GLSL Multiband and LiDAR Inventory 120-502

Description: This project investigates multiband orthophotography (and integrated airborne LiDAR) for its potential value in the production of enhanced forest inventories in the Great Lakes St. Lawrence Forest. It will allow for the efficient acquisition and extensive testing of these high-tech inventory precursors at specific and diverse sites over a broad geographical area within the Great Lakes St. Lawrence Forest region of Ontario.

Multi-return LiDAR (Light Detection and Ranging) offers an opportunity to capture dense point data defining the first surface (canopy) and penetration into the vegetation cover with many points also hitting the forest floor. These multi return points permit the mapping of the forest canopy, the bare earth, and many of the structural characteristics such as canopy height, volume and basal area - attributes desireable in a state of the art forest inventory.

This project will evaluate new technology (high resolution digital imagery and airborne LiDAR) in the production of a 21st century forest inventory; an inventory capable of supporting the sustainability questions we ask of it every day when planning and implementing forest management practices. Long term spin off benefits would include significantly improved forest management planning, modeling, operations, silviculture, road building, forest values identification, wetlands evaluation, ecological land classification, carbon cycling; and potentially major corresponding cost decreases for both forest industry and the OMNR.

The desired outcome of the project is to allow identification, comparison, evaluation and common understanding of new and existing technology relating to the development and production of a 21st century forest inventory through the following objectives:

1. Explore the capabilities and application of high resolution digital imagery, LiDAR, and remote sensing technologies and applications, to forest operations and to forest inventory derivation and production in an operational setting in the GLSL forest region.

2. Engage technical experts (University and private sector organizations) in an investigation and evaluation of the high resolution digital imagery, LiDAR and remote sensing technologies and applications.

3. Evaluate and compare automated interpretive tools (software) for forest resource inventory database production (ie. ERDAS, Softcopy, MicroDEM, TauDEM, and other analytical software).

4. Provide an opportunity for the OMNR and other partners to work together in exploring new technological means of improving base fabric information for sustainable forest management decisions.

5. Where appropriate, use the outputs of the project (digitalimagery, LiDAR, etc.) to undertake and enhance other ongoing projects (ie. SOLRIS landcover/use mapping, Ecological Land Classification typing, wetland mapping, enhanced forest hydrology mapping, etc.)

The Project Team: Murray Woods, Southern Science and Information, OMNR, Paul Courville, FRP, Bill Cole, OFRI, Kevin DelGuidice, Tembec, Paul Treitz, Queen's University, Richard Mussakowski, OMNR, and Don Leckie, CFS.


Murray Woods, Project Leader  and   Paul Courville, Logistics Coordinator



Report: Semi-Automated Species Classification in Ontario Great Lakes–St. Lawrence Forest Conditions (Chubey et al)

Woods, M., Lim., K., and P. Treitz. 2008. Predicting forest stand variables from LiDAR data in the Great Lakes – St. Lawrence forest of Ontario. For. Chron. 84: 827- 839.


Tree Tips:

Tree Tip: Prism Crusing

Tree Tip: Stream Prediction

Tree Tip: DNRGarmin

Tree Tip: ArcPad



Semi-Automated Natural Resource Inventory Production Through Fusing New Technologies

LiDAR Boreal Seminar 2008


Status Reports:

Annual Report (2007-2008)

Project Work Report (2007-2008)

Status Report (2008-2009)

Financial Summary (2008-2009) 

For Additional Information Contact:

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