基于MODIS数据的全球叶片可燃物含水率时空变化与差异分析

Spatiotemporal variation and difference analysis of global leaf fuel moisture content based on MODIS data

  • 摘要: 基于Google Earth Engine(GEE)平台生成了全球可燃物含水率(fuel moisture content,FMC)的时空分布图,分析在不同区域和植被类型中FMC的时空差异,以揭示FMC的动态变化.首先,基于地面实测数据和光谱指数分别构建森林和草地植被类型的等效水厚度(equivalent water thickness,EWT)和干物质含量(dry matter content,DMC)的经验估算模型;随后通过EWT和DMC作为中间变量,构建了FMC的分步估算模型;最终生成了2010—2022年的全球月尺度FMC时空分布图.为了评估估算结果的精度,本文结合站点数据(Global-LFMC)和标准化降水蒸散指数(standardized precipitation evapotranspiration index,SPEI),进行了直接和间接的验证.结果表明,对于森林类别,利用归一化红外指数(normalized difference infrared index,NDII)估算EWT(r = 0.89)和利用归一化水指数(normalized difference water index,NDWI)估算DMC(r = 0.26),以及对于草地,利用简单比值水指数(simple ratio water index,SRWI)估算EWT(r = 0.85)和利用NDWI估算DMC(r = 0.27)均表现出较高的精度,因此被用于构建FMC的分步估算模型.直接验证结果显示,FMC估算值与实测值之间的相关系数为0.79(显著性水平P < 0.0001),说明二者存在显著的相关性;间接验证结果表明,FMC与SPEI的变化趋势一致,但在时间上存在滞后效应.在此基础上,本研究探讨了全球植被FMC的时空动态变化.结果显示,FMC存在显著的季节性变化,南北半球的不同植被类型之间FMC分布特征存在显著差异.热带地区全年FMC值较高,且变化幅度较小,温带地区的FMC在春夏季节明显上升,极地和干旱地区FMC全年较低.中高纬度地区的FMC受季节性极端气候事件的显著影响,低FMC区域与火灾高发区域相吻合.常绿森林全年维持较高的含水量,而落叶林和草地的FMC则随季节显著波动.此外,研究还揭示了FMC异常值的空间分布特征,异常值分析反映了气候和光照等因素对植被水分状况的显著影响,进一步揭示了极端气候对FMC的影响.本研究生成的2010—2022年MODIS每月全球FMC时空分布图,揭示了不同地区和植被类型的FMC分布特征,为制定高火险区域的科学防控策略提供了重要依据,对于森林火灾防治和生态系统管理具有重要的科学意义和应用价值.

     

    Abstract: Forest fires are major natural disasters and key to the loss of forest biodiversity and degradation of ecosystem functions. Fuel moisture content (FMC) is closely related to occurrence of forest fires and is a critical parameter in forest fire risk assessment and in calculating fire spread rates. In this study, we used Google Earth Engine (GEE) platform to generate spatiotemporal distribution maps of global FMC and analyzed spatiotemporal differences in FMC across various regions and vegetation types. First, ground-measured data and spectral indices are used to construct empirical estimation models for equivalent water thickness (EWT) and dry matter content (DMC) for forest and grassland vegetation types, respectively. Then, EWT and DMC are used as intermediate variables, to develop a stepwise estimation model for FMC, to ultimately produce global monthly FMC spatiotemporal distribution maps for the period from 2010 to 2022. To assess estimation accuracy, both direct and indirect validations are conducted by integrating site data (Global-LFMC) and standardized precipitation evapotranspiration index (SPEI) data. For forests, normalized difference infrared index (NDII) is used to estimate EWT (r = 0.89) and normalized difference water index (NDWI) is used to estimate DMC (r = 0.26). For grasslands, simple ratio water index (SRWI) is used to estimate EWT (r = 0.85) and NDWI used to estimate DMC (r = 0.27), both yielding high accuracy. Thus, these methods are used to construct stepwise estimation model for FMC. Direct validations indicate that the correlation coefficient between estimated FMC and measured values is 0.79 (with a significance level of P < 0.000 1), demonstrating significant correlation. Indirect validations reveal that FMC exhibits a trend consistent with SPEI, albeit with a temporal lag. The spatiotemporal dynamics of global vegetation FMC is then further explored. FMC shows significant seasonal variations and marked differences between the Northern and Southern Hemispheres as well as among different vegetation types. In tropical regions, FMC values remain relatively high throughout the year with minimal fluctuations. In temperate regions, FMC values increase significantly during the spring and summer. In polar and arid regions, FMC values are relatively low year-round. In mid-to-high latitude areas, FMC is significantly influenced by extreme seasonal climate events. Areas with low FMC coincide with regions of high fire occurrence. Evergreen forests maintain high moisture content throughout the year, whereas FMC in deciduous forests and grasslands fluctuates markedly with seasons. Spatial distribution characteristics of FMC outliers are confirmed. Analysis of these outliers reflects the significant impact of factors such as climate and sunlight on vegetation water status, further elucidating influence of extreme climate on FMC. MODIS-derived global monthly FMC spatiotemporal distribution maps generated for 2010–2022 reveal FMC distribution characteristics across different regions and vegetation types, providing ground for formulating scientific prevention and control strategies in high fire-risk areas, and they are of significant values for forest fire prevention and ecosystem management.

     

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