Armed with new data, but with no way to use it
Tropical dry forests are subject to some of the highest rates of deforestation and degradation around the world. These ecosystems are particularly at risk due their fragility and the high demand for forest goods and services, which are required to support the livelihoods of large numbers of the world’s poorest people. Indeed, African miombo forests alone, one of the habitat types captured in the biome, are thought to contribute to the livelihoods of more than 100 million people in urban and rural areas on the continent. Despite their importance, tropical dry forests are among the least studied of the world’s forested ecosystems. Improved forest management requires better approaches for monitoring forests and assessing forest degradation.
Recent major breakthroughs in satellite earth observation (EO) data provision provide unprecedented views of the Earth and present an opportunity to address existing limitations in forest monitoring capabilities. Satellites launched within the past several years by the European Space Agency (ESA), NASA, and US Geological Survey (USGS) have vastly increased the amount of data available to researchers and policymakers – yet very few new tools have yet been developed and institutionalised within government processes in order to take advantage of this new data source.
In 2014 and 2015, the European Space Agency (ESA) launched two satellite missions – Sentinel-1 and Sentinel-2 – which are the first part of the Copernicus programme aimed at responding to the challenges of monitoring the global environment. Both satellites are revolutionary in terms of wide coverage, high spatial resolution, and frequent repeat coverage, and Sentinel-2 provides a unique wide-range of spectral measurements. These satellites, among others, represent a valuable new source of information for monitoring our world.
Meeting the challenge
Together with the Global Environmental Facility (funder), the World Bank (coordinator) initiated the Satellite Monitoring for Forest Management (SMFM) project as a way to develop new methods and improve global knowledge about tropical dry forest ecosystems. Equally important is building capacity in countries around the world to deploy these tools in pursuit of their own forest management commitments and goals. Pilots have been launched in Mozambique and Zambia (with another country forthcoming) in order to test out the newly developed tools and methods. LTS, part of the NIRAS group, was contracted to lead a technical team with the following objectives:
- Develop new satellite EO methods for monitoring tropical dry forest landscapes and forest degradation assessment.
- Produce dry forest and forest degradation map products for sub-national areas, suitable for forest monitoring and management & planning purposes.
- Improve national technical capacity for monitoring of tropical dry forest landscapes and forest degradation assessment through training in new satellite EO methods and data.
- Enhance global knowledge of new satellite EO methods and analysis through capacity building and south-south knowledge exchange (SSKE) programmes.
The intended project outcomes will equip dry forest countries around the world to create more precise accurate comprehensive sustainable forestry management plans, including helping them meet their obligations under the United Nation’s Reducing Emissions from Deforestation and Degradation in Developing Countries (REDD) programme.
Developing methods and tools from the ground up
A first step in creating the most useful tools for EO is an assessment of what current capacity and methods exist. Therefore, LTS has reviewed the present state of the art by conducting a gap analysis at national as well as global level, with an emphasis on establishing limitations in present capacity as pertains to satellite EO monitoring of tropical dry forest landscapes. Drawing upon this review, as well as the new technical capabilities of the ESA’s satellites and other relevant datasets, we will design new methods to monitor ecosystem degradation.
The products developed will be consistent with the framework provided by the Global Forest Observations Initiative (GFOI), and we will showcase the tools at the 2019 GFOI Plenary in April. Perhaps more fundamentally, it will be important to help define ‘degradation’ and establish related proxy indicators in order to allow for a more consistent understanding of this complex concept. Finally, during the development process, it will be necessary to validate our findings by in-situ data collection, ensuring that our products yield reliable data and information.
Some of the tools SMFM is creating include land cover/use mapping to significantly streamline and improve the processing of satellite imagery to create cloud-free images; above-ground biomass estimation, which also compares biomass year-on-year and identifies areas of change; and deforestation and degradation mapping, which uses dense satellite data and machine learning to generate maps estimating current rates of deforestation and degradation based on historic change data– a notoriously challenging prospect – while also identifying causes and drivers – an exciting new possibility which remains experimental.
Deploying new capacities in critical places around the world
Creating methods and tools to take advantage of these new data sources is only effective if those tools are deployed. In a first stage, capacity-building plans are being implemented in Mozambique and, Zambia, with a third country to follow.
This effort will integrate multiple training events with SSKE so that all participants have mastery of the fundamentals of the process as well as specific knowledge of how to utilise the methods developed by LTS during the initial phases of the project. The guidance developed for these capacity building initiatives will also be available for other actors in sustainable forestry management worldwide in order to most effectively capitalise on the newly developed tools and to help ensure their uptake and suitable use in the future.
Read more about the current status of the SMFM project here.