This summer was very busy, with four new researchers joining AWARE. Doug Bolton started in May and is working on Q1 at UBC. Catherine Frizzle (Q17b, Sherbrooke) and Ayla Brombach (Q9, Nipissing) began their research at the beginning of September. Our most recent addition, Rana Parvez (Q23, UQAM) is expected to start in mid to late September. With the new people, AWARE is now doing research on 22 of the 25 research questions in our mandate.
Last of all, congratulations to Dr. Paul Arp and Shane Furze! Their research group, the Forest Watershed Research Centre, won the 2017 CIF Forest Management Group Achievement Award.
Shawn Donovan is a Master’s student studying at the University of New Brunswick (UNB) and currently working on Question 15 of Theme 2 for the AWARE research project. Shawn’s research is supervised by Dr. David MacLean and is investigating how satellite and high spatial resolution optical imagery can be used to augment spruce budworm forest health surveys. Specifically, Shawn’s research is focusing on quantifying spruce budworm (SBW) defoliation using digital hemispherical imagery and EO-1 Hyperion hyperspectral satellite data collected in the Gaspé Peninsula, Quebec, Canada. Current progress of Shawn’s research has him editing his first manuscript for journal publication which investigated hemispherical imagery for estimating defoliation and also has started analysis work for the second manuscript which uses the EO-1 Hyperion hyperspectral satellite data.
Shawn first discovered a passion for the environment and especially forestry while working in Northern Alberta for the oil industry conducting seismic testing. Eventually, Shawn pursued his interests and completed a Wildlife Conservation Diploma and Wildlife Conservation Enforcement certificate in 2009 at Holland College in Prince Edward Island. Shawn transferred to UNB in 2012 to focus more in the field of forestry completing his BScF in 2015. His interest in conducting meaningful research lead towards beginning his Masters the following semester.
Shawn plans to pursue a research career and potentially a professorship after finishing his Masters in the spring of 2018.
When not immersed in his studies Shawn enjoys mountain biking, bird watching and especially flying fishing for Atlantic salmon and trout throughout New Brunswick and P.E.I. Shawn also is involved in the Forestry Graduate Student Association as Vice-president which brings together forestry and environment grad students by holding socials, luncheons and monthly events which sometimes lead to the campus grad house where cold ales are of abundance.
Enhancing Forest Growth and Yield Predictions with Airborne Laser Scanning Data
Piotr Tompalski is a PDF working with Nicholas Coops and Peter Marshall (University of British Columbia). In his research project (Question 13 of Theme 2) Piotr looks for methods to integrate forest growth models with lidar-enhanced forest inventory data. He is using data collected at two different times (T1 and T2) to improve forest growth and yield projections of important forest stand attributes. He integrates ALS data with digital aerial photogrammetry (DAP) point clouds, which were acquired in 2007 and 2015, respectively. Existing stand-based growth model (GYPSY) is used as a source of growth information that is assigned at a 20 x 20 m cell level.
In his previous work Piotr showed how current ALS-based estimates of stand attributes can be used to derive growth and yield at a 20 m grid cell level (Tompalski et al., 2016). He demonstrated how stand-level growth model can be used to create a yield curve database, and how can ALS data be used to match yield curve to each cell. The approach was based on point cloud acquired at a single point in time only.
In the current project Piotr aims to demonstrate an approach whereby two three-dimensional point cloud datasets (one from ALS and one from DAP), acquired over the same forest stands, at two points in time (circa 2008 and 2015), are used to derive forest inventory information. The area-based approach (ABA) is first used to predict top height (H), basal area (BA), total volume (V), and stem density (N) for Time 1 and Time 2 (T1, T2). Individual yield curves are then assigned to 20 m grid cells in two scenarios. The first scenario uses T1 estimates only (approach 1), while the second scenario combined T1 and T2 estimates (approach 2). Yield curves are matched by comparing the predicted cell-level attributes with a yield curve template database generated using GYPSY. Results indicate that the yield curves in approach 2 were matched with higher accuracy, and that the accuracy of curve matching is dependent on the ABA prediction error. The relative root mean squared error of curve matching in approach 2 for H, BA, V, and N, was 18.4, 11.5, 25.6, and 27.53% for observed data, and 13.2, 44.6, 50.4 and 112.3% for predicted data, respectively. Additionally, projections based on yield curves matched with the combined T1 and T2 data were lower for BA, V, and N, which was especially evident when comparing mean annual increment values for the whole study area. The analyses presented in this study provide additional detail than existing inventory projection approaches, thereby improving the information available to inform long-term, sustainable forest management.
Figure 1. A 2 x 2 km subset of the wall-to-wall yield curve projection result, based on approach 2. Each row of graphs demonstrates a progression of a cell-level attribute (H – top height, BA – basal area, V – total volume, N – stem density) through time. Attributes are projected to 25, 50, 100, and 150 years (columns). Yield curves are shown for four representative cells.
Tompalski, P., Coops, N., White, J., & Wulder, M. (2016). Enhancing Forest Growth and Yield Predictions with Airborne Laser Scanning Data: Increasing Spatial Detail and Optimizing Yield Curve Selection through Template Matching. Forests, 7(12), 255. doi:10.3390/f7110255
The Y2 AGM drew more participants than last year’s AGM, with 62 attendees, including contingents from CWFC, NHRI, NB-DNR and JD Irving. One of the highlights of this AGM was the field trip to the Black Brook Forest, where we were able to visit sites to see examples of Sean Lamb’s research on commercial thinning (Q6) and Rachel Perron’s research on species identification (Q22).
Next year, the AGM will be held in Montreal from June 4-6th. For this AGM, one day has been set aside to show AWARE’s research results to forestry professionals who are not directly participating in AWARE. If you know of anyone who might be interested in AWARE’s research, have them sign up to the newsletter on our website.
For those who are still on the fence about attending Silvilaser (http://www.cpe.vt.edu/silvilaser2017/), several of our researchers will be presenting their results at Silvilaser. This is an excellent chance to hear about the latest research developments in AWARE. Our researchers will be presenting their latest results on many topics including species identification, metrics, and growth modelling.
Introductory LiDAR Workshops
AWARE and CWFC are also putting on several workshops to show forestry professionals how to use LiDAR. More information can be found on our website (https://aware.forestry.ubc.ca/news)