The EO Dashboard has multiple global maps for air quality and greenhouse gases in GeoTIFF file format. Your challenge is to create a spatial analysis and time series plotting tool for EO Dashboard air quality and greenhouse gas GeoTIFF datasets.
Our project develops a plotting tool that generates graphs for the CO2 and CH4 data for different regions around the globe. It shows the variations in the level of greenhouse gas emissions and how they impact nature. The current features of the tool include availability of indicators on the global map which shows regions that are under the threat of global warming, ability to hover through points on the map, ability to generate graphs that display regional information of CO2 and CH4, and ability to download the CSV files of CO2 and CH4.
We developed a frontend containing features that gives availability of red indicators on the global map which shows the regions under threat / the regions that contribute the most to global warming, ability to hover through the points on map and hence display the ppm level of greenhouse gases and status of that region in the past 24 hours, plotting tools which generate line charts displaying regional information of CO2 and CH4, and ability to download the CSV files of the greenhouse gases(CO2 and CH4) for both layers of troposphere for a particular region . The plotting tool gives clear visualizations of the greenhouse gases(CO2 and CH4) in different regions(Texas, New Delhi, etc.) and how they change over time using the datasets from NASA, JAXA, and ESA.
These features and visualizations are important because they help track the amount of CO2 and CH4 gases in specific regions and see how they behave over time to predict or study any environmental effects on the regions such as rise in temperature, air pollution etc. They also allow individuals to have access to data-driven results which promotes open science across communities. Scientists/Researchers can also have access to these tools and visualizations which will help them enhance their study on data and climate.
The plotting tool is able to display the map and point out the level of greenhouse gases in different regions i.e. green for low, yellow for moderate and red for high. By clicking at a particular region using the indicator, a user is able to generate a visualization of the amount of CO2 and CH4 in that particular region and how it changes over time. It has the ability to display the ppm level of greenhouse gases and status of the region in the past 24 hrs. A user is also able to download the csv files of the graph generated.
Climate change is affecting our planet very rapidly. It is essential that during this critical time we take advantage of all the resources available to us in order to better understand and track the changes in our climate. The aim of our team is to take advantage of the datasets available and build tools with which the general public can have access to data driven results that directly address the essential greenhouse gases that harmfully impact the environment in different regions. We also hope to provide the users with a user-friendly interface that is easy to navigate and produce effective and clear/accurate results for the users. Through this effort, we hope to promote open science across different communities
Our team chose this challenge because the content area of air quality and data on greenhouse gases which contribute to the understanding of it is a big factor in studying climate. We wanted to explore the datasets available in the hackathon to help us create valuable tools for the dashboard and feed our curiosity of how these datasets can be utilized.
The very first approach in this challenge was to understand the expectations and goals of the problem as a team and brainstorming ways in which we can address the solutions. It significantly helped our team divide the tasks between team members during the course of the challenge which helped us work effectively. We divided our tasks into developing code for the frontend, searching for datasets that we can use for the dashboard, creating codes for developing visualizations based on the datasets we decided to work with and building our presentations and storytelling methods.
We used HTML,CSS and JAVASCRIPT for developing the frontend. We also used python language in the Jupyter Notebook interface to derive graphs and visualization tools on the datasets using numpy, matplotlib and pandas. Lastly, we used google drive, PowerPoint, and Canva to communicate our project and how we solved the challenge.
Our team faced two challenges during the course of the project. We faced difficulties accessing the datasets and couldn’t solidify our technical developments due to time constraints based on the deadline. Although we faced difficulties accessing the datasets, our team was able to communicate these concerns and strategize ways of how we can access the data in csv formats. Our team was finally able to get our hands on the datasets which directly had information on the greenhouse gases we wanted to explore(CO2 and CH4). In addition, our team maintained excellent communication skills by holding meetings everyday and being able to divide the tasks up between the members. All of the tasks were interconnected and through our communication, we were able to complete these tasks as effectively as we could.
We obtained the datasets from GOSAT, for different regions. By monitoring the levels of CO2 and CH4 through these datasets, we would be able to find out the regions under threat(the ones contributing to global warming the highest) in the near future(5 years or more from now)from our mathematical analysis and would be marking those areas as red zones.
We would also be able to monitor climate change, temperature fluctuations of the particular region, effects on crops through datasets obtained from Sentinel-2, Sentinel-5P, and GOSAT.
From the datasets of NASA, ESA, and JAXA we would be able to locate the regions in a particular city that contributes to the highest carbon level by monitoring the greenhouse gases, CO2, temperature through time-series and hence produce the future vision of how possibly the city and its nearby places will be affected if the same mean rate continues.
Additionally we also used Euro Data Cube for analysis and computational purposes.
The following link contains the source code of our solution (Read Me section of the code contains snapshots and demo):
https://github.com/rashafathima/eodash_hackathon
The following link contains the source code of the visualizations:
https://drive.google.com/file/d/13RBERuflUsqARaP-ouvvNdfqTC7ghmo7/view
So far, we have developed a separate application for the plotting which can be further integrated with EO Dashboard.
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This project has been submitted for consideration during the Judging process.