@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} } @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} }