Electronic Coastal Monitoring and Assessment Program – EcoMap

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Half of all world mangroves have been lost over the past 50 years due to a variety of both anthropogenic and natural disturbances. To prevent these dieoffs from continuing, it is necessary to develop a system to monitor and predict regions under highest vulnerability from these various loss drivers.

EcoMap , Electronic Coastal Monitoring and Assessment Program, seeks to establish an interactive, user-friendly portal that can be used by stakeholders and coastal communities to map the world’s most vulnerable regions of mangroves, identify the proximity and urgency of various loss drivers, and provide real-time alerts about local mangrove health.

Our team member, Liza, has been working on this project with member Dr. Lagomasino.

* EcoMap Video Demo 2018


Future Steps:

EcoMap is currently being transferred from using static data to using real-time data in its risk detection algorithms. Real-time alerts for local risk of mangrove loss may then be sent to coastal communities in order enable preventative action in the case of high vulnerability in a particular region.

Structural modeling using LIDAR, TanDEM-X, and SRTM data may also be incorporated into EcoMap ’s risk determination software in order to correlate any changes in mangrove structure or biomass with any observed NDVI changes. These correlations will allow for a more accurate representation of any degradation seen around the world.

In the future, we hope to establish a global network of mangrove monitoring in coastal communities, through equipping schools, decision-makers, and local citizens with the EcoMap software. By giving coastal communities access to the tools needed to detect and predict loss in local mangroves, mangrove resources allocation, policy, and restoration efforts can proceed in a more informed manner.

Workshop: Remote Sensing Applications for Wetland Inventories

Coastal wetlands hold tremendous economic, recreational, and commercial value. These system are highly sensitive to changes in salinity and hydrology that are brought upon by sea level rise, saltwater intrusion, and human manipulation. Remote sensing is an effective tool to monitor changes to wetland ecosystems, and model ecosystem structural and functional parameters.

In June of 2017, we held a brief workshop at the 2017 Society of Wetland Scientists Meeting in Puerto Rico. At this workshop, we exchanged information about the opportunities and challenges associated with the use of remote sensing applications to estimate wetland biomass and discussed the design of mangrove forest inventories using remote sensing products. In particular, we focused on emerging high-resolution radar and optical techniques that can accurately estimate forest structure and be used in stratified sampling protocols.

Topics covered in this workshop included:

(1) Land cover and land cover change,

(2) canopy height and terrain modeling,

(3) stand age and growth rates,

(4) biomass and carbon modeling, and

(5) forest inventory design.

The main goals of the workshop are to:

  1. Familiarize audience with recent remote sensing applications in mangrove forests
  2. Examine remote sensing based forest inventory decision support tools

Overall, the workshop will help to inform the wetland community on the current and emerging observations and research activities in wetland ecosystems that may be applicable to local, regional, and global modeling of wetlands.

Workshop Summary and Resources

Remote Sensing Workshop (pdf w/ notes)

Remote Sensing Workshop (ppt)



Welcome to Mangrove Science

This is Mangrove Science

Mangrove Science pulls together research from a network of forest ecologists, remote sensing scientists, hydrologists, local officials, and members of the community to understand how the landscape of mangrove forests change over time.

Browse the website to find out more about our past, current and pending projects. Mangrove data products including mangrove extent maps, canopy height models, biomass maps, and more will be continually updated on the “Data Portal“.

Check back often or sign up for updates to stay informed of new mangrove science and datasets.

African Blue Carbon

Mangroves and tidal wetlands have the highest carbon density among terrestrial ecosystems. Although they only represent 3 % of the total forest area (or 0.01 % of land area), C emissions from mangrove destruction alone at
current rates could be equivalent to 10 % of carbon emissions from deforestation. Due to their location along highly populated coastlines, they are under significant threat from anthropogenic activity as well as sea level rise. In fact, it is estimated that over 50% of mangrove forests and tidal marshes have been destroyed over the past 60 years, with continued annual deforestation rates of 1% to 2%. The high C sequestration coupled with the high risk of destruction makes mangroves a prime candidate for carbon mitigation initiatives such as the United Nations Collaborative Programme on Reducing Emissions from Deforestation and Degradation in Developing Countries (UNREDD and REDD+).

One of the main challenges to implementing carbon mitigation projects is measuring carbon, efficiently, effectively, and safely. In mangroves especially, the extreme difficulty of the terrain has hindered the establishment of sufficient field plots needed to accurately measure carbon on the scale necessary to relate remotely sensed measurements with field measurements at accuracies of 10% to 20% as required for REDD and other C trading mechanisms. Furthermore, most intensive mangrove sites are established in South-East Asia, Australia and Latin America, with a large gap in knowledge in African mangrove ecosystems.

Our goals are to develop the methodologies for, and produce the initial remote sensing products necessary to implement an MRV (monitoring, reporting and verification) system in Coastal Blue Carbon ecosystems in collaboration with local in country and international partners. We propose to estimate forest change, structure and biomass of mangrove forests to improve carbon management, monitoring and carbon cycle science, and to inform REDD+ and carbon emission mitigation projects in Tanzania, Mozambique and Gabon.

Get more information at carbon.nasa.gov

SilvaCarbon – Bangladesh

Mangrove ecosystems can store large quantities of carbon that are used to determine their global value for programs such as REDD and REDD+. Bangladesh has 710 kilometers of coasts. The coastal zone covers 19 coastal districts in the Bay of Bengal. This includes Bangladesh’s Sundarbans, which is the world’s largest contiguous mangrove forest. Coastal mangrove afforestation initiatives have been in place since 1966 after a devastating cyclone took a massive toll on human lives and destroyed property. Bangladesh is a pioneer in protecting coastal areas from natural disasters and has also been active in promoting urban forestry. The ecosystem services mangrove provides are invaluable. Some of these services include stabilizing the soil, reducing erosion, protecting the shore from natural disasters, sequestering carbon to help with climate change mitigation and adaptation, and providing sustenance wildlife and humans.

SilvaCarbon – Bangladesh

South Florida Coastal Ecosystem Vulnerability

Mangroves represent only 3% of the global forest cover, but the current degradation of pantropical mangrove forests is responsible for approximately 10% of the total carbon emissions from deforestation worldwide (Donato et al, 2011). Beyond being one of the most carbon dense ecosystems due to their high carbon sequestration rates (Donato et al, 2011; Pendleton et al, 2012), mangrove forests and other blue carbon wetlands (e.g., coastal sawgrass marsh) are economically and biologically important from local to global scales (Alongi et al, 2002). The large carbon stocks along with the many ecosystem services they provide, and threats from rising seas, saltwater intrusion, degradation and urban expansion, make mangrove environments globally important ecosystems.

Blue carbon ecosystems store and sequester most of their carbon stocks in peat soils (Donato et al, 2011; Stringer et al, 2015) as long as they can maintain a balance between sediment accretion and sea-level rise (McKee, 2011). Sea-level rise and seawater intrusion pose high-risks of change to mangrove forests and coastal marshes, which can result in extraordinary changes to inundation and salinity that impact both above and below ground carbon cycling (Weston et al, 2006; Bouillon et al, 2008). Plant productivity, community structure, soil stability, microbial activity, and root dynamics can all be affected by these environmental changes. As a result, rapid changes in inundation or salinity brought upon by climate change, accelerated sea-level rise, storm surges, or increased water flow through restoration efforts will collectively have an impact on regional and global carbon cycling.

The main goal of this project will consist of developing a new analytical framework from the fusion of multiple readily available ground, airborne, and spaceborne remote sensing datasets to quantify and predict rapid changes or collapse of the blue carbon landscapes. These types of data are now available or planned over the Florida Everglades as part of other NASA and other institutionally funded research. Spectral reflectance and fluorescence measurements can reveal when vegetation is enduring biophysical stress. Multi-scale lidar and radar measurements provide information regarding the horizontal and vertical physical structure. Combining these datasets will enable us to estimate forest and ecosystem changes, identify areas vulnerable to collapse, and model changes to regional carbon and water cycling to inform current restoration and research efforts in the Everglades.


Summary of Activities to Date

G-LiHT Planning and Coordination
NASA Goddard’s LiDAR, Hyperspectral and Thermal airborne imager (G-LiHT) collected data over Everglades National Park in May 2015. These acquisitions targeted critical vegetation, hydrologic, and salinity gradients that were also areas with existing ground plots in the marsh and mangrove forests. Repeat flights were also conducted in March of 2017 and after Hurricane Irma in December of 2017. G-LiHT data from the study domain will provide a link for upscaling ground data to long-term satellite observations in order to measure temporal landscape dynamics. Combining measurements of vegetation structure, foliar spectra, and surface temperatures using the G-LiHT imager has delivered well-calibrated results and has proven successful in determining forest inventories and individual tree structures. This data is important to examine the differential impacts to the vertical and spatial structure of coastal ecosystems across all of South Florida

Initial Land Cover Maps
Combining 3D mapping methodologies with biophysical spectral responses we can quantitatively access forest and vegetation structure and health. Using both the structure and function of the environment we can identify areas of ecosystem change and whether that change could lead to ecosystem degradation or regrowth. The main goal of this research is to develop a new monitoring framework from the fusion of readily-available ground, airborne, and spaceborne remote sensing datasets to quantify and predict rapid changes or collapse of the blue carbon landscapes. Because of the extensive Landsat archive and recent canopy height models we can assess changes to mangrove ecosystems across the globe, with the ability to compare processes between geomorphological settings.

Preliminary land cover change maps have been generated for the study region, Everglades National Park, for the period of 1993 through 2014. Initial results show mangrove forest lost along the southwestern tip of Florida, suggestive of typical landward erosion. In addition, there also appears to be substantial degradation within interior mangrove areas located in the western and southern portions of the Park . There has been relatively minimal gain in mangrove cover, except for a few areas around the islands of Florida Bay, and near 10,000 Islands.


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