Predicting dead fine fuel moisture at regional scales using vapour pressure deficit from MODIS and gridded weather data
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- 作者：Rachael H. Nolana ; b ; 1 ; Rachael.Nolan@uts.edu.au" class="auth_mail" title="E-mail the corresponding author ; Ví ; ctor Resco de Diosa ; 2 ; Matthias M. Boera ; Gabriele Caccamob ; 3 ; Michael L. Gouldenc ; Ross A. Bradstockb
- 关键词：Remote sensing ; Land surface temperature ; MODIS ; Wildfire
- 刊名：Remote Sensing of Environment
- 出版年：2016
- 出版时间：1 March 2016
- 年：2016
- 卷：174
- 期：Complete
- 页码：100-108
- 全文大小：904 K
文摘
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Dead fine fuel moisture content (FM) is crucial for modelling wildfire risk.
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Vapour pressure deficit (D) is a good predictor of FM.
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MODIS land surface temperature is a good predictor of D.
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We used MODIS land surface temperature to model FM at a regional scale.
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FM predictions of similar accuracy were achieved with gridded weather data.