Detection and monitoring of forest fires using Himawari-8 geostationary satellite data in South Korea

Geostationary satellite remote sensing systems are a useful tool for forest fire detection and monitoring because of their high temporal resolution over large areas. In this study, we propose a combined 3-step forest fire detection algorithm (i.e., thresholding, machine learning-based modeling, and post processing) using Himawari-8 geostationary satellite data over South Korea. This threshold-based algorithm filtered the forest fire candidate pixels using adaptive threshold values considering the diurnal cycle and seasonality of forest fires while allowing a high rate of false alarms. The random forest (RF) machine learning model then effectively removed the false alarms from the results of the threshold-based algorithm (overall accuracy ~99.16%, probability of detection (POD) ~93.08%, probability of false detection (POFD) ~0.07%, and 96% reduction of the false alarmed pixels for validation), and the remaining false alarms were removed through post-processing using the forest map. The proposed algorithm was compared to the two existing methods. The proposed algorithm (POD ~ 93%) successfully detected most forest fires, while the others missed many small-scale forest fires (POD ~ 50-60%). More than half of the detected forest fires were detected within 10 min, which is a promising result when the operational real-time monitoring of forest fires using more advanced geostationary satellite sensor data (i.e., with higher spatial and temporal resolutions) is used for rapid response and management of forest fires

Fire behavior modeling for operational decision-making

Simulation frameworks are necessary to facilitate decision-making to many fire agencies. An accurate estimation of fire behavior is required to analyze potential impact and risk. Applied research and technology together have improved the implementation of fire modeling, and decision-making in operational environments.Dr Cardil acknowledges the support of Technosylva USA and Wageningen University in his research stays in the USA and the Netherlands to develop this work. The authors of this paper acknowledges the support of the EUfunded PYROLIFE project (Reference: 860787; https://pyrolife.lessonsonfire.eu/), a project in which a new generation of experts will be trained in integrated wildfire management

Characterizing the rate of spread of large wildfires in emerging fire environments of northwestern Europe using visible infrared imaging radiometer suite active fire data

In recent years fires of greater magnitude have been documented throughout northwest Europe. With several climate projections indicating future increases in fire activity in this temperate area, it is imperative to identify the status of fire in this region. This study unravels unknowns about the state of the fire regime in northwest Europe by characterizing one of the key aspects of fire behavior, the rate of spread (ROS). Using an innovative approach to cluster Visible Infrared Imaging Radiometer Suite (VIIRS) hotspots into fire perimeter isochrones to derive ROS, we identify the effects of land cover and season on the rate of spread of 102 landscape fires that occurred between 2012 and 2022. Results reveal significant differences between land cover types, and there is a clear peak of ROS and burned area in the months of March and April. Median ROS within these peak months is approximately 0.09 km h−1 during a 12 h overpass, and 66 % of the burned area occurs in this spring period. Heightened ROS and burned area values persist in the bordering months of February and May, suggesting that these months may present the extent of the main fire season in northwest Europe. Accurate data on ROS among the represented land cover types, as well as periods of peak activity, are essential for determining periods of elevated fire risk, the effectiveness of available suppression techniques, and appropriate mitigation strategies (land and fuel management).This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 860787 (PyroLife Innovative Training Network; https://pyrolife.lessonsonfire.eu/, last access: January 2023), a project in which a new generation of experts is trained in integrated fire manageme

HOTSPOT VALIDATION OF THE HIMAWARI-8 SATELLITE BASED ON MULTISOURCE DATA FOR CENTRAL KALIMANTAN

The Advanced Himawari Imager (AHI) is the sensor aboard the remote-sensing satellite Himawari-8 which records the Earth’s weather and land conditions every 10 minutes from a geostationary orbit. The imagery produced known as Himawari-8 has 16 bands which cover visible, near infrared, middle infrared and thermal infrared wavelength potentials to monitor forestry phenomena. One of these is forest/land fires, which frequently occur in Indonesia in the dry season. Himawari-8 can detect hotspots in thermal bands 5 and band 7 using absolute fire pixel (AFP) and possible fire pixel (PFP) algorithms. However, validation has not yet been conducted to assess the accuracy of this information. This study aims to validate hotspots identified from Himawari images based on information from Landsat 8 images, field surveys and burnout data. The methodology used to validate hotspots comprises AFP and PFP extraction, determining firespots from Landsat 8, buffering at 2 km from firespots, field surveys, burnout data, and calculation of accuracy. AFP and PFP hotspot validation of firespots from Landsat-8 is found to have higher accuracy than the other options. In using Himawari-8 hotspots to detect land/forest fires in Central Kalimantan, the AFP algorithm with 2km radius has accuracy of 51.33% while the PFP algorithm has accuracy of 27.62%

Advances in Remote Sensing-based Disaster Monitoring and Assessment

Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones

Anak Krakatau triggers volcanic freezer in the upper troposphere

Volcanic activity occurring in tropical moist atmospheres can promote deep convection and trigger volcanic thunderstorms. These phenomena, however, are rarely observed to last continuously for more than a day and so insights into the dynamics, microphysics and electrification processes are limited. Here we present a multidisciplinary study on an extreme case, where volcanically-triggered deep convection lasted for six days. We show that this unprecedented event was caused and sustained by phreatomagmatic activity at Anak Krakatau volcano, Indonesia during 22-28 December 2018. Our modelling suggests an ice mass flow rate of similar to 5x10(6)kg/s for the initial explosive eruption associated with a flank collapse. Following the flank collapse, a deep convective cloud column formed over the volcano and acted as a 'volcanic freezer' containing similar to 3x10(9)kg of ice on average with maxima reaching similar to 10(10)kg. Our satellite analyses reveal that the convective anvil cloud, reaching 16-18km above sea level, was ice-rich and ash-poor. Cloud-top temperatures hovered around -80 degrees C and ice particles produced in the anvil were notably small (effective radii similar to 20 mu m). Our analyses indicate that vigorous updrafts (>50m/s) and prodigious ice production explain the impressive number of lightning flashes (similar to 100,000) recorded near the volcano from 22 to 28 December 2018. Our results, together with the unique dataset we have compiled, show that lightning flash rates were strongly correlated (R=0.77) with satellite-derived plume heights for this event

A deep learning model using geostationary satellite data for forest fire detection with reduced detection latency

Although remote sensing of active fires is well-researched, their early detection has received less attention. Additionally, simple threshold approaches based on contextual statistical analysis suffer from generalization problems. Therefore, this study proposes a deep learning-based forest fire detection algorithm, with a focus on reducing detection latency, utilizing 10-min interval high temporal resolution Himawari-8 Advanced Himawari Imager. Random forest (RF) and convolutional neural network (CNN) were utilized for model development. The CNN model accurately reflected the contextual approach adopted in previous studies by learning information between adjacent matrices from an image. This study also investigates the contribution of temporal and spatial information to the two machine learning techniques by combining input features. Temporal and spatial factors contributed to the reduction in detection latency and false alarms, respectively, and forest fires could be most effectively detected using both types of information. The overall accuracy, precision, recall, and F1-score were 0.97, 0.89, 0.41, and 0.54, respectively, in the best scheme among the RF-based schemes and 0.98, 0.91, 0.63, and 0.74, respectively, in that among the CNN-based schemes. This indicated better performance of the CNN model for forest fire detection that is attributed to its spatial pattern training and data augmentation. The CNN model detected all test forest fires within an average of 12 min, and one case was detected 9 min earlier than the recording time. Moreover, the proposed model outperformed the recent operational satellite-based active fire detection algorithms. Further spatial generality test results showed that the CNN model had reliable generality and was robust under varied environmental conditions. Overall, our results demonstrated the benefits of geostationary satellite-based remote sensing for forest fire monitoring

Satellite Remote Sensing contributions to Wildland Fire Science and Management

No funding was received for this particular review, but support research was funded by the European Space Agency’s Climate Change Initiative Programme to Dr. Chuvieco.This paper reviews the most recent literature related to the use of remote sensing (RS) data in wildland fire management. Recent Findings Studies dealing with pre-fire assessment, active fire detection, and fire effect monitoring are reviewed in this paper. The analysis follows the different fire management categories: fire prevention, detection, and post-fire assessment. Extracting the main trends from each of these temporal sections, recent RS literature shows growing support of the combined use of different sensors, particularly optical and radar data and lidar and optical passive images. Dedicated fire sensors have been developed in the last years, but still, most fire products are derived from sensors that were designed for other purposes. Therefore, the needs of fire managers are not always met, both in terms of spatial and temporal scales, favouring global over local scales because of the spatial resolution of existing sensors. Lidar use on fuel types and post-fire regeneration is more local, and mostly not operational, but future satellite lidar systems may help to obtain operational products. Regional and global scales are also combined in the last years, emphasizing the needs of using upscaling and merging methods to reduce uncertainties of global products. Validation is indicated as a critical phase of any new RS-based product. It should be based on the independent reference information acquired from statistically derived samples. The main challenges of using RS for fire management rely on the need to improve the integration of sensors and methods to meet user requirements, uncertainty characterization of products, and greater efforts on statistical validation approaches.European Space Agenc

Object Tracking Based on Satellite Videos: A Literature Review

Video satellites have recently become an attractive method of Earth observation, providing consecutive images of the Earth’s surface for continuous monitoring of specific events. The development of on-board optical and communication systems has enabled the various applications of satellite image sequences. However, satellite video-based target tracking is a challenging research topic in remote sensing due to its relatively low spatial and temporal resolution. Thus, this survey systematically investigates current satellite video-based tracking approaches and benchmark datasets, focusing on five typical tracking applications: traffic target tracking, ship tracking, typhoon tracking, fire tracking, and ice motion tracking. The essential aspects of each tracking target are summarized, such as the tracking architecture, the fundamental characteristics, primary motivations, and contributions. Furthermore, popular visual tracking benchmarks and their respective properties are discussed. Finally, a revised multi-level dataset based on WPAFB videos is generated and quantitatively evaluated for future development in the satellite video-based tracking area. In addition, 54.3% of the tracklets with lower Difficulty Score (DS) are selected and renamed as the Easy group, while 27.2% and 18.5% of the tracklets are grouped into the Medium-DS group and the Hard-DS group, respectively