Fire Characterization by Using an Original RST-Based Approach for Fire Radiative Power (FRP) Computation

Fire radiative power (FRP) is a basic parameter for fire characterization since it represents the heat emission rate of fires. Moreover, its temporal integration (fire radiative energy, FRE) is used as a proxy for estimating biomass burning and emissions. From satellite, FRP is generally computed by comparing the Medium InfraRed (MIR) signal of the fire pixel with the background value on the event image. Such an approach is possibly affected by some issues due to fire extent, clouds and smoke over the event area. The enlargement of the background window is the commonly used gimmick to face these issues. However, it may include unrepresentative signals of the fire pixel because of very different land use/cover. In this paper, the alternative Background Radiance Estimator by a Multi-temporal Approach (BREMA), based on the Robust Satellite Technique (RST), is proposed to characterize background and compute FRP. The approach is presented using data from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) platform. Moreover, BREMA is here combined with the RST-FIRES (RST for FIRES detection) technique for fire pixel identification and the -SEVIRI retrieval algorithm for transmittance evaluation. Results compared to the operational SEVIRI-based FRP-PIXEL product, although highly correlated in terms of background radiance (r2=0.95) and FRP values (r2=0.96), demonstrated a major capability of BREMA to estimate background radiances regardless of cloudiness or smoke presence during the event and independently on fire extent. The possible impact of the proposed approach on the estimates of CO2 emissions was also evaluated for comparison with the Global Fire Emissions Database (GFED4s)

Working with the enemy? Social work education and men who use intimate partner violence

This article examines service user involvement in social work education. It discusses the challenges and ethical considerations of involving populations who may previously have been excluded from user involvement initiatives, raising questions about the benefits and challenges of their involvement. The article then provides discussion of an approach to service user involvement in social work education with one of these populations, men who use violence in their intimate relationships, and concludes by considering the implications of their involvement for the social work academy

Developing a wildfire surveillance algorithm for geostationary satellites

Wildfire surveillance is an important aspect of effective wildfire management, requiring near continuous observations to detect and monitor fires. Geostationary satellites have the potential to meet this challenge, capturing full disk images every 10 to 30 minutes at ground sample distances down to 500 m for some sensors. However, the MIR (Middle Infrared) and TIR (Thermal Infrared) channels on geostationary satellite sensors have a coarse ground sample distance of 2-4 km. Currently, fire detection algorithms depend on these channels to detect thermal anomalies. The coarse spatial resolution in the MIR and TIR channels limits the application of geostationary satellite for wildfire surveillance. This thesis looks to fully exploit the potential of geostationary satellites for wildfire surveillance through a multi-spatial and multi-temporal approach. The first research question in this thesis, develops and tests an algorithm to improve the wildfire surveillance capabilities of the geostationary satellites. The new algorithm utilises the MIR, NIR and visible channels, linking them to biophysical processes on the ground. The MIR channel is used to detect thermal anomalies, the NIR channel is used to detect changes in vegetation cover, and the visible channel detects smoke from the fire. By combining these detections, or observations, fire surveillance can be achieved at the highest ground sampling resolution available (typically in the visible wavelength channels). Initial algorithm development and testing were conducted on the Advanced Himawari Imager (AHI) sensor onboard the Himawari-8 satellite. The MIR, NIR and RED channels on AHI have 2 km, 1 km and 500 m ground sampling distances respectively, enabling the new algorithm to detect 2 km thermal anomalies and 500 m fire-line pixels. Fire-line pixels is a new product designed to Adetect the trailing edge of the fire. Quantifiable methods for assessing algorithm performance in geostationary satellites are dicult to apply due to their high temporal resolution and lack of concurrent in-situ information. The second research question investigates methods for assessing the performance by considering the near continuous temporal sampling of geostationary satellites and the higher spatial ground sampling resolution a↵orded from LEO (Low Earth Orbiting) satellite observations. The study examines di↵erent evaluation methods and suggests a three-step process to provide the optimum performance evaluation for geostationary wildfire surveillance products, inter-compared with LEO satellite-based thermal anomaly detections. Algorithm performance is further evaluated in research question three using the intercomparison method developed in research question 2 and applied to case study fires over Northern Australia. Subsequently, the algorithm is evaluated using an annual dataset (2016) comprising of nine study areas across Australia (totalling 360.000 km2) stratified by tree canopy cover. The algorithm reported an omission error of 27 % at 2 km ground resolution when compared to VIIRS (Visible Infrared Imaging Radiometer Suite) hotspots over the nine study grids. In Northern Australia, the algorithm detected fires up to three hours before LEO observations due to the high temporal frequency of observations. Furthermore, in comparison to MODIS (Moderate Resolution Imaging Spectroradiometer) hotspots, there was a 73 % chance of detecting fire activity at the location of the MODIS hotspot, before the MODIS overpass. The algorithm also demonstrated a 40 % detection probability for fires less than 14 ha over Northern Australian woodlands. The fire-line pixels with a ground sampling distance of 500 m demonstrated a 25 % commission error when compared to VIIRS hotspots over the nine study grids. Over Northern Australia, this figure was 7 % inter-compared to Landsat-8 burnt scars. The fourth research question applied the developed algorithm to the SEVIRI (Spinning Enhanced Visible and Infrared Image) sensor onboard the European Meteosat Second Generation (MSG) satellite. SEVIRI has an operational fire product (FIR (Active Fire Monitoring)) which provides 3 km ground resolution hotspots using the MIR and TIR channels. The algorithm initially developed for AHI was modified to work with SEVIRI 3 km MIR channel and the High-Resolution Visible (HRV) channel (1 km). An inter-comparison of the modified algorithm with FIR products showed a 28 % and 16 % improvement in commission and omission errors respectively over a large case study fire in Portugal. The modified algorithm also improved the SEVIRI wildfire surveillance ground sampling resolution to 1 km taking advantage of the HRV channel. The algorithm developed in this study demonstrates a novel approach to utilise geostationary satellites for wildfire surveillance with improved spatial resolution. Compared to the 2 km thermal anomaly hotspots derived through existing algorithms for AHI, the new algorithm provides 2 km thermal anomaly detections and 500 m fire-line pixels with performance comparable to that of medium resolution LEO satellites. Near-real time implementation of the algorithm has the potential to provide high temporal fire surveillance capabilities. The fire-line pixels from the algorithm could also be used to derive fire behaviour parameters such as heading and speed, providing an essential tool for wildfire surveillance in remote parts of Australia and other areas, where resources can only be deployed for a hand full of high-risk fires

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

Determining ground-level composition and concentration of particulate matter across regional areas using the Himawari-8 satellite

Speciated ground-level aerosol concentrations are required to understand and mitigate health impacts from dust storms, wildfires and other aerosol emissions. Globally, surface monitoring is limited due to cost and infrastructure demands. While remote sensing can help estimate respirable (i.e. ground level) concentrations, current observations are restricted by inadequate spatiotemporal resolution, uncertainty in aerosol type, particle size, and vertical profile. One key issue with current remote sensing datasets is that they are derived from reflectances observed by polar orbiting imagers, which means that aerosol is only derived during the daytime, and only once or twice per day. Sub-hourly, infrared (IR), geostationary data, such as the ten-minute data from Himawari-8, are required to monitor these events to ensure that sporadic dust events can be continually observed and quantified. Newer quantification methods using geostationary data have focussed on detecting the presence, or absence, of a dust event. However, limited attention has been paid to the determination of composition, and particle size, using IR wavelengths exclusively. More appropriate IR methods are required to quantify and classify aerosol composition in order to improve the understanding of source impacts. The primary research objectives were investigated through a series of scientific papers centred on aspects deemed critical to successfully determining ground-level concentrations. A literature review of surface particulate monitoring of dust events using geostationary satellite remote sensing was undertaken to understand the theory and limitations in the current methodology. The review identified (amongst other findings) the reliance on visible wavelengths and the lack of temporal resolution in polar-orbiting satellite data. As a result of this, a duststorm was investigated to determine how rapidly the storm passed and what temporal data resolution is required to monitor these and other similar events. Various IR dust indices were investigated to determine which are optimum for determining spectral change. These indices were then used to qualify and quantitate dust events, and the methodology was validated against three severe air quality events of a dust storm; smoke from prescribed burns; and an ozone smog incident. The study identified that continuous geostationary temporal resolution is critical in the determination of concentration. The Himawari-8 spatial resolution of 2 km is slightly coarse and further spatial aggregation or cloud masking would be detrimental to determining concentrations. Five dual-band BTD combinations, using all IR wavelengths, maximises the identification of compositional differences, atmospheric stability, and cloud cover and this improves the estimated accuracy. Preliminary validation suggests that atmospheric stability, cloud height, relative humidity, PM2.5, PM10, NO, NO2, and O3 appear to produce plausible plumes but that aerosol speciation (soil, sea-spray, fires, vehicles, and secondary sulfates) and SO2 require further investigation. The research described in the thesis details the processes adopted for the development and implementation of an integrated approach to using geostationary remote sensing data to quantify population exposure (who), qualify the concentration and composition (what), assess the temporal (when) and spatial (where) concentration distributions, to determine the source (why) of aerosols contribution to resulting ground-level concentration