Type: Article
Title: The green window view index: automated multi-source visibility analysis for a multi-scale assessment of green window views
Authors: Bolte, Anna-Maria
Niedermann, Benjamin
Kistemann, Thomas
Haunert, Jan-Henrik
Dehbi, Youness 
Kötter, Theo
Issue Date: 11-Mar-2024
Keywords: Visibility analysis; Urban green; Open source data; CityGML; LiDAR; ALKIS
Context: Providing accessible urban green spaces is crucial for planning and ensuring healthy, resilient, and sustainable cities. The importance of visually accessible urban green spaces increases due to inner urban development processes.
Objectives: This article proposes a new index, the Green Window View Index (GWVI) for analyzing and assessing visible vegetation, that promotes an integrated planning of urban green spaces and buildings at different scales and levels. It is defined as the proportion of visible vegetation area in a field of view when looking out of a specific window with a defined distance to the window.
Methods: The method for estimating GWVI consists of three steps: (a) the modeling of the three-dimensional environment, (b) the simulation of the two-dimensional window views using modern rendering engines for three-dimensional graphics, (c) the computation of the GWVI. The method is proposed and tested through a case study of the urban area of Bonn, Germany, using a Digital Terrain Model (DTM), CityGML-based semantic 3D City Model at level of detail (LoD) 2, airborne Light Detection and Ranging (LiDAR) data, and 2D land use data from the official German property cadaster information system (ALKIS).
Results: With an average processing time of 0.05 s per window view, an average GWVI of 26.00% could be calculated for the entire study area and visualized in both 2D and 3D.
Conclusion: The proposed engine generates multi-scale visibility values for various vegetation shapes. These values are intended for use in participatory citizenship and decision-making processes for analysis by architects, real-estate appraisers, investors, and urban as well as landscape planners.
Subject Class (DDC): 710: Landschaftsgestaltung, Raumplanung
HCU-Faculty: Computational Methods 
Journal or Series Name: Landscape Ecology 
Volume: 39
Publisher: Springer
ISSN: 0921-2973
Publisher DOI: 10.1007/s10980-024-01871-7
URN (Citation Link): urn:nbn:de:gbv:1373-repos-12703
Directlink: https://repos.hcu-hamburg.de/handle/hcu/989
Language: English
Creative Commons License: https://creativecommons.org/licenses/by/4.0/
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