@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-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} }