Model-Assisted Forest Stand Delineation to Make Forest Assessment, Valuation, and Management Planning More Accessible

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Commodities

• Additional Plants: trees

Practices

• Crop Production: forest/woodlot management
• Education and Training: decision support system, participatory research

44,000 family forests cover 3.3 million acres in Oregon and Washington; 74% “have good stewardship attitudes but are not highly engaged in managing their land” [1]. Social science research identifies shortfalls and calls for experimentation with modern technology and marketing to better engage landowners [2]–[5]. Preparing a Forest Management Plan (FMP) may involve days-to-months navigating dozens of websites, learning software, or visiting offices, and remains a challenging prerequisite for cost-share or incentive programs or to justify investments in forest management or acquisition decisions. Mapping and characterizing a property’s forest stands remains a particularly costly and time-consuming bottleneck.

Our motivating research questions are:

• Can machine learning models trained with publicly-available imagery, lidar, climate, and soils data produce actionable maps delineating and characterizing forest attributes required for FMPs (e.g., dominant tree species, size, and abundance)?
• Beyond basic FMP requirements, which attributes sought by owners and managers to assess forests and make confident management decisions can be adequately modeled or easily recorded with ubiquitous technology (e.g., smartphones)?
• Will a user-friendly web app delivering forest information reduce time and effort for FMP completion? Will it facilitate increased landowner awareness or adoption of new management and conservation practices?

Following best practices to coproduce actionable science, our team will involve stakeholders and end-users (landowners and foresters) throughout the project, including qualitative research to define user needs and requirements, assessing models that estimate forest conditions, honing tools deemed suitable for broader use, and delivering them with web technologies and extension activities.

We will engage stakeholders who develop and review FMPs to ensure our deliverables comply with relevant formatting and data sufficiency requirements. Producer Advisors will participate in qualitative research including interviews with forest owners and managers in Oregon and Washington to clearly define User Needs, Use Cases, and Functional Requirements for new tools and technologies. We will then train, document, and publish open-source machine learning models using hundreds-of-thousands of plots and stand-delineations from public agencies across Oregon and Washington, apply them to generate predictive maps of basic forest conditions, and refine our models following forester and landowner feedback and ground-truthing. Based on our research, we will integrate new features/products into existing open-source web apps to serve maps and tabular data that auto-populate relevant sections of a FMP template.

Our work will lead to the following outcomes:

1. A benchmarking dataset with hundreds of thousands of forest observations alongside corresponding 2D and 3D remotely-sensed data enables rapid advances in state-of-the-art forestry and computer vision applications and a repository open to comparable data from other regions.
2. An open-source framework for data processing, model-fitting, and predictive forest mapping demonstrated across Oregon and Washington facilitates replication in other US regions.
3. An equitable coproduction process motivated by needs and interests of non-industrial forest owners provides new technologies and resources that increase landowner awareness and adoption of forest conservation and management activities.

This project is organized around five objectives:

1. Employ an equitable and inclusive process to co-produce actionable science. Diverse stakeholders are engaged throughout the entire research cycle including problem definition, methodology, interpretation of results, and extension activities. This involvement ensures science-based outcomes and products are salient, credible, and legitimate [12].
2. Establish a large benchmarking dataset for machine learning and remote sensing applications in forestry. Extensive data covering most of Oregon and Washington including stem-mapped plots, stand delineations with corresponding inventory data, aerial and satellite imagery, lidar point clouds and lidar-derived raster and vector layers, and other climatic and environmental layers. Data provenance, formatting, and processing will be clearly documented along with reproducible computing pipelines allowing others to submit new data.
3. Establish a reproducible pipeline for generating actionable maps and supporting data delineating and characterizing forest stands throughout Oregon and Washington. This will include fundamental attributes such as species composition, size class, stocking level, and other forest structure attributes, as well as an assessment of this approach for additional indicators such as timber value, fire and forest health threats, and wildlife habitat suitability.
4. Reduce the time and expense for non-industrial forest owners and managers to assess forest conditions, conduct forestland valuation, and develop management plans. Deploy user-friendly open-source technology focused on serving Tribes, consulting foresters and NRCS Technical Service Providers, and family forest owners with actionable forestry information.
5. Increase landowner engagement and activation. Increase awareness and use of time- and cost-saving tools to accelerate the learning curve, completion of Forest Management Plans, and adoption of sustainable forest management and conservation practices. Increase landowner interest, preparedness, adoption, and participation in programs that support practices to improve forest health, wildlife habitat, mitigate and adapt to climate change, and reduce pest and wildfire risks.