【作者】 红梅；

【导师】 都瓦拉；

【作者基本信息】 内蒙古师范大学 ， 地理学， 2022， 硕士

【摘要】 随着全球环境的快速变化导致草原火灾频繁发生,由草原火灾造成的危害日趋严重,特别是中蒙边境地区。活可燃物含水率定义为单位质量可燃物的水分含量,是影响野火行为的关键植被变量,它与火灾的点燃和传播高度相关。杜拉尔林场位于内蒙古自治区阿尔山西北部,处于下风口,易受西北风的影响下蒙古国火灾蔓延至该区域,使杜拉尔林场处于高火险区。本文以中蒙边境地区的阿尔山杜拉尔林场林缘草地活可燃物含水率为研究对象,利用2021年9月Radarsat-2双极化SAR数据、地面实测数据,通过构建半经验模型来反演草地活可燃物含水率。根据Sentinel-2卫星数据提取研究区的草地空间分布,最后在此基础上生成了林缘草地活可燃物含水率空间分布图并对其进行了特征分析。本文得出的结果如下:(1)根据水云模型耦合裸土线性模型构建反演草地活可燃物含水率的半经验模型。通过最小二乘法计算耦合模型中的经验系数。最后表明,此模型模拟得到的HH极化雷达后向散射系数与SAR影像提取的HH极化雷达后向散射系数的相关性较好,其中模型训练的决定系数(R~2)和均方根误差(RMSE)分别为0.57、1.137d B,模型验证的R~2=0.42,RMSE=2.002d B,而全部样本的R~2=0.44,RMSE=1.404d B。(2)利用查找表法(Look-up-table)对建立的半经验模型反向运算可实现草地活可燃物含水率的高精度反演。查找表算法反演的草地活可燃物含水率与实测草地活可燃物含水率的相关性较高。模型训练的R~2和RMSE分别为0.45、5.69%,模型验证的R~2和RMSE分别为0.44、5.47%,全部样本的R~2和RMSE分别为0.46、5.64%。因此表明,此半经验模型对于反演该区域的草地活可燃物含水率是切实可行的。(3)利用建立的查找表估算整个研究区域的草地活可燃物含水率。结果显示,中蒙边境林缘草地活可燃物含水率主要集中在35%-55%之间。其中含水率较低(<35%)地区主要分布在杜拉尔林场西南中蒙边境地区。而含水率较高(>55%)地区主要集中在北部和中部地区。更多还原

【Abstract】 With the rapid changes in the global environment,grassland fires frequently occur,and the harm caused by grassland fires is becoming more and more serious,especially in the border areas of China and Mongolia.Among them,live fuel moisture content,defined as the moisture content per unit mass of fuel,is a key vegetation variable affecting wildfire behavior because it is highly correlated with fire ignition and propagation.The Dural Forest Farm is located in the northwest of Arshan,Inner Mongolia Autonomous Region,and is at the downwind.It is vulnerable to the influence of the northwest wind and the Mongolian fire spreads to this area,making the Dural Forest Farm in a high fire hazard area.In this paper,the live fuel moisture content in the forest margin grassland of Arxan Dural Forest Farm in the Sino-Mongolian border area is used as the research object.Using the Radarsat-2 dual-polarization SAR data and the ground measured data in September 2021,a semi-empirical model was constructed for retrieving the water content of grassland live fuel.According to Sentinel-2 satellite data,the grassland spatial distribution in the study area was extracted,and finally,the spatial distribution map of forest margin grassland living fuel moisture content was generated and its characteristics were analyzed.The results obtained in this paper are as follows:(1)Based on the water cloud model and the bare soil scattering ray model,a semi-empirical model for inversion of grassland Fuel Moisture Content is constructed.There is a good correlation between the HH polarimetric radar backscatter coefficient obtained by model simulation and the HH polarimetric radar backscatter coefficient extracted from SAR images.Among them,the coefficient of determination(R~2)and root mean square error(RMSE)of model training were 0.57 and 1.137d B,respectively,R~2=0.42 and RMSE=2.002d B for model validation,while R~2=0.44 and RMSE=1.404d B for all samples.(2)Using the look-up-table method to reverse the established semi-empirical model can realize the high-precision inversion of the moisture content of the grassland living combustibles.There is a high correlation between the moisture content of grassland live fuel inverted by the look-up table algorithm and the measured grassland live fuel moisture content.The R~2 and RMSE of model training are 0.45 and 5.69%,respectively,the R~2and RMSE of model validation are 0.44 and 5.47%,respectively,and the R~2 and RMSE of all samples are 0.46 and 5.64%,respectively.Therefore,it is shown that this model is feasible for inversion of grassland live fuel moisture content in this area.(3)The established look-up table was used to invert the moisture content of grassland live fuels in the entire study area.The results showed that the moisture content of live combustibles in forest margin grassland in the border between China and Mongolia was mainly between 35%and55%.Among them,the areas with low moisture content(<35%)are mainly distributed in the border areas between China and Mongolia in the southwest of Dural Forest Farm.The areas with higher water content(>55%)are mainly concentrated in the northern and central regions.更多还原