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.

Machine learning algorithms (MLAs) are used to solve complex non-linear and high-dimensional problems. The objective of this study was to identify the MLA that generates an accurate spatial distribution model of bark beetle (Ips typographus L.) infestation spots. We first evaluated the performance of 2 linear (logistic regression, linear discriminant analysis), 4 non-linear (quadratic discriminant analysis, k-nearest neighbors classifier, Gaussian naive Bayes, support vector classification), and 4 decision trees-based MLAs (decision tree classifier, random forest classifier, extra trees classifier, gradient boosting classifier) for the study area (the Horní Planá region, Czech Republic) for the period 2003–2012. Each MLA was trained and tested on all subsets of the 8 explanatory variables (distance to forest damage spots from previous year, distance to spruce forest edge, potential global solar radiation, normalized difference vegetation index, spruce forest age, percentage of spruce, volume of spruce wood per hectare, stocking). The mean phi coefficient of the model generated by extra trees classifier (ETC) MLA with five explanatory variables for the period was significantly greater than that of most forest damage models generated by the other MLAs. The mean true positive rate of the best ETC-based model was 80.4%, and the mean true negative rate was 80.0%. The spatio-temporal simulations of bark beetle-infested forests based on MLAs and GIS tools will facilitate the development and testing of novel forest management strategies for preventing forest damage in general and bark beetle outbreaks in particular.

In many regions of Europe, large wild herbivores alter forest community composition through their foraging preferences, hinder the forest's natural adaptive responses to climate change, and reduce ecosystem resilience. We investigated a widespread European forest type, a mixed forest dominated by Picea abies, which has recently experienced an unprecedented level of disturbance across the continent. Using the forest landscape model iLand, we investigated the combined effect of climate change and herbivory on forest structure, composition, and carbon and identified conditions leading to ecosystem transitions on a 300‐year timescale. Eight climate change scenarios, driven by Representative Concentration Pathways 4.5 and 8.5, combined with three levels of regeneration browsing, were tested. We found that the persistence of the current level of browsing pressure impedes adaptive changes in community composition and sustains the presence of the vulnerable yet less palatable P. abies. These development trajectories were tortuous, characterized by a high disturbance intensity. On the contrary, reduced herbivory initiated a transformation towards the naturally dominant broadleaved species that was associated with an increased forest carbon and a considerably reduced disturbance. The conditions of RCP4.5 combined with high and moderate browsing levels preserved the forest within its reference range of variability, defining the actual boundaries of resilience. The remaining combinations of browsing and climate change led to ecosystem transitions. Under RCP4.5 with browsing effects excluded, the new equilibrium conditions were achieved within 120 years, whereas the stabilization was delayed by 50–100 years under RCP8.5 with higher browsing intensities. We conclude that forests dominated by P. abies are prone to transitions driven by climate change. However, reducing herbivory can set the forest on a stable and predictable trajectory, whereas sustaining the current browsing levels can lead to heightened disturbance activity, extended transition times, and high variability in the target conditions.

The paper demonstrates the multipurpose application of the normalized difference vegetation index (NDVI) derived from MODIS products for forest monitoring across the Central-European macro-region Slovakia and Hungary (i.e., the Western Carpathians and Pannonian basin). Relationships between forest dynamics and NDVI were analysed and used for determining the onset of phenophases in spring and autumn and for the assessment of forest growth and health condition. To identify the phenophases, the NDVI profile during the year was established by fitting a double logistic sigmoid function to data and phenological metrics were developed based on the calculated extreme values of the sigmoid function and its derivatives. According to our analyses, leaf unfolding and leaf fall were significantly delayed or advanced in 2018 with the increase of altitude and latitude (p < 0.01). The longitudinal aspect was significant only in the autumn phenophase with earlier onset of leaf fall towards to the east. The duration of the growing season varied extensively within the region, mainly according to altitudinal and latitudinal occurrence of beech forests. Positive associations between annual tree-ring width and standardized summer NDVI were found for conifers at local scale. The highest correlation period was between July 12 and August 12 as the most critical periods for forest growth. Slight positive correlation can be observed during March – April that could be associated with the varying start of the growing seasons. In the forest health study, whereas NDVI values could well identify the location and extent of a recent forest damage due to a combination of snow break and wind break, an urgent demand for more detailed field data was obvious.