Launching CQuest.Earth

After the inception of CQuest earlier this year, we are proud to launch our new earth observation (EO) platform: CQuest.Earth.

CQuest aims to enable global climate action through data-driven carbon sequestration. We all know that there is too much CO2 in our atmosphere. A bundle of approaches to mitigate this are nature-based climate solutions (NCS), ranging from reforestation and agroforestry to sustainable grazing and conservation agriculture. 

Throughout the past months we talked to several project developers of NCS, who are implementing these projects on the ground. They are always excited to hear of the potential of remote sensing and EO technologies. Yet, they often ask, how they can tap this potential. Unfortunately, there is no simple answer. Despite the growing supply of EO resources and platforms, it still remains difficult for non-technical users to access the information they need. With CQuest.Earth we offer a modest first contribution to make EO data more accessible to end-users, particularly to project developers of NCS. Our apps shall provide a playground for NCS-project developers to test the applicability and feasibility of remote sensing data for existing monitoring, reporting and verification methodologies and offer them first insights into their potential regions of interest.

To kick it off, we start with the “bread and butter” of remote sensing for vegetation monitoring: the normalized difference vegetation index (NDVI). Healthy leaves reflect green light and absorb red light which they use for photosynthesis. Interestingly, they also reflect near-infrared (NIR) light and much stronger than any of the light visible to us. Thus, by subtracting the measurable quantities of red light reflected off vegetation from the quantity of near-infrared light we can obtain a relative data point of plant photosynthetic activity as a proxy for plant health. Normalizing this by dividing it by the sum of the quantity of red and NIR reflectance, we obtain a unitless index generally ranging from 0 to 1 with higher values hinting at more area covered by leaves (leaf-area index, LAI) and better plant health.

While this is a simple quantitative measure that does not provide qualitative information about the plant type or specific nutrient deficiencies, its simplicity makes it a powerful tool for comparison. One can easily compare the NDVI of two different places to claim which one is more densely or healthily vegetated. Likewise, we can observe NDVI time-series to see temporal changes in vegetation cover (upward and downward trends or breaks). To provide you with this functionality, we offer you our first apps on CQuest.Earth. On the landing page you can view a global map of annual NDVI values over the last twenty years (see Figure 1).

Figure 1: CQuest.Earth Landing Page App – MODIS Median NDVI from 2000 to 2020

In the coming months we want to extend our offerings on CQuest.Earth to include other satellite sensors, data products and information services. In our next blogpost we will share how we programmed the first apps on the Google Earth Engine. 

Stay tuned for more details on our tech stack,

Julian, CTO of CQuest

Figure 2: CQuest.Earth MODIS vegetation index time-series App

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