This work aims to assess the spatial distribution and concentration of sulfur in the topsoil layer and to determine the relationships between sulfur concentration, soil pH, soil electrical conductivity, and plant cover at the reforested site of the former sulfur mine (Southern Poland). Soil samples were collected from 0 to 20 cm (topsoil) from a total of 86 sampling points in a regular square grid with sides of 150 m. Plant cover was assayed in circular plots with an area of 100 m, divided into a woody plant layer and herbaceous plant layer. Soil properties such as particle size distribution, pH in KCl and HO, soil electrical conductivity (EC), soil organic carbon (SOC), total nitrogen (N), and total sulfur (S) were determined. The degree of soil contamination with sulfur was assessed based on the guidelines of the Institute of Soil Science and Plant Cultivation (IUNG), Poland. The results indicate that remediation and application of lime were not fully effective in spatial variation, because 33 points with sulfur contamination above 500 mg kg were observed. These spots occurred irregularly in the topsoil horizons. This high sulfur concentration in the soil did not result in severe acidification (below 4.5) in all cases, most likely due to neutralization from the application of high doses of flotation lime. High vegetative cover occurred at some points with high soil sulfur concentrations, with two points having S concentration above 40,000 mg kg and tree cover about 60%. Numerous points with high soil EC above 1500 μS cm as well as limited vegetation and high soil sulfur concentrations, however, indicate that the reclamation to forest is still not completely successful.

In the presented study, the Sentinel-2 vegetation indices (VIs) were evaluated in context of estimating defoliation of Scots pine stands in western Poland. Regression and classification models were built based on reference data from 50 field plots and Sentinel-2 satellite images from three acquisition dates. Three machine-learning (ML) methods were tested: k-nearest neighbors (kNN), random forest (RF), and support vector machines (SVM). Regression models predicted stands defoliation with moderate accuracy. R 2 values for regression models amounted to 0.53, 0.57, 0.57 for kNN, RF and SVM, accordingly. Analogically, the following values of normalized root mean squared error were obtained: 12.2%, 11.9% and 11.6%. Overall accuracies for two-class classification models were 78%, 75%, 78% for kNN, RF and SVM methods. The Green Normalized Difference Vegetation Index and MERIS Terrestrial Chlorophyll Index VIs were found to be most robust defoliation predictors regardless of the ML method. We conclude that Sentinel-2 satellite images provide useful information about forest defoliation and may contribute to forest monitoring systems.

The study was performed for the part of the administrative district Milicz. The authors analysed the parcels where the changes in land use, compared to the cadastral data, were found. The areas of interest were the parcels, where agricultural use was abandoned and the forest succession progressed. This paper investigates the possibility of applying satellite images Sentinel-2A for the automation of land use/land cover change detection, mainly in the aspect of monitoring uncontrolled forest succession. The results of the supervised classification of images Sentinel-2A were referred to the results of the traditionally applied manual vectorization of aerial orthophotomap. The difference for area covered by trees or shrub was 3.85% of the analysed parcels area. Analysing the results for each parcel in which the process of succession occurred, the mean difference is on average 2.25% for one parcel. The mean difference in the absolute value of the total area of participation in individual land use plots was about 1.54% of the analysed area.ARTICLE HISTORY

Abstract-The paper presents the results from the study concerning the application of airborne laser scanning (ALS) data and derived raster products like the digital surface model (DSM) and the digital terrain model (DTM) for the assessment of the degree of change of the land use based on the forest succession example. Simultaneously, an automated method of ALS data processing was developed based on the normalized (nDSM) and cadastral GIS information. Besides delivering precise information on forest succession, ALS technology is an excellent tool for time-changes spatial analyses. Usage of the ALS data can support the image interpretation process decreasing the subjectivity of the operator. In parallel, a manual vectorization and object classification (objectbased image analysis-OBIA) were performed; both based on aerial orthophoto and ALS data. By using integrated ALS point clouds and digital aerial images, one can obtain fast OBIA processing and the determination of areas where the land cover has changed. The Milicz District (central west part of Poland) was chosen as the test site where ALS was to be performed in 2007, together with the digital aerial photos (Vexcel camera; pixel 0.15 m; CIR). The aerial photos were then processed to a CIR orthophoto. The area of study consisted of 68 private parcels (some of them were abandoned; 68.57 ha; scanned cadastral maps from the local survey office; land use information) in the direct neighbourhood of the State Forest, on which a forest succession could often be observed. The operator vectorized forest (trees and shrubs) succession areas on the 2D CIR orthophoto. They were then compared with the results from the OBIA and GIS analysis, based on the normalized digital surface model. The results showed that areas with high vegetation cover were three times larger than the official land cover database (cadastral maps).Key words: CIR aerial orthophoto, airborne laser scanning (ALS), object based image analysis (OBIA), GIS analysis, digital surface model (DSM), digital terrain model (DTM), secondary forest succession.

The work aims to assess the degree of soil sulphur contamination of the various abandoned reclamation efficiencies, within the microhabitats formed in the "Jeziórko" inoperative boreholes of former sulphur-mining areas. These areas have been reclaimed to the forest. Three plot categories were initially determined in the post-mining areas: category D-degraded, i.e. ineffectively reclaimed and unsuccessfully afforested plots, with low cover-abundance or complete lack of vegetation, category (P)-pine stands and category (B)-birch stands successfully aff orested. Afterwards, four circular plots were defined within each of the determined categories (4 replications, i.e. a total of 12 plots). For each plot, cover-abundance (according to the Braun-Blanquet scale) and dominant herbaceous vegetation species, tree species and stand density were determined. Height (Ht) and diameter at breast height (DBH) measurements were taken, and a vitality assessment was completed, according to the IUFRO classification. Soil samples were collected at each plot, in five points, at two different depths (0-5 cm and 5-40 cm). Finally, laboratory analysis was undertaken. Soil properties such as texture, pH, electrical conductivity (EC), hydrolytic acidity (Hh), the contents of soil organic carbon SOC, total nitrogen TN, total sulphur TS, and exchangeable cations (Ca 2+ , Mg 2+ , K + , Na +) were determined. Soils from the D plot category were characterised by high sulphur contamination, excess salinity (EC) and strong acidity in top soil. These parameters indicated that completed neutralization was not performed effectively at certain sites. Pine (P) and birch (B) stands categories showed good growth rates and soil parameters, indicating that the reclamation treatments were completed successfully.