China
Africa
Explore chapters and articles related to this topic
Slope failure prediction using a spatial probabilistic modeling approach integrated with Monte Carlo simulation and GIS
Published in Katsuhiko Sugawara, Yuzo Obara, Akira Sato, Rock Stress, 2020
G. Zhou, T. Esaki, Y. Miani, J. Mori
A prepared digital terrain map (contour polyline) was used to build TIN (Triangulated Irregular Network) model in the GIS. The TIN is a form of surface model which partitions a surface into a set of contiguous, non-overlapping, triangles. A height value is recorded for each triangle node. Heights between nodes can be interpolated thus allowing for the definition of a continuous surface. In order to calculate the slope angle, the digital elevation model (DEM) was produced from TIN with certain grid (pixel) size. A sensitivity study of the grid size influence on calculated slope angle is carried out by using grid size of 2 m, 5 m, 10 m, 20 m and 50 m. It is found that the slope angle distributions are not remarkably changed when grid size is less than 20 m. By considering work efficiency and a common Japanese house size, a grid size of 10 m is chosen as optimum. The calculation of slope angle for a cell uses the neighboring 8 cell’s value.
Two Dimensional (2D) Surface Model
Published in Ahmad Fikri Bin Abdullah, A Methodology for Processing Raw Lidar Data to Support Urban Flood Modelling Framework, 2020
A TIN is a digital data structure, used in a Geographic Information System (GIS), for the representation of a surface. A TIN is a vector based representation of the physical land surface or sea bottom, made up of irregularly distributed nodes and lines, with three dimensional coordinates (x, y, and z) that are arranged in a network of non-overlapping triangles (refer to Figure 3.4). An advantage of using a TIN over a raster DTM in mapping and analysis, is that the points of a TIN are distributed variably, based on an algorithm that determines which points are most necessary for an accurate representation of the terrain. Data input is therefore flexible and fewer points need to be stored than in a raster DTM with regularly distributed points. A TIN may be less suited to certain kinds of GIS applications than a raster DTM, such as for the analysis of a surface's slope and aspect.
Multiview Image Matching for 3D Earth Surface Reconstruction
Published in Yuhong He, Qihao Weng, High Spatial Resolution Remote Sensing, 2018
There are many data formats for representing the 3D Earth's surface, such as the digital surface model (DSM), the TIN, and 3D point clouds. In object-space matching, each grid cell with a known X and Y and an optimal Z* after multi-image matching generates a 2.5D DSM; the iteration over all grid cells within the image boundary generates elevations as attributes for each grid. In the image-space matching case, a pixel or feature point in the reference image creates an accurate ground point after matching; iteration over all image pixels or detected feature points creates point clouds inside the image boundary. Point clouds can be further processed to produce a TIN or DSM. A TIN is usually generated after matching different primitives, such as points and edges (Wu et al., 2012). There are also other ground definition methods in multi-image matching, such as the inclined planar surface patch (Jiang, 2004) as well as the voxel in a space-sweep method (Collins, 1996) and a volume graphic cut matching method (Vogiatzis et al., 2005). Currently, DSM is still the most popular 3D Earth surface representation for satellites and aerial images acquired via narrow baseline.
A procedural footprint enhancement of global topographic surface with multiple levels of detail
Published in International Journal of Digital Earth, 2020
The triangulated irregular network (TIN) representation of terrain is a classic type of 2D GIS layer which has been employed by early GIS applications since the 1970s. Since then, a number of methods addressing the integration of points, poly-lines, areas, or even volumetric objects into TINs were developed. Two general approaches can be identified for the integration of poly-lines into TIN. The first approach inserts poly-lines directly into the triangulation as constrained edges, presented by Daehlen, Fimland, and Hjelle (2001), for example. However, this approach can cause inappropriate trenches or ridges in the resulting TIN-based terrain model. In the second approach, poly-lines are overlaid on top of the terrain (e.g. Wartell et al. 2003). Eppstein (1994) achieved this by adding Steiner points to the poly-lines inserted into the TIN. None of these approaches are perfect. The suitability of the chosen approach to feature-terrain association is data-dependent and the semantics of integrated datasets must be considered, like in Koch and Heipke (2005).
Assessment of reservoir sedimentation of irrigation dams in northern Ghana
Published in Lake and Reservoir Management, 2020
Thomas A. Adongo, Nicholas Kyei-Baffour, Felix K. Abagale, Wilson A. Agyare
For each reservoir, the processed bathymetric survey vectorial data (X, Y, Z) were exported to ArcGIS 10.4 software in ESRI shape format. Using customizing tools in the GIS program and a kridging method, the depth points were interpolated and converted into raster maps. A triangulated irregular network model (TIN) was generated from the data stored in the GIS database using the 3D Analyst module. TIN is a collection of triangles generated using the data points as the corners. The triangles were created using the Delaunay triangulation method, so that all points are connected using their two nearest neighbors to form triangles (Wilson and Richards 2006). Consequently, a bathymetric TIN map was produced for each reservoir. The current storage capacity and water surface area of each reservoir at full supply level were then computed using the TIN model tool in ArcGIS. The current storage capacity and the water surface area of the reservoirs as obtained from the bathymetry and the initial storage capacity and water surface area of the reservoirs were used to compute the loss in storage capacity and water surface area of the reservoirs (equations 2 and 6). The storage capacity lost in a reservoir represents the volume of sediment accumulated in it.