Ai, B.; Xia, Y.; Li, Z.; Yang, F., and Ji, M., 2020. Three-dimensional dynamic visualization of coastal zones for sea-land integration. In: Zheng, C.W.; Wang, Q.; Zhan, C., and Yang, S.B. (eds.), Air-Sea Interaction and Coastal Environments of the Maritime and Polar Silk Roads. Journal of Coastal Research, Special Issue No. 99, pp. 85-91. Coconut Creek (Florida), ISSN 0749-0208.The three-dimensional visualization of coastal zones for sea-land integration faces difficulties such as multisource terrain data fusion, tidal dynamic changes, and sea surface simulation. This paper proposes a method to convert the depth reference of water depth data into the 1985 national elevation benchmarks, which integrates multisource sea-land terrain data by vertical datums, converts rectangular coordinates and establishes a digital elevation model for sea-land terrain integration. Based on the dynamic characteristics of the sea surface with tides, this paper calculates the tidal level of the nearshore tide station using numerical tidal harmonic analysis and a prediction model. The dynamic sea surface height field away from the mainland is established by inverse distance weighted spatial interpolation. The paper simulates the sea surface by Phillips spectrum analysis and fast Fourier transform and achieves dynamic effects for the sea surface by concave-convex mapping and wave mapping, enhancing the reality of dynamic sea surface expression. In addition, this paper designs and develops the Coastal Zone Terrain Three-Dimensional Dynamic Visualization System, which realizes functions such as sea-land integrated three-dimensional terrain roaming, sea surface simulation visualization and the automatic generation of instantaneous coastlines at any time.
When it comes to feature retention in multi-scale representations of ocean flow fields, not all data points are equal. Therefore, this paper proposes a method of selecting data points based on their importance. First, an autocorrelation analysis is performed on flow speed and the rate of change in flow direction. Then, the magnitude of speed and variation in the rate of change in flow direction are classified. Feature regions are determined according to autocorrelation aggregation and classification analysis. Then, rough set theory and evidence theory are applied, using these results to determine the weights of different points. Finally, these weights are used to construct multi-scale representations of ocean flow fields, which effectively retain flow-field characteristics.
Triangulated irregular networks (TINs) are widely used in terrain visualization due to their accuracy and efficiency. However, the conventional algorithm for multi-scale terrain rendering, based on TIN, has many problems, such as data redundancy and discontinuities in scale transition. To solve these issues, a method based on a detail-increment model for the construction of a continuous-scale hierarchical terrain model is proposed. First, using the algorithm of edge collapse, based on a quadric error metric (QEM), a complex terrain base model is processed to a most simplified model version. Edge collapse records at different scales are stored as compressed incremental information in order to make the rendering as simple as possible. Then, the detail-increment hierarchical terrain model is built using the incremental information and the most simplified model version. Finally, the square root of the mean minimum quadric error (MMQE), calculated by the points at each scale, is considered the smallest visible object (SVO) threshold that allows for the scale transition with the required scale or the visual range. A point cloud from Yanzhi island is converted into a hierarchical TIN model to verify the effectiveness of the proposed method. The results show that the method has low data redundancy, and no error existed in the topology. It can therefore meet the basic requirements of hierarchical visualization.
Satellite laser altimetry can obtain sub-meter or even centimeter-scale surface elevation data over large areas, but it is inevitably affected by scattering caused by clouds, aerosols, and other atmospheric particles. This laser ranging error caused by scattering cannot be ignored. In this study, we systematically combined existing atmospheric scattering identification technology used in satellite laser altimetry and observed that the traditional algorithm cannot effectively estimate the laser multiple scattering of the GaoFen-7 (GF-7) satellite. To solve this problem, we used data from the GF-7 satellite to analyze the importance of atmospheric scattering and propose an identification scheme for atmospheric scattering data over land and water areas. We also used a look-up table and a multi-layer perceptron (MLP) model to identify and correct atmospheric scattering, for which the availability of land and water data reached 16.67% and 26.09%, respectively. After correction using the MLP model, the availability of land and water data increased to 21% and 30%, respectively. These corrections mitigated the low identification accuracy due to atmospheric scattering, which is significant for facilitating satellite laser altimetry data processing.
There are some limitations in traditional ocean scalar field visualization methods, such as inaccurate expression and low efficiency in the three-dimensional digital Earth environment. This paper presents a spherical volume-rendering method based on adaptive ray casting to express ocean scalar field. Specifically, the minimum bounding volume based on spherical mosaic is constructed as the proxy geometry, and the depth texture of the seabed terrain is applied to determine the position of sampling points in the spatial interpolation process, which realizes the fusion of ocean scalar field and seabed terrain. Then, we propose an adaptive sampling step algorithm according to the heterogeneous depth distribution and data change rate of the ocean scalar field dataset to improve the efficiency of the ray-casting algorithm. In addition, this paper proposes a nonlinear color-mapping enhancement scheme based on the skewness characteristics of the datasets to optimize the expression effect of volume rendering, and the transparency transfer function is designed to realize volume rendering and local feature structure extraction of ocean scalar field data in the study area.
Bike-sharing data are an important data source to study urban mobility in the context of the coronavirus disease 2019 (COVID-19). However, studies that focus on different bike-sharing activities including both riding and rebalancing are sparse. This limits the comprehensiveness of the analysis of the impact of the pandemic on bike-sharing. In this study, we combine geospatial network analysis and origin-destination (OD) clustering methods to explore the spatiotemporal change patterns hidden in the bike-sharing data during the pandemic. Different from previous research that mostly focuses on the analysis of riding behaviors, we also extract and analyze the rebalancing data of a bike-sharing system. In this study, we propose a framework including three components: (1) a geospatial network analysis component for a statistical and spatiotemporal description of the overall riding flows and behaviors, (2) an origin-destination clustering component that compensates the network analysis by identifying large flow groups in which individual edges start from and end at nearby stations, and (3) a rebalancing data analysis component for the understanding of the rebalancing patterns during the pandemic. We test our framework using bike-sharing data collected in New York City. The results show that the spatial distribution of the main riding flows changed significantly in the pandemic compared to pre-pandemic time. For example, many riding trips seemed to expand the purposes of riding for work–home commuting to more leisure activities. Furthermore, we found that the changes in the riding flow patterns led to changes in the spatiotemporal distributions of bike rebalancing, such as the shifting of the rebalancing peak time and the increased ratio between the number of rebalancing and the total number of rides. Policy implications are also discussed based on our findings.
This paper takes the map load as the evaluation indicator for multi-scale representations of ocean flow fields using Euler method. First, the map load of the ocean flow fields is calculated and the suitable map load interval is analysed in order to obtain the key scale for the multi-scale representation of ocean flow fields. Then, the load is limited by appropriate load intervals to fit the relationship between map load and scale. Finally, a mathematical model for multilevel comprehensive evaluation in fuzzy mathematics is established, and the usability of the resulting ocean flow field map is evaluated using questionnaires.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)