How does data accuracy influence the reliability of digital viewshed models? A case study with wind turbines

Klouček, Tomáš; Lagner, Ondřej; Šímová, Petra

doi:10.1016/j.apgeog.2015.09.005

Cited by 34 publications

(25 citation statements)

References 30 publications

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“…However, these packages offer limited functions for computing viewsheds and require many other operations to conduct complete visibility analyses. The use of these tools to measure ecosystem services or quantify green infrastructure is still relatively limited (Klouček et al, 2015;Loures et al, 2015).…”

Section: Visual Landscape Analysismentioning

confidence: 99%

Visual structure of landscapes seen from built environment

Hilal

Joly

Roy

et al. 2018

Urban Forestry & Urban Greening

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Vegetation in cities keeps climate warming down and improves the health of people and ecosystems while making for a pleasant urban setting. Contemporary urban planning promotes sustainable green cities. Green and blue infrastructures, which help maintain an eco-friendly environment, are the primary instruments of this movement. This paper attempts to show the relative weight of plant life (trees, grass, orchards, etc.) seen from buildings in different urban settings (local urban patterns of high or low density of buildings). Landscapes open to view are identified by combining a digital elevation model and an 11-class land-use layer (including buildings, facilities, grey infrastructures, green and blue surfaces) in a computational tool that calculates viewsheds. The results show that vegetation is very much present in urban landscapes. In high-density built areas of city centres, the landscape is varied although not open and is dominated by trees, low-rise residential buildings and grass. Grey infrastructures and bushes are also very common. In low-density built areas the rank order of objects in view is similar but the landscape is more panoramic.

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Section: Visual Landscape Analysismentioning

confidence: 99%

Visual structure of landscapes seen from built environment

Hilal

Joly

Roy

et al. 2018

Urban Forestry & Urban Greening

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“…Ruiz [27] examined the effect of DTM error on an estimate of viewshed. Kloucek et al [28] used random control points to confirm that a model with higher resolution enables a more accurate sight distance output. Castro et al [29] described the impact of DTM resolution and the spacing between path stations on highway sight distance studies.…”

Section: Introductionmentioning

confidence: 99%

Using Small Unmanned Aerial Vehicle in 3D Modeling of Highways with Tree-Covered Roadsides to Estimate Sight Distance

et al. 2019

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The safe and efficient operation of highways largely depends on the adequate provision of sight distance. Small unmanned aerial vehicles (UAVs) can be utilized to efficiently complete data acquisition very soon after identifying an issue when searching for potential highway safety risks. A double grid flight is proposed to obtain an adequate three-dimensional (3D) recreation of the road environment, ensuring an unbiased sight distance output. Then, a dense cloud point is derived through a Structure from Motion Multi-View Stereo process. The point cloud is classified to produce both a terrain model, characterized by its resolution, and a 3D-object model, characterized by the maximum edge length of the entities. The resulting road environment model is utilized to calculate sight distance in a geographic information system. The results enabled the detection of accident-prone locations caused by sight distance limitations. Moreover, the impact of the 3D modeling parameters on the results was assessed.

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“…In addition to the computational algorithm, the reliability of the resulting visibility model also depends on the quality of the input digital surface model (DSM) (Klouček, Lagner, & Šímová, 2015;Lake, Lovett, Bateman, & Day, 2000;Sander & Manson, 2007), and Fisher (1992) previously noted that it would be an error to assume the input DSM to be accurate. Although many studies have dealt with improving algorithms, only a few studies have focused on the effect the spatial accuracy of input data has on the reliability of results from visibility analyses, even though, as can been seen in older visibility studies (Fisher, 1992;Huss & Pumar, 1997) and spatial uncertainty research in other areas (for review see Barry & Elith, 2006;Moudrý & Šímová, 2012), it is highly probable that decreased data quality correlates with decreased quality of results.…”

Section: Introductionmentioning

confidence: 99%

“…Problems with implementing vegetation and structures into DSMs do not arise when using LiDAR-based surface models, which already contain objects on the surface and are considered by many authors to be currently the most accurate data input for visibility analyses (see Castro, García-Espona, & Iglesias, 2015;Lake et al, 2000;Murgoitio, Shrestha, Glenn, & Spaete, 2014). Using the example of wind turbine visibility and comparing modelled visibility with actual visibility in the field, Klouček et al (2015) demonstrated that use of a LiDAR-based DSM can result in an approx. 90% match rate with reality while the use of DSMs based on vector layers of various scales resulted in only 50-80% match rates.…”

Section: Introductionmentioning

confidence: 99%

Peer Review #1 of "Impact of input data (in)accuracy on overestimation of visible area in digital viewshed models (v0.1)"

2018

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Viewshed analysis is a GIS tool in standard use for more than two decades to perform numerous scientific and practical tasks. The reliability of the resulting viewshed model depends on the computational algorithm and the quality of the input digital surface model (DSM). Although many studies have dealt with improving viewshed algorithms, only a few studies have focused on the effect of the spatial accuracy of input data. Here, we compare simple binary viewshed models based on DSMs having varying levels of detail with viewshed models created using LiDAR DSM. The compared DSMs were calculated as the sums of digital terrain models (DTMs) and layers of forests and buildings with expertly assigned heights. Both elevation data and the visibility obstacle layers were prepared using digital vector maps differing in scale (1:5,000, 1:25,000, and 1:500,000) as well as using a combination of a LiDAR DTM with objects vectorized on an orthophotomap. All analyses were performed for 104 sample locations of 5 km 2 , covering areas from lowlands to mountains and including farmlands as well as afforested landscapes. We worked with two observer point heights, the first (1.8 m) simulating observation by a person standing on the ground and the second (80 m) as observation from high structures such as wind turbines, and with five estimates of forest heights (15, 20, 25, 30, and 35 m). At all height estimations, all of the vector-based DSMs used resulted in overestimations of visible areas considerably greater than those from the LiDAR DSM. In comparison to the effect from input data scale, the effect from object height estimation was shown to be secondary.

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How does data accuracy influence the reliability of digital viewshed models? A case study with wind turbines

Cited by 34 publications

References 30 publications

Visual structure of landscapes seen from built environment

Visual structure of landscapes seen from built environment

Using Small Unmanned Aerial Vehicle in 3D Modeling of Highways with Tree-Covered Roadsides to Estimate Sight Distance

Peer Review #1 of "Impact of input data (in)accuracy on overestimation of visible area in digital viewshed models (v0.1)"

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