Point Cloud V2 Issue 3 (Nov 2016)

November Edition of the Point Cloud

The Point Cloud, AWARE’s Electronic Newsletter
Vol. 2, Issue 3. Date: 11/14/2016
aware.forestry.ubc.ca
Previous Issue
Not displaying correctly?
View this email in your browser

Welcome to the fall edition of the Point Cloud. In this edition:

 

Introduction
Welcome to the November edition of the Point Cloud. While AWARE is focussed on using LiDAR and remote sensing for forestry research, we’re not the only ones researching new uses for LiDAR. In a new application of LiDAR, NASA is considering using a LiDAR to look for extra-terrestrial life on Mars. Photonics – Is there life of Mars? Back on Earth, AWARE is now officially into the second half of its second year and we are making great progress. Two of our students are getting close to completing their degrees and we also expect to see our first publication in the not so distant future.

Feature Researcher

Shane Furze

Shane Furze is a PhD candidate with the Forest Watershed Research Center at the University of New Brunswick (UNB) under the supervision of Dr. Paul Arp. Shane completed his BSc in Environmental Science in Biology at St. Francis Xavier University in 2010 before obtaining a Master’s of Environmental Management (MEM) at UNB in 2013.
Shane has always been interested in the natural environment and his passion for the outdoors began at an early age while spending his childhood fishing and hunting with his father in New Brunswick. It was an easy decision for Shane to follow his passion and focus his education around natural resources and environmental management. It was during his Master’s degree when Shane was introduced to GIS while assessing the impacts of forestry operations and forest roads on Atlantic salmon spawning habitat. From this point forward Shane has tied his passion into his education and current projects directed toward digital soil mapping and forest productivity.
Shane’s contributions to AWARE focus on improving forest plantation growth predictions based on digital terrain and soil modelling (theme 2, question 10). In this, soil properties are considered to change continuously from ridge top to valleys, as influenced by increasing soil moisture as well as landform type and position. Resulting from this work, Shane is the recipient of the 2015 ESRI International Young Scholar for Canada Award and the 2015 New Brunswick Innovation Foundation Tri Council Doctoral Alternate Award.
When time permits, Shane can be found outdoors guiding fishermen and hunters, fly fishing, photographing nature, paddling, and cycling.

Research Snapshot

 

A Comparison of Direct and Indirect Mapping Strategies of Forest Attributes : Test Case for the Boreal Forest of Newfoundland, Canada.

 

Mélodie Bujold is a Master’s student under the supervision of Richard Fournier (Université de Sherbrooke) and Joan Luther (Natural Resources Canada). Her research project (Question 3 of Theme 1) aims to develop a multi-level strategy for mapping forest attributes using a combination of ground plots, airborne LiDAR samples, full coverage satellite imagery and ancillary site and geographic data. Her project will investigate the utility of the various datasets, compare various imputation methods, and produce island-wide assessments of key attributes of Newfoundland forests. The purpose is to improve the accuracy of forest attribute maps over large areas.

Traditional inventory programs rely on aerial photographs and experienced interpreters to map forest attributes over large areas. This requires a large amount of resources. An alternative mapping strategy is to combine field measurements from forest plots with spectral values from satellite images. Statistical relationships can then be made between the plot measurements and the spectral values so that the attribute information can be generalized for the area covered by the satellite images. Strategies using aerial photographs or satellite imagery have been used extensively in past decades to extrapolate information from forest plots, produce maps of forest type and stand cover density, and to estimate stand volume and biomass. However, in more recent years, Airborne Laser Scanning (ALS) is used increasingly to produce forest attribute maps with improved accuracy. In fact, ALS has provided such an improvement over traditional methods using aerial photographs or satellite images, that many national inventory programs have adopted ALS data as a baseline for their inventory. However, ALS data remains more expensive than aerial photographs or satellite images for large area mapping. Consequently many areas can only be covered partially by ALS. Meanwhile the need for more accurate maps of large areas remains. Therefore, a potential solution to address this need is an indirect mapping strategy that takes advantage of partial ALS coverage with more resource-efficient satellite images.

Overall, this project aims to compare the results achieved from a direct strategy with an indirect strategy that involves two steps to map forest attributes (from forest plots to satellite images). The first step uses measurements from ground plots to model relationships with ALS data. This step is spatially limited to the area where ALS data is collected. The second step spatially expands estimates for the area covered by ALS data to a much larger area covered by optical satellite images. Improving accuracy using ALS in an indirect mapping strategy is expected to help the planning and monitoring of forest resources for sustainable resource management.

The use of an indirect mapping strategy overcomes several constraints related to field plots for map production. Measurements at field plots are costly to acquire. This research project will explore how to reduce the number of plots required for mapping and how to improve the use of the available ones. This project will be applied to the island of Newfoundland, Canada. Several statistical options will be explored in the direct and indirect strategies. For instance both parametric (ordinary least-square regression – OLS) and non-parametric (random forests – RF) techniques will be compared. The steps involved in the indirect mapping strategy are illustrated in Figure 1. ALS transect data collected in 2011 will be used for a first set of results. A second ALS dataset collected in 2016 will be used to assess method sensitivity to data specifications. This project will result in the implementation and error assessment of the indirect strategy for the Island of Newfoundland. Since the strategy has the potential to be generalized to other large areas in Canada, we also plan to test it on another boreal region in Quebec with independent datasets.

Figure 1. Indirect strategy used to predict forest attributes over a large forest area.

 


AWARE Research Opportunities

 

Graduate Students


TERRAIN BASED INDICATORS IN PREDICTIVE SPATIAL REPRESENTATIONS OF FOREST GROWTH AND WOOD FIBRE ATTRIBUTES

Download Full Description PDF

A MESc. Student at Nipissing University will investigate the potential to derive a suite of terrain based indicators from newly acquired LiDAR data and other geospatial information at the Newfoundland core site and integrate these with existing soil and forest structure information available from the provincial growth plot network. The student will then assess the capacity of these indicators to be used as inputs into land classification schemes which can be used to produce predictive spatial representations of forest growth and wood fibre attributes.

The ideal candidate will have some background in quantitative analysis at the undergraduate level, a Bachelor’s degree in forestry, biology or geography and some experience in growth and yield modelling or forest inventory.

Interested candidates are encouraged to submit a cover letter and curriculum vitae by Jan. 1, 2017 to:

Dr. Jeff Dech
Associate Professor
Department of Biology and Chemistry
Nipissing University
100 College Drive
North Bay, ON, Canada P1B 8L7
Tel: (705) 474-3450 x.4701
Fax: (705) 474-1947
jeffrey@nipissingu.ca

 


Post-Doctoral Positions

 

DEVELOPMENT OF GENERALIZED AIRBORNE LIDAR METHODS FOR TREE SPECIES IDENTIFICATION TRANSFERABLE ACROSS CANADIAN FOREST SITES

Download Full Description PDF

AWARE (Assessment of Wood Attributes from Remote Sensing) is an NSERC Collaborative Research and Development (CRD) project spanning eight universities, federal and provincial governments and seven industrial partners that seeks to use remote sensing to enhance Canada’s forest inventory and to improve the modelling of forested ecosystems (see aware.forestry.ubc.ca).

Within this framework, we are seeking applicants for a postdoctoral fellowship to join AWARE researchers, other postdoctoral fellows and graduate students who are actively conducting research on a variety of themes related to the project’s overall objectives. The selected postdoctoral fellow will be mentored by Prof. Benoît St-Onge, Ph.D. (Department of Geography, University of Quebec at Montreal) and will work collaboratively with other project members locally, and across Canada.

Application

Persons interested in this position should send a statement of interest outlining relevant research qualifications (maximum of two pages), a CV (showing the actual or planned Ph.D. defence date), and up to three relevant publications and contact information for three references. At the end of the selection process, a copy of the PhD diploma will be required.

Deadline for applications: 16 January 2017

Please submit your application, or any request for further information, to:

Prof. Benoît St-Onge
Phone: +1 (514) 987-3000 ext. 0280
email: st-onge.benoit@uqam.ca


METHODS DEVELOPMENT FOR TERRESTRIAL LIDAR APPLIED TO FOREST INVENTORY WITH EMPHASIS ON INTEGRATING A TREE ARCHITECTURAL MODEL

 

Download Full Description PDF

Location: Applied Geomatics Department, Université de Sherbrooke, Sherbrooke, Quebec, Canada

Timeframe: Up to two years starting between April 2016 and September 2016

The Terrestrial LiDAR research group at the Applied Geomatics Department of the Université de Sherbrooke is seeking to hire a post-doctoral candidate. The research will be focussed on the development of methods to estimate tree and stand structural attributes from point cloud data acquired with terrestrial scanners in forested environments. These new methods will take advantage of architectural models to describe detailed branching structure and foliage distribution in trees.

Review of applications will begin on April. 1, 2016 and continue until the position is filled. Interested parties should send their CV and two letters of reference to:

Dr. Richard Fournier
Department of Applied Geomatics (FLSH)
Université de Sherbrooke
2500 boul de l’Université, Sherbrooke (Quebec) Canada J1K 2R1
E-mail: Richard.Fournier@USherbrooke.ca
Tel.: 1-819-821-8000 ext 63209

Multi-Spectral LiDAR Update

Teledyne Optech Titan data (Petawawa Research Forest): Data processing update

As mentioned in the September issue of the Point Cloud newsletter, we were fortunate enough to be able to acquire Titan multispectral lidar data over Petawawa Research Forest (PRF) as part of the AWARE Summer 2016 field activities for the Ontario region. Teledyne Optech was very quick in providing us the data for the July flight, the data was received in August. Since there are 3 laser beams in the Titan system (with wavelengths of 532 nm, 1064 nm and 1550 nm) there are 3 separate sets of LAS files that comprise the 2016 PRF dataset. Each set is composed of 33 flight strips with overlap between them, the overlap varies between 20 and 100 m. We received the digitized full waveforms from the 1064 nm channel as well. Over 110 GB of data was collected during the flight.

Initial processing was centered around the quality control of the obtained data. Rapidlasso’s LAStools and TU Wien’s OPAL software were primarily used in this step. The point density (all returns) was calculated to be 9 points/m2 for the two IR channels (1064 nm and 1550 nm) and 3 points/m2 for the green channel. The spacing between lidar points was 0.33 m for the 2 IR channels and 0.57 m for the green. This disparity is explained by the fact that the green laser has twice the beam divergence (0.70 mrad vs 0.35 mrad) as the two IR ones due to eye safety regulations. Additionally, there were altitude flight restrictions over PRF since it is part of the Petawawa Garrison military base. Therefore, less energy from the green laser pulses was reflected back to the sensor resulting in lower point densities. This effect has been observed on other Titan lidar flights. The figure shows the difference in point densities between one of the IR channels (1550 nm) and the green one. Strip alignment was also verified and found to be excellent with no alignment issues between the strips. Overall, the data quality for the Titan lidar flight over PRF is excellent.

Current work is concentrated on evaluating different ground filtering processing alternatives. For our research purposes, we need to be able to calculate Canopy Height Models (CHM). A Digital Terrain Model (DTM) representing the bare earth is required and we need to filter our Titan point cloud to identify ground points. Several software alternatives (PDAL, LAStools, FUSION, ArcGIS Pro) are being evaluated for this step. The quality of the DTM is an integral element of the precision forestry work flow and is a cornerstone for the further statistical and classification work that will be performed on this data set.

Upcoming Events

 

On Nov. 23rd-24th, AWARE’s Richard Fournier will be hosting a workshop in conjunction with INRA in Avignon (http://aware.forestry.ubc.ca/news/).

Next summer, IUFRO will hold a conference in Vancouver from June 12-16th (www.IUFROdiv5-2017.ca). The week after, from June 20-22nd, the Earth Observation summit Earth Observation Summit will be happening at UQAM. AWARE is planning to present at both these events next summer.

Last of all, we will be holding our AGM in New Brunswick in the early summer. The date and location in New Brunswick will be determined in the coming months. More details will be provided in the New Year.


 

Copyright © 2016 University of British Columbia, All rights reserved.