CLIMATE REGULATING ECOSYTEM SERVICES OF THE FOREST LANDSCAPE IN THE UKRAINIAN CARPATHIANS

Ecosystems provide multiple services for humans. Among them, a group of supporting and regulating ecosystem services is often less recognized by people as benefit and has been less studied by researchers. Amid various manifestations of climate change, more attention has been paid to particular subset of this group of services called climate regulating. Despite these there still few quantitative studies in this field. Trying to fill this research gap we conducted a study aimed at exploration of relation between climate regulating ecosystem services and their spatial determinants in the forest landscape within Ukrainian Carpathians. For that we chose the territory within Rakhiv and Tsiachiv districts in Transcarpathian region which represents all diversity of forest mountain ecosystems. For this study we used information on land surface temperature (LST) extracted from Landsat 8 thermal band for summer season of 2015. In order to account for vertical thermal gradient in mountains the LST data underwent normalization and in further analysis a dependent variable we employed normalized LST (nLST). Set of independent variables included geomorphometric indicators (altitude, slope, aspect, TPI) and data on forest cover (disturbance, density, dominant species, and disturbance in the neighborhood). For key study area of Velykyi watershed of 4059 ha we additionally used data on forest biomass and tree age. In general, all forest ecosystems in present research have been divided into three distinct classes – “natural”, “disturbed” and “other” forests. Using boosted regression trees method we built three statistical models for each of the forest classes called “global” models. Also we developed 12 “local” models that showed the link between nLST and analyzed independent variables within each altitudinal bioclimatic zone with considering also forest class. Three separate statistical models have been built for each of the forest classes for key study area. Our results suggest that both maximum and mean values of nLST within particular altitudinal bioclimatic zone are the lowest in “natural” forests and the highest in “disturbed” ones.. The statistical model performance based on the variance explained indicator ranged from 32 to 74 %, whilst for models for key study area it was between 77 and 89 %. The set of influential variables for different forest classes varied substantially, but the most often they included aspect, forest density and elevation despite of normalization applied before. In models created for class “disturbed” forests between 19 and 35 % of all explained variance has been contributed by variable indicating time of disturbance. In “local” models for class “natural” forests we revealed gradual decrease of influence of the geomorphometric indicators (elevation, slope, and TPI) when move from warmer to cooler altitudinal zones while for topographic aspect and forest density the trends were just the opposite. In case of key study area a wood stock and tree age variables along with elevation and aspect were amongst the most influential ones. We can conclude that depending on the state of naturalness of forest ecosystems they have different climate regulating potential which might be severely depleted by human and natural disturbances. Keywords: forest landscape, ecosystem services, remote sensing, climate regulation, climate change, Landsat satellite images, Ukrainian Carpathians.

On the Wind Turbine Wake and Forest Terrain Interaction

Future wind power developments may be located in complex topographic and harsh environments; forests are one type of complex terrain that offers untapped potential for wind energy. A detailed analysis of the unsteady interaction between wind turbines and the distinct boundary layers from those terrains is necessary to ensure optimized design, operation, and life span of wind turbines and wind farms. Here, laboratory experiments were carried to explore the interaction between the wake of a horizontal-axis model wind turbine and the boundary layer flow over forest-like canopies and the modulation of forest density in the turbulent exchange. The case of the turbine in a canonical boundary layer is included for selected comparison. The experiments were performed in a wind tunnel fully covered with tree models of height H/zhub≈0.36, where zhub is the turbine hub height, which were placed in a staggered pattern sharing streamwise and transverse spacing of Δx/dc=1.3 and 2.7, where dc is the mean crown diameter of the trees. Particle image velocimetry is used to characterize the incoming flow and three fields of view in the turbine wake within x/dT∈(2,7) and covering the vertical extent of the wake. The results show a significant modulation of the forest-like canopies on the wake statistics relative to a case without forest canopies. Forest density did not induce dominant effects on the bulk features of the wake; however, a faster flow recovery, particularly in the intermediate wake, occurred with the case with less dense forest. Decomposition of the kinematic shear stress using a hyperbolic hole in the quadrant analysis reveals a substantial effect sufficiently away from the canopy top with sweep-dominated events that differentiate from ejection-dominated observed in canonical boundary layers. The comparatively high background turbulence induced by the forest reduced the modulation of the rotor in the wake; the quadrant fraction distribution in the intermediate wake exhibited similar features of the associated incoming flow.

The NDVI algorithm utilization on the google earth engine platform to monitor changes in forest density in mining area

Periodic forest monitoring needs to be done to avoid forest degradation. In general, forest monitoring can be conducted manually (field surveys) or using technological innovations such as remote sensing data derived from aerial images (drone results) or cloud computing-based image processing. Currently, remote sensing technology provides large-scale forest monitoring using multispectral sensors and various vegetation index processing algorithms. This study aimed to evaluate the use of the Google Earth Engine (GEE) platform, a geospatial dataset platform, in the Vale Indonesia mining concession area to improve accountable forest monitoring. This platform integrates a set of programming methods with a publicly accessible time-series database of satellite imaging services. The method used is NDVI processing on Landsat multispectral images in time series format, which allows for the description of changes in forest density levels over time. The results of this NDVI study conducted on the GEE platform have the potential to be used as a tool and additional supporting data for monitoring forest conditions and improvement in mining regions.

Analysis of marine debris and mangrove forest density in Purnama village, Dumai city, Riau province

Mangrove ecosystems naturally function as trap of waste produced from anthropogenic activities including marine debris. This study aims to analyze marine debris and density of mangrove forests in the Purnama Village. The survey research method was carried out in two stations: Station I (Estuary of Sungai Masjid) and Station II (coastal waters of the Unri Marine Station), which was held from April to July 2021. Marine debris was collected in five plots in a quadrant transect measuring 100 x 50 meter. Identification results of mangrove species in Station I found 3 species of mangrove (Xylocarpus granatum, Rhizophora apiculata and Bruguiera gymnorriza), while at Station II found 3 species of mangroves (Rhizophora apiculata, Xylocarpus granatum and Avecennia alba). This research results obtained the release of marine debris at Station II is more than that of Station I. While in Station II there are 172 items (marine debris density of 0.172 item/m2), weight of marine debris 12.665 grams/m2 and mangrove density 2222 individu/ha (category very close). At Station I there are 35 items (the density of marine debris is 0.035 item/m2), the total weight of marine debris is 3.194 grams/m2 and the density of mangroves is 1678 individu/ha (category very close).

Detrainment and braking of snow avalanches interacting with forests

Mountain forests provide natural protection against avalanches. They can both prevent avalanche formation in release zones and reduce avalanche mobility in runout areas. Although the braking effect of forests has been previously explored through global statistical analyses on documented avalanches, little is known about the mechanism of snow detrainment in forests for small and medium avalanches. In this study, we investigate the detrainment and braking of snow avalanches in forested terrain, by performing three-dimensional simulations using the Material Point Method (MPM) and a large strain elastoplastic snow constitutive model based on Critical State Soil Mechanics. First, the snow internal friction is evaluated using existing field measurements based on the detrainment mass, showing the feasibility of the numerical framework and offering a reference case for further exploration of different snow types. Then, we systematically investigate the influence of snow properties and forest parameters on avalanche characteristics. Our results suggest that, for both dry and wet avalanches, the detrainment mass decreases with the square of the avalanche front velocity before it reaches a plateau value. Furthermore, the detrainment mass significantly depends on snow properties. It can be as much as ten times larger for wet snow compared to dry snow. By examining the effect of forest configurations, it is found that forest density and tree diameter have cubic and square relations with the detrainment mass, respectively. The outcomes of this study may contribute to the development of improved formulations of avalanche–forest interaction models in popular operational simulation tools and thus improve hazard assessment for alpine geophysical mass flows in forested terrain.

Holocene wildfire regimes in forested peatlands in western Siberia: interaction between peatland moisture conditions and the composition of plant functional types

Wildfire is the most common disturbance type in boreal forests and can trigger significant changes in forest composition. Waterlogging in peatlands determines the degree of tree cover and the depth of the burning horizon associated with wildfires. However, interactions between peatland moisture, vegetation composition and flammability, and fire regime in forested peatland in Eurasia remain largely unexplored, despite their huge extent in boreal regions. To address this knowledge gap, we reconstructed the Holocene fire regime, vegetation composition, and peatland hydrology at two sites in Western Siberia near Tomsk Oblast, Russia. The palaeoecological records originate from forested peatland areas in predominantly light taiga (Pinus-Betula) with the increase in dark taiga communities (Pinus sibirica, Picea obovata, Abies sibirica) towards the east. We found that the past water level fluctuated between 8 and 30 cm below the peat surface. Wet peatland conditions promoted broadleaf trees (Betula), whereas dry peatland conditions favoured conifers and a greater forest density (dark-to-light-taiga ratio). The frequency and severity of fire increased with a declining water table that enhanced fuel dryness and flammability and at an intermediate forest density. We found that the probability of intensification in fire severity increased when the water level declined below 20 cm suggesting a tipping point in peatland hydrology at which wildfire regime intensifies. On a Holocene scale, we found two scenarios of moisture-vegetation-fire interactions. In the first, severe fires were recorded between 7.5 and 4.5 ka BP with lower water levels and an increased proportion of dark taiga and fire avoiders (Pinus sibirica at Rybanya and Abies sibirica at Ulukh Chayakh) mixed into the dominantly light taiga and fire-resister community of Pinus sylvestris. The second occurred over the last 1.5 ka and was associated with fluctuating water tables, a declining abundance of fire avoiders, and an expansion of fire invaders (Betula). These findings suggest that frequent high-severity fires can lead to compositional and structural changes in forests when trees fail to reach reproductive maturity between fire events or where extensive forest gaps limit seed dispersal. This study also shows prolonged periods of synchronous fire activity across the sites, particularly during the early to mid-Holocene, suggesting a regional imprint of centennial to millennial-scale Holocene climate variability on wildfire activity. Increasing human presence in the region of the Ulukh-Chayakh Mire near Teguldet over the last four centuries drastically enhanced ignitions compared to natural background levels. Frequent warm and dry spells predicted for the future in Siberia by climate change scenarios will enhance peatland drying and may convey a competitive advantage to conifer taxa. However, dry conditions, particularly a water table decline below the threshold of 20 cm, will probably exacerbate the frequency and severity of wildfire, disrupt conifers’ successional pathway and accelerate shifts towards more fire-adapted broadleaf tree cover. Furthermore, climate-disturbance-fire feedbacks will accelerate changes in the carbon balance of forested boreal peatlands and affect their overall future resilience to climate change.

Wildfire Risk Assessment and Zoning by Integrating Maxent and GIS in Hunan Province, China

Forest wildfire is an important threat and disturbance facing natural forest ecosystems. Conducting wildfire risk assessments and zoning studies are of great practical significance in guiding wildfire prevention, curbing fire occurrence, and mitigating the environmental effects of wildfire. Taking Hunan Province, China as the case area, this study used remotely sensed high-temperature fire data as the wildfire sample. Twelve factors related to topography, climatic conditions, vegetation attributes, and human activities were used as environmental variables affecting wildfire occurrence. Then, a Maxent wildfire risk assessment model was constructed with GIS, which analyzed the contribution, importance, and response of environmental variables to wildfire in Hunan Province. The results show that (1) the Maxent model has high applicability and feasibility when applied to wildfire risk assessment after a test of wildfire sample sites; (2) the importance of meteorological conditions and vegetation status variables to wildfire are 54.64% and 25.40%, respectively, and their contribution to wildfire are 43.03% and 34.69%, respectively. The interaction between factors can enhance or weaken the contribution of factors on wildfire. (3) The mechanism for the effects of environmental variables on wildfire is not linear as generally believed; temperature, aridity, land use type, GDP, distance from the road, and population density have a nonlinear positive correlation with the probability of wildfire occurrence. Elevation, slope, precipitation, wind speed, and vegetation cover within the suitable interval positively contribute to the probability of wildfire, while the environmental conditions outside the suitable interval curb the occurrence of wildfire. The response of wildfire probability to forest density is U-shaped, which means either too high or too low will promote the occurrence of wildfire. (4) There is geographical variation of wildfire risk in Hunan Province. The areas at high risk and below account for 74.48% of the total area, while the areas at significantly high risk and above account for a relatively low proportion, 25.52%.

Statistical Evidence for Managing Forest Density in Consideration of Natural Volatile Organic Compounds

Rapid deforestation, coupled with the growing population seeking forest therapy, urges the necessity for research on how to maximize forests’ therapeutic functions when cultivating damaged or unmanaged forests. This study was formulated to provide a basis for forest stand density management to maximize the therapeutic effects of forests with a focus on natural volatile organic compounds (NVOCs), a representative component of forest therapy through analysis of variance and regression analyses. The results of this study revealed all studied stand densities yield the highest total NVOC (TNVOC) emissions in summer, especially in the study site which has a forest density of 700/ha. In addition, treeless areas (0/ha) were found to have the most significant difference in average NVOC emissions when cultivated at a density of 700/ha. When managing forests with a density of 900/ha to 1000/ha, it has been shown that it is most desirable, in terms of therapeutic function efficiency, to control a density of 500/ha to 700/ha. Finally, regression equations for the five experimental sites with significant explanatory power were derived. Based on the results of the conducted analyses, 700/ha of forest density is recommended to maximize the therapeutic effects of forests, compared to other ranges of forest density.

Using Eco-Geographical Zoning Data and Crowdsourcing to Improve the Detection of Spurious Land Cover Changes

To address problems in remote sensing image change detection, this study proposes a method for identifying spurious changes based on an eco-geographical zoning knowledge base and crowdsourced data mining. After preliminary change detection using the super pixel cosegmentation method, eco-geographical zoning is introduced, and the rules of spurious change are collected based on the knowledge of expert interpreters, and from statistics on existing land cover products according to each eco-geographical zone. Uncertain changed patches with a high possibility of spurious change according to the eco-geographical zoning rule were published in the form of a map service on an online platform, and then crowd tagging information on spurious changed patches was collected. The Hyperlink-Induced Topic Search (HITS) algorithm was used to calculate the spurious change degree of changed patches. We selected the northern part of Laos as the experimental area and the Chinese GF-1 Wide Field View (WFV) images for change detection to verify the effectiveness of the method. The results show that the accuracy of change detection improves by 23% after removing the spurious changes. Spurious changes caused by clouds, river water turbidity, spectral differences in cultivated land before and after harvest, and changes in shrubs, grassland, and forest density, can be removed using an eco-geographical zoning knowledge base and crowdsourced data mining methods.

Development and Application of Earth Observation Based Machine Learning Methods for Characterizing Forest and Land Cover Change in Dilijan National Park of Armenia between 1991 and 2019

Dilijan National Park is one of the most important national parks of Armenia, established in 2002 to protect its rich biodiversity of flora and fauna and to prevent illegal logging. The aim of this study is to provide first, a mapping of forest degradation and deforestation, and second, of land cover/land use changes every 5 years over a 28-year monitoring cycle from 1991 to 2019, using Sentinel-2 and Landsat time series and Machine Learning methods. Very High Spatial Resolution imagery was used for calibration and validation purposes of forest density modelling and related changes. Correlation coefficient R2 between forest density map and reference values ranges from 0.70 for the earliest epoch to 0.90 for the latest one. Land cover/land use classification yield good results with most classes showing high users’ and producers’ accuracies above 80%. Although forest degradation and deforestation which initiated about 30 years ago was restrained thanks to protection measures, anthropogenic pressure remains a threat with the increase in settlements, tourism, or agriculture. This case study can be used as a decision-support tool for the Armenian Government for sustainable forest management and policies and serve as a model for a future nationwide forest monitoring system.