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.
Highlights
Fire is a common form of disaster in forested and grassland areas across temperate regions of the world
moisture content indices (MCI) calculated using data collected in the field are shown in Figure 2 and show that most of the species have a large range of values except for Ainus Subcordata, there was no inter-species variation between all the four MCI metrics
MCI (LFMC, Relative Water Content (RWC), Leaf Dry Matter Content (LDMC), and Dead Fuel Moisture Content (DFMC)) calculated from Landsat ETM+ (6 August 2010) using the Artificial Neural Network (ANN) method is shown in Figure 4
Summary
Fire is a common form of disaster in forested and grassland areas across temperate regions of the world. Intervention of forest fires can occur at an early stage of fire ignition by monitoring and forecasting the factors such as leaf moisture content and leaf dry matter content (MCI) that influence biomass burning. Indices of vegetation moisture have a relationship with the fire probability because when plants experience water deficits or nutrients stress, leaves may eventually become more yellow and brown so the vegetation indices will change. Fuel moisture and plant dry matter are considered to be important parameters in any forest fire modeling that attempts to predict fuel ignition. Such models can be used to provide early warning of increased forest fire potential at the pre-ignition stage
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