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1.
On 25th January 2019, the tailings dam of the Brumadinho iron mine operated by Vale S/A failed catastrophically. The death toll stood at 259 and 11 people remained missing as of January 2020. This tragedy occurred three years after Mariana’s tailings dam rupture – the most significant tailing dam disaster in Brazilian history. Thus far, a systematic investigation on the cause and effect of the failure has yet to be conducted. Here, we use satellite-driven soil moisture index, multispectral high-resolution imagery and Interferometric Synthetic Aperture Radar (InSAR) products to assess pre-disaster scenarios and the direct causes of the tailings dam collapse. A decreasing trend in the moisture content at the surface and the full evanescence of pond water through time (2011–2019) suggest that the water was gradually penetrating the fill downwards and caused the seepage erosion, saturating the tailings dam. Large-scale slumping of the dam (extensional failure) upon the rupture indicates that the materials of the fill were already saturated. InSAR measurements reveal a dramatic, up to 30 cm subsidence in the dam (at the rear part) within the past 12 months before the dam collapse, signifying that the sediments had been removed from the fill. Although the information on the resistance level of the tailings dam to infiltrations is not available, these pieces of evidence collectively indicate that the seepage erosion (piping) is the primary cause for the chronic weakening of the structure and, hence, the internal “liquefaction” condition. Upon the collapse, the fully saturated mud tailings flowed down the gentle slope area (3.13 × 106 m2), where 73 % were originally covered by tree, grass or agricultural tracts. The toxic mud eventually reached the Paraopeba River after travelling 10 km, abruptly increasing the suspended particulate matter (SPM) concentration and the toxic chemical elements in the river, immediately affecting the local livelihoods that depend on its water. The Paraopeba River is a major tributary of the San Francisco River, the second-longest river in Brazil reaching the Atlantic Ocean. We anticipate that the environmental repercussions of this toxic seepage will be felt throughout the entire basin, especially riverine communities located downstream.  相似文献   
2.
Himawari-8静止气象卫星具有高空间分辨率、高观测频次和高时效特点,对于火点检测具有很强优势。对Himawari-8卫星的3.9μm和11.2μm两通道亮温进行了连续时相变化研究,得出两通道的亮温在时间上的变化差值稳定且规律明显。根据两通道的亮温时相特征,考虑白天可见光对3.9μm通道的影响,并结合火点产生时引起的亮温变化特征,提出了适用于晴空条件下改进的火点检测算法。在多处进行了此算法的实验,例如2018-11-27 T 16:40(UTC时)河北张家口市桥东区一化工厂附近发生的严重爆炸起火事件以及2019-02-28澳大利亚西南部发生的火灾事件,均快速有效的检测到了火点。实验表明,改进的火点检测算法能很好的进行火点检测,并能解决晨昏交界、冰雪下垫面、常规火源点、太阳耀光等火点检测的难题。  相似文献   
3.
Sentinel-2卫星落叶松林龄信息反演   总被引:1,自引:0,他引:1  
林龄结构信息能够有效反映区域森林群落不同生长阶段的固碳能力,对于评估森林生态系统的健康状况具有重要意义。本研究以中国温带典型优势树种落叶松林为研究对象,分别选择其芽萌动期、展叶期和落叶期时段的Sentinel-2影像,采用多元线性回归(MLR)、随机森林(RF)、支持向量机回归(SVR)、前馈反向传播神经网络(BP)以及多元自适应回归样条(MARS)等5种方法依次构建落叶松林龄反演模型。通过相关性分析首先确定最佳遥感反演物候期,并在此基础上根据相关性差异筛选出5个最优特征变量用于模型反演,分别为冠层含水量(CWC),归一化水体指数(NDWI),叶面积指数(LAI),光合有效辐射吸收率(FAPAR)和植被覆盖度(FVC)。研究结果表明,展叶期为落叶松林最佳遥感反演物候期。除植被衰减指数(PSRI)以及落叶期的NDVI、RVI外,落叶松林龄与各指标之间均呈负相关关系,其中与冠层含水量(CWC)的相关性最高,pearson相关系数达到-0.74(p<0.01)。此外,不同模型反演结果表明,随机森林模型(RF)为最佳落叶松林龄估测模型,其平均决定系数R2和平均均方根误差RMSE分别为0.89和2.91 a;多元线性回归模型(MLR)的林龄估测结果最差,其平均决定系数R2和平均均方根误差RMSE仅为0.57和5.69 a,非线性模型能更好的解释林龄与建模变量之间的关系。  相似文献   
4.
基于 SRP 概念模型的祁连山地区生态脆弱性评价   总被引:1,自引:0,他引:1       下载免费PDF全文
基于生态敏感性-生态恢复力-生态压力度(SRP)概念模型,从地形、气候、植被和社会经济 因子选取 8 个评价指标,利用遥感和 GIS 技术,采用主成分分析方法求取权重,对祁连山地区启动 水源涵养区生态环境保护和综合治理规划研究前后近 10 a 的生态脆弱性程度进行系统、定量地评 估,旨在揭示生态脆弱性的分布特征、时空演变及动因,为区域生态保护、资源利用和可持续发展 提供参考。结果表明:(1)从研究区生态脆弱性分布来看,祁连山地区主要以轻度和重度脆弱为 主,脆弱性程度从西北向东南地区逐渐减弱,西北地区植被覆盖度小,海拔高,生态环境较为恶劣 是导致脆弱性程度较高的原因;(2)祁连山地区 3 期生态脆弱程度呈逐渐下降趋势,综合指数分别 为 3.307、3.118 和 3.103;2005 年 生 态 脆 弱 性 较 高 ,极 度 脆 弱 面 积 为 28 610 km2,2010 年 下 降 为 11 723 km2,2015 年降低为 6 174 km2,极度脆弱面积逐渐减少;(3)从祁连山地区生态脆弱性演变动 因来看,8 个指标对生态脆弱性影响均较为显著,但在不同的时间影响程度各不相同,2005—2015 年 3 期数据中对生态脆弱性影响最大的均为植被指数,降水次之,地形因子影响最小。总体来看, 近年来祁连山地区生态脆弱性程度有所降低,但仍然需要加强保护力度,促进生态环境可持续 发展。  相似文献   
5.
陕北地区退耕还林还草工程土壤保护效应的时空特征   总被引:1,自引:1,他引:0  
This paper looks at the Green for Grain Project in northern Shaanxi Province.Based on remote sensing monitoring data,this study analyzes the locations of arable land in northern Shaanxi in the years 2000,2010 and 2013 as well as spatio-temporal changes over that period,and then incorporates data on the distribution of terraced fields to improve the input parameters of a RUSLE model and simulate and generate raster data on soil erosion for northern Shaanxi at different stages with a accuracy verification.Finally,combined with the dataset of farmland change,compared and analyzed the characteristics of soil erosion change in the converted farmland to forest(grassland)and the unconverted farmland in northern Shaanxi,so as to determine the project’s impact on soil erosion over time across the region.The results show that between 2000 and 2010,the soil erosion modulus of repurposed farmland in northern Shaanxi decreased 22.7 t/ha,equivalent to 47.08%of the soil erosion modulus of repurposed farmland in 2000.In the same period,the soil erosion modulus of non-repurposed farmland fell 10.99 t/ha,equivalent to 28.6%of the soil erosion modulus of non-repurposed farmland in 2000.The soil erosion modulus for all types of land in northern Shaanxi decreased by an average of 14.51 t/ha between 2000 and 2010,equivalent to 41.87%of the soil erosion modulus for the entire region in 2000.This suggests that the Green for Grain Project effectively reduced the soil erosion modulus,thus helping to protect the soil.In particular,arable land that was turned into forest and grassland reduced erosion most noticeably and contributed most to soil conservation.Nevertheless,in the period 2010 to 2013,which was a period of consolidation of the Green for Grain Project,the soil erosion modulus and change in volume of soil erosion in northern Shaanxi were significantly lower than in the previous decade.  相似文献   
6.
The acquisition of spatial-temporal information of frozen soil is fundamental for the study of frozen soil dynamics and its feedback to climate change in cold regions. With advancement of remote sensing and better understanding of frozen soil dynamics, discrimination of freeze and thaw status of surface soil based on passive microwave remote sensing and numerical simulation of frozen soil processes under water and heat transfer principles provides valuable means for regional and global frozen soil dynamic monitoring and systematic spatial-temporal responses to global change. However, as an important data source of frozen soil processes, remotely sensed information has not yet been fully utilized in the numerical simulation of frozen soil processes. Although great progress has been made in remote sensing and frozen soil physics, yet few frozen soil research has been done on the application of remotely sensed information in association with the numerical model for frozen soil process studies. In the present study, a distributed numerical model for frozen soil dynamic studies based on coupled water-heat transferring theory in association with remotely sensed frozen soil datasets was developed. In order to reduce the uncertainty of the simulation, the remotely sensed frozen soil information was used to monitor and modify relevant parameters in the process of model simulation. The remotely sensed information and numerically simulated spatial-temporal frozen soil processes were validated by in-situ field observations in cold regions near the town of Naqu on the East-Central Tibetan Plateau. The results suggest that the overall accuracy of the algorithm for discriminating freeze and thaw status of surface soil based on passive microwave remote sensing was more than 95%. These results provided an accurate initial freeze and thaw status of surface soil for coupling and calibrating the numerical model of this study. The numerically simulated frozen soil processes demonstrated good performance of the distributed numerical model based on the coupled water-heat transferring theory. The relatively larger uncertainties of the numerical model were found in alternating periods between freezing and thawing of surface soil. The average accuracy increased by about 5% after integrating remotely sensed information on the surface soil. The simulation accuracy was significantly improved, especially in transition periods between freezing and thawing of the surface soil.  相似文献   
7.
蒙其古尔铀矿床为伊犁盆地南缘大型层间氧化带砂岩型铀矿床,为查明该矿床含矿层中黄铁矿成因及其形成机制,探讨微生物参与铀成矿过程。文章对含矿层砂岩中黄铁矿与铀矿物矿物学特征、黄铁矿S同位素与碳酸盐胶结物的C-O同位素开展细致研究。研究表明:①蒙其古尔铀矿床中铀主要以铀矿物与吸附铀形式存在,吸附铀主要为有机质吸附铀,铀矿物以沥青铀矿为主,多与黄铁矿、炭屑共生;②蒙其古尔铀矿床含矿层砂岩中黄铁矿主要以自形晶、草莓状和不规则状集合体产出,多与沥青铀矿、碳酸盐胶结物共生,其中黄铁矿S同位素(δ~(34)S_(V-CDT)=-68.4‰~22.1‰)与碳酸盐胶结物的C-O同位素(δ~(13)C_(V-PDB)=-10.2‰~-7.4‰,δ~(18)O_(V-PDB)=-9.6‰~-5.8‰)分析表明黄铁矿具有细菌硫酸盐还原(BSR)与有机物热解2种成因,并探讨了这2种不同成因黄铁矿的形成机制。③结合前人研究成果,认为硫酸盐还原菌(SRB)参与蒙其古尔铀矿床铀成矿过程,以间接还原方式为主,在有机质、黏土矿物与颗粒表面吸附U(Ⅵ)的基础上,通过硫酸盐还原菌(SRB)还原SO_4~(2-)产生的H_2S将U(Ⅵ)被还原成U(Ⅳ),形成铀矿物。  相似文献   
8.
对取自渤海湾的307个表层沉积物进行了元素测试和粒度分析。渤海湾表层沉积物的常微量元素呈现4种组合:富集于粗粒沉积区的SiO_2、Na_2O亲碎屑元素组合;富集于细粒沉积区的以Al_2O_3和重金属元素为代表的亲黏土元素组合;与缺氧环境有关的MnO、V、TOC元素组合和与河流输入有关的陆源CaO、TiO_2元素组合。粒度粗细主导了渤海湾元素含量分布的整体格局;海域河流物源供应不同较大影响了渤海湾南部(富Na_2O、CaO和SiO_2)和北部(富Ba和P_2O_5)在元素含量上的差异;高流速潮流对海底的冲刷再分配导致曹妃甸南侧Sr、Ca元素的条带状富集异常;有机质在细粒沉积区的富集导致缺氧环境的形成和K_2O、Mn、V、自生黄铁矿的海洋自生化学沉积;人类活动导致以Pb为代表的重金属污染在河流入海口、港口及沿岸海域的元素分布异常;曹妃甸沙坝内侧的泻湖(海洋钙质生物沉积)与沙坝外侧水下岸坡(陆源碎屑沉积)的截然不同的物源,导致了独特地貌沉积环境下元素分布的局部差异。  相似文献   
9.
桑日群广泛分布于冈底斯火山岩浆弧的中东段,是新特提斯洋俯冲作用的代表性记录。本文对冈底斯中段厅宫地区桑日群比马组开展了碎屑锆石U-Pb同位素测年和地球化学特征研究,结果表明:比马组所含的碎屑锆石具有85.7~143.5Ma、160.2~191.2Ma、334.4~364.1Ma和904Ma四个年龄段,其中最年轻的碎屑锆石年龄为85.7±1.9Ma,结合古生物资料,认为比马组形成于早—晚白垩世,其物源主要来自于拉萨地块,尤其是冈底斯岩基剥蚀源区;岩石地球化学特征显示,比马组沉积主要来源于长英质岩石,是上地壳源区物质经风化剥蚀后搬运沉积形成。  相似文献   
10.
赵志飞 《地质与勘探》2020,56(2):328-336
青海省夏日哈-什多龙成矿远景区位于青藏高原东昆仑东段,西起都兰县夏日哈镇,东至兴海县什多龙一带,跨祁漫塔格北坡-夏日哈岩浆弧和北昆仑岩浆弧,是青海省重要的成矿区。为了在该区寻找新的成矿有利地段,本文在对该区区域地质背景、矿产特征及区内典型矿床地质特征系统研究的基础上,利用多元素衬值累加地球化学找矿方法,在区域上优选出一批找矿靶区。多元素衬值累加地球化学找矿方法首先收集远景区1∶5万水系沉积物测量成果,经过数据处理,绘制远景区衬值累计地球化学图,进而建立典型矿床找矿模式,最终通过对比分析,选出新的找矿靶区。对圈定的找矿靶区进行野外踏勘检查,均发现不同程度的矿化线索,为今后在该区进一步找矿起到了很好的指导作用。通过本次工作,发现了该方法在靶区圈定方面的优势:(1)可快速圈定异常,提高工作效率;(2)编制的地球化学图浓集中心更显著;(3)圈定的找矿靶区找矿成果显著,尤其针对陆相火山岩型多金属矿、斑岩型铜钼矿、矽卡岩型多金属矿找矿成果突出;(4)能直观反映背景变化趋势,有助于评价异常。  相似文献   
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