Experimental Fire Measurement with UAV Multimodal Stereovision

Vito Ciullo,Antoine Pieri,Lucile Rossi

doi:10.3390/rs12213546

Abstract

In wildfire research, systems that are able to estimate the geometric characteristics of fire, in order to understand and model the behavior of this spreading and dangerous phenomenon, are required. Over the past decade, there has been a growing interest in the use of computer vision and image processing technologies. The majority of these works have considered multiple mono-camera systems, merging the information obtained from each camera. Recent studies have introduced the use of stereovision in this field; for example, a framework with multiple ground stereo pairs of cameras has been developed to measure fires spreading for about 10 meters. This work proposes an unmanned aerial vehicle multimodal stereovision framework which allows for estimation of the geometric characteristics of fires propagating over long distances. The vision system is composed of two cameras operating simultaneously in the visible and infrared spectral bands. The main result of this work is the development of a portable drone system which is able to obtain georeferenced stereoscopic multimodal images associated with a method for the estimation of fire geometric characteristics. The performance of the proposed system is tested through various experiments, which reveal its efficiency and potential for use in monitoring wildfires.

Highlights

Every year, wildfires cause ecological, economic, and human disasters [1]
Other anthropogenic factors have contributed to the increase in fire risk worldwide [2,6]; for instance, anarchic extension of the urbanization in wildland–urban interfaces (WUIs) or fire exclusion policies, which contribute to fuel accumulation, can potentially increase the impacts of wildfires
This paper presents a unmanned aerial vehicle (UAV) multimodal stereovision system for the measurement of the geometric characteristics of fires propagating over unlimited distances

Summary

Introduction

Wildfires cause ecological, economic, and human disasters [1]. The term “wildfires”— known as “forest fires” or “wildland fires”, is commonly used to refer to unwanted fires that burn forests and wildlands [2,3]. Other anthropogenic factors have contributed to the increase in fire risk worldwide [2,6]; for instance, anarchic extension of the urbanization in wildland–urban interfaces (WUIs) or fire exclusion policies, which contribute to fuel accumulation, can potentially increase the impacts of wildfires. With the combination of these factors, wildfires may occur more frequently. This evolving scenario requires strategies that are able to reduce the risk of fires and thereby decrease their economic, environmental, and social impacts. Management, prevention, and control actions are performed. The effectiveness of these types of actions is related to the knowledge of the phenomena that occur during the outbreak and spread of fire. Wildfire research has been conducted for more than 50 years, in order to understand these phenomena, to develop propagation and behavior models, to predict the evolution of wildfires [7,8,9,10,11] and the heat they transfer [12,13,14], and to develop decision management systems for fire fighting and land-use planning [15,16,17,18,19]

Methods

Findings

Discussion

Conclusion