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Norway spruce (Picea abies (L.) H. Karst.) and Scots pine (Pinus sylvestris L.) are among the most valuable tree species in the Lithuanian forests. Pure stands, which comprise approximately one-quarter of Lithuania’s forest area, provide an important framework for studying tree responses to thinning and susceptibility to species-specific diseases and damage. This study investigated stem health and quality in two experimental Scots pine stands (32 and 39 years old) and four experimental Norway spruce stands (36–43 years old) to assess the influence of the initial stand density and thinning intensity. Each stand consisted of five plots with different initial densities and was subjected to varying thinning regimes from stand establishment. Tree locations were mapped using the pseudolite-based positioning system TerraHärp, and local tree density was calculated. Stem health and damage were assessed using ICP-Forests methodology. Our results showed that across initial densities of 1000–4400 trees ha−1, tree dimensions (diameter and height) were similar, regardless of thinning intensity. The highest levels of stem damage and competition-induced mortality occurred in the densest, unthinned stands, with deer browsing and scraping from fallen trees being the most common damage agents. In contrast, thinned stands exhibited a higher incidence of stem rot (Heterobasidion annosum (Fr.) Bref.), particularly for Norway spruce. Finally, stand density alone did not consistently explain the patterns of tree mortality in either the pine or spruce stands. These findings suggest that cultivating Scots pine and Norway spruce at lower initial densities with minimal thinning may reduce the damage and losses caused by fungal infection. Finally, novel techniques, such as the pseudolite-based positioning system for geolocating trees and drone imaging for assessing tree health, have proven valuable in facilitating field surveys.

One may consider the application of remote sensing as a trade-off between the imaging platforms, sensors, and data gathering and processing techniques. This study addresses the potential of hyperspectral imaging using ultra-light aircraft for vegetation species mapping in an urban environment, exploring both the engineering and scientific aspects related to imaging platform design and image classification methods. An imaging system based on simultaneous use of Rikola frame format hyperspectral and Nikon D800E adopted colour infrared cameras installed onboard a Bekas X32 manned ultra-light aircraft is introduced. Two test imaging flight missions were conducted in July of 2015 and September of 2016 over a 4000 ha area in Kaunas City, Lithuania. Sixteen and 64 spectral bands in 2015 and 2016, respectively, in a spectral range of 500–900 nm were recorded with colour infrared images. Three research questions were explored assessing the identification of six deciduous tree species: (1) Pre-treatment of spectral features for classification, (2) testing five conventional machine learning classifiers, and (3) fusion of hyperspectral and colour infrared images. Classification performance was assessed by applying leave-one-out cross-validation at the individual crown level and using as a reference at least 100 field inventoried trees for each species. The best-performing classification algorithm—multilayer perceptron, using all spectral properties extracted from the hyperspectral images—resulted in a moderate classification accuracy. The overall classification accuracy was 63%, Cohen’s Kappa was 0.54, and the species-specific classification accuracies were in the range of 51–72%. Hyperspectral images resulted in significantly better tree species classification ability than the colour infrared images and simultaneous use of spectral properties extracted from hyperspectral and colour infrared images improved slightly the accuracy over the 2015 image. Even though classifications using hyperspectral data cubes of 64 bands resulted in relatively larger accuracies than with 16 bands, classification error matrices were not statistically different. Alternative imaging platforms (like an unmanned aerial vehicle and a Cessna 172 aircraft) and settings of the flights were discussed using simulated imaging projects assuming the same study area and field of application. Ultra-light aircraft-based hyperspectral and colour-infrared imaging was considered to be a technically and economically sound solution for urban green space inventories to facilitate tree mapping, characterization, and monitoring.

Landscape naturalness is an important indicator for supporting sustainable development-driven policies and suggesting associated decisions in land management. This study used CORINE Land Cover data to estimate the changes in land cover naturalness in Lithuania since 1995. All the land cover types were ranked according to naturalness level, ranging from purely anthropogenic to natural landscapes. Spatial patterns of the increase or decline in landscape naturalness were investigated at the level of municipalities. Then, publicly available geographic data were mobilised to explain the reasons behind the trends observed. A minor increase in land cover naturalness in the whole area of Lithuania was observed; however, this increase was statistically insignificant. Nevertheless, statistically significant clusters with both increasing and decreasing levels of land cover naturalness were identified when moving to the level of municipalities. The trends in the development of landscape naturalness were associated with the specificity of agricultural and forestry activities in the municipalities. The suitability of lands for agriculture due to soil, terrain, current land use specifics, and related drivers, such as the availability of land reclamation installations and the intensity of land use, were the main drivers for the declining level of land cover naturalness, usually concentrated in northern and central Lithuania. The land cover naturalness did increase in less suitable areas for agriculture, i.e., in the more forested southeastern municipalities. The study emphasised the need for a systematic and spatially explicit monitoring of the land cover patterns and their changes as well as elaborated proposals for land management policies over the next decade, which were mostly in the line with current European Union and national strategies.

Strong winds may uproot and break trees and represent a major natural disturbance for European forests. Wind disturbances have intensified over the last decades globally and are expected to further rise in view of the effects of climate change. Despite the importance of such natural disturbances, there are currently no spatially explicit databases of wind-related impact at a pan-European scale. Here, we present a new database of wind disturbances in European forests (FORWIND). FORWIND is comprised of more than 80 000 spatially delineated areas in Europe that were disturbed by wind in the period 2000–2018 and describes them in a harmonized and consistent geographical vector format. The database includes all major windstorms that occurred over the observational period (e.g. Gudrun, Kyrill, Klaus, Xynthia and Vaia) and represents approximately 30 % of the reported damaging wind events in Europe. Correlation analyses between the areas in FORWIND and land cover changes retrieved from the Landsat-based Global Forest Change dataset and the MODIS Global Disturbance Index corroborate the robustness of FORWIND. Spearman rank coefficients range between 0.27 and 0.48 (p value < 0.05). When recorded forest areas are rescaled based on their damage degree, correlation increases to 0.54. Wind-damaged growing stock volumes reported in national inventories (FORESTORM dataset) are generally higher than analogous metrics provided by FORWIND in combination with satellite-based biomass and country-scale statistics of growing stock volume. The potential of FORWIND is explored for a range of challenging topics and scientific fields, including scaling relations of wind damage, forest vulnerability modelling, remote sensing monitoring of forest disturbance, representation of uprooting and breakage of trees in large-scale land surface models, and hydrogeological risks following wind damage. Overall, FORWIND represents an essential and open-access spatial source that can be used to improve the understanding, detection and prediction of wind disturbances and the consequent impacts on forest ecosystems and the land–atmosphere system. Data sharing is encouraged in order to continuously update and improve FORWIND. The dataset is available at https://doi.org/10.6084/m9.figshare.9555008 (Forzieri et al., 2019).

This paper introduces a method for rapid forest damage assessment using satellite images and stand-wise forest inventory data. Two Landsat 5 Thematic Mapper (TM) images from June and September 2010 and data from a forest stand register developed within the frameworks of conventional stand-wise forest inventories in Lithuania were used to assess the forest damage caused by wind storms that occurred on August 8, 2010. Satellite images were geometrically and radiometrically corrected. The percentage of damage in terms of wind-fallen or broken tree volume was then predicted for each forest compartment within the zone potentially affected by the wind storm, using the non-parametric k-nearest neighbor technique. Satellite imagery-based difference images and general forest stand characteristics from the stand register were used as the auxiliary data sets for prediction. All auxiliary data were available from existing databases, and therefore did not involve any added data acquisition costs. Simultaneously, aerial photography of the area damaged by the wind storm was carried-out and color infrared (CIR) orthophotos with a resolution of 0.5 x 0.5 m were produced. A precise manual interpretation of the effects of the wind storm was used to validate satellite image-based estimates. The total wind damaged volume in pine dominating forest (~1.180.000 m3) was underestimated by 2.2%, in predominantly spruce stands (~233.000 m3) by 2.6% and in predominantly deciduous stands (~195.000 m3) by 4.2%, compared to validation data. The overall accuracy of identification of wind-damaged areas was around 95-98%, based solely on difference data from satellite images gathered on two dates.