Advancing Forest Fire Risk Evaluation: An Integrated Framework for Visualizing Area-Specific Forest Fire Risks Using UAV Imagery, Object Detection and Color Mapping Techniques
Michal Aibin,
Yuanxi Li,
Rohan Sharma,
Junyan Ling,
Jiannan Ye,
Jianming Lu,
Jiesi Zhang,
Lino Coria,
Xingguo Huang,
Zhiyuan Yang,
Lili Ke,
Panhaoqi Zou
Affiliations
Michal Aibin
Khoury College of Computer Sciences, Northeastern University, 410 West Georgia Street, Vancouver, BC V6B 1Z3, Canada
Yuanxi Li
Khoury College of Computer Sciences, Northeastern University, 410 West Georgia Street, Vancouver, BC V6B 1Z3, Canada
Rohan Sharma
Khoury College of Computer Sciences, Northeastern University, 410 West Georgia Street, Vancouver, BC V6B 1Z3, Canada
Junyan Ling
Khoury College of Computer Sciences, Northeastern University, 410 West Georgia Street, Vancouver, BC V6B 1Z3, Canada
Jiannan Ye
Khoury College of Computer Sciences, Northeastern University, 410 West Georgia Street, Vancouver, BC V6B 1Z3, Canada
Jianming Lu
Khoury College of Computer Sciences, Northeastern University, 410 West Georgia Street, Vancouver, BC V6B 1Z3, Canada
Jiesi Zhang
Khoury College of Computer Sciences, Northeastern University, 410 West Georgia Street, Vancouver, BC V6B 1Z3, Canada
Lino Coria
Khoury College of Computer Sciences, Northeastern University, 410 West Georgia Street, Vancouver, BC V6B 1Z3, Canada
Xingguo Huang
Khoury College of Computer Sciences, Northeastern University, 410 West Georgia Street, Vancouver, BC V6B 1Z3, Canada
Zhiyuan Yang
Khoury College of Computer Sciences, Northeastern University, 410 West Georgia Street, Vancouver, BC V6B 1Z3, Canada
Lili Ke
Khoury College of Computer Sciences, Northeastern University, 410 West Georgia Street, Vancouver, BC V6B 1Z3, Canada
Panhaoqi Zou
Khoury College of Computer Sciences, Northeastern University, 410 West Georgia Street, Vancouver, BC V6B 1Z3, Canada
Forest fires have significant implications for the Earth’s ecological balance, causing widespread devastation and posing formidable challenges for containment once they propagate. The development of computer vision methods holds promise in facilitating the timely identification of forest fire risks, thereby preventing potential economic losses. In our study conducted in various regions in British Columbia, we utilized image data captured by unmanned aerial vehicles (UAVs) and computer vision methods to detect various types of trees, including alive trees, debris (logs on the ground), beetle- and fire-impacted trees, and dead trees that pose a risk of a forest fire. We then designed and implemented a novel sliding window technique to process large forest areas as georeferenced orthogonal maps. The model demonstrates proficiency in identifying various tree types, excelling in detecting healthy trees with precision and recall scores of 0.904 and 0.848, respectively. Its effectiveness in recognizing trees killed by beetles is somewhat limited, likely due to the smaller number of examples available in the dataset. After the tree types are detected, we generate color maps, indicating different fire risks to provide a new tool for fire managers to assess and implement prevention strategies. This study stands out for its integration of UAV technology and computer vision in forest fire risk assessment, marking a significant step forward in ecological protection and sustainable forest management.
