| dc.description.abstract | Accurate volume estimations are pivotal for effective forest resource management,
influencing stakeholders throughout the forestry industry. Traditionally, estimations relied on
stem diameter measurements and geometric assumptions. However, advancements in remote
sensing have revolutionized volume calculations, offering new possibilities for precision and
efficiency. This thesis delves into volume estimations within the Romeo Mallette Forest of
Northeastern Ontario’s Boreal Forest, employing a multifaceted approach that includes ground
surveys, Ontario Forest Resource Inventory (FRI) data, and Remotely Piloted Aircraft System
(RPAS)-based remote sensing. The objectives encompass evaluating the accuracy of FRI data,
assessing ground surveys' precision, investigating RPAS. Additionally, the study aims to leverage
FPInnovations' Single Tree Metrics and Stand assessment (STEMS), a pre-harvest inventory tool
that utilizes consumer-grade RGB imagery from an RPAS, and to scrutinize the variance between
estimated volumes and actual mill volumes. The study meticulously evaluates the efficacy of the
STEMS algorithm against ground surveys and FRI merchantable volume estimates, utilizing the
final Bill of Lading (BOL) as the control measurement. Remarkably, the initial RPAS flight path,
harnessing STEMS technology, emerged as the most precise in estimating merchantable volume,
yielding 129 m3
/ha compared to the final BOL measurement of 122 m3
/ha. In contrast, ground
surveys anticipated 134 m3
/ha, while the FRI data was the only underestimation at 106 m3
/ha.
This singular study underscores the potential of STEMS in accurately estimating merchantable
volumes in forestry, signaling a significant advancement in volume estimation methodologies. | en_US |
