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

Managing fire events and invasive species

Habitat loss and invasion by alien plant species pose the biggest threats to South Africa's native vegetation, and current studies report that 10% of the most threatened vegetation types are converted per decade to other land cover types, often without environmental authorisation (Skowno, Jewitt & Slingsby, 2021). 

Existing tools that describe this land cover change in South Africa are not updated frequently enough to inform conservationists of the underlying drivers of change, or cannot be used for the prosecution of illegal transformation. 

Reference: Skowno, A., Jewitt, D., & Slingsby, J. (2021). Rates and patterns of habitat loss across AUTHORS: South Africa’s vegetation biomes. South African Journal Of Science, 117(1/2). doi: 10.17159/sajs.2021/8182

Photo by Adam Wilson

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. 

Accurately allocating limited resources

EO Data Science provided the license for SAEON to use GEE as an operational product that will help build a monitoring system that has longevity and will bridge the gap between research and decision-making tools. 

The information SAEON collects will be critical to making decisions about where to devote extremely limited resources for the management and conservation of protected lands. 

Photo by Adam Wilson

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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