Continuous forest inventory framework
Chris Mulverhill, PDF
While OB.3 focuses on broader scale strategic monitoring, there is also a need for improved monitoring of key stand forest metrics that drive the silvicultural decision-making process. Key to prescriptions such as thinning, fertilising and final harvesting is accurate tactical, tree and stand level, information such as tree location, height, as well as stand density, composition and health. Forest companies such as JDI require continual update of these conditions to implement agile, adaptive forest management. To support two MSc students in OB.5b and OB.5c a two-year PDF will first have the objective to develop a framework to determine which key stand attributes could be integrated into an continuous monitoring framework, at what level of spatial detail (ideally within a 20m grid) and at what level of accuracy (ideally at the 1ha level). The PDF will first undertake an evaluation of needs in terms of attributes and their desired accuracy, and then make an assessment of the most appropriate remote sensing tools to meet these desired outcomes across a number of HUB sites. Outcome (OB.5a): A continuous forest inventory updating framework using a range of remote sensing data sources for implementation at operational scales at relevant Hub sites.
Chris Mulverhill, PDF at University of British Columbia
Main Partner: J.D. Irving Ltd.
Professor Nicholas Coops
Collaborator Alexis Achim
Mulverhill, C., Coops, N.C., Achim, A. (2023). Continuous monitoring and sub-annual change detection in high-latitude forests using Harmonized Landsat Sentinel-2 data. ISPRS Journal of Photogrammetry and Remote Sensing, 197: 309-319. https://doi.org/10.1016/j.isprsjprs.2023.02.002
Coops, N., Tompalski, P., Goodbody, T.R,H., et al. (2022). Framework for near real-time forest inventory using multi source remote sensing data. Forestry cpac015. https://doi.org/10.1093/forestry/cpac015
Keay, L., Mulverhill, C., Coops, N.C., McCartney, (2022) Automated Forest Harvest Detection With a Normalized PlanetScope Imagery Time Series. Canadian Journal of Remote Sensing, 1-15; https://doi.org/10.1080/07038992.2022.2154598