Vegetation monitoring in the Cape Floristic Region

The Cape Floral Region, located in the south-western extremity of South Africa, is recognised as one of the world’s ‘hottest hotspots’ for its diversity of endemic and threatened plants. 

Invasive alien species and fires are a huge threat to the region. There is currently no other place on Earth (excluding islands) where alien plants have invaded natural vegetation to such a comparable extent. 

Scientists from the South African Environmental Observation Network (SAEON) are working on a project to build operational models that will characterise the region's vegetation types with near real-time monitoring to help manage fire events and invasive alien species. 

Through the GEO-GEE Program, the EO Data Science team is providing SAEON with essential Google Earth Engine (GEE)  training, support and billing capabilities to help integrate GEE into their project and characterise non-forest vegetation types

Monitoring vegetation in near real-time with GEE

The SAEON project team is working to develop an operational system for monitoring the state of vegetation in the Cape Floristic Region of South Africa in near real-time. This data can be used to report changes to relevant authorities and the public in easily-interpretable and usable formats. 

To be effective, model predictions must be compared regularly to satellite data. The cost of data transfer, storage and computational power to achieve this would make the project impractical and too expensive. Therefore, the greatest benefit of using GEE is the ability to prototype ideas using in-built algorithms and switch between alternative satellite datasets seamlessly. 

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: