A nonlinear deformation modulus based on rock mass classification

Summary This paper describes the development of an empirical nonlinear stress dependent expression for the deformation modulus of rock masses based on rock mass classification. The expression defines the deformation modulus as the ratio of the deviator stress at failure to the major principal strain at failure. The Hoek and Brown failure criterion is used to predict the deviator stress at failure. Research was directed toward developing a failure criterion defining the major principal strain at failure. The expression for the deformation modulus was extended to rock mass conditions through correlations with observed deformations from case history studies and predicted deformations from finite element analyses.     

基于NDVI时间序列影像的张掖市农作物种植结构提取

HJ卫星(环境与灾害监测预报小卫星)数据具有较高的时间和空间分辨率,可以有效地进行农作物种类识别,利用其时间序列数据进行作物信息提取具有很大优势。以甘肃省张掖市为研究区,利用2012年4月~2012年10月15个时相的HJ卫星CCD(电荷耦合元件,charge-coupled device)数据,通过分析玉米、油菜、大麦和小麦农作物在整个生长阶段的时间序列NDVI(归一化植被指数,normalized difference vegetation index)变化特点,提取四类农作物NDVI随时间变化的特征曲线,利用决策树分类方法,对2012年张掖市农作物进行分类,并以外业调查得到的实际地面验证点对分类结果进行验证。验证结果表明,可以有效地识别玉米、油菜、大麦和小麦4类农作物,制图精度分别为94.74%、82.35%、78.95%和90%;总体分类精度达到86.43%。而后提取了张掖市2014年农作物种植面积和分布格局,通过对比分析发现,2012~2014年,张掖市玉米、油菜的种植面积有不同程度的增加,大麦和小麦的种植面积各有一定程度的较少。可以为张掖市主要作物的种植结构调整提供科学依据,还可以为黑河流域农作物信息提取研究提供参考。     

测绘地理信息档案分类探讨

该文介绍了测绘地理信息档案分类法、档案实体分类和档案信息分类理论,并结合测绘地理信息档案的特点,探讨了它们在测绘地理信息档案管理中的应用,提出了包含数字档案在内的测绘地理信息实体分类方法,探讨了基于档案管理系统的测绘地理信息档案分类目录的组织形式。     

西北太平洋迅速加强热带气旋的统计特征和识别预报试验

利用2000—2014年热带气旋(TC)最佳路径、最终分析资料和静止卫星红外云顶亮温(TBB)资料,对比分析了西北太平洋(WNP),以及南海(SCS)的迅速加强(RI),与非迅速加强(non-RI)TC样本的环境背景和TBB统计特征,其中non-RI样本细分为不同的强度变化率即:缓慢加强(SI),强度稳定、缓慢减弱和迅速减弱等。结果表明,相对于SI,WNP海域的RI样本处于海表温度较高、海洋上层热容量较大、最大可能强度较大、高层辐散较强、风垂直切变(VWS)较弱和高层纬向风(U200)偏东分量较大等环境背景条件下;SCS海域的RI样本较易发生在VWS较弱的环境背景条件下。此外,相对于non-RI,支持RI发展的有利条件还包括中低层相对湿度较大、高层环境温度较低等。RI样本通常具备的TBB特征为TC内核的对流云覆盖率较大、TBB平均值相对较小。采用K最近邻分类算法进行RI预报试验,交叉检验结果表明,该方法对RI样本有一定的识别预报能力,RI样本概括率达到74.2%,技巧评分达到0.717。     

论热液角砾岩构造系统及研究内容、研究方法和岩相学填图应用

含矿热液角砾岩类和非含矿角砾岩类的成岩成矿机制、独立填图单元确定和构造岩相学填图等问题,一直是困惑地质学家的难题。有效解决这些难题,对于研究和恢复矿田构造具有重要价值。在热液角砾岩成岩成矿系统中,热液角砾岩类不但是流体–岩石的多期次地球化学耦合与叠加作用、强烈流体交代作用的物理–化学反应库,也是各类叠加地质作用过程和结果的构造岩相学物质记录。因此,对其深入研究有助于提升成岩成矿系统理论认识水平。在综述以往研究基础上,本文提出了热液角砾岩构造系统概念、研究内容、研究方法和技术组合。在对角砾岩相系分类基础上,针对热液角砾岩相系的复杂性,通过岩相学填图实例解剖,总结专项填图中构造岩相学填图单元建立方法和填图技术,探索采用岩相学填图恢复热液角砾岩构造系统的新方法及技术组合。通过专项研究认为热液角砾岩系统主要形成有利的构造地质背景有:(1)复式侵入岩体在多期次岩浆侵入过程中,岩浆结晶分异和不混溶作用、岩浆冷却与围岩–先存构造多重耦合过程、同岩浆侵入期的脆韧性剪切带耦合、侵入岩体在后期构造–流体叠加过程等,对于形成热液角砾岩构造系统有利;(2)在火山岩相系中,早期次火山侵入体、晚期次火山岩侵入体和后期岩浆侵入岩体等,对于形成热液角砾岩体构造系统十分有利;(3)在沉积盆地后期叠加改造过程中,先存火山角砾岩、岩溶角砾岩和沉积角砾岩相系等,在后期盆地流体注入和多期次岩浆侵入过程中,有利于形成叠加热液角砾岩体构造系统;(4)在多期次的构造–岩浆–角砾岩杂岩带中,有利于形成热液角砾岩构造系统。研究认为多期次岩浆侵入体、火山–次火山岩侵入体和盆地中热流体是形成热液角砾岩构造系统的主要机制,包括与多期次复式侵入体有关的岩浆热液角砾岩构造系统、火山–次火山热液角砾岩构造系统、构造热液角砾岩构造系统和复合热液角砾岩系统等,它们均属矿田构造类型,也是多矿种共生矿床的成岩成矿机制。采用构造岩相学专项填图技术,对不同类型热液角砾岩构造系统及成岩成矿中心进行重建,有助于寻找和发现深部隐蔽构造和隐伏铁氧化物铜金型(IOCG)矿床找矿预测。     

A regulation-based classification system for Marine Protected Areas (MPAs)

Marine protected areas (MPAs) are a global conservation and management tool to enhance the resilience of linked social-ecological systems with the aim of conserving biodiversity and providing ecosystem services for sustainable use. However, MPAs implemented worldwide include a large variety of zoning and management schemes from single to multiple-zoning and from no-take to multiple-use areas. The current IUCN categorisation of MPAs is based on management objectives which many times have a significant mismatch to regulations causing a strong uncertainty when evaluating global MPAs effectiveness. A novel global classification system for MPAs based on regulations of uses as an alternative or complementing the current IUCN system of categories is presented. Scores for uses weighted by their potential impact on biodiversity were built. Each zone within a MPA was scored and an MPA index integrates the zone scores. This system classifies MPAs as well as each MPA zone individually, is globally applicable and unambiguously discriminates the impacts of uses.     

Projected Shifts in Kppen Climate Zones over China and Their Temporal Evolution in CMIP5 Multi-Model Simulations

Previous studies have examined the projected climate types in China by 2100. This study identified the emergence time of climate shifts at a 1?scale over China from 1990 to 2100 and investigated the temporal evolution of K o¨ppen–Geiger climate classifications computed from CMIP5 multi-model outputs. Climate shifts were detected in transition regions(7%–8% of China's land area) by 2010, including rapid replacement of mixed forest(Dwb) by deciduous forest(Dwa) over Northeast China, strong shrinkage of alpine climate type(ET) on the Tibetan Plateau, weak northward expansion of subtropical winterdry climate(Cwa) over Southeast China, and contraction of oceanic climate(Cwb) in Southwest China. Under all future RCP(Representative Concentration Pathway) scenarios, the reduction of Dwb in Northeast China and ET on the Tibetan Plateau was projected to accelerate substantially during 2010–30, and half of the total area occupied by ET in 1990 was projected to be redistributed by 2040. Under the most severe scenario(RCP8.5), sub-polar continental winter dry climate over Northeast China would disappear by 2040–50, ET on the Tibetan Plateau would disappear by 2070, and the climate types in 35.9%and 50.8% of China's land area would change by 2050 and 2100, respectively. The results presented in this paper indicate imperative impacts of anthropogenic climate change on China's ecoregions in future decades.     

Object-oriented and pixel-based classification approaches to classify tropical successional stages using airborne high–spatial resolution images

Airborne high–spatial resolution images were evaluated for mapping purposes in a complex Atlantic rainforest environment in southern Brazil. Two study sites, covered predominantly by secondary evergreen rainforest, were surveyed by airborne multispectral high-resolution imagery. These aerophotogrammetric images were acquired at four spectral bands (visible to near-infrared) with spatial resolution of 0.39 m. We evaluated different data input scenarios to suit the object-oriented classification approach. In addition to the four spectral bands, auxiliary products such as band ratios and digital elevation models were considered. Comparisons with traditional pixel-based classifiers were also performed. The results showed that the object-based classification approach yielded a better overall accuracy, ranging from 89% to 91%, than the pixel-based classifications, which ranged from 62% to 63%. The individual classification accuracy of forest-related classes, such as young successional forest stages, benefits the object-based approach. These classes have been reported in the literature as the most difficult to map in tropical environments. The results confirm the potential of object-based classification for mapping procedures and discrimination of successional forest stages and other related land use and land cover classes in complex Atlantic rainforest environments. The methodology is suggested for further SAAPI acquisitions in order to monitor such endangered environment as well as to support National Land and Environmental Management Protocols.     

An evaluation of unsupervised and supervised learning algorithms for clustering landscape types in the United States

Knowledge of landscape type can inform cartographic generalization of hydrographic features, because landscape characteristics provide an important geographic context that affects variation in channel geometry, flow pattern, and network configuration. Landscape types are characterized by expansive spatial gradients, lacking abrupt changes between adjacent classes; and as having a limited number of outliers that might confound classification. The US Geological Survey (USGS) is exploring methods to automate generalization of features in the National Hydrography Data set (NHD), to associate specific sequences of processing operations and parameters with specific landscape characteristics, thus obviating manual selection of a unique processing strategy for every NHD watershed unit. A chronology of methods to delineate physiographic regions for the United States is described, including a recent maximum likelihood classification based on seven input variables. This research compares unsupervised and supervised algorithms applied to these seven input variables, to evaluate and possibly refine the recent classification. Evaluation metrics for unsupervised methods include the Davies–Bouldin index, the Silhouette index, and the Dunn index as well as quantization and topographic error metrics. Cross validation and misclassification rate analysis are used to evaluate supervised classification methods. The paper reports the comparative analysis and its impact on the selection of landscape regions. The compared solutions show problems in areas of high landscape diversity. There is some indication that additional input variables, additional classes, or more sophisticated methods can refine the existing classification.     

机器学习法在面向对象影像分类中的对比分析

针对如何选择遥感影像面向地理对象分类方法的问题,该文面向地理国情普查中的地表覆盖分类应用,以3个典型区域(山区、平原、城区)的多源高分辨率遥感影像为实验数据,从分类效果、分类精度等方面对比分析3种分类方法(支持向量机、决策树、随机森林)的优劣。在相同影像分割、特征提取、样本采集条件下,通过333组分类实验,得出以下规律:支持向量机分类方法稳定性强,分类速度快,但对特征数的要求高,特征数目与总体精度、地物环境之间的规律性不强,从而增加了特征提取与选择的难度,而随着特征的增加,决策树、随机森林的总体分类精度均为先升高后降低,最后趋于平衡。最后,综合随机森林对特征的优选机制和支持向量机的高分类精度,得到新的组合分类器。