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基于遥感的滑坡和洪水对土地覆盖变化影响的研究——以吉尔吉斯斯坦纳伦河盆地为例
Merim Pamirbek kyzy
Subtype硕士
Thesis Advisor艾里西尔·库尔班
2018-06-01
Degree Grantor中国科学院大学
Place of Conferral新疆乌鲁木齐
Degree Discipline理学硕士
Keyword纳伦河流域 土地覆盖 Landsat 洪水 滑坡 遥感
Abstract目前,包括吉尔吉斯斯坦在内的世界各国受突发事件的风险逐年增加。在吉尔吉斯斯坦,共有 5000 个山体滑坡区, 3103 条河流受泥沙影响。现有的风险和新的威胁要求在预防和应对紧急情况时寻求新的方法。本文介绍了自然灾害带来的后果:洪水和山体滑坡对纳伦河流域土地覆盖的影响。洪水和山体滑坡是造成人类,财产和环境灾难性损失的自然灾害之一。 不能避免和阻止它的发生,但可以通过有效途径减少其影响,如建立预警防范系统、基于社区的灾害管理培训等。通过绘制用于识别高风险区域的洪水和滑坡地图可以达到实现预警防范的目的。风险评估有利于城市基础设施建设者和风险管理者在极端天气中制定应对自然灾害的应急措施。本研究的目的是利用遥感卫星图像和地理信息系统工具结合气候,地质和河流流量数据,为纳伦河流域创建山体滑坡和洪水路线的风险图。在本研究中,使用了 Landsat TM / ETM + / OLI 和 SRTM DEM 数据。对洪水,滑坡和土地覆盖变化进行了准确性评估。最终所有使用的数据都叠加到ArcMap 中,以便为研究区编制滑坡和洪水风险地图。研究结果表明,洪水是研究区的主要自然灾害。根据结果显示,大约 10%的面积被归类为洪水高危区, 43%被归类为中等风险区, 34%被类归为低风险区,而只有 15%被归类为没有已知洪水风险区。对于山体滑坡测绘,对滑坡发生的 8 个因素(斜率,曲率,海拔, TWI, NDVI, NDSI,土地覆盖,降水)进行了统计分析。频率比模型和对比预测模型分别利用总数据的 72.1%和 69%进行预测分析。最后,对不同年份的土地覆盖变化进行了评估。创建了 500 米和 1000 米的河岸带缓冲区。结果显示: 500 米缓冲区发生了 39%-42%的变化, 1000 米缓冲区发生了 18%-21%。没有发生变化的区域约为 33%-37%。
Other AbstractIn recent years, the global risks of emergencies largely increased year by year.Nowadays, the Third World countries have difficulty meeting the high costs ofcontrolling natural hazards. In Kyrgyzstan Republic, there are more than 5000landslides sites, among them 3103 sites are mud flowing rivers. Hence, the existingrisks and new threats dictate to research for new approaches to controlling naturalhazards. Flooding and landslide are one of the annually natural disasters that causedisastrous consequences for human and environment. It is impossible to bypass allrisks or stop their morbidity, but it is possible to reduce their impact by using variousmethods, such as early warning systems, community-based disaster managementtraining, etc.In the present investigation, flood and landslides mapping were used toidentify high-risk areas which can be a powerful tool for this purpose. Comparison ofrisk will be beneficial for urban, infrastructure, risk management, and response tonatural disasters or emergency services during extreme and intense precipitation. Thepurpose of this study was to create a risk mapping of Flooding landslides and awatercourse for the Naryn River basin (NRB) by using temporary satellite images andGIS tools with a combination between climate data, geology, and river flow. In thisstudy, Landsat TM / ETM + / OLI and SRTM DEM data were used. Assessment ofthe accuracy of flood maps, landslides risk maps, and land cover changes were carriedout. All the data used in this study was finally merged into ArcMap to compile a finalhazard map for the area under investigation.The results of this study revealed that river floods are a major hazard for thestudied area which can be particularly classified to approximately 10% of the studiedsite can be classified as a high flood risk, 43% are assessed as a moderate risk, 34% asa low risk and only 15% are classified as no flood risk. In addition, for landslidesmapping, we used statistical analysis for eight causal factors such as slope, curvature,elevation, TWI, NDVI, NDSI, land cover, and precipitation. The analysis of thefrequency Ratio and Pairwise Comparison models predicted that about 72.1% and 69%of the empirical data, respectively which showed a close agreement between the twomodels These maps can be used to demonstrate the effectiveness of two-dimensionalstatistical model through the relationship between each factor with a resultantlandslide susceptibility. Our findings provide new insights into the evaluating of thevegetation changes between 2012 and 2017 by using satellite images and socioeconomic data. Spatial and temporal dynamics of vegetation changes showed thatland cover changed was very high in river bank (500-1000m) is about 39%-42%, andwhile the soil moisture is very high 33%-37%.
Subject Area地图学与地理信息系统
Language英语
Document Type学位论文
Identifierhttp://ir.xjlas.org/handle/365004/14952
Collection研究系统_荒漠环境研究室
Affiliation中国科学院新疆生态与地理研究所
First Author Affilication中国科学院新疆生态与地理研究所
Recommended Citation
GB/T 7714
Merim Pamirbek kyzy. 基于遥感的滑坡和洪水对土地覆盖变化影响的研究——以吉尔吉斯斯坦纳伦河盆地为例[D]. 新疆乌鲁木齐. 中国科学院大学,2018.
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