Integration of multi-source, multi-scale remotely sensed imagery with ground survey information to provide forest health and inventory data

• Main Author: Coggins, Sam
• Language: English
• Published: University of British Columbia 2011
• Online Access: http://hdl.handle.net/2429/30610

Bark beetle infestations in western Canada have caused damage at previously unrecorded levels. Conventional forest health surveys are conducted to collect information on these infestations; however, due to the widespread nature of attack digital remote sensing technologies have the potential to offer new methods to augment forest inventories. This thesis will investigate the utility of remotely sensed data to detect and monitor insect infestations and provide innovative approaches to determine forest health information. In the first section of the thesis the accuracies of conventional forest health surveys were reviewed and assessed in a series of plots at the edge of the infestation. Mitigation levels were shown to be 43%, which was inadequate to stop a doubling expansion rate. A review of the detection rates of digital remote sensing was also conducted and used in a simple expansion model to assess the capacity of digital techniques. In the second part of the thesis a series of innovative methods were applied over a hierarchy of remotely sensed data sets. Attacked trees identified during field surveys were delineated on fine scale imagery with an accuracy of 80.2%. From these delineations, tree [stem diameter (r = 0.71, p <0.001)] and stand level [stocking density (r = 0.95, p <0.001)] information was accurately predicted and used to initiate an infestation spread model. Using this technique, an adaptive cluster sampling approach was applied in an innovative way to develop regional estimates of infestations. A relative efficiency estimator confirmed the adaptive approach was twice as efficient as conventional sampling schemes. With confidence in the approach, adaptive cluster sampling was applied to consecutive annual images determining a doubling infestation rate. Finally, an advanced remote sensing model was applied to stratify the landscape based on predictions of stocking and crown size, to predict the susceptibility of attack over the study area. Ultimately, this research successfully used a hierarchy of remotely sensed data to provide forest health and inventory information at a variety of scales from individual tree to stands and regions, which can augment existing forestry databases.