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Remote Sens. 2016, 8(11), 961; doi:10.3390/rs8110961

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

1
Faculty of Geography and Environmental Science, Hakim Sabzevari University, Sabzevar, Khorasan Razavi 9617976487, Iran
2
Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia, Johor 81310, Malaysia
3
Centre for Environmental Sustainability and Water Security, Research Institute for Sustainable Environment, Universiti Teknologi Malaysia, Johor 81310, Malaysia
4
School of Earth and Environment, University of Western Australia (M004), 35 Stirling Highway, Crawley WA 6009, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Sangram Ganguly, Compton Tucker, Ioannis Gitas, Clement Atzberge and Prasad S. Thenkabail
Received: 27 June 2016 / Revised: 4 November 2016 / Accepted: 7 November 2016 / Published: 19 November 2016
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
View Full-Text   |   Download PDF [21134 KB, uploaded 19 November 2016]   |  

Abstract

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), and Leaf Dry Matter Content (LDMC) (hereinafter known as moisture content indices (MCI)) were calculated in the field for different forest species at 32 sites in a temperate humid forest (Zaringol forest) located in northeastern Iran. These data and several relevant vegetation-biophysical indices and atmospheric variables calculated using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data with moderate spatial resolution (30 m) were used to estimate MCI of the Zaringol forest using Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) methods. The prediction of MCI using ANN showed that ETM+ predicted MCI slightly better (Mean Absolute Percentage Error (MAPE) of 6%–12%)) than MLR (MAPE between 8% and 17%). Once satisfactory results in estimating MCI were obtained by using ANN from ETM+ data, these data were then upscaled to estimate MCI using MODIS data for daily monitoring of leaf water and leaf dry matter content at 500 m spatial resolution. For MODIS derived LFMC, LDMC, RWC, and DLMC, the ANN produced a MAPE between 11% and 29% for the indices compared to MLR which produced an MAPE of 14%–33%. In conclusion, we suggest that upscaling is necessary for solving the scale discrepancy problems between the indicators and low spatial resolution MODIS data. The scaling up of MCI could be used for pre-fire alert system and thereby can detect fire prone areas in near real time for fire-fighting operations. View Full-Text
Keywords: leaf moisture content; leaf dry matter content; temperate humid forest; remote sensing; artificial neural network; multiple linear regression; upscaling; fire danger leaf moisture content; leaf dry matter content; temperate humid forest; remote sensing; artificial neural network; multiple linear regression; upscaling; fire danger
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Adab, H.; Devi Kanniah, K.; Beringer, J. Estimating and Up-Scaling Fuel Moisture and Leaf Dry Matter Content of a Temperate Humid Forest Using Multi Resolution Remote Sensing Data. Remote Sens. 2016, 8, 961.

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