Ecosystem Monitoring for Management Application (EMMA)
Project Summary
Ecosystems provide essential goods and services, and maintaining their health is of crucial importance to meet the Sustainable Development Goals (SDGs). Shrubland ecosystems, where trees may be present but not dominant, make up >40% of the global total ecosystem organic carbon and contain a substantial proportion of the world’s biological diversity. Advances in Earth Observation, in situ measurements, and ecological models allow the development of near real-time monitoring tools that report on the state and changes in vegetation, the ecosystem functions, processes and services they drive, and the pressures they face. Unfortunately, to the best of our knowledge, no operational tools of this nature exist for dynamic shrubland ecosystems that are prone to fire and other natural disturbances, short-term (event-driven) variability, and long-term trends. This project is developing an operational system and tools for monitoring the vegetation state of a fire-prone shrubland ecosystem. Our study region is the Cape Floristic Region of South Africa, which contains 20% of Africa’s plant diversity and is a Global Biodiversity Hotspot and UNESCO World Heritage Site. Our main end-user organization is the South African Environmental Observation Network (SAEON), which will further facilitate the adoption of our developed tools by other government departments and conservation organizations in the region. The indigenous vegetation in the study region is threatened by climate change, human-induced habitat loss, and invasion by alien species. Due to a lack of readily available, reliable, and up-to-date information on the state of the ecosystem, our end users face challenging decisions about where to allocate extremely limited human and other resources for the management and conservation of protected areas. This project combines Earth observations (e.g., NDVI and fire data from MODIS), in situ observations, and ecological forecasting models to characterize the spatial and temporal variation of the vegetation state (including structure, productivity, natural disturbance dynamics, and seasonal phenology) for near real-time monitoring and change detection in the study region. We will produce an operational system and tools which leverage iterative ecological forecasting and deep learning to predict natural land surface processes, evaluate near-real-time changes in the vegetation state, and classify the factors causing abnormal vegetation changes. Once completed, the system and tools will support the decisions of our end users by providing various types of near real-time information, such as unmapped fires, locations where vegetation has been damaged or cleared, areas where alien plant species are invading indigenous vegetation, and the locations where high plant mortality has occurred. The system and tools will be deployed on SAEON’s computational infrastructure for operational forecasts and monitoring. Our developed system and tools are readily extensible to similar ecosystems (e.g., the Californian Chaparral, Australian Kwongan, and parts of the Mediterranean Basin), and can be adapted to ecosystems with different dynamics. This project focuses on subsection 2.1 of the ROSES call on measuring and monitoring protected area outcomes.
Project Workflow and Partners:
Our primary end-user is the South African Environmental Observation Network (SAEON), where Co-Is Slingsby and Moncrieff work as scientists. This project will help SAEON develop and share long term ecological research data and decision-relevant information for natural resource management and environmental protection policy-making under several mandates: 1) informing protected area managers of ecosystem changes (GEO Global Ecosystem Initiative); 2) reporting on changes in biodiversity at national and international levels through South Africa’s National Biodiversity Assessment (NBA) and the CBD Aichi Biodiversity Targets (and post-2020 Global Biodiversity Framework); 3) informing the “red listing” of threatened ecosystems (iucnrle.org), which further informs conservation priorities and environmental authorizations in South Africa (NEMA Act 107 of 1998, NEMBA Act 10 of 2004); and 4) identifying areas of established and nascent alien plant invasions. The developed system will help SAEON provide more comprehensive and accurate information for its stakeholders, such as South African National Parks, CapeNature, World Wildlife Fund, and The Nature Conservancy, to improve decisions related to the monitoring and management of protected areas in the region, while also providing a long-term record of ecological change for research purposes.
Project Team
Jasper Slingsby, co-I
Glenn Moncrieff, co-I
Yingjie Hu, co-I
Adam M. Wilson, PI
Development Workshop Overview
On September 9, 2021, we held a virtual workshop at the Fynbos Forum (https://fynbosforum2020.co.za/workshop-03/), an annual meeting of scientists and managers entitled Satellite monitoring of the Fynbos biome: identifying user needs. The workshop was attended by about 80 participants from a variety of organizations across South Africa including the Council for Scientific and Industrial Research, Nelson Mandela University, SAEON, Botanical Society of South Africa, SANBI, SANParks, CapeNature, The Nature Conservancy, DEA&DP Biodiversity Management, and the University of Stellenbosch. At this meeting, the participants selected to join one or more of the following working groups: fire management, monitoring species and ecosystems, invasive species management, hydrology, and illegal vegetation clearing. We then presented the initial ideas of the project and co-designed a set of potential applications/decisions that could be enhanced. Many people within this group offered to provide feedback on the prototype system during development.