Identification of Forest Road Construction Year from Historical Remotely Sensed Data by Integrating Change Detection and Geometric Connectivity
Loading...
Authors
Noltie, Kirsten R.
Date
Type
thesis
Language
eng
Keyword
Forest road , Logging , Best Available Pixel (BAP) Composite , Road Extraction , Change Detection , Classification , Logic Rules , Post-Processing , Remote Sensing , Geographic Information Systems (GIS) , Forestry
Alternative Title
Abstract
Current and highly accurate forest disturbance information is critical for monitoring and managing forested ecosystems. The types, rates, and responses to past disturbances allow for improved predictions for how forests will respond to current and future disturbances. Forest roads, a short-term forest stand replacing disturbance, are associated with ecosystem degradation. Determining the age of different elements of forest road networks can enable long term monitoring and assist with sustainable forestry management. To determine the age of construction of forest roads within Ontario’s managed forests, a semi-automatic road extraction and Post-Classification Change Detection (PCCD) approach was developed with Best Available Pixel (BAP) composites of Landsat imagery. A study site was selected near Savant Lake, Ontario for the years between 1974 and 2019. The BAP composite approach uses sensor, day of year (DOY), distance to cloud or cloud shadow, opacity, and pixel brightness scores to create yearly composites which are free of clouds or anomalies and optimized for the intended analysis. An unsupervised classifier, Jenks Optimization, was used to differentiate road and non-road pixels from an image masked with current known road areas. This method aims to reduce the average deviation of each value from the mean of the class and increase the deviation of each class from the mean of other classes. PCCD was completed with the pre- and post-1984 periods resulting in overall accuracies of 80% and 68%, respectively. Four logic rules, which leverage the topological relations between road segments and address road gaps due to occlusions or sensor anomalies, were applied twice throughout the process. The logic rules increased the overall accuracy up to 3% for each annual binary road network and up to 3% for the final year of construction road network. Understanding historic types, rates, trends, and patterns of ecosystem change across large areas with high spatial detail is essential for effective forest monitoring and sustainable management. Time series analyses based on optimized annual BAP composites and post-processing logic rules can provide information to support these endeavors.
Description
Citation
Publisher
License
Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution-NonCommercial 4.0 International
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution-NonCommercial 4.0 International