A fuel moisture content and flammability monitoring methodology for continental Australia based on optical remote sensing
|Title||A fuel moisture content and flammability monitoring methodology for continental Australia based on optical remote sensing|
|Publication Type||Journal Article|
|Year of Publication||2018|
|Authors||Yebra, M, Quan, X, Riaño, D, Larraondo, PRozas, van Dijk, A, Cary, GJ|
|Journal||Remote Sensing of Environment|
|Keywords||Fire occurrence, Fire risk, Forests., GEOPROSAIL inversion, Grasslands, MODIS, PROSAIL inversion, Shrubs|
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 “Area Under the Curve” calculated from the Receiver Operating Characteristic plot method of 0.70, 0.78 and 0.71, respectively, indicating reasonable skill in fire risk prediction.