3S技术在上海滩涂管理中的应用

针对海塘滩涂基础信息数据在信息管理系统建设中的重要性，详细介NT3S技术在海塘滩涂行业数据采集、处理、更新和分析中的应用，分别阐述了GPS、RS和GIS技术在海塘滩涂行业日常管理中发挥的重要作用。     

GPS高程转换的应用研究

在对GPS高程测量的原理和方法进行初步的探讨的基础上,结合工程应用实际,以数值拟合为主,建立了高程转换的数学模型,同时用VB开发了GPS高程控制转换系统,经试实验测试得出了一些参考性的结论。     

基于最小二乘支持向量机的组合模型在区域似大地水准面拟合中的应用

针对最小二乘支持向量机（least squares support vector machine,LSSVM）在区域似大地水准面拟合过程中存在较大模型误差的问题,构建LSSVM-Shepard GPS高程异常转换组合模型。采用LSSVM拟合高程异常中的中长波项,利用Shepard插值模型来泛化去除中长波项的残余项。结合平原地区和高原山区工程实例,分别采用二次曲面模型、LSSVM、Shepard插值模型、二次曲面-Shepard模型、LSSVM-Shepard模型进行高程转换及精度对比。结果表明,新组合模型高程转换精度高于各单一模型,且在平原地区转换效果与二次曲面-Shepard模型基本一致,在高原山区拟合效果优于二次曲面-Shepard模型。     

Coupling near-surface geomorphology with mangrove community diversity at the estuarine scale: A case study at Dongzhaigang Bay,China

Coastal wetlands are key features of the Earth's surface and are characterized by a diverse array of coupled geomorphological and biological processes. However, the links between the distribution of biodiversity (for example, species and structural diversity) and the formation of coastal geomorphology are not well-understood on a landscape scale most useful to coastal zone managers. This study describes the relationship between select geomorphological and biological mangrove community features (i.e. species composition and functional root type) in a landscape-distributed coastal zone of Dongzhaigang Bay, north-eastern Hainan Island, China. A total of 11 mangrove species and five functional aerial root types were encountered, with the location of species by root types being controlled by the elevation of the soil surface. Plank roots, prop roots and pneumatophores occupied the lowest intertidal elevations, while knee roots and fibrous roots of the mangrove fern, Acrostichum aureum, preferred the highest intertidal elevations. Surface sediment deposition in areas with mangroves was greater than deposition in non-mangrove forest zones, establishing an important biological mechanism for this large-area response because surface erosion/compaction was also more prominent within mangrove roots. Indeed, functional root type influenced the magnitude of deposition, erosion and compaction, with knee roots and pneumatophores being more effective in promoting deposition and preventing surface erosion/compaction than prop roots. These results indicate a potential role for vegetation type (especially functional root type) to influence coastal geomorphological processes at large landscape scales. While soil surface elevation is correlated to the distribution of mangrove species and functional root types, a significant feedback exists between elevation change and the capacity of those root types to influence coastal geomorphological differentiation within sustainable intertidal elevations. An enhanced understanding of geomorphological development, mangrove species distribution and functional root type may improve management to support nature-based solutions that adjust more effectively to sea-level rise through feedbacks.     

Image classification-based ground filtering of point clouds extracted from UAV-based aerial photos

With the advent of unmanned aerial vehicles (UAVs) for mapping applications, it is possible to generate 3D dense point clouds using stereo images. This technology, however, has some disadvantages when compared to Light Detection and Ranging (LiDAR) system. Unlike LiDAR, digital cameras mounted on UAVs are incapable of viewing beneath the canopy, which leads to sparse points on the bare earth surface. In such cases, it is more challenging to remove points belonging to above-ground objects using ground filtering algorithms generated especially for LiDAR data. To tackle this problem, a methodology employing supervised image classification for filtering 3D point clouds is proposed in this study. A classified image is overlapped with the point cloud to determine the ground points to be used for digital elevation model (DEM) generation. Quantitative evaluation results showed that filtering the point cloud with this methodology has a good potential for high-resolution DEM generation.     

Drainage skeletonization from flow-accumulated area without the use of threshold

This study presents a method of automatic drainage skeletonization from flow-accumulated area without the use of threshold which conserves drainage geometry at chosen digital elevation model (DEM) scale. To get all possible drainage at the chosen scale, stream order raster is generated by incorporating flow accumulation and flow direction raster derived from corresponding DEM. This allows generation of drainage network without the use of threshold. Resultant stream order raster, termed as raw stream order raster (RSOR), is tested against threshold defined stream order raster to evaluate its efficiency. Use of RSOR allows extraction of stream heads to greater stream head extent. Again, DEM downscaling takes care of overestimation in number of streams. So, the proposed technique is effective in controlling two basic aspects of drainage characteristics – stream number and extent. In our case, drainage estimated from re-sampled medium-scale DEM has the closest matching with that of the reference topographical map.     

数字高程模型(DEM)成果裁切探讨

随着计算机技术及测绘产品的不断发展,数字高程模型(DEM)已成为地理信息空间系统和"数字地球"的重要组成部分。在测绘技术蓬勃发展的今天,数字高程模型(DEM)的生产已成为各生产部门较关注的问题之一,特别是近年来数字龙江地理空间框架建设一期工程对数字高程模型(DEM)产品成果的要求有很大提高,数字高程模型(DEM)成果的裁切也显得尤为重要,本文从工作中遇到的DEM成果裁切问题入手,结合笔者多年的测绘生产经验,研讨DEM成果裁切问题。     

基于DWT的DEM重点区域多分辨率伪装技术

在数据共享中,保护重要高程数据的信息安全十分重要,提出了一种针对DEM重点区域进行多分辨率伪装和分权限还原的信息保护方法。结合传统信息保护技术的特点,分析DEM数据重点区域多分辨率伪装的流程和特点,介绍了重点区域数据置乱以及原始数据高程漏洞填充等进行信息伪装的数据准备和预处理方法。结合DWT算法能够进行多分辨率分解的特点,实现了DEM重点区域的多分辨率信息伪装与还原。通过实验分析,证明了该方法的可行性,可以为不同权限的用户提供适当精度的高程数据,能够保证DEM数据特别是重点区域在存储和传输中的信息安全。     

低空无人机双介质水下礁盘深度测量试验与分析

以双介质摄影测量原理为基础,利用海岛无人机影像开展了航空双介质摄影测量试验与分析。首先简单介绍了双介质摄影测量原理,论述了双介质摄影测量对海水折射率的精度要求,提出了一种解算高程改正系数方法,在此基础上开展了基于某岛低空无人机影像的双介质水下礁盘深度量测试验。试验表明,航空双介质摄影测量在实际应用中是可行的,对航空摄影条件要求比较高。     

Modelling of piping collapses and gully headcut landforms:Evaluating topographic variables from different types of DEM

The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model(DEM)data.The unique terrain characteristics of a particular landscape are derived from DEM,which are responsible for initiation and development of ephemeral gullies.As the topographic features of an area significantly influences on the erosive power of the water flow,it is an important task the extraction of terrain features from DEM to properly research gully erosion.Alongside,topography is highly correlated with other geo-environmental factors i.e.geology,climate,soil types,vegetation density and floristic composition,runoff generation,which ultimately influences on gully occurrences.Therefore,terrain morphometric attributes derived from DEM data are used in spatial prediction of gully erosion susceptibility(GES)mapping.In this study,remote sensing-Geographic information system(GIS)tech-niques coupled with machine learning(ML)methods has been used for GES mapping in the parts of Semnan province,Iran.Current research focuses on the comparison of predicted GES result by using three types of DEM i.e.Advanced Land Observation satellite(ALOS),ALOS World 3D-30 m(AW3D30)and Advanced Space borne Thermal Emission and Reflection Radiometer(ASTER)in different resolutions.For further progress of our research work,here we have used thirteen suitable geo-environmental gully erosion conditioning factors(GECFs)based on the multi-collinearity analysis.ML methods of conditional inference forests(Cforest),Cubist model and Elastic net model have been chosen for modelling GES accordingly.Variable's importance of GECFs was measured through sensitivity analysis and result show that elevation is the most important factor for occurrences of gullies in the three aforementioned ML methods(Cforest=21.4,Cubist=19.65 and Elastic net=17.08),followed by lithology and slope.Validation of the model's result was performed through area under curve(AUC)and other statistical indices.The validation result of AUC has shown that Cforest is the most appropriate model for predicting the GES assessment in three different DEMs(AUC value of Cforest in ALOS DEM is 0.994,AW3D30 DEM is 0.989 and ASTER DEM is 0.982)used in this study,followed by elastic net and cubist model.The output result of GES maps will be used by decision-makers for sustainable development of degraded land in this study area.