Student Symposium 2008 Centre for Earth and Environmental Technologies

1. Student Symposium 2008 Centre for Earth and Environmental Technologies

2. Where Next Happens Multi-Cohort Forest Classification Using LiDAR OCE Student Symposium, Ramada Conference Centre, Guelph, 7February 2008 Ben Kuttner, Ph.D. Candidate, Faculty of Forestry, University of Toronto Mike Burrell, M.Sc.F Candidate, Faculty of Forestry, University of Toronto Dr. Jay Malcolm, Faculty of Forestry, University of Toronto

3. Partners and Collaborators Project Partners and Collaborators: KBM Forestry Consultants Inc., Faculty of Forestry (Toronto), Tembec Inc., Forestry Research Partnership, Ontario Ministry of Natural Resources, Lake Abitibi Model Forest

4. Partners and Collaborators Multi-Cohort Forest Classification Using LiDARProject Leader – Jay Malcolm, Univ. Toronto HQP – Ben Kuttner (Ph.D.), Mike Burrell (M.Sc.F.)Other Research Participants – Murray Woods (MNR), Wally Bidwell (LAMF) John Pineau (FRP [currently CIF])Industry Partners – Arnold Rudy (KBM) Ken Durst (Tembec Inc.)

5. Original Project Goals • Objectives of the Multi-Cohort Classification using LiDAR project are: • To explore the utility of LiDAR to re-construct, and possibly improve upon, a ground‑based forest stand structural classification that separates relatively uniform even-aged forests from increasingly complex uneven-aged multi-cohort forests • To develop and deliver a software package that automates the multi-cohort forest structure classification process using LiDAR and FRI data and make it available to project partners for integration in a comprehensive forest inventory management system • Future work: • Commercialization of the technologies developed during this project that will ultimately increase the competitiveness of Ontario’s forest sector

6. Milestones – On schedule

7. Background Concepts and Technology LiDAR – Laser Light Detection and Ranging Airborne LiDAR systems emit pulses of laser light that are reflected by the ground and objects above the ground surface and calculate distances to reflection points. LiDAR is of particular interest in forestry because of its potential to penetrate forest canopies and accurately measure 3-dimensional structure

8. Background Concepts and Technology What is multi-cohort forest classification? • The multi-cohort stand structure concept separates relatively young even-aged forest stands consisting of a single cohort of trees from more structurally complex stands in which multiple cohorts of trees (and tree ages) are represented. • Many structural features of complex older natural stands are not likely to re-develop in clearcut and planted areas before they are scheduled again for harvesting; MFM proposes partial cutting to emulate multi-cohort structural conditions in some proportion of managed stands

9. Background Concepts and Technology MFM CHALLENGES…. • Traditional forest inventories lack the information required to assess forest structural conditions. • Ground-based multi-cohort structural classification approaches are costly and labour intensive • Remote sensing technologies are required to integrate MCM information into enhanced forest inventories

10. Bird Sub-Project: New addition • Unique opportunity to test the utility of the LiDAR-based 3-dimensional forest structure and cohort classification in predicting wildlife communities • took advantage of synergies between OCE and Forestry Futures Trust projects to sample bird communities in a subset of the ground-truth plots • Forest’s 3-D structure thought to be fundamental in understanding wildlife-habitat relationships • LiDAR data provide structural information at large spatial scales that have not been possible to evaluate in boreal forests before Photos: P. Drapeau

11. Milestones: Bird sub-project

12. Technology Technologies used and under development…. USED: Map Windows GIS: free open source GIS technology FUSION: USDA Forest Service Lidar visualization viewer Cloudpeak LASedit: Commercial Lidar ground classification and visualization software used for the ground classification of our raw data ESRI: We are using the ARC software suite DEVELOPED: CustomESRI scripts in for generating LiDAR shapefiles LAS utilities: plug-in for Map Windows to work with LiDAR data LiDAR Height Frequency visualization tools: web-based application

13. Technology ESRI LiDAR toolbox developed to build integrated LiDAR GIS: LASedit to visualize and classify ground points

14. Technology LAS Utilities: MapWindow GIS plug-in developed to manage large amounts of LiDAR data

15. Technology LAS Utilities: Given the large study area (>500,000 ha), LAS utilities was designed to tile and manage the data for integration in a GIS with other forest information (stand boundaries, linear features, etc.)

16. Technology • Custom GIS data management framework: • GIS allows Lidar to be clipped to FRI stand boundaries • tiled 20 m X 20 m • Plot cell ID • Edge cell ID

17. Technology • LiDAR HEIGHT FREQUENCY VISUALIZATION TOOLS • Custom application developed to visualize the frequencies of LiDAR returns by height class • Web based • variable scale summaries (i.e. plot and stand level)

18. Current Status – on schedule Data processing and management: • Software tools and scripts developed to date have allowed data to be managed independently in GIS at the plot and subplot levels that are organized to allow scaling and combination of datasets Software Development: • LAS utilities development; custom visualization tool development • Data management scripts and codes are being tested and evaluated for use in software products Project extension: • Integrated GIS used to support bird and other wildlife habitat studies

19. Project Outcome Additional anticipated outcomes: • Improved understanding of LiDAR as a tool in SFM • Papers in peer reviewed journals, conference presentations • Integration of software modules into enhanced forest inventories and wildlife studies • Positioning as leaders in the processing of LiDAR data for forestry applications • Potential further development and commercialization of softwares/approaches • Employment for at least one OCE-funded student with R&D partner

20. End Users Industry, government, and academia Tembec Inc. will be able to implement LiDAR-derived forest structure information in inventories KBM Forestry Consultants Inc. will use experience and outcomes to gain market share in LiDAR data analysis for forestry Software tools will inform forest management policy development, planning, monitoring, and evaluation capabilities for government Data products will support ongoing studies with cutting edge information to address complex ecological questions

21. Success Story Our students and KBM Forestry Consultants are already enjoying the benefits of being pioneers in the management and use of LiDAR derived data for forestry applications • This Ontario-based consultancy is currently commercializing the knowledge gained and developing new LiDAR-based mapping products for an Industry client in Alberta with the help of one of our students Enabling cutting edge research in forest ecology • Training HQP: A new student (outside our original mandate) has been added to use products developed here to investigate bird habitat responses • Leveraging of new funding by the Wildlife Ecology group to investigate key habitat and biodiversity questions

22. What is Next Working with OCE, several next steps are possible: • Before project completion: OCE’s Talent programs • Value Added Personnel (VAP) – R&D partner is planning a Toronto-based subsidiary to be led by the project student • Professional Outreach Awards – support student professional dev. • Post-project: OCE Commercialization Programs • Martin Walmsley Fellowship for Technological Entrepreneurship • Investment Accelerator Fund • Post project: • Further development and extension of technology developed during the project and marketing of new technologies and unique skills

23. Multi-Cohort Forest Classification Using LiDAR • Student Researchers in Forestry at the University of Toronto go Hi-Tech with LiDAR and OCE • In collaboration with KBM Forestry Consultants Inc., enhanced forest information that integrates new remote sensing technologies (LiDAR) with Forest Resource Inventory is being assembled in a unique Geographic Information System (GIS) • Custom software modules and tools are being developed to manage LiDAR data, enable LiDAR-based classification of forest structure, and test the utility of LiDAR in understanding wildlife-habitat relationships • Delivery of cutting edge tools to integrate LiDAR data in forest inventories, a solid development platform for commercial software, and unique skills development for researchers, industry collaborators, and other partners in support of a more competitive Ontario forest sector.