Spatiotemporal Changes of Vegetation Cover in Response to Climate Change on the Tibetan Plateau

The Tibetan Plateau is one of the most important ecological barriers in China.Resolving the internal relations and dynamics ruling the association between regional vegetation and climate change is important to understand and protect the regional ecosystems.Based on vegetation,temperature and precipitation data of the Tibetan Plateau from 2001 to 2010,we analyze the spatial and temporal variations of vegetation cover over the past 10 years and discuss the vegetation response to climate change using empirical orthogonal function and singular value decomposition.Our results reveal the following：（1） vegetation cover gradually decreases from the southeast to the northwest of the Tibetan Plateau; （2） vegetation cover has increased on the Tibetan Plateau over the past 10 years,mainly in the central and eastern zones; and （3） a significant positive relationship was suggested between vegetation cover during growing season and the temperature in the entire region and with precipitation in the central and southern zones.     

中国东部浅水湖泊参照状态确定的非参数方法

根据中国东部浅水湖泊受人类活动影响较严重的情况,将季节分解的非参数局部线性回归模型、频率分析和几何分块自助法有机结合,提出了一种基于非参数方法的湖泊参照状态确定的新方法。该方法首先将季节分解模型用于湖泊营养盐及其响应物的观测值,选取出适合用于推断参照状态的时间段;其次使用频率分析法分析此时间段内的观测值,并给出湖泊总氮、总磷和叶绿素a的参照状态值;最后用几何分块自助法给出各自的置信区间。该方法能有效克服前人提出方法的缺点。以太湖为例,采用该方法推断了参照状态浓度,总氮为0.78mg/L,总磷为0.030mg/L,叶绿素a为2.63μg/L;给出相应的95%置信区间分别为0.57~0.83mg/L、0.025~0.046mg/L和1.86~2.65μg/L。该方法也可适用受人类活动影响较大的中国东部其他浅水湖泊。     

基于EWT-分量阈值与SVD的微震信号降噪方法

针对微震信号采集过程中存在大量不同频率的干扰信号,导致信号初至拾取难度大的问题,提出一种经验小波变换（EWT）结合分量阈值重构规则及奇异值分解（SVD）技术对微震信号进行降噪的方法。该方法利用EWT自适应分解和抗模态混叠的特点分解微震信号,得到各分量信号。对于高信噪比信号,选取相关系数大于0.3且方差贡献率大于15%的固有模态分量进行重构,具有较好的降噪效果;对于低信噪比信号,在高信噪比降噪方法的基础上利用SVD去除高频分量中的噪声,并与EWT分解出的低频有效分量进行重构。实验表明,对不同信噪比的构造信号与实际微震信号进行降噪,信噪比均得到明显改善;以修改的能量比法和分形维数法拾取实际微震降噪信号初至到时作为验证,拾取相对误差均小于1%,证明了本文降噪方法的有效性。     

中国区际价值链协作的网络特征与空间格局及其演变——基于增加值分解和复杂网络的分析

利用增加值分解和复杂网络分析方法,基于2007年、2010年和2012年的省际投入产出表,构建中国区际价值链协作网络,考察区际价值链协作的网络特点与空间格局及其演变。结果表明,中国区际价值链协作存在显著的网络特点,显示出小世界网络特征;各地区的网络中心性有明显的地区差异,且该差异在不断缩小,而工业化水平等是引起节点网络中心性地区差异的重要因素。节点聚类和社区划分的结果发现,中国区际价值链协作网络存在3~4个网络社区,地区间的局部网络互动显著;且各网络社区内部的节点地区表现出显著的空间毗邻与地理成片,特别是上海等8个地区在网络中的局部互动最为稳定。     

毛乌素沙地东南缘植被NDVI时空变化及其对气候因子的响应

了解植被生长对气候变化的响应是厘清生态系统动态关系的重点。基于1990—2018年气象数据和归一化植被指数（NDVI）,应用偏相关分析与地理探测器等方法,分析了在生长季,毛乌素沙地东南缘不同类型植被年均NDVI的变化趋势,探讨了年均气温与年总降水量对各类型植被的影响。结果表明：（1） 1990—2018年生长季研究区植被年均NDVI显著与极显著增加面积达97.9%,整体生态环境质量大幅度改善。2005年之前植被年均NDVI增速缓慢,此后以0.011·a^-1的速率发生了突变增加,其中灌丛类植被年均NDVI增长幅度最大。（2） 2000年为年总降水量与年均气温的趋势突变点,突变前年总降水量以-5.510 mm·a^-1的速率减少,此后以5.541 mm·a^-1的速率增加,且主要依赖于大雨雨量的增加;年均高温与年均低温在突变前上升速率分别为0.122 ℃·a^-1与0.230 ℃·a^-1,突变后,年均高温下降速率为-0.014 ℃·a^-1,而年均低温上升速率为0.022 ℃·a^-1。（3） 在植被年均NDVI缓慢增长阶段（1990—2005年）,年均低温对植被影响较大,与不同类型植被年均NDVI多呈显著正相关;在植被年均NDVI快速增长阶段（2006—2018年）,年总降水量与不同类型植被年均NDVI呈显著正相关,大降雨事件的频发使得降水量对于植被的生长起主导作用。年总降水量与年均气温尤其是年均低温的交互作用是促进植被生长的关键。     

ParCYCLIC: finite element modelling of earthquake liquefaction response on parallel computers

This paper presents the computational procedures and solution strategy employed in ParCYCLIC, a parallel non‐linear finite element program developed based on an existing serial code CYCLIC for the analysis of cyclic seismically‐induced liquefaction problems. In ParCYCLIC, finite elements are employed within an incremental plasticity, coupled solid–fluid formulation. A constitutive model developed for simulating liquefaction‐induced deformations is a main component of this analysis framework. The elements of the computational strategy, designed for distributed‐memory message‐passing parallel computer systems, include: (a) an automatic domain decomposer to partition the finite element mesh; (b) nodal ordering strategies to minimize storage space for the matrix coefficients; (c) an efficient scheme for the allocation of sparse matrix coefficients among the processors; and (d) a parallel sparse direct solver. Application of ParCYCLIC to simulate 3‐D geotechnical experimental models is demonstrated. The computational results show excellent parallel performance and scalability of ParCYCLIC on parallel computers with a large number of processors. Copyright © 2004 John Wiley & Sons, Ltd.     

MULTI-SCALE DECOMPOSITION OF BOUGUER GRAVITY ANOMALY AND SEISMIC ACTIVITY IN NORTH CHINA

1　IntroductionThecrustalstructureofNorthChinahasbeenstudiedalotwithgravitymethod[1 ,2 ] .Theresultsareundoubtedlyofgreatsignificancetoabetterun derstandingofthecrustalstructureofthisregion .Butsofar,neithervertical/lateraldistributionscalesnorcharacteristicsofanomalousdensitybodiesofrockmediaintheregionhavebeenrevealedsystem aticallyintermsofrockdensity .Assurfacegravityanomalyreflectstheoveralleffectofgravityanoma liesofsubsurfaceanomalousgeologicalbodiesatdif ferentdepths,ondifferentscales…     

基于全极化Radarsat-2影像的建筑物震害评估

利用震后灾区全极化SAR影像可快速提取建筑物震害信息,为应急救援的快速有效实施提供重要的灾情信息支持。本文建立基于极化散射矩阵的Pauli-Wishart监督极化分类的建筑物震害信息提取和以街区为尺度的震害程度评估方法,包括Pauli分解、Wishart监督分类和遥感震害指数提取,并利用玉树县城区2010年4月14日青海玉树7.1级地震震后全极化Radarsat-2影像,提取了建筑物震害信息。经统计,确定结果总体分类精度达到0.81,Kappa值为0.61,表明本文提取建筑物震害的方法是可行的。     

基于MATLAB的定点形变观测数据时频分析软件设计及应用研究

为方便台站工作人员快速准确分析定点形变观测数据的时频响应特征,利用MATLAB软件研发了基于小波分析和同步挤压时频变换的交互式数据处理与成图软件包。该软件包遵循模块化设计原则,利用导入模块读取从中国地震前兆台网数据处理系统下载的原始数据,利用预处理模块对缺失数据进行插值补全,利用小波分解与重构模块从原始数据中提取待分析的目标信号分量,利用同步挤压时频分析模块对提取目标进行高精度时频分析,并在各模块关键节点中增加绘图功能,全部处理过程采用绘图-参数输入-绘图交互的方式进行,参数灵活可调,且每步计算结果直观清晰。应用该软件包对2020年1月至2021年6月西昌小庙台DSQ型水管仪和SS-Y型伸缩仪整时值采样数据进行固体潮时频计算,并与理论固体潮时频结果进行对比,结合时频辅助分析方法,从时频谱角度初步评价了2套仪器观测资料的质量情况,为台站日常数据跟踪分析提供了参考。     

A general-purpose framework for parallel processing of large-scale LiDAR data

ABSTRACT

Light detection and ranging (LiDAR) data are essential for scientific discoveries such as Earth and ecological sciences, environmental applications, and responding to natural disasters. While collecting LiDAR data over large areas is quite possible the subsequent processing steps typically involve large computational demands. Efficiently storing, managing, and processing LiDAR data are the prerequisite steps for enabling these LiDAR-based applications. However, handling LiDAR data poses grand geoprocessing challenges due to data and computational intensity. To tackle such challenges, we developed a general-purpose scalable framework coupled with a sophisticated data decomposition and parallelization strategy to efficiently handle ‘big’ LiDAR data collections. The contributions of this research were (1) a tile-based spatial index to manage big LiDAR data in the scalable and fault-tolerable Hadoop distributed file system, (2) two spatial decomposition techniques to enable efficient parallelization of different types of LiDAR processing tasks, and (3) by coupling existing LiDAR processing tools with Hadoop, a variety of LiDAR data processing tasks can be conducted in parallel in a highly scalable distributed computing environment using an online geoprocessing application. A proof-of-concept prototype is presented here to demonstrate the feasibility, performance, and scalability of the proposed framework.