论文利用2005年Terra/MODIS卫星8天合成的250m地表反射率数据（MODIS Terra Sur-face Reflectance 8-Day L3 Global 250 m：MOD09Q1）构建的时间序列数据集，通过计算NDVI指数，结合典型地物的谱间特征，并借助SRTM数字高程数据，采用...论文利用2005年Terra/MODIS卫星8天合成的250m地表反射率数据（MODIS Terra Sur-face Reflectance 8-Day L3 Global 250 m：MOD09Q1）构建的时间序列数据集，通过计算NDVI指数，结合典型地物的谱间特征，并借助SRTM数字高程数据，采用多源信息提取的方法对洞庭湖地区2005年水域面积变化进行了动态监测。结合Terra/MODIS数据特点，提出了全年最大淹没时间指数的概念，并通过对该指数的构建，完成了对洞庭湖地区重点水域的淹没风险评价。结果表明：①文中提出的基于Terra/MODIS MOD09Q1数据的多源信息水体提取方法，通过更高空间分辨率ENVISAT/ASAR数据以及水文测站水位数据的检验表明，是切实可行的；②2005年洞庭湖地区水域面积变化特征总体表现为，在11-4月份期间水域面积较小，而在5-10月份期间较大。其中，4月份最小，9月份最大，两者相差了几乎1.5倍，受地区季节性降雨的年内分布规律及长江主汛期的影响显著；③通过全年最大淹没时间指数的计算发现，占研究区总面积84.13%的年内持久陆地和持久水域区域，基本上没有防洪压力，而剩余的15.87%的年内变化水域，由于潜在淹没风险的存在，则需要抗洪防险部门进行重点防控；④文中多源信息水面提取方法的实现以及全年最大淹没时间指数概念的提出，为今后更加深入地探讨三峡工程建成运营对洞庭湖地区水域变化以及江湖关系的影响奠定了基础。展开更多
This research presents the remote sensing data on hotspots in four national parks located in Chiang Mai province, Thailand: Sri Lanna National Park, Huai Nam Dang National Park, Doi Pahom Pok National Park, and Doi In...This research presents the remote sensing data on hotspots in four national parks located in Chiang Mai province, Thailand: Sri Lanna National Park, Huai Nam Dang National Park, Doi Pahom Pok National Park, and Doi Inthanon National Park. To mitigate the devastating impacts of these wildfires, effective monitoring and management strategies are necessary. Remote sensing technology provides a promising approach for mapping burnt areas and understanding fire regimes at a regional scale. The primary focus of this research is to employ the MODIS Aqua/Terra satellite system for obtaining historical remote sensing data on hotspots. The advantages of remote sensing include accurate identification and mapping of burnt areas, regular monitoring, rapid data acquisition, and historical data analysis. The MODIS sensor, specifically designed for fire monitoring, offers enhanced fire detection and diagnosis, multiple channels for qualitative and quantitative analysis, and precision positioning capabilities. The research results presented in the analysis contribute to the understanding of fire incidents and hotspot occurrences within the four national parks studied. This paper suggests the optimization of early detection of forest and land fires through the utilization of Artificial Intelligence (AI), presenting it as a recommendation for future endeavors. The research emphasizes the significance of implementing efficient policies and management strategies to effectively tackle the challenges associated with fires in these ecologically significant areas.展开更多
文摘论文利用2005年Terra/MODIS卫星8天合成的250m地表反射率数据（MODIS Terra Sur-face Reflectance 8-Day L3 Global 250 m：MOD09Q1）构建的时间序列数据集，通过计算NDVI指数，结合典型地物的谱间特征，并借助SRTM数字高程数据，采用多源信息提取的方法对洞庭湖地区2005年水域面积变化进行了动态监测。结合Terra/MODIS数据特点，提出了全年最大淹没时间指数的概念，并通过对该指数的构建，完成了对洞庭湖地区重点水域的淹没风险评价。结果表明：①文中提出的基于Terra/MODIS MOD09Q1数据的多源信息水体提取方法，通过更高空间分辨率ENVISAT/ASAR数据以及水文测站水位数据的检验表明，是切实可行的；②2005年洞庭湖地区水域面积变化特征总体表现为，在11-4月份期间水域面积较小，而在5-10月份期间较大。其中，4月份最小，9月份最大，两者相差了几乎1.5倍，受地区季节性降雨的年内分布规律及长江主汛期的影响显著；③通过全年最大淹没时间指数的计算发现，占研究区总面积84.13%的年内持久陆地和持久水域区域，基本上没有防洪压力，而剩余的15.87%的年内变化水域，由于潜在淹没风险的存在，则需要抗洪防险部门进行重点防控；④文中多源信息水面提取方法的实现以及全年最大淹没时间指数概念的提出，为今后更加深入地探讨三峡工程建成运营对洞庭湖地区水域变化以及江湖关系的影响奠定了基础。
文摘This research presents the remote sensing data on hotspots in four national parks located in Chiang Mai province, Thailand: Sri Lanna National Park, Huai Nam Dang National Park, Doi Pahom Pok National Park, and Doi Inthanon National Park. To mitigate the devastating impacts of these wildfires, effective monitoring and management strategies are necessary. Remote sensing technology provides a promising approach for mapping burnt areas and understanding fire regimes at a regional scale. The primary focus of this research is to employ the MODIS Aqua/Terra satellite system for obtaining historical remote sensing data on hotspots. The advantages of remote sensing include accurate identification and mapping of burnt areas, regular monitoring, rapid data acquisition, and historical data analysis. The MODIS sensor, specifically designed for fire monitoring, offers enhanced fire detection and diagnosis, multiple channels for qualitative and quantitative analysis, and precision positioning capabilities. The research results presented in the analysis contribute to the understanding of fire incidents and hotspot occurrences within the four national parks studied. This paper suggests the optimization of early detection of forest and land fires through the utilization of Artificial Intelligence (AI), presenting it as a recommendation for future endeavors. The research emphasizes the significance of implementing efficient policies and management strategies to effectively tackle the challenges associated with fires in these ecologically significant areas.
