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

Dinâmica de incêndios florestais e alterações biofísicas na Amazônia e Cerrado brasileiros a partir de séries temporais de sensoriamento remoto

Tese (doutorado)—Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-Graduação em Geografia, 2019.Os biomas brasileiros se adaptaram a diferentes padrões de presença ou ausência do fogo. Dados derivados de sensoriamento remoto têm sido uma das principais bases para a detecção de incêndios florestais e os danos na estrutura da vegetação, especialmente com o desenvolvimento de sensores com alta resolução temporal e espectral, e o estabelecimento de longas séries contínuas. Nesse sentido, esta tese buscou aprofundamento em três pontos: (1) Qual a potencialidade de produtos de sensoriamento remoto para a descrição da dinâmica do fogo no Brasil? (2) Como detectar cicatrizes de queimadas a partir de séries temporais em ambientes amazônicos?; e por fim (3) Quais os danos na vegetação resultantes da alteração do regime histórico do fogo e como podem ser quantificados por sensoriamento remoto? Para ampliar o conhecimento sobre essas questões foram utilizados diversos produtos derivados dos sensores Moderate Resolution Imaging Spectroradiometer (MODIS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) e Operational Land Imager (OLI), além de diversos dados espaciais, em três escalas: uma para todo o território nacional, uma área específica do Cerrado e duas áreas específicas da Amazônia. A metodologia básica consistiu na análise de séries temporais MODIS para detecção e quantificação dos efeitos do fogo. Os resultados permitiram concluir que: (1) Os produtos globais MODIS de detecção de cicatrizes de queimadas apresentaram altas taxas de erros de omissão no Brasil, superiores a 78% em média no território nacional, sendo seu uso recomendado apenas para análises regionais ou globais. Os produtos de queimadas apresentaram as menores acurácias nos biomas dos Pampas, Amazônia e Mata Atlântica e as maiores acurácias nos biomas do Cerrado e da Caatinga. Apesar desta limitação, o produto MCD64 permitiu descrever o regime do fogo no país, as principais regiões de ocorrência e a influência da umidade e classe de vegetação neste padrão. Foram estabelecidas como limite para a ação do fogo, as zonas sem estiagem, como o Oeste da Amazônia e litoral leste do Brasil, assim como as áreas do semiárido nordestino. (2) Dentre os métodos analisados de diferença sazonal e normalização temporal, a normalização pela média da banda espectral do Infravermelho Próximo foi responsável pela maior acurácia na detecção de cicatrizes de queimadas na Amazônia, retificando a utilização de alguns índices especializados originalmente para vegetações temperadas, como o Normalized Burn Ratio (NBR). Outros métodos analisados, como a diferença sazonal e normalização por z-score, apresentaram melhor acurácia que imagens originais, mas inferior em comparação com a normalização pela média. (3) A alteração da recorrência do fogo teve influência direta no padrão biofísico e fenológico da vegetação nas áreas de estudo na Amazônia e no Cerrado. As variáveis de produtividade primária bruta e albedo apresentaram baixa representatividade espacial. As mudanças com maior inclinação da tendência, do Enhanced Vegetation Index (EVI) e temperatura superficial, foram tanto relacionadas com a recorrência do fogo, quanto com a classe de uso da vegetação, como nas terras indígenas. A inclinação da tendência, no EVI e temperatura superficial, foi maior na área do Cerrado, reforçando a necessidade urgente de conservação deste bioma. A pesquisa atestou a importância de dados de sensoriamento remoto para avaliação da dinâmica do fogo e dos seus efeitos na vegetação. A utilização de séries temporais do sensor MODIS permitiu tanto identificar as áreas queimadas com maior acurácia que outros produtos disponíveis, quanto quantificar as fragilidades da vegetação relacionadas ao padrão de fogo atual.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).Brazilian biomes have adapted to different patterns of presence or absence of fire. Data derived from remote sensing have been one of the main techniques for the detection of forest fires and damage to vegetation structure, especially with the development of high temporal and spectral resolution sensors and the establishment of long continuous series. Thus, we intend to focus on three points in this thesis: (1) What is the potential of remote sensing products for the description of fire dynamics in Brazil? (2) How to detect burn scars from remote sensing time series in Amazonian environments? And finally (3) What damages in the vegetation resulting from the alteration of the historical fire regime and how can they be quantified by remote sensing? In order to increase the knowledge about these issues, several products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) sensors were used, in addition to diverse spatial data, in three scales: one for the whole national territory, one specific area of the Cerrado and two specific areas of the Amazon. The basic methodology consisted of the analysis of MODIS time series for the detection and quantification of fire effects. The results allowed to conclude that: (1) MODIS global burned area products presented high omission errors rates in Brazil, higher than 78% on average in the national territory, and their use is recommended only for regional or global analyzes. The burned area products showed the lowest value in the biomes of the Pampas, Amazon Forest and Atlantic Forest, and the highest values in the biomes of the Cerrado and Caatinga. In spite of this limitation, the product MCD64 allowed to describe the fire regime in the country, the main regions of occurrence and the influence of moisture and vegetation class in this pattern. Were established as a limit for the action of the fire the areas without drought, such as the Western Amazon and the east coast of Brazil, as well as areas with low availability of rainfall and fuel, such as the semi-arid in the Northeast. (2) Among the analyzed methods of seasonal difference and temporal normalization, the normalization of the Near Infrared spectral band by the zero-mean, was responsible for the greater accuracy in the detection of burn scars in the Amazon region, rectifying the use of some indices originally specialized for temperate vegetation, such as the Normalized Burn Ratio (NBR). Other methods analyzed, such as the seasonal difference and z-score normalization, showed better accuracy than original images, but lower than normalization by the zero-mean. (3) The alteration of fire recurrence had a direct influence on the biophysical and phenological pattern of vegetation the study areas of Amazon and Cerrado. The variables of gross primary productivity and albedo showed low spatial representativeness. The changes with higher trend slope, of Enhanced Vegetation Index (EVI) and surface temperature, were related both to fire recurrence and to the vegetation use class, as in indigenous lands. The slope of the trend in EVI and surface temperature was higher in the Cerrado area, reinforcing the urgent need for conservation of this biome. The research attested the importance of remote sensing data for the evaluation of fire dynamics and its effects on vegetation. The use of MODIS time series allowed both identifying the burned areas with greater accuracy than other available products, and quantifying the fragilities of the vegetation related to the current fire pattern

Google earth engine as multi-sensor open-source tool for supporting the preservation of archaeological areas: The case study of flood and fire mapping in metaponto, italy

In recent years, the impact of Climate change, anthropogenic and natural hazards (such as earthquakes, landslides, floods, tsunamis, fires) has dramatically increased and adversely affected modern and past human buildings including outstanding cultural properties and UNESCO heritage sites. Research about protection/monitoring of cultural heritage is crucial to preserve our cultural properties and (with them also) our history and identity. This paper is focused on the use of the open-source Google Earth Engine tool herein used to analyze flood and fire events which affected the area of Metaponto (southern Italy), near the homonymous Greek-Roman archaeological site. The use of the Google Earth Engine has allowed the supervised and unsupervised classification of areas affected by flooding (2013–2020) and fire (2017) in the past years, obtaining remarkable results and useful information for setting up strategies to mitigate damage and support the preservation of areas and landscape rich in cultural and natural heritage

Landfill elevated internal temperature detection and landfill fire index assessment for fire monitoring

Landfill fires are becoming a real threat to both people and environment due to lack of predictions and control methods. Processing of the infrared band from level-1 satellite images was employed and decades worth of archived data from USGS Earth Explorer databases were analyzed to obtain surface temperature values of Atlantic Waste Landfill, Virginia and Bridgeton Landfill, Missouri. Multitemporal thermal maps and frequency of maxima analysis maps of these two landfills showed the hotspots spreading through the waste site. A Landfill Fire Index (LFI) was created by investigating eight factors that give information about the hazardousness of the landfill conditions relative to the presence of a fire occurrence. The application of Analytical Hierarchy Method (AHP) resulted in the determination of the degree of importance of each Landfill Fire Index factor. Several monitoring well data sets were used to calculate the LFI for Bridgeton Landfill, Missouri, and Burlington County Landfill, New Jersey

UAV Assisted Spatiotemporal Analysis and Management of Bushfires: A Case Study of the 2020 Victorian Bushfires

Australia is a regular recipient of devastating bushfires that severely impacts its economy, landscape, forests, and wild animals. These bushfires must be managed to save a fortune, wildlife, and vegetation and reduce fatalities and harmful environmental impacts. The current study proposes a holistic model that uses a mixed-method approach of Geographical Information System (GIS), remote sensing, and Unmanned Aerial Vehicles (UAV)-based bushfire assessment and mitigation. The fire products of Visible Infrared Imager Radiometer Suite (VIIRS) and Moderate-resolution Imaging Spectroradiometer (MODIS) are used for monitoring the burnt areas within the Victorian Region due to the 2020 bushfires. The results show that the aggregate of 1500 m produces the best output for estimating the burnt areas. The identified hotspots are in the eastern belt of the state that progressed north towards New South Wales. The R2 values between 0.91–0.99 indicate the fitness of methods used in the current study. A healthy z-value index between 0.03 to 2.9 shows the statistical significance of the hotspots. Additional analysis of the 2019–20 Victorian bushfires shows a widespread radius of the fires associated with the climate change and Indian Ocean Dipole (IOD) phenomenon. The UAV paths are optimized using five algorithms: greedy, intra route, inter route, tabu, and particle swarm optimization (PSO), where PSO search surpassed all the tested methods in terms of faster run time and lesser costs to manage the bushfires disasters. The average improvement demonstrated by the PSO algorithm over the greedy method is approximately 2% and 1.2% as compared with the intra route. Further, the cost reduction is 1.5% compared with the inter-route scheme and 1.2% compared with the intra route algorithm. The local disaster management authorities can instantly adopt the proposed system to assess the bushfires disasters and instigate an immediate response plan

Development of burned area algorithms on a global scale

El trabajo de tesis titulado "Desarrollo de algoritmos de área quemada a escala global - Development of burned area algorithms on a global scale" ha sido desarrollado y financiando en el marco del proyecto fire_cci dentro del programa de cambio climático de la Agencia Espacial Europea. El objetivo principal de esta tesis doctoral ha sido desarrollar un algoritmo para la caracterización de áreas quemadas (AQ) a escala global a partir de información del sensor MERIS. Dentro de la tesis se ha buscado contextualizar la relevancia del fuego a escala global. Se han revisado los métodos para caracterizar los incendios desde el espacio, llevando a cabo una revisión bibliográfica del estado del arte. Se ha desarrollado y probado el algoritmo de área quemada, basando su configuración final en los distintos métodos implementados y en los resultados de las pruebas realizadas. El algoritmo obtenido puede clasificarse dentro de la categoría de algoritmo híbrido, ya que combina la información obtenida del contraste térmico (proporcionada por el producto MODIS HS) y de los cambios temporales en las reflectividades de los datos MERIS. El algoritmo consta de dos fases: semillado y crecimiento. En la primera fase, se identifican los píxeles semilla, es decir los puntos más claramente clasificables como quemados. Para ello se obtienen de forma dinámica estadísticas locales (basadas en regiones de 10x10 grados) de forma mensual que permiten definir condiciones para clasificar los píxeles semilla. En la fase de crecimiento se realiza un análisis de los píxeles vecinos a estas semillas, estableciendo su carácter quemado si verifican a su vez una serie de condiciones. Se ha llevado a cabo un análisis y discusión de las estimaciones de área quemada obtenidas mediante este algoritmo a nivel global para los años 2006 a 2008. Estos resultados se han validado e inter-comparado con otros productos de área quemada. Se incluyen así mismo en la tesis las conclusiones obtenidas del desarrollo del algoritmo, y los posibles futuros pasos a seguir. El principal logro del trabajo realizado en el marco de este trabajo de investigación ha sido el desarrollo del primer algoritmo de áreas quemadas a escala global a partir del sensor MERIS. Esto permite obtener productos de AQ a mayor resolución que la proporcionada por las colecciones de AQ existentes en la actualidad, y mejorando la calidad de las colecciones obtenidas a nivel europeo

Development of burned area algorithms on a global scale

El trabajo de tesis titulado "Desarrollo de algoritmos de área quemada a escala global - Development of burned area algorithms on a global scale" ha sido desarrollado y financiando en el marco del proyecto fire_cci dentro del programa de cambio climático de la Agencia Espacial Europea. El objetivo principal de esta tesis doctoral ha sido desarrollar un algoritmo para la caracterización de áreas quemadas (AQ) a escala global a partir de información del sensor MERIS. Dentro de la tesis se ha buscado contextualizar la relevancia del fuego a escala global. Se han revisado los métodos para caracterizar los incendios desde el espacio, llevando a cabo una revisión bibliográfica del estado del arte. Se ha desarrollado y probado el algoritmo de área quemada, basando su configuración final en los distintos métodos implementados y en los resultados de las pruebas realizadas. El algoritmo obtenido puede clasificarse dentro de la categoría de algoritmo híbrido, ya que combina la información obtenida del contraste térmico (proporcionada por el producto MODIS HS) y de los cambios temporales en las reflectividades de los datos MERIS. El algoritmo consta de dos fases: semillado y crecimiento. En la primera fase, se identifican los píxeles semilla, es decir los puntos más claramente clasificables como quemados. Para ello se obtienen de forma dinámica estadísticas locales (basadas en regiones de 10x10 grados) de forma mensual que permiten definir condiciones para clasificar los píxeles semilla. En la fase de crecimiento se realiza un análisis de los píxeles vecinos a estas semillas, estableciendo su carácter quemado si verifican a su vez una serie de condiciones. Se ha llevado a cabo un análisis y discusión de las estimaciones de área quemada obtenidas mediante este algoritmo a nivel global para los años 2006 a 2008. Estos resultados se han validado e inter-comparado con otros productos de área quemada. Se incluyen así mismo en la tesis las conclusiones obtenidas del desarrollo del algoritmo, y los posibles futuros pasos a seguir. El principal logro del trabajo realizado en el marco de este trabajo de investigación ha sido el desarrollo del primer algoritmo de áreas quemadas a escala global a partir del sensor MERIS. Esto permite obtener productos de AQ a mayor resolución que la proporcionada por las colecciones de AQ existentes en la actualidad, y mejorando la calidad de las colecciones obtenidas a nivel europeo

Advances in Remote Sensing and GIS applications in Forest Fire Management: from local to global assessments

This report contains the proceedings of the 8th International Workshop of the European Association of Remote Sensing Laboratories (EARSeL) Special Interest Group on Forest Fires, that took place in Stresa, (Italy) on 20-21 October 2011. The main subject of the workshop was the operational use of remote sensing in forest fire management and different spatial scales were addressed, from local to regional and from national to global. Topics of the workshops were also grouped according to the fire management stage considered for the application of remote sensing techniques, addressing pre fire, during fire or post fire conditions.JRC.H.7-Land management and natural hazard

Proceedings of the 6th International Workshop of the EARSeL Special Interest Group on Forest Fires Advances in Remote Sensing and GIS Applications in Forest Fire Management Towards an Operational Use of Remote Sensing in Forest Fire Management

During the last two decades, interest in forest fire research has grown steadily, as more and more local and global impacts of burning are being identified. The definition of fire regimes as well as the identification of factors explaining spatial and temporal variations in these fire characteristics are recently hot fields of research. Changes in these fire regimes have important social and ecological implications. Whether these changes are mainly caused by land use or climate warming, greater efforts are demanded to manage forest fires at different temporal and spatial scales. The European Association of Remote Sensing Laboratories (EARSeL)’s Special Interest Group (SIG) on Forest Fires was created in 1995, following the initiative of several researchers studying Mediterranean fires in Europe. It has promoted five technical meetings and several specialised publications since then, and represents one of the most active groups within the EARSeL. The SIG has tried to foster interaction among scientists and managers who are interested in using remote sensing data and techniques to improve the traditional methods of fire risk estimation and the assessment of fire effect. The aim of the 6th international workshop is to analyze the operational use of remote sensing in forest fire management, bringing together scientists and fire managers to promote the development of methods that may better serve the operational community. This idea clearly links with international programmes of a similar scope, such as the Global Monitoring for Environment and Security (GMES) and the Global Observation of Forest Cover/Land Dynamics (GOFC-GOLD) who, together with the Joint Research Center of the European Union sponsor this event. Finally, I would like to thank the local organisers for the considerable lengths they have gone to in order to put this material together, and take care of all the details that the organization of this event requires.JRC.H.3-Global environement monitorin

Improving our understanding of individual wildfires by combining satellite data with fire spread modelling

Doutoramento em Engenharia Florestal e dos Recursos Naturais - Instituto Superior de AgronomiaWildfires pose real threats to life and property. In Portugal, the recent year of 2017 had the largest burnt area extent and number of casualties. A knowledge gap still exists in wildfire research related with better understanding individual wildfires, which has important implications for fire suppression, management, and policies. Wildfire spread models have been used to study individual wildfires, however, associated uncertainties and the lack of systematic evaluation methods hamper their capability for accurately predicting their spread. Understanding how fire spread predictions can be improved is a critical research task, as they will only be deemed useful if they can provide accurate and reliable information to fire managers. The present Thesis proposes to contribute to improve fire spread predictions by: i) Developing a methodology to systematically evaluate fire spread predictions ii) Thoroughly characterizing input data uncertainty and its impact on predictions; iii) Improving predictions using data-driven model calibration. The spread of large historical wildfires were studied by combining satellite data and models. The major findings of the present Thesis were: i) Satellite data accurately contributed to provide accurate fire dates and ignition information for large wildfires. ii) The evaluation metrics were very useful in identifying areas and periods of low/high spatio-temporal agreement, highlighting the strong underprediction bias and poor accuracy of the predictions. iii) Uncertainties in wind speed and direction, fuel model assignment and typology, location and timing of ignitions, had a major impact on prediction accuracy. iv) Predictions iii) Uncertainties in wind speed and direction, fuel model assignment and typology, location and timing of ignitions, had a major impact on prediction accuracy. iv) Predictions were improved by ‘learning’ from past wildfires, significantly reducing the impact of data uncertainty on the accuracy of fire spread predictions Overall, the work contributed to advance the body of knowledge regarding individual wildfires and identified future research steps towards a reliable operational fire system capable of supporting more effective and safer fire management decisions with the aim of reducing the dramatic impacts of wildfiresN/