A Method of Optimizing Terrain Rendering Using Digital Terrain Analysis

Terrain rendering is an important issue in Geographic Information Systems and other fields. During large-scale, real-time terrain rendering, complex terrain structure and an increasing amount of data decrease the smoothness of terrain rendering. Existing rendering methods rarely use the features of terrain to optimize terrain rendering. This paper presents a method to increase rendering performance through precomputing roughness and self-occlusion information making use of GIS-based Digital Terrain Analysis. Our method is based on GPU tessellation. We use quadtrees to manage patches and take surface roughness in Digital Terrain Analysis as a factor of Levels of Detail (LOD) selection. Before rendering, we first regularly partition the terrain scene into view cells. Then, for each cell, we calculate its potential visible patch set (PVPS) using a visibility analysis algorithm. After that, A PVPS Image Pyramid is built, and each LOD level has its corresponding PVPS. The PVPS Image Pyramid is stored on a disk and is read into RAM before rendering. Based on the PVPS Image Pyramid and the viewpoint’s position, invisible terrain areas that are not culled through view frustum culling can be dynamically culled. We use Digital Elevation Model (DEM) elevation data of a square area in Henan Province to verify the effectiveness of this method. The experiments show that this method can increase the frame rate compared with other methods, especially for lower camera flight heights.

A history of TOPMODEL

The theory that forms the basis of TOPMODEL (a topography-based hydrological model) was first outlined by Mike Kirkby some 45 years ago. This paper recalls some of the early developments, the rejection of the first journal paper, the early days of digital terrain analysis, model calibration and validation, the various criticisms of the simplifying assumptions, and the relaxation of those assumptions in the dynamic forms of TOPMODEL. A final section addresses the question of what might be done now in seeking a simple, parametrically parsimonious model of hillslope and small catchment processes if we were starting again.

A history of TOPMODEL

The theory that forms the basis of Topmodel was first outlined by Mike Kirkby some 45 years ago. This paper recalls some of the early developments; the rejection of the first journal paper; the early days of digital terrain analysis; model calibration and validation; the various criticisms of the simplifying assumptions; and the relaxation of those assumptions in the dynamic forms of Topmodel. A final section addresses the question of what might be done now in seeking a simple, parametrically parsimonious model of hillslope and small catchment processes if we were starting again.

Leading Progress in Digital Terrain Analysis and Modeling

Digital Terrain analysis (DTA) and modeling has been a flourishing interdisciplinary field for decades, with applications in hydrology, geomorphology, soil science, engineering projects and computer sciences. Currently, DTA is characterized by a proliferation of multispectral data from new sensors and platforms, driven by regional and national governments, commercial businesses, and scientific groups, with a general trend towards data with higher spatial, spectral or temporal resolutions. Deriving meaningful and interpretable products from such a large pool of data sources sets new challenges. The articles included in this special issue (SI) focuses on terrain analysis applications that advance the fields of hydrology, geomorphology, soil science, geographic information software (GIS), and computer science. They showcase challenging examples of DTA tackling different subjects or different point of views on the same subject.

Automatic crater detection by mining existing crater map

<p><strong>Abstract.</strong> Existing geomorphologic type maps manually delineated by experts contain implicit expert knowledge on the spatial structure of real geomorphologic objects. Taking crater map as example, this study presents an attempt of mining the implicit expert knowledge contained in existing crater maps to automatically detect craters from digital elevation models (DEMs) of an area with similar environment as the area of the crater map.</p><p>The proposed automatic crater detection approach trains random forests classifiers based on existing crater map of an area and the spatial structural information derived by digital terrain analysis on DEM in the corresponding area, and then applies the trained classifiers to the application area with DEM.</p><p>A case study with the Lunar Orbiter Laser Altimeter (LOLA) crater map (Head et al., 2010) and Chang’E-1 lunar DEM (Li et al., 2010) with a resolution of 500&amp;thinsp;m shows that the proposed approach performed better than AutoCrat (Stepinski et al., 2009; 2012), a representative of existing crater detection approaches based on digital terrain analysis which have not effectively considered the spatial structural information of real craters.</p>