Nowadays, maritime infrastructure is heavily exploited, which requires monitoring. The article presents the implementation of spatial information which are point clouds for monitoring and analysis of the area around the port (buildings and wharves). For this study, point clouds coming from terrestrial (TLS) and airborne laser scanning (ALS), each of them having different accuracy, were used. An important part of the analysis was the integration of the two data sources. Through integration, the acquisition of information on areas not covered by the measurement in the presented case, one of the methods was possible for use (e.g. the roofs in case of TLS, or the lack of some of the walls of buildings in case of ALS).Another aspect was to use this data. Measurement of the shape and geometry of objects was executed. Additionally, the planeness analysis of individual elements of port infrastructure has been carried out. An interesting analysis was to determine the water level, based on relation to specific characteristics of the light reflectance.

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Trees growing on private property have become an essential part of urban green policies. In many places, restrictions are imposed on tree removal on private property. However, monitoring compliance of these regulations appears difficult due to a lack of reference data and public administration capacity. We assessed the impact of the temporary suspension of mandatory permits on tree removal, which was in force in 2017 in Poland, on the change in urban tree cover (UTC) in the case of the municipality of Racibórz. The bi-temporal airborne laser scanning (ALS) point clouds (2011 and 2017) and administrative records on tree removal permits were used for analyzing the changes of UTC in the period of 2011–2017. The results show increased tree removal at a time when the mandatory permit was suspended. Moreover, it appeared that most trees on private properties were removed without obtaining permission when it was obligatory. The method based on LiDAR we proposed allows for monitoring green areas, including private properties.

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