Forest resources have increasingly been seen as critical resources not only for their economic value but also as carbon storage in the face of increased threats from climate change.
On the other hand, forests also face a number of threats, including from land use practices and direct and indirect effects from climate change. Such threats can threaten their viability in different ecosystems.
Now, new tools are helping to better manage forest resources and the threats they face.
Forest management tools
Forest management tools such as ForEST, a tool created for the state of Maine in the United States, is an example of how machine learning, satellite imagery, and data on forest ecology can be brought together to create better information for resource managers and enable decision support.
The state of Maine estimates that about $400 million in the economy could be affected by spruce budworm, a moth that has larva that feed off mostly fir and spruce trees.
This moth can cause severe damage by causing defoliation and tree mortality.
Using the ForEST tool, which is a form of Intelligent Geosolutions System (IGS) that includes semi-automated image processing and machine learning classification along with Sentinel and Landsat data, managers can create forecast maps of potentially threatening outbreaks of spruce budworms. Maps are created that use input data, along with imagery, to determine forest type, tree species abundance, and measures for canopy disturbance based on current and forecasted spreads of spruce budworms.
Machine learning models can take the data to then estimate how given outbreaks of spruce budworms may harm canopy growth over given years. This includes models such as Support Vector Machines (SVM) and genetic algorithms.
The tool could, for instance, inform that early harvesting of potentially threatened trees could be useful for the timber industry, while allow conservation efforts to also mitigate the spread of the spruce budworm through early warning and informing on where to focus conservation efforts.
The image processing and machine learning software are all part of the Supervised Adaptive Multi-objective Mapper (SAMM) tool, which has been created in collaboration between the University of Maine’s Center for Research of Sustainable Forests (CRSF) and the university’s Advanced Computing Group.
Near real-time monitoring of forests
Monitoring of forests and providing near real-time and historical data are critical to decisions from sourcing areas for carbon credits to determining how forests are impacted by invasive species and land use change. Increasingly, industry is seeing the economic and social benefits of forest monitoring.
The company terraPulse has been focusing on this area, as one example, where their product suite is used to measure satellite imagery and data on woody vegetation and determining tree heights.
For instance, imagery from different periods can be used together to measure if tree heights have changed say 3 to 5 meters while comparing to historical data.
Imagery from the 1980s to more recent periods can be used in providing forest managers a historical perspective. Machine learning algorithms then provide estimates such as wood yield, degradation or thinning, estimates on utility of given forests for wildlife, or even estimates on fire risk.
In the UK, a similar tool was developed that enables local management down to individual trees. Social, economic, or land management goals can be set and monitored using the application.
The cloud-based system can be shared across different devices so that a team managing a given forest can access real-time data, which could incorporate satellite and ground-based data.
Mapping the social benefits of forests
Many tools have focused on economic monitoring and benefits of forest ecosystems. However, some newer tools have begun to look at the social benefits that forests provide that may not have a clear or monitory impact. For instance, this measures the aesthetic or recreational benefits that forests may provide.
One such tool, called Social Values for Ecosystem Services (SolVES), is an open-source tool that is integrated with QGIS. It can be used to provide quantitative information on ecosystem services and can be modified to incorporate different stakeholder interests for forests.
Data, such as surveys on social value of ecosystems, can be incorporated to spatially model relationships between human value perception and underlying environmental characteristics. For instance, people placing a high value on the mental benefits of forests and who can easily access these systems could be measured to demonstrate how many people could benefit from maintaining given forest health.
Another use could be to monitor how accessibility to forest resources could impact public use of given spaces. The benefit of this tool is it allows different social value measures to be estimated for forest ecosystems in conjunction with the more usual physical data, helping resource managers recognize how the public may value what they manage.
What tools such as SolVES attempt to do, in contrast to tools mainly focused on remote sensing or ground-based monitors, is incorporate a social dimension to natural resources, particularly surrounding communities to get a better understanding on how they see given resources and value their protection. This could help managers to best determine strategies that help protect resources by understanding how to mobilize communities and incorporate their interests in management plans.
Current tools for managing and protection against forest threats have increasingly incorporated satellite-based remote sensing and machine learning. Some of these tools are free or can be developed for bespoke projects and areas to monitor.
Researchers have also sensed the need to better capture a human element in forest management, particularly how people experience or interact with their natural resources. These measures might be also important in long-term management of ecosystems.
We might expect more forest management tools that integrate both physical measures of forest health as well as social values and perceptions in the near future.
 For more on Forester, see: https://www.forestresearch.gov.uk/tools-and-resources/fthr/forester/.
 For more on SolVES and its utility in measuring social value of ecosystems such as forests, see: Sherrouse, B.C., Semmens, D.J., Ancona, Z.H., 2022. Social Values for Ecosystem Services (SolVES): Open-source spatial modeling of cultural services. Environmental Modelling & Software 148, 105259. https://doi.org/10.1016/j.envsoft.2021.105259