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Objectives 1 & 2 - Measurement Automation and Model Integration
Pete Bettinger, Chad Bolding, Bronson Bullock, Joe Conrad, Bruno da Silva, Stephen Kinane, Roger C. Lowe, III, Fred Maier, Krista Merry, Khaled Rasheed, Sheng-I Yang, and Dehai Zhao
Objective 1: Develop scalable automated data capture systems with integration of multimodal, multiplatform, and multitemporal data to assist stakeholder data-driven forest management practices and decision-making.
Task 1.1 iForester: Develop an integrated AI-assisted iForester (measurement tool - ground truth/inventory measurements) smartphone application that automatically inventories major tree species for the eastern United States forest with two key functions: (1) AI-assisted species recognition using tree bark and (2) Light Detection and Ranging (LiDAR) - Red, Green, Blue (RGB) image-enabled measurements of key tree biometric measures.
PERSEUS Interns at UGA are collecting bark images of southeastern tree species to help aid in AI model training. To date, they have collected over 6000 images!
Task 1.2 StemMapper: Create a LiDAR-based and AI-assisted StemMapper (proximal and near proximal remote sensing-derived models and products) to provide stem-level inventory at the stand and tract level with large-scale automated inventory for multi-objective, data-driven management for forestry professionals
Task 1.3 Data Coverage: Generate multiscale data products (e.g., fiber, habitat, carbon) with higher spatial and temporal resolution for every acre across the eastern U.S. to facilitate both local- and regional-level management optimization
Southeast region-wide GIS databases have been developed and include roads, streams, soils, and site index for various species. These data will help with implementing models in Objective 2.
Model development has been completed for estimating biomass, volume, and basal area, using super computing and machine learning, for the Talladega National forest.
Using ALS and GEDI LiDAR, significant progress has been made to scale-up woody fuel estimates from field measurement plots.
An assessment of TreeMap data, which is being evaluated as an input into Objective 2 modeling efforts, is nearing completion.
GNSS accuracy assessments have been completed of both an iOS and Android smartphone along with a high precision antenna.
Objective 2: Create a multimodal ensemble that is locally calibrated and capable of projecting forest ecosystem services under a range of conditions.
Task 2.1 Landowner Optimization: Link available forest data (from PERSEUS and existing datasets such as FIA) to the integrated multimodal ensemble to optimize ecosystem services at a local scale (1-1,000,000 ha).
Task 2.2 Broad Simulation: Co-develop (with stakeholders) a dynamic simulation system to present broadscale (>1M ha) assessments of alternative policy and market scenarios, while facilitating fine-scale assessments of tradeoffs among management and ecosystem services for regional decision-making.
Non-spatial simulation of management alternatives for large areas (states) based on FIA data, SEES simulation model, is moving to a web-based platform with two graduate students developing an online interface.
Spatial simulation development of large areas using LANDIS-II are ongoing. Investigations are underway on the LADIS-II model's abilities to derive growth projections of current forests that would then be inputs for simulating alternative management activities.
Development of an alternative spatial simulation of large areas using a novel landscape model integrating existing U.S.D.A. Forest Service products with predictive algorithms. Simulations will be guided by TPO harvest reports.
Task 2.3 Value Chain: Develop and refine methods to investigate potential efficiencies through forest management activities in the eastern U.S.
Development of value chain improvement assessments related to forest product systems to identify if opportunities exist to improve the percent of time logging trucks travel. The outcome will be a spatial model focusing on specific wood basket areas implementing a shortest path model.
Task 2.4 Data Visualization: Develop a cloud-based data warehouse to allow key project data to be stored, visualized and shared with stakeholders.
Options for visualization are currently being explored and has included discussions of metadata standardization. Visualization efforts will be informed by feedback received through Objective 3 outreach efforts.
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