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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 #267257
Title: Remote sensing of fuel moisture content from ratios of narrow-band vegetation water and dry-matter indices

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

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/10/2012
Publication Date: 2/15/2013
Citation: Wang, L., Hunt, E.R., Qu, J.J., Hao, X. 2013. Remote sensing of fuel moisture content from ratios of narrow-band vegetation water and dry-matter indices. Remote Sensing of Environment. 129:103-110.

Interpretive Summary: Fuel moisture content (FMC) is an important variable for determining fire danger ratings by field personnel, and predicting the occurrence and spread of wildfire over a landscape. FMC is calculated from the fresh and dry weights of leaves. Remote sensing is used to estimate the amount of water in leaves using vegetation water indices, but alone, these vegetation water indices are not useful for predicting FMC. A new hyperspectral remote sensing index, the normalized dry matter index (NDMI), was developed for estimating leaf dry weights. The ratio of one vegetation water index (the normalized difference infrared index) and NDMI was strongly related to FMC using both measured leaf reflectance data and computer simulation modeling. Future satellites, such as NASA's Hyperspectral Infrared Imager (HyspIRI), are expected to have the sensitivity to assess fire danger ratings over large areas.

Technical Abstract: Fuel moisture content (FMC) is an important variable for predicting the occurrence and spread of wildfire. FMC can be calculated by dividing leaf water content (Cw) by dry matter content (Cm). This study explored potential for remote sensing FMC by investigating the ratio of Cw/Cm from various water indices and the normalized dry matter index (NDMI). Based on PROSPECT-SAIL model simulations, at the leaf level, any of the selected water indices divided by NDMI has significant correlations with FMC. The R2 values are generally greater than 0.9. At the canopy level, the water indices which employ the normalized calculation demonstrated higher performance compared with simple ratios. Among all the indices, NDII and NDWI have higher R2 with values of 0.899 and 0.864, respectively. The evaluation by using the leaf data show that fuel moisture contents were retrievable by combining NDII, NDWI and GVMI with NDMI with R2 of 0.808, 0.700 and 0.754 and regression slopes of 0.790, 0.582 and 1.013. Further investigation needs to be conducted to evaluate the effectiveness of this approach over a variety of vegetation types at canopy scales.