Bark beetle outbreaks are a significant cause of high tree mortality rates, dramatically impacting the resilience of forests. Understanding the triggers and impacts of these outbreaks is critical for effective forest management strategies. In this context, we studied windfall and bark beetle outbreaks in the period 2015-2021 in the southern part of Kurilskiy Nature Reserve, North Pacific Ocean region. Massive bark beetle outbreaks on Kunashir Island were not previously studied. The dominant tree species are Yezo spruce (Picea jezoensis) and Sakhalin fir (Abies sachalinensis), which collectively form spruce -fir forests on Kunashir Island. Glehn spruce (Picea glehnii), although less common on the island, forms pure spruce forests. Typically, spruce bark beetle (Ips typographus L.) attacks Yezo and Glehn spruce, and fir bark beetle (Polygraphus proximus) attacks Sakhalin fir. Significant tree mortality was observed in the aftermath of a substantial bark beetle outbreak, induced by gale -force winds. The total disturbance area was 620.5 ha, which is about 4% of the study area, 72% of the windfall area, and 28% of the bark beetle -infested area. Utilising a forest loss dataset (Global Forest Change dataset) and Sentinel 2 imagery, we identified windfall areas and standing tree mortality through unsupervised classification, accompanied by field sampling. Subsequently, the authors analysed the main drivers of disturbances caused by wind and bark beetle outbreaks using datasets combined with forest inventory data. Field data showed a pattern of tree infestation by both bark beetle species at the tree level, and the potential infestation of Sakhalin fir by the spruce bark beetle. We used boosted regression tree (BRT) models to analyse the main drivers using the presence and severity of wind damage and bark beetle outbreaks by phases. As predictors, we used a set of forest characteristics (tree species percentage, height, diameter of trunk, age, growth class) and environmental characteristics (slope, elevation, potential solar radiation, soil pH). The bark beetle outbreak was split into two phases: the first phase (2017-2019) involved the transition of bark beetles from colonised downed trees to standing trees, and the second phase (2020-2021) occurred during the spreading of beetles in standing trees. Stand tree characteristics were of greater significance for the likelihood of a bark beetle outbreak than environmental characteristics, across both phases for the southern part of the reserve. The percentage and the age of Glehn spruce and Yezo spruce were the main influencing factors for the presence and severity of an outbreak.

Wildfires considerably disturb the structure and forest ecosystem functioning. The disturbances estimation as well as the extent of damage to the soil and vegetation soon after the fire is crucial information for planning of restoration efforts. Because of the financial resources needed for field work and the involvement of experts, remote aerospace methods and data are extensively employed in monitoring ecological research. The aim of this paper is to assess postfire forest disturbances and initial regrowth processes using the tasseled cap derived Direction Angle (DA). DA is an index introduced by the authors in previous research - the angle between the Greenness component from the TCT (tasseled cap transformation) and VIC (Vector of Instantaneous Condition). The proposed method is based on linear orthogonal transformation of multispectral satellite images and is characterized with higher accuracy compared to standard methodologies using vegetation indices. The higher accuracy of the methodology is based on the linear orthogonal transformation of multispectral satellite images (TCT), which increases the degree of identification of the three main components changing during fire - soil, vegetation, and moisture/water. The methodology proposed in this paper is characterized by high accuracy in assessing the recovery of undergrowth, that is difficult to differentiate using standard monitoring methodologies based on vegetation indices. The DA raster images show the direction of change of the green tasseled cap component (TCG) relative to the VIC, which allows to estimate the degree of recovery of the vegetation component for different moments of the study period. The variations observed in DA values illustrate the pattern of the green component at various points during the investigation period, enabling the assessment of disturbances and the monitoring of regrowth processes. The test area is located in the Middle Rhodopes, near the village of Hvoyna (Smolyan region), Bulgaria, where on 28/08/2023 a wildfire broke out. 1,500 decares have been burnt by the fire, including deciduous and coniferous forest. The wildfire affected 100-130 years old black pine (Pinus nigra) forests.

Gully erosion is the most destructive type of soil erosion, induced by soil structure detachment. As a result, modest to massive incisions are made in the field. The process can degrade the quantity and quality of soil and potentially cause structural damage. Field studies are used to map the position of gullies, but they are inefficient in terms of time and cost, especially on a regional scale. Therefore, another approach is applied to visualize the probability of gully erosion development using geoenvironmental factors. Remote sensing data can be used to examine the condition of the land, leading to an accurate representation of the earth's surface. This research's primary goal is to predict the location of gully erosion using remote sensing data in the upper of the Sapi Watershed, Banjarnegara, Indonesia. This location primarily consists of mountainous areas used for massive cultivation. Parameters comprising land use and vegetation area (ie. anomaly NDVI) derived from SENTINEL 2A, and topographic data from DEMNAS. The mapping process considers the actual location of the gully and other geographical characteristics using Random Forest. A total of 85 gully location records were collected and verified using Google Earth and field surveys. Nongully data were obtained using median DoD filters to distinguish between river and mountain top. 70% of the data are used for modelling and the rest for validation of model results. RF-generated prediction maps could provide an important instrument for planning and land conservation in the early phases of gully formation.