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Student Symposium 2008

Centre for Earth and Environmental Technologies

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Multi-Cohort Forest Classification Using LiDAR

OCE Student Symposium, Ramada Conference Centre, Guelph, 7 February 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

Where Next Happens

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KBM Forestry Consultants Inc., Faculty of Forestry (Toronto), Tembec Inc., Forestry Research Partnership, Ontario Ministry of Natural Resources, Lake Abitibi Model Forest

Project Partners and Collaborators:

Partners and Collaborators

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Multi-Cohort Forest Classification Using LiDAR

Project 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.)

Partners and Collaborators

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Objectives of the Multi-Cohort Classification using LiDAR project are:

1. 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

2. 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

Original Project Goals

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Task Description Start Finish

Develop LiDAR based DEM and ground classification

Obtain and evaluate softwares to process raw LiDAR data; process raw LiDAR data to separate ground and vegetation returns

April 2007 July 2007

Evaluate relationships between ground- and LiDAR-derived classification variables

Create custom GIS package to integrate LiDAR, ground plot, and Forest Resource Inventory data; derivation of analysis variables; analyses using bivariate and multivariate techniques

August 2007 March 2008

Extension to landscape scale

Establishment of appropriate scales of measurement; software module development

April 2008 December 2008

Software development

Development and testing of a GIS-based interface that integrates FRI and LiDAR data to undertake landscape-scale forest classification

January 2009

March 2010

Milestones – On schedule

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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

Background Concepts and Technology

LiDAR – Laser Light Detection and Ranging

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• 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

Background Concepts and Technology

What is multi-cohort forest classification?

9Background 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

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Photos: P. Drapeau

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

Bird Sub-Project: New addition

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Task Description Start Finish

Site selection Site selection in the R-M forest to sample a variety of cohort types, disturbance regimes, and forest ages

May 2007

June 2007

Bird community sampling

Sampling of bird communities and key indicator species via playbacks and point counts

June 2007

July 2008

Ground-based sampling

Measurement of key habitat variables, including ground-based 3-dimensional forest structure

August 2007

August 2007

Evaluation of multi-cohort concept and LiDAR data

Bivariate and multivariate analyses at multiple spatial scales

February 2008

July 2009

Feedback to software module development

Examination and testing of the utility of software products in the context of bird habitat modelling

February 2008

July 2009

Milestones: Bird sub-project

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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:

Custom ESRI 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

Technology

Technologies used and under development….

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

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.)

LAS Utilities:

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

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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)

Technology

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

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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

Project Outcome

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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

End Users

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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

Success Story

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

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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

What is Next

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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.