@article {bnh-8355, title = {Representing vapour and capillary rise from the soil improves a leaf litter moisture model}, journal = {Journal of Hydrology}, volume = {612}, year = {2022}, month = {06/2022}, abstract = {

Litter moisture content plays a critical role in fire danger rating systems and forest ecosystems. Soil moisture content has been proposed to affect litter moisture due to vapour flux and capillary rise from the soil. However, few models consider soil water content when predicting litter moisture, and to our knowledge, no model includes capillary fluxes. In this study, we represented soil moisture coupling in a physics-based litter moisture prediction model by describing the vapour and capillary fluxes from the soil to litter. We aimed to evaluate if litter moisture predictions can be improved by incorporating the hydrological process at the soil-litter interface and explored the possible role of soil moisture in litter moisture simulations. Three model versions were compared against observations at a dry and wet experimental site in Australia: the original physics-based model, a model version extended with soil vapour flux only, and a version that incorporates both soil vapour flux and capillary rise. The simulation results suggest that soil moisture considerably influences litter moisture through soil vapour flux and capillary rise, which can lead to more than 10\% of oven-dried weight, particularly under wet soil conditions. The corresponding model showed the best performance in comparison with field observations. The contribution of upward soil moisture fluxes was small after long dry and warm periods but noticeable during more moist periods. Further research is needed to evaluate the revised model for a broader range of weather, soil and litter conditions.

}, keywords = {capillary rise, fuel moisture content, leaf litter, Soil moisture, vapour flux}, doi = {https://doi.org/10.1016/j.jhydrol.2022.128087}, url = {sciencedirect.com/science/article/abs/pii/S002216942200662X}, author = {Li Zhao and Marta Yebra and Albert van Dijk and Geoffrey J. Cary} } @article {bnh-8100, title = {Continental-scale prediction of live fuel moisture content using soil moisture information}, journal = {Agricultural and Forest Meteorology}, volume = {307}, year = {2021}, month = {06/2021}, abstract = {

Live fuel moisture content (LFMC) is a key factor that determines the flammability of vegetation in ecosystems. Soil moisture (SM) is one of the variables that is known to influence plant water use. The present study analyses the LFMC-SM relationship over Australia using gridded, remote sensing-based LFMC and land surface model-based SM products. A lag-correlation analysis conducted over 60 selected sites shows that the strength of the relationship between LFMC and SM varies from site to site and, in general, is moderately strong (median lag-correlation of ~0.5). However, the strength of the relationship changes with vegetation type and also with soil profile depth. At all the sites, SM is found to be a leading indicator of LFMC. The lag also varies with the location and is found to range from days to months. Based on the location-based correlation analysis, we identify the 0-35 cm SM profile (SM0-35cm) to be the best predictor of LFMC. We developed a simple model to predict daily LFMC, where it is hypothesised that daily variations in LFMC from its annual cycle can be predicted using daily deviations from the annual cycle in SM0-35cm. The annual cycles of LFMC and SM0-35cm are modelled using Fourier cosine series. The averaged (over 60 sites) correlation obtained for the validation period is 0.74 when a time-lag of 14 days is assumed at all locations. When the model is applied nationally at a 5 km grid, the normalised root mean squared error for the validation period is found to be less than 25\% in general. The results from the present study highlight a modelling strategy that can be used to address a critical gap in the forecast of spatially and temporally continuous LFMC at regional scales in advance for operational fire management applications.

}, keywords = {AFMS, Fire, JASMIN, JULES, live fuel moisture, Soil moisture}, doi = {https://doi.org/10.1016/j.agrformet.2021.108503}, url = {https://www.sciencedirect.com/science/article/abs/pii/S0168192321001866}, author = {Vinod Kumar and Imtiaz Dharssi and Marta Yebra and Paul Fox-Hughes} } @article {bnh-7761, title = {The influence of soil moisture on surface and sub-surface litter fuel moisture simulation at five Australian sites}, journal = {Agricultural and Forest Meteorology}, volume = {298-299}, year = {2021}, month = {03/2021}, abstract = {

Fuel moisture content (FMC) of the litter is a critical factor determining fire ignition and spread and is an important input for most fire behaviour prediction models. Several models, ranging from empirical regression functions to physics-based models, have been developed to forecast litter FMC. Soil moisture below the litter layer has been shown to influence litter FMC, but few models explicitly consider its effect. This study aimed to evaluate how soil moisture content may affect litter FMC by coupling soil moisture as a boundary condition to the physics-based {\textquoteleft}Koba{\textquoteright} model, which simulates radiation, energy and moisture fluxes in the surface and subsurface litter layer. The coupled model was tested at five sites in Victoria, Australia, where litter FMC values were recorded continuously during 2014-2015 using calibrated fuel stick sensors. Two versions of the model were compared against the observations: an uncoupled model and a model version accounting for the vapour flux from soil to litter. The simulation results show that the influence of soil moisture depends on environmental conditions. Soil moisture appeared to have a minor influence on FMC when both soil and litter are dry, but a stronger influence when the soil is relatively wet. Correspondingly, the coupled model explained observed FMC better than the uncoupled model for the two wetter study sites. As expected, the subsurface litter in contact with the soil appeared more sensitive to soil moisture conditions than the surface litter. The influence of soil vapour flux on litter FMC can be considerable during the transition from wet to dry litter and soil conditions. This has implications for hazard reduction burning, which is typically planned to take advantage of transitional fuel moisture conditions. Further research is needed to understand the influence of the structure and thickness of litter on the importance of soil vapour flux.

}, keywords = {Coupled model, Dead fuel, forecast, fuel moisture content, Soil moisture}, doi = {https://doi.org/10.1016/j.agrformet.2020.108282}, url = {https://www.sciencedirect.com/science/article/abs/pii/S0168192320303841?dgcid=coauthor}, author = {Li Zhao and Marta Yebra and Albert van Dijk and Geoffrey J. Cary and Stuart Matthews and Gary J. Sheridan} } @article {bnh-8190, title = {Soil and fuel moisture precursors of fire activity during the 2019-20 fire season, in comparison to previous seasons - Black Summer final report}, number = {686}, year = {2021}, month = {08/2021}, institution = {Bushfire and Natural Hazards CRC}, address = {MELBOURNE}, abstract = {

Australia experienced unprecedented bushfires during the 2019-20 fire season. Millions of hectares were burned, almost 3,000 homes were destroyed, there were 26 fatalities, and communities were exposed to smoke for extended periods.\  Low soil and vegetation moisture content due to antecedent dry conditions were a key driver of fire activity.

This report extends the work in two Bushfire and Natural Hazards CRC projects to examine the 2019-20 fire season:

The national spatial datasets produced by these projects are consistent and regularly updated, separately examining soil and vegetation moisture variability, as both quantities influence fuel availability and therefore fire behaviour.

This project explores the overlaps between these data sets, as well as comparing other available data (including the Australian Landscape Water Balance) to identify atmospheric, soil and fuel moisture characteristics that contributed to fire risk during Black Summer.

Spatially coherent historical and regularly updated soil and fuel moisture datasets are critically important to fire managers so that an accurate, quantifiable assessment of potential fire activity in the landscape can be made, and this can be benchmarked against historical conditions. This information is crucial to making accurate assessments of risk and identifying situations that fall outside of historical records. One of the valuable aspects of JASMIN is that it can also be used in a predictive way and is therefore useful in anticipating soil moisture and therefore fire risk into the future in a quantitative manner on weather (short-term) and seasonal timescales. Research work in the {\textquoteright}Land Dryness{\textquoteright} project also demonstrated the capacity of soil moisture to predict future vegetation moisture content and thus more directly anticipate fire potential.

This project examines the interaction of atmospheric parameters with soil and fuel moisture content over scales of days, weeks to months, and years. Such an integration has not been undertaken previously.\ Consideration of each of these timeframes is important when making accurate assessments of fire risk. The interaction between them is also critical, as a compounding of the individual processes at the different scales occurs and this was seen in the cumulative effects of antecedent dry years, low winter rainfall and heatwaves during the Black Summer.\ 

This project examined six fire events that were nominated for investigation by the relevant jurisdictions. The fires are:

The scope of the project included examining the following:

Key findings of the project include:\ 

The findings confirm impressions of fire practitioners regarding the extremity of conditions antecedent to the 2019-20 fire season. Importantly, the datasets used in the project present a measurable and spatially coherent approach to estimating fire risk from observed and modelled soil and fuel moisture. Operational application of the datasets and approaches used here will assist in producing accurate soil and vegetation moisture forecasts for prediction of fire risk in the future.

Some immediate benefits for fire managers of this work include:

Each of the meteorological and moisture variables investigated in this study contributed useful information to an understanding of increased fire risk at each site during the 2019-20 Australian summer. Fuel moisture content provided perhaps the most immediate indicator of present fire risk. Soil moisture content permitted an assessment of future changes in fuel moisture content. Both of these integrated changes in meteorological parameters. As such, the meteorological parameters (temperature, precipitation, atmospheric moisture represented by vapour pressure) contributed an understanding of why soil and fuel moisture changed in the ways that they did and offered information on how they would change in the future. Monitoring these quantities can help fire and land managers understand how and why fire risk changes across a landscape.\ 

Future work

This project was conducted over a short time period with one objective being to establish a pathway for future research and operational application.

The following opportunities for future research with likely operational benefits have been identified:

In summary, better understanding and spatial mapping of the influence of atmospheric, soil and fuel moisture will assist fire and land managers predict fire risk, which is critical for strategic and tactical planning. Many of the recommendations could be inclusions to the Australian Fire Danger Rating System (AFDRS), and therefore of immediate benefit to fire and land managers following the introduction of the AFDRS to operational use over the next two fire seasons.

}, keywords = {black summer, Fire, fire activity, fuel, moisture, precursors, soil}, issn = {686}, author = {Paul Fox-Hughes and Marta Yebra and Shukhrat Shokirov and Vinod Kumar and Dowdy, Andrew J and Pandora Hope and Mika Peace and Sugata Narsey and Francois Delage and Huqiang Zhang} } @article {bnh-6993, title = {Case study: use of remote sensing data to derive spatial and temporal explicit fuel accumulation curves across defence lands}, number = {579}, year = {2020}, month = {06/2020}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

Fuel loads are a main driver of fire rate of spread. Therefore, a spatially explicit estimation of fuel loads, coupled with their variation through time may improve wildland fuel management and contribute to the design of more efficient active fire response strategies. However, the high frequency of planned and unplanned fires in large wild areas linked to varying fire severity levels that affect the rate at which fuels re-accumulate, make the continuous monitoring of wildland fuels challenging with field-based survey methods. Here we propose the use of satellite remote sensing to map fuel loads with a revisit time of 16 days. Fuel load maps are produced for five Defence Lands using Landsat and Sentinel-2 optical remote sensing data available at Digital Earth Australia and the National Computation Infrastructure. The fuel load maps are obtained by calculating the time series of the Vegetation Structure Perpendicular Index, an index that measures post-fire disturbance, and fitting these to fuel accumulation curves derived from literature.

}, keywords = {data analysis, defence lands, fuel accumulation, remote sensing}, issn = {579}, author = {Andrea Massetti and Marta Yebra and James Hilton and Christoph R{\"u}diger} } @article {bnh-7470, title = {Exploring the soil moisture-live fuel moisture relationship}, number = {624}, year = {2020}, month = {10/2020}, institution = {Bushfire and Natural Hazards CRC}, address = {MELBOURNE}, abstract = {

Live fuel moisture content is a key factor that determines the flammability of vegetation in ecosystems. Prediction of live fuel moisture content is inherently a very difficult problem since it is modulated by the complex physiological, phenological and ecological processes characteristic of the plant species. Soil moisture is one of the key variables that is known to influence plant water use. Recently, a new live fuel moisture content near-real-time product has been developed for Australia using a radiative transfer model inversion technique on the MODerate Resolution Imaging Spectroradiometer reflectance data. This live fuel moisture content product forms the basis of the Australian Flammability Monitoring System. At the same time, an advanced soil moisture analysis system has been developed by the Bureau of Meteorology recently, called the Joint United Kingdom Land Environment Simulator based Soil Moisture Information (JASMIN). JASMIN can estimate soil moisture at 5 km resolution on a daily timestep for the whole of Australia.

The present study brings together the above two products and explores the live fuel moisture content{\textendash}soil moisture relationship on a national scale. This study will report the preliminary work carried out in understanding live fuel moisture content{\textendash}soil moisture relationship and suggests an approach that may be constructive in advancing the ability to predict live fuel moisture content reliably to support fire management. A preliminary analysis is being conducted over 60 selected locations where JASMIN is found to have good skill. These 60 sites together represent a range of land cover types and climate zones typical of the Australian landscape. All the possible soil moisture profiles that can be derived from the four JASMIN soil layers are used for the analysis. Lag-correlation analysis shows that the strength of the relationship between live fuel moisture content and soil moisture varies from site to site and in general, is moderately strong (median lag-correlation of ~0.5). However, the strength of the relationship varies with vegetation type and also with soil profile depth. At all the sites, soil moisture is found to be an (important) leading indicator of live fuel moisture content. The lag also varies with the location and is found to range from days to months. Except for the forested sites, the top two soil layers exhibit a higher correlation with live fuel moisture content compared to the deeper layers. We develop a simple model to predict live fuel moisture content. The model is found to have good skill with an average R2 of 0.64 over the 60 sites. The normalized root mean square error for the model prediction is found to be less than 25\% in general.

}, keywords = {live fuel moisture, Soil moisture}, issn = {624}, author = {Vinod Kumar and Imtiaz Dharssi and Marta Yebra and Paul Fox-Hughes} } @article {bnh-7479, title = {A global canopy water content product from AVHRR/Metop}, journal = {Remote Sensing}, volume = {162}, year = {2020}, month = {04/2020}, pages = {77-93}, abstract = {

Spatially and temporally explicit canopy water content (CWC) data are important for monitoring vegetation status, and constitute essential information for studying ecosystem-climate interactions. Despite many efforts there is currently no operational CWC product available to users. In the context of the Satellite Application Facility for Land Surface Analysis (LSA-SAF), we have developed an algorithm to produce a global dataset of CWC based on data from the Advanced Very High Resolution Radiometer (AVHRR) sensor on board Meteorological{\textendash}Operational (MetOp) satellites forming the EUMETSAT Polar System (EPS). CWC reflects the water conditions at the leaf level and information related to canopy structure. An accuracy assessment of the EPS/AVHRR CWC indicated a close agreement with multi-temporal ground data from SMAPVEX16 in Canada and Dahra in Senegal, with RMSE of 0.19\ kg\ m-2\ and 0.078\ kg\ m-2\ respectively. Particularly, when the Normalized Difference Infrared Index (NDII) was included the algorithm was better constrained in semi-arid regions and saturation effects were mitigated in dense canopies. An analysis of spatial scale effects shows the mean bias error in CWC retrievals remains below 0.001\ kg\ m-2\ when spatial resolutions ranging from 20\ m to 1\ km are considered. The present study further evaluates the consistency of the LSA-SAF product with respect to the Simplified Level 2 Product Prototype Processor (SL2P) product, and demonstrates its applicability at different spatio-temporal resolutions using optical data from MSI/Sentinel-2 and MODIS/Terra \& Aqua. Results suggest that the LSA-SAF EPS/AVHRR algorithm is robust, agrees with the CWC dynamics observed in available ground data, and is also applicable to data from other sensors. We conclude that the EPS/AVHRR CWC product is a promising tool for monitoring vegetation water status at regional and global scales.

}, keywords = {AVHRR/MetOp, Canopy Water Content (CWC), EUMETSAT, Gaussian Process Regression (GPR), MODIS, Polar System (EPS), Sentinel-2}, doi = {https://doi.org/10.1016/j.isprsjprs.2020.02.007}, url = {https://www.sciencedirect.com/science/article/abs/pii/S0924271620300411}, author = {Francis Javier Garcia-Haro and Manuel Campos-Taberner and Alaro Moreno and Hakan Torbern Tagesson and Fernando Camacho and Beatriz Martinez and Sergio Sanchez and Maria Piles and Gustau Campas-Valls and Marta Yebra and Maria Amparo Gilabert} } @article {bnh-6974, title = {A Live Fuel Moisture Content Product from Landsat TM Satellite Time Series for Implementation in Fire Behavior Models}, journal = {Remote Sensing}, volume = {12}, year = {2020}, month = {05/2020}, abstract = {

Live Fuel Moisture Content (LFMC) contributes to fire danger and behavior, as it affects fire ignition and propagation. This paper presents a two layered Landsat LFMC product based on topographically corrected relative Spectral Indices (SI) over a 2000{\textendash}2011 time series, which can be integrated into fire behavior simulation models. Nine chaparral sampling sites across three Landsat -5 Thematic Mapper (TM) scenes were used to validate the product over the Western USA. The relations between field-measured LFMC and Landsat-derived SIs were strong for each individual site but worsened when pooled together. The Enhanced Vegetation Index (EVI) presented the strongest correlations (r) and the least Root Mean Square Error (RMSE), followed by the Normalized Difference Infrared Index (NDII), Normalized Difference Vegetation Index (NDVI) and Visible Atmospherically Resistant Index (VARI). The relations between LFMC and the SIs for all sites improved after using their relative values and relative LFMC, increasing r from 0.44 up to 0.69 for relative EVI (relEVI), the best predictive variable. This relEVI served to estimate the herbaceous and woody LFMC based on minimum and maximum seasonal LFMC values. The understory herbaceous LFMC on the woody pixels was extrapolated from the surrounding pixels where the herbaceous vegetation is the top layer. Running simulations on the Wildfire Analyst (WFA) fire behavior model demonstrated that this LFMC product alone impacts significantly the fire spatial distribution in terms of burned probability, with average burned area differences over 21\% after 8 h burning since ignition, compared to commonly carried out simulations based on constant values for each fuel model. The method could be applied to Landsat-7 and -8 and Sentinel-2A and -2B after proper sensor inter-calibration and topographic correction.

}, keywords = {data normalization, fire behavior simulator, fire danger, fire propagation, Landsat-5 TM, live fuel moisture content}, doi = {https://doi.org/10.3390/rs12111714}, url = {https://www.mdpi.com/2072-4292/12/11/1714}, author = {Mariano Garcia and David Ria{\~n}o and Marta Yebra and Javier Salas and Adrian Cardil and Santiago Monedero and Joaquin Ramirez and Pillar Martin and Lara Vilar and John Gajardo and Susan Ustin} } @article {bnh-7390, title = {Mapping bushfire hazard and impacts {\textendash} final project report}, number = {616}, year = {2020}, month = {10/2020}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

Fuel condition (e.g. moisture content, horizontal and vertical continuity and the quantity or load) affect flammability and therefore is a key determinant of the ease of ignition and the spread rate of a fire. Many research institutions and management services are investing an important amount of resources on field-based fuel condition monitoring to understand its dynamics and support operational fire management activities. However, field sampling is expensive and time-consuming, sometimes subjective and implies also a delay time until the data are processed and made available. The Bushfire \& Natural Hazards CRC project {\textquoteleft}Mapping Bushfire Hazards and Impacts{\textquoteright} used cutting-edge technology to produce near-real-time spatial information on fuel condition, fire hazard and impact to support a wide range of fire risk management and response activities such as hazard reduction burning and pre-positioning firefighting resources and, in the longer term, the new Australian Fire Danger Rating System (AFDRS) given it considers fuel condition as ne of the key factors required to determine fire danger and support other decision -making.

The first phase of this project (2014-2017) involved the parallel investigation of several promising data sources and methods that can be categorised as either {\textquoteleft}in-field{\textquoteright} (grassland curing on-ground sensors and ground-based LiDAR laser scanning for fuel structure characterisation) or {\textquoteleft}national-scale{\textquoteright} (the Australian Flammability Monitoring System-AFMS, and the High-resolution Fire Risk and Impact (HiFRI) model-data fusion framework) methods. Generally, information derived from the national-scale methods appeared to represent enough accuracy and better return on investment and generated greater interest among end-users.

Consequently, over 2017-2020, this research project focused on ensuring the continuity of the AFMS but further developing the algorithms to estimate fuel moisture content (FMC), and through this, its acceptance and adoption. The AFMS provides information on FMC and flammability across Australia at 500m resolution every four days. It also displays information on soil moisture content near the surface (0-10 cm) and shallow soils (10-35 cm) as research outcomes from the project {\textquotedblleft}Improving land dryness measures and forecast{\textquotedblright}. Our research project also improved estimates of litter FMC by incorporating the effect of soil moisture content in existing physics-based models, developed a methodology to map fuel loads using satellite data and an objective and observation-based approach to assess fire danger that considers spatial data on the occurrence of actual fires as well as on fire factors that are already routinely produced every day for Australia.

The improved litter FMC, fuel load and fire danger retrieval algorithms and products derived from this project are not yet included in the AFMS nor generated at near real-time and across Australia. Further research and development is needed to make that step and providing landscape scale, cost-effective way to comprehensively monitor fuel condition and fire danger at such a broad scale. Consequently, future research should focus on developing a multi-sensor system that will display daily information on fire danger and fuel condition from whatever satellite sensor has collected an image over Australia, including high-resolution satellite imagery. This will improve the temporal and spatial resolution of the information we currently provide at the AFMS that will allow the identification of, for example, local fuel moisture gradients in the landscape that are currently not identifiable using the 500m pixel resolution of the product we currently serve in the AFMS.

The use of high-resolution satellite imagery will provide an unprecedented level of detail and accuracy when estimating fuel condition, bringing the system closer to use in operations. However, the data volumes and large compute resources required to store and generate these high-resolution products becomes a challenge. Consequently, future research should also focus on developing an alternative methodology for computing fuel condition that can provide up to date estimations by reducing the required storage and computing resources using state-of-the-art artificial intelligence algorithms.

}, keywords = {Bushfire, hazard, impacts, mapping}, issn = {616}, author = {Marta Yebra and Albert van Dijk and Geoffrey J. Cary} } @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-5688, title = {Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation}, journal = {Remote Sensing}, volume = {11}, year = {2019}, month = {07/2019}, abstract = {

Fuel moisture content (FMC) is a crucial variable affecting fuel ignition and rate of fire spread. Much work so far has focused on the usage of remote sensing data from multiple sensors to derive FMC; however, little attention has been devoted to the usage of the C-band Sentinel-1A data. In this study, we aimed to test the performance of C-band Sentinel-1A data for multi-temporal retrieval of forest FMC by coupling the bare soil backscatter linear model with the vegetation backscatter water cloud model (WCM). This coupled model that linked the observed backscatter directly to FMC, was firstly calibrated using field FMC measurements and corresponding synthetic aperture radar (SAR) backscatters (VV and VH), and then a look-up table (LUT) comprising of the modelled VH backscatter and FMC was built by running the calibrated model forwardly. The absolute difference (MAEr) of modelled and observed VH backscatters was selected as the cost function to search the optimal FMC from the LUT. The performance of the presented methodology was verified using the three-fold cross-validation method by dividing the whole samples into equal three parts. Two parts were used for the model calibration and the other one for the validation, and this was repeated three times. The results showed that the estimated and measured forest FMC were consistent across the three validation samples, with the root mean square error (RMSE) of 19.53\% (Sample 1), 12.64\% (Sample 2) and 15.45\% (Sample 3). To further test the performance of the C-band Sentinel-1A data for forest FMC estimation, our results were compared to those obtained using the optical Landsat 8 Operational Land Imager (OLI) data and the empirical partial least squares regression (PLSR) method. The latter resulted in higher RMSE between estimated and measured forest FMC with 20.11\% (Sample 1), 26.21\% (Sample 2) and 26.73\% (Sample 3) than the presented Sentinel-1A data-based method. Hence, this study demonstrated that the good capability of C-band Sentinel-1A data for forest FMC retrieval, opening the possibility of developing a new operational SAR data-based methodology for forest FMC estimation.

}, keywords = {bare soil backscatter linear model, dual polarimetric Sentinel-1A, fuel moisture content, ignition, remote sensing, vegetation backscatter water cloud model}, doi = {https://doi.org/10.3390/rs11131568}, url = {https://www.mdpi.com/2072-4292/11/13/1568}, author = {Long Wang and Xingwen Quan and Binbin He and Marta Yebra and Minfeng Xing and Xiangzhuo Liu} } @article {bnh-6000, title = {Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications}, journal = {Scientific Data}, volume = {6}, year = {2019}, month = {08/2019}, abstract = {

Globe-LFMC is an extensive global database of live fuel moisture content (LFMC) measured from 1,383 sampling sites in 11 countries: Argentina, Australia, China, France, Italy, Senegal, Spain, South Africa, Tunisia, United Kingdom and the United States of America. The database contains 161,717 individual records based on in situ destructive samples used to measure LFMC, representing the amount of water in plant leaves per unit of dry matter. the primary goal of the database is to calibrate and validate remote sensing algorithms used to predict LFMC. However, this database is also relevant for the calibration and validation of dynamic global vegetation models, eco-physiological models of plant water stress as well as understanding the physiological drivers of spatiotemporal variation in LFMC at local, regional and global scales. Globe-LFMC should be useful for studying LFMC trends in response to environmental change and LFMC influence on wildfire occurrence, wildfire behavior, and overall vegetation health.

}, keywords = {database, Emergency management, land management, Natural disasters, Wildfire spread}, doi = {https://doi.org/10.1038/s41597-019-0164-9}, url = {https://www.nature.com/articles/s41597-019-0164-9.epdf?author_access_token=HISJcfE-VovHPab3al2NwNRgN0jAjWel9jnR3ZoTv0OARKV_7w7xO9p9PGwHd2zKbrs5f-VkYE5AC2lYTydBxaTKy0JaWSgXKUWz0U-fruuzViNrn1JJFl8mARAjGudmQfIcQsd98fM0zv-fk4bXxA\%3D\%3D}, author = {Marta Yebra and Gianluca Scortechini and Abdulbaset Badi and Maria Eugenia Beget and Matthias M. Boer and Ross Bradstock and Emilio Chuvieco and F. Mark Danson and Philip Dennison and Victor Resco de Dios and Carlos M. Di Bella and Greg Forsyth and Philip Frost and Mariano Garcia and Abdelaziz Hamdi and Binbin He and Matt Jolly and Tineke Kraaij and Pillar Martin and Florent Mouillot and Glenn J Newnham and Rachael Nolan and Grazia Pellizzaro and Yi Qi and Xingwen Quan and David Ria{\~n}o and Dar Roberts and Momadou Sow and Susan Ustin} } @article {bnh-5437, title = {Historical background and current developments for mapping burned area from satellite Earth observation}, journal = {Remote Sensing of Environment}, volume = {225}, year = {2019}, month = {05/2019}, pages = {45-64}, abstract = {

Fire has a diverse range of impacts on Earth{\textquoteright}s physical and social systems. Accurate and up to date information on areas affected by fire is critical to better understand drivers of fire activity, as well as its relevance for\ biogeochemical cycles, climate, air quality, and to aid fire management. Mapping burned areas was traditionally done from field sketches. With the launch of the first Earth\ observation satellites, remote sensing quickly became a more practical alternative to detect burned areas, as they provide timely regional and global coverage of fire occurrence. This review paper explores the physical basis to detect burned area from satellite observations, describes the historical trends of using\ satellite sensors\ to monitor burned areas, summarizes the most recent approaches to map burned areas and evaluates the existing burned area products (both at global and regional scales). Finally, it identifies potential future opportunities to further improve burned area detection from Earth observation satellites.

}, keywords = {Burned area, Climate change, Fire, fire impacts, Lidar, Radar}, doi = {https://doi.org/10.1016/j.rse.2019.02.013}, url = {https://www.sciencedirect.com/science/article/pii/S0034425719300689}, author = {Emilio Chuvieco and Florent Mouillot and Guido van der Werf and Jes{\'u}s San Miguel and Mihai Tanasse and Nikos Koutsias and Mariano Garcia and Marta Yebra and Marc Padilla and Ioannis Gitas and Angelika Heil and Todd Hawbaker and Louis Giglio} } @article {bnh-5430, title = {Mapping Bushfire Hazard and Impacts Annual Report 2017-2018}, number = {462}, year = {2019}, month = {03/2019}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {
Understanding and predicting fire behaviour is a priority for fire services, land managers and sometimes individual businesses and residents. This is an\ enormous scientific challenge given bushfires are complex phenomena, with\ their behaviour and resultant severity driven\ by\ complicated interactions\ among living and dead vegetation, topography and weather conditions.\ A good understanding of fire risk across the landscape is critical in preparing\ and responding to bushfire events and managing fire regimes, and remote\ sensing data will enhance this understanding. However, the vast array of spatial\ data sources available is not being used ve\ ry effectively in fire management.\ This project uses remote sensing observations\ to\ produce spatial information on\ fire hazard and impacts needed by planners, land managers and emergency\ services\ to effectively manage fire at landscape scales. The group works\ closely with ACT Parks and Conservation Service and agencies beyond the\ ACT to better understand their procedures and information needs, comparing\ these with the spatial data and mapping methods that are readily available,\ and developing the next generation of mapping technologies to help them\ prepare and respond to bushfires.
}, keywords = {Bushfire, fire impacts, hazards, mapping fire}, author = {Marta Yebra and Albert van Dijk and Geoffrey J. Cary} } @article {bnh-5928, title = {Mapping bushfire hazards and impacts annual report 2018-2019}, number = {511}, year = {2019}, month = {09/2019}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

This annual report is the output from the Bushfire and National Hazards CRC (BNHCRC), Project A1 {\textquoteleft}Mapping Bushfire Hazard and Impacts{\textquoteright}. It summarises the project objectives, introduces the team members as well as documents the project progress and outcomes during the financial year 2018/2019.

During this financial year we have:

  1. Worked on a comprehensive characterization of fire risk conditions through the integration of fire weather, dead Fuel Moisture Content (FMC) and total biomass into the Australian Flammability Monitoring System (AFMS);
  2. Worked on AFMS website version 3 that incorporated feedback on the trial utilisation of the data service;
  3. Evaluated the feasibility and benefits of the operational use of alternative satellite data in AFMS to ensure long-term data continuity;
  4. Coupled a process-based model that simulates litter fuel moisture and a distributed biophysical model (the Australian Water Resources Assessment system Landscape model) to predict dead fuel moisture content;
  5. Published six journal manuscripts, seventeen conference abstracts and one milestone reports with another one in preparation. We have also had eight appearances in the media;
  6. Hosted two international exchange visits and were approached by more than 30 domestic and international applicants for a PhD scholarship or postdoc position in bushfire research; and
  7. Submitted an application for utilisation funds from the BNHCRC to develop a high-resolution prototype version of AFMS.

Over the next year (2019-2020), this research project will focus on finishing the analysis on the fire risk index as well as using remote sensing data to derive spatial and temporal explicit fuel accumulation curves. In terms of utilization, we will liaise with Geoscience Australia (GA) on transitioning the AFMS as GA has agreed to be in charge of the long term operationalization and maintenance.

}, keywords = {bushfires, fire behavior, fire impacts, hazards, impacts, mapping}, issn = {511}, author = {Marta Yebra and Albert van Dijk and Geoffrey J. Cary and Li Zhao and Honghao Zeng} } @conference {bnh-6397, title = {Towards comprehensive characterisation of flammability and fire danger }, booktitle = {Bushfire and Natural Hazards CRC Research Day AFAC19}, year = {2019}, month = {12/2019}, address = {Melbourne}, abstract = {

Fire danger ratings inform the community and fire managing agencies of fire risk. Fire danger ratings are currently primarily based on fire danger indices (FDIs) calculated using methods that were developed many decades ago. The challenges and issues with these methods are well documented. We aimed to develop a more objective and observation-based approach to fire danger assessment that considers spatial data on the occurrence of actual fires as well as on fire factors that are already routinely produced every day for Australia. A preliminary assessment suggested a very good potential of the methodology to formally and objectively incorporate any new fire danger predictors. The method can be combined with forecasted rather than observed weather and fuel conditions to produce forward predictions of expected FDI.

}, keywords = {Fire danger rating, fire factors, risk managemnt}, url = {https://knowledge.aidr.org.au/resources/australian-journal-of-emergency-management-monograph-series/}, author = {Albert van Dijk and Marta Yebra and Geoffrey J. Cary and Sami Shah} } @article {bnh-5388, title = {The Vegetation Structure Perpendicular Index (VSPI): A forest condition index for wildlife predictions}, journal = {Remote Sensing of Environment}, volume = {224}, year = {2019}, month = {04/2019}, chapter = {167}, abstract = {

Wildfires are a major\ natural hazard, causing substantial damage to infrastructure as well as being a risk to lives and homes. An understanding of their progression and behaviour is necessary to reduce risks and to develop operational management strategies in the event of an active fire. Many empirical fire-spread models have been developed to predict the spread and overall behaviour of a wildfire, based on a range of parameters such as weather and fuel conditions. However, these parameters may not be available with sufficient accuracy or spatiotemporal resolution to provide reliable fire spread predictions. Fuel condition data include variables such as vegetation quantity, structure and moisture content and, in the event of previous wildfires, the burn severity and stage of ecosystem recovery. In this study, an index called the\ Vegetation Structure\ Perpendicular Index (VSPI) is introduced. The VSPI utilises the short-wave infrared reflectance in bands centred at 1.6 and 2.2 μm, essentially representing the amount and structure of the vegetation{\textquoteright}s woody biomass (as opposed to the photosynthetic activity and moisture content). The VSPI is quantified as the divergence from a linear regression between the two bands in a time series and represents vegetation disturbance and recovery more reliably than indices such as the Normalised Burn Ratio (NBR) and\ Normalised Difference Vegetation Index\ (NDVI). The VSPI index generally shows minor inter-annual variability and stronger post-wildfire detection of disturbance over a longer period than NBR and\ NDVI. The index is developed and applied to major wildfire events within eucalypt forests throughout southern Australia to estimate both burn severity and time to recovery. The VSPI can provide an improved information layer for fire risk evaluation and operational predictions of wildfire behaviour.

}, keywords = {Forest condition, Landsat, Vegetation recovery, Wildfire spread}, doi = {https://doi.org/10.1016/j.rse.2019.02.004}, url = {https://www.sciencedirect.com/science/article/pii/S0034425719300586?dgcid=coauthor}, author = {Andrea Massetti and Christoph R{\"u}diger and Marta Yebra and James Hilton} } @article {bnh-5201, title = {Australian Flammability Monitoring System Version 1.0: User feedback and priorities for further development}, year = {2018}, month = {12/2018}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

In response to the information requirements expressed by end users, the Bushfire \& Natural Hazards CRC project {\textquoteleft}Mapping Bushfire Hazards and Impacts{\textquoteright} (Project A1) developed the Australian Flammability Monitoring System (AFMS)
website (version 1.0). The AFMS makes spatial information on fuel condition and flammability easier and faster to access.

This report summarises feedback received from end users and discusses priorities for future development that may potentially be addressed by the BNHCRC and the project team in future.

All end users involved in the project, and participating in several seminars and presentations featuring the AFMS, recognised the value of the system to improve fire management in Australia. However, they also identified some significant barriers to adoption by the Australian bushfire sector. Firstly, neither FMC nor FI are presently used in any models or systems. This means that the sector needs to explore the information and develop work processes for using it. Another key barrier is the timeliness of the system. It presently takes 15 days to acquire the satellite data, process and publish. This delay is too long to allow practitioners to trial the system during operations.

Consequently, future priorities for further development should focus on activities that will contribute to strengthening the usability of the AFMS and adoption. As a first step, we need to invest more time working with end users to develop specific operational applications and integrate the information displayed in the AFMS into current decision processes and tools (e.g. the ACT Parks and Conservation Service{\textquoteright}s Prescribe Burn Decision Tool). Meanwhile, we also need to improve timeliness, robustness, visual presentation and explanation of the information displayed on the AFMS website to make it easier for users to extract the important details.

The implementation of the AFMS into daily fire management operations will take fire management in Australia to a new level.

}, issn = {437}, author = {Marta Yebra and Albert van Dijk and Geoffrey J. Cary} } @article {bnh-5102, title = {Estimating fire severity and carbon emissions over Australian tropical savannahs based on passive microwave satellite observations}, journal = {International Journal of Remote Sensing}, year = {2018}, month = {04/2018}, abstract = {

We investigated the use of a recently developed satellite-based vegetation optical depth (VOD) data set to estimate fire severity and carbon emission over Australian tropical savannahs. VOD is sensitive to the dynamics of all aboveground vegetation and available nearly every two days. For areas burned during 2003{\textendash}2010, we calculated the VOD change (ΔVOD) pre- and post-fire and the associated loss in the above ground biomass carbon. ΔVOD agreed well with the Normalized Burn Ratio change (ΔNBR) which is the metric used to estimate fire severity and carbon loss compared well with modelled emissions from the Global Fire Emissions Database (GFED). We found that the ΔVOD and ΔNBR are generally linearly related. The Pearson correlation coefficients (r) between VOD- and GFED-based fire carbon emissions for monthly and annual total estimates are very high, 0.92 and 0.96, respectively. A key feature of fire carbon emissions is the strong inter-annual variation, ranging from 21.1 Mt in 2010 to 84.3 Mt in 2004. This study demonstrates that a reasonable estimate of fire severity and carbon emissions can be achieved in a timely manner based on multiple satellite observations over Australian tropical savannahs, which can be complementary to the currently used approaches.

}, doi = {https://doi.org/10.1080/01431161.2018.1460507}, url = {https://www.tandfonline.com/doi/abs/10.1080/01431161.2018.1460507}, author = {Xi Chen and Y.Y Liu and Jason P. Evans and R.M. Parinussa and Albert van Dijk and Marta Yebra} } @article {bnh-5217, title = {Evaluation of the feasibility and benefits of operational use of alternative satellite data in the Australian Flammability Monitoring System to ensure long-term data continuity}, year = {2018}, month = {12/2018}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

The Australian Flammability Monitoring System (AFMS) is the first, continental-scale prototype web service providing spatial information on Live Fuel Moisture Content (FMC) and landscape flammability conditions derived from satellite observations. The system uses data from the MODIS (MODerate resolution Imaging Spectrometer) instruments on board the Terra and Aqua satellites, providing estimates of FMC and flammability over a fortnight leading up to the day of interest and at 500 m spatial resolution. The Terra and Aqua satellites have already exceeded the expected lifetime and, at some point in the not-too-distant future, will become inoperative.

To support the AFMS continuity and redundancy strategy, we evaluate the feasibility and relative benefits of using alternative remote sensing imagery in the AFMS to provide finer spatial and temporal resolutions. The evaluated data sources include the geostationary Japanese Himawari-8 satellite (10min, 2km), the European Sentinel-2 (5 days, 20 m), the Landsat (16 days, 30m) and VIIRS (daily, 750 m) satellites.

We use Radiative Transfer Models to build a database of simulated spectra for the different studied sensors and corresponding to different FMC conditions. The database is then used to test the suitability of the different sensor to retrieve FMC based on their different spectral characteristics and goodness of retrieval (e.g. r2 and RMSE between retrieved and modelled).

VIIRS obtained the highest accuracy retrieval (r2=0.8, RMSE=19\%, n=6178) followed by Sentinel-2 (r2=0.8, RMSE=23, n=6178), Landsat-8 (r2=0.8, RMSE=24, n=6178) and Himawari-8 (r2=0.7, RMSE=24, n=6178). These results were expected as VIIRS has a larger number of sensor bands (5 bands) in the Short Wave Infrared (SWIR) followed by Sentinel-2 (3 bands), Landsat and Himawari-8 (two bands each).

VIIRS, therefore, is likely the best candidate to ensure the AFMS website data provision continuity. Sentinel-2 (3 bands) and Himawari-8 are the best second and third candidates that obtain similar accuracies while increasing the spatial (Sentinel-2, 20m) and temporal (Himawari-8, 10 minutes) resolutions of the products displayed in the AFMS.

Future work will focus on integrating estimates from these different data sources to better support a range of fire management activities such as prescribed burning and pre-positioning of firefighting resources and inform the future National Fire Danger Rating System.

}, issn = {448}, author = {Marta Yebra and Albert van Dijk and Geoffrey J. Cary} } @article {bnh-4586, title = {A fuel moisture content and flammability monitoring methodology for continental Australia based on optical remote sensing}, journal = {Remote Sensing of Environment}, volume = {212}, year = {2018}, month = {06/2018}, pages = {12}, chapter = {260}, abstract = {

Fuel Moisture Content (FMC) is one of the primary drivers affecting fuel flammability that lead to fires. Satellite observations well-grounded with field data over the highly climatologically and ecologically diverse Australian region served to estimate FMC and flammability for the first time at a continental-scale. The methodology includes a physically-based retrieval model to estimate FMC from MODIS (Moderate Resolution Imaging Spectrometer) reflectance data using radiative transfer model inversion. The algorithm was evaluated using 360 observations at 32 locations around Australia with mean accuracy for the studied land cover classes (grassland, shrubland, and forest) close to those obtained elsewhere (r2 = 0.58, RMSE = 40\%) but without site-specific calibration. Logistic regression models were developed to generate a flammability index, trained on fire events mapped in the MODIS burned area product and four predictor variables calculated from the FMC estimates. The selected predictor variables were actual FMC corresponding to the 8-day and 16-day period before burning; the same but expressed as an anomaly from the long-term mean for that date; and the FMC change between the two successive 8-day periods before burning. Separate logistic regression models were developed for grassland, shrubland and forest. The models obtained an {\textquotedblleft}Area Under the Curve{\textquotedblright} calculated from the Receiver Operating Characteristic plot method of 0.70, 0.78 and 0.71, respectively, indicating reasonable skill in fire risk prediction.

}, keywords = {Fire occurrence, Fire risk, Forests., GEOPROSAIL inversion, Grasslands, MODIS, PROSAIL inversion, Shrubs}, doi = {10.1016}, url = {https://www.sciencedirect.com/science/article/pii/S0034425718302116$\#$.WvZrg57h8og.twitter}, author = {Marta Yebra and Xingwen Quan and David Ria{\~n}o and Pablo Rozas Larraondo and Albert van Dijk and Geoffrey J. Cary} } @conference {bnh-4775, title = {A lidar-derived fuel map for the ACT}, booktitle = {AFAC18}, year = {2018}, month = {09/2018}, publisher = {Bushfire and Natural Hazards CRC}, organization = {Bushfire and Natural Hazards CRC}, address = {Perth}, abstract = {

Many Australian fire managers rely on fuel maps derived from vegetation classifications and time-since-fire via an accumulation curve (e.g. Phoenix Rapidfire). However fuel maps derived using this method lack potentially critical information about pockets of high and low fuel loads. This is important because a poor understanding of fuels was found to be a critical factor in investigations of both the Margaret River and Lancefield prescribed burn escapes. A potential solution for this problem is the use of LiDAR to create spatially-explicit fuel information.

A LiDAR dataset covering the whole of the ACT was flown over a number of months in 201516 using the LAS 1.4 format. The density of returns was 8ppm over the urban area and 4ppm over the remainder of the territory including a large alpine national park that covers 45 percent of the ACT. Bushfire \& Natural Hazards Cooperative Research Centre (BNHCRC) research partners arranged for the dataset to be included in feasibility analyses conducted by the Terrestrial Ecology Research Network (TERN) AusCover Landscape Observatory project. A number of fuel-related layers were created based on the Overall Fuel Hazard Assessment (OFHA) and inputs to Project Vesta.

The fuel layers were created at resolutions of 1m, 2m, 5m and 25m in the NetCDF file format. The 25m data were selected for initial assessment because these were the only whole-of-territory datasets easily manipulated in ArcGIS 10. Qualitative assessments indicate: 1) a discernible pattern of fuel variation related to topography; 2) good agreement between LiDAR-derived elevated and near-surface fuel layers and OFHA data collected during the LiDAR acquisition; and 3) good agreement between the LiDAR and fire severity assessments made on burns that were conducted a short time before the acquisition. The next step is to conduct quantitative assessments.

}, author = {Adam J. Leavesley and Albert van Dijk and Marta Yebra} } @article {bnh-5051, title = {Mapping bushfire hazards and impacts: annual project report 2017-18}, year = {2018}, month = {11/2018}, institution = {Bushfire and Natural Hazards CRC}, abstract = {

Understanding and predicting fire behaviour is a priority for fire services, land managers and sometimes individual businesses and residents. This is an enormous scientific challenge given bushfires are complex phenomena, with\  their behaviour and resultant severity driven by complicated interactions among living and dead vegetation, topography and weather conditions.\ 

}, issn = {426}, author = {Marta Yebra and Geoffrey J. Cary and Albert van Dijk} } @article {bnh-6039, title = {Near real-time extracting wildfire spread rate from Himawari-8 satellite data}, journal = {Remote Sensing}, volume = {10}, year = {2018}, month = {10/2018}, pages = {1654}, abstract = {

Fire Spread Rate (FSR) can indicate how fast a fire is spreading, which is especially helpful for wildfire rescue and management. Historically, images obtained from sun-orbiting satellites such as Moderate Resolution Imaging Spectroradiometer (MODIS) were used to detect active fire and burned area at the large spatial scale. However, the daily revisit cycles make them inherently unable to extract FSR in near real--time (hourly or less). We argue that the Himawari-8, a next generation geostationary satellite with a 10-min temporal resolution and 0.5{\textendash}2 km spatial resolution, may have the potential for near real-time FSR extraction. To that end, we propose a novel method (named H8-FSR) for near real-time FSR extraction based on the Himawari-8 data. The method first defines the centroid of the burned area as the fire center and then the near real-time FSR is extracted by timely computing the movement rate of the fire center. As a case study, the method was applied to the Esperance bushfire that broke out on 17 November, 2015, in Western Australia. Compared with the estimated FSR using the Commonwealth Scientific and Industrial Research Organization (CSIRO) Grassland Fire Spread (GFS) model, H8-FSR achieved favorable performance with a coefficient of determination (R2) of 0.54, mean bias error of {\textendash}0.75 m/s, mean absolute percent error of 33.20\% and root mean square error of 1.17 m/s, respectively. These results demonstrated that the Himawari-8 data are valuable for near real-time FSR extraction, and also suggested that the proposed method could be potentially applicable to other next generation geostationary satellite data.

}, keywords = {fire spread rate; fire center; fire behavior; Himawari-8; near real-time}, doi = {https://doi.org/10.3390/rs10101654}, url = {https://www.mdpi.com/2072-4292/10/10/1654}, author = {Xiangzhuo Liu and Binbin He and Xingwen Quan and Marta Yebra and Shi Qiu and Changming Yin and Zhanmang Liao and Hongguo Zhang} } @conference {bnh-3914, title = {The Australian Flammability Monitoring System}, booktitle = {AFAC17}, year = {2017}, month = {09/2017}, publisher = {Bushfire and Natural Hazards CRC}, organization = {Bushfire and Natural Hazards CRC}, address = {Sydney}, abstract = {

The Australian Flammability Monitoring System (AFMS) is the first, continental-scale prototype web explorer providing spatial information on current Live Fuel Moisture Content (FMC) and landscape-scale fuel flammability derived from satellite observations. The satellite observations are converted into FMC using a radiative transfer modelling inversion approach. Evaluation of the FMC estimates using 408 observations at 35 locations around Australia shows similar accuracies (r2=0.60, RMSE=39\%) across the vegetation classes studied (grassland, shrubland and forest) to those derived elsewhere globally. Flammability estimates are calculated using logistic regression models relating fire occurrence to FMC. Separate prediction models were developed for grassland, shrubland and forest, obtaining performance metrics (Area Under the Curve) of 0.70, 0.78 and 0.71, respectively (where skillful predictions range between 0.5 and1). A web-based data explorer will be available to fire and land management agencies and any other interested parties in all states and territories. The AFMS can support a range of fire management activities such as prescribed burning and pre-positioning of firefighting resources and can inform the future National Fire Danger Rating System.

}, author = {Marta Yebra and Albert van Dijk and Geoffrey J. Cary and Xingwen Quan and Joel Rahman and Zac H. Dodds and Pablo Rozas} } @article {bnh-3919, title = {Development of a predictive model for estimating forest surface fuel load in Australian eucalypt forests with LiDAR data}, journal = {Environmental Modelling \& Software}, volume = {97}, year = {2017}, month = {11/2017}, pages = {61-71}, chapter = {61}, abstract = {

Accurate description of forest surface fuel load is important for understanding bushfire behaviour and suppression difficulties, predicting ongoing fires for operational activities, assessing potential fire hazards and assisting in fuel hazard-reduction burns to reduce fire risks to the community and the environment. Bushfire related studies and current operational activities have a common challenge in quantifying fuels, since the fuel load varies across the landscape. This paper developed a predictive model that efficiently and accurately estimates quantities of surface fuel in Australian southeast Eucalypt forests. Model coefficients were determined through a three-step process that attempts to evaluate how the spatial variation in surface fuel load relates to litter-bed depth, fuel characteristics, topography and previous fire disturbance. First, the forest surface fuel depth-to-load relationship was established; second, key quantitative variables of environmental factors were added; and third, important qualitative variables of fuel characteristics were included. The verification of model prediction was conducted through leave-one-out cross-validation (CV). Light Detection and Ranging was used to quantify forest structural characteristics and terrain features. The calibrated model had a\ R2\ of 0.89 (RMSE\ =\ 20.7\ g) and performed better than the currently used surface fuel load models, including McArthur{\textquoteright}s (R2\ =\ 0.61 and\ RMSE\ =\ 39.6\ g) and Gilroy and Tran{\textquoteright}s (R2\ =\ 0.69 and\ RMSE\ =\ 36.5\ g) models. This study describes a novel approach to forest surface fuel load modelling using forest characteristics and environmental factors derived from LiDAR data through statistical analysis. The model established in this study can be used as an efficient approach to assist in forest fuel management and fire related operational activities.

}, doi = {10.1016/j.envsoft.2017.07.007}, url = {http://www.sciencedirect.com/science/article/pii/S1364815216304418}, author = {Chen, Yang and Xuan Zhou and Marta Yebra and Sarah Harris and Nigel Tapper} } @conference {bnh-3232, title = {Estimation of forest surface fuel load using airborne LiDAR data}, booktitle = {SPIE Remote Sensing}, year = {2017}, month = {09/2017}, publisher = {SPIE}, organization = {SPIE}, address = {Warsaw, Poland}, abstract = {

Accurately describing forest surface fuel load is significant for understanding bushfire behaviour and suppression difficulties, predicting ongoing fires for operational activities, as well as assessing potential fire hazards. In this study, the Light Detection and Ranging (LiDAR) data was used to estimate surface fuel load, due to its ability to provide threedimensional
information to quantify forest structural characteristics with high spatial accuracies. Firstly, the multilayered eucalypt forest vegetation was stratified by identifying the cut point of the mixture distribution of LiDAR point density through a non-parametric fitting strategy as well as derivative functions. Secondly, the LiDAR indices of heights, intensity, topography, and canopy density were extracted. Thirdly, these LiDAR indices, forest type and previous fire disturbances were then used to develop two predictive models to estimate surface fuel load through multiple regression analysis. Model 1 was developed based on LiDAR indices, which produced a R2 value of 0.63. Model 2 (R2 = 0.8) wasderived from LiDAR indices, forest type and previous fire disturbances. The accurate and consistent spatial variation in surface fuel load derived from both models could be used to assist fire authorities in guiding fire hazard-reduction burns and fire suppressions in the Upper Yarra Reservoir area, Victoria, Australia.

}, author = {Chen, Yang and Xuan Zhou and Marta Yebra and Sarah Harris and Nigel Tapper} } @article {bnh-4196, title = {Mapping bushfire hazards and impacts: annual project report 2016-17}, number = {308}, year = {2017}, month = {09/2017}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

This annual report is output from the Bushfire and National Hazards CRC, {\textquoteleft}Mapping Bushfire Hazard and Impacts{\textquoteright}. It summarises the project objectives, introduces the team members as well as documents the project progress and outcomes during the financial year 2016/2017.

In consultation with the end users we have identified a subset of key outputs that have the highest utilisation potential, and that will be the focus of our work program in the future.

On those lines, the project team has made considerable progress this financial year towards the validation and increased accessibility to the {\textquotedblleft}Australian Flammability Monitoring System{\textquotedblright} for operational prediction of live fuel moisture content (FMC) and flammability in Australia. In this regard, we have created a prototype web service to make spatial information on FMC and flammability easier and faster to access.

Additionally we have:

1)\ \ \  Evaluated the suitability of coarse resolution near-surface soil moisture data to improve the McArthur forest fire danger index

2)\ \ \  Undertaken development of a predictive model for estimating forest surface fuel load with LiDAR data

3)\ \ \  Developed a satellite-based method for the estimation of grassland aboveground biomass

4)\ \ \  Published four journal manuscript with another three currently in review and one invited book chapter in preparation, ten conference abstracts and two milestone reports;

5)\ \ \  Hosted two international exchange visits and were approached by more than 30 domestic and international applicants for a PhD scholarship or postdoc position in bushfire research; and

Over the next three years (2017-2020), this research project will focus on increasing the understanding, reliability and long-term continuity of the AFMS, and through this, its acceptance and adoption. Also, a small number of promising, low-cost in-field techniques will continue to be investigated to improve their cost/benefit ratio and utility.

}, issn = {308}, author = {Marta Yebra and Albert van Dijk and Geoffrey J. Cary} } @article {bnh-3229, title = {A radiative transfer model-based method for the estimation of grassland aboveground biomass}, journal = {International Journal of Applied Earth Observation and Geoinformation}, volume = {54}, year = {2017}, month = {02/2017}, abstract = {

This paper presents a novel method to derive grassland aboveground biomass (AGB) based on the PROSAILH (PROSPECT\ +\ SAILH) radiative transfer model (RTM). Two variables, leaf area index (LAI, m2m-2, defined as a one-side leaf area per unit of horizontal ground area) and dry matter content (DMC, gcm-2, defined as the dry matter per leaf area), were retrieved using PROSAILH and reflectance data from Landsat 8 OLI product. The result of LAI\ {\texttimes}\ DMC was regarded as the estimated grassland AGB according to their definitions. The well-known ill-posed inversion problem when inverting PROSAILH was alleviated using ecological criteria to constrain the simulation scenario and therefore the number of simulated spectra. A case study of the presented method was applied to a plateau grassland in China to estimate its AGB. The results were compared to those obtained using an exponential regression, a partial least squares regression (PLSR) and an artificial neural networks (ANN). The RTM-based method offered higher accuracy (R2\ =\ 0.64 and RMSE\ =\ 42.67\ gm-2) than the exponential regression (R2\ =\ 0.48 and RMSE\ =\ 41.65\ gm-2) and the ANN (R2\ =\ 0.43 and RMSE\ =\ 46.26\ gm-2). However, the proposed method offered similar performance than PLSR as presented better determination coefficient than PLSR (R2\ =\ 0.55) but higher RMSE (RMSE\ =\ 37.79\ gm-2). Although it is still necessary to test these methodologies in other areas, the RTM-based method offers greater robustness and reproducibility to estimate grassland AGB at large scale without the need to collect field measurements and therefore is considered the most promising methodology.

}, doi = {http://dx.doi.org/10.1016/j.jag.2016.10.002}, url = {http://www.sciencedirect.com/science/article/pii/S0303243416301726}, author = {Xingwen Quan and Binbin He and Marta Yebra and Changming Yin and Zhanmang Liao and Xueting Zhang and Xing Li} } @article {bnh-5085, title = {Retrieval of forest fuel moisture content using a coupled radiative transfer model}, journal = {Environmental Modelling \& Software}, volume = {95}, year = {2017}, month = {09/2017}, pages = {290-302}, chapter = {290}, abstract = {

Forest fuel moisture content (FMC) dynamics are paramount to assessing the forest wildfire risk and its behavior. This variable can be retrieved from remotely sensed data using a\ radiative transfer\ model (RTM). However, previous studies generally treated the background of\ forest canopy\ as soil surface while ignored the fact that the soil may be covered by grass canopy. In this study, we focused on retrieving FMC of such forestry structure by coupling two RTMs: PROSAIL and PRO-GeoSail. The spectra of lower grass canopy were firstly simulated by the PROSAIL model, which was then coupled into the PRO-GeoSail model. The results showed that the accuracy level of retrieved FMC using this coupled model was better than that when the PRO-GeoSail model used alone. Further analysis revealed that low FMC condition fostered by fire weather condition had an important influence on the breakout of a fire during the study period.

}, doi = {https://doi.org/10.1016/j.envsoft.2017.06.006}, url = {https://www.sciencedirect.com/science/article/pii/S1364815216304431}, author = {Xingwen Quan and Binbin He and Marta Yebra and Changming Yin and Zhanmang Liao and Xing Li} } @article {bnh-3920, title = {Using alternative soil moisture estimates in the McArthur Forest Fire Danger Index}, journal = {International Journal of Wildland Fire}, year = {2017}, month = {08/2017}, abstract = {

McArthur{\textquoteright}s Forest Fire Danger Index (FFDI) incorporates the Keetch{\textendash}Byram Drought Index (KBDI) estimate of soil dryness. Improved approaches for estimating soil moisture now exist, with potential for informing the calculation of FFDI. We evaluated the effect, compared with KBDI, of two alternative methods of estimating soil moisture: the rainfall-based Antecedent Precipitation Index and soil moisture from the Soil Moisture Ocean Salinity satellite mission. These methods were used to calculate FFDI over a sample period of 5\ years (2010{\textendash}14) at seven locations around Australia. The effect of substituting the alternatives for KBDI, and of entirely replacing the Drought Factor (DF) (a measure of fuel availability in FFDI) with the alternatives was explored by studying the effect on magnitude, distribution and timing of FFDI and associated Fire Danger Rating (FDR). Both approaches predicted drier soil conditions than KBDI, resulting in fewer Low{\textendash}Moderate FDR days and more days of High FDR and above. The alternative methods replacing KBDI had little effect on seasonal patterns of FDR. Of all approaches, replacing DF entirely with the soil moisture alternatives most closely mimicked McArthur{\textquoteright}s FFDI. Overall, if alternative measures of soil moisture are adopted for FFDI, the entire replacement of the DF term should be considered.

}, doi = {10.1071/WF16217}, url = {http://www.publish.csiro.au/WF/WF16217}, author = {C.M Holgate and Albert van Dijk and Geoffrey J. Cary and Marta Yebra} } @article {bnh-3422, title = {Deriving comprehensive forest structure information from mobile laser scanning observations using automated point cloud classification}, journal = {Environmental Modelling \& Software}, volume = {82}, year = {2016}, month = {08/2016}, pages = {142-151}, chapter = {142}, abstract = {

The advent of mobile laser scanning has enabled time efficient and cost effective collection of forest structure information. To make use of this technology in calibrating or evaluating models of forest and landscape dynamics, there is a need to systematically and reproducibly automate the processing of LiDAR point clouds into quantities of forest structural components. Here we propose a method to classify vegetation structural components of an open-understorey eucalyptus forest, scanned with a {\textquoteleft}Zebedee{\textquoteright} mobile laser scanner. It detected 98\% of the tree stems (N\ =\ 50) and 80\% of the elevated understorey components (N\ =\ 15). Automatically derived DBH values agreed with manual field measurements with r2\ =\ 0.72, RMSE\ =\ 3.8\ cm, (N\ =\ 27), and total basal area agreed within 1.5\%. Though this methodological study was restricted to one ecosystem, the results are promising for use in applications such as fuel load, habitat structure, and biomass estimations.

}, doi = {10.1016/j.envsoft.2016.04.025}, url = {http://www.sciencedirect.com/science/article/pii/S1364815216301207}, author = {Marselis, S and Marta Yebra and Jovanovic, T and Albert van Dijk} } @article {bnh-3094, title = {Mapping bushfire hazard and impacts: Annual project report 2015-2016}, number = {227}, year = {2016}, month = {09/2016}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

This annual report is an output from the Bushfire and National Hazards CRC, Project {\textquoteleft}Mapping Bushfire Hazard and Impacts{\textquoteright}. It summarizes the project objectives, introduces the team members as well as documents the project progress and outcomes during the financial year 2015/2016.

The project team has made considerable progress this financial year and has generated a number of nationwide products. More specifically this financial year we have:

1)\ \ \  developed and tested the {\textquotedblleft}Bushfire Information System{\textquotedblright} with potential for operational prediction of live fuel moisture content and fire occurrence in Australia;

2)\ \ \  analyzed the suitability of near-surface soil moisture data to improve the McArthur forest fire danger index;

3)\ \ \  tested a forest carbon uptake estimation model constrained with remote sensing data at 25 m to 5 km resolution and evaluated against field data; and

4)\ \ \  Developed a predictive model for estimating forest surface fuel load with LiDAR data.

We have also completed some activities initialized during the previous financial year including:

1)\ \ \  the development and testing of a new software tool to automatically derive detailed vegetation structure information from ground-based LiDAR; and

2)\ \ \  testing an on-ground network of curing/FMC sensors.

We have maintained an effective and engaging relationship with project end users. The most intensive interactions have been with ACT Parks and Conservation, with whom the team collaborates closely, including in an operational setting. However, now that we have generated a number of nationwide products, more end users are identifying how they can practically engage in the research. This has led to a large number of follow-up projects that have been requested and will be addressed as much as possible over the next years, contingent on future project funding.

Additionally we:

1)\ \ \  published 1 journal manuscript with another 6 currently in review and one invited book chapter in preparation, 10 conference abstracts, one conference paper and 3 milestone reports;

2)\ \ \  hosted three international exchange visits and were approached by more than 15 domestic and international applicants for a PhD scholarship or postdoc position in bushfire research; and

3)\ \ \  in consultation with the end users, identified a subset of key outputs that have the highest utilisation potential, and that will be the focus of our work program in the future.

}, issn = {227}, author = {Marta Yebra and Albert van Dijk and Geoffrey J. Cary} } @article {bnh-1844, title = {Global vegetation gross primary production estimation using satellite-derived light-use efficiency and canopy conductance}, journal = {Remote Sensing of Environment}, volume = {163}, year = {2015}, month = {06/2015}, pages = {206-216}, chapter = {206}, abstract = {

Climate and physiological controls of vegetation gross primary production (GPP) vary in space and time. In many ecosystems, GPP is primary limited by absorbed photosynthetically-active radiation; in others by canopy conductance. These controls further vary in importance over daily to seasonal time scales. We propose a simple but effective conceptual model that estimates GPP as the lesser of a conductance-limited (Fc) and radiation-limited (Fr) assimilation rate.\ Fc\ is estimated from canopy conductance while\ Fr\ is estimated using a light use efficiency model. Both can be related to vegetation properties observed by optical remote sensing. The model has only two fitting parameters: maximum light use efficiency, and the minimum achieved ratio of internal to external CO2\ concentration. The two parameters were estimated using data from 16 eddy covariance flux towers for six major biomes including both energy- and water-limited ecosystems. Evaluation of model estimates with flux tower-derived GPP compared favourably to that of more complex models, for fluxes averaged; per day (r2\ =\ 0.72, root mean square error, RMSE\ =\ 2.48\ μmol\ C\ m2\ s-\ 1, relative percentage error, RPE\ =\ -\ 11\%), over 8-day periods (r2\ =\ 0.78 RMSE\ =\ 2.09\ μmol\ C\ m2\ s-\ 1,RPE\ =\ -\ 10\%), over months (r2\ =\ 0.79, RMSE\ =\ 1.93\ μmol\ C\ m2\ s-\ 1, RPE\ =\ -\ 9\%) and over years (r2\ =\ 0.54, RMSE\ =\ 1.62\ μmol\ C\ m2\ s-\ 1, RPE\ =\ -\ 9\%). Using the model we estimated global GPP of 107\ Pg\ C\ y-\ 1\ for 2000{\textendash}2011. This value is within the range reported by other GPP models and the spatial and inter-annual patterns compared favourably. The main advantages of the proposed model are its simplicity, avoiding the use of uncertain biome- or land-cover class mapping, and inclusion of explicit coupling between GPP and plant transpiration.

}, keywords = {Photosynthesis; Canopy conductance; Light use efficiency; FLUXNET; MODIS; Gross primary production; GPP; Vegetation}, doi = {doi:10.1016/j.rse.2015.03.016}, url = {http://www.sciencedirect.com/science/article/pii/S0034425715001121}, author = {Marta Yebra and Albert van Dijk and Ray Leuning and Guerschman, Juan Pablo} } @article {bnh-2402, title = {Mapping bushfire hazard and impact}, year = {2015}, author = {Marta Yebra and Albert van Dijk and Geoffrey J. Cary} } @article {bnh-2311, title = {Mapping bushfire hazard and impacts: Annual project report 2014-2015}, number = {119}, year = {2015}, month = {10/2015}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

This annual report is an output from the Bushfire and National Hazards CRC Project {\textquoteleft}Mapping Bushfire Hazard and Impacts{\textquoteright}. It summarises the project objectives, introduces the team members as well as documents the project progress and outcomes during the finantial year 2014/2015.\ The project team has made great progress this year as demonstrated by:

1)\ \ \  Developing a model framework, the High-resolution Fire Risk and Impact (HiFRI) framework, to provide best possible estimates on forest fuel load and moisture content and fire impacts on landscape values such as water resource generation and carbon storage.

2)\ \ \  Developing and tested a new software tool based in MATLAB to automatically derive detailed vegetation structure information from ground-based LiDAR

3)\ \ \  Successfully obtaining two student top-up scholarships from BNHCRC, what will have further positive impacts on the quality of our research.

4)\ \ \  Maintaining a healthy and engaging relationship with end users. The strongest connections have been with ACT Parks and Conservation, with whom the team collaborate closely and hands-on. However, the expectations are that once the project team have been able to demonstrate the practical value of research in the ACT, additional end users will find it easier to see how they can concretely engage in the research.

5)\ \ \  Publishing 1 journal paper, 4 conference abstracts and 2 milestone reports. In addition another manuscript is currently under review with Remote Sensing of Environment,top-ranked in the category of remote sensing.

6)\ \ \  Communicating progress on the research goals to end users by organising two workshops with operational stakeholders and other researchersandpublishing divulgative material in blogs and other media.

7)\ \ \  Receiving international researchers exchange visits and being approached by more than 15 domestic and international requests for PhD scholarships or postdoc positions on the topic of bushfire research. Of particular interest has been our innovative work in LiDAR vegetation mapping.

}, issn = {119}, author = {Marta Yebra and Albert van Dijk and Geoffrey J. Cary} } @article {bnh-1883, title = {A model-data fusion framework for estimating fuel properties, vegetation growth, carbon storage and the water balance at hillslope scale}, year = {2015}, month = {06/2015}, abstract = {

This progress report is an output from the Bushfire and National Hazards CRC, Project A1 {\textquoteleft}Mapping Bushfire Hazard and Impacts{\textquoteright}. It documents the first steps in the development of a model-data fusion framework to provide estimates on historic fire impacts on landscape values, as well as potentially real-time estimates of current fuel load and flammability. Model-data fusion refers to the blending of observations with model predictions by considering the error in both.

A case study area was undertaken to analyse the value of alternative airborne and remote sensing observations in a model-data fusion framework. The data were used to set up a spatial forest growth, water use and carbon uptake model at high (25 m) spatial resolution and daily time step. The model shares a common heritage with the Australian Water Resources Assessment Landscape (AWRA-L) water balance model used by the Bureau of Meteorology, and includes coupled models of water, carbon and biomass (fuel) dynamics that can be applied at high resolution. The model-data fusion framework is provisionally referred to here as the High-resolution Fire Risk and Impact (HiFRI) framework.

The case study comprised the period 2000-2010 for the western ACT, including native forests, plantation forests and grasslands. Much of the area was burnt in 2003. The data integrated within HiFRI included high-resolution Landsat imagery, a digital elevation model and daily climate grids. The case study demonstrated that it is feasible to produce estimates of water and carbon balance variables. The results show strong slope effects associated with solar irradiance. Integrating satellite observations also showed the expected influence from vegetation regrowth after the fire.

}, author = {Albert van Dijk and Marta Yebra and Geoffrey J. Cary} } @article {bnh-1661, title = {Using LiDAR for forest and fuel structure mapping: options, benefits, requirements and costs}, number = {64}, year = {2015}, month = {03/2015}, institution = {Bushfire and Natural Hazards CRC}, abstract = {

Understanding fuel structure is important for assessing suppression difficulty, risk of damage from bushfires, monitoring fuel build up and planning hazard reduction programs. Technologies such as airborne Light Detection and Ranging (LiDAR) can provide precise information about fuel structure over larger areas. However, the use of LiDAR by fire managers is still in the early stages and has not been implemented through any routine operational program in Australia.

This report aims to address this situation by describing and evaluating the maturity and suitability of airborne LiDAR to derive the different types of information needed in forest fuel assessment. It does so through a set of questions scoped in consultation with fire managers through the Bushfire and Natural Hazards CRC project {\textquoteleft}Mapping bushfire hazard and impacts{\textquoteright}. The language and technical detail is aimed at a wide audience with fields of expertise outside LiDAR.

This report first covers some of the basic principles on LiDAR and then focuses on the analysis of the information content and accuracy of airborne LiDAR to retrieve the forest fuel attributes that are important for fire management. The information that can be derived about the height, cover fraction and density of different over- and understorey layers is assessed, along with other useful information that may be derived. Additional measurements that help to make more optimal use of airborne LiDAR data are presented, including terrestrial laser scanning, UAV-borne LiDAR, and airborne imaging. Guidance is provided on discovering existing LiDAR data, factors determining the cost of new LiDAR data acquisition, and options for processing the data. Finally, the current and future development in the use of LiDAR for fire management are discussed.

Summarising, airborne LiDAR may be considered a mature data product that is commercially available, using established data standards. However, standardised data specifications and processing methods for applications in fuel mapping do not yet exist. Essential aspects to consider are the type of fuel information, accuracy and spatial detail desired. Greater data density can increase accuracy and spatial detail, but will also increase the cost of acquisition. In forests with a dense overstorey canopy high data density may be the only way to obtain information on the understorey. In small-scale applications, field or UAV-mounted LiDAR systems may be a suitable alternative for airborne LiDAR.

Priority areas for research and development to achieve more cost-effective and successful use of LiDAR by the fire management community were identified. This includes the development of standardised methods to acquire and process airborne LiDAR data for fuel mapping, the validation of these methods using field measurements, and investigation of full-waveform airborne LiDAR as a promising alternative to current LiDAR data collection methods. The Bushfire and Natural Hazards CRC project {\textquoteleft}Mapping bushfire hazard and impacts{\textquoteright} is working with end users to pursue each of these lines of enquiry.

}, issn = {64}, author = {Marta Yebra and Marselis, S and Albert van Dijk and Geoffrey J. Cary and Chen, Yang} }