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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #283920
Title: Remote Sensing of fuel moisture content from the ratios of canopy water indices with a foliar dry matter index

Author
item Hunt Jr, Earle
item WANG, LINGLI - George Mason University
item QU, JOHN - George Mason University
item HAO, X - George Mason University

Submitted to: Proceedings of SPIE
Publication Type: Proceedings
Publication Acceptance Date: 8/6/2012
Publication Date: 10/17/2012
Citation: Hunt Jr, E.R., Wang, L., Qu, J., Hao, X. 2012. Remote Sensing of fuel moisture content from the ratios of canopy water indices with a foliar dry matter index. Proceedings of SPIE. SPIE Optics & Photonics Conference, August 12-16, 2012, San Diego, CA. Optics & Photonics 2012: Remote Sensing. Vol 8513. Paper 8513-1.

Interpretive Summary:

Technical Abstract: Fuel moisture content (FMC) is an important variable for predicting the occurrence and spread of wildfire. Foliar FMC was calculated as the ratio of leaf foliar water content (Cw) and dry matter content (Cm). Recently, the normalized dry matter index (NDMI) was developed for the remote sensing of Cm using high-spectral resolution data. This study explored the potential for remote sensing of FMC using the ratio of various vegetation water indices with NDMI. For leaf-scale simulations, all index ratios were significantly related to FMC. For canopy-scale simulations, ratio indices of the normalized difference infrared index (NDII) and normalized difference water index (NDWI) with NDMI predicted FMC with R2 values of 0.900 and 0.864, respectively. NDII/NDMI determined from leaf reflectance data had the highest correlation with FMC. Further investigation needs to be conducted to evaluate the effectiveness of this approach at canopy scales with airborne remote sensing data.