Mapping Soil Moisture as an Indicator of Wildfire Risk Using Landsat 8 Images in Sri Lanna National Park, Northern Thailand
Mapping Soil Moisture as an Indicator of Wildfire Risk Using Landsat 8 Images in Sri Lanna National Park, Northern Thailand
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DOI：
10.5539/jas.v8n10p107
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发表时间：
2016-09
期刊：
影响因子：
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通讯作者：
Kansuma Burapapol;R. Nagasawa
中科院分区：
文献类型：
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作者：
Kansuma Burapapol;R. Nagasawa
严重干燥的气候对泰国山火的发生起着重要作用。土壤水分亏缺加剧了干旱条件，导致野火燃烧得更猛烈、时间更长。利用温度植被干燥指数（TVDI）和归一化差异干旱指数（NDDI）估算旱季土壤湿度，探索其在野火风险评估中的应用。结果表明，归一化差异湿润指数（NDWI）和地表温度（LST）可用于TVDI计算。 NDWI/LST 和归一化植被指数 (NDVI)/LST 的散点图呈现出理论 TVDI 的典型三角形形状。然而，NDWI 与 LST 的相关性比 NDVI 更显着。为提取最大和最小LST（LSTmax、LSTmin）而进行的线性回归分析表明，NDWI 定义的LSTmax 和LSTmin 比NDVI 定义的LSTmax 和LSTmin 更好地满足共线性要求。因此，NDWI-LST关系更适合计算TVDI。这个修改后的指数称为 TVDINDWI-LST，与 NDDI 一起应用来建立土壤湿度估计的回归模型。土壤湿度模型与实际土壤湿度和模型生成的估计土壤湿度的一致性达到76.65%，满足统计要求。根据我们的模型估计的土壤湿度与叶片燃料湿度之间的关系表明，土壤湿度可以用作评估野火风险的补充数据集，因为土壤湿度和燃料湿度含量（FMC）在干燥条件下表现出相同或相似的行为。
Severely dry climate plays an important role in the occurrence of wildfires in Thailand. Soil water deficits increase dry conditions, resulting in more intense and longer burning wildfires. The temperature vegetation dryness index (TVDI) and the normalized difference drought index (NDDI) were used to estimate soil moisture during the dry season to explore its use for wildfire risk assessment. The results reveal that the normalized difference wet index (NDWI) and land surface temperature (LST) can be used for TVDI calculation. Scatter plots of both NDWI/LST and the normalized difference vegetation index (NDVI)/LST exhibit the triangular shape typical for the theoretical TVDI. However, the NDWI is more significantly correlated to LST than the NDVI. Linear regression analysis, carried out to extract the maximum and minimum LSTs (LSTmax, LSTmin), indicate that LSTmax andLSTmin delineated by the NDWI better fulfill the collinearity requirement than those defined by the NDVI. Accordingly, the NDWI-LST relationship is better suited to calculate the TVDI. This modified index, called TVDINDWI-LST, was applied together with the NDDI to establish a regression model for soil moisture estimates. The soil moisture model fulfills statistical requirements by achieving 76.65% consistency with the actual soil moisture and estimated soil moisture generated by our model. The relationship between soil moisture estimated from our model and leaf fuel moisture indicates that soil moisture can be used as a complementary dataset to assess wildfire risk, because soil moisture and fuel moisture content (FMC) show the same or similar behavior under dry conditions.