seed.nsw.gov.au/dataset/shellharbour-seabed-landforms-derived-from-mbes-data-2022 https://datasets.seed.nsw.gov.au/dataset/nsw-marine-habitat-data

This dataset summarises 35 years of seagrass data collection (1984-2018) within the Great Barrier Reef World Heritage Area into one GIS shapefile containing seagrass presence and absence survey data for 81,387 sites. Managing seagrass resources in the GBRWHA requires adequate baseline information on where seagrass is (presence/absence), what species are present, and date of collection. This baseline is particularly important as a reference point against which to compare seagrass loss or change through time. The scale of the GBRWHA (1000s of kilometres) and the remoteness of many seagrass meadows from human populations present a challenge for research and management agencies reporting on the state of seagrass ecological indicators. Broad-scale and repeated surveys/studies of areas this large are logistically and financially impracticable. However seagrass data is being collected through various projects which, although designed for specific reasons, are amenable to collating a picture of the extent and state of the seagrass resource. James Cook University’s Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Seagrass Group (The Seagrass Group was part of the Queensland Government Department of Fisheries prior to 2013) has been collecting spatial data on GBR seagrass since the early 1980s. In this project TropWATER updated a previous synthesis of seagrass site data (NESP Project 3.1: https://eatlas.org.au/data/uuid/77998615-bbab-4270-bcb1-96c46f56f85a), with more recent data collected 2014-2018 to make this publicly available. Data included here from Cleveland Bay was used to classify seagrass community types, set desired state targets and for connecting sediment load targets to ecological outcomes for seagrass (NESP Project 3.2.1). In making this data publicly available for management, the authors from the TropWATER Seagrass Group request being contacted and involved in decision making processes that incorporate this data, to ensure its limitations are fully understood. Methods: The sampling methods used to study, describe and monitors seagrass meadows were developed by the TropWATER Seagrass Group and tailored to the location and habitat surveyed; these are described in detail in the relevant publications (https://research.jcu.edu.au/tropwater). Methods for data sets collected by CSIRO are reported in Pitcher et al (2007). 1. Location – Latitudes and longitudes are from converted RADAR fix or GPS. 2. Depth – Depth for subtidal sites only estimated for each site using Beaman, R.J. (2017): High-resolution depth model for the Great Barrier Reef - 30 m (http://pid.geoscience.gov.au/dataset/115066). Depth for intertidal sites = 0. 3. Sediment – Dominant sediment type from deck description. Seagrass metrics –Observers recorded seagrass presence/absence and presence/absence of each seagrass species using video transects, grabs, free diving, helicopter and walking: Video transect: Commonly used for subtidal meadows at each transect site. A CCTV camera was lowered to the bottom and towed at drift speed (less than one knot) for approximately 100m. Latitude/longitude represent the start of each transect. Footage was observed on a TV monitor and digitally recorded. The recording was paused at random times and frames selected to determine presence/absence for seagrass and each seagrass species. The camera sled included a small collecting net to obtain a specimen for identification. van Veen grab: Commonly used for subtidal meadows. A sample of seagrass was collected using a van Veen grab (grab area 0.0625 m2) to determine presence/absence for seagrass and each seagrass species at each site. Free diving, helicopter and walking: Presence/absence for seagrass and each seagrass species was estimated at each site, with a site representing approximately 10m2. Geographic Information System (GIS) All survey data were entered into a Geographic Information System (GIS) using MapInfo (generally pre-2005) then ArcMap® software. MapInfo

국립해양조사원은 매년 해안선 변화에 관한 조사를 통해 해안선 공간정보 및 지자체별 해안선 길이 현황을 제공하고 있습니다. 해안선 변화조사에 따른 지자체별 해안선 길이 현황입니다.

This dataset corresponds to land area polygons of Australian coastline and surrounding islands. It was generated from 10 m Sentinel 2 imagery from 2022 - 2024 using the Normalized Difference Water Index (NDWI) to distinguish land from water. It was estimated from composite imagery made up from images where the tide is above the mean sea level. The coastline approximately corresponds to the mean high water level. This dataset was created as part of the NESP MaC 3.17 northern Australian Reef mapping project. It was developed to allow the inshore edge of digitised fringing reef features to be neatly clipped to the land areas without requiring manual digitisation of the neighbouring coastline. This required a coastline polygon with an edge positional error of below 50 m so as to not distort the shape of small fringing reefs. We found that existing coastline datasets such as the Geodata Coast 100K 2004 and the Australian Hydrographic Office (AHO) Australian land and coastline dataset did not meet our needs. The scale of the Geodata Coast 100K 2004 was too coarse to represent small islands and the the positional error of the Australian Hydrographic Office (AHO) Australian land and coastline dataset was too high (typically 80 m) for our application as the errors would have introduced significant errors in the shape of small fringing reefs. The Digital Earth Australia Coastline (GA) dataset was sufficiently accurate and detailed however the format of the data was unsuitable for our application as the coast was expressed as disconnected line features between rivers, rather than a closed polygon of the land areas. We did however base our approach on the process developed for the DEA coastline described in Bishop-Taylor et al., 2021 (https://doi.org/10.1016/j.rse.2021.112734). Adapting it to our existing Sentinel 2 Google Earth processing pipeline. The difference between the approach used for the DEA coastline and this dataset was the DEA coastline performed the tidal calculations and filtering at the pixel level, where as in this dataset we only estimated a single tidal level for each whole Sentinel image scene. This was done for computational simplicity and to align with our existing Google Earth Engine image processing code. The images in the stack were sorted by this tidal estimate and those with a tidal high greater than the mean seal level were combined into the composite. The Sentinel 2 satellite follows a sun synchronous orbit and so does not observe the full range of tidal levels. This observed tidal range varies spatially due to the relative timing of peak tides with satellite image timing. We made no accommodation for variation in the tidal levels of the images used to calculate the coastline, other than selecting images that were above the mean tide level. This means tidal height that the dataset coastline corresponds to will vary spatially. While this approach is less precise than that used in the DEA Coastline the resulting errors were sufficiently low to meet the project goals. This simplified approach was chosen because it integrated well with our existing Sentinel 2 processing pipeline for generating composite imagery. To verify the accuracy of this dataset we manually checked the generated coastline with high resolution imagery (ArcGIS World Imagery). We found that 90% of the coastline polygons in this dataset have a horizontal position error of less than 20 m when compared to high-resolution imagery, except for isolated failure cases. During our manual checks we identified some areas where our algorithm can lead to falsely identifying land or not identifying land. We identified specific scenarios, or 'failure modes,' where our algorithm struggled to distinguish between land and water. These are shown in the image "Potential failure modes": a) The coastline is pushed out due to breaking waves (example: western coast, S2 tile ID 49KPG). b) False land polygons are created because of very turbid water due to suspended

This data set contains the 1995-era and 2000-era classifications of US Coast zone 66 and can be used to analyze change. This imagery was collected as part of the Multi-Resolution Land Characteristics program in a multi-agency effort to provide baseline multi-scale environmental characteristics and to monitor environmental change. This data set utilized 47 full or partial Landsat 5 and 7 scenes which were analyzed according to the Coastal Change Analysis Program (C-CAP) protocol to determine land cover.

This dataset is a composition of various datasets to form a map at the 1:100000 scale of the seagrass and underwater habitats from Shark Bay to NSW border going south and including Tasmania. Data extends up to 50Km offshore. The dataset was compiled largely by Dr Hugh Kirkman and subsequently updated by Dr. Ian Hahmdorf, (Bureau of Rural Sciences) and became part of CAMRIS as the CAMRIS Seagrass Dataset. Additional details (including a link to download the data as shapefile) are accessible via the www.environment.gov.au website (see links section).

This data set contains the 1995-era and 2005-era classifications of US Northeast, zone 65 and zone 66 and can be used to analyze change. This imagery was collected as part of the Multi-Resolution Land Characteristics program in a multi-agency effort to provide baseline multi-scale environmental characteristics and to monitor environmental change. This data set utilized 41 (zone 65) and 47 (zone 66) full or partial Landsat 5 and 7 scenes which were analyzed according to the Coastal Change Analysis Program (C-CAP) protocol to determine land cover.

This data set contains the 2000-era and 2005-era classifications of US Gulf Coast, zone 46 and can be used to analyze change. This imagery was collected as part of the Multi-Resolution Land Characteristics program in a multi-agency effort to provide baseline multi-scale environmental characteristics and to monitor environmental change. This data set utilized 72 full or partial Landsat 5 and 7 scenes which were analyzed according to the Coastal Change Analysis Program (C-CAP) protocol to determine land cover.