Environmental and climate stress index

Locating converging environmental and climate stresses 

The United Nations Environment Programme (UNEP) along with Earth Blox and scientists from the University of Edinburgh are working to develop an early warning system to identify where climate change could impact areas already affected by natural disasters or conflict. 

The tool, known as Strata: Earth Stress Monitor, is a platform that will use data captured through satellite imagery and cloud computing to improve the mapping of environmental and climate stresses globally. More specifically, this tool will reveal where harmful climate change overlaps with other environmental stressors, such as war, temperature, noise and crowds.

In 2020, Strata was awarded grant funding and a support package through the GEO-GEE program run by Google Earth Engine, GEO and EO Data Science. The EO Data Science team is providing essential Google Earth Engine (GEE) training, support and billing capabilities to the Strata team to help integrate GEE into their platform and achieve their project outcomes. 

Making informed decisions with Earth Observation (EO) data

The challenge faced by the in-country United Nations (UN) teams is the ability to make informed decisions on a policy and programme level. This, in turn, impacts each team’s ability to provide environmental stewardship, improvements to human wellbeing and the protection of livelihoods of citizens within their countries.

The in-country UN teams require a simple system that will allow them to rapidly determine where environmental and climate stresses are converging and the accuracy of those indicators. Hence, there is a need for easy access to advanced geospatial analytics and EO data. 

Accessing Google Earth Engine datasets

The GEE license provided by EO Data Science will allow the Strata team to experiment with high-quality resources and to test various algorithms for environmental and climate risk assessments. Additionally, the access to technical expertise provided by Google and EO Data Science will help the Strata team when transferring their scientific methodologies into practical algorithms to visualise stress hotspots in Strata.

GEE also provides access to a vast range of environmental, climatic and wider geospatial datasets that contribute to hotspots of environmental and climate stresses. These data sets are global and, in many cases, cover extensive time periods. Such a repository of data will enable the team to offer a solution that can be tailored by users to meet their specific requirements and scenarios. 

How Strata will help reduce climate and environmental risks

The Strata platform will assist UN country teams to improve how they use geospatial and Earth Observation (EO) data to assess, and ultimately reduce climate and environmental risks. Through code-free and effortless access to planetary datasets, Strata will become a crucial tool that promotes sustainable livelihoods and facilitates more informed decision making at both a policy and programme level.

The UNEP and their teams across the globe will be the end users, so Strata will be co-designed by them to ensure it is fit for purpose and can be tailored to meet specific situations in their countries, while operating alongside their existing methodologies and platforms.

EO Data Science's role in the GEO-GEE program

EO Data Science partnered with Google Earth Engine and the Group on Earth Observations to launch the GEO-GEE Program in July 2020. Across 22 countries, 32 projects were selected for the program which offers $3 million USD towards product licenses and $1 million USD in technical support from EO Data Science to help operationalise their science as they strive to tackle the world’s biggest sustainable development challenges. So far EO Data Science has: 

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Conducted 38 Google Earth Engine training courses attended by 680 program participants

Resolved over 105 support tickets with a live support desk

Organised five virtual meetups for projects across the world to exchange knowledge and network with one another

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