Currently, our country is striving to achieve the goal of carbon peaking. “Blue Carbon,” represented by mangrove wetlands, is an indispensable component in the field of carbon sink. In 2020, the Ministry of Natural Resources issued the “Special Action for Mangrove Conservation and Restoration (2020-2025),” and significant progress has been made recent year. As a marine-centric city, Shenzhen boasts relatively abundant mangrove resources. A comprehensive investigation of the current status of typical coastal mangrove ecosystems and mangrove species is essential. This not only facilitates a better understanding of the species composition and community structure within the region but also allows for the evaluation of the achievements of mangrove conservation plans.Based on the geographical distribution and community structure of the city's mangroves, nine typical mangrove monitoring transects and 24 monitoring plots were selected in the summer of 2023. An area-weighted average method was utilized to determine the per-unit area biomass of the city’s mangrove vegetation, via unmanned aerial vehicles, combined with on-site inspections and fixed plot surveys. The above-ground plant biomass of Shenzhen's coastal mangrove was calculated using the allometric growth equation method, in conjunction with the results of plot surveys to get the determination of the distribution range and area of the mangrove forests along Shenzhen's coastline. Field measurements and recordings of various plant indices were conducted, along with on-site identification of plant species composition, to record community indices of the mangrove forests. Ultimately, the dataset was obtained. This dataset exhibits several characteristics: (1) It contains rich content, including the geographic coordinates of sampling points, biological information, community structure, and community characteristics. (2) It covers a wide geographical range, including all concentrated mangrove locations within the Shenzhen city area. (3) Field surveys and fixed plot sampling methods were employed, resulting in minimal errors. Utilizing this dataset enables the exploration of the governance and distribution status of mangrove wetlands in the Greater Bay Area. Furthermore, it can be integrated with investigations on carbon flux, carbon storage, water quality, and atmospheric conditions, which is of significant importance for ecological environmental monitoring and research.

To improve scientific understanding of the extent and distribution of mangrove forests of the world the status and distribution of global mangroves were mapped using recently available Global Land Survey (GLS) data and the Landsat archive.The project interpreted approximately 1000 Landsat scenes using hybrid supervised and unsupervised digital image classification techniques. Results were validated using existing GIS data and the published literature to map ‘true mangroves’.The total area of mangroves in the year 2000 was 137,760 km2 in 118 countries and territories in the tropical and subtropical regions of the world. Approximately 75% of world's mangroves are found in just 15 countries, and only 6.9% are protected under the existing protected areas network (IUCN I-IV). Our study confirms earlier findings that the biogeographic distribution of mangroves is generally confined to the tropical and subtropical regions and the largest percentage of mangroves is found between 5° N and 5° S latitude.The remaining area of mangrove forest in the world is less than previously thought; the estimate provided in this study is 12.3% smaller than the most recent estimate by the Food and Agriculture Organization (FAO) of the United Nations. This data set presents the most comprehensive, globally consistent and highest resolution (30 m) global mangrove database ever created

Mangrove forests are wetland ecosystems in the coastal zone with important ecological and eco-economic service values. At the same time, mangrove forests are vulnerable ecosystems, being under threat from both natural and anthropogenic forces, so mangrove monitoring is a permanent task. The development of remote sensing technology has provided an efficient and convenient means for mangrove monitoring. In this study, a dataset of mangrove forest changes on Hainan Island from 2015 to 2019 was obtained using Gaofen-2 (GF-2) data in 2015, 2017 and 2019. The dataset was compiled through a support vector machine (SVM) classification method and field survey data. It can be used as the basic data for the analysis of spatial and temporal changes of mangrove forests, and can also provide decision support for the restoration, protection and management of mangrove wetland ecosystems, and provide basic data for the ecological environment supervision in Hainan Province.

Abstract

The Bangladesh Forest Inventory (BFI) was developed to support sustainable forest management and promoting forest monitoring system. BFI contains the biophysical inventory and socio-economic survey. The design and analysis of the components was supported by remote sensing-based land cover mapping. The inventory methodology was prepared with technical consultations with national and international forest inventory and land monitoring experts and employed by the Forest Department under the Ministry of Environment, Forests and Climate Change to establish the BFI as an accurate and replicable national forest assessment. Biophysical inventory involved visiting of 1781 field plots, and the socioeconomic surveying covered 6400 households. Semi-automated segmented land cover mapping used for object-based land characterisation, mobile application for onsite tree species identification, Open Foris tool for data collection and processing, R statistical package for analysis and differential GPS for plot referencing used. Seven major criteria and relevant indicators were developed to monitor sustainable forest management, informing both management decisions and national and international reporting. Biophysical and socioeconomic data was integrated to estimate these indicators. BFI provided data and information on tree and forest resources, land use, and ecosystem services valuations to the country. BFI made the sample plots as permanent for the continuous assessment of forest resources and monitoring over time.

Geographic coverage

National

Analysis unit

Fields/plots

Universe

The country was divided into five distinct strata/zone for allocation of sample plots to represent the forest and trees outside forest properly. Also, the interaction of community with forest and trees and their dependency were also considered. For monitoring purposes, the samples were made permanent, and the boundary of the zones are defined in such a way that may not change easily. The five zones of Bangladesh Forest Inventory are Sundarbans (natural Mangrove Forest) Zone, Coastal (coastal plantations including mangrove plantation) Zone, Hill (evergreen and semi evergreen hilly forest areas) Zone, Sal (Deciduous Forest) Zone and Village (mainly tree outside forest and social forestry) Zone. The universe is the tree populations across the country, included trees in and outside forest land in all five subpopulations.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling strategy for the National Forest Inventory (NFI) in Bangladesh comprises multiple steps, including Zoning, Land Cover development, Biophysical Inventory, and Socioeconomic Survey. Zone: The country is divided into five zones—Sal, Sundarbans, Village, Hill, and Coastal—based on geographical conditions, species diversity, forest types, and human interaction. The socioeconomic survey zones correspond to the biophysical zones, with the Sundarbans zone referred to as the Sundarbans periphery zone. Land Cover: The Land Representation System of Bangladesh (LRSB) was developed using an object-based classification approach with the Land Cover Classification System (LCCS v3) and satellite imagery. The 33 land cover classes from the 2015 Land Cover Map were aggregated into Forest or Other Land categories following FRA definitions. Biophysical Inventory Design: The biophysical component employs a pre-stratified systematic sampling design with variable intensities for each zone. Sample intensity was determined by a 5% confidence interval target for tree resource estimates, utilizing Neyman allocation for plot distribution. Plots were randomly placed within a hexagonal grid, with distances ranging from 5900 to 10400 meters, resulting in 2245 plot locations, of which 1858 required field visits. Each plot included subplots of 19m radius in the Sundarbans and 5 subplots in other zones. Trees with DBH ? 30 cm, 10-30 cm, and 2-10 cm were measured in 19m, 8m, and 2.5m radius plots, respectively. Soil samples were collected at 8m from the subplot centre at a 270° bearing. Socioeconomic Survey Design: The socioeconomic survey utilized a multi-stage random sampling method. It was based on the hypothesis that tree and forest ecosystem services correlate with tree cover per household. Tree cover data from 2014 Landsat images and household data from the 2011 Census were used to calculate Household Tree Availability. The five zones were divided into four strata each, based on tree cover availability. In each pre-selected union, 20 households were surveyed (totalling 6400 households) by navigating to random GPS points. Additionally, 100 qualitative surveys were conducted through Focus Group Discussions across the zones, involving community leaders and special forest user groups. This comprehensive sampling strategy ensures robust data collection on forest resources and their socioeconomic interactions.

Sampling deviation

Biophysical inventory: around 4% deviation took place because of inaccessibility issues mostly in hill regions Socioeconomic survey: 0 deviation from the sample design

Mode of data collection

Field measurement [field]

Cleaning operations

In BFI field data collection, data cleaning, quality control, and data archiving were part of a simultaneous process performed both in the field and in the central office. Open Foris Collect was used for data collection and processing. Collected data were submitted to the central office unit for managing, cleaning, archiving and further processing. As field data were collected, they were checked for outliers or suspect data entries, both manually and with R scripts. If an obvious correction was needed, it was updated in the Open Foris database, otherwise the field teams were consulted about suspect data to understand the problem and take further decision. At the same time, four QA/QC teams performed quality assessments of data collection directly in the field through hot and cold checks. Hot checks allowed for the opportunity to improve data in the field. Cold checks provided the issues to be considered and identify the check list also the data will be acceptable or not, in case of unacceptable data remeasurement of plat took place. In the biophysical inventory, 39 hot checks, 54 cold checks were conducted, which is about 5% of the sampled plots. The total number of plots re-measured was 52. For the socio-economic survey, 254 hot checks, 13 cold checks, which is about 4% of the total number of households sampled. Microsoft Access database was prepared using the data exported from collect, which also enabled to generate reports with images collected from the field. With new data, the database was updated accordingly. Data cleansing conducted using Collect desktop and R statistical tool. Manual checks for records were done in Collect. R generated quality control checks were also used to identify possible inconsistencies. Inconsistencies were confirmed and corrected through consulting to the field crews or data collectors by and updated later in the collect database.

Response rate

Biophysical Inventory: 1781 sample plots were inventoried among the total of 1857 plots, which is around 96%. Socioeconomic Survey: 100% of targeted household numbers were surveyed, which is 6400.

Sampling error estimates

Please refer to Table 5.10 of the Report on the Bangladesh Forest Inventory for more information on the estimates of sampling error.

Data appraisal

Socioeconomic survey had nonresponse 0%, whereas Biophysical inventory had non-response around 4%. However, knowing the fact that non-response introduce bias, and to minimize the bias estimates methods developed. 1. Treat as zeros but show the area by inaccessible class. This is transparent, so it is recommended to always present the proportion of inaccessible plots. 2. Partition inaccessible zones and report them as such. This clearly identifies regions that could not be sampled. 3. Drop the plots from estimation. This treats the inaccessible plots as if they had the strata mean. For partially accessible plots, a special estimator must be used, such as the ratio-to-size estimator or that used by FIA (Bechtold and Scott 2005) to account for the missing portion of the plot. Mostly methods 1 and 3 used. Method 2 were used if there are inaccessible regions.

The mangrove map displays data from multiple sources, providing the most accurate and current information for this region to date. The first step towards this final product began with mapping regional data which covered as many countries in the CLME region as possible. This included information from 3 separate datasets: (1.) For the insular Caribbean, the best available information was compiled by Brigham Young University students through a TNC contract in 2011. This effort also began with a compilation of various datasets, supplemented with any original digitization efforts. This information was then checked against high resolution satellite imagery where available from Bing and Esri imagery basemaps. Errors and misrepresentations in the base information were modified through heads up digitization from the high resolution imagery. Omissions of coverage were corrected by creating new polygons to match visible coral structures. Outside of the insular Caribbean, (2.) the data from the Mesoamerican region was collected in 2007 as well as (3.) a "World Atlas of Mangroves" file collected from Mark Spalding in 2012. This dataset has been created as part of a collaboration between Food and Agriculture Organization of the United Nations (FAO), International Society for Mangrove Ecosystems (ISME), the International Tropical Timber Organization (ITTO), the United Nations Educational, Scientific and Cultural Organization Man and the Biosphere Programme (UNESCO-MAB), the United Nations Environment Programme – World Conservation Monitoring Centre (UNEP-WCMC) and the United Nations University Institute for Water, Environment and Health (UNU-INWEH) to revise the 1997 World Alas of Mangroves. The regional data was then replaced with sub-regional, national or site level information where this information was available (and wasn't already used in the starting regional file) to obtain a finer scale product. For mangroves, the following separate datasets were included: (1.) Grenada and St. Vincent and the Grenadines were replaced with data collected from the At the Water’s Edge Project led by the Nature Conservancy. The spatial information comes from four different datasets. a.) Contract work by Matthew Jones where he used existing data from Landsat and improved upon these shapes using Bing and ESRI imagery base maps in 2011. b.) The Ph.D. work of Kim Baldwin at the University of the West Indies Cave Hill and the generation of the Marine Space-use Information System (MarSIS) in 2009 resulted in a benthic habitat file which she created by heads up digitizing effort using IKONOS imagery. c.) Brigham Young University students, under contract by the Nature Conservancy, digitized polygons for the country of Grenada using WorldView2 2010 orthophotos. Mangroves for St. Vincent and the Grenadines were digitized from 2007 aerial photos. d.) field verification of mangrove patches by Gregg Moore of the University of New Hampshire in 2012 (2.) Some of the USVI and Puerto Rico polygons were replaced with and some were added from data collected from NOAA's benthic habitat mapping effort in 1999. Twenty-one distinct benthic habitat types within eight zones were mapped using visual interpretation of orthorectified aerial photographs. (3.) St. Lucia was replaced with data collected for the 2009 National Ecological GAP Assessment. Mangrove locations were collected from Mr. Allan Smith and manually digitized from 2004 aerial photography. (4.) Barbuda was missing from the regional data. Polygons were added using a national level landcover file created from 2004 imagery. (5.) Andros Island in the Bahamas was replaced with data collected from the Andros Island Conservation Assessment Project (CAP) in 2006 mapped by using a combination of local and scientific knowledge and land cover classes delineated from Landsat 7 imagery.

The mangrove map displays data from multiple sources, providing the most accurate and current information for this region to date. The first step towards this final product began with mapping regional data which covered as many countries in the CLME region as possible. This included information from 3 separate datasets: (1.) For the insular Caribbean, the best available information was compiled by Brigham Young University students through a TNC contract in 2011. This effort also began with a compilation of various datasets, supplemented with any original digitization efforts. This information was then checked against high resolution satellite imagery where available from Bing and Esri imagery basemaps. Errors and misrepresentations in the base information were modified through heads up digitization from the high resolution imagery. Omissions of coverage were corrected by creating new polygons to match visible coral structures. Outside of the insular Caribbean, (2.) the data from the Mesoamerican region was collected in 2007 as well as (3.) a "World Atlas of Mangroves" file collected from Mark Spalding in 2012. This dataset has been created as part of a collaboration between Food and Agriculture Organization of the United Nations (FAO), International Society for Mangrove Ecosystems (ISME), the International Tropical Timber Organization (ITTO), the United Nations Educational, Scientific and Cultural Organization Man and the Biosphere Programme (UNESCO-MAB), the United Nations Environment Programme – World Conservation Monitoring Centre (UNEP-WCMC) and the United Nations University Institute for Water, Environment and Health (UNU-INWEH) to revise the 1997 World Alas of Mangroves. The regional data was then replaced with sub-regional, national or site level information where this information was available (and wasn't already used in the starting regional file) to obtain a finer scale product. For mangroves, the following separate datasets were included: (1.) Grenada and St. Vincent and the Grenadines were replaced with data collected from the At the Water’s Edge Project led by the Nature Conservancy. The spatial information comes from four different datasets. a.) Contract work by Matthew Jones where he used existing data from Landsat and improved upon these shapes using Bing and ESRI imagery base maps in 2011. b.) The Ph.D. work of Kim Baldwin at the University of the West Indies Cave Hill and the generation of the Marine Space-use Information System (MarSIS) in 2009 resulted in a benthic habitat file which she created by heads up digitizing effort using IKONOS imagery. c.) Brigham Young University students, under contract by the Nature Conservancy, digitized polygons for the country of Grenada using WorldView2 2010 orthophotos. Mangroves for St. Vincent and the Grenadines were digitized from 2007 aerial photos. d.) field verification of mangrove patches by Gregg Moore of the University of New Hampshire in 2012 (2.) Some of the USVI and Puerto Rico polygons were replaced with and some were added from data collected from NOAA's benthic habitat mapping effort in 1999. Twenty-one distinct benthic habitat types within eight zones were mapped using visual interpretation of orthorectified aerial photographs. (3.) St. Lucia was replaced with data collected for the 2009 National Ecological GAP Assessment. Mangrove locations were collected from Mr. Allan Smith and manually digitized from 2004 aerial photography. (4.) Barbuda was missing from the regional data. Polygons were added using a national level landcover file created from 2004 imagery. (5.) Andros Island in the Bahamas was replaced with data collected from the Andros Island Conservation Assessment Project (CAP) in 2006 mapped by using a combination of local and scientific knowledge and land cover classes delineated from Landsat 7 imagery.

An overall strategy of this project is to conduct integrated research at a number of different locations to address key questions related to global climate change impacts on the coastal mangrove forests and adjacent marshes. The integrated elements of the project include hydrology, vegetation and fauna. This project established, runs and maintains the Mangrove Hydrology Monitoring Network, a series of 17 stations arrayed along upstream downstream gradients in major rivers on the southwest coast of the Park and in the C-111 basin. The sites are also used for sampling vegetation, and soil elevation changes. Additionally the project adds a key research element concerning mangrove fauna, that is not present in related projects dealing with the mangrove dominated coastal zone. The network provides data on water (ground and surface) stage and conductivity that are used by the TIME and other modeling groups. Water year reports have been prepared and data are available via the TIME website and Everglades NP "Data for Ever" database. Open File Reports are being generated which provide historical aerial photographs in digital format. The data generated by this project is being used in models (hydrological and ecological) for gauging restoration success. The data are also being used in the formulation of Performance Measures. For example, spatial data on the movement of the mangrove / marsh ecotone (derived from the digital historical aerial photographs) will be used to provide a pre-drainage baseline of the Everglades ecosystem and metrics of success in restoration.

This project is addressing several key hypothesis related to global change impacts on the flora and fauna of the mangrove forested ecosystems which occur at the downstream end of the greater Everglades: 1) Mangroves in a geomorphic setting with relatively more edge (open-water/mangrove interface) support greater fishery productivity as measured by density and biomass/area than near-by mangroves with relatively little edge; 2) fishery productivity along complex environmental gradients is a function of the frequency and duration of tidal flooding, and of the variability in a suite of physicochemical parameters; 3) fires along the mangrove-marsh ecotone promote invasion of mangroves into adjacent marshes; and, 4) shifts in the position of the mangrove-marsh ecotone are linked to the passage of major tropical storms and hurricanes.

Guangdong province is one of the regions with the largest mangroves area, the widest range and the most abundant species in China. It is very significant to find out the location and situation of mangroves along the coast of Guangdong province. The dataset collects 119 Gaofen-2(GF-2) satellite remote sensing images of Guangdong province with less cloud cover from 2015 to 2020. After all of the collected GF-2 images were preprocessed, object-based image analysis (OBIA) is adopted to extract vector data of mangroves. Accuracy assessment demonstrates that the overall classification accuracy, Kappa coefficient and production accuracy of the data set are higher than 96%, 0.88 and 82%, respectively. This product presents the status and area changes of mangroves in Guangdong province coast during 2015-2020, which can be used as the basic data for the protection, restoration and management of mangrove resources, as well as wetland dynamic monitoring and marine environment change in the region.

The 2008 - Present Ecosystem History of South Florida's Estuaries Database contains listings of all sites (modern and core) and modern monitoring site survey information (water chemistry, floral and faunal data, etc.). Two general types of data are contained within this database: 1) Modern Field Data and 2) Core data - location information. Data are available for modern sites (from 2008 to present) and cores in the general areas of Florida Bay, Biscayne Bay, and the southwest (Florida) coastal mangrove estuaries. Specific sites in the Florida Bay area include Taylor Creek, Bob Allen Key, Russell Bank, Pass Key, Whipray Basin, Rankin Bight, park Key, and Mud Creek core). Specific Biscayne Bay sites include Manatee Bay, Featherbed Bank, Card bank, No Name Bank, Middle Key, Black Point North, and Chicken Key. Sites on the southwest coast include Alligator Bay, Big Lostmans Bay, Broad River Bay, Roberts River mouth, Tarpon Bay, Lostmans River First and Second Bays, Harney River, Shark River near entrance to Ponce de Leon Bay, and Shark River channels. Modern field data contain (1) general information about the site, bottom type, description, latitude and longitude, date of data collection, (2) water chemistry information (salinity, temperature, pH, etc.), and (3) descriptive text of fauna and flora observed at the site. Core data contain basic location information.

TERN Ecosystem Data Services and Analytics

TERN Research Data Repository

Simplify your research data collection with the help of the research data repository managed by the Terrestrial Ecosystem Research Network. Our collection of ecosystem data includes ecoacustics, bio acoustics, lead area index information and much more.

The TERN research data collection provides analysis-ready environment data that facilitates a wide range of ecological research projects undertaken by established and emerging scientists from Australia and around the world. The resources which we provide support scientific investigation in a wide array of environment and climate research fields along with decision-making initiatives.

Explore Our Ecosystem Data Portals

Open access ecosystem data collections via the TERN Data Discovery Portal and sub-portals:

Access all TERN Environment Data

Discover datasets published by TERN’s observing platforms and collaborators. Search geographically, then browse, query and extract the data via the TERN Data Discovery Portal.

Search EcoPlots data

Search, integrate and access Australia’s plot-based ecology survey data.

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Extract, prepare, visualise and analyse TERN Ecosystem Surveillance monitoring data in R.

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Search and download Leaf Area Index (LAI), Phenocam and Photopoint images.

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Tools that support the discovery, anaylsis and re-use of data:

Visualise the data

We’ve teamed up with ANU to provide 50 landscape and ecosystem datasets presented graphically.

Access CoESRA Virtual Desktop

A virtual desktop environment that enables users to create, execute and share environmental data simulations.

Submit data with SHaRED

Our user friendly tool to upload your data securely to our environment database so you can contribute to Australia’s ecological research.

Other data portals, tools and services

The Soil and Landscape Grid of Australia provides relevant, consistent, comprehensive, nation-wide data in an easily-accessible format. It provides detailed digital maps of the country’s soil and landscape attributes at a finer resolution than ever before in Australia.

The annual Australia’s Environment products summarise a large amount of observations on the trajectory of our natural resources and ecosystems. Use the data explorer to view and download maps, accounts or charts by region and land use type. The website also has national summary reports and report cards for different types of administrative and geographical regions.

TERN’s ausplotsR is an R Studio package for extracting, preparing, visualising and analysing TERN’s Ecosystem Surveillance monitoring data. Users can use the package to directly access plot-based data on vegetation and soils across Australia, with simple function calls to extract the data and merge them into species occurrence matrices for analysis or to calculate things like basal area and fractional cover.

The Australian Cosmic-Ray Neutron Soil Moisture Monitoring Network (CosmOz) delivers soil moisture data for 16 sites over an area of about 30 hectares to depths in the soil of between 10 to 50 cm. In 2020, the CosmOz soil moisture network, which is led by CSIRO, is set to be expanded to 23 sites.

The TERN Mangrove Data Portal provides a diverse range of historical and contemporary remotely-sensed datasets on extent and change of mangrove ecosystems across Australia. It includes multi-scale field measurements of mangrove floristics, structure and biomass, a diverse range of airborne imagery collected since the 1950s, and multispectral and hyperspectral imagery captured by drones, aircraft and satellites.

The TERN Wetlands and Riparian Zones Data Portal provides access to relevant national to local remotely-sensed datasets and also facilitates the collation and collection of on-ground data that support validation.

ecocloud provides easy access to large volumes of curated ecosystem science data and tools, a computing platform and resources and tools for innovative research. ecocloud gives you 10GB of persistent storage to keep your code/notebooks so they are ready to go when you start up a server (R or Python environment). It uses the JupyterLabs interface, which includes connections to GitHub, Google Drive and Dropbox.

Analysis Ready Ecosystem Data

Our research data collection makes it easier for scientists and researchers to investigate and answer their questions by providing them with open data, research and management tools, infrastructure, and site-based research tools.

The TERN data portal provides open access ecosystem data. Our tools support data discovery, analysis, and re-use. The services which we provide facilitate research, education, and management. We maintain a network of monitoring site and sensor data streams for long-term research as part of our research data repository.

The database contains data points extracted from the systematic review on the effect of land-use and land-cover changes on mangrove blue carbon at global scale. The data points are summarised into four spreadsheets comprising carbon stocks, soil greenhouse gas (GHG), forest structure and soil properties data from land-based mangrove conversion and regeneration. Dataset 1 contains carbon stock, forest structure, soil properties, and soil GHG (CO2 and CH4) effluxes data from paired study sites between undisturbed control and disturbed treatment (tree removal, aquaculture, rice field, pasture, and other category). Dataset 2 contains carbon stock, forest structure, soil properties, and soil GHG (CO2 and CH4) effluxes data from regenerated mangroves.

This project investigated the cause of the extensive areas of mangroves across the Gulf of Carpentaria which died in late 2015. Images from local fisherman showed extended impacted areas of more than 1,000 km where at least 7,400ha of mangroves had died in a matter of months. The project mapped the extent of the mass die-back, conducted aerial surveys to quantify shoreline condition, field studies to validate remote assessments and engaged with local aboriginal ranger groups to raise capacity for monitoring.

View the imagery via the map interface: https://maps.eatlas.org.au/index.html?intro=false&z=10&ll=136.96547,-15.79844&l0=ea_nesp4%3AGOC_NESP-4-13_JCU_AerialSurveys_2017_2019_Shoreline_DB,ea_ea-be%3AWorld_Bright-Earth-e-Atlas-basemap,google_HYBRID,google_TERRAIN,ea_nesp-mac-3%3AAU_NESP-MaC-3-17_AIMS_S2-comp_low-tide_p30-trueColour&v0=,f,,f

View and download the imagery as a gallery: https://nextcloud.eatlas.org.au/apps/sharealias/a/GOC_NESP-4-13_JCU_Mangrove-Shoreline-Aerial-Surveys_2017_2019

The aerial surveys are the first comprehensive record of oblique and continuous views of coastal shorelines for this large section of the Gulf of Carpentaria – providing a working database of more than 30,000 high-resolution images. This record is a lasting primary reference for baseline visual characterisations of shorelines for 2017 and 2019.

The aim of aerial shoreline surveys was to systematically record and investigate the presence of 2015 mangrove dieback, the overall condition of shorelines, processes affecting the mangrove vegetation, and the health of tidal wetlands along Gulf shorelines, as well as in the mouths of major estuarine systems. These surveys were repeated in 2017 and in 2019 to gain insight and knowledge of the issues affecting shorelines, and the severity of factors influencing Gulf shorelines.

The aerial surveys provided a baseline database or library of more than 19,534 geotagged oblique locations in 2017 and 2019 covering every metre of shoreline plus a series of inland profiles extending to the upper limits of tidal inundation in 37 estuarine outlets.

This dataset consists of the complete set of imagery and compiled observations of current drivers of change and severity of impacts for 37 major estuarine sites. From east to west, these sites included Mission River, Embley River, Watson River, Holroyd River, Christmas Creek, Mitchell River, South Mitchell River, Nassau River, Staatan River, Gilbert River, Accident Inlet, Norman River, Flinders River, Leichhardt River, Albert River, Nicholson River, John’s Creek, Syrell Creek, Massacre Inlet, Dugong River, Toongoowahgun River, Elizabeth River, Sandalwood Place River, Calvert River, Robinson River, Wearyan River, McArthur River, Mule Creek, Limmen Bight River, Towns River, Roper River, Miyangkala Creek, Rose River, Muntak River, Walker River, and Koolatong River. (Note: we are still to publish the estuary survey data on the eAtlas).

Shoreline and estuarine evaluations identified more than 30 issues in tidal wetland and shoreline habitats divided into direct and indirect human causes, or natural causes: shoreline retreat & landward transgressions of saline water and tidal wetland vegetation, rising sea levels, severe and frequent storms, feral animals plus other seemingly uncontrolled but damaging local land management practices.

Methods: Aerial surveys were conducted in two series during 2017 and 2019. Those in 2017 were completed over 11 days from 1–11 December and included the shoreline survey plus surveys of 37 estuary mouths. The shoreline distance surveyed in 2017 was 2,633 km with a total flying distance of 4,646 km over 173 hours. A follow-up survey in 2019 was completed over nine days from 12–21 September and included the shoreline survey plus surveys of 31 estuary mouths.

Aerial surveys were made using an R-44 helicopter flying at around 150 metres altitude. The aircraft windows and doors were removed to aid easier and best quality image capture. The entire shoreline from Mission River at Weipa (Queensland) to Koolatong River in Blue Mud Bay (Northern Territory) was surveyed. Shoreline and target estuaries were assessed in their order of occurrence travelling in a westerly direction.

Shoreline filming captured the complete coastline used in the current evaluations of shoreline and estuarine habitat condition, with geotagged high-resolution digital images of shorelines, taken obliquely at low elevations ~150 metres altitude.. These photographs were comprised of three categories of images – survey, scenic and general. Survey photos made up ~60% and consisted of high-resolution images using a Nikon D800E camera with AF-S Nikkor 50 mm 1:1.4 G-series lens and di-GPS. These images were taken to give overlapping continuous coverage of shorelines centred up from the mean sea level contour – as the seaward edge of mangroves. Scenic photos made up ~33% and consisted of high resolution images using a Nikon D850 camera with AF-S Nikkor 28–300 mm 1:3.5–5.6 Gseries and di-GPS. A similar number and types of images were acquired in 2019.

Summary of scenic photos:

2017 survey: Day 1: R44 Helicopter and crew on ground, jabiru in flight, crocodile on beach Day 2: Aerial view of mangrove die back, feral pigs on beach, jabiru in flight, large flock of egrets in mangrove forest Day 3: Aerial view of snubfin dolphin at surface, mangrove die back, smoke plume from bush fire in the distance creating a cloud, helicopter taking off. Day 4: Close up of rubber vine in a mangrove, pelican in flight, aerial image of swimming crocodile in turbid water with fish in its mouth, aerial shot of salt pans, crab pot on a mud flat. Day 5: Dead patch in mangrove forest due to lightning strike, aerial photos of mangrove die back, seagrass (long thin), aerial image of a dugong feeding trail through a seagrass meadow. Day 6: Indigenous coastal fish traps, sea eagle in flight, dugong stranded in mud, jabiru walking on sand, aerial photos of mangrove die back, shorebirds in flight Day 7: Dingy upside down on remote beach, aerial view of lush highly dense seagrass meadow (2 species, one with long leaves), dingy stranded in middle of mangrove forest, dark grey heron flying low over a river, fringing coral reef?, sea turtle, bent aero plane propeller partly covered in oysters, ghost net on shore Day 8: Large vertebra bone (whale?) on salt pan, turtle, helicopter in flight, aerial view of river mouth with seagrass meadows, Norm Duke standing in water holding seagrass Day 9: Large wrack of seagrass (tubular leaves), shore bird, mangrove die back, crocodile swimming with head out of water, boat ramp with two vehicles, industrial harbor, areas with large mangrove die back Day 10: Stranded dingy, aerial view of four green turtles, mangrove forest with large flock of black birds, ghost net caught up in mangrove, egret in flight over water, helicopter taking off from near mangrove, small town, aerial view of area with dense seagrass meadow Day 11: Closeup of water buffalo walking through water with long leafed seagrass, aerial view of 10 water buffalo, possibly indigenous fish traps, small town, river mouth with dense seagrass, large areas of mangrove die back

2019 survey: 0_sortedPhotos: images sorted by categories: Burdekin duck, crab pots, crocs, depositional gain, erosion, fire, inner fringe collapse, jabiru, jellies, large litter, light gaps, pelicans, root burial, shorebird Day 11: Ghost nets Day 12: Crocodile on mud flat, eight pelicans floating on water, pair of jabiru, collection of large tires on the shoreline presumably to help catch fish or crabs, samphire on a salt pan, helicopter in flight, crab pot at the edge of a mangrove, small boat wreckage on dry land at edge of mangrove, mangroves with fire in the distance, ghost nets on a beach, red dirt cliff where the shore is receding, weathered dead mangrove stumps, aerial shot crocodile swimming in clear water, aerial shot of wide sandy beach, two dead sharks on beach presumably caught and abandoned, jabiru in flight, flock of brolgas in flight, partly buried cage (protect turtle nest?) Day 13: tire and dog paw tracks on sandy beach with a dug up area (looking for turtle eggs?), black feral pig on beach, partly buried cage (to protect turtle nest?), blackened burnout grass neighbouring salt pan, pelican flying over water, dolphin and calf, person with fishing net on shore, small boat on water, photo of mangrove with a shadow of the helicopter on the water Day 14: giant milkweed growing on beach, grey mangrove saplings growing within trunks of dead mangrove trunks, flock of pelicans flying over water, old rotted mangrove tree stumps along the shoreline, mangrove forest with mixed species, many pig tracks on beach and rooting mounds, turtle tracks on beach, dust storm over salt flats, close up of shells on beach, many dead tree trunks on shoreline, dead trees covered in vines on shoreline, aerial view of wired fence going down beach into the intertidal region, three brolgas walking on beach, jabiru standing in a small lagoon near the shore, field of rotted mangrove tree stumps, large flock (> 30) of brolgas flying over mangroves and flats, brolgas on the shoreline at edge of grey mangrove forest, winding estuary lined with mangroves with large patches of die back, crocodile lying on muddy foreshore. Day 15

Both surveys were conducted during lower tidal levels where this was logistically feasible to do to gain the greatest visibility of the shoreline intertidal vegetation – positioned between the mean sea level and highest tide levels.

Limitations of the data: The original aerial imagery data was reprocessed for presentation on the web. The the original aerial photos (which were 6144x4080 pixels) were down-sampled (3000x2000 pixels) and compressed (85% JPEG quality) to shrink the dataset size and make rapid previewing of the imagery much faster. This compression of the images reduced the

This dataset contains detailed inventories of 7 large plots of mangrove forests in the Utría National Park in the Colombian Pacific Coast. The inventory consists of individual geo-referenced tree masks for the endemic Pelliciera rhizophorae species (/pelliciera_trees/Pelliciera.shp), and area coverages for the Rhizophora mangle species, as well as Mud and Water areas (/other_classes_coverage/.tiff). For each individual tree of the Pelliciera rhizophorae species we provide the predicted height, crown diameter and crown area (/pelliciera_trees/trees.csv). We also provide the cover area of the other predicted classes (/other_area_coverage/area_coverages.csv). The inventories were automatically produced with trained Artificial Intelligence (AI) algorithms. The algorithms were trained with orthomosaic images and digital surface models (DSMs) produced from Unoccupied Aerial System (UAS) imagery with Structure-from-Motion software, both paired with expert annotations of the trees and areas (/annotations/.shp). In this dataset we provide all the input data for the algorithms, as well as the predicted geo-referenced data products, such as: predicted Pelliciera rhizophorae tree masks, Rhizophora mangle areas, Water areas, Mud areas, canopy height models (CHM), digital elevation models (DEM), digital terrain models (DTM) and various ancillary images. We also provide the initial orthomosaic files (/orthomosaic.tif) and the DSM files (/DSM.tif), that were produced with SfM software Agisoft Metashape v1.6.2 from the aerial footage captured in 2019 (19–22 February) using two consumer-grade UASs: the DJI Phantom 4 and DJI Mavic Pro (SZ DJI Technology Co., Ltd—Shenzhen, China). The DJI Phantom 4 has an integrated photo camera, the DJI FC330 and the DJI Mavic Pro was equipped with the integrated DJI FC220. The flights were programmed to follow the trajectories in an automated mode by means of the commercial application "DroneDeploy". Ground control points (GCPs) were positioned in the field, and their geographic location was acquired. We used two single-band global navigation satellite system (GNSS) receivers: an Emlid Reach RS+ single-band real-time kinematics (RTK) GNSS receiver (Emlid Tech Kft.—Budapest, Hungary) as a base station, and a Bad Elf GNSS Surveyor handheld GPS (Bad Elf, LLC—West Hartford, AZ, USA). RINEX static data from the base station was processed with the Precise Point Positioning Service (PPP) of the Natural Resources of Canada, while rover position was processed using the RTKLib software through a post processed kinematics (PPK) workflow. The final absolute positional accuracy of the products is below one meter because the results of the PPP workflow has a positional accuracy between 0.2 m and 1 m.

This polygon spatial dataset provides new information on mangrove communities in Darwin Harbour, Shoal Bay and Charles Point. The survey area covers approximately 32,000 hectares of mangrove and salt flats. The purpose of the project was to update the original mapping undertaken in 1996 at a finer scale of 1:8,000 and extends from Bynoe Harbour to Adelaide River. This project was funded by the INPEX-led Ichthys LNG Project as part of the Darwin Harbour Integrated Marine Monitoring and Research Program (IMMRP). This mapping now forms the base line for on-going mangrove monitoring in the IMMRP region

Surface water concentrations of ammonium, nitrite, nitrate, phosphate, DON, DOP, chlorophyll a, were sampled monthly in two tropical, tidally-dominated creeks in north Queensland for 30 months between October 1993 and May 1996. Climatic factors (rainfall and temperature), and physico-chemical information from the creeks (salinity, pH, DO, temperature) were analysed using ANOVA and multiple regression. Seasonal and climatic fluctuations were significantly correlated with fluctuations in several of the observed parameters. The dynamics of salinity at upstream and downstream locations in the creeks was the major factor which had a significant effect on the dynamics of the nutrient and chlorophyll a concentrations. Rainfall in the catchment area feeding these creeks is also significantly correlated with associated fluctuations in concentrations of dissolved nutrients and chlorophyll a. Natural fluctuations in climatic events, as demonstrated here, need to be taken into account when assessing the relative impact of human activities upon the same ecosystem.. To identify potential environmental impact of prawn farm aquaculture discharge on adjacent tidal mangrove creeks and forests. This project was conducted under contract from Mossman Central Mill Co. Ltd. Research was centred on Muddy Creek adjacent to the Sea Ranch Pty Ltd prawn aquaculture facility.Additional information in database: monthly mangrove forest litter fall, mangrove soil salinity, mangrove soil nutrients.

Land-use change in the coastal zone has led to worldwide degradation of marine coastal ecosystems and a loss of the goods and services they provide. Restoration is the process of assisting the recovery of an ecosystem that has been degraded, damaged, or destroyed and is critical for habitats where natural recovery is hindered. Uncertainties about restoration cost and feasibility can impede decisions on whether, what, how, where, and how much to restore. Here, we perform a synthesis of 235 studies with 954 observations from restoration or rehabilitation projects of coral reefs, seagrass, mangroves, saltmarshes, and oyster reefs worldwide, and evaluate cost, survival of restored organisms, project duration, area, and techniques applied. Findings showed that while the median and average reported costs for restoration of one hectare of marine coastal habitat were around US$80 000 (2010) and US$1 600 000 (2010), respectively, the real total costs (median) are likely to be two to four times higher. Coral reefs and seagrass were among the most expensive ecosystems to restore. Mangrove restoration projects were typically the largest and the least expensive per hectare. Most marine coastal restoration projects were conducted in Australia, Europe, and USA, while total restoration costs were significantly (up to 30 times) cheaper in countries with developing economies. Community- or volunteer-based marine restoration projects usually have lower costs. Median survival of restored marine and coastal organisms, often assessed only within the first one to two years after restoration, was highest for saltmarshes (64.8%) and coral reefs (64.5%) and lowest for seagrass (38.0%). However, success rates reported in the scientific literature could be biased towards publishing successes rather than failures. The majority of restoration projects were short-lived and seldom reported monitoring costs. Restoration success depended primarily on the ecosystem, site selection, and techniques applied rather than on money spent. We need enhanced investment in both improving restoration practices and large-scale restoration. Database on cost and feasibility of marine coastal restorationThis database represents the core part of the synthesis review "The cost and feasibility of marine coastal restoration". It contains information on cost and feasibility of restoration projects worldwide. The database is divided into five sections according to each investigated ecosystem: coral reefs, seagrass, mangroves, saltmarshes, and oyster reefs. Each ecosystem-specific database section contains the full reference, general information about the publication and project, the restoration action undertaken, species involved, location, a description on the type of cost reported, information on funding sources, project duration (in years), the area restored in hectare (ha), the converted restoration cost in 2010 US$ ha-1, feasibility information (including reasons for success or failure), and restoration success in terms of % survival of restored organisms. For coral reefs, we also accounted for pre-transplant (i.e. survival of coral spat/larvae in culture before rearing them in nursery or out-planting), transplant (i.e. survival of coral fragments during nursery period), post-transplant (i.e. survival of coral fragments after out-planting to the reef) survival as well as for the overall survival averaged over the former three categories. See ‘Approach’ of the publication for a detailed database description and Table A1-A5 in Appendix A for a detailed summary of the database information.Restoration_database.zip

Assessing biodiversity in marine nearshore ecosystems is crucial for effective management, especially in the context of climate change and overexploitation of marine resources. Conventional methods often fall short in providing comprehensive information for managing seagrass ecosystems. However, the emergence of environmental DNA (eDNA) techniques has transformed the field by enabling non-invasive surveys that are cost-effective and provide detailed information with high resolution. In this study, we utilized eDNA to assess fish diversity and compared its effectiveness to conventional techniques such as catch assessment surveys and underwater surveys. We sampled three habitats (A: mangrove-seagrass, B: seagrass only, and C: coral-seagrass) with 4 replicates. Site A recorded 8 fish species, site B had 16 species, and site C, characterized by coral and seagrass habitats, exhibited the highest fish diversity with 45 species (mean H' index = 2.455), underscoring its ecological importance. To ensure accurate taxonomic identification, we utilized an updated MiFish reference database containing a larger number of fish species compared to the initial library. This expanded reference database with 9,569 fish species, facilitated more precise identification and enhanced the reliability of our findings. Notably, the eDNA technique outperformed conventional methods by detecting 23 additional fish species that went undetected using traditional surveys. Moreover, our study documented five fish species previously unknown to occur within the study region, further emphasizing the value of eDNA analysis in uncovering hidden biodiversity. These findings strongly advocate for integrating eDNA techniques into the monitoring and assessment of biodiversity in shallow tropical habitats of the Western Indian Ocean. By leveraging eDNA surveys, we can gain valuable insights into fish diversity, discover hidden species, and make informed decisions for the conservation and management of these ecologically significant areas. Methods Study site Located in Kenya’s South Coast region and covering ~7 km2, Gazi Bay’s seagrass meadows are surrounded by a dynamic fishing community (Hemminga et al., 1995; Tuda & Wolff, 2018; Pendleton et al., 2012). It is located, (4°25’S, and 39°30’E), ~55 km from Mombasa City. The bay is shallow with a mean depth of ~5 m, ~1.75-3.5 km wide and 3.25 km long with a surface area of ~10 km2 (Bouillon et al., 2007)⁠. Being a shallow tropical coastal water system (Musembi et al., 2019)⁠, it is surrounded by fringing mangrove forests on the landward side, coral reefs sheltering the bay from the eastern seaward side, freshwater inflow from two rivers and extensive seagrass bed on the shallow continental waters (Figure 1). The bay opens into the Indian Ocean through a relatively wide but shallow (3-8 m deep) entrance in the southern part and are two creeks (Western and Eastern creeks). The western creek is characterised by two freshwater inflows: River Kidogoweni to the north and River Mkurumunji to the west. Among the most active landing sites on Kenya's South Coast with dominance in fishing and fishery-related activities, Gazi Bay has long supported small-scale artisanal multi-species and multi-gear fishing (Kimani et al., 1996; Musembi et al., 2019). Samples and data collection All reusable apparatus and reagents used in this study were sterilized by autoclaving at 121°C for 15 minutes. Heat-labile apparatuses were UV sterilized for 1 hour, while heat-labile reagents were filter-sterilized using a 0.22 µm nitrocellulose filter membrane. All working surfaces and equipment were decontaminated using 10% bleach and 70% ethanol. Sample processing including DNA extraction, quantification, and amplification, was performed by a single individual in separate rooms. The sampling activities were conducted on 12th November 2020, and started just before the ebb current. The sampling scheme was customized following the guidance of the eDNA Society's sampling standardization method (Miya & Sado, 2019). Three sampling sites (10m*8m transect) within the bay were identified and labeled as Site A, B, and C (Figure 1) with their respective GPS coordinate (Table S1). The sampling sites were selected based on the proximity between seagrass and other habitats. Site A represented the seagrass-mangrove habitat, Site B represented the seagrass-only habitat, and Site C represented the seagrass-coral reef habitat. At each site, we randomly collected 1-liter water samples from the sea surface using a sterile Nansen bottle in four replicates at 2-minute intervals, as recommended by Ficetola et al. (2015). In addition to the sampling process, three 0.5-liter bottles filled with nuclease-free water were intentionally left open during the collection to serve as negative controls. These control samples were handled in the same manner as the actual samples, including exposure to the surrounding environment, but without any target organisms. By including these negative controls, we could monitor and account for any potential contamination or false positive results that might arise from the sampling and laboratory procedures. The physical-chemical parameters of the seawater were measured, and the description of the habitat was recorded (see Table S2). The sampling process took approximately 10 min per site. The collected water samples were immediately preserved in a cooler box with ice packs and transported to the molecular biology laboratory at the Kenya Marine and Fisheries Research Institute (KMFRI) for filtration. Once the seawater sampling was completed, an underwater visual survey and multi-gear fish catch survey followed up immediately. Underwater visual surveys were conducted to directly observe fish species present in the study site covering a predetermined transect of 30m * 30m coinciding with the seawater sampling points. Divers equipped with snorkels visually surveyed the underwater habitats, including seagrass beds, mangroves, and coral reefs immediately after seawater sampling. They carefully recorded the fish species observed and their abundance. Multi-gear fish catch survey in this study involved the use of various fishing gears, including basket traps, hand lines, and reef seines. This was conducted by the local fishers as part of their routine work. Basket traps were deployed by submerging them in submerged seagrass locations within the bay for 6 hours to capture fish. Hand lines, consisting of a line with a baited hook, were used to catch fish manually. Reef seines, which are large nets with weights and floats, were dragged along water columns to capture fish. These fishing gears were deployed in different locations and depths within the bay guided by the fishers to sample the fish population. Catch landing was carefully documented and four voucher specimens of the most dominant fish (Siganus sutor (Valenciennes, 1835)) in the landed catch were obtained and preserved in a cooler box.

DNA extraction, amplification and library preparation

The water samples were processed using a manifold filtration system, and sterile 0.45µm nitrocellulose filter papers were used to filter the samples. These filters were then stored at -80°C until further processing. Total genomic DNA was extracted from the filter papers, as well as from four fish voucher specimens (Siganus sutor (Valenciennes, 1835)) and three DNA extraction negative controls. The DNA extraction was performed using a CTAB-based method, following the protocol described by Miya and Sado (2019). To assess the concentration and purity of the extracted DNA, spectrophotometry was performed using an Eppendorf Bio Spectrometer with software version 4.3.5.0. The DNA samples were evaluated for their quality and quantity. For amplification of the hypervariable region of the 12S rRNA gene, a universal primer pair MiFish-U-F: GTCGGTAAAACTCGTGCCAGC and MiFish-U-R: CATAGTGGGGTATCTAATCCCAGTTTG was used (Miya et al., 2015). The expected amplicon length was approximately 172 bp, ranging from 163 to 185 bp. In addition, specific primers F1: TCAACCAACCACAAAGACATTGGCAC and R1: TAGACTTCTGGGTGGCCAAAGAATCA (Tabassum et al., 2017) targeting the Cytochrome C oxidase subunit I were used for the identification of the voucher specimens. The amplification reaction was conducted in a 12 μl reaction volume comprising the following components: 6.0 μl of 2 × KAPA HiFi HotStart ReadyMix (KAPA Biosystems), 1.4 μl of each primer (5 μM primer F/R), 2.6 μl of sterile distilled water, and 2.0 μl of DNA template. The amplification reaction was designed in accordance with Miya et al., (2015) while amplification was performed with an initial denaturation step at 95°C for 5 min, followed by 35 cycles of denaturation at 95°C for 30 sec, annealing at 55°C for 30 sec, extension at 72°C for 1 min, and a final extension step at 72°C for 10 min. The PCR products from three rounds of amplification were pooled together. Negative control samples from the field, as well as from the DNA extraction and amplification steps, were also pooled into one sample. The pooled samples, including the amplicons and the negative controls, were sent to Inqaba Biotechnical Industries, a commercial next-generation sequencing (NGS) service provider in Pretoria, South Africa, for sequencing. The amplicons were purified, end-repaired, and ligated to Illumina-specific adapter sequences using the NEBNext Ultra II DNA library prep kit. After quantification, the samples were individually indexed using NEBNext Multiplex Oligos for Illumina (Dual Index Primers Set 1), and an additional purification step was performed using AMPure XP beads. The libraries were quantified using Agilent Technologies 2100 Bioanalyzer, normalized, and sequenced on the Illumina MiSeq platform using a MiSeq v3 (600 cycles) kit. Additionally, the four voucher specimens were subjected to Sanger sequencing for further confirmation and

The database contains surface reflectance values from 147 Sentinel-2 images for monospecific red (Rhizophora mangrove), black (Avicennia germinans) and white (Laguncularia racemosa) mangrove forests at the La Mancha site (19° 35' 12'' N, 96° 23' 09'' W), Veracruz, in the Gulf of Mexico, for the period 2015 to 2021. Data are labelled by species and arranged in columns including blue (band 2 - 0.490 µm), green (band 3 - 0.560 µm), red (band 4 - 0.665 µm), red edge (bands 5 - 0.705 µm, 6 - 0.740 µm, 7 - 0.783 µm and 8A - 0.865 µm), near infrared (band 8 - 0.842 µm) and shortwave infrared (bands 11 - 1.610 µm and 12 - 2.190 µm), all with a spatial resolution of 10 metres. Mangrove species distribution was determined by creating masks in QGIS, using as reference an orthophoto taken with a DJI Mavic Mini 2 in July 2021 and 100 field control points.

Activation date: 2018-05-16
Event type: Storm

Activation reason:
Support monitoring of recovery after the passage of the Category 5 Hurricane Matthew on the 4th of October 2016. Six areas were selected to analyse several environmental aspects, including agricultural activities, forest in protected areas, mangrove areas, and finally coastline modifications. The geographical focus of this work is the Western area of the Grand’Anse, Haiti, which was severely affected.The aim is to build a comprehensive database to support recovery aid organizations in recovery monitoring of the critical resources destroyed. This information is to be used by local governmental organisation and users.Proposed solution and results:Map of tree-cover before and after Hurricane Matthew and, documenting damage assessment for parklands and mangroves areas.Map agricultural activities before and after Hurricane Matthew including landscape changes within agricultural areas.Map the coastline before and after Hurricane Matthew to indicate coastal erosion due to the wave action of the storm.

A better understanding of the biogeochemical cycling of nutrients entering Florida Bay is a key issue regarding the restoration of the Everglades. In addition to precipitation, the other major source of freshwater to Florida Bay is from Taylor Slough and the C-111 Basin in the northeast section of the Bay. While it is known that these areas deliver significant amounts of N to the Bay, a significant portion of this is in the form of dissolved organic N (DON). The sources, environmental fate and bioavailability to microorganisms of this DON are however, not known. Should this DON be readily available, any increased load as a function of restoration changes might have an impact on internal phytoplankton bloom dynamics. No significant flocculation or precipitation of DOM occurred with increase in salinity, meaning that terrestrial DOM does not get trapped in the sediments but stays in the water column where it subjected to photolysis and advective transport. Sunlight has a significant effect on the chemical characteristics of DOM. While the DOC levels did not change significantly during photo-exposure, the optical characteristics of the DOM were modified. The environmental implications of this are conflicting: photo-induced polymerization may stabilize the DOM by reducing its bioavailability while photolysis may make the DOM more labile. Overall, DON bioavailability was relatively low in this region. Even though the amount of DON loaded to the bay may be significant, the fraction of DON available for microbial cycling is much smaller. The amount of N supplied by recycling may be a significant portion of the total DIN pool. All this must be considered in context with the proposed CERP modifications to flows. As of the latest initial Comprehensive Everglades Restoration Project (CERP) update, the flows to Taylor Slough and C-111/Panhandle Basis are not predicted to change very much from base conditions. Therefore we do not expect any great increases in TN loading in this region. In contrast, the proposed flow increases to the Shark River Slough are large and may have significant effects on transport of DOM to the Southwest Florida Shelf. We believe that future efforts in DON characterization and bioavailability should be concentrated in this area.