Assessment of the Effect of PROSAILH for Open and Closed Shrublands Live Fuel Moisture Content Retrieval

Lai, Gengke; Quan, Xingwen; He, Bin

doi:10.1109/igarss39084.2020.9323386

Cited by 1 publication

(1 citation statement)

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“…Further research and analysis have shown good agreement between vegetation parameters derived from physical models or look-up table-based methods and measured data [28]. Multiple-model analyses and accuracy evaluations of inverse canopy FMC estimation further revealed that the PROGEOSAIL model outperformed the Liberty model for sparse broadleaf and mixed conifer forests [29,30].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Forest Canopy Fuel Moisture Content in Dali Prefecture by Combining Vegetation Indices and Canopy Radiative Transfer Models from MODIS Data

Yang,

Tang,

et al. 2024

Forests

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Forest canopy fuel moisture content (FMC) is a critical factor in assessing the vulnerability of a specific area to forest fires. The conventional FMC estimation method, which relies on look-up tables and loss functions, cannot to elucidate the relationship between FMC and simulated data from look-up tables. This study proposes a novel approach for estimating FMC by combining enhanced vegetation index (EVI) and normalized difference moisture index (NDMI). The method employs the PROSAIL + PROGeoSAIL two-layer coupled radiation transfer model to simulate the vegetation index, the water index, and the FMC value, targeting the prevalent double-layer structure in the study area’s vegetation distribution. Additionally, a look-up table is constructed through numerical analysis to investigate the relationships among vegetation indices, water indices, and FMC. The results reveal that the polynomial equations incorporating vegetation and water indices as independent variables exhibit a strong correlation with FMC. Utilizing the EVI–NDMI joint FMC estimation method enables the direct estimation of FMC. The collected samples from Dali were compared with the estimated values, revealing that the proposed method exhibits superior accuracy (R2 = 0.79) in comparison with conventional FMC estimation methods. In addition, we applied this method to estimate the FMC in the Chongqing region one week before the 2022 forest fire event, revealing a significant decreasing trend in regional FMC leading up to the fire outbreak, highlighting its effectiveness in facilitating pre-disaster warnings.

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Section: Introductionmentioning

confidence: 99%