Name
Abstract | ||
|---|---|---|
One of the major forces for understanding forest fire risk and behavior is the fire fuel. Fire risk and behavior depend on the fuel properties such as moisture content. Context information on vegetation water content is vital for understanding the processes involved in initiation and propagation of forest fires. In that sense, a novel method was tested to estimate vegetation canopy water content (CWC) from simulated MODIS satellite data. An inversion of a radiative transfer model called Forest Light Interaction-Model (FLIM) from performed using evolutionary computation. CWC is critical, among other applications, in wildfire risk assessment since a decrease in CWC causes higher probability to have wildfire occurrence. Simulations were carried out with the FLIM model for a wide range of forest canopy characteristics and CWC values. A 50 subsample of the simulations was used for the training process and 50 for the validation providing a RMSE=0.74 and r2=0.62. Further research is needed to apply this method on real MODIS images. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-73055-2_29 | IWINAC (2) |
Keywords | Field | DocType |
moisture content,wildfire risk assessment,vegetation water content,understanding forest fire,vegetation canopy water content,forest fire,evolutionary computation,cwc value,fire risk,fire fuel,forest canopy characteristic,context information,forest fire risk,forest canopy,water content,radiative transfer model,evolutionary computing,risk assessment | Meteorology,Tree canopy,Inversion (meteorology),Computer science,Vegetation water content,Mean squared error,Risk assessment,Evolutionary computation,Atmospheric radiative transfer codes,Artificial intelligence,Water content,Machine learning | Conference |
Volume | ISSN | Citations |
4528 | 0302-9743 | 1 |
PageRank | References | Authors |
0.37 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| L. Usero | 1 | 4 | 2.78 |
| Angel Arroyo | 2 | 7 | 2.33 |
| J. Calvo | 3 | 1 | 0.37 |