Structural Evolution and Hydrocarbon Potential of the Upper Paleozoic Northern Ordos Basin, North China

The hydrocarbon potential of the Hangjinqi area in the northern Ordos Basin is not well known, compared to the other areas of the basin, despite its substantial petroleum system.Restoration of a depth-converted seismic profile across the Hangjinqi Fault Zone(HFZ) in the eastern Hangjinqi area shows one compression that created anticlinal structures in the Late Triassic, and two extensions in ~Middle Jurassic and Late Early Cretaceous, which were interrupted by inversions in the Late Jurassic–Early Early Cretaceous and Late Cretaceous, respectively.Hydrocarbon generation at the well locations in the Central Ordos Basin(COB) began in the Late Triassic.Basin modeling of Well Zhao-4 suggests that hydrocarbon generation from the Late Carboniferous–Early Permian coal measures of the northern Shanbei Slope peaked in the Early Cretaceous, predating the inversion in the Late Cretaceous.Most source rocks in the Shanbei Slope passed the main gas-migration phase except for the Hangjinqi area source rocks(Well Jin-48).Hydrocarbons generated from the COB are likely to have migrated northward toward the anticlinal structures and traps along the HFZ because the basin-fill strata are dipping south.Faulting that continued during the extensional phase(Late Early Cretaceous) of the Hangjinqi area probably acted as conduits for the migration of hydrocarbons.Thus, the anticlinal structures and associated traps to the north of the HFZ might have trapped hydrocarbons that were charged from the Late Carboniferous–Early Permian coal measures in the COB since the Middle Jurassic.     

基于遥感空间信息的洪水风险识别与动态模拟研究进展

对地观测卫星遥感能够提供广泛可靠的空间信息，是洪水风险识别与动态模拟的重要支撑技术之一。为阐明卫星遥感技术对洪水研究的推动作用，回顾了洪水风险识别与动态模拟研究的发展历程及技术需求，以对地观测卫星遥感三大阶段的发展轨迹为主线，分析了遥感空间信息在洪水研究中的历史性贡献和阶段性效用，讨论总结了危险分区法、水文模型和微波遥感监测等3种洪水研究典型方法的应用进展。提出未来洪水风险识别与动态模拟研究的重点：遥感空间信息与模型算法的深度结合，遥感反演算法与系统的开发及应用，典型洪水研究方法集成系统的开发与应用，大数据方法与手段的应用。以期为提升洪水应急响应能力与灾害风险管理水平提供有效参考。     

高分辨率遥感影像主干道路提取的感知编组方法

针对高分辨率遥感影像的道路提取受"同物异谱、同谱异物"干扰的问题,该文提出一种基于感知编组的高分辨率遥感影像主干道路自动提取方法:首先,使用Line Segment Detector算法提取影像中直线段信息。然后,利用高分辨率遥感影像上的道路几何特征对直线段进行感知编组;最后,经过长度约束得到道路信息。使用两景影像进行了主干道路提取实验。实验结果表明,两个实验中主干道路提取的完整率、正确率和检测质量都在96%以上。     

基于遥感反演参数与双约束空间聚类算法的湖泊营养物生态分区

中国湖泊营养物水平和富营养化效应存在显著区域分异特征,开展湖泊营养物生态分区研究,是实现湖泊分区控制的前提和基础。本文以云贵高原亚热带湿润区为案例区,通过遥感反演得到研究区域湖泊水体的叶绿素a(Chl-a)、总氮(TP)和总磷(TN)含量,从自然地理要素、生态系统和人类活动3 方面建立指标体系;根据研究区域 DEM数据划分小流域,将各指标因子投影到小流域;在小流域尺度上,采用数理统计方法定量分析各指标分别对 Chl-a/P 与Chl-a/N 的作用,计算两种湖泊营养物生态分区评价分值;采用双约束空间聚类算法对小流域尺度上两种湖泊营养物生态分区评价分值共同进行聚类,形成湖泊营养物生态分区结果。研究发现：①通过遥感反演模型可以将少数呈点状分布的监测数据推演到面上,这在一定程度上弥补了有限的野外监测数据量难以支撑湖泊营养物生态分区的不足;②通过构建各指标因子分别与湖泊水体中Chl-a/P、Chl-a/N 的定量关系,实现了湖泊营养物生态分区中湖泊水体微观指标与湖泊流域宏观指标的有机结合;③采用双约束空间聚类进行湖泊营养物生态分区,保证了分区结果在空间上的连续性和评价分值上的接近性;④根据自然地理条件、土地利用和人类活动强度的不同,将云贵高原亚热带湿润区分为4 个区：北云南高原湖区、滇南河谷山原湖区、桂西-滇东-黔南岩溶湖区、贵州高原东北部湖区。本文提出的基于双约束空间聚类的湖泊营养物生态分区方法也可以作为其它地区乃至国家尺度湖泊营养物生态分区的借鉴。     

基于微型无人机遥感的亚热带林冠物种识别

通过微型无人机平台搭载微单相机监测亚热带地区森林生态系统冠层,发现在低空飞行时（飞行高度100m）,仅能识别马尾松、湿地松和大叶相思3 个冠层树种;但在超低空飞行时（高度50 m）的影像分辨率较高,通过提取冠层的轮廓、纹理、结构、颜色等信息,能成功分辨出近20 种冠层物种,甚至可以识别出部分林隙中的林下物种。根据微型无人机遥感,可提取多种亚热带林冠信息,为森林保护或生态系统管理提供有效帮助。     

中天山前寒武纪基底中新发现铁铜矿床

池西铁铜矿床是近年来新发现的赋存在中天山前寒武纪基底卡瓦布拉克群内的块状硫化物型矿床,具上铁下铜的分带性,在成因上可能与海相火山作用有关。已发现的2个矿体呈层状、透镜状产出,产状与围岩地层一致。该矿床可能是中天山地区卡瓦布拉克群内首次发现的块状硫化物型矿床,对今后在中天山乃至东天山地区寻找与海相火山作用有关的块状硫化物型矿床(尤其是铁铜共生矿床)具有重要的参考意义。     

基于可移动角点的航空和地面LiDAR数据配准

针对航空和地面LiDAR数据配准中点云数据的共轭特征较少且精度差异较大的问题,提出了一种基于可移动角点的航空和地面LiDAR数据配准方法:从航空和地面LiDAR数据中分别提取相应的建筑物角点,采用6参数模型对角点进行初始配准;以地面角点为参照,利用迭代移动方法对误差较大的航空角点进行修正;最后根据移动后的航空和地面角点计算获得点云配准关系。实验结果表明,该文方法可取得较好的点云配准效果,角点修正后能有效提升点云配准精度,适合于含有角点特征的航空和地面LiDAR数据配准。     

高分辨率遥感土地覆盖分类技术的应用研究——以重庆市黔江贫困区为例

针对贫困区生态环境与资源的地表覆盖精细化调查需求,本文利用高分辨率遥感影像开展了土地覆盖信息提取的方法和应用研究.重点分析了高分辨率影像均值漂移分割,多特征提取与分析,对象级样本采集以及监督分类等技术,并综合实现了流程化的对象级土地覆盖分类.结果表明,本文串联的高分辨率影像分类技术能生成较精细的土地覆盖专题图,可及时为贫困区生态资源环境评价,碳核算等应用提供较可靠的地表覆盖数据.     

长江三峡库区(奉节-巴东段)移民工程遥感动态监测

在长江三峡库区移民工程中 ,我们采用 ETM+、SPOT、IKONOS等多源、多时相、多分辨率航天、航空遥感数据组合 ,监测移民工程的动态变化 ,通过对库区奉节 -巴东段移民搬迁建与安置、新开发用地分布、土地利用与土地结构变化、地质灾害及衍生地质灾害与防治、库区环境及生态平衡与改善经济结构以及移民工程遥感动态监测等 ,取得突破性进展 ,获取了一批重要成果。为三峡移民工程进一步全面、安全地实施、改进规划管理工作及库区经济可持续发展 ,提供了科学依据和决策性建议。     

High spatial resolution hyperspectral mapping of in-stream habitats, depths, and woody debris in mountain streams

This article evaluates the potential of 1-m resolution, 128-band hyperspectral imagery for mapping in-stream habitats, depths, and woody debris in third- to fifth-order streams in the northern Yellowstone region. Maximum likelihood supervised classification using principal component images provided overall classification accuracies for in-stream habitats (glides, riffles, pools, and eddy drop zones) ranging from 69% for third-order streams to 86% for fifth-order streams. This scale dependency of classification accuracy was probably driven by the greater proportion of transitional boundary areas in the smaller streams. Multiple regressions of measured depths (y) versus principal component scores (x₁, x₂,…, x_n) generated R² values ranging from 67% for high-gradient riffles to 99% for glides in a fifth-order reach. R² values were lower in third-order reaches, ranging from 28% for runs and glides to 94% for pools. The less accurate depth estimates obtained for smaller streams probably resulted from the relative increase in the number of mixed pixels, where a wide range of depths and surface turbulence occurred within a single pixel. Matched filter (MF) mapping of woody debris generated overall accuracies of 83% in the fifth-order Lamar River. Accuracy figures for the in-stream habitat and wood mapping may have been misleadingly low because the fine-resolution imagery captured fine-scale variations not mapped by field teams, which in turn generated false “misclassifications” when the image and field maps were compared.The use of high spatial resolution hyperspectral (HSRH) imagery for stream mapping is limited by the need for clear water to measure depth, by any tree cover obscuring the stream, and by the limited availability of airborne hyperspectral sensors. Nonetheless, the high accuracies achieved in northern Yellowstone streams indicate that HSRH imagery can be a powerful tool for watershed-wide mapping, monitoring, and modeling of streams.