Knowledge Resource Center for Ecological Environment in Arid Area
| 咸海湖盆盐/沙尘变化过程及其影响因素遥感研究 | |
| 其他题名 | Research on the Variation Process and Influencing Factors of Salt or Dust Events Originated from the Aral Sea Lake Basin Based on Remote Sensing |
| 沈浩 | |
| 出版年 | 2018 |
| 学位类型 | 博士 |
| 导师 | 吉力力 |
| 学位授予单位 | 中国科学院大学 |
| 中文摘要 | 咸海作为亚洲中部干旱区为数不多的超大型内陆湖泊之一，其生态功能在区域尺度上的重要性不言而喻。自上世纪60年代开始，在流域大规模开展灌溉农业生产以及气候变化的综合作用下，咸海水域发生灾难性萎缩，大面积湖床干涸暴露于地表，形成富含盐分的新型荒漠。这片荒漠由于积累了巨厚的湖相或海相沉积物，在强物理风化作用下，干裂破碎形成松散颗粒物质，受地带性强风的频繁侵蚀，极易形成裹挟大量盐类物质的沙尘暴，对咸海区域的生态安全和周边居民的生理健康构成巨大威胁。已经得到广泛研究证实风蚀盐/沙尘及形成的气溶胶通过多种形式，直接或间接影响地气能量交换及全球物质循环过程，在地球表观系统中起着至关重要的作用。针对咸海湖盆频发的盐/沙尘灾害现象，本文基于地面调查、遥感和GIS技术，从中亚五国所在区域大气气溶胶的大背景调查出发，首先对该地区气溶胶的时空分布特征及其主要来源作了探讨，并重点研究了气溶胶热点区——咸海湖盆的气溶胶变化。为查明与盐/沙起源密切相关的下垫面状况，借助多尺度遥感数据研究了咸海湖盆尺度地表覆被变化及演变趋势。在利用MODIS遥感影像监测盐/沙尘事件中，针对多种沙尘监测指数所存在的阈值不稳定的问题，提出指数改进方案，通过监测效果对比验证了改进指数的优异性，并由此获取自2000年以来起源于咸海湖盆的盐/沙尘暴序列以及点源的空间分布。最后通过建立包括地表覆被类型、植被覆盖度、地形因子以及近地表风速等风蚀影响因素的综合评价体系，对湖盆尺度的风蚀风险进行评估和制图。同时结合HYSPLIT模式，模拟咸海湖盆盐/沙尘的扩散传输路径和影响范围。主要研究结论如下：（1）气溶胶指数（AI）和气溶胶光学厚度（AOD）均显示2005年以来，中亚地区气溶胶整体水平呈明显上升趋势，气溶胶高值主要出现在春季和夏季，具体指4、5、6、7四个月份。其中咸海及其周边荒漠是中亚气溶胶的热点，表明气溶胶主要来源于沙尘和盐尘。咸海湖盆作为气溶胶高值中心，从1979年开始AI水平一直在上升，增幅达100%，AOD从2002年至2016年的年均增幅也达到3.12%。（2）基于Landsat数据的地表覆被变化结果表明，1977年至2015年咸海水域面积减少82.45%，形成大面积含盐表层（盐壳和盐土）和其它裸地，荒漠化面积达到47566.45 km2（占湖盆面积的70.28%），植被覆盖仅占15.38%。根据Markov-CA（马尔科夫-元胞自动机）模型的预测，若以过去40年来的地表覆被转化概率，2025年咸海东南部将完全干涸。针对MODIS时序数据的集成学习分类实验表明相对于AdaBoost、Bagging和随机森林算法，旋转森林算法能显著提高分类精度。基于旋转森林方法的2000～2015年分类结果揭示了16年间最显著的地表覆被变化是咸海东南部水体。覆被变化的驱动力分析表明，引水灌溉等人文因素占主导作用，区域气候变化，尤其是明显升温则在一定程度上加剧了咸海湖盆的荒漠化进程。（3）通过线性波谱分离方法，引入沙尘光学浓度(α)改进NDDI所形成的EDI，在与BTD、NDDI、MEDI和BADI几种通用沙尘监测指数对典型盐/沙尘事件监测效果的对比评估中， EDI有效解决了监测阈值不稳定的问题，而且与AOD的相关性最高，在识别沙尘空间分布和浓度方面的表现更好，适用于长距离传输的沙尘事件监测。由EDI监测并建立的2000～2016年的盐/沙尘暴事件序列表明咸海湖盆的盐/沙尘活动一直很活跃，年均出现频率达到15.3次，峰值出现在2014年达25次。由高值频率法分析AI和AOD产品的结果显示，东部早期干涸湖岸是盐/沙尘点源的集中分布地带。（4）根据HYSPLIT前向径迹模拟，起源于咸海湖盆的盐/沙尘传输扩散模式的季节变异较大，在盐/沙尘暴多发的春季和夏季，其中春季41%向南部扩散，44%向东北方向扩散；夏季53%向西南方向扩散，总体上看，咸海南部受盐/沙尘影响最为严重。综合地形、地表覆被类型、植被覆盖度和近地表风速等四种风蚀影响因素的风蚀风险评估模型及制图显示，咸海东部风蚀风险最为严重，风蚀可能性高达93%，其次是中部（82%）和南部（67%）。 |
| 英文摘要 | Aral Sea, one of the largest inland lakes located in the Central Asian Arid Zone, plays a very important role in matainning the ecological functions on the regional scale. However, dramatic desciccation has occurred on this huge sea-lake because of overexploitation of water resources for agricultural irrigation from the whole Aral Sea drainage basin since 1960s. Then the special saline desert Aralkum formed on the vast exposed dry lake bed. It has accumulated huge amounts of poisonous sediments and become a major source for salt and other chemical dust, which has generated frequent salt or dust events and been posing great threat to the regional ecological environment and human health in Central Asia. In addition, extensive studies have confirmed that aeolian salt, dust and aerosols are very important in the earth surface system through various forms, directly or indirectly affecting the energy balance and material cycle of the earth.To this end, we organized this thesis to fully comprehend the salt or dust events orginated from the Aralkum desert. As mentioned above, dust activities would creat much atmospheric aerosols, therefore the temporal and spatial distribution characteristics of aerosol and its main sources in Central Asia were firstly analyzed based on ground survey data, remote sensing methods as well as GIS technique. Since the outbreak of dust events are closely associated with the condition of land surface, then the land cover change and its evolution trend on the scale of Aral Sea lake basin was obtained by analyzing multi-scale remotely-sensed imageries. In the salt/dust event detection based on MODIS imageries an newly improved index was proposed to resolve the common problem of unfixed threshold. And its advantage was confirmed through comparisons of salt/dust events detection performance with several widely-used dust monitoring indexes, then built the sequence of salt/dust storms originated from the Aral Sea basin since 2000. Finally, we established a comprehensive wind erosion risk evaluation model which involvs in land surface cover types, vegetation coverage, terrain factors and near surface wind regime, and performed the evaluation and mapping of the deflation risk on the basin scale. Additionally, the salt/dust transport patterns was simulated via the HYSPLIT model.We could obtain the following major conclusions：(1) analysis of AI and AOD showed that the overall level of aerosol in Central Asia increased significantly since 2005, and mainly took placed in late spring and summer, specifically April, May, June and July. The Aral Sea and the surrounding deserts were the hot spots of aerosols in Central Asia, indicating that the aerosol mainly comes from dust and salt dust. As an aerosol high value center, the AI level of the Aral Sea Lake Basin has been on the rise from the beginning of 1979, increased nearly by 100%, and the annual increase of AOD from 2002 to 2016 had reached 3.1% as well. (2) Landsat data based land cover change results showed that the water body of Aral Sea decreased by 82.45% from 1977 to 2015, directly forming large area with saline surface layer (namely salt crust and soil) and other bare land, hence the desertification area reached up to 47566.45 km2 (70.28% of the lake basin), while vegetation covers only accounted 15.38%. And according to the simulation of Markov-CA model, the southeastern part of Aral Sea would be completely dry by 2025. Experiments on ensemble learning for MODIS time series data showed that rotation forest algorithm could significantly improve classification accuracy in contrast with AdaBoost, Bagging, and random forest ensemble learning methods. The classification results of 2000~2015 years based on rotation forest revealled the most significant land cover change was the water body of Southeastern Aral Sea durng the 16 years. As to the drivers of the LUCC, to some extent climate change did increase the water incomings as well out-comings of the Aral Sea. But the most dramatic land use changes were driven by the rapid and massive expansion of irrigation, water diversion and conversion of desert rangelands into irrigated croplands. (3) By comparisons of several common dust monitoring indexes like BTD, NDDI, MEDI and BADI on the detection performance of typical dust events, then introduced dust optical concentration (α) to improve NDDI and propose EDI, the results showed that EDI and AOD had the highest correlation, and EDI had the better performance in identifying the spatial distribution and concentration of dust, long-distance-transport dust events monitoring, numerical analysis of dust outbreaks, transport and deposition process. And salt/sand storm events sequence in 2000~2016 years built by EDI monitoring showed that salt/dust activity had been very active in the Aral Sea basin, and the peak value of frequency reached 25 in the year of 2014. High value frequency analysis based on AOD and AI revealed that the majority of point sources for dust events were located in the eastern former lake shore.(4) The HYSPLIT trajectory mode revealled that the salt/dust diffusion model transported from the Aral Sea lake basin showed large seasonal variability. Focused on the two seasons with high frequencies of salt/dust storms, in spring 41% of the dust spread to the south, 44% spread to the northeast, and 53% spread to the southwest in summer. Overall, the southern to the Aral Sea sufferred the most severe salt/dust. The integration of land cover types, vegetation coverage, terrain, and the near surface wind speed of wind erosion risk assessment model and mapping showed that the eastern part of Aral Sea had the highest wind erosion risk, the possibility was of up to 93%, followed by the central part (82%) and southern part (67%). |
| 中文关键词 | 盐/沙尘暴 ; 土地覆被变化 ; 沙尘监测指数 ; 风蚀险 ; 咸海湖盆 |
| 英文关键词 | Salt/dust storms Land surface dynamics Dust index Risk of deflation Aral Sea lake basin |
| 语种 | 中文 |
| 国家 | 中国 |
| 来源学科分类 | 自然地理学 |
| 来源机构 | 中国科学院新疆生态与地理研究所 |
| 资源类型 | 学位论文 |
| 条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/288181 |
| 推荐引用方式 GB/T 7714 | 沈浩. 咸海湖盆盐/沙尘变化过程及其影响因素遥感研究[D]. 中国科学院大学,2018. |
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