Recipient Organization
CORNELL UNIVERSITY
(N/A)
ITHACA,NY 14853
Performing Department
Ecology & Evolutionary Biology
Non Technical Summary
Sustainable management and stewardship of forest ecosystems are limited by accurate estimates of their ecological and economic values. Tree biomass/volume is acritical indicator of forest ecosystem values.However, an accurate estimate of tree biomass or volume and its allocation into commercial volume and harvest residuals becomes a challenge to further increase our ability of forest management. The need becomes more urgent becausethere has yet been a tool for stakeholders to estimate the cost and length of deer-preventing slash walls built from harvest residuals that has been shown to be a sustainable management practice.Accurate fine-scale tree growth information is also lacking to support effective sustainable forest management to optimize forest carbon accumulation and other forest productionssuch as maple syrup production.Terrestrial LiDAR technology can provide non-destructive and high-resolution (down to the millimeter) measurements on tree-level biomass dynamics. We will use novel portable and cost-effective terrestrial laser scanners (TLS) to generate allometric equations for commercial volume and harvest residuals. We will also use multi-year TLS data to analyze what drives the differences in individual-level tree growth and syrup production (from long-term ground data in the Arnot Forest of Cornell).
Animal Health Component
50%
Research Effort Categories
Basic
25%
Applied
50%
Developmental
25%
Goals / Objectives
Our overarching goal is to improvethe utility of forest timber products and sustainable forestry management for both timber and syrup production with the novel, cost-effective, portableterrestrial laser scanner(TLS) technology that can quantify tree aboveground structures non-destructively. In particular, the project will generate useful new tools to accurately and rapidly estimate carbon storage,commercial value, and harvest residuals that are increasingly used for the construction of deer-preventing slash walls. Long-term forest plot monitoring with TLS can also increase our scientific understanding of the controlling factors on variations in tree biomass growth and syrup production.We have four specific objectives during the course of the project.Objective 1(Summer 2021 - Spring 2022): Evaluate the accuracy of portableTLS and the state-of-the-art digital tree segmentation algorithms to quantify aboveground tree volume and its 3-dimensional structure from branch trimming experiments and traditional destructive measurements leveraging on the planned tree harvest at the Arnot Forests.Objective 2(Fall 2021 - Fall 2022): Constructbiomass/volume allometric relationships for both commercial tree volume and harvest residuals based on TLS samplings over key commercial species in central New York from extensive field campaigns with TLS.Objective 3(Fall 2021 - Fall 2024): Monitortree volume gain and loss from multi-year TLS measurements over a few permanent plots (including maple syrup production plots). Based on the results, investigate the biotic (e.g. crown area, crown position, competition stress from neighbors) and abiotic (e.g. climate, soil) drivers of the spatial and temporal variations in individual-level volume dynamics.Objective 4(Fall 2023 - Fall 2024): Compare growth records from TLS measurements with maple syrup production data at the Arnot Forest to understand the relationship between syrup production and sugar concentration with tree structure and volume growth.
Project Methods
Scientific MethodsWe will conduct the research project in the Arnot Teaching and Research Forest of Cornell University. We will use a state-of-the-art portable (1kg in weight) and cost-effective (~20k$ compared with over 100k$ for traditional terrestrial lidar systems) terrestrial laser scanner (TLS), BLK360 from Leica Geosystems.To evaluate the accuracy of the TLS (Objective 1), we plan to conduct a partial trimming experiment in the Arnot forest over trees scheduled to be harvested. We will select 3-5 adult trees (DBH>10cm) for common commercial species in central NY. For each tree, we will gradually trim branches from smaller size (~1cm) to bigger size (5-10cm). Before each trimming, we will scan the tree's 3D structure with TLS. We will compare the tree volume and biomass changes estimated from TLS scans and the ground truth measurements from the trimming experimentsTo improve the allometric equation for tree volume and biomass (Objective 2), we will carry out field campaigns in the Arnot Forest to estimate tree volume and biomass non-destructively from TLS. We will establish 15-30 0.1 hectare circular plot covering a wide range of species and size classes during each campaign. Together with wood density data, volume and biomass of the whole tree, as well as the fraction to commercial volume and harvest residuals, can be accurately estimated for hundreds of trees. We will then conduct statistical analysis to estimate the best allometric equation to estimate biomass/volume from easy-to-measure traits such as diameter at breast height and height.To improve our understanding tree growth and sugarbush syrup production (Objectives 3&4), we will set up permanent forest plots in the Arnot Forest and conduct multi-year TLS measurements. The multi-year data setswill provide unprecedented data set to analyze individual-level growth variations, which will be analyzed together with maple syrup production data from our collaborators.Efforts to Deliver Research ProductsDuring the project, key extension informants will be engaged twice annually to review and discuss the project and develop audience-specific fact sheets and presentations. Biannual updates on the project will be posted online. Annual field tours to stakeholder groups and annual webinars will acquaint stakeholders with this technology and its utility to forest and sugarbush management. We will also involve key stakeholders in field research trials to visualize applications of lidar and to field test tools and predictive models.We will also engage Cornell undergraduates through summer research opportunities and the data and knowledge generated from the projects will serve as course materials for BioEE1610 Ecology and Environment at Cornell.Project EvaluationFor the research component, we will have high-quality well-curated TLS data sets as key milestones after each year's field campaign. We will then share the new knowledge and data to the broader scientific community through high-profilepeer-reviewed journals in forestry and ecology such as Forest Ecology and Management, Remote Sensing of Environment.For extension and education efforts, we will provide more accurate estimates of biomass and timber values in the Arnot forest and estimate the cost saved in slash wall construction with pre-harvest TLS measurement. We will also collect feedback from participants about precision forestry with TLS and shareguidelines and tools for stakeholders interested in adopting the new technology.