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Increases in wildfire activity and the resulting impacts have prompted the development of high-resolution wildfire behavior models for forecasting fire spread. Recent progress in using satellites to detect fire locations further provides…
Bushfire is one of the major natural disasters that cause huge losses to livelihoods and the environment. Understanding and analyzing the severity of bushfires is crucial for effective management and mitigation strategies, helping to…
Due to severe societal and environmental impacts, wildfire prediction using multi-modal sensing data has become a highly sought-after data-analytical tool by various stakeholders (such as state governments and power utility companies) to…
Forest fires have a profound impact on the atmospheric environment and air quality across the ecosystems. The recent west coast forest fire in the United States of America (USA) has broken all the past records and caused severe…
This study presents FCI-FireDyn, a new algorithm developed to monitor wildfire dynamics using the Flexible Combined Imager (FCI) onboard the Meteosat Third Generation satellite. Leveraging the high temporal resolution of FCI (10-minute…
The study aimed to evaluate the applicability of environmental indices in the monitoring of smouldering coal-waste dumps. A dump located in the Upper Silesian Coal Basin served as the research site for a multi-method analysis combining…
The spread and burned areas of wildfires in Alberta, Canada during a 50 year period, from 1961 through 2010 are studied here. Meteorological factors that control the spread and burn area have been discussed for a long time. In this paper,…
The increasing frequency and severity of wildfires highlight the need for accurate fire and plume spread models. We introduce an approach that effectively isolates and tracks fire and plume behavior across various spatial and temporal…
Forest fires pose a natural threat with devastating social, environmental, and economic implications. The rapid and highly uncertain rate of spread of wildfires necessitates a trustworthy digital tool capable of providing real-time…
Wildfires are becoming increasingly frequent, with potentially devastating consequences, including loss of life, infrastructure destruction, and severe environmental damage. Low Earth orbit satellites equipped with onboard sensors can…
Monitoring the dynamics of global land-ocean cover is fundamental for regulating the Earth's climate and sustaining terrestrial and marine ecosystems. However, existing datasets and research often exhibit limitations in temporal resolution…
Dynamic mean field theory is applied to the problem of forest fires. The starting point is the Monte Carlo simulation in a lattice of million cells. The statistics of the clusters is obtained by means of the Hoshen--Kopelman algorithm. We…
The main properties of the climate of waves in the seasonally ice-covered Baltic Sea and its decadal changes since 1990 are estimated from satellite altimetry data. The data set of significant wave heights (SWH) from all existing nine…
With fires becoming increasingly frequent and severe across the globe in recent years, understanding climate change's role in fire behavior is critical for quantifying current and future fire risk. However, global climate models typically…
In recent years, advances in computational power and spatial data analysis (GIS, remote sensing, etc) have led to an increase in attempts to model the spread and behaviour of wildland fires across the landscape. This series of review papers…
The 2019-2020 Black Summer bushfires in Australia devastated 19 million hectares, destroyed 3,000 homes, and lasted seven months, demonstrating the escalating scale and urgency of wildfire threats requiring better forecasting for effective…
Fire has been an integral part of the Earth for millennia. Several recent wildfires have exhibited an unprecedented spatial and temporal extent and their control is beyond national firefighting capabilities. Prescribed or controlled burning…
The Amazon rainforest (ARF) is threatened by deforestation and climate change, which could trigger a regime shift to a savanna-like state. Previous work suggesting declining resilience in recent decades was based only on local resilience…
This paper discusses the development of a convolutional architecture of a deep neural network for the recognition of wildfires on satellite images. Based on the results of image classification, a fuzzy cognitive map of the analysis of the…
The global occurrence, scale, and frequency of wildfires pose significant threats to ecosystem services and human livelihoods. To effectively quantify and attribute the antecedent conditions for wildfires, a thorough understanding of Earth…