Scientific visualization of landscapes and landforms

Mitášová, Helena; Harmon, Russell S.; Weaver, Kristen R.; Lyons, Nathan J.; Overton, Margery F.

doi:10.1016/j.geomorph.2010.09.033

Cited by 50 publications

(24 citation statements)

References 32 publications

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“…Surface visualization using 2D shaded relief images or 3D perspective view, fully integrated within most GIS and RS software systems, has become a core tool for the analysis of landscapes and landforms [49]. Nowadays, geographic WEB portals like Google Earth ® or Bing Maps TM provide free 3D models, employable in the initial stage of landslide investigation, or even for extracting information on previous landslides [50,51] or validating inventories and databases.…”

Section: Optical Passive Sensors For Landslide Recognitionmentioning

confidence: 99%

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Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives

et al. 2014

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Landslides represent major natural hazards, which cause every year significant loss of lives and damages to buildings, properties and lifelines. In the last decades, a significant increase in landslide frequency took place, in concomitance to climate change and the expansion of urbanized areas. Remote sensing techniques represent a powerful tool for landslide investigation: applications are traditionally divided into three main classes, although this subdivision has some limitations and borders are sometimes fuzzy. The first class comprehends techniques for landslide recognition, i.e., the mapping of past or active slope failures. The second regards landslide monitoring, which entails both ground deformation measurement and the analysis of any other changes along time (e.g., land use, vegetation cover). The third class groups methods for landslide hazard analysis and forecasting. The aim of this paper is to give an overview on the applications of remote-sensing techniques for the three categories of landslide investigations, focusing on the achievements of the last decade, being that previous studies have already been exhaustively reviewed in the existing literature. At the end of the paper, a new classification of remote-sensing techniques that may be pertinently adopted for investigating specific typologies of soil and rock slope failures is proposed.

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Section: Optical Passive Sensors For Landslide Recognitionmentioning

confidence: 99%

“…Hill-shaded visualizations derived from ALS HR-DEM's help delineate morphological features of landslides [49]. Mainly, two different methods are applied for landslide detection: (1) the heuristic visual analysis of the topographic surface; and (2) the semi-automatic recognition of morphometric features [94].…”

Section: Airborne and Terrestrial Laser Scanningmentioning

confidence: 99%

Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives

et al. 2014

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“…The combination of VR and GIS facilitates exploring large regions at high resolution leading to field scale experiences with varying levels of immersion. Examples include Geowall, which is a low-cost interactive 3D stereoscopic projection system that provides 3D immersion using a single wall, Vision Dome, that creates a full 360°projection with 180°field of view, and Cave Automatic Virtual Environment (CAVE™) that projects images on multiple walls of a room-sized cube (Mitasova, Weaver, & Lyons, 2011).…”

Section: Virtual Reality Definedmentioning

confidence: 99%

“…When Pittman penned his paper, the Virtual Environments Laboratory he describes at the School of Design at North Carolina State University introduced design concepts at that university with relatively new levels of accuracy. As additional labs have become established the ability to accurately represent environments has increased and the use of VR has broadened (e.g., Atwell & Gretlein, 2013;Gill et al 2013;Lange, 2011;Mitasova et al, 2011).…”

Section: How Real Must Real Be?mentioning

confidence: 99%

To go where no man has gone before: Virtual reality in architecture, landscape architecture and environmental planning

Portman

Natapov

Fisher-Gewirtzman

2015

Computers, Environment and Urban Systems

333

147

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“…New algorithms continue to appear, for example by Magillo et al (2007Magillo et al ( , 2008, Hashemi (2008) for peaks, Rana 485 (2010), Wang (2014) for drainage networks, and Chen and Zhou (2013) for multiscale VIPs. Mitasova et al (2012) have rigorously explored the role of the surface network in terrain visualization and made use of open source tools in raising terrain analysis to new 490 levels of sophistication, including adding back flow lines. Guilbert (2013) continues to explore the multiscale effects on terrain and its network, especially with new higher resolution data.…”

Section: Contemporary Research On the Surface Network 480mentioning

confidence: 99%