| dc.description.abstract | Snags are an integral component of forest ecosystems as they provide
habitat for a number of different species and add complexity to vertical forest
structure. However, snags also may pose as potential hazards to people and
property. Efficient and effective methods to locate and assess snags/hazard
trees holds value to resource and conservation managers. This study aimed to
assess the feasibility of using drone-based, multi-spectral sensors for detecting
snags/hazard trees. The methods used in the study included an autonomous
drone flight over the study areas, orthomosaic processing, object-based image
analysis (OBIA), an accuracy assessment, and a field ground truth. The results
provided sufficient evidence of drone-based, multi-spectral sensors being
effective at detecting snags/hazard trees. However, the methods used in this
study were found to only be accurate at detecting high quality/hazard snags.
Segmentation parameters had a significant impact on the degree of
quality/hazard of snag that the algorithm could detect. The orthomosaic
classification was considered as highly accurate with an overall accuracy of
93.4%. Resource and conservation managers can effectively use the methods
from this study for a variety of applications that aim to promote biodiversity
and/or minimize public hazards. | en_US |
