Revisioning precision forestry through tree level monitoring and modeling

Background

Revisioning Precision Forestry designs, develops, and tests the accuracy and precision of novel imaging technologies using drones (UAS) and other systems to implement improved processes for derivingforest inventory metrics within operational management standards. Our priority is to resolve constraints on access and use of terrestrial and aerial remote sensing technology to monitor, measure, and manage western United States forested ecosystems, which are under significant stress due to climate change, wildfires, insect attacks, increasing wood utilization demand, and a burgeoning carbon offset market. We propose to reduce access constraints by developing and publishing automated, open-access software algorithms forprocessing terrestrial and UAS remote sensing data into formats that aid visualizing, managing, and communicating forest stand and tree attributes using currently available, open-source forest modeling software. Specifically, we envision democratizing precision forestry through exploring novel principles, such as allometric relationships, to more precisely fuse (co-register) above and below canopy point clouds derived from terrestrial and UAS remote sensing platforms. We will then assess the automation of deriving individual tree and forest stand attributes from fused point clouds that would be directly ingestible by the Forest Vegetation Simulator program, the most widely used forest growth and yield software program within the United States. Lastly, we will evaluate the cost/benefit of utilizing photogrammetry alone to estimate forest and tree attributes, relative to more costly lidar remote sensing platforms. We will use advancements in computational machine learning (e.g., XGBoost), coupled with open-source analytical platforms (SQL, Python, R) to test and develop new methods for modeling the forested environment through readily accessible and relatively inexpensive remote sensing technologies.This research project is designed to solve a key national problem limiting the optimization of forest resource management. Namely, that many organizations and land managers have limited or no access to software support systems that enable comprehensive remote sensing quantification of tree and forest attributes. Accurate and comprehensive quantification of forest and tree attributes, in actionable and meaningful formats, is critical to proactively maintaining healthy and productive forests, capable of supplying long-term ecosystem services (e.g., fiber, carbon sequestration, habitat, water quality, cultural traditions). We intend to overcome these software and data access limitations by developing open-access software algorithms for processing point clouds of forested environments. Open-access is critical, as the threats and opportunities facing our forested ecosystems and the services they provide culturally, ecologically, and economically are only increasing. Access should not be limited to those with significant financial resources, but to all who have an interest and responsibility to manage our national forest resource. Project outcomes will provide software tools accessible by all stakeholders in the forest management community to allow precision forestry to 1) Promote sustainable forest ecosystem services, 2) Sustain natural resource-based economies, and 3) Advance the integration of technology into organizational forest management workflows.

Goals / Objectives
Ourgoals are to: 1) democratize precision forestry through testing and validating low-cost off the shelf remote sensing platforms to accurately measure forest attributes above and below forest canopy (stem size, species, and location); 2) develop open-source algorithms for co-registering above and below canopy point clouds derived from lidar and photogrammetry; and 3) create an open-source software to convert point clouds into tree lists digestible by forest modeling platforms. These goals will be achieved through the completion of the following four objectives.Objective 1 seeks to enhance existing point cloud co-registration algorithms by integrating allometric models into the alignment process. We hypothesize that the allometric informed approaches will reduce above and below canopy point cloud co-registering errors. We will deliver open-source software allowing any individual or organization to produce an allometric informed co-registered point cloud collected above and below a forest canopy.Objective 2 will assess tree measurement accuracy from co-registered above and below canopy point clouds derived from Structure from Motion (SfM - inexpensive) against co-registered below canopy handheld mobile lidar scanning (HMLS - expensive) and above canopy SfM. Accuracy comparisons will occur across multiple forest productivity zones and species compositions in the western U.S. Stem mapped plots will be used to evaluate platform accuracy across Idaho, Oregon, and Colorado within Douglas-fir and ponderosa pine forests - the two most ecologically and economically important species in the western U.S.Objective 3 will perform a cost-benefit analysis of the remote sensing platform accuracy, error propagation, acquisition time, and tradeoffs between equipment cost and post-acquisition processing requirements.Objective 4 will develop an open-source software package to process SfM/HMLS derived point clouds, co-register, and produce an individual tree list for the scanned area. This tree list will be formatted to be compatible with the most widely used forest modeling software package in the U.S. - the US Forest Service Forest Vegetation Simulator.

Project PIs: Mark Kimsey (U. Idaho), Bogdan Strimbu (Oregon State University), Wade Tinkham (USFS)