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Retrieval of fuel moisture content by using radiative transfer models from optical remote sensing data

  • role： First author第一作者
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

  • Email：xingwen.quan@uestc.edu.cn
  • Introduction：全兴文，1986年生，男，博士研究生，研究方向为光学遥感建模及反演、数据同化与数据挖掘。E-mail：xingwen.quan@uestc.edu.cn
Xingwen QUAN1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

    Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China

  • Email：binbinhe@uestc.edu.cn
  • Introduction：何彬彬，1972年生，男，教授，研究方向为定量遥感、时空大数据挖掘。E-mail：binbinhe@uestc.edu.cn
Binbin HE12*,  
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

Xiangzhuo LIU1,  
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

Zhanmang LIAO1,  
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

Shi QIU1,  
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

Changming YIN1

résumé

Wildfire is a natural agent of many ecosystems since fire can affect nutrient cycles, vegetation succession patterns, and insect plague resistance. However, wildfire also has a wide range of negative impacts on ecosystems, such as soil erosion and degradation, destruction of vegetation water conservation function, emissions of atmospheric greenhouse gases, and human life and welfare. The three major forces that are essential for understanding forest fire risk and its behavior are as follows: weather, fuel, and topography. In this context, fuel moisture content (FMC), which is the proportion of water content over dry mass, is one of the key factors used for the risk assessment of wildfire because it affects fire ignition and spreading. However, traditional field measurement of the variable is time consuming, and extending to large-scale and dynamic application is impossible. Thus, a near real-time, multi-temporal and multi-spatial remotely sensed data can address the solution. In this work, the FMC of grassland and forest for six study areas distributed in the Western China were retrieved using the PROSAIL and PROGeoSAIL radiative transfer models from Landsat-5 TM and Landsat-8 OLI data. For accurate and robust retrieval of FMC, the ill-posed inversion problem, the weak sensitivity characteristic of FMC, and the way in modeling the spectra of two-layered forest canopy should be addressed. In this study, we focused on alleviating the ill-posed inverse problem based on the ecological rule to obtain the improvement of the FMC retrieval. Previous studies generally treated the free parameters independent while ignoring that these parameters were naturally correlated. The correlations that naturally existed between the model parameters were introduced into their prior joint probability distribution of the requried free parameters. This treatment can reduce the probabilities of unrealistic combinations that may confuse the retrieval process and therefore increase the accuracy level of the retrieved FMC. FMC is calculated from the ratio of the model input parameters, namely, equivalent water thickness (EWT) and dry matter content (DMC). Both these two variables have the sensitivity in the near infrared (NIR) and shortwave infrared (SWIR) bands. However, the effect of DMC in NIR and SWIR is often confused by the EWT, which renders the accurate estimation of DMC (or FMC) slightly challenging. For the accurate retrieval of FMC, all available bands from Landsat-5 TM and Landsat-8 OLI data were used for obtaining additional information from these remotely sensed data. Regarding to the forest with two-layered canopy (lower grass and upper tree canopies), this study coupled two radiative transfer models, namely, PROSAIL and PROGEOSAIL to characterized its spectra. First, the spectra of the lower grass canopy were simulated via the PROSAIL model. Then, the soil spectra required in the PROGeoSAIL model were replaced using the simulated spectra of the low grass canopy layer. These coupled models allowed to better resemble the two-layered canopy configuration in the study area. Result show that the accuracy of the retrieved forest FMC (R2=0.71, RMSE=30.82%) was better than the grassland FMC (R2 = 0.64, RMSE = 47.57%) with overall accuracy of R2 = 0.64, RMSE = 44.86%. The multi-temporal FMC maps were generated to further demonstrate the performance of the retrieved FMC in assessing the wildfire risk in Jinhe Village of Yunnan Province where a forest fire occurred in March 02, 2011. Result further show that FMC noticeably decreased during the fire, thereby implying the potential usage of retrieved FMC in the early warning of the wildfire risk.

mots-clés

fuel moisture content;radiative transfer model;weak sensitivity of FMC;two-layered forest canopy;wstern China;wildfire

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