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The paper presents the applicability of LiDAR data in limnological research with respect to the identification of the shoreline of lakes, and consequently the scale of overgrowing of lakes. Data obtained from the Head Office of Geodesy and Cartography from the period 2011 2016 provided the basis for the identification of shorelines for 14 lakes of the Pomeranian Lakeland. The paper employs the orthophotomap for the determination of the range of lake vegetation. Using the line determined based on the orthophotomap and that determined based on LiDAR points, the zone of overgrowing of the analysed lakes was delineated. In all the analysed cases combined, the water surface area was found to be reduced in comparison to the shoreline identified through LiDAR scanning by 161.5 ha, suggesting a 21.2% share of vegetation in the surface area of lakes.

Precisely determining agricultural land abandonment (ALA) in an area is still difficult, even with recent progress in data collection and analysis. It is especially difficult in fragmented areas that need more tailor-made methods. The aim of this research was to determine ALA using airborne laser scanning (ALS) data, which are available in Poland with 4 to 6 points per square metre resolution. ALS data were processed into heat maps and modified with chosen kernel functions: triweight and Epanechnikov. The results of ALS data processing were compared to the control method, i.e., visual interpretation of an orthophotomap. This study shows that ALS data modelled with kernel functions allow for a good identification of ALA. The accuracy of results shows 82% concordance as compared to the control method. When comparing triweight and Epanechnikov functions, higher accuracy was achieved when using the triweight function. The research shows that ALS data processing is a promising method of detection of ALA and could provide an alternative to well-known methods such as the analysis of satellite images.

This paper presents the results of an analysis of the pace of filling one of the deepest European granite quarries with water. A DTM (digital terrain model) based on data from LiDAR ALS (light detection and ranging airborne laser scanning) was used to create a model of the pit of the Strzelin I granite quarry and to determine the reach and surface area of the direct catchment of the excavation pit. The increase in the volume of water in the excavation pit was determined. Analogue maps and DTM were used to calculate the maximum depth of the pit (113.3 m), its surface area (9.71 ha), and its capacity (5.1 million m3). The volume of water collected in the excavation pit during the years 2011&ndash, 2018 was determined based on the analogue base map and the DTM. The result was 0.335 million m3. Based on the data made available by the mining company, the correlation of the DTM with the orthophotomap of the mining area and additional field measurements, the ordinates of the water level in the years 2011&ndash, 2018 were determined. Initially, the water surface level in the quarry was located on the ordinate of 66.6 m a.s.l. (July 20, 2011). After the pumping of water was discontinued, the level rose to 96.1 m a.s.l. (January 28, 2018). The increase in the water volume in the quarry pit during specific periods was determined (actual retention increase). The obtained data on the volume of the retained water referred to the period during which it accumulated in the quarry. On average, the net increase in water retention in the excavation pit was 138.537 m3∙d&minus, 1, and the calculated net supply from the direct catchment (16.04 ha) was 101.758 m3∙d&minus, 1. The use of DTM and measurements of the water level in the excavation pit seem to be an efficient means of estimating the pace of spontaneous filling of the quarry with water supplied from the direct physiographic catchment.

Mapping plant communities is a difficult and time consuming endeavor. Methods relying on field surveys deliver high quality data but are usually limited to relatively small areas. In this paper we apply airborne hyperspectral data to vegetation mapping in remote and hard to reach areas. We classified 22 vegetation communities in the Giant Mountains on 3.12-m Airborne Prism Experiment (APEX) hyperspectral images, registered in 288 spectral bands (10 September 2012). As the classification algorithm, Support Vector Machines (SVM) was used. APEX data were corrected geometrically and atmospherically, and three dimensionality reduction methods were performed to select the best dataset. As reference we used a non-forest vegetation map containing vegetation communities of Polish Karkonosze National Park from 2002, orthophotomap and field surveys data from 2013 to 2014. We obtained the post-classification maps of 22 vegetation communities, lakes and areas without any vegetation. Iterative accuracy assessment repeated 100 times was used to obtain the most objective results for individual communities. The median value of overall accuracy (OA) was 84%. Fourteen out of twenty-four classes were classified of more than 80% of producer accuracy (PA) and sixteen out of twenty-four of user accuracy (UA). APEX data and SVM with the use of iterative accuracy assessment are useful for the mountain communities classification. This can support both Polish and Czech national parks management by giving the information about diversity of communities in the whole transboundary area, helping with identification especially in changing environment caused by humans.