Reitberger, RolandRolandReitbergerPattnaik, NayaneshNayaneshPattnaikLu, YingYingLu2024-10-212024-10-212024-09-1835. Forum Bauinformatik, fbi 2024: 228-235https://hdl.handle.net/11420/49610Trees provide various ecosystem services to humans. Therefore, increasing their number in urban areas is necessary. Identifying suitable tree planting locations is essential for cities' resource allocation. However, city-wide information, such as underground pipes, is hardly available. In this paper, we introduce a GIS-based method to identify potential tree planting locations within Munich, utilizing aboveground data. Our methodology combines imperviousness-levels with existing tree data from remote observations. This enables us to investigate tree distribution across different imperviousness levels, identifying areas unsuitable for tree planting. This approach is applied to several land use typologies, thereby identifying suitable planting distances from buildings. We perform a distance analysis for existing trees to identify the nearest neighboring tree for each individual tree. By analyzing the distribution and counts of tree-to-tree distances, a suitable distance between two trees is determined from real-world data. Our case study in Munich exemplifies the methodology. The key findings are that locations with up to 81 % imperviousness can sustain a significant probability for tree planting. Suitable planting distances vary by land use typology. The common spacing between tree pairs is 2.97 meters. The results offer insights into urban areas with a high potential for additional trees, based on above-ground data.enhttps://creativecommons.org/licenses/by/4.0/Empirical Tree PlacementGIS AnalysisTree LocationsUrban Green InfrastructureComputer Science, Information and General Works::006: Special computer methodsSocial Sciences::333: Economics of Land and Energy::333.7: Natural Resources, Energy and EnvironmentConference Paper10.15480/882.1351710.15480/882.13517Conference Paper