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LiDAR Data Standards - Queens 120-701

Description: The goal of this project is to develop standards for collecting, processing and analysing lidar data to derive forest inventory attributes that lead to the production of an enhanced forest resource inventory (eFRI) and associated predictive ecosite classification for Ontario forests. Similar to our ongoing research in the application and testing of lidar for forestry, this research aims to address enhanced forest resource inventory specifically, and the standards around its production and management. There is a strong need to make commercial lidar data operational within a clearly established set of standards for forest inventory derivation and ecosite classification. A software module that incorporates these standards and allows quality analysis of lidar data to generate accurate and repeatable estimates of typical forest inventory attributes and forest ecosite classification, ultimately incorporated within a comprehensive forest inventory management system is the project’s desired outcome.

 

The primary objectives of the project are:

1) The evaluation and development of lidar data acquisition standards for forestry applications - our research will analyze lidar data that have been acquired with different lidar systems and under different operating conditions across a range of forest conditions in Ontario. This will provide the basis for the development of lidar data acquisition and processing standards, and their ultimate incorporation into a large-scale forest inventory management system.

2) The development of a predictive forest ecosite classification using lidar data. Using a combination of vegetation cover-type, vertical structure and much improved land base feature delineation, the project will investigate the derivation of associated eFRI ecosites. The potential exists to develop lidar data analysis and modeling techniques to the point where it efficiently, accurately and cost-effectively enhances the prediction of forest ecosite classes across Ontario.

3) The development of a peer-reviewed and accepted approach to lidar use in the interpretation and production of enhanced forest resource inventory and forest ecosite classification across a range of Ontario forest types and conditions, and a modular software application that automates this classification and is made available as a component part of a full-scale and comprehensive forest inventory management system; this software module will assign ecosite classifications based on an appropriate modeling approach that combines lidar and other related datasets.

 

The Project Team:  Paul Treitz, Queen's University, Murray Woods, OMNR, Al Stinson, OMNR-FRP, Paul Courville, FRP

 

Papers:

Lim, K.S. and P.M. Treitz. 2004. Estimation of Above ground Forest Biomass from Airborne Discrete Return Laser Scanner Data Using Canopy-based Quantile Estimators. Scand. J. For. Res. 19: 558-570.

Hopkinson C. et al. 2005. Vegetation class dependent errors in lidar ground elevation and canopy height estimates in a boreal wetland environment. Can. J. Remote Sensing 31:191–206.

Chasmer, L., Hopkinson, C. and P. Treitz. 2006. Investigating laser pulse penetration through a conifer canopy by integrating airborne and terrestrial lidar. Can. J. Remote Sensing 32: 116–125.

Chasmer, L., Hopkinson, C., Smith, B. and P. Treitz. 2006. Examining the Influence of Changing Laser Pulse Repetition Frequencies on Conifer Forest Canopy Returns. Photogrammetric Engineering & Remote Sensing 72: 1359–1367.

Hopkinson, C., Chasmer, L., Lim, K., Treitz, P. and I. Creed. 2006. Towards a universal lidar canopy height indicator. Can. J. Remote Sensing 32: 139–152.

Thomas, V., Treitz, P., McCaughey, J.H. and I. Morrison. 2006. Mapping stand-level forest biophysical variables for a mixedwood boreal forest using lidar: an examination of scanning density. Can. J. For. Res. 36: 34–47.

Thomas, V., Oliver, R.D., Lim, K. and M. Woods. 2008. LiDAR and Weibull modeling of diameter and basal area. For. Chron. 84: 866-875.

Lim, K., Hopkinson, C. and P. Treitz. 2008. Examining the effects of sampling point densities on laser canopy height and density metrics. For. Chron. 84: 876-885.

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.

Pitt, D. and J. Pineau. 2009. Forest inventory research at the Canadian Wood Fibre Centre: Notes from a research coordination workshop, June 3–4, 2009, Pointe Claire, QC. For. Chron. 85: 859-869.

AFRIT Final Report (April 17, 2010): Spatially Explicit Predictions of Forest Inventory Variables for the Romeo Malette Forest

Woods, M. et al. 2011. Operational implementation of a LiDAR inventory in Boreal Ontario. For. Chron. 87: 512-528.

 Paul Treitz, Kevin Lim, Murray Woods, Doug Pitt, Dave Nesbitt and Dave Etheridge (2010). LiDAR Sampling Density for Forest Resource Inventories in Ontario, Canada. Remote Sens. 4: 830-848.

Tree Tip:

Tree Trip Project 120-701

 

Presentations:

Precision Planning Inventory Tools for Forest Value Enhancement

Estimating Forest Canopy Chlorophyll Concentration Using Complementary Remote Sensing Technologies: LiDAR and Hyperspectral Data

Evaluation and Development of LiDAR Data Acquisition Standards for Forest Inventory Applications and Predictive Ecosite Classification

Application of Airborne and Ground-based LiDAR Data for Forest Inventory and Monitoring

Evaluation and Development of Lidar Data Acquisition Standards for Forest Inventory Applications and Predictive Forest Ecosite Classification

 

Status Reports:

OCE Metrics Report

Annual Report (2007-2008)

Project Work Report (2007-2008)

Status Report (2008-2009)

Financial Summary (2008-2009)

Status Report (2009-2010)

Status Report (2010-2011)

 

For Additional Information Contact:

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