## 3 Materials and Methods  
The present research was carried out in 2019-2020 based on the following datasets:  
* PlanetScope imagery (planet), date: 22 September 2019; channels: B--blue, G--green, R--red, and NIR; 3.0 m resolution; source: Planet's Education and Research (E&R) Program [40];
* ALS point clouds; source: IT System for the Protection of Poland (pl. ISOK) project, point cloud density: 6 pts/m\({}^{2}\); a minimum of 4 reflections; the altitude accuracy (mean error) of the ALS point clouds after alignment was \(\leq\) 0.15 m; situational accuracy \(\leq\) 0.50 m;
* Orthophotomaps and cadastral data. The PlanetScope imagery classification was performed using the photointerpretation key (RGB and Color InfraRed (CIR: R, G, NIR)) composition prepared during the field study (2019) using a Spectra Precision GNSS receiver. We chose points based on 5 classes  
Figure 2: Scenes for the Jeziórko LULC classes: (**a**) mineral extraction sites, bare sand with sulfur contamination on the surface; (**b**) transitional woodland shrub, trees and bushes; (**c**) broad-leaved forest, i.e., oak, birch; (**d**)iferous forest—scots pine. (Figure 3[33]): mineral extraction sites (places without vegetation cover/insufficient reclamation), broad-leaved forest, coniferous forest, transitional woodland shrub, and water. In Figure 3, we provide examples of 5 classes marked with circles larger (for visibility) than the real measured field points. Exploitation was carried out throughout the entire area, and there were 8000 holes in about 2000 ha. Reclamation was successful in most of the area but not in certain places. Figure 3 presents an example mineral extraction site after the reclamation process that is still without vegetation cover. The preliminary processing of the PlanetScope imagery (the product was orthorectified with 4-band imagery featuring geometric, radiometric, and atmospheric corrections) was carried out to create the color compositions of RGB and CIR. Images were classified using the methods (ArcGIS Pro, Esri) of random trees, support vector machine, and maximum likelihood. Tree classifiers were tested to choose the better one. The total number of training areas (area of interest (AOI)) was around 600 [33] (Figure 4): broad-leaved forest, 168 samples (3952 pixels, 12.32% of the total number of pixels), and coniferous forest, 205 (8164 and 25.45%, respectively); transitional woodland shrub, 136 (10,018 and 31.23%, respectively); mineral extraction sites, 46 (558 and 1.74%, respectively), and water, 43 (9383 and 29.25%, respectively). The image classification results were assessed using the create accuracy assessment points tool in ArcGIS Pro. Classes were verified based on the CIR and RGB color composition in PlanetScope and orthophotomaps. Based on the validation points (total number: 500, different than the training points), using the compute confusion matrix tool, provided the following parameters: producer accuracy, user accuracy, and overall accuracy. The kappa coefficient was also calculated. As the final step of the processing, we used the generalization tools of boundary clean and majority filter (ArcGIS Pro). The LiDAR point cloud processing began by deriving the models: DTM (tool: Grid-Surface Create), DSM (canopy model) in FUSION Version 3.50 (Pacific Northwest Research Station: Seattle, WA, USA) [41], and normalized DSM (nDSM = DSM \(-\) DTM) using map  
Figure 3: Examples of LULC classes for the Jeziórko former sulfur mine research area: (**a**) RGB; (**b**) CIR color composition (source: Planet imagery, 2019). algebra (ArcGIS Pro). For both models, DTM and DSM were rendered in grayscale (hills shade). There are many vegetation structural metrics needed to understand the overall vegetation structures of different forest types that can be derived from LiDAR data to provide details about the vertical and horizontal variations in the forest structure.