多波束数据处理中的UTM直角坐标与经纬度坐标转换技术

简要介绍了地图投影的一般概念,重点描述了UTM的优点及其与大地坐标之间的相互转换,该转换方法软件化后形成的技术可以为多波束数据后处理商业软件提供坐标转换的补丁技术。最后附上部分经实际检验的Fortran程序供参考。     

新疆焉耆盆地绿洲景观的空间格局及其变化

通过GIS技术的叠加和提取功能建立了焉耆盆地两个时间段的景观类型转移矩阵。在此基础上,分析了焉耆盆地内部绿洲景观类型的动态演化过程和空间分布特征。结果表明:焉耆盆地经过近40年的土地开垦后,人工绿洲呈现出上升的趋势,农田向自然绿洲和荒漠的扩张在整个研究阶段均可见;农田开垦的对象表现为从草地(含部分沼泽地)→多汁木本盐柴类荒漠→超旱生灌木半灌木荒漠迁移,体现了农田绿洲开垦从易到难,自然绿洲不断缩小,人工绿洲不断扩大的过程;对土地和水资源的不合理利用,导致盆地内湖泊水体、湖泊周边及农田绿洲出现明显的生态退化。焉耆盆地今后的规划和发展必须高度重视人工绿洲生态系统和自然生态之间的平衡,盆地环境治理很大程度决定于水资源的合理利用和合理调节。     

IKONOS影像生产1∶10 000地理信息产品的研究

简要介绍了IKONOS卫星遥感影像适用于地理信息产品生产的特点,然后通过试验生产,并与传统的航片相比较,详细分析了利用IKONOS影像作为数据源生产1∶10000基础信息产品在精度、生产流程、工作效率、生产周期、外业工作量和价格等方面的情况。     

基于RS、GIS的东喜马拉雅南迦巴瓦峰地区断裂构造的定量研究

东喜马拉雅南迦巴瓦峰地区地质构造十分复杂,对该区板块缝合带的空间位置问题,长期以来,一直认为沿雅鲁藏布江呈弧形展布。笔者以ETM+为主要信息源,以遥感与GIS为技术支撑,对南峰地区的断裂构造进行了详细的遥感解译分析,从构造统计分析的角度对断裂构造进行定量研究,从分维值及趋势值的空间分布及变化特征分析,认为缝合带的南东段、北段大致沿雅鲁藏布江展布,而北西段位于雅鲁藏布江的北西侧,沿东久-米林(断裂带)展布,这也与近几年来开展了1∶25万区域地质调查获得的新认识相一致。     

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。     

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.