Jellyfish| Scanning for Lifeforms
Scanning for Lifeforms
Mangrove monitoring in climate change
Summary
The goal of the project is to analyze the effect of sea-level rise on the distribution of mangrove in Can Gio district, Ho Chi Minh City, Vietnam. We measured the plant health by using enhance vegetation index (EVI), analyzed the image statistics and classified healthy and unhealthy plants. The same technique was applied for calculating the soil salinity using normalized difference salinity index (NDSI) for classifying saltwater areas. Finally, we combined these indices to detect and identify the change in the distribution of species due to the increase in salinity caused by sea level-rise.
How We Addressed This Challenge
One of the issues that climate change is creating is sea level rise. Sea level rise has many impacts on the environment, especially on biodiversity on a global and regional scale. In Can Gio district, Ho Chi Minh City, we rely on biodiversity for coastal safety. The mangrove forest along the southeastern coast of our city protects the city from flooding when sea level rises. That is why monitoring and protecting the mangrove forest is so important.
We developed a program on Google Earth Engine to classify vegetation, soil, and water to study the change of mangrove forest in coastal Can Gio between the time period from 2015 to 2020. The program analyzed the satellite images to identify mangrove trees in Can Gio. We examined the result from this analysis to study the changes in mangrove distribution. We hope to achieve a better understanding of the effects of sea level rise on mangrove forest from this project.
How We Developed This Project
We are a team of six Space Engineering students. We live and study in Ho Chi Minh City. Our city is one of the regions of Southeast Asia that will receive the worst impact when sea level rises. We love this city and we want to lend a hand in protecting it from the effects of global warming. That is why we care deeply about climate change and mangrove protection.
And what better way to monitor mangrove than to use satellite data? We approached this project from a Remote Sensing / Earth Observation point of view. We learned how to access and analyze satellite data online using the Google Earth Engine platform.
In the course of our project, we encountered numerous problems, but the challenges are worth it. We had to learn how to program in JavaScript in order to use Google Earth Engine. We had to do research about mangrove and sea level rise to determine which satellite and sensor to study. We had to examine and compare the result from mangrove analysis and ocean analysis to study the effect of sea level rise on mangrove distribution change.
How We Used Space Agency Data in This Project
The data we used is from NASA. We accessed the data by programming using the Google Earth Engine platform. We mainly used data products from Landsat 8 - an Earth observation data used for environmental and climate monitoring. We accessed the dataset of the atmospherically corrected surface reflectance from the Landsat 8 OLI/TIRS.
Project Demo
A demonstration of our challenge solution is available at:
https://docs.google.com/presentation/d/1iflsm8XxEozGD7uew_tMqwe98jENTEkzv3q3RHapw4g/edit?usp=sharing
Data & Resources
Data:
https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_SR
Reference:
- Claudia Kuenzer Andrea Bluemel ,Steffen Gebhardt,Tuan Vo Quoc,and Stefan Dech - Remote Sensing of Mangrove Ecosystems: A Review. MDPI Journal (2011)
- Chaichoke Vaiphasa - Remote Sensing Techniques for mangrove mapping
- Daniel M. Alongi - Mangrove forests: Resilience, protection from tsunamis, and responses to global climate change
- Vu Thi Hoai Thu Toshinori Tabata, Kazuaki Hiramatsu Trieu Anh Ngoc, and Masayoshi Harada - Effects of sea level rise and sea dike construction on the downstream end of Saigon River Basic. JARQ (2018)
- https://medium.com/regen-network/remote-sensing-indices-389153e3d947?fbclid=IwAR3fhAxPNE25J-Kz3-D1m7PeRm4pkppkv6Qd4dJ7ZTnmTNWB4RM2kefLXow
- Phuong Ha Tran, Anh Kim Nguyen, Yuei-An Liou, Phung Phi Hoang, and Hung ThanhNguyen - Estimation of Salinity Intrusion by Using Landsat 8 OLI in the Mekong Delta River
- https://www.indexdatabase.de/