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

Modelagem de abundância com drones : detectabilidade, desenho amostral e revisão automática de imagens em um estudo com cervos-do-pantanal

Entender como a abundância de uma espécie se distribui no espaço e/ou no tempo é uma questão fundamental em ecologia e conservação e ajuda, por exemplo, a elucidar relações entre a heterogeneidade de paisagens e populações ou compreender influência de predação na distribuição de indivíduos. Informações de tamanho populacional também são essenciais para avaliar risco de extinção, monitorar populações ameaçadas e planejar ações de conservação. Modelar a abundância de cervos-do-pantanal (Blastocerus dichotomus), sendo um grande herbívoro da América do Sul, pode ser importante para entender relações da espécie com a variação espacial da produtividade primária, das áreas úmidas que a espécie ocupa e do seu principal predador, a onça- pintada. Além disso, por estar ameaçado de extinção, estimar a abundância de cervos pode contribuir para avaliar populações relictuais da espécie, assim como monitorar populações após grandes eventos, como os incêndios de 2020 no Pantanal. Porém, acessar estimativas de abundância confiáveis de maneira eficiente requer métodos robustos que levem em conta os possíveis erros nas contagens e que forneçam as estimativas em tempo hábil, além de um desenho amostral otimizado para aproveitar os recursos geralmente escassos. Os drones tem aparecido como uma ferramenta versátil e custo-efetiva para amostragem de populações animais e vêm sendo aplicados para várias espécies diferentes nos mais variados contextos ecológicos. Como um método emergente, o uso de drones na ecologia fornece oportunidades para explorar novas possibilidades de amostragem e análise de dados, ao mesmo tempo em que pode apresentar novos desafios. Nesta tese, i) exploro oportunidade e desafios na utilização de drones para modelagem de abundância de animais, abordando questões de erros de detecção, desenho amostral e como lidar com os grandes bancos de imagens gerados; e ii) aplico os métodos desenvolvidos para estudar a variação na abundância de cervo-do- pantanal, assim como estabelecer uma abordagem para monitoramento robusto e efetivo dessa espécie. Assim, no primeiro capítulo, conduzo uma revisão na literatura descrevendo os potenciais erros de detecção que podem enviesar estimativas de abundância com drones, buscando soluções atuais para lidar com esses erros e identificando lacunas que precisam de desenvolvimento. Nessa revisão, destaco o potencial dos modelos hierárquicos para estimar abundância em amostragens com drone. No segundo capítulo, aplico amostragens espaço-temporalmente replicadas com drone, analisadas com modelos hierárquicos N-mixture, para entender o efeito de processos topo-base (distribuição de onças-pintadas) e base-topo (disponibilidade de forragem de qualidade e corpos d’água) na distribuição da abundância de cervos-do- pantanal. Nesse estudo, encontrei que, na época seca, os cervos se concentram em áreas de alta qualidade (maior disponibilidade de forragem e próximas a corpos d’água), mesmo sendo a região em que é esperado maior efeito da predação. No capítulo 3, em um estudo com simulações, avalio o desempenho de modelos N-mixture para estimativas de abundância a partir de amostragens espaço-temporalmente replicadas, explorando otimização de esforço amostral e o impacto de um protocolo com observadores duplos na acurácia das estimativas. No capítulo 4, desenvolvo uma abordagem para estimar abundância com drone usando observadores múltiplos na revisão das imagens, sendo um dos observadores baseado em um processamento semiautomático usando algoritmos de inteligência artificial. Nesse estudo, exploro técnicas de aprendizado profundo de máquina, com redes neurais convolucionais, acessíveis para ecólogos, treinando algoritmos para detectar cervos nas imagens de drone. Além de ajudar a elucidar questões sobre as relações do cervo-do-pantanal com aspectos diferentes da paisagem do Pantanal, as abordagens exploradas e desenvolvidas aqui têm um grande potencial de aplicação, ajudando a estabelecer os drones como uma ferramenta eficiente para modelagem e monitoramento populacional de diversas espécies animais, e particularmente de cervos.Understanding how abundance distributes in space and/or time is a fundamental question in ecology and conservation, and it helps, for example, to elucidate relationships between landscape heterogeneity or predation and populations. Information on the population size also is essential to evaluate extinction risk, monitor threatened species and plan conservation actions. Abundance modeling of marsh deer (Blastocerus dichotomus), as a large herbivore of South America, may be important to understand the relationships of this species with spatial variation in primary productivity, in the availability of wetlands that the species inhabits, and in the distribution of its main predator, the jaguar. Moreover, since marsh deer threatened to extinction, estimating its abundance can be contribute in assessments of relictual populations, as well as in monitoring the species after big events, such as the Pantanal 2020 megafires. However, efficiently assessing reliable abundance estimates require robust methods that account for possible sources of error in counts while providing the estimates timely. An optimized sampling design is also important, in order to make the best use of the usual scarce resources. Drones have raised as a versatile and cost- effective tool for sampling animal populations, and they have been applied for several species in a wide variety of ecological contexts. As being an emergent method, the use of drones in ecology provides opportunities to explore novel possibilities of sampling and analyzing data, while potentially presenting new challenges. In this thesis I: i) explore opportunities and challenges in the use of drones for animal abundance modeling, approaching issues about detection errors, sampling design and how to deal with the huge image sets generated from drone flights; and ii) apply the developed methods to study the spatial variation in marsh deer abundance and to establish an approach to monitor this species robustly and efficiently. Thus, in the first chapter, I carry on a literature review describing potential sources of errors that may bias abundance estimation with drones and the current solutions to address them, identifying gaps that need development. In this review, I highlight the potential of hierarchical models for abundance estimations from drone-based surveys. In the second chapter, I apply spatiotemporally replicated drone surveys, analyzed with N-mixture models, to understand the influence of bottom-up (forage and water) and top-down (jaguar density) variables on the spatial variation of marsh deer local abundance. In such study, I found that, in the dry season, the deer concentrate in high quality areas (high-quality forage available and close to water bodies), even these regions being expected to present higher predation risks. In chapter 3, in a simulation study, I evaluate the performance of N- mixture models for abundance estimation from spatiotemporally replicated surveys, exploring optimization of sampling effort and the impact of a double-observer protocol on estimation accuracy. In chapter 4, I develop a pipeline to estimate abundance from drone-based surveys using a multiple-observer protocol in which one of the observer is a semiautomated procedure based on deep learning algorithms. In such study, I explore deep learning techniques with convolutional neural networks that are accessible for ecologists, and train algorithms to detect marsh deer in drone imagery. Besides helping to elucidate questions about the relationships of marsh deer with landscape variables in Pantanal, the approaches explored and developed here have a great potential of application in order to establish drones as an efficient technique for population modeling and monitoring of several wildlife species, and particularly the marsh deer

A Comprehensive Review of AI-enabled Unmanned Aerial Vehicle: Trends, Vision , and Challenges

In recent years, the combination of artificial intelligence (AI) and unmanned aerial vehicles (UAVs) has brought about advancements in various areas. This comprehensive analysis explores the changing landscape of AI-powered UAVs and friendly computing in their applications. It covers emerging trends, futuristic visions, and the inherent challenges that come with this relationship. The study examines how AI plays a role in enabling navigation, detecting and tracking objects, monitoring wildlife, enhancing precision agriculture, facilitating rescue operations, conducting surveillance activities, and establishing communication among UAVs using environmentally conscious computing techniques. By delving into the interaction between AI and UAVs, this analysis highlights the potential for these technologies to revolutionise industries such as agriculture, surveillance practices, disaster management strategies, and more. While envisioning possibilities, it also takes a look at ethical considerations, safety concerns, regulatory frameworks to be established, and the responsible deployment of AI-enhanced UAV systems. By consolidating insights from research endeavours in this field, this review provides an understanding of the evolving landscape of AI-powered UAVs while setting the stage for further exploration in this transformative domain

Feasibility of Warehouse Drone Adoption and Implementation

While aerial delivery drones capture headlines, the pace of adoption of drones in warehouses has shown the greatest acceleration. Warehousing constitutes 30% of the cost of logistics in the US. The rise of e-commerce, greater customer service demands of retail stores, and a shortage of skilled labor have intensified competition for efficient warehouse operations. This takes place during an era of shortening technology life cycles. This paper integrates several theoretical perspectives on technology diffusion and adoption to propose a framework to inform supply chain decision-makers on when to invest in new robotics technology

Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey

The ongoing amalgamation of UAV and ML techniques is creating a significant synergy and empowering UAVs with unprecedented intelligence and autonomy. This survey aims to provide a timely and comprehensive overview of ML techniques used in UAV operations and communications and identify the potential growth areas and research gaps. We emphasise the four key components of UAV operations and communications to which ML can significantly contribute, namely, perception and feature extraction, feature interpretation and regeneration, trajectory and mission planning, and aerodynamic control and operation. We classify the latest popular ML tools based on their applications to the four components and conduct gap analyses. This survey also takes a step forward by pointing out significant challenges in the upcoming realm of ML-aided automated UAV operations and communications. It is revealed that different ML techniques dominate the applications to the four key modules of UAV operations and communications. While there is an increasing trend of cross-module designs, little effort has been devoted to an end-to-end ML framework, from perception and feature extraction to aerodynamic control and operation. It is also unveiled that the reliability and trust of ML in UAV operations and applications require significant attention before full automation of UAVs and potential cooperation between UAVs and humans come to fruition.Comment: 36 pages, 304 references, 19 Figure

How much does a man cost? A dirty, dull, and dangerous application

Thesis (M.A.) University of Alaska Fairbanks, 2017This study illuminates the many abilities of Unmanned Aerial Vehicles (UAVs). One area of importance includes the UAV's capability to assist in the development, implementation, and execution of crisis management. This research focuses on UAV uses in pre and post crisis planning and accomplishments. The accompaniment of unmanned vehicles with base teams can make crisis management plans more reliable for the general public and teams faced with tasks such as search and rescue and firefighting. In the fight for mass acceptance of UAV integration, knowledge and attitude inventories were collected and analyzed. Methodology includes mixed method research collected by interviews and questionnaires available to experts and ground teams in the UAV fields, mining industry, firefighting and police force career field, and general city planning crisis management members. This information was compiled to assist professionals in creation of general guidelines and recommendations for how to utilize UAVs in crisis management planning and implementation as well as integration of UAVs into the educational system. The results from this study show the benefits and disadvantages of strategically giving UAVs a role in the construction and implementation of crisis management plans and other areas of interest. The results also show that the general public is lacking information and education on the abilities of UAVs. This education gap shows a correlation with negative attitudes towards UAVs. Educational programs to teach the public benefits of UAV integration should be implemented

Quantifying the effect of aerial imagery resolution in automated hydromorphological river characterisation

Existing regulatory frameworks aiming to improve the quality of rivers place hydromorphology as a key factor in the assessment of hydrology, morphology and river continuity. The majority of available methods for hydromorphological characterisation rely on the identification of homogeneous areas (i.e., features) of flow, vegetation and substrate. For that purpose, aerial imagery is used to identify existing features through either visual observation or automated classification techniques. There is evidence to believe that the success in feature identification relies on the resolution of the imagery used. However, little effort has yet been made to quantify the uncertainty in feature identification associated with the resolution of the aerial imagery. This paper contributes to address this gap in knowledge by contrasting results in automated hydromorphological feature identification from unmanned aerial vehicles (UAV) aerial imagery captured at three resolutions (2.5 cm, 5 cm and 10 cm) along a 1.4 km river reach. The results show that resolution plays a key role in the accuracy and variety of features identified, with larger identification errors observed for riffles and side bars. This in turn has an impact on the ecological characterisation of the river reach. The research shows that UAV technology could be essential for unbiased hydromorphological assessment