This study aims to analyze and assess studies published from 1992 to 2019 and listed in the Web of Science (WOS) and Current Contents (CC) databases, and to identify agricultural abandonment by application of remote sensing (RS) optical and microwave data. We selected 73 studies by applying structured queries in a field tag form and Boolean operators in the WOS portal and by expert analysis. An expert assessment yielded the topical picture concerning the definitions and criteria for the identification of abandoned agricultural land (AAL). The analysis also showed the absence of similar field research, which serves not only for validation, but also for understanding the process of agricultural abandonment. The benefit of the fusion of optical and radar data, which supports the application of Sentinel-1 and Sentinel-2 data, is also evident. Knowledge attained from the literary sources indicated that there exists, in the world literature, a well-covered problem of abandonment identification or biomass estimation, as well as missing works dealing with the assessment of the natural accretion of biomass in AAL.

Monitoring plant phenology is one of the means of detecting the response of vegetation to changing environmental conditions. One approach for the study of vegetation phenology from local to global scales is to apply satellite-based indices. We investigated the potential of phenological metrics from moderate resolution remotely sensed data to monitor the altitudinal variations in phenological phases of European beech (Fagus sylvatica L.). Phenological metrics were derived from the NDVI annual trajectories fitted with double sigmoid logistic function. Validation of the satellite-derived phenological metrics was necessary, thus the multiple-year ground observations of phenological phases from twelve beech stands along the altitudinal gradient were employed. In five stands, the validation process was supported with annual (in 2011) phenological observations of the undergrowth and understory vegetation, measurements of the leaf area index (LAI), and with laboratory spectral analyses of forest components reflecting the red and near-infrared radiation. Non-significant differences between the satellite-derived phenological metrics and the in situ observed phenological phases of the beginning of leaf onset (LO_10); end of leaf onset (LO_100); and 80% leaf coloring (LC_80) were detected. Next, the altitude dependent variations of the phenological metrics were investigated in all beech-dominated pixels over the area between latitudes 47°44′ N and 49°37′ N, and longitudes 16°50′ E and 22°34′ E (Slovakia, Central Europe). In all cases, this large-scale regression revealed non-linear relationships. Since spring phenological metrics showed strong dependence on altitude, only a weak relationship was detected between autumn phenological metric and altitude. The effect of altitude was evaluated through differences in local climatic conditions, especially temperature and precipitation. We used normal values from the last 30 years to evaluate the altitude-conditioned differences in the growing season length in 12 study stands. The approach presented in this paper contributes to a more explicit understanding of satellite data-based beech phenology along the altitudinal gradient, and will be useful for determining the optimal distribution range of European beech under changing climate conditions.

For the sustainability of an important renewable resource, such as wood, it is important to significantly increase the efficiency of its processing. A large part of this raw material ends up in the wood processing industry, where it is used for the production of pulp, paper, construction and furniture timber, floors and others. Therefore, it is very important to gain the knowledge needed for optimal valuation of raw wood material, through quality detection and classification into quality classes. There are many defectoscopic methods working on different physical principles. The most familiar of these methods are semi-destructive and non-destructive, as they do not cause damage to the tree or wood during assessment. The aim of this article is to describe, assess and compare known semi-destructive and non-destructive methods for the assessment of wood properties. This article describes basic visual inspection, basic semi-destructive methods (Pilodyn, Resistograph) and advanced semi-destructive methods (SilviScan®, DiscBot®) as well. Non-destructive methods use mostly acoustic wave motion (acoustic, ultrasonic), high-frequency waves (using georadar, microwave) and methods based on visual evaluation (image, laser). At last, there are X–ray methods with the latest technology using three-dimensional (3D) computed tomography (CT). The implementation of modern non-destructive methods is of great importance for the application of principles of Industry 4.0, where these methods provide collecting of data on the material properties, in its entire production flow of log processing.

Heterogeneity in the tree trunks’ shapes and quality is not often reached fully using raw material potential in grading processes of tree and stand and the following sawmill processing. Therefore, optimization of given processes is a current topic of research and is part of the operational practice. In the contribution we submit a survey of solving the given problems in the European and Slovak conditions. A significant impulse for solving problem at a new level is a significant progress in the field of industrial computed tomography. New and fast CT scanners have been developed and they enable to increase valuation by 15% in coniferous trees and by 24% in broadleaf trees. In the contribution we analyze period of returns of CT scanner’s implementation into the sawmill process within Slovak context for small, medium-sized and big sawmills. Results show that period of returns for big sawmills is approximately for years, for medium-sized sawmills is eight years when processing coniferous softwood or three to eight years in case of broadleaved processing. In the final synthesis we present a concept of interlinking the 3D scanner and technologies of laser woodcutting with the outcomes allowing to optimize stand grading and maximize profit of the given raw wood in the sawmill processing.