Accuracy Analysis of Real-time Open Burning Imagery for Early Wildfire Monitoring By Employing Drones

Pnam Donovan^*
*Correspondence: Pnam Donovan, Department of Environmental Science and Biology, Mahidol University, Bangkok, Thailand, Email:

Description

Wildfires have become an increasingly pressing concern in recent years due to their devastating impact on both human communities and the environment. Early detection and timely response are crucial in mitigating the destructive consequences of wildfires. Traditional wildfire monitoring techniques often rely on ground-based observations, satellite imagery, and weather forecasts. However these methods have limitations, such as delayed response times and restricted access to remote areas. In recent years the integration of drones with real-time open burning imagery analysis has emerged as a promising solution to address these shortcomings and enhance wildfire monitoring accuracy.

The concept of using drones to capture real-time imagery of open burning areas for early wildfire monitoring. We will delve into the technology and methods involved in this approach, discuss the advantages and challenges, and conduct an in-depth analysis of the accuracy of the collected data [1]. By evaluating the effectiveness of drones in detecting and monitoring wildfires, we aim to shed light on their potential to revolutionize wildfire management strategies. The use of drones also known as Unmanned Aerial Vehicles (UAVs) has seen significant growth in various fields including agriculture, environmental conservation and disaster management. In the context of wildfire monitoring, drones offer several advantages that make them an attractive tool for early detection and monitoring.

Drones equipped with high-resolution cameras and thermal imaging sensors can capture real-time imagery of wildfire-prone areas, providing instant data to wildfire management teams. Drones can reach remote and rugged terrain that is often challenging for ground-based teams to access, ensuring a more comprehensive monitoring coverage. Compared to manned aircraft, drones are more cost-effective for continuous monitoring of large areas over extended periods. Using drones reduces the risk to human lives by minimizing the need for ground-based personnel to enter dangerous fire zones [2]. Infrared cameras mounted on drones can detect variations in temperature, allowing for the identification of hotspots and fire fronts even in low-visibility conditions. Real-time data transmission capabilities enable immediate analysis and decision-making by authorities, allowing for faster response times. Geographic Information Systems (GIS) can be used to map and track the spread of wildfires based on the data collected by drones, providing a comprehensive overview of the situation. One of the primary advantages of using drones for wildfire monitoring is their ability to detect fires at an early stage.

Once a wildfire is detected, a rapid response is essential to prevent its spread and minimize damage. Drones equipped with real-time open burning imagery analysis can provide instant information to fire fighting teams, helping them make informed decisions about resource allocation and deployment. This agility can be a game-changer in containing wildfires before they become uncontrollable [3]. Drones provide a bird's-eye view of the wildfire, offering valuable situational awareness to incident commanders. This perspective allows for better understanding of the fire's behavior, direction and potential threats to communities and infrastructure.

Compared to traditional methods involving manned aircraft, drones offer a cost-effective solution for continuous wildfire monitoring. Drones can be deployed as needed, reducing operational expenses and allowing for more extensive coverage over time. This cost-efficiency is particularly valuable for regions with limited fire fighting resources. While the use of drones in wildfire monitoring holds great promise, several challenges and limitations must be addressed. Drones are susceptible to adverse weather conditions such as strong winds, rain, and fog, which can hinder their flight capabilities. Drones operate within regulated airspace, and their use near wildfires may conflict with fire fighting aircraft or other emergency responders. Coordination and communication between drone operators and authorities are crucial to avoid airspace conflicts [4].

The sensitivity of the detection algorithms used to analyze imagery data plays a significant role in accuracy. These algorithms must distinguish between genuine wildfires and false positives caused by factors like controlled burns or industrial activity. An accurate system should minimize both false positives (detecting fires that don't exist) and false negatives (failing to detect actual fires). Striking the right balance is critical to ensure that resources are not wasted on false alarms while real threats are promptly addressed. Accuracy assessments should involve validation against ground truth data, which can be obtained from ground-based observations or other reliable sources.

California has been plagued by wildfires in recent years, making it a hotspot for drone deployment in wildfire monitoring. In a study conducted during the 2020 California wildfire season, drones equipped with advanced thermal imaging sensors were used to detect wildfires at night and in smoky conditions. The Amazon rainforest is another region prone to wildfires, often caused by deforestation and illegal land clearing. Drones equipped with high-resolution cameras have been used to monitor open burning activity and detect wildfires in this vast and remote area. Accuracy assessments demonstrated the ability of drones to identify and report wildfires in real-time, contributing to conservation efforts. The advantages of early detection, rapid response, enhanced situational awareness, and reduced risk to first responders are compelling reasons to invest in this technology. By addressing the challenges and continually refining the methodology, we can harness the full potential of drones to protect lives, property, and the environment from the devastating impact of wildfires [5].

Acknowledgement

None.

Conflict of Interest

There are no conflicts of interest by author.

References

1. Jeong, Mira, MinJi Park, Jaeyeal Nam and Byoung Chul Ko, et al. “Light-weight student LSTM for real-time wildfire smoke detection.” Sensors 20 (2020): 5508.

Google Scholar, Crossref, Indexed at

2. Carta, Francesco, Chiara Zidda, Martina Putzu and Daniele Loru, et al. “Advancements in forest fire prevention: A comprehensive survey.” Sensors 23 (2023): 6635.

Google Scholar, Crossref, Indexed at

3. Chen, Liang-Chieh, George Papandreou, Iasonas Kokkinos and Kevin Murphy, et al. “DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution and fully connected CRFs.” IEEE Trans Pattern Anal Mach Intel 40 (2018): 834–848.

Google Scholar, Crossref, Indexed at

4. Phairuang, Worradorn, Panwadee Suwattiga, Thaneeya Chetiyanukornkul and Surapa Hongteiab, et al. “The influence of the open burning of agricultural biomass and forest fires in Thailand on the carbonaceous components in size-fractionated particles.” Environ Poll 247 (2019): 238–247.

Google Scholar, Crossref, Indexed at

5. Dash, Jonathan P., Grant D. Pearse and Michael S. Watt. “UAV multispectral imagery can complement satellite data for monitoring forest health.” Remote Sens 10 (2018): 1216.

Google Scholar, Crossref