@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-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} } @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-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} }