Estimation of Fuel Moisture Content by Integrating Surface and Satellite Observations Using Machine Learning

Branko Kosovic; Pedro Jimenez; Tyler McCandless; Bill Petzke; Steven Massie; Amanda Siems-Anderson; Amy Decastro; Domingo Munoz-Esparza; Sue Ellen Haupt

doi:10.1109/IGARSS39084.2020.9323134

Estimation of Fuel Moisture Content by Integrating Surface and Satellite Observations Using Machine Learning

Branko Kosovic, Pedro Jimenez, Tyler McCandless, Bill Petzke, Steven Massie, Amanda Siems-Anderson, Amy Decastro, Domingo Munoz-Esparza, Sue Ellen Haupt

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Fuel moisture content (FMC) is an important fuel property and an important parameter controlling the rate spread of a wildland fire. Currently the dead FMC is estimated based on relatively sparse observations over Conterminous United States while the live FMC is sampled manually and infrequently. An effective operational wildland fire prediction requires real-time, high-resolution fuel moisture content data set. We have therefore developed a fuel moisture content data set by combining satellite and surface observations as well as National Water Model output using a machine learning model. The new FMC data set is integrated in the Colorado Fire Prediction System (CO-FPS) for operational wildland fire prediction.

Original language	English
Title of host publication	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3626-3628
Number of pages	3
ISBN (Electronic)	9781728163741
DOIs	https://doi.org/10.1109/IGARSS39084.2020.9323134
State	Published - Sep 26 2020
Externally published	Yes
Event	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Virtual, Waikoloa, United States Duration: Sep 26 2020 → Oct 2 2020

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Country/Territory	United States
City	Virtual, Waikoloa
Period	09/26/20 → 10/2/20

Keywords

Fuel moisture content
MODIS satellite observations
National Water Model
machine learning model
random forest algorithm

Access to Document

10.1109/IGARSS39084.2020.9323134

Cite this

Kosovic, B., Jimenez, P., McCandless, T., Petzke, B., Massie, S., Siems-Anderson, A., Decastro, A., Munoz-Esparza, D., & Haupt, S. E. (2020). Estimation of Fuel Moisture Content by Integrating Surface and Satellite Observations Using Machine Learning. In 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings (pp. 3626-3628). Article 9323134 (International Geoscience and Remote Sensing Symposium (IGARSS)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS39084.2020.9323134

Kosovic, Branko ; Jimenez, Pedro ; McCandless, Tyler et al. / Estimation of Fuel Moisture Content by Integrating Surface and Satellite Observations Using Machine Learning. 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 3626-3628 (International Geoscience and Remote Sensing Symposium (IGARSS)).

@inproceedings{444e989eb5a24a38ac6a62175c7275e8,

title = "Estimation of Fuel Moisture Content by Integrating Surface and Satellite Observations Using Machine Learning",

abstract = "Fuel moisture content (FMC) is an important fuel property and an important parameter controlling the rate spread of a wildland fire. Currently the dead FMC is estimated based on relatively sparse observations over Conterminous United States while the live FMC is sampled manually and infrequently. An effective operational wildland fire prediction requires real-time, high-resolution fuel moisture content data set. We have therefore developed a fuel moisture content data set by combining satellite and surface observations as well as National Water Model output using a machine learning model. The new FMC data set is integrated in the Colorado Fire Prediction System (CO-FPS) for operational wildland fire prediction.",

keywords = "Fuel moisture content, MODIS satellite observations, National Water Model, machine learning model, random forest algorithm",

author = "Branko Kosovic and Pedro Jimenez and Tyler McCandless and Bill Petzke and Steven Massie and Amanda Siems-Anderson and Amy Decastro and Domingo Munoz-Esparza and Haupt, \{Sue Ellen\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 ; Conference date: 26-09-2020 Through 02-10-2020",

year = "2020",

month = sep,

day = "26",

doi = "10.1109/IGARSS39084.2020.9323134",

language = "English",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

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Kosovic, B, Jimenez, P, McCandless, T, Petzke, B, Massie, S, Siems-Anderson, A, Decastro, A, Munoz-Esparza, D & Haupt, SE 2020, Estimation of Fuel Moisture Content by Integrating Surface and Satellite Observations Using Machine Learning. in 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings., 9323134, International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc., pp. 3626-3628, 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020, Virtual, Waikoloa, United States, 09/26/20. https://doi.org/10.1109/IGARSS39084.2020.9323134

Estimation of Fuel Moisture Content by Integrating Surface and Satellite Observations Using Machine Learning. / Kosovic, Branko; Jimenez, Pedro; McCandless, Tyler et al.
2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. p. 3626-3628 9323134 (International Geoscience and Remote Sensing Symposium (IGARSS)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Estimation of Fuel Moisture Content by Integrating Surface and Satellite Observations Using Machine Learning

AU - Kosovic, Branko

AU - Jimenez, Pedro

AU - McCandless, Tyler

AU - Petzke, Bill

AU - Massie, Steven

AU - Siems-Anderson, Amanda

AU - Decastro, Amy

AU - Munoz-Esparza, Domingo

AU - Haupt, Sue Ellen

PY - 2020/9/26

Y1 - 2020/9/26

N2 - Fuel moisture content (FMC) is an important fuel property and an important parameter controlling the rate spread of a wildland fire. Currently the dead FMC is estimated based on relatively sparse observations over Conterminous United States while the live FMC is sampled manually and infrequently. An effective operational wildland fire prediction requires real-time, high-resolution fuel moisture content data set. We have therefore developed a fuel moisture content data set by combining satellite and surface observations as well as National Water Model output using a machine learning model. The new FMC data set is integrated in the Colorado Fire Prediction System (CO-FPS) for operational wildland fire prediction.

AB - Fuel moisture content (FMC) is an important fuel property and an important parameter controlling the rate spread of a wildland fire. Currently the dead FMC is estimated based on relatively sparse observations over Conterminous United States while the live FMC is sampled manually and infrequently. An effective operational wildland fire prediction requires real-time, high-resolution fuel moisture content data set. We have therefore developed a fuel moisture content data set by combining satellite and surface observations as well as National Water Model output using a machine learning model. The new FMC data set is integrated in the Colorado Fire Prediction System (CO-FPS) for operational wildland fire prediction.

KW - Fuel moisture content

KW - MODIS satellite observations

KW - National Water Model

KW - machine learning model

KW - random forest algorithm

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DO - 10.1109/IGARSS39084.2020.9323134

M3 - Conference contribution

AN - SCOPUS:85102007300

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SP - 3626

EP - 3628

BT - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020

Y2 - 26 September 2020 through 2 October 2020

ER -

Kosovic B, Jimenez P, McCandless T, Petzke B, Massie S, Siems-Anderson A et al. Estimation of Fuel Moisture Content by Integrating Surface and Satellite Observations Using Machine Learning. In 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. p. 3626-3628. 9323134. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS39084.2020.9323134

Estimation of Fuel Moisture Content by Integrating Surface and Satellite Observations Using Machine Learning

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Keywords

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