skip to main content


Title: A micro-UAS to Start Prescribed Fires
Prescribed fires have many benefits, but existing ignition methods are dangerous, costly, or inefficient. This paper presents the design and evaluation of a micro-UAS that can start a prescribed fire from the air, while being operated from a safe distance and without the costs associated with aerial ignition from a manned aircraft. We evaluate the performance of the system in extensive controlled tests indoors. We verify the capabilities of the system to perform interior ignitions, a normally dangerous task, through the ignition of two prescribed fires alongside wildland firefighters  more » « less
Award ID(s):
1638099
PAR ID:
10026209
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
International Symposium on Experimental Robotics
Volume:
1
Issue:
1
Page Range / eLocation ID:
12-24
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    Decadal trends in fire activity can reveal important human and climate-driven influences across a multitude of landscapes from croplands to savannas. We use 16 years of daily satellite observations from 2003 to 2018 to search globally for stationary temporal shifts in fire activity during the primary burning season. We focus on southwest Russia and north Australia as case study regions; both regions experienced nearly 40 d shifts over a 16 year period but in opposite directions. In southwest Russia, a major wheat-growing region, we trace the delay in post-harvest fires to several potential drivers: modernization in the agricultural system and recent droughts, followed by government restrictions on wheat exports. In north Australia, prescribed burns in the early dry season are a key practice in Aboriginal fire management of savannas, and the increasing trend of such fires has limited the size and extent of fast-spreading late dry season fires, thereby shifting overall fire activity earlier. In both regions, human action, through controlling fire ignition and extent, is an important driver of the temporal shifts in fire activity with climate as both a harbinger and an amplifier of human-induced changes.

     
    more » « less
  2. Fires in boreal forests of Alaska are changing, threatening human health and ecosystems. Given expected increases in fire activity with climate warming, insight into the controls on fire size from the time of ignition is necessary. Such insight may be increasingly useful for fire management, especially in cases where many ignitions occur in a short time period. Here we investigated the controls and predictability of final fire size at the time of ignition. Using decision trees, we show that ignitions can be classified as leading to small, medium or large fires with 50.4±5.2% accuracy. This was accomplished using two variables: vapour pressure deficit and the fraction of spruce cover near the ignition point. The model predicted that 40% of ignitions would lead to large fires, and those ultimately accounted for 75% of the total burned area. Other machine learning classification algorithms, including random forests and multi-layer perceptrons, were tested but did not outperform the simpler decision tree model. Applying the model to areas with intensive human management resulted in overprediction of large fires, as expected. This type of simple classification system could offer insight into optimal resource allocation, helping to maintain a historical fire regime and protect Alaskan ecosystems. 
    more » « less
  3. In recent years, the increasing threat of devastating wildfires has underscored the need for effective prescribed fire management. Process-based computer simulations have traditionally been employed to plan prescribed fires for wildfire prevention. However, even simplified process models are too compute-intensive to be used for real-time decision-making. Traditional ML methods used for fire modeling offer computational speedup but struggle with physically inconsistent predictions, biased predictions due to class imbalance, biased estimates for fire spread metrics (e.g., burned area, rate of spread), and limited generalizability in out-of-distribution wind conditions. This paper introduces a novel machine learning (ML) framework that enables rapid emulation of prescribed fires while addressing these concerns. To overcome these challenges, the framework incorporates domain knowledge in the form of physical constraints, a hierarchical modeling structure to capture the interdependence among variables of interest, and also leverages pre-existing source domain data to augment training data and learn the spread of fire more effectively. Notably, improvement in fire metric (e.g., burned area) estimates offered by our framework makes it useful for fire managers, who often rely on these estimates to make decisions about prescribed burn management. Furthermore, our framework exhibits better generalization capabilities than the other ML-based fire modeling methods across diverse wind conditions and ignition patterns. 
    more » « less
  4. Abstract

    Increasing fire activity and the associated degradation in air quality in the United States has been indirectly linked to human activity via climate change. In addition, direct attribution of fires to human activities may provide opportunities for near term smoke mitigation by focusing policy, management, and funding efforts on particular ignition sources. We analyze how fires associated with human ignitions (agricultural fires and human-initiated wildfires) impact fire particulate matter under 2.5µm (PM2.5) concentrations in the contiguous United States (CONUS) from 2003 to 2018. We find that these agricultural and human-initiated wildfires dominate fire PM2.5in both a high fire and human ignition year (2018) and low fire and human ignition year (2003). Smoke from these human levers also makes meaningful contributions to total PM2.5(∼5%–10% in 2003 and 2018). Across CONUS, these two human ignition processes account for more than 80% of the population-weighted exposure and premature deaths associated with fire PM2.5. These findings indicate that a large portion of the smoke exposure and impacts in CONUS are from fires ignited by human activities with large mitigation potential that could be the focus of future management choices and policymaking.

     
    more » « less
  5. Abstract

    Cloud‐to‐ground lightning with minimal rainfall (“dry” lightning) is a major wildfire ignition source in the western United States (WUS). Although dry lightning is commonly defined as occurring with <2.5 mm of daily‐accumulated precipitation, a rigorous quantification of precipitation amounts concurrent with lightning‐ignited wildfires (LIWs) is lacking. We combine wildfire, lightning and precipitation data sets to quantify these ignition precipitation amounts across ecoprovinces of the WUS. The median precipitation for all LIWs is 2.8 mm but varies with vegetation and fire characteristics. “Holdover” fires not detected until 2–5 days following ignition occur with significantly higher precipitation (5.1 mm) compared to fires detected promptly after ignition (2.5 mm), and with cooler and wetter environmental conditions. Further, there is substantial variation in precipitation associated with promptly‐detected (1.7–4.6 mm) and holdover (3.0–7.7 mm) fires across ecoprovinces. Consequently, the widely‐used 2.5 mm threshold does not fully capture lightning ignition risk and incorporating ecoprovince‐specific precipitation amounts would better inform WUS wildfire prediction and management.

     
    more » « less