Introducing Spatially Distributed Fire Danger from Earth Observations (FDEO) Using Satellite-Based Data in the Contiguous United States
Wildfire danger assessment is essential for operational allocation of fire management resources; with longer lead prediction, the more efficiently can resources be allocated regionally. Traditional studies focus on meteorological forecasts and fire danger index models (e.g., National Fire Danger Rat...
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doaj-a5ceb3392e364e9abf05e05d35fb34fe2020-11-25T02:02:55ZengMDPI AGRemote Sensing2072-42922020-04-01121252125210.3390/rs12081252Introducing Spatially Distributed Fire Danger from Earth Observations (FDEO) Using Satellite-Based Data in the Contiguous United StatesAlireza Farahmand0E. Natasha Stavros1John T. Reager2Ali Behrangi3Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USAJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USAJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USADepartment of hydrology and atmospheric sciences, The University of Arizona, Tucson, AZ 85721, USAWildfire danger assessment is essential for operational allocation of fire management resources; with longer lead prediction, the more efficiently can resources be allocated regionally. Traditional studies focus on meteorological forecasts and fire danger index models (e.g., National Fire Danger Rating System—NFDRS) for predicting fire danger. Meteorological forecasts, however, lose accuracy beyond ~10 days; as such, there is no quantifiable method for predicting fire danger beyond 10 days. While some recent studies have statistically related hydrologic parameters and past wildfire area burned or occurrence to fire, no study has used these parameters to develop a monthly spatially distributed predictive model in the contiguous United States. Thus, the objective of this study is to introduce Fire Danger from Earth Observations (FDEO), which uses satellite data over the contiguous United States (CONUS) to enable two-month lead time prediction of wildfire danger, a sufficient lead time for planning purposes and relocating resources. In this study, we use satellite observations of land cover type, vapor pressure deficit, surface soil moisture, and the enhanced vegetation index, together with the United States Forest Service (USFS) verified and validated fire database (FPA) to develop spatially gridded probabilistic predictions of fire danger, defined as expected area burned as a deviation from “normal”. The results show that the model predicts spatial patterns of fire danger with 52% overall accuracy over the 2004–2013 record, and up to 75% overall accuracy during the fire season. Overall accuracy is defined as number of pixels with correctly predicted fire probability classes divided by the total number of the studied pixels. This overall accuracy is the first quantified result of two-month lead prediction of fire danger and demonstrates the potential utility of using diverse observational data sets for use in operational fire management resource allocation in the CONUS.https://www.mdpi.com/2072-4292/12/8/1252wildfire dangervapor pressure deficit (VPD)soil moistureenhanced vegetation index, predictionGRACEAIRS |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Alireza Farahmand E. Natasha Stavros John T. Reager Ali Behrangi |
spellingShingle |
Alireza Farahmand E. Natasha Stavros John T. Reager Ali Behrangi Introducing Spatially Distributed Fire Danger from Earth Observations (FDEO) Using Satellite-Based Data in the Contiguous United States Remote Sensing wildfire danger vapor pressure deficit (VPD) soil moisture enhanced vegetation index, prediction GRACE AIRS |
author_facet |
Alireza Farahmand E. Natasha Stavros John T. Reager Ali Behrangi |
author_sort |
Alireza Farahmand |
title |
Introducing Spatially Distributed Fire Danger from Earth Observations (FDEO) Using Satellite-Based Data in the Contiguous United States |
title_short |
Introducing Spatially Distributed Fire Danger from Earth Observations (FDEO) Using Satellite-Based Data in the Contiguous United States |
title_full |
Introducing Spatially Distributed Fire Danger from Earth Observations (FDEO) Using Satellite-Based Data in the Contiguous United States |
title_fullStr |
Introducing Spatially Distributed Fire Danger from Earth Observations (FDEO) Using Satellite-Based Data in the Contiguous United States |
title_full_unstemmed |
Introducing Spatially Distributed Fire Danger from Earth Observations (FDEO) Using Satellite-Based Data in the Contiguous United States |
title_sort |
introducing spatially distributed fire danger from earth observations (fdeo) using satellite-based data in the contiguous united states |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2020-04-01 |
description |
Wildfire danger assessment is essential for operational allocation of fire management resources; with longer lead prediction, the more efficiently can resources be allocated regionally. Traditional studies focus on meteorological forecasts and fire danger index models (e.g., National Fire Danger Rating System—NFDRS) for predicting fire danger. Meteorological forecasts, however, lose accuracy beyond ~10 days; as such, there is no quantifiable method for predicting fire danger beyond 10 days. While some recent studies have statistically related hydrologic parameters and past wildfire area burned or occurrence to fire, no study has used these parameters to develop a monthly spatially distributed predictive model in the contiguous United States. Thus, the objective of this study is to introduce Fire Danger from Earth Observations (FDEO), which uses satellite data over the contiguous United States (CONUS) to enable two-month lead time prediction of wildfire danger, a sufficient lead time for planning purposes and relocating resources. In this study, we use satellite observations of land cover type, vapor pressure deficit, surface soil moisture, and the enhanced vegetation index, together with the United States Forest Service (USFS) verified and validated fire database (FPA) to develop spatially gridded probabilistic predictions of fire danger, defined as expected area burned as a deviation from “normal”. The results show that the model predicts spatial patterns of fire danger with 52% overall accuracy over the 2004–2013 record, and up to 75% overall accuracy during the fire season. Overall accuracy is defined as number of pixels with correctly predicted fire probability classes divided by the total number of the studied pixels. This overall accuracy is the first quantified result of two-month lead prediction of fire danger and demonstrates the potential utility of using diverse observational data sets for use in operational fire management resource allocation in the CONUS. |
topic |
wildfire danger vapor pressure deficit (VPD) soil moisture enhanced vegetation index, prediction GRACE AIRS |
url |
https://www.mdpi.com/2072-4292/12/8/1252 |
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