Student Symposium 2009 Centre for Earth and Environmental Technologies

1. Student Symposium 2009 Centre for Earth and Environmental Technologies Evaluation and Development of Lidar Data Acquisition Standards for Forest Inventory Applications and Predictive Forest Ecosite Classification February 3rd, 2009 Neal Pilger, PhD candidate Laboratory for Remote Sensing of Earth and Environmental Systems (LARSEES) Project Leader: Dr. Paul Treitz

2. Project Basics Program Type: Collaborative ResearchProject Length: 36 monthsProject Start: 01/2007Progress: 67%

3. Partners, Collaborators, and others who have contributed

4. Partners and Collaborators

5. Context Research into the application of airborne light detection and ranging (lidar) has demonstrated that accurate and precise forest inventory variables can be modeled from lidar height metrics.Critical parameters derived from such height metrics include: - height, basal area, volume - crown closure, leaf area index (LAI) - biomass and carbon stocks

6. Context From an industry perspective where costs of data acquisition and processing are paramount, standards for the use of lidar data for ecositeclassification and enhanced forest inventory do not currently exist in Ontario or Canada. This lack of standards slows the operational adoption and application of lidar even though it is proven as a reliable technology for forestry. Addressing this issue will allow both government and industry forest sectors to attain a distinct competitive advantage in achieving a truly sustainable forest management that encompasses economic, ecological and social values.

7. Original Project Goals The objectives of this work are to: Evaluate and develop lidar data acquisition and processing standards for forestry applications and their ultimate incorporation into a large-scale forest inventory management system (FIMS); Develop a predictive forest ecosite classification using various lidar derived variables in combination with vegetation cover-type; and Develop a peer-reviewed and accepted approach to lidar use in the context of enhanced Forest Resource Inventory (eFRI) and Forest Ecosite Classification (FEC) across a range of Ontario forest types and conditions, and a modular software application that automates this classification and can be part of a full-scale and comprehensive FIMS.

8. Current Focus Goal: The development of standards for LiDAR data acquisition in support of modelling forest inventory variables. Objectives/Tasks: Examine the impact of changes in pulse densities on modelling forest inventory variables. decimation levels all vs. first returns z thresholds Original Project Goals

9. Future Directions • Future research should include the: • Development of precision planning inventory tools for forest value optimization (including biomass for bioenergy). • Development of a tactical forest management decision support system with inputs from an eFRI.

10. Milestones

11. Airborne lidar for forest inventory:Use of airborne lidar for forest inventory, forest structure and biophysical variables estimation across a range of Ontario forest types and conditions: -plantations and natural forest stands of various cover types and at various stages of successionOptimization of the FRI process through:1. The development of lidar standards.2. The combination and integration of lidar and high resolution digital imagery.3. The integration of 1 and 2 into a modular software application. Technology

12. LiDAR Data Period: Summer 2007 Sensor: Optech ALTM 3100 Altitude: 1000 m Overlap: N/A Speed: 120 knots System PRF: 100 kHz Scan Freq: 54 Hz Scan Half Angle: 13° Cross Track Resolution: 0.499 m Down Track Resolution: 0.572 m Technology ~ 3 point/m2

13. Forest vertical structure and lidar intensity in:A: stand initiation stage (one layer)B: stem exclusion / understory reinitiation stage (multi-layered)C: understory reinitiation / old growth stage (complex) Technology A B C 3 hits/m2 10 hits/m2

14. Technology Multi-layered plot - point cloud (3 h/m2) Complex plot - point cloud (3 h/m2) VCI - vertical complexity index (modified from Shannon Weaver diversity index and Pielou evenness index)

15. Technology How does Vertical Complexity Index (VCI) relate to canopy layering and LAI estimates?

16. Field plots have been established in a variety of forest ecosystems in Ontario: - Romeo-Mallette: boreal mixed woods. - Swan Lake Forest Reserve: mature shade and mid-tolerant hardwoods, conifers, and limited mid-tolerant and intolerant hardwoods. - Petawawa Research Forest: mixed mature forest with hardwoods, conifers and mixed woods.LiDAR at multiple densities have been acquired for all sites Current Status

17. Current Status Field / LiDAR processing is underway to determine new forest metrics and indices for the prediction of basal area, volume, biomass, and leaf area index based on coupled LiDAR and multispectral image data.Of particular interest are the: Development of improved DEM analyses and vertical vegetation structural indices. Transition from stand-based to area-based analysis in order to combine lidar derived variables with vegetation cover-types. Development of a dynamic rather than a static predictive ecosite model.

18. Three (3) main Ontario study sites: Swan Lake (SL) Reserve (n=32) Petawawa Research Forest (PRF) (n=32) Romeo Mallette (RM) Forest (n=135) Current Status 18

19. Field Data Plots grouped by study site Circular plots – 0.10ha for SL; 0.04ha for PRF and RM DBH threshold of 9.1 cm Parameters measured/recorded: DBH Species Status of Tree Crown Class Visual Quality Height to Base of Live Crown Total Tree Height Current Status

20. Forest Variables Top Height (m) (TOPHT) Average Height (m) (AVGHT) Density (stems ha-1) (DENSITY) Quadratic Mean Diameter (cm) (QMDBH) Basal Area (m2 ha-1) (SUMBA) Gross Total Volume (m3 ha-1) (SUMGTV) Gross Merchantable Volume (m3 ha-1) (SUMMV) Total Aboveground Biomass (kg ha-1) (SUMBIO) Current Status

21. Three Decimation Levels Current Status ~ 3 point/m2 ~ 1.6 point/m2 ~ 0.4 point/m2 Modified from Raber, G.T., Jensen, J.R., Hodgson, M.E., Tullis, J.A., Davis, B.A., and Berglund, J. 2007. Impact of Lidar Nominal Post-spacing on DEM Accuracy and Flood Zone Delineation. Photogrammetric Engineering & Remote Sensing, 73(7): 793-804.

22. Current Status Comparison of Predictors – Mean Height Swan Lake – Tolerant Hardwoods

23. Current Status Comparison of Predictors – Maximum Height PRF – Great Lakes Pr/Pw

24. Current Status Results – Swan Lake

25. Current Status Results – Petawawa Research Forest

26. Current Status Key Outcomes of Research Project • We are almost breaching the 10% RMSE level, which would meet acceptable error tolerances. • We are challenging the assumption that higher sampling point densities lead to better estimates of forest inventory variables. • We are testing the robustness of the workflow and exploring ways to improve upon it.

27. Papers: Lim, K., C. Hopkinson, and P. Treitz. 2008. Examining the effects of sampling point densities on laser canopy height and density metrics for forest studies at the plot level, Forestry Chronicle, 84(6): 876-885. Woods, M., K. Lim, and P. Treitz. 2008. Predicting forest stand variables from LiDAR data in the Great Lakes St. Lawrence Forest of Ontario, Forestry Chronicle, 84(6): 827-839. Thomas, V., Treitz, P., McCaughey, J.H., Noland, T., and Rich, L., 2008. Canopy chlorophyll concentration estimation using hyperspectral and lidar data for a boreal mixedwood forest in northern Ontario, Canada. International Journal of Remote Sensing, 29(4): 1029-1052. Thomas, V., J.H. McCaughey, P. Treitz, D..A. Finch, T. Noland and L. Rich., Spatial modelling of photosynthesis for a boreal mixedwood forest by integrating micrometeorological, lidar and hyperspectral remote sensing data. Agricultural and Forest Meteorology (2008), doi:10.1016/j.agrformet.2008.10.016 Thomas, V., Oliver, R.D., Lim, K., and Woods, M. (2008) LiDAR and Weibull modeling of diameter and basal area. Forestry Chronicle, 84(6): 866-875. Pilger, N., Treitz, P, and St-Onge, B. Coupling Lidar and Multispectral data for estimation of aboveground biomass. (in preparation for the Canadian Journal of Remote Sensing). Pilger, N., Treitz, P, and St-Onge, B. Predicting Leaf Area Index from LiDAR Remote Sensing. (in preparation for the Forestry Chronicle). Project Outcome Project Outcomes - Academic Team

28. Presentations: Lim, K. (2007) LiDAR Metrics and Models. Presented at: Enhancing Resource Inventories Workshop, 22 – 24 May 2007, Mattawa, ON. Pilger, N., 2007. LiDAR and Multi-spectral Data Integration for Modeling Carbon, biomass and Leaf Area Index. Presented at: Enhancing Resources Inventories Workshop, 22 – 24 May 2007, Mattawa, ON. Lim, K., Woods, M., Treitz, P. and Courville, P.(2008) Enhanced Forest Resource Inventories: Going Operational with LIDAR. Presented at: International Lidar Mapping Forum, Denver, CO, Feb 21-22, 2008. Pilger, N., Treitz, P, and St-Onge, B.(2008) Coupling LiDAR and multispectral data for estimating forest biomass. In proceedings:American Society for Photogrammetry and Remote Sensing, Annual Conference, Portland, OR. April 27 - May 2, 2008. Pilger, N., Treitz, P., St-Onge, B., Woods, M., and Courville, P. (2007) LiDAR Point Density Analysis for Forest Parameter Extraction. Presented at: CAGONT 2007, October 19-20, 2007 Laurentian University, Sudbury, ON Pilger, N., Treitz, P., St-Onge, B., Woods, M., and Courville, P. (2008) Optimal Lidar Point Density for Calculating Leaf Area Index in Mixed-wood Great Lakes / St. Lawrence Forests. Presented at: Canadian Association of Geographers (CAG) Annual Meeting, May 20-24, 2008 Universite de Laval, Quebec, QC Fedrigo, M., P. Treitz and G. Barber, 2008. Comparison of Digital Elevation Data derived from Topographic Maps and Airborne Lidar Acquisition under varying Forest canopy Densities. Canadian Association of Geographers Annual General Meeting, May 20-24, University of Laval, Quebec City, Quebec.  Van Ewijk, K., P. Treitz, N. Scott, and M. Wood, 2008. The Characterization of Vertical Forest Structure using Lidar Derived Complexity Indices to enhance Forest Vegetation classification in Central Ontario, Canadian Association of Geographers Annual General Meeting, May 20-24, University of Laval, Quebec City, Quebec. Project Outcome Project Outcomes - Academic Team

29. Project Outcomes - Partners Adaptation by Collaborators: It is expected that the partners, particularly OMNR, will be able to build a substantial part of the research results into their provincial enhanced Forest Resource Inventory (FRI). Forest inventory and ecosite classification using lidar remote sensing provides more information at a faster rate than the traditional inventory methods currently in operation. Job Opportunities: It is expected that MSc and BSc students involved in this project will likely find employment within the forestry or geomatics sector. e.g. with partners such as OMNR, Tembec Industries Inc.

30. The synthesis and development of lidar data acquisition standards, which will be applicable to many of the lidar derived forest inventory, forest structure and biophysical variable estimations, provide an opportunity to ENHANCE the outputs and methods that provincial forest resource inventories, and related predictive ecosite classifications, are conducted. Hence, some of the expected end users of the research results will be:OMNR Tembec Industries Inc. Forestry Consulting firms Lidar Data Acquisition firms Other End Users Gross Total Volume

31. Commercialization • It has not yet been determined how the proposed solution will be commercialized. • The intent will be to ‘roll-up’ this project’s associated lidar forest resource inventory software module as part of the comprehensive forest inventory management system. • It is anticipated that the entire system for this stand-alone module would be available for licensed use by individual organizations.  • The interested receptors would include the ‘Forest Inventory Section’ of the OMNR and various companies/consultants specializing in FRI interpretation and production. • Given the Ontario Ministry of Natural Resources recent resumption of responsibility for forest resource inventory production and the availability of both in-house and consultant expertise, they manifest the characteristics of the receptor of choice.

32. Success Stories Graduate Student Theses: Chasmer, Laura (2008). Canopy Structural and Meteorological Influences on CO2 Exchange for MODIS Product Validation in a Boreal Jack Pine Chronosequence. PhD Thesis, Department of Geography, Queen’s University, Kingston, Ontario, Canada 188 p. - currently a post-doctoral fellow (Wilfrid Laurier University) Undergraduate Student Theses: Gralewicz, Nicholas (2008). Lidar Estimation of Biophysical Variables in Pristine Northern Tolerant Hardwood Stands. BSc Thesis,Department of Geography, Queen’s University, Kingston, Ontario, Canada 55 p. - currently MSc candidate (University of Victoria) Fedrigo, Melissa (2008). Comparison of Digital Elevation Data Derived from Topographic Maps and Airborne Lidar Acquisition under Varying Forest Canopy Densities. BSc Thesis,Department of Geography, Queen’s University, Kingston, Ontario, Canada 109 p. - currently MA candidate (University of Edinburgh)

33. Carry on with project plan OCE’s Talent programs: Professional Outreach Awards: Conference travel Value Added Personnel: Management and Teamwork – Project Management Core Course What is Next • Finish analysis for 3 forest types in RMF • Assess the benefits of using FIRST returns. • Assess the benefits of using a z threshold.

34. HT/dbh Allometry Field Data HT/Cr.Rad Allometry Subset to Plot Image Data Classification Validation Lidar Data Height Quantiles HT/dbh Allometry Validation Validation Validation Biomass Equations Volume Equations Plot Volume Plot Biomass What is Next Species ID DBH Crown Radius Height Subset to Plot Integration of lidar and high- resolution digital image data for volume and biomass estimation across mixed-wood forest environments. Computation of lidar-derived Leaf Area Index c and d a and b Species ID Species Distribution Species Specific Ratios Subset to Plot LPI  DBH Mean Height Max Height

35. What is Next Predictive modeling for carbon sequestration

36. Thank You