Estimating and Up-Scaling Fuel Moisture and Leaf Dry Matter Content of a Temperate Humid Forest Using Multi Resolution Remote Sensing Data

Estimating and Up-Scaling Fuel Moisture and Leaf Dry Matter Content of a Temperate Humid Forest Using Multi Resolution Remote Sensing Data

Vegetation moisture and dry matter content are important indicators in predicting the behavior of fire and it is widely used in fire spread models. In this study, leaf fuel moisture content such as Live Fuel Moisture Content (LFMC), Leaf Relative Water Content (RWC), Dead Fuel Moisture Content (DFMC...

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Bibliographic Details
Main Authors:	Hamed Adab, Kasturi Devi Kanniah, Jason Beringer
Format:	Article
Language:	English
Published:	MDPI AG 2016-11-01
Series:	Remote Sensing
Subjects:	leaf moisture content leaf dry matter content temperate humid forest remote sensing artificial neural network multiple linear regression upscaling fire danger
Online Access:	http://www.mdpi.com/2072-4292/8/11/961

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