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Estimating and up-scaling fuel moisture and leaf dry matter content of a temperate humid forest using multi resolution remote sensing data

Adab, H. and Kanniah, K. D. and Beringer, J. (2016) Estimating and up-scaling fuel moisture and leaf dry matter content of a temperate humid forest using multi resolution remote sensing data. Remote Sensing, 8 (11). ISSN 2072-4292

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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 RelativeWater 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.

Item Type:Article
Uncontrolled Keywords:Fire extinguishers, Fire fighting equipment, Fires, Forestry, Fuels, Image resolution, Linear regression, Moisture, Moisture determination, Neural networks, Radiometers, Vegetation, Fire danger, Leaf dry matter, Multiple linear regressions, Temperate humid forest, Upscaling, Remote sensing
Subjects:G Geography. Anthropology. Recreation > G Geography (General) > G70.39-70.6 Remote sensing
Divisions:Geoinformation and Real Estate
ID Code:71772
Deposited By: Fazli Masari
Deposited On:15 Nov 2017 11:11
Last Modified:15 Nov 2017 11:11

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