Remote Sensing Boreal Coarse Woody Debris

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Coarse woody debris (CWD) are vital components of forested environments, affecting the physical structure and biochemistry of forests, supplying habitats, nutrients and food for many organisms. Additionally, CWD is an especially important element in boreal forest management in Alberta, Canada. Large quantities of CWD can configure a fire hazard, whereas moderate quantities of CWD in linear disturbances can aid newly planted seedlings to survive and hinder the hunting effectiveness of predators of endangered caribou herds. Forest managers and ecologists can benefit from large-scale high-accuracy quantitative mapping of CWD in complex boreal environments. This thesis represents the first high-resolution remote sensing study of CWD within the context of Alberta’s boreal forest. The research conducted here tested the effectiveness of a geographical object-based image analysis (GEOBIA) workflow with random forest classification for mapping CWD logs and snags in a 4300-hectare study area in northeastern Alberta, Canada. Additionally, zero-adjusted models were selected for accurate estimation of CWD volume in the study area using Akaike’s information criterion. The developed models successfully mapped (up to 93.4% completeness and 94.5% correctness) and estimated volume of CWD (0.623 R2, 0.224 RMSE) with good accuracies. Light detection and ranging (LiDAR) data improved the distinction between logs and snags in CWD maps (~6% better distinction; significant at α 0.05), and multispectral LiDAR data improved the estimation of CWD volume occluded by superimposed vegetation (~ 0.1 higher R2 and ~0.018 lower RMSE). Models developed in a calibration area could be applied to a verification area 4 km distant from all training data without substantial differences in accuracy (average 9% drop in mapping accuracy, no decrease in R2 or increase in RMSE when estimating volume). Given the potential of emerging multispectral LiDAR technologies, it is likely that future improvements to sensors will make ever more accurate CWD predictions possible. Site managers, as well as ecologists and foresters interested in studying the spatiality of CWD can make use of the developed workflows to obtain accurate and extensive map products in forested areas.
coarse woody debris, snag, log, CWD, boreal forest, GEOBIA, random forest, machine learning, LiDAR
Lopes Queiroz, G. (2019). Remote Sensing Boreal Coarse Woody Debris (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from