Broad‐Scale Monitoring of Live Fuel Moisture

Yool, Stephen R.

doi:10.1111/j.1749-8198.2009.00267.x

Cited by 1 publication

(2 citation statements)

References 78 publications

(145 reference statements)

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“…Two different sets of equations were generated, one for DFMC and another one for LFMC, RWC, and LDMC as shown in Equations (9)- (12). The first and second principal components in Equations (9) and (10) explained 83% and 90% of the total variation in Equations (11) and (12) Principle components 1 (PC1) and 2 (PC2) and Varimax1 and Varimax2 (Equations (9)- (12)) were used to estimate LFMC, RWC, LDMC, and DFMC using MLR as shown in Equations (13)- (16). Approximately 77% of the study area (2029 pixels) was used to train the model, and 15% (606 pixels) was used to validate the performance of the model.…”

Section: Upscaling Fuel Moisture and Leaf Dry Matter Content Derived mentioning

confidence: 99%

“…According to the electromagnetic spectrum, there is a link between a plant's moisture content and its spectrum. The spectral responses for live fuel moisture levels were explored in the visible, NIR (near-infrared), SWIR (short-wave infrared) and thermal infrared wavelengths, so these wavelengths are used to estimate fuel moisture content [16]. On the other hand, dead fuel moisture is primarily affected by soil moisture (which would also be affected by weather) near the dead fuel surface [17].…”

Section: Introductionmentioning

confidence: 99%

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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

2016

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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 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.

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Section: Upscaling Fuel Moisture and Leaf Dry Matter Content Derived mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%