@article {bnh-7505, title = {Active fire detection using the Himawari-8 satellite - annual report 2019-2020}, number = {628}, year = {2020}, month = {11/2020}, institution = {Bushfire and Natural Hazards CRC}, address = {MELBOURNE}, abstract = {

This project is a critical part of the BNHCRC{\textquoteright}s value to the broader Australian government. The government run Sentinel Hotspots application is used by all levels of government, private sector, researchers and the public.\  This project will assist the Australian government to develop and validate Himawari-8 as a data source for the Sentinel Hotspots application.\  Our vision is to create a world leading approach to monitor fire activity.\  To achieve this, we propose the use of remote sensing technologies for active fire detection and monitoring.

This project is a critical part of the BNHCRC{\textquoteright}s value to Geoscience Australia and the broader Australian government. The Sentinel Hotspots application is used by all levels of government, private sector, researchers and the public {\textendash} this system would not be trusted by those parties without sound validation. This project will continue to assist the Australian government in developing and validating the capability of the Himawari-8 data source. Further, the project will assist in the ongoing improvement of vital bushfire information acquired through state-of-the-art remote sensing technology as needed by fire and emergency management now, and into the future.

The aim this project is to next generation, remote sensing satellite information to enhance Australia{\textquoteright}s operational capabilities and information systems for bushfire monitoring across a range of spatial scales and landscapes. Ultimately the outcomes of this research will enable measures of active fires in terms of areal extent and magnitude, which in turn will have the potential to inform decisions about bushfire response, fuel hazard management and ecosystem sensitivity to fire during fire events and post-fire rehabilitation efforts.

The following sections describe the background, research approaches, key milestones, utilisation study and outputs of our Himawari-8 active fire detection research.

}, keywords = {fire detection, HIMAWARI-8, Satellite}, issn = {628}, author = {Simon Jones and Karin Reinke and Chermelle Engel} } @article {bnh-6881, title = {Satellite Remote Sensing Contributions to Wildland Fire Science and Management}, journal = {Current Forestry Reports}, year = {2020}, month = {04/2020}, abstract = {

Purpose

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.

Summary

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.

}, keywords = {fire detection, management, remote sensing, Wildfire}, doi = {https://doi.org/10.1007/s40725-020-00116-5}, url = {https://link.springer.com/article/10.1007/s40725-020-00116-5?wt_mc=socialmedia.twitter.1.SEM.ArticleAuthorOnlineFirst}, author = {Emilio Chuvieco and Inmaculada Aguado and Javier Salas and Mariano Garcia and Marta Yebra and Patricia Oliva} } @article {bnh-7826, title = {A Seasonal-Window Ensemble-Based Thresholding Technique Used to Detect Active Fires in Geostationary Remotely Sensed Data}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, year = {2020}, abstract = {

This article introduces a new algorithm to detect active fires in geostationary remotely sensed data. The algorithm calculated dynamic statistical multispectral thresholds based on, and sensitive to, biogeographical region, subseason, and time-of-day. The spectral characteristics of nonfire and noncloud mid-infrared values were found to vary with biogeographical region, subseason, and time-of-day. These differences were exploited to define a new seasonal Biogeographical Region and Individual Geostationary HHMMSS Threshold (BRIGHT) multivariate adaptive threshold geostationary satellite fire anomaly algorithm. The algorithm was demonstrated on 12 months of daytime data acquired from the geostationary satellite system, the Advanced Himawari Imager (on Himawari-8) over Australia (7.69 million km{\texttwosuperior}). The resulting hotspots were compared with those from the existing Moderate Resolution Imaging Spectrometer (MODIS) polar-orbiting fire-hotspot algorithm. The intercomparison showed that BRIGHT wildfire hotspots, detected using Himawari-8 and Interim Biogeographic Regionalisation of Australia (IBRA) data, were also detected by MODIS polar-orbiting fire hotspots 88\% of the time. While MODIS hotspots were detected by BRIGHT hotspots only 39\% of the time; the majority of the undetected MODIS hotspots had low radiative power. BRIGHT provides a new method for the remote sensing of active fires providing reliable observations at spatial and temporal scales useful for fire managers.

}, keywords = {Advanced Baseline Imager, Advanced Himawari Imager, algorithm development, clear-sky, cloud detection, fire anomaly, fire detection, geostationary, HIMAWARI-8}, doi = {10.1109/TGRS.2020.3018455}, url = {https://ieeexplore.ieee.org/document/9186808/keywords$\#$keywords}, author = {Chermelle Engel and Simon Jones and Karin Reinke} }