中国近海及临近海域海浪的季节特征及其时间变化

利用1992年12月-2005年3月TOPEX卫星高度计资料,对中国近海波浪季节特征及其时间变化进行了分析。分析结果表明,冬季平均波高最大,台湾海峡、南海北部、中南半岛东南海域以及吕宋海峡外侧是冬季的大浪区;夏季平均波高最小;春、秋两季为过渡期。对冬季大浪所在区域波浪时间变化的研究表明,年变化是其主要时间变化特征,而季节内变化是该海区的另一重要特征,并且以5 a为周期的年际变化与ENSO事件有着很好的对应关系。     

New bounds for the height limit of a vertical slope

Using the finite element method, the static and kinematic methods of limit analysis provide tools to solve many stability problems in mechanics of continuous media. The classic problem of the height limit of a Tresca or Mises vertical slope subjected to the action of gravity stems naturally from this theory in plane strain. Although the exact solution to this problem remains unknown, the present work has produced precise bounds using the static and kinematic approaches conjointly: the height limit is now between 3·760 and 3·786 C/ γ, γ being the weight per unit volume and C the soil cohesion. These tests also show that both methods, used on current workstations with industrial optimization codes such as XPRESS or OSL, are capable of solving any plane problem of limit loads in geotechnics or in structural calculus. Copyright © 2000 John Wiley & Sons, Ltd.     

移动式三维激光扫描技术在隧道扫描中的应用研究

移动式三维激光扫描技术通过发射激光并以螺旋线形式对隧道进行全断面高密度扫描,初步获取隧道表面特征信息,再通过内业分析发射和接收激光信号的强度,可以获得隧道衬砌内表面的影像信息.使用这些数据信息,可以对隧道状态、病害、限界、变形、收敛情况等进行分析和评价.本文采用移动式三维激光扫描技术选取杭州地铁5号线一个车站和区间进行...     

砂性土细颗粒起动临界水力坡降计算方法

地下水渗流作用下内部不稳定砂性土将发生潜蚀现象,潜蚀作用引起的土体渗透破坏会对土工建筑物或地基造成不良影响。考虑土体有效应力和细颗粒应力折减,建立渗流场中细颗粒受力模型,根据极限受力平衡状态得到潜蚀过程中砂性土细颗粒起动临界水力坡降计算公式,并通过DEM-CFD耦合方法以及现有试验数据进行验证。结果表明：砂性土中细颗粒以滚动方式起动,起动临界水力坡降受渗流水流、土体特性以及颗粒自身特性共同影响;砂性土表层细颗粒起动临界水力坡降受埋深影响较大,埋深1 cm的细颗粒最高、最低起动临界水力坡降相差10.169%,埋深10 cm时差异减少至1.061%。该计算方法与数值模拟和渗流试验结果的最大标准误差分别为6.038%、11.211%,可以较为准确地预测砂性土细颗粒起动临界水力坡降。     

“关键资源地球化学勘查”专辑特邀主编寄语——关键元素分布与关键资源勘查

关键资源已经成为工业4.0和低碳能源不可替代的原材料。构成关键资源的元素有50余种, 这些元素是现代社会的先进制造、电子产品、低碳能源、国防安全、生物医药等必须的原材料, 因此也称为关键元素。地球化学勘查是研究和探测关键元素分布和超常富集, 寻找关键资源行之有效的方法。中国在关键元素的分布和深部探测地球化学领域走在了国际前列, 形成了从全球尺度、全国尺度、矿区尺度直到纳米尺度地球化学探测技术体系, 实现从二维到三维、从浅表矿到千米深度矿产探测能力的飞跃, 不仅圈定了一批全国稀土、锂、铀的超常富集区, 而且取得了多处深部金矿和铜矿勘查的重大突破。本专辑选择部分研究成果介绍了稀土元素、新能源金属(铀、锂、钴)、金、铜等关键资源地球化学勘查中取得的新进展和成果。为了响应国家号召“把论文写在祖国大地上”, 专辑作者把优质素材形成的论文发表在自己的杂志上, 作为本期特邀主编对他们的奉献精神表示衷心感谢和崇高的敬意！     

基于Google Earth遥感影像的城市建筑物高度反演

基于光学遥感影像中建筑物的阴影分布,推导了建筑物高度与阴影分布的关系函数,在此基础上构建了建筑物高度反演方法,并在北京市典型区进行了实例研究。结果表明：利用建筑物在夏季的阴影全长或在冬季可视部分的阴影长提取高度,分别有90.9%和84.8%的建筑物高度误差控制在实测值的5%以内;在提取居民区或高层建筑物的高度时应尽量采用夏季的遥感影像,减少阴影遮挡的情况;低矮建筑物的高度提取,宜采用阴影更为显著的冬季影像可视阴影部分进行反演计算;在缺乏卫星轨道等影像参数时,利用典型已知建筑的高度反推出相关参数,并推求同景影像中其他建筑物高度的方法是可行的,为利用城市代表性建筑物推求建筑群高度提供了可能。     

High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 data

Forest canopy height is an important indicator of forest carbon storage, productivity, and biodiversity. The present study showed the first attempt to develop a machine-learning workflow to map the spatial pattern of the forest canopy height in a mountainous region in the northeast China by coupling the recently available canopy height (H_canopy) footprint product from ICESat-2 with the Sentinel-1 and Sentinel-2 satellite data. The ICESat-2 H_canopy was initially validated by the high-resolution canopy height from airborne LiDAR data at different spatial scales. Performance comparisons were conducted between two machine-learning models – deep learning (DL) model and random forest (RF) model, and between the Sentinel and Landsat-8 satellites. Results showed that the ICESat-2 H_canopy showed the highest correlation with the airborne LiDAR canopy height at a spatial scale of 250 m with a Pearson’s correlation coefficient (R) of 0.82 and a mean bias of -1.46 m, providing important evidence on the reliability of the ICESat-2 vegetation height product from the case in China’s forest. Both DL and RF models obtained satisfactory accuracy on the upscaling of ICESat-2 H_canopy assisted by Sentinel satellite co-variables with an R-value between the observed and predicted H_canopy equalling 0.78 and 0.68, respectively. Compared to Sentinel satellites, Landsat-8 showed relatively weaker performance in H_canopy prediction, suggesting that the addition of the backscattering coefficients from Sentinel-1 and the red-edge related variables from Sentinel-2 could positively contribute to the prediction of forest canopy height. To our knowledge, few studies have demonstrated large-scale vegetation height mapping in a resolution ≤ 250 m based on the newly available satellites (ICESat-2, Sentinel-1 and Sentinel-2) and DL regression model, particularly in the forest areas in China. Thus, the present work provided a timely and important supplementary to the applications of these new earth observation tools.     

Woody vegetation cover,height and biomass at 25-m resolution across Australia derived from multiple site,airborne and satellite observations

Detailed spatial information on the presence and properties of woody vegetation serves many purposes, including carbon accounting, environmental reporting and land management. Here, we investigated whether machine learning can be used to combine multiple spatial observations and training data to estimate woody vegetation canopy cover fraction (‘cover’), vegetation height (‘height’) and woody above-ground biomass dry matter (‘biomass’) at 25-m resolution across the Australian continent, where possible on an annual basis. We trained a Random Forest algorithm on cover and height estimates derived from airborne LiDAR over 11 regions and inventory-based biomass estimates for many thousands of plots across Australia. As predictors, we used annual geomedian Landsat surface reflectance, ALOS/PALSAR L-band radar backscatter mosaics, spatial vegetation structure data derived primarily from ICESat/GLAS satellite altimetry, and spatial climate data. Cross-validation experiments were undertaken to optimize the selection of predictors and the configuration of the algorithm. The resulting estimation errors were 0.07 for cover, 3.4 m for height, and 80 t dry matter ha^-1 for biomass. A large fraction (89–94 %) of the observed variance was explained in each case. Priorities for future research include validation of the LiDAR-derived cover training data and the use of new satellite vegetation height data from the GEDI mission. Annual cover mapping for 2000–2018 provided detailed insight in woody vegetation dynamics. Continentally, woody vegetation change was primarily driven by water availability and its effect on bushfire and mortality, particularly in the drier interior. Changes in woody vegetation made a substantial contribution to Australia’s total carbon emissions since 2000. Whether these ecosystems will recover biomass in future remains to be seen, given the persistent pressures of climate change and land use.     

孤立波作用下植物带消浪效果影响因素分析

近岸植物带能有效削弱波浪作用,植物带消浪影响因素分析对布置护岸工程、维护岸线稳定有重要意义。在模型实验基础上,探究相对波高、模型相对宽度、模型相对高度和体积分数对透射系数的影响。设置波浪水槽试验,研究孤立波在不同水深、入射波高及植物排布方式等组合条件下对植物消浪效果的影响。采用遗传编程法得出各复杂度下的透射系数计算公式,并分析各参数在方程中的表现情况,发现遗传编程能定性给出参数的敏感度。相比于非线性回归拟合结果,遗传编程方法得到的函数关系更加精确。采用人工神经网络方法进一步分析与验证各影响因素对植物消浪效果的影响程度,结果表明人工神经网络得出的结果与遗传编程方法相近,体积分数对消浪效果起主导作用。     

基于人工神经网络的台风浪高快速计算方法

采用2010—2017年南海5个浮标波高观测资料和中国气象局热带气旋最佳路径集中的热带气旋参数, 基于前馈型误差反向传播(Forward Feedback Back Propagation, FFBP)神经网络(Artificial Neural Network, ANN)方法, 分别建立了各浮标站的台风浪高快速计算模型。研究显示, 基于热带气旋中心坐标、中心最低气压、近中心最大风速、热带气旋中心与浮标之间的距离和方位4个参数建立的神经网络模型经反复训练后, 模型输出结果可以很好地拟合观测数据, 各浮标有效波高计算值与观测值的均方根误差小于0.3m, 平均相对误差为5.78%~7.23%, 相关系数大于0.9, 属高度相关。独立测试结果显示, “山竹”( 国际编号: 1822)影响期间有效波高最大值的神经网络模型预报结果与观测值基本吻合, 相对误差为-31.06%~0.98%, 但计算的最大值出现时间和观测情况不完全一致。该计算方法可应用于热带气旋影响期间的有效波高最大值计算, 因而在海洋工程领域和海洋预报领域具有应用前景。