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. R² 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.

The main objectives of our study were to evaluate soil contamination on a zinc-lead spoil heap in the Upper Silesian Industrial Region in southern Poland using pollution indices, and to investigate the relation between soil properties and the natural succession of vegetation. Organic carbon and nitrogen, pH, soil texture, base cations, and heavy metal content were analyzed in soil samples at depths of 0–15 cm below the organic horizon over a regular grid of 14 sampling plots. The contents of Zn, Pb, and Cd exceeded by several times the acceptable thresholds. Measurements of soil enzyme activity were used to evaluate the progress of vegetation development in relation to soil chemical properties. The results indicate that heavy metals had a significant impact on soil enzyme activity and the development of vegetation cover. High contents of Pb and Cd reduced enzyme activity, while this activity increased with increasing amounts of soil organic matter. Further, the accumulative capacities of heavy metals in needles of Scots pine (Pinus sylvestris L.) and aboveground biomass of bladder campion (Silene vulgaris (Moench) Garcke) were examined. A high accumulation of Zn, Pb, and Cd in the aboveground tissues of S. vulgaris indicated an unusual tolerance of this species to heavy metals and the possibility of using this species in phytoremediation of post-industrial sites.

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 (object-based 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).

This paper investigates the possibility of applying light detection and ranging (LiDAR) point clouds and geographic information system (GIS) analyses for land use and land cover (LULC) change detection, mainly with a view to monitoring uncontrolled forest succession occurring on postagricultural lands. The research was conducted in a part of the administrative district of Milicz (in the central-west area of Poland). The areas of interest were parcels in which agricultural use has been abandoned and forest succession processes have progressed. The airborne laser scanning (ALS) data (acquired in 2007, 2012, and 2015) revealed detailed changes in land cover as a result of the progression in the forest succession process. Using the ALS data, the LULC changes and the progress of secondary forest succession are shown, and the vegetation parameters (LiDAR metrics) are presented.