海洋防灾减灾信息服务体系构建理论与方法

海洋灾害的发生同所处的地理、气候等自然条件紧密相关,所以海洋防灾减灾信息系统研究需要围绕区域性发展规律开展。本研究从信息基础设施建设理论的角度,为了避免"信息壁垒、信息孤岛、重复建设",定义了"区域性防灾减灾信息服务体系",解析了6个特征和基础要素并提出了面向集成的以业务流为核心的"5层沙漏"模型。进而,以台湾海峡区域防灾减灾信息服务体系构建与应用的案例,论证了其实际应用的意义。最后,结合我国智慧海洋建设,提出了未来的发展方向与研究重点。     

梯级水库作用下的长江流域水文设计成果修订研究

长江流域水文成果复核选取了48个水文站点作为研究对象,考虑站点上游已建具有较强调节能力的大型水库调蓄影响,对径流、洪水进行还原分析计算以保证系列的一致性,并对径流、洪水设计成果的变化进行对比分析。径流成果分析表明,延长系列后干支流主要控制站点的径流成果较为稳定,多年平均年径流量和各站点20%、50%和75%频率年径流设计值的相对变化均在5%以内;洪水成果分析表明,延长洪水系列后的百年一遇设计洪水成果与前期不同阶段采用成果相比变化不大,基本均在4%以内。     

Lithospheric flexural modelling of the seaward and trenchward of the subducting oceanic plates

ABSTRACT

Based on a two-segment plate flexural modelling, we investigated the effective elastic thickness of global subducting oceanic lithosphere. Our results show that for the plate age of 0 to 50 Ma, the seaward effective elastic thickness T _e ^M values are located between 600 and 900°C isotherms, and do not track any isotherm, while the majority of the trenchward effective elastic thickness T _e ^m values are located between 300 and 600°C isotherms. For the plate age older than 50 Ma, T _e ^M values basically matches the 600°C isotherm with some fluctuations for the age older than 110 Ma, while T _e ^m values mainly fall between 200 and 400°C isotherms. The reduction in effective elastic thickness (T _e ^M-T _e ^m) varies from 2.6 to 30.1 km, or 11–68% of seaward T _e ^M. Thus, the absolute value of the decrease in the effective elastic thicknesses (T _e ^M-T _e ^m) increases with the plate age, while the percentage reduction in the effective elastic thickness (1-T _e ^m/T _e ^M) has no obvious relationship with the age, but more related to the curvature of bending plate. Almost all bending-related earthquakes occurred above the T _e ^M line, but many normal-faulting earthquakes are deeper than the T _e ^M-T _e ^m line, implying that the plate may still retain some thickness of an elastic property core in the areas (depth) where earthquakes occur.     

基于强度折减法的土石混合体边坡长期稳定性研究

土石混合体边坡是自然界中常见的一种边坡类型,具有明显的不均匀性和不连续性。土体的蠕变是造成土石混合体边坡变形及失稳的主要原因之一,而目前关于土石混合体边坡稳定性的研究几乎均未考虑土体的蠕变性。首先运用数字图像处理技术对我国某水电站库区的一个土石混合体边坡进行建模,而后利用FLAC3D软件中的强度折减法对其进行稳定性分析,并着重研究了土体的蠕变特征及其蠕变参数对土石混合体边坡变形及稳定性的影响。计算结果表明,土体的蠕变特征会明显降低土石混合体边坡的安全系数,增大边坡变形,进而对边坡的稳定性造成不利影响;其中蠕变黏性系数对边坡的长期稳定性具有较大影响,黏性系数越大,则土石混合体边坡的安全系数越低。     

Identification of failure slip surfaces for landslide risk assessment using smoothed particle hydrodynamics

ABSTRACT

Location of failure slip surfaces plays a critical role in landslide risk assessment and mitigation, particularly for unstable slopes, because it is a key input to design of stabilisation measures for unstable slopes and it determines the volume of the sliding soil mass (i.e. landslide consequence). The failure slip surfaces in the numerical analysis (e.g. finite element/different method, FEM/FDM) are often identified using shear strength reduction (SSR) method. A careful examination of FEM results showed that, although the SSR method performs well for stable slopes, it might provide misleading results for unstable slopes. To properly locate failure slip surfaces for unstable slopes, this paper presents a particle-based numerical method called smoothed particle hydrodynamics (SPH), which is mesh-free, immune to the mesh distortion problem in FEM/FDM, and able to directly simulate large deformation of soils that occurs during landslides. A series of slope stability analyses is performed using an in-house SPH programme. Failure slip surfaces are properly identified by SPH for both stable and unstable slopes. Furthermore, because SPH provides a spatial distribution of the post-landslide large displacement of soils, the failure slip surfaces can be identified conveniently using soil displacement. A displacement-based criterion is proposed to locate the failure slip surfaces.     

PhenoCrop: An integrated satellite-based framework to estimate physiological growth stages of corn and soybeans

The accurate and timely estimates of crop physiological growth stages are essential for efficient crop management and precise modeling of agricultural systems. Satellite remote sensing has been widely used to retrieve vegetation phenology metrics at local to global scales. However, most of these phenology metrics (e.g., green-up) are different from crop growth stages (e.g., emergence) used in crop management and modeling. As such, an integrated framework referred to as PhenoCrop was developed to: 1) establish a connection between remote sensing-derived phenology metrics and key crop growth stages based on Wang and Engle plant phenology model and 2) use fused MODIS-Landsat 30 m 8-day reflectance data generated using Kalman Filter-based data fusion technique to produce onset dates of key growth stages of corn (Zea mays L.) and soybeans (Glycine max L.) at 30 m spatial resolution. In this paper, we described the PhenoCrop framework, and tested its performance for the State of Nebraska for 2012–2016 by comparison to observations of estimated key growth stages at four experimental sites, and state-level statistical data from Crop Progress Reports (CPRs) published by the United States Department of Agriculture’s (USDA) National Agricultural Statistical Services (NASS). In addition, to evaluate the suitability of using coarse or high spatial resolution satellite imagery, fused MODIS-Landsat-based estimates were compared with those produced using EOS MODIS 250 m (MOD9Q1) reflectance data.The PhenoCrop estimates captured the typical spatial trends of gradual delay in the progression of the growing season from southeast to northwest Nebraska. Also inter-annual differences due to factors such as weather fluctuations and change in management strategies (e.g., early season in 2012) were evident in the estimates. Validation results revealed that average root mean square error (RMSE) of the state-level estimates of corn and soybean growth stages ranged from 1.10 to 4.20 days and from 3.81 to 7.89 days, respectively, while pixel level estimates had a RMSE ranging from 3.72 to 8.51 days for corn and 4.76–9.51 days for soybean growth stages. Although MODIS 250 m based estimates showed similar general spatial patterns observed in the fused MODIS-Landsat based estimates, the accuracy and ability to capture field scale variations was improved with fused MODIS-Landsat data. Overall, results showed the ability of PhenoCrop framework to provide reliable estimates of crop growth stages that can be highly useful in crop modeling and crop management during the growing season.     

不同树种叶片养分含量提取的高光谱方法及精度评价

以深圳市坝光银叶园和大鹏半岛自然保护区19种湿地森林树种叶片可见光近红外光谱与全氮（Total Nitrogen, TN）、全磷（Total Phosphorus, TP）、全钾（Total Potassium, TK）含量关系为基础,分析了11种光谱预处理方式、3种光谱数据降维方式和2种建模方法对模型精度的影响。结果表明,标准正态变换（Standard Normal Variate, SNV）结合一阶导数（first derivative, 1 ^st）预处理方式下模型精度最高;主成分分析（Principal Component Analysis, PCA）降维处理对模型的降维效果最好;支持向量回归（Support Vector Regression, SVR）的建模效果精度最高。对于TN、TP、TK最佳模型的预测确定系数均在0.80以上,模型RPD值也在2.0以上,SVR模型可以用于树种叶片TN、TP、TK的快速检测。     

长江经济带市域尺度减贫成效测度及其时空演替

减贫成效评估是近年来国际减贫战略研究中的热点议题,也是中国未来扩展减贫深度、提高减贫质量所面临的关键问题。论文基于“两不愁、三保障”标准,构建涵盖经济、人口、社会和绿色4个维度12项指标的减贫成效评估指标体系,利用熵权法与模糊集方法揭示长江经济带126个地级市域减贫成效状况,引入空间自相关理论解析长江经济带减贫成效的空间分异及演化特征。结果表明:① 2011—2017年间长江经济带整体减贫成效显著,区域间存在明显空间差异,上游地区市域减贫提升潜力较大;② 研究期内市域Moran's I均大于0且逐年上升,市域减贫成效存在空间正相关性且集聚特征逐年增强;③ 研究期内减贫成效指数的LISA值存在区域差异,上、中游地区局域空间自相关较为显著,尤其在集中连片贫困区呈现“连块集聚”的关联特征。研究成果可为当前推动长江经济带可持续减贫,统筹区域经济、社会和生态协调发展提供参考。     

高光谱图像的JM变换自适应降维

在无需先验标签样本的情况下,非监督降维可以有效简化高光谱图像的特征空间,避免目标分类中的霍夫效应。本文提出JM非线性变换优化的自适应降维模型来研究面向图像目标分类的高光谱波段选择问题。该方法考虑波段的信息量和独立性等两个重要因子,针对其测度方法的差异性问题,引入JM变换函数进行规范化优化。选用线阵高光谱和面阵显微光谱等两个图像数据集,在k最邻近和随机森林分类器下,进行了多组监督分类实验,结果表明,在Kappa系数、总体分类精度和平均分类精度上,本文方法均优于3种非监督方法MABS、InfFS和LSFS。说明本文提出的JM变换的自适应降维模型能够有效降低特征维度,满足高光谱图像分类的高精度要求。     

栈式稀疏自编码网络的多时相全极化SAR散射特征降维

利用极化合成孔径雷达（PolSAR）能够实现地物的识别和分类,而多时相全极化SAR可以获取地物更多的散射特征,提升地物识别精度,但高维散射特征的引入会带来严重的维数灾难问题。为了实现对高维散射特征的有效降维,本文提出一种基于栈式稀疏自编码网络S-SAE（Stacked Sparse AutoEncoder）的多时相PolSAR散射特征降维方法。该方法首先对PolSAR数据进行极化目标分解以获取高维散射特征;然后使用S-SAE对获取的多维特征进行降维处理,其中S-SAE降维方法首先采用无监督训练方式进行逐层贪婪训练;再结合Sigmod分类器,利用监督训练的方式对S-SAE进行参数优化,实现高维特征的有效降维;最后以降维后的特征作为支持向量机（SVM）和卷积神经网络（CNN）分类器的输入,实现地物分类。通过仿真和实测的两组多时相Sentinel-1数据处理结果表明,双隐层的S-SAE降维方法在各分类器上均取得最优的降维效果;对比各降维方法在SVM分类器上的分类精度,S-SAE较于局部线性嵌入（LLE）与主成分分析（PCA）降维方法,总体分类精度分别至少提升了9%和14%;在CNN分类器上,S-SAE较于LLE与PCA降维方法,总体分类精度分别至少提升了7%和9%。