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Journal of Applied Remote Sensing

Remote sensing of fuel moisture content from canopy water indices and normalized dry matter index
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Paper Abstract

Fuel moisture content (FMC), an important variable for predicting the occurrence and spread of wildfire, is the ratio of foliar water content and foliar dry matter content. One approach for the remote sensing of FMC has been to estimate the change in canopy water content over time by using a liquid-water spectral index. Recently, the normalized dry matter index (NDMI) was developed for the remote sensing of dry matter content using high-spectral-resolution data. The ratio of a spectral water index and a dry matter index corresponds to the ratio of foliar water and dry matter contents; therefore, we hypothesized that FMC may be remotely sensed with a spectral water index divided by NDMI. For leaf-scale simulations using the PROSPECT (leaf optical properties spectra) model, all water index/NDMI ratios were significantly related to FMC with a second-order polynomial regression. For canopy-scale simulations using the SAIL (scattering by arbitrarily inclined leaves) model, two water index/NDMI ratios, with numerators of the normalized difference infrared index (NDII) and the normalized difference water index (NDWI), predicted FMC with R2 values of 0.900 and 0.864, respectively. Leaves from three species were dried or stacked to vary FMC; measured NDII/NDMI was best related to FMC. Whereas the planned NASA mission Hyperspectral Infrared Imager (HyspIRI) will have high spectral resolution and very high signal-to-noise properties, the planned 19-day repeat frequency will not be sufficient for monitoring FMC with NDII/NDMI. Because increased fire frequency is expected with climatic change, operational assessment of FMC at large scales may require polar-orbiting environmental sensors with narrow bands to calculate NDMI.

Paper Details

Date Published: 3 December 2012
PDF: 11 pages
J. Appl. Rem. Sens. 6(1) 061705 doi: 10.1117/1.JRS.6.061705
Published in: Journal of Applied Remote Sensing Volume 6, Issue 1
Show Author Affiliations
E. Raymond Hunt, Agricultural Research Service (United States)
Lingli Wang, George Mason Univ. (United States)
John J. Qu, George Mason Univ. (United States)
Xianjun Hao, George Mason Univ. (United States)

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