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Hello! We are excited to bring you CQuest.Earth (Alpha), a prototype application that will allow you to begin harnessing insights into your current NCS projects and potential project areas around the world. If you would like to read more about how this platform was built please check out our CTO Julian Kremers’ blog posts on our website.
Why we built this?
Why we built this: CQuest is tackling the problem of expensive and lagging metrics into NCS projects for scouting and MRV. CQuest.ai is our remote sensing and machine learning foundry where we will extract useful, repeatable, and affordable insights from various data sources. These insights will be delivered to you, our customers, through CQuest.Earth. The current version of CQuest.Earth allows users to easily extract vegetative insights and knowledge from some of the MODIS datasets. We will regularly improve and update CQuest.Earth and plan to add additional features in the near future. CQuest.Earth is currently completely free to use. We just ask that you spend a few minutes to give us your feedback.
Are you interested in learning about degrading or non-degrading landscapes in current or potential project areas? Our tool allows you to easily access NDVI data to determine vegetation trends over the past 20 years.
Without further ado, let's jump in!
The Landing Page
To begin, please click CQuest.Earth, or copy and paste into your browser. The page you see should look similar to the page below.
The landing page shows a map of the NDVI values. You can use the slider to change the year, and visually track the changes over the years dating back to 2000.
In the left navbar you can see various options
- Metric of interest:
- Geometry type point 1
- Geometry type point 2
- Show Time Series
So, let's do an analysis! This app is designed to be intuitive, but here are some tips and tricks to improve your user experience. If you have any questions feel free to email jakob@CQuest.ai at any time!
- In box 1, select the type of analysis you would like to see. NDVI, GPP, NPP, etc. We will continuously add additional datasets.
- In box 2, select the geometry type. Please remember to select your point, or area before moving on to box 3!
- In box 3 is the same as box 2. Generally it makes the most sense to select the same type of geometry (point, or area) as you did in box 2.
- Once you have selected your analysis click the “Show Time Series Button”
- Depending on the metric you chose, and the size of the selected area, the chart can take a few minutes to render (see image below). If it seems like the system is stuck, please refresh your page!
And after a few moments voilà, we have a very cool comparative chart between the two locations you selected. The chart includes a legend with the color coded location of the point and a trend line to easily show the multiyear trend of the ndvi measurements. Now let's dive even deeper!
Zooming in on the graph, there is a button on the top right corner. Click this to enlarge the chart and see some additional options.
Excellent. Now we have the option to download the graph image as an SVG or PNG file or to access the raw data in a CSV!
Thank you for showing interest in growing the role of remote sensing and machine learning in NCS. As mentioned earlier, this is just a prototype platform that we have built to begin to scratch the surface. Stay tuned for updates and exciting new features in the near future.
We kindly ask that you take a few minutes to provide feedback. This would be a huge help as we continue to grow our solution.
Common Questions about CQuest.Earth
How much does it cost to use CQuest.earth?
CQuest.Earth is currently free. However, we will add premium features in the future that can be accessed through a subscription plan.
What is the spatial resolution?
We are starting out with MODIS vegetation indices which come with a native spatial resolution of 250×250 Meters. When drawing polygons, the values displayed in the chart are averages over the drawn areas. Stay tuned for more satellite sensor products with more fine-grained spatial resolution in the near future.
Where are you sourcing your data?
We aggregate information from various reputable public sources. Among them are datasets from ESA’s Copernicus Satellites, NASA’s Landsat program and many others. We will continuously be adding features to the platform, so stay tuned!
What Engine are you using?
We are using the Google Earth Engine to compute many of our analysis. Have a look at the blog post our CTO and co-founder Julian has written about the topic.