The paper presents an efficient methodology of water body extent estimation based on remotely sensed data collected with UAV (Unmanned Aerial Vehicle). The methodology includes the data collection with selected sensors and processing of remotely sensed data to obtain accurate geospatial products that are finally used to estimate water body extent. Three sensors were investigated: RGB (Red Green Blue) camera, thermal infrared camera, and laser scanner. The platform used to carry each of these sensors was an Aibot X6—a multirotor type of UAV. Test data was collected at 6 sites containing different types of water bodies, including 4 river sections, an old river bed, and a part of a lake shore. The processing of collected data resulted in 2.5-D and 2-D geospatial products that were used subsequently for water body extent estimation. Depending on the type of used sensor, the created geospatial product, and the type of the water body and the land cover, three strategies employing image processing tools were developed to estimate water body range. The obtained results were assessed in terms of classification accuracy (distinguishing the water body from the land) and geometrical planar accuracy of the water body extent. The product identified as the most suitable in water body detection was four bands RGB+TIR (Thermal InfraRed) ortho mosaic. It allowed to achieve the average kappa coefficient of the water body identification above 0.9. The planar accuracy of water body extent varied depending on the type of the sensor, the geospatial product, and the test site conditions, but it was comparable with results obtained in similar studies.

We present the results of a four-year collaborative project involving researchers and agencies that manage the Pełcznica Valley near Książ Castle in southwestern Poland. The findings show that long-term historical inquiry clarifies the consequences of past land-use practices and the limits of passive conservation, while protecting scenic value can serve as common ground between heritage and nature conservation. We highlight the dynamic, context-dependent nature of visual relationships and underscore the need for flexible management, continuous monitoring, and durable cross-sector cooperation in protected landscapes. Integrating interdisciplinary knowledge, GIS-based analysis, and sustained stakeholder engagement strengthened collective interpretation and supported collaborative landscape learning. The combination of scientific, historical, and local expertise enabled the development of realistic strategies for restoring historic views while accounting for current formal, technical, and ecological constraints.