Publications related to the GRACE Missions (no abstracts)

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Introducing Spatially Distributed Fire Danger from Earth Observations (FDEO) Using Satellite-Based Data in the Contiguous United States

Farahmand, Alireza, Stavros, E. Natasha, Reager, John T., and Behrangi, Ali, 2020. Introducing Spatially Distributed Fire Danger from Earth Observations (FDEO) Using Satellite-Based Data in the Contiguous United States. Remote Sensing, 12(8):1252, doi:10.3390/rs12081252.

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@ARTICLE{2020RemS...12.1252F,
       author = {{Farahmand}, Alireza and {Stavros}, E. Natasha and {Reager}, John T. and {Behrangi}, Ali},
        title = "{Introducing Spatially Distributed Fire Danger from Earth Observations (FDEO) Using Satellite-Based Data in the Contiguous United States}",
      journal = {Remote Sensing},
     keywords = {wildfire danger, vapor pressure deficit (VPD), soil moisture, enhanced vegetation index, prediction, GRACE, AIRS, MODIS},
         year = 2020,
        month = apr,
       volume = {12},
       number = {8},
          eid = {1252},
        pages = {1252},
     abstract = "{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{\textemdash}NFDRS) for predicting fire danger.
        Meteorological forecasts, however, lose accuracy beyond
        \raisebox{-0.5ex}\textasciitilde10 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.}",
          doi = {10.3390/rs12081252},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2020RemS...12.1252F},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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