Project Title: Great Lakes St Lawrence Lidar Inventory Project

Status Report to the Forestry Research Partnership

Project Title: Great Lakes St Lawrence LiDAR inventory Project

FRP Project No. 6252-00

Project Leader: Murray Woods, Southern Science & Information Section, OMNR

Project Facilitator: Al Stinson, FRP/OMNR

Project Start Date:

Project Reporting Period: April 1 2008 - March 31st 2009

Report Date: March 31st 2009

1. Project Description and Activities:

The focus of the 2008-2009 project was to:

1. publish results of the EFPP project in the Forestry Chronicle
2. Re-analyze using ITC approach the Swan Lake Research Forest with the acquired ADS40-SHII digital imagery

2. Project Results vs. Objectives:

The 2008-09 GLSL LiDAR inventory program was an opportunity to publish and transfer project results. In addition, lessons learned thorough the project were implemented in another attempt at operationally developing an ITC inventory project for the Swan Lake Research Forest through a partnership/consultant agreement with Silvatech Consulting Ltd.

The project funded the costs for two project publications in the November/December issue of the Forestry Chronicle.

• Predicting forest stand variables from LiDAR data in the Great Lakes – St. Lawrence forest of Ontario. 2008. M. Woods, K. Lim and P. Treitz.

• LiDAR and Weibull modeling of diameter and basal area. 2008. V. Thomas, R.D. Oliver, K. Lim and M. Woods

An internal report summarizing lessons learned and results of ITC inventory development for Petawawa Research Forest and Swan Lake Research Forest was completed. It has posted at

• Automated Species Classification in Ontario Great Lakes–St. Lawrence Forest Conditions. 2009. M. Chubey, K. Stehle, R. Albricht, F. Gougeon, D. Leckie, S. Gray, M. Woods, and P. Courville

Silvatech Consulting was contracted to perform a re-analysis of the Swan Lake Forest Research Reserve utilizing the lessons learned during the GLSL LiDAR project and with the new recently acquired provincial imagery (Leica ADS40). The project has progressed to a second classification effort with final results and report due in May 2009. Final results, report and invoice for that portion of the project will be submitted by the end of May 2009.

1

Page

4. Next Year’s Work Plan: Describe the next year’s activities; anticipated results; milestones and deliverables and provide the budget estimate. The budget estimate should be the budget detail table, with footnotes, that was part of the approved project’s financial plan. Therefore, please complete the Table below.

Expenditures

2008-09

LLT Request

Tembec

MNR

CFS

Other

Cash

In-kind

Cash

In-kind

Cash

In-kind

Cash

In-kind

Travel

Equipment

Supplies

Support Services

$8,460.00

$1,000

Holdback

Final analysis and report on ITC re-analysis of the Swan Lake Research Reserve will be delivered early in fiscal 2009-10.

1

Page

5. Information and Publications: List produced project database(s), information and publications to date as applicable; where and when the information can be accessed; describe how this information was transferred to practitioners.

November/December Issue of the Forestry Chronicle

• Predicting forest stand variables from LiDAR data in the Great Lakes – St. Lawrence forest of Ontario. 2008. M. Woods, K. Lim and P. Treitz.

• LiDAR and Weibull modeling of diameter and basal area. 2008. V. Thomas, R.D. Oliver, K. Lim and M. Woods

An internal report summarizing lessons learned and results of ITC inventory development for Petawawa Research Forest and Swan Lake Research Forest was completed. It has posted at

• Automated Species Classification in Ontario Great Lakes–St. Lawrence Forest Conditions. 2009. M. Chubey, K. Stehle, R. Albricht, F. Gougeon, D. Leckie, S. Gray, M. Woods, and P. Courville

6. Project Synopsis:

LiDAR and semi-automated image analysis approaches have been refined to work within an operational forest inventory program. This project has demonstrated how low intensity LiDAR can complement standard inventory outputs with additional stand level estimates of attributes such as basal area, density, DBHq, volume, biomass and measures of stand height. Indications through efforts of this project are that LiDAR can also provide estimates of horizontal stand structure – diameter class distributions or size class distributions. Individual Tree Classification (ITC) has evolved to a state where, with controlled image acquisition and processing, it can provide objective stand level estimates of the common forest resource inventory attributes (species composition, height, and crown closure). Work continues to refine the crown segmentation routine to better delineate one crown from another and to minimize over-delineation of hardwood species. With these enhancements, the ITC Suite software will be capable of providing individual tree attributes. The fusion of these two technologies (LiDAR and ITC) hold great potential to assist photo interpreters do their job more quickly, objectively, accurately and with additional attributes.

1

Page