Flood and landslide applications of near real-time satellite rainfall products

Floods and associated landslides account for the largest number of natural disasters and affect more people than any other type of natural disaster. With the availability of satellite rainfall analyses at fine time and space resolution, it has also become possible to mitigate such hazards on a near-global basis. In this article, a framework to detect floods and landslides related to heavy rain events in near-real-time is proposed. Key components of the framework are: a fine resolution precipitation acquisition system; a comprehensive land surface database; a hydrological modeling component; and landslide and debris flow model components. A key precipitation input dataset for the integrated applications is the NASA TRMM-based multi-satellite precipitation estimates. This dataset provides near real-time precipitation at a spatial-temporal resolution of 3 h and 0.25° × 0.25°. In combination with global land surface datasets it is now possible to expand regional hazard modeling components into a global identification/monitoring system for flood/landslide disaster preparedness and mitigation.     

Use of satellite remote sensing data in the mapping of global landslide susceptibility

Satellite remote sensing data has significant potential use in analysis of natural hazards such as landslides. Relying on the recent advances in satellite remote sensing and geographic information system (GIS) techniques, this paper aims to map landslide susceptibility over most of the globe using a GIS-based weighted linear combination method. First, six relevant landslide-controlling factors are derived from geospatial remote sensing data and coded into a GIS system. Next, continuous susceptibility values from low to high are assigned to each of the six factors. Second, a continuous scale of a global landslide susceptibility index is derived using GIS weighted linear combination based on each factor’s relative significance to the process of landslide occurrence (e.g., slope is the most important factor, soil types and soil texture are also primary-level parameters, while elevation, land cover types, and drainage density are secondary in importance). Finally, the continuous index map is further classified into six susceptibility categories. Results show the hot spots of landslide-prone regions include the Pacific Rim, the Himalayas and South Asia, Rocky Mountains, Appalachian Mountains, Alps, and parts of the Middle East and Africa. India, China, Nepal, Japan, the USA, and Peru are shown to have landslide-prone areas. This first-cut global landslide susceptibility map forms a starting point to provide a global view of landslide risks and may be used in conjunction with satellite-based precipitation information to potentially detect areas with significant landslide potential due to heavy rainfall.     

基于高分卫星遥感数据的冀东地区矿山开发现状及环境问题研究

为了基本查明冀东地区矿产资源开发过程中占损土地、恢复治理和矿山环境现状及变化趋势,利用国产高分遥感卫星数据作为主要数据源,辅以野外调查和走访,对该区矿产资源开发和矿山环境问题进行了调查与研究.在广泛收集基础地质资料和遥感资料基础上,结合野外验证建立了该区矿山开发占地的解译标志;利用ArcGIS平台对矿山占损土地进行了统计与变化分析;对区内采矿引起的植被破坏、水体污染和采空沉陷等系列矿山环境问题进行了调查与研究.结果表明:①区内2016年度矿山占损土地总面积为49755.47 hm2,占研究区总面积的2.26%,且有逐年扩大趋势;②相较开发占损土地状况,区内矿山环境恢复治理程度低,治理进程滞后,2016年治理率仅为4.7%;③区内206个金属矿开采点距离重要水源较近,对水体安全构成威胁;④采矿引起的采空沉陷面积较大,影响周边村庄和生态环境稳定性,直接影响区域总面积达24351 hm2;⑤国产高分遥感卫星数据具有空间分辨率高、快速、经济等特点,在矿山开发占地和矿山环境调查研究中可发挥重要作用.     

1986—2015年长江源各拉丹冬地区冰川变化遥感监测研究

以长江源各拉丹冬为研究区,针对该地区地物特点,选取了1986—2015年间云量较少、成像质量较高的相关卫星影像作为数据源,在充分了解环境特征与影像特点的基础上,基于"波段阈值比值法",通过人机交互调整阈值,对大范围冰川区域进行快速边界提取,并基于提取结果,结合数字高程数据、气象数据等相关数据展开了分析。结果表明:1986—2015年间研究区冰川面积减小92.06km2,减少速率为0.33%·a^-1;其中1986—1994年、1994—2001年、2001—2009年、2009—2015年分别减少32.95km2、27.37km2、13.11km2和18.63km2,减少速率分别为0.47%·a^-1、0.41%·a^-1、0.17%·a^-1和0.34%·a^-1。同时,依据空间组织方式进行了研究区冰川变化的分区分析,结果表明在不同分区、不同规模上该地区冰川变化呈现出不同的趋势;部分区域内冰川的降级、分裂现象较为明显,对不同等级规模冰川的变化趋势有一定影响。研究区冰川面积的坡向变化以东...