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Developing a MODIS-based index to discriminate dead fuel from photosynthetic vegetation and soil background in the Asian steppe area

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Dead fuel (DF) coverage and biomass are important parameters for wildfire danger rating and fire behaviour modelling. Although a hyperspectral Cellulose Absorption Index (CAI) has been proven to be a good tool for discriminating non-photosynthetic vegetation (NPV), plant litter and crop residues, the narrow bands are not well suited for multi-band Moderate Resolution Imaging Spectroradiometer (MODIS) to achieve global and real-time wildfire risk assessment. Meanwhile, Landsat TM-based indices, such as Normalized Difference Index (NDI), Soil Adjusted Corn Residue Index (SACRI) and Crop Residue Index Multiband (CRIM), have been proposed to extract NPV only in the case of two components of NPV and soil. This study intended to discriminate DF and estimate its coverage in three-component mixtures of photosynthetic vegetation (PV), DF and soil using MODIS simulated data. The methods used included: (1) analysing field spectra of PV, DF and soil; (2) developing a four-band Dead Fuel Index (DFI) based on MODIS band ranges; and (3) simulating spectral mixtures to determine the lower thresholds for DF detection. DFI as well as NDI, SACRI, CRIM and CAI were evaluated based on individual spectra and linearly simulated spectral mixtures of PV, DF and soil. Results suggested that DFI was the best index for DF discrimination, with a minimal fractional coverage of only about 0.20 in one pixel required to confirm the existence of DF with MODIS simulated data. However, DFI is less sensitive to long-term DFs. For top-of-atmosphere (TOA) reflectance simulation under different atmospheric conditions, the minimal fractions were 0.32, 0.42, 0.53 and 0.64 for optical depths at 550 nm of 0.2 (clear), 0.4, 0.6 and 0.8 (hazy), respectively. The results of this study suggest that DFI has good potential to estimate DF coverage in steppe areas.

Document Type: Research Article


Affiliations: 1: State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, PR, China,Graduate School of Environmental Studies, Nagoya University, Japan 2: State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, PR, China 3: Graduate School of Life and Environmental Sciences, University of Tsukuba, Japan 4: Graduate School of Environmental Studies, Nagoya University, Japan

Publication date: 2010-02-01

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