Unmanned Aerial Systems for Wildland and Forest Fires

Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. In recent years, we witness an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small scale environments. However, wildfires cover large areas making some of the proposed ground-based systems unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial Systems (UAS) were proposed. UAS have proven to be useful due to their maneuverability, allowing for the implementation of remote sensing, allocation strategies and task planning. They can provide a low-cost alternative for the prevention, detection and real-time support of firefighting. In this paper we review previous work related to the use of UAS in wildfires. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered. In addition, we present some of the recent frameworks proposing the use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at: https://doi.org/10.3390/drones501001

Advancements in Forest Fire Prevention: A Comprehensive Survey

Nowadays, the challenges related to technological and environmental development are becoming increasingly complex. Among the environmentally significant issues, wildfires pose a serious threat to the global ecosystem. The damages inflicted upon forests are manifold, leading not only to the destruction of terrestrial ecosystems but also to climate changes. Consequently, reducing their impact on both people and nature requires the adoption of effective approaches for prevention, early warning, and well-coordinated interventions. This document presents an analysis of the evolution of various technologies used in the detection, monitoring, and prevention of forest fires from past years to the present. It highlights the strengths, limitations, and future developments in this field. Forest fires have emerged as a critical environmental concern due to their devastating effects on ecosystems and the potential repercussions on the climate. Understanding the evolution of technology in addressing this issue is essential to formulate more effective strategies for mitigating and preventing wildfires

Fire Immediate Response System Workshop Report

California's recent wildfires, exacerbated by extreme weather conditions, have focused the nation's attention on the problem of managing fire at the wildland urban interface. With the goal of understanding how new or re-imagined technologies could improve early fire detection and response, the Gordon and Betty Moore Foundation hosted a "Fire Immediate Response System" workshop (April 24 -26, 2019). The workshop identified the following priorities and recommendations, which are described in detail in the report.* Develop a shared, integrated platform for diverse sources of data, intelligence and information* Conduct new wildfire risk assessments with high-resolution mapping technologies* Improve scientific understanding of "megafires" through retrospective analysis* Enhance fire behavior models and associated inputs for real-time prediction* Perform a cost-benefit analysis of investment in solutions vs. reactive management* Target investments in the development and adoption of new technologies* Expand multi-stakeholder dialogue, collaboration and actio

Video-based Smoke Detection Algorithms: A Chronological Survey

Over the past decade, several vision-based algorithms proposed in literature have resulted into development of a large number of techniques for detection of smoke and fire from video images. Video-based smoke detection approaches are becoming practical alternatives to the conventional fire detection methods due to their numerous advantages such as early fire detection, fast response, non-contact, absence of spatial limits, ability to provide live video that conveys fire progress information, and capability to provide forensic evidence for fire investigations. This paper provides a chronological survey of different video-based smoke detection methods that are available in literatures from 1998 to 2014.Though the paper is not aimed at performing comparative analysis of the surveyed methods, perceived strengths and weakness of the different methods are identified as this will be useful for future research in video-based smoke or fire detection. Keywords: Early fire detection, video-based smoke detection, algorithms, computer vision, image processing

Smoke, Air, Fire, Energy (SAFE) in Rural California: Critical Reflections on an Interdisciplinary Research Collaboration

This article provides a synthesis of the interconnected problems of tenuous energy access, wildfires, and exposures to high air pollution in Indigenous communities in rural California through the lens of ongoing collaborative research being carried out by researchers at Cal Poly Humboldt, Schatz Energy Research Center, Karuk Department of Natural Resources, and the Blue Lake Rancheria Tribe. The collaboration is funded by the Strategic Growth Council of the state of California, and we hope is the beginning of a longer term relationship between all partners. We are an interdisciplinary team of researchers drawing on energy engineering, air pollution science, and qualitative social sciences to better understand the intersecting challenges of expanding clean energy access, and building climate resilience in Tribal communities in rural California in the context of the multiple challenges of climate change, increasing risk of dangerous wildfires, and high exposures to air pollution. Individuals and communities need to make decisions about energy and air quality infrastructure with implications for public health, climate change, energy resilience, and Tribal sovereignty. This article will reflect on the joys, challenges, ethical questions, and epistemological constraints involved with academic researchers working on interdisciplinary research projects across disciplines, and in partnership with Tribal nations. Grounded in the reflections and experience of an ongoing project, this article sheds light on the challenges and unique opportunities of conducting collaborative interdisciplinary research in close engagement with communities, and also reflects on the structural constraints posed within current institutional structures

Using a Semi-autonomous Drone Swarm to Support Wildfire Management – A Concept of Operations Development Study

This paper provides insights into a human factors-oriented Concept of Operations (ConOps), which can be applied for future semi-autonomous drone swarms to support the management of wildfires. The results provide, firstly, an overview of the current practices to manage wildfires in Finland. Secondly, some of the current challenges and future visions about drone usage in a wildfire situation are presented. Third, a description of the key elements of the developed future ConOps for operating a drone swarm to support the combat of wildfires is given. The ConOps has been formulated based on qualitative research, which included a literature review, seven subject matter expert interviews and a workshop with 40 professionals in the domain. Many elements of this ConOps may also be applied to a variety of other swarm robotics operations than only wildfire management. Finally, as the development of the ConOps is still in its first stage, several further avenues for research and development are proposed

Towards an ultra‐low‐power low‐cost wireless visual sensor node for fine‐grain detection of forest fires

Advances in electronics, sensor technologies, embedded hardware and software are boosting the application scenarios of wireless sensor networks. Specifically, the incorporation of visual capabilities into the nodes means a milestone, and a challenge, in terms of the amount of information sensed and processed by these networks. The scarcity of resources – power, processing and memory – imposes strong restrictions on the vision hardware and algorithms suitable for implementation at the nodes. Both, hardware and algorithms must be adapted to the particular characteristics of the targeted application. This permits to achieve the required performance at lower energy and computational cost. We have followed this approach when addressing the detection of forest fires by means of wireless visual sensor networks. From the development of a smoke detection algorithm down to the design of a low‐power smart imager, every step along the way has been influenced by the objective of reducing power consumption and computational resources as much as possible. Of course, reliability and robustness against false alarms have also been crucial requirements demanded by this specific application. All in all, we summarize in this paper our experience in this topic. In addition to a prototype vision system based on a full‐custom smart imager, we also report results from a vision system based on ultra‐low‐power low‐cost commercial imagers with a resolution of 30×30 pixels. Even for this small number of pixels, we have been able to detect smoke at around 100 meters away without false alarms. For such tiny images, smoke is simply a moving grey stain within a blurry scene, but it features a particular spatio‐temporal dynamics. As described in the manuscript, the key point to succeed with so low resolution thus falls on the adequate encoding of that dynamics at algorithm levelMinisterio de Economía y Competitividad TEC2012‐38921‐C02, IPT‐2011‐1625‐430000, IPC‐ 20111009 CDTIJunta de Andalucía TIC 2338‐201

Automatic Forest-Fire Measuring Using Ground Stations and Unmanned Aerial Systems

This paper presents a novel system for automatic forest-fire measurement using cameras distributed at ground stations and mounted on Unmanned Aerial Systems (UAS). It can obtain geometrical measurements of forest fires in real-time such as the location and shape of the fire front, flame height and rate of spread, among others. Measurement of forest fires is a challenging problem that is affected by numerous potential sources of error. The proposed system addresses them by exploiting the complementarities between infrared and visual cameras located at different ground locations together with others onboard Unmanned Aerial Systems (UAS). The system applies image processing and geo-location techniques to obtain forest-fire measurements individually from each camera and then integrates the results from all the cameras using statistical data fusion techniques. The proposed system has been extensively tested and validated in close-to-operational conditions in field fire experiments with controlled safety conditions carried out in Portugal and Spain from 2001 to 2006

Deep Learning Based Burnt Area Mapping Using Sentinel 1 for the Santa Cruz Mountains Lightning Complex (CZU) and Creek Fires 2020

The study presented here builds on previous synthetic aperture radar (SAR) burnt area estimation models and presents the first U-Net (a convolutional network architecture for fast and precise segmentation of images) combined with ResNet50 (Residual Networks used as a backbone for many computer vision tasks) encoder architecture used with SAR, Digital Elevation Model, and land cover data for burnt area mapping in near-real time. The Santa Cruz Mountains Lightning Complex (CZU) was one of the most destructive fires in state history. The results showed a maximum burnt area segmentation F1-Score of 0.671 in the CZU, which outperforms current models estimating burnt area with SAR data for the specific event studied models in the literature, with an F1-Score of 0.667. The framework presented here has the potential to be applied on a near real-time basis, which could allow land monitoring as the frequency of data capture improves