This data was collected as part of a sampling protocol to monitor ecotonal movement of mangroves and upslope freshwater marshes in Everglades National Park and Big Cypress National Preserve. Remote sensing approaches are used to delineate the physical location of the ecotone (boundaries) between mangroves and freshwater marsh. Fourteen 3 kilometer (1.9 mile [mi]) segments are systematically placed along the ecotone within the two parks. Aerial imagery is used to digitize the mangrove-marsh ecotone in each segment and must be 1 meter (3.3 feet [ft]) or better resolution and have positional accuracy of ± 3 meters (9.8 feet [ft]) or better. Four ground-truth field point locations (marker points) are established approximately 1 kilometer (0.62 miles [mi]) apart along the ecotone of each segment to provide field-based measurements and ground-truthing of the digitized line. Vegetation data is collected at each sampling point, with this vegetation coverage determining where the placement of the ecotone should be. generally, this is where mangrove cover drops below 50% cover. The vegetation data also verifies if it is mangroves (typical Red) that is the dominant tree on the tress side of the ecotonal edge. The vegetation data also indicated the dominant graminoid on the more inland side of the ecotone. We have found 3 major groups for the marsh dominant vegetation type. This could be important mitigating factor or a indicator of future conditions. NOTE - Data for this data release was pulled from the mangrove marsh ecotone database and preliminarily cleaned and organized in May 2025. The data should undergo further QAQC before being used in analysis.

This dataset consists of a file geodatabase containing the digitized ecotone boundaries between mangroves and freshwater marsh in 2013 and 2019 at 14 systematically selected segments in Big Cypress National Preserve and Everglades National Park (southwest Florida, USA).

This dataset consists of a file geodatabase containing the digitized ecotone boundaries between mangroves and freshwater marsh in 2013 and 2019 at 14 systematically selected segments in Big Cypress National Preserve and Everglades National Park (southwest Florida, USA).

Twenty-three long-term mangrove vegetation plots were established between 1992-1997 within the southwest coastal region of Everglades National Park (Florida, USA) to quantify tree damage and recovery following hurricanes and to understand the effects of global climate change. Many of the mangrove vegetation plots were associated with hydrology, soil porewater, and soil surface elevation monitoring stations. Within each plot, trees were tagged, mapped, and identified to species. The diameter at breast height (DBH) and status of each tree were measured at one to two year intervals through 2011. This dataset includes 51,730 tree measurements.

This data set contains biological information from important coastal mangrove areas situated at the border of, or near the targeted seascapes of the Caribbean Marine Biodiversity Program (CMBP). The data covers: raw species density, diameter at breast height, canopy cover and average heights; GIS data on all locations where data was collected. Targeted seascapes and corresponding countries include: 1) The Mangroves of Bajo Yuna and Los Corozos - Samana Bay - Dominican Republic 2) The Mangroves of Protected Area of Three Bays - Haiti This file reports data on mangroves of Bajo Yuna and Los Corozos in Samana Bay, Dominican Republic. The two datasets, "data" and "density" together describe each area.

Mangrove inventory data from J.N. Ding Darling National Wildlife Refuge, Sanibel Island, Florida, USA collected in 2016 and 2017. Plot data includes X and Y downed dead wood count, mangrove species information and site descriptions. Tree data includes the three species found on the refuge: Avicennia germinans (Black mangroves), Laguncularia racemosa (White mangroves) and Rhizophora mangle (Red mangroves). They were inventoried for diameter at breast height (DBH), height, and dead status.

Mangrove inventory data from J.N. Ding Darling National Wildlife Refuge, Sanibel Island, Florida, USA collected in 2016 and 2017. Plot data includes X and Y downed dead wood count, mangrove species information and site descriptions. Tree data includes the three species found on the refuge: Avicennia germinans (Black mangroves), Laguncularia racemosa (White mangroves) and Rhizophora mangle (Red mangroves). They were inventoried for diameter at breast height (DBH), height, and dead status.

This dataset contains point clouds of three-dimensional (3D) mangrove forest structure and volume collected from 10 sites in Everglades National Park, Florida. Data were collected during NASA CMS "Blueflux" campaigns in March 2022, October 2022, and March 2023. Products were acquired using a RIEGL VZ-400i terrestrial laser scanner (TLS). TLS is a non-destructive and quantitative method for in situ 3D forest structure measuring and monitoring. Data are provided in LAS (*.las) format.

This data set contains biological information from important coastal mangrove areas situated at the border of, or near the targeted seascapes of the Caribbean Marine Biodiversity Program (CMBP). The data covers: raw species density, diameter at breast height, canopy cover and average heights; GIS data on all locations where data was collected. Targeted seascapes and corresponding countries include: 1) The Mangroves of Bajo Yuna and Los Corozos - Samana Bay - Dominican Republic 2) The Mangroves of Protected Area of Three Bays - Haiti This file reports data on mangroves of the Mangroves of Protected Area of Three Bays, Haiti. The two datasets, "data" and "density" together describe each area.

This dataset contains point clouds of three-dimensional (3D) mangrove forest structure and volume collected from 10 sites in Everglades National Park, Florida. Data were collected during NASA CMS "Blueflux" campaigns in March 2022, October 2022, and March 2023. Products were acquired using a RIEGL VZ-400i terrestrial laser scanner (TLS). TLS is a non-destructive and quantitative method for in situ 3D forest structure measuring and monitoring. Data are provided in LAS (*.las) format.