(Feature image: Adam Wilson)
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.
The project is one of the 32 selected as part of the Group on Earth Observations (GEO) - Google Earth Engine (GEE) Program that provides funding to tackle environmental and social challenges using open Earth data.
We spoke with the project’s Geospatial Data Scientist, Glenn Moncrieff, to learn more about their project and how they will be using GEE to characterise non-forest vegetation types.
Glenn, can you describe the challenge your team wants to tackle with this project?
South Africa has strong, progressive policies and legislation that govern the management of key natural resources such as biodiversity, water and carbon. However, the implementation and enforcement of these policies is marred by the lack of readily available, reliable and up-to-date information on the current state of ecosystems.
Habitat loss and invasion by alien species poses 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.
How will your project address these problems?
We want 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.
The key contributions that our near-real time satellite-based observation system will include are:
- Identifying recently burnt areas that are not mapped by managing authorities or detected by remote sensing products
- Mapping where vegetation has been damaged or cleared
- Locating alien plant species invasions
- Detecting areas where high plant mortality has occurred due to drought or disease
An online web-application displaying these layers will be regularly accessed by regional land management and decision-making authorities.
How will this help the conservation community in South Africa and the world?
The information we collect will be critical to making decisions about where to devote extremely limited resources for the management and conservation of protected lands. For example, the cost-benefit analysis of clearing alien vegetation is driven by the location and density of alien plants and is used to determine where contractors are deployed first for clearing.
Currently, reporting of unlawful vegetation removal in the region occurs on an ad-hoc basis, often relying on public complaints received by the provincial Department of Environmental Affairs and Development Planning. If a member of the public does not stumble upon an environmental crime by change, years may go by before it is detected and reported. However, an automated detection of anomalous vegetation loss could instead form the basis for a complaint report, and lead to further investigation.
How does GEE help you achieve your project-related goals?
To be effective, model predictions must be compared regularly to satellite data. The cost of data transfer, storage and compute 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.
How will the GEO-GEE funding help your project?
GEE is licensed only for research, education, and nonprofit use. But to ensure that the tool we produce is sustainable in the long term it needs to be deployed as an operational tool with a view to commercialise it. The license to use GEE as an operational product from the GEO-GEE program will help build a monitoring system that has longevity and will bridge the gap between research and decision-making tools.
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, which supports GEO member countries to operationalise their science as they strive to tackle the world’s biggest sustainable development challenges.
In July 2020, 32 projects across 22 countries were selected into the program which offers $3 million USD towards product licenses and $1 million USD in technical support from EO Data Science. This funding and support will help these projects tackle global challenges using open Earth data. Read the announcement and list of winners here.
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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