Germination is an essential phenomenon in the life cycle of plants, and a variety of external and internal factors influence it. Fire and the produced smoke have been vital environmental stimulants for the germination of seeds in many plant species, like Leucospermum cordifolium and Serruria florida. These plants do not germinate at all if fire and smoke are not present. This phenomenon of germination in plant species has existed in the ecosystem since ancient times. Various studies to study the response of seeds to smoke and its extracts have been undertaken for stimulation of germination by burning various plant materials and bubbling the smoke produced through water. The application of plant-derived smoke and smoke water is well known for promoting germination, breaking dormancy, and checking abiotic stress. This significantly indicates that plant-derived smoke contains some bioactive metabolites responsible for the physiological metabolism of seed germination and is involved in enhancing seed vigor. The present review deals with the ancient use of smoke and smoke extracts for seed priming, the cost-efficient method of its preparation, the mode of action of karrikins relating to its perception by plants, and its significant effects on various crops, including its ability to check biotic and abiotic stresses.

Forest fires pose significant environmental and socioeconomic threats, particularly in regions such as Central India, where forest ecosystems are vital for biodiversity and local livelihoods. Understanding forest fire dynamics and identifying fire risk zones are crucial for effective mitigation. The current study explores the spatiotemporal dynamics of forest fires in the Khandwa and North Betul forest divisions in the Central Indian region over 22 years using Mann-Kendall and Sen\'s slope tests on MODIS (Moderate Resolution Imaging Spectroradiometer) fire point data. We found a nonsignificant increase in forest fires in both divisions. Khandwa showed a nonsignificant slope rise of more than three events per year, while North Betul revealed an increase of around one event per year. The lack of statistical significance suggests that upward trends of forest fire events may result from random fluctuations rather than consistent patterns. Spatial autocorrelation analysis revealed significant clustering of fire incidents in both regions. Khandwa confirmed moderate clustering (Moran\'s I = 0.043), whereas North Betul showed robust clustering (Moran\'s I = 0.096). Kernel density estimation further identified high-risk clusters in both divisions, necessitating zonal-wise targeted fire management strategies. Fire risk zonation was developed using the analytic hierarchy process (AHP), combining 10 environmental and socioeconomic factors. The AHP model, validated using MODIS fire data, showed reliable accuracy. The results revealed many of both divisions in the high- to very high-risk categories. Approximately, 45% of the area of the Khandwa and nearly 50% of the area of North Betul fall under high to very high fire risk zones. Khandwa\'s high-risk areas mainly lie in the northern and southeastern parts, while North Betul lies in the northwestern and north-eastern regions. The identified fire-prone areas indicate the pressing need for local or region-specific fire prevention and mitigation strategies. Thus, the findings of this study provide valuable insights into forest fire risk management and contribute to more focused research and methodological developments.

Forest ecosystems face increasing wildfire threats, demanding prompt and precise detection methods to ensure efficient fire control. However, real-time forest fire data accessibility and timeliness require improvement. Our study addresses the challenge through the introduction of the Unmanned Aerial Vehicles (UAVs) based forest fire database (UAVs-FFDB), characterized by a dual composition. Firstly, it encompasses a collection of 1653 high-resolution RGB raw images meticulously captured utilizing a standard S500 quadcopter frame in conjunction with a RaspiCamV2 camera. Secondly, the database incorporates augmented data, culminating in a total of 15560 images, thereby enhancing the diversity and comprehensiveness of the dataset. These images were captured within a forested area adjacent to Adana Alparslan Türkeş Science and Technology University in Adana, Turkey. Each raw image in the dataset spans dimensions from 353 × 314 to 640 × 480, while augmented data ranges from 398 × 358 to 640 × 480, resulting in a total dataset size of 692 MB for the raw data subset. In contrast, the augmented data subset accounts for a considerably larger size, totaling 6.76 GB. The raw images are obtained during a UAV surveillance mission, with the camera precisely angled a -180-degree to be horizontal to the ground. The images are taken from altitudes alternating between 5 - 15 meters to diversify the field of vision and to build a more inclusive database. During the surveillance operation, the UAV speed is 2 m/s on average. Following this, the dataset underwent meticulous annotation using the advanced annotation platform, Makesense.ai, enabling accurate demarcation of fire boundaries. This resource equips researchers with the necessary data infrastructure to develop innovative methodologies for early fire detection and continuous monitoring, enhancing efforts to protect ecosystems and human lives while promoting sustainable forest management practices. Additionally, the UAVs-FFDB dataset serves as a foundational cornerstone for the advancement and refinement of state-of-the-art AI-based methodologies, aiming to automate fire classification, recognition, detection, and segmentation tasks with unparalleled precision and efficacy.

Forest fires in Thailand are a recurring and formidable challenge, inflicting widespread damage and ranking among the nation\'s most devastating natural disasters. Most detection methods are labor-intensive, lack speed for early detection, or result in high infrastructure costs. An essential approach to mitigating this issue involves establishing an efficient forest fire warning system based on amalgamating diverse available data sources and optimized algorithms. This research endeavors to develop a binary machine-learning classifier based on Thailand\'s forest fire occurrences from January 2019 to October 2022 using data acquired from satellite resources, including the Google Earth engine. We use four gas variables including carbon monoxide, sulfur dioxide, nitrogen dioxide, and ozone. The study explores a range of classification models, encompassing linear classifiers, gradient-boosting classifiers, and artificial neural networks. The XGBoost model is the top-performing option across various classification evaluation metrics. The model provides the accuracy of 99.6 % and ROC-AUC score of 0.939. These findings underscore the necessity for a comprehensive forest fire warning system that integrates gas measurement sensor devices and geospatial data. A feedback mechanism is also imperative to enable model retraining post-deployment, thereby diminishing reliance on geospatial attributes. Moreover, given that decision-tree-based algorithms consistently yield superior results, future research in machine learning for forest fire prediction should prioritize these approaches.

From the beginning of May 2023 to the end of August 2023, the Northern Hemisphere experienced significant wildfire activity with the most widespread fires occurring in Canada. Forest fires in Canada destroyed more than 15.6 million hectares of forests. These wildfires worsened air quality across the region and other parts of the world. The smoke reached southern Europe by the end of June 2023. To better understand the consequences of such forest fires far from the site of origin, aerosol optical, microphysical and radiative properties were analyzed during this event for southern Europe using data from the Visible Infrared Imaging Radiometer Suite (VIIRS), TROPOspheric Monitoring Instrument (TROPOMI), and Aerosol Robotic Network (AERONET). TROPOMI aerosol index (AI) and the carbon monoxide (CO) product confirm that the smoke originated directly from these forest fires. AERONET data from the El Arenosillo site in southern Spain showed maximum aerosol optical depth (AOD) values on June 27 reached 2.36. Data on Angstrom Exponent (AE), aerosol volume size distribution (VSD), single scattering albedo (SSA), fine mode fraction (FMF), volume particle concentration, effective radius (REff), absorption AOD (AAOD), extinction AE (EAE) and absorption AE (AAE) showed that fine-mode particles with carbonaceous aerosols contribution predominated in the atmosphere above the El Arenosillo site. Direct aerosol radiative forcing (DARF) at the top (DARFTOA) and bottom of atmosphere (DARFBOA) were -103.1 and -198.93 Wm-2, respectively. The atmospheric aerosol radiative forcing (DARFATM) was found to be 95.83 Wm-2 and with a heating rate 2.69 K day-1, which indicates the resulting warming of the atmosphere.

This study investigates the dynamic deployment of unmanned aerial vehicles (UAVs) using edge computing in a forest fire scenario. We consider the dynamically changing characteristics of forest fires and the corresponding varying resource requirements. Based on this, this paper models a two-timescale UAV dynamic deployment scheme by considering the dynamic changes in the number and position of UAVs. In the slow timescale, we use a gate recurrent unit (GRU) to predict the number of future users and determine the number of UAVs based on the resource requirements. UAVs with low energy are replaced accordingly. In the fast timescale, a deep-reinforcement-learning-based UAV position deployment algorithm is designed to enable the low-latency processing of computational tasks by adjusting the UAV positions in real time to meet the ground devices\' computational demands. The simulation results demonstrate that the proposed scheme achieves better prediction accuracy. The number and position of UAVs can be adapted to resource demand changes and reduce task execution delays.

Mountain forests play an essential role in protecting people and infrastructure from natural hazards. However, forests are currently experiencing an increasing rate of natural disturbances (including windthrows, bark beetle outbreaks and forest fires) that may jeopardize their capacity to provide this ecosystem service in the future. Here, we mapped the risk to forests\' protective service across the European Alps by integrating the risk components of hazard (in this case, the probability of a disturbance occurring), exposure (the proportion of forests that protect people or infrastructure), and vulnerability (the probability that the forests lose their protective structure after a disturbance). We combined satellite-based data on forest disturbances from 1986 to 2020 with data on key forest structural characteristics (cover and height) from spaceborne lidar (GEDI), and used ensemble models to predict disturbance probabilities and post-disturbance forest structure based on topographic and climatic predictors. Wind and bark beetles are dominant natural disturbance agents in the Alps, with a mean annual probability of occurrence of 0.05%, while forest fires were less likely (mean annual probability <0.01%), except in the south-western Alps. After a disturbance, over 40% of forests maintained their protective structure, highlighting the important role of residual living or dead trees. Within 30 years after wind and bark beetle disturbance, 61% of forests were likely to either maintain or recover their protective structure. Vulnerability to fires was higher, with 51% of forest still lacking sufficient protective structure 30 years after fire. Fire vulnerability was especially pronounced at dry sites, which also had a high fire hazard. Combining hazard and vulnerability with the exposure of protective forests we identified 186 Alpine municipalities with a high risk to protective forests due to wind and bark beetles, and 117 with a high fire risk. Mapping the disturbance risk to ecosystem services can help identify priority areas for increasing preparedness and managing forests towards lower susceptibility under an intensifying disturbance regime.

The frequency and intensity of forest fires are amplified by climate change. Substantial quantities of PM1 emitted from forest fires can undergo gradual atmospheric dispersion and long-range transport, thus impacting air quality far from the source. However, the chemical composition and physical properties of PM emitted from forest fires and its changes during atmospheric transport remain uncertain. In this study, the evolution of organic carbon (OC), elemental carbon (EC), water-soluble ions, and water-soluble metals in the particulate phase of smoke emitted from the typical forest vegetation combustion in Southwest China before and after photo-oxidation was investigated in the laboratory. Two aging periods of 5 and 9 days were selected. The OC and TC mass concentrations tended to decrease after 9-days aged compared to fresh emissions. OP, OC2, and OC3 in PM1 are expected to be potential indicators of fresh smoke, while OC3 and OC4 may serve as suitable markers for identifying aged carbon sources from the typical forest vegetation combustion in Southwest China. K+ exhibited the highest abundant water-soluble ion in fresh PM1, whereas NO3- became the most abundant water-soluble ion in aged PM1. NH4NO3 emerged as the primary secondary inorganic aerosol emitted from typical forest vegetation combustion in Southwest China. Notably, a 5-day aging period proved insufficient for the complete formation of the secondary inorganic aerosols NH4NO3 and (NH4)2SO4. After aging, the mass concentration of the water-soluble metal Ni in PM1 from typical forest vegetation combustion in Southwest China decreased, while the mean mass concentrations of all other water-soluble metals increased in varying degrees. These findings provide valuable data support and theoretical guidance for studying the atmospheric evolution of forest fire aerosols, as well as contribute to policy formulation and management of atmospheric environment safety and human health.

Climate change and anthropogenic activities have influenced the frequency and magnitude of forest fires both globally and regionally. While skilful short- to extended-range prediction of forest fires is essential for effective mitigation in local communities, it is also important to identify the implications of forest fires on different sectors, including water resources and sustainable development. Limited studies have investigated the association between forest fires and hydrometeorological variables at the regional scale in developing countries due to the lack of necessary datasets, which can now be leveraged using the newly hosted global reanalysis of fire danger indices (referred to as fire indices). The current study presents a comprehensive analysis of the spatio-temporal variations of eight fire indices across India, as well as their association with hydro-meteorological variables, such as precipitation, temperature, and the streamflow of a major river basin (Mahanadi) in India. The accuracy of these indices in capturing real fire events and the potential benefit of incorporating fire indices into long-term hydrologic simulations are also explored. The results show that fire indices can accurately yield fire seasons (i.e., post-monsoon and summer) in India. Furthermore, forest fires are found to be strongly associated with hydro-meteorological variables, typically resulting in low streamflow regimes. Fire indices can also capture actual fire events, maintaining high scalar accuracy. Finally, an improvement in uncalibrated hydrologic model simulations is observed when simulated streamflow is post-processed using the fire indices as predictors. Overall, the current study has valuable implications for fire indices forecasting and hydrologic simulations in ungauged basins.

Recently burned boreal forests have lower aboveground fuel loads, generating a negative feedback to subsequent wildfires. Despite this feedback, short-interval reburns (≤20 years between fires) are possible under extreme weather conditions. Reburns have consequences for ecosystem recovery, leading to enduring vegetation change. In this study, we characterize the strength of the fire-fuel feedback in recently burned Canadian boreal forests and the weather conditions that overwhelm resistance to fire spread in recently burned areas. We used a dataset of daily fire spread for thousands of large boreal fires, interpolated from remotely sensed thermal anomalies to which we associated local weather from ERA5-Land for each day of a fire\'s duration. We classified days with >3 ha of fire growth as spread days and defined burned pixels overlapping a fire perimeter ≤20 years old as short-interval reburns. Results of a logistic regression showed that the odds of fire spread in recently burned areas were ~50% lower than in long-interval fires; however, all Canadian boreal ecozones experienced short-interval reburning (1981-2021), with over 100,000 ha reburning annually. As fire weather conditions intensify, the resistance to fire spread declines, allowing fire to spread in recently burned areas. The weather associated with short-interval fire spread days was more extreme than the conditions during long-interval spread, but overall differences were modest (e.g. relative humidity 2.6% lower). The frequency of fire weather conducive to short-interval fire spread has significantly increased in the western boreal forest due to climate warming and drying (1981-2021). Our results suggest an ongoing degradation of fire-fuel feedbacks, which is likely to continue with climatic warming and drying.