Model-driven estimation of closed and open shrublands live fuel moisture content

Live fuel moisture content (LFMC) is a crucial variable affecting the ignition potential of shrublands. Different remote sensing-based models (either empirical or physical) have been adopted to estimate LFMC in shrublands but with mixed success potentially owing to differences in vegetation cover (closed vs. open shrublands). This study aimed to evaluate and discuss LFMC estimation in open and closed shrublands using different remote sensing approaches. For each case, three broadly used radiative transfer models (RTMs) (PROSAILH, PROGeoSail, and PROACRM), and two empirical models were selected and compared. The empirical models were calibrated by a stepwise regression approach using a spectral index (SI) and its normalized form (SImax-min). Results showed that both RTMs and empirical models performed well in retrieving LFMC of closed shrublands (RTMs: R2 = 0.60–0.66, RMSE = 14.96–18.51%, bias = −5.99–4.36%, and empirical models: R2 = 0.69–0.72, RMSE = 10.67–11.30%, bias = 0.18–0.35%). However, all RTMs failed to retrieve LFMC for open shrublands (R2 = 0.01–0.09, RMSE = 45.21–48.66%, bias = 9.76–14.75%) potentially due to the high heterogeneity of vegetation in this vegetation type. In contrast, the SImax-min-based model outperformed the RTMs for the open shrublands LFMC estimation (R2 = 0.44, RMSE = 32.32%, bias = −0.34%) but saturated at high LFMC values (> 120%). In conclusion, PROACRM and the empirical model using SImax-min as an explanatory variable are recommended to model closed and open shrublands LFMC, respectively. This study gives insights into developing effective models for improving shrubland LFMC estimation by considering various fractions of covers of shrublands that were not previously considered

Evaluation of Himawari-8 for live fuel moisture content retrieval

Near-real-time monitoring live fuel moisture content (LFMC) from remote sensing is paramount to wildfire early management at a large scale since LFMC is a critical variable in affecting fire ignition and fire spread rate. The geostationary satellite Himawari-8 observes the land surface every 10 minutes, making near-real-time LFMC retrieval achievable. To this end, the potential of Himawari-8 data for LFMC retrieval using the radiative transfer model was explored in this study. The performance of retrieved LFMC was validated using 16 LFMC samplings located in Australia involving two land cover types: croplands and tree cover lands. Additionally, the MODIS data was also applied and compared for the LFMC retrieval. The results showed that Himawati-8 data performed poor accuracy level with R-2 and RMSE of 0.26 and 42.16%, respectively. Whereas better accuracy level was found for MODIS data, R-2 and RMSE were 0.67 and 29.17%, respectively. This result indicated that the LFMC estimated from Himawari-8 is challenged. Detailed fieldwork and methodology improvements adopted for this data are needed for improving the LFMC estimate in the future

Remote Sensing of Burn Severity Using Coupled Radiative Transfer Model: A Case Study on Chinese Qinyuan Pine Fires

Burn severity mapping is critical to quantifying fire impact on key ecological processes and post-fire forest management. Satellite remote sensing has the advantages of high spatial-temporal resolution and large-scale monitoring and provides a more efficient way to evaluate forest fire burn severity than traditional field or aerial surveys. However, the proportion of tree canopy cover (TCC) affects the spectral signal received by remote sensing sensors from the background charcoal and ash. Consequently, not considering this factor normally leads a spectral confusion in burn severity retrieval. In this study, the burn severity of two Qinyuan forest fires was estimated using a coupled Radiative Transfer Model (RTM) and Sentinel-2A Multi-Spectral Instrument (MSI) reflectance data. A two-layer Canopy Reflectance Model (ACRM) RTM was coupled with the GeoSail RTM by replacing the spectra of the background input of GeoSail RTM to simulate the spectra of the three-layered forests for burn severity retrieval measured as the Composite Burn Index (CBI). The TCC data was then served to RTM parameterization and constrain the backward inversion procedure of the coupled RTM to alleviate spectral confusion. Finally, the inversion retrievals were evaluated using 163 field measured CBI. The coupled RTM can simulate the radiative transfer characteristics of three-layer vegetation and has greater potential to accurately estimate burn severity worldwide. To evaluate the merit of our proposed method, the CBI was estimated through coupled RTM inversion with TCC constraint (CP_RTM+TCC), coupled RTM inversion with global optimal search (CP-RTM+GOS), Forest Reflectance and Transmittance (FRT) RTM inversion with TCC constraint (FRT+TCC), and random forest (RF) algorithm. The results showed that the method proposed in this study (CP_RTM+TCC) yielded the highest estimation accuracy (R2 = 0.92, RMSE = 0.2) among the four methods used as benchmark, indicating its reasonable ability to assist forest managers to better understand post-fire vegetation regeneration and forest management

Global fuel moisture content mapping from MODIS

International audienceFuel moisture content (FMC) of live vegetation is a crucial wildfire risk and spread rate driver. This study presents the first daily FMC product at a global scale and 500 m pixel resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) and radiative transfer models (RTMs) inversion techniques. Firstly, multi-source information parameterized the PROSPECT-5 (leaf level), 4SAIL (grass and shrub canopy level) and GeoSail (tree canopy level) RTMs to generate three look-up tables (LUTs). Each LUT contained the most realistic model inputs range and combination, and the corresponding simulated spectra. Secondly, for each date and location of interest, a global landcover map classified fuels into three classes: grassland, shrubland and forest. For each fuel class, the best LUT-based inversion strategy based on spectral information, cost function, percentage of solutions, and central tendency determined the optimal model for the global FMC product. Finally, 3,034 FMC measurements from 120 worldwide sites validated the statistically significant results (R2 = 0.62, RMSE = 34.57%, p < 0.01). Filtering out low quality field measurements achieved better accuracy (R2 = 0.71, RMSE = 32.36%, p < 0.01, n = 2008). It is anticipated that this global FMC product can assist in wildfire danger modeling, early prediction, suppression and response, as well as improve awareness of wildfire risk to life and property