Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316.2/44622
Title: | Data likelihood of active fires satellite detection and applications toignition estimation and data assimilation | Authors: | Haley, James Caus, Angel Farguell Kochanski, Adam K. Schranz, Sher Mandel, Jan |
Keywords: | Active Fires;MODIS;VIIRS;Coupled Fire-Atmosphere Modeling;Remote Sensing;Maximum Likelihood;Data Assimilation;Data-Driven Simulation | Issue Date: | 2018 | Publisher: | Imprensa da Universidade de Coimbra | Journal: | http://hdl.handle.net/10316.2/44517 | Abstract: | Data likelihood of fire detection is the probability of the observed detection outcome given the state of the fire spread model. We derive fire detection likelihood of satellite data as a function of the fire arrival time on the model grid. The data likelihood is constructed by a combination of the burn model, the logistic regression of the active fires detections, and the Gaussian distribution of the geolocation error. The use of the data likelihood is then demonstrated by an estimation of the ignition point of a wildland fire by the maximization of the likelihood of MODIS and VIIRS data over multiple possible ignition points. | URI: | https://hdl.handle.net/10316.2/44622 | ISBN: | 978-989-26-16-506 (PDF) | DOI: | 10.14195/978-989-26-16-506_105 | Rights: | open access |
Appears in Collections: | Advances in forest fire research 2018 |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
data_likelihood_of_active_fires.pdf | 1.3 MB | Adobe PDF | ![]() |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.