基于可见光遥感测深技术的长江口南支河段河势演变规律研究

以长江口南支河段为研究区,利用LandsatETM+遥感影像反演的1999年水下地形和该河段2002年实测水深数据,结合长江口来水来沙现状和河段特征,分析了河段的冲淤变化和沙体迁移规律,提出了需采取的治理措施。研究结果表明:(1)从1999到2002年南支河段总体发生冲刷,年平均冲刷量为0.1926亿m3,河道内白茆沙、新浏河沙和新浏河沙包的沙头年平均后退467,374和421m;(2)上、下扁担沙之间由于串沟发育,两者有分离的趋势,沙体5m等深线的变动格局显示两沙体沙尾向下移动导致新桥水道上段水深加深、下段南侧水深变浅;(3)固定白茆沙和上、下扁担沙沙体,对维持南支河段航道的航运、稳定南北槽河势具有重要作用。     

基于RS和GIS的松花江流域植被覆盖动态变化研究

利用遥感和地理信息系统技术对1989,1995年的Landsat TM数据和2002年Landsat ETM＋三期遥感数据进行处理,反演和计算松花江流域的归一化植被指数（NDVI）,在此基础上,获取研究区域植被覆盖度。在ArcGIS9.2软件空间分析模块的支持下,对研究区域三期植被覆盖影像进行叠加分析,以流域尺度和栅格尺度分析植被覆盖变化的时间和空间特性,获取研究区域植被覆盖度空间格局分布特征,为该区域植被覆盖度的自动化监测提供很好的技术支持。     

基于3S的荣成湾沿岸土地覆被变化及驱动力研究

运用3S技术对最近40年来荣成湾沿岸土地覆被动态变化及驱动力进行研究.结果表明,该区海岸湿地土地覆被总面积和陆域土地覆被总面积变化不大,但其各自类型结构和分布均发生了显著调整,而且二者变化在时间上不同步.从面积上讲,前者以1981年为界,渴湖和沙地先减少后波动增加,滩涂和沼泽地先增加后波动减少;养殖池从无到有并迅速增加...     

遥感技术在我国海冰研究方面的进展

本文首先对遥感技术在我国海冰研究中的发展情况作了详细地论述,并介绍了几种常用于海冰研究的遥感数据;其次,从海冰厚度识别、海冰运动速度矢量计算、海冰资源量测算以及海冰灾害监测等四个方面,简单论述了遥感技术在海冰研究中的应用。最后,以渤海海冰为例,阐述了遥感技术的具体应用,同时指出了今后的发展方向。     

随机森林模型在遥感水深反演中的应用

随着我国浅海测绘需求的日益增长,文中利用四波段的WorldView-2高分辨率遥感影像,选取我国南海西沙群岛中的甘泉岛和台湾南湾地区作为典型试验区,开展水深反演研究。引入随机森林算法构建了随机森林水深反演模型,并同常用的3种水深反演模型进行精度对比。结果表明,在甘泉岛和南湾地区随机森林模型反演的水深值和真实水深值的RMSE分别为0.85 m和1.59 m,MRE分别为8%和12%,均优于其他3种模型。     

渤海岸线及水深变化对水动力影响的数值模拟

为探究渤海岸线及水深变化对水动力的影响,基于Delft3D水动力学模型,选用2003年和2015年作为围 填海前后的典型年份,建立了围填海前后岸线及水深条件下的渤海三维水动力模型,并对水动力场进行了模拟。通过对围填海前后潮波和潮余流的分析,得到了岸线及水深变化对渤海水动力场的影响。结果表明:填海后,岸线及水深变化会对渤海主导分潮M₂分潮产生较大影响,秦皇岛附近无潮点向西北方向偏移,渤海海域M₂ 分潮振幅总体减小;潮致余流场受岸线及水深变化影响较大,其中渤海湾曹妃甸港南部形成复杂的涡流,沿岸海域余流增大;滨海新区附近形成多个小范围环流,且天津港到黄骅港北部沿岸海域2015年余流比2003年增加3~5 cm/s;黄骅港南部形成一个逆时针环流,并且该处余流减小2~5 cm/s。辽东湾辽河口附近由于水深增加导致余流减小2~7 cm/s。莱州湾黄河口附近的逆时针环流向东南方向移动,黄河口北部余流略有减小,东南部余流明显增大,增加量最多能达到9 cm/s。刁龙嘴南侧顺时针环流减小,北侧顺时针环流增大4~9 cm/s。     

东北典型丘陵漫岗区沟谷侵蚀动态及空间分析

以黑龙江省克山县为例,分析1954年和2000年侵蚀沟密度变化,探讨研究区土壤侵蚀的时空动态变化,并以侵蚀沟密度变化值为依据进行不同分区侵蚀因素的耦合分析。结果表明:过去46年间,克山县东南部侵蚀沟密度趋于减少,西北部侵蚀沟密度增大。1954～2000年侵蚀沟密度变化值在侵蚀减弱区与高程和坡度呈负相关,在侵蚀增强区呈正相关。随着坡长增加,侵蚀沟密度变化量呈增加趋势。侵蚀沟变化与坡长相关性最大,R²达0.9743,其次为高程,R²为0.6893,与坡度的相关性最小,R²为0.3437。结果表明,高程和坡度不是东北丘陵漫岗地区土壤流失的主要影响因子,坡长是该区侵蚀的一个重要影响因子。土地利用结构变化与土壤侵蚀强度变化密切相关,耕地面积大幅度增加与草地面积明显减少加速侵蚀沟发育。     

Capabilities of the bathymetric Hawk Eye LiDAR for coastal habitat mapping: A case study within a Basque estuary

The bathymetric LiDAR system is an airborne laser that detects sea bottom at high vertical and horizontal resolutions in shallow coastal waters. This study assesses the capabilities of the airborne bathymetric LiDAR sensor (Hawk Eye system) for coastal habitat mapping in the Oka estuary (within the Biosphere Reserve of Urdaibai, SE Bay of Biscay, northern Spain), where water conditions are moderately turbid. Three specific objectives were addressed: 1) to assess the data quality of the Hawk Eye LiDAR, both for terrestrial and subtidal zones, in terms of height measurement density, coverage, and vertical accuracy; 2) to compare bathymetric LiDAR with a ship-borne multibeam echosounder (MBES) for different bottom types and depth ranges; and 3) to test the discrimination potential of LiDAR height and reflectance information, together with multi-spectral imagery (three visible and near infrared bands), for the classification of 22 salt marsh and rocky shore habitats, covering supralittoral, intertidal and subtidal zones. The bathymetric LiDAR Hawk Eye data enabled the generation of a digital elevation model (DEM) of the Oka estuary, at 2 m of horizontal spatial resolution in the terrestrial zone (with a vertical accuracy of 0.15 m) and at 4 m within the subtidal, extending a water depth of 21 m. Data gaps occurred in 14.4% of the area surveyed with the LiDAR (13.69 km²). Comparison of the LiDAR system and the MBES showed no significant mean difference in depth. However, the Root Mean Square error of the former was high (0.84 m), especially concentrated upon rocky (0.55–1.77 m) rather than in sediment bottoms (0.38–0.62 m). The potential of LiDAR topographic variables and reflectance alone for discriminating 15 intertidal and submerged habitats was low (with overall classification accuracy between 52.4 and 65.4%). In particular, reflectance retrieved for this case study has been found to be not particularly useful for classification purposes. The combination of the LiDAR-based DEM and derived topographical features with the near infrared and visible bands has permitted the mapping of 22 supralittoral, intertidal and subtidal habitats of the Oka estuary, with high overall classification accuracies of between 84.5% and 92.1%, using the maximum likelihood algorithm. The airborne bathymetric Hawk Eye LiDAR, although somewhat limited by water turbidity and wave breaking, provides unique height information obscured from topographic LiDAR and acoustic systems, together with an improvement of the habitat mapping reliability in the complex and dynamic coastal fringe.     

Remote sensing of water depths in shallow waters via artificial neural networks

Determination of the water depths in coastal zones is a common requirement for the majority of coastal engineering and coastal science applications. However, production of high quality bathymetric maps requires expensive field survey, high technology equipment and expert personnel. Remotely sensed images can be conveniently used to reduce the cost and labor needed for bathymetric measurements and to overcome the difficulties in spatial and temporal depth provision. An Artificial Neural Network (ANN) methodology is introduced in this study to derive bathymetric maps in shallow waters via remote sensing images and sample depth measurements. This methodology provides fast and practical solution for depth estimation in shallow waters, coupling temporal and spatial capabilities of remote sensing imagery with modeling flexibility of ANN. Its main advantage in practice is that it enables to directly use image reflectance values in depth estimations, without refining depth-caused scatterings from other environmental factors (e.g. bottom material and vegetation). Its function-free structure allows evaluating nonlinear relationships between multi-band images and in-situ depth measurements, therefore leads more reliable depth estimations than classical regressive approaches. The west coast of the Foca, Izmir/Turkey was used as a test bed. Aster first three band images and Quickbird pan-sharpened images were used to derive ANN based bathymetric maps of this study area. In-situ depth measurements were supplied from the General Command of Mapping, Turkey (HGK). Two models were set, one for Aster and one for Quickbird image inputs. Bathymetric maps relying solely on in-situ depth measurements were used to evaluate resultant derived bathymetric maps. The efficiency of the methodology was discussed at the end of the paper. It is concluded that the proposed methodology could decrease spatial and repetitive depth measurement requirements in bathymetric mapping especially for preliminary engineering application.     

自动表面船用于岛礁水深测绘

岛礁具有重要的地位,然而对其附近水深的调查手段却不充分。比较了几种水深测绘手段的优缺点,设计了一种小型自动表面船用于岛礁水深测绘。该表面船底部安装声学测深仪,具备自动定位、导航与运动功能;其尾部安装两个电动推进器提供动力,通过独立控制两个推进器的转速,可实现船体的前进、后退和转向;利用无线电与岸基单元进行数据通讯。根据不同水深测绘条件,该船可以用三种控制方式进行水深测量:直接控制、间接控制和自主控制。测量数据实时传输到岸基单元,并自动存储于船载存储单元。初步试验结果表明,设计的自动表面船测量系统具有可行性。