Monitoring forest dynamics in Costa Rica
Deforestation and forest degradation are responsible for approximately 11% of global carbon emissions. That is more than the entire global transportation sector and second only to the energy sector.
The National Center for Geoenvironmental Information (CENIGA) within the Ministry of Environment and Energy (MINAE)
in Costa Rica and their partner organisations are working to operationalise the change detection algorithms that are used to accurately detect deforestation and forest degradation.
Further, they are developing an early warning system to provide near real-time alerts of deforestation to mitigate greenhouse gas emissions, reduce illegal deforestation activities and contribute to the United Nations REDD+ programme
Through the GEO-GEE Program
run by Google, GEO and EO Data Science, the EO Data Science team is providing CENIGA with essential Google Earth Engine training, support and billing capabilities.
Detecting small deforestation patches
The team at CENIGA want to address Costa Rica's challenge of estimating precise rates of deforestation, forest degradation, and reforestation using sample-based area estimation. Detecting these activities in Costa Rica is particularly complex because they occur in relatively small patches that are dispersed throughout the country.
Building change maps with Google Earth Engine
Costa Rica seeks to develop an operational production pipeline using Google Earth Engine (GEE) to process both optical and radar time series data. The pipeline will become part of a new subsystem of Costa Rica’s National Land Use, Land Cover and Ecosystems Monitoring System (SIMOCUTE).
The optical and radar time series data will be used to produce the change maps to support sample-based area estimation and generate near real-time detection alerts of deforestation.
With GEE, the CENIGA team can access vast archives of pre-processed imagery and radar data, and collaborate in an environment that facilitates knowledge sharing. Using GEE also enables them to process these data at lightning speeds in order to do rapid prototyping and testing.
Accurate change detection maps
This project will refine and operationalise the analysis of Earth Observation (EO) data to produce more accurate change detection maps. These maps, in conjunction with the system for sample-based area estimation, will improve the precision of estimates of greenhouse gas emissions that are a result of deforestation and forest degradation.
Furthermore, the CENIGA team is developing an early warning system that will detect deforestation events in near real-time and provide information to law enforcement to enable rapid response when illegal activities are detected. Both mapping systems will provide information on where deforestation, forest degradation, and restoration activities are occurring to inform decision makers and identify the drivers of illegal activities.
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:
Conducted 38 Google Earth Engine training courses attended by 680 program participants
Resolved 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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