The Open Collections website will be undergoing maintenance on Wednesday December 7th from 9pm to 11pm PST. The site may be temporarily unavailable during this time.
UBC Theses and Dissertations
Forest structure for avian conservation using multi-scale remote sensing data in Newfoundland Herniman, Samuel
The diversity of forest birds has long been linked to forest structure. Forest structure can be quantified by remote sensing with airborne laser scanning (ALS). To date, ALS based wildlife monitoring studies have been limited in geographic scope, principally due to limited aerial coverage of the ALS datasets. This thesis first assesses the ability of ALS and other remote sensing and geospatial datasets to estimate bird occurrence over a focus site of boreal forest in Newfoundland, Canada and then develops an approach to extend predictions of bird occurrence in areas that have no ALS coverage. To do so, I developed models using best subset regression of avian habitat suitability using ALS data acquired in Newfoundland, Canada. Model data included plot surveys, multispectral information, topographic, landscape, climatic and ALS datasets. While field plot metrics outperformed remote sensing metrics from a single sensor; models combining ALS, multispectral and/or environmental data, were most significant. Given the models were developed in highly managed areas of boreal forest, the results suggest these approaches can help monitor the environmental impacts of management decisions and climate change on biodiversity. Second, I used discontinuous ALS and spectral time series from Landsat to predict wall- to-wall structural metrics across Newfoundland. Three forest structural metrics (kurtosis, height and understory) were selected as strong predictors of avian habitat quality. Using imputation approaches, I extended these structural metrics over the entirety of Newfoundland with results indicating kurtosis was not predicted accurately (r 2 =0.08) when compared to independent ALS data, however, height (r 2 =0.43) and understory (r 2 =0.37) showed strong correlations. Using a random forests modelling approach, I produced habitat suitability models to predict the occurrence of nine bird iv species using citizen science data from eBird and provincial permanent sample plots (PSPs). All species had higher area under the receiver operator curve values using eBird data (min=0.73, median=0.85, max=0.91) compared to PSP data (min=0.52, median=0.60, max=0.71), demonstrating the strength of citizen science data. Overall, the results demonstrate structural metrics from ALS, combined with avian observations from forest plots and citizen science data can produce accurate predictions of avian occurrence and abundance over large areas.
Item Citations and Data
Attribution-ShareAlike 4.0 International