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Part 1: Document Description
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Citation |
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Title: |
Monitoring of the mangrove ecosystem on the coastal of Ecuador during the period 2018-2022 |
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Identification Number: |
doi:10.7910/DVN/RDTUZC |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2023-12-14 |
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Version: |
1 |
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Bibliographic Citation: |
Caiza Morales,Lorena, 2023, "Monitoring of the mangrove ecosystem on the coastal of Ecuador during the period 2018-2022", https://doi.org/10.7910/DVN/RDTUZC, Harvard Dataverse, V1 |
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Citation |
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Title: |
Monitoring of the mangrove ecosystem on the coastal of Ecuador during the period 2018-2022 |
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Identification Number: |
doi:10.7910/DVN/RDTUZC |
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Authoring Entity: |
Caiza Morales,Lorena (Fundación Ecociencia) |
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Other identifications and acknowledgements: |
SERVIR- AMAZONIA |
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Other identifications and acknowledgements: |
ECOCIENCIA |
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Other identifications and acknowledgements: |
CAMBIUM RESEARCH GROUP UVA SORIA |
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Producer: |
Fundación Ecuatoriana de estudios Ecológicos Ecociencia |
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Centro Internacional para la investigación del Fenómeno del Niño |
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Date of Production: |
2023 |
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Distributor: |
Harvard Dataverse |
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Distributor: |
Fundación Ecuatoriana de estudios Ecológicos Ecociencia |
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Distributor: |
Bioversity International and the International Center for Tropical Agriculture |
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Access Authority: |
Ecociencia |
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Access Authority: |
Rodrigo Torres |
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Access Authority: |
Caiza Morales,Lorena |
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Depositor: |
Castaño, Silvia-Elena |
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Date of Deposit: |
2023-12-12 |
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Date of Distribution: |
2023-12-12 |
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Holdings Information: |
https://doi.org/10.7910/DVN/RDTUZC |
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Study Scope |
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Keywords: |
Earth and Environmental Sciences, carbon sequestration, biodiversity, ecosystem services, biomass, ecology, land degradation, agroforestry systems, mangrove cover, mangrove ecosystem, mapping, random forest, vegetation index, carbon sink, deforestation, shrimp farms, cloud computing, ecosystem services, classification, Sentinel 1, Sentinel 2, SAR, Google Earth Engine, GEE, SERVIR Amazonia, USAID, Ecuador, Guayas, Latin America and the Caribbean, Multifunctional landscapes, Mangrove ecosystem |
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Topic Classification: |
mangrove cover, biomass |
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Abstract: |
<p>SERVIR AMAZONIA is part of SERVIR Global, a joint development initiative between the National Aeronautics and Space Administration (NASA) and the United States Agency for International Development (USAID). As part of the SERVIR-Amazonia program, the EcoCiencia Foundation, as a local partner in Ecuador, signed a memorandum of understanding with the International Research Center for El Niño Phenomenon (CIIFEN) to generate various tools and services for comprehensive land management. </p> <p>To contribute to coastal management and facilitate mangrove monitoring, the EcoCiencia Foundation developed MANGLEE, an open, multi-user tool based on Synthetic Aperture Radar data, optical satellite images, cloud computing (Google Earth Engine), and machine learning. </p> <p><b>MANGLEE</b> includes data preprocessing for Sentinel-1 and Sentinel-2, Random Forest classification (mangrove - non-mangrove), distributed change detection in three modules, and a visualization app. MANGLEE's performance was tested in the Ecuadorian mangroves during the period 2018-2022, resulting in 10-meter coverage maps. </p> <p>The maps generated in this service are also available on the APP <a https://lorenacaizamorales.users.earthengine.app/view/mangleeapp> MANGLEE </a> </p> <p>The methodology and the diffusion workshops can be found at: </p> https://sites.google.com/view/mangleetrain/inicio |
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Time Period: |
2018-03-01-2022-12-31 |
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Date of Collection: |
2022-05-01-2022-12-31 |
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Country: |
Ecuador |
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Geographic Coverage: |
Guayas |
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Geographic Bounding Box: |
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Kind of Data: |
Geospatial Data |
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Kind of Data: |
Geographic Data |
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Kind of Data: |
Shape Data |
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Notes: |
<p><b>Methodology:</b></p> <b>MANGLEE</b> compiles and preprocesses optical (Sentinel-2) and synthetic aperture radar (SAR) Sentinel-1 data for a given year, calculates vegetation indexes, then with a training file and the Sentinels 1 and 2 stack, it uses a Random Forest classification to obtain the binary mangrove and non-mangrove map for the selected year, of 10 m spatial resolution. Using two coverage maps MANGLEE detects the changes greater than one-half hectare (gain and loss) pixel by pixel cataloging them in degradation or gain and presents all the results in a viewer that allows to compare the optical image of two different years. These results were evaluated and improved with ground truth data and visual interpretation. The process was repeated for both periods. |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Materials |
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<p><b>Original Publication</b></p> Mapa de Cobertura de manglar de costa de Ecuador 2018 -2020-2022 |
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Label: |
01 MAN_EC_2018.zip |
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Text: |
Ecuador Mangrove Cover Map Guayas 2018 |
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Notes: |
application/zip |
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Label: |
02 MAN_EC_2020.zip |
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Text: |
Ecuador Mangrove Cover 2020 |
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Notes: |
application/zip |
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Label: |
03 MAN_EC_2022.zip |
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Text: |
Ecuador Mangrove Cover Map 2022 |
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Notes: |
application/zip |
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Label: |
Change_EC_18_22.zip |
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Text: |
04 Ecuador Mangrove Change Map 2018-2022 |
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Notes: |
application/zipped-shapefile |