Widespread dieback of natural Mongolian pine (Pinus sylvestris var. mongolica) forests in Hulunbuir sandy land since 2018 has raised concerns about their sustainability in afforestation programs. We hypothesized that this dieback is driven by two interrelated mechanisms: (1) anthropogenic fire suppression disrupting natural fire regime, and (2) climate change-induced winter warming reducing snow cover duration and depth. To test these, we quantified dieback using Green Normalized Difference Vegetation Index (GNDVI) across stands with varying fire histories via UAV-based multispectral imagery, alongside long-term climatic observations (1960-2024) of temperature, precipitation, and snow dynamics from meteorological stations combined with remote sensing datasets. Results showed that an abrupt change point in 2018 for both annual precipitation and mean temperature was identified, coinciding with dieback. Significant negative linear relationship between GNDVI (forest health) and last fire interval indicated prolonged fire exclusion exacerbating dieback, possibly via pathogen/pest accumulation. Winter temperature rose significantly during 1960-2023, with notable acceleration following abrupt change point in 1987. Concurrently, winters during 2018-2023 exhibited pronounced warming, with snow cover duration reduced by 23 days and snow depth diminished by 7.6 cm. These conditions reduced snowmelt -derived soil moisture (critical water source) recharge in early spring, exacerbating drought stress during critical growth periods and predisposing trees to pest and disease infestations. Our results support both hypotheses, demonstrating that dieback is synergistically driven by fire regime alteration and climate-mediated snowpack reductions. Converting pure pine forests into mixed pine-broadleaf forests via differentiated water sources was proposed to restore ecological resilience in sandy ecosystems.

Flash floods are often responsible for deaths and damage to infrastructure. The objective of this work is to create a data-driven model to understand how predisposing factors influence the spatial variation of the triggering factor (rainfall intensity) in the case of flash floods in the continental area of Portugal. Flash floods occurrences were extracted from the DISASTER database. We extracted the accumulated precipitation from the Copernicus database by considering two days of duration. The analysed predisposing factors for flooding were extracted considering the whole basin where each occurrence is located. These factors include the basin area, the predominant lithology, drainage density, and the mean or median values of elevation, slope, stream power index (SPI), topographic wetness index (TWI), roughness, and four soil properties. The Random Forest algorithm was used to build the models and obtained mean absolute percentage error (MAPE) around 19%, an acceptable value for the objectives of the work. The median of SPI, mean elevation and the area of the basin are the top three most relevant predisposing factors interpreted by the model for defining the rainfall input for flash flooding in mainland Portugal.

Gunung Bromo Education Forest is a forest that functions as a buffer area to maintain the balance of the surrounding area. However, the undulating to hilly topography, the presence of rivers, and land management for annual crops can make the area vulnerable to erosion-induced degradation. This research aims to analyze and classify the erosion hazard level in Gunung Bromo Education Forest and analyze the relationship between research parameters and erosion in Gunung Bromo Education Forest. Erosion was predicted using the MUSLE method. This research used an explorative-descriptive method incorporating a survey and laboratory analysis. Furthermore, data analysis used was Analysis of Variance (ANOVA), Duncan's Multiple Range Test (DMRT) at a 5% significance level, and Pearson correlation test. The results showed that Gunung Bromo Education Forest erosion ranged from 0.025 to 78.36 t ha(-1)y(-1). The erosion hazard level in Gunung Bromo Education Forest is in the very light to heavy class and is dominated by the light class. The factors of erosivity (R), erodibility (K), slope (LS), and crop management (C) are positively correlated with erosion values. The conservation factor (P) is negatively correlated with erosion values. Making remedial efforts according to the erosion hazard level is important to avoid greater damage.

Thaw hazards in high-latitude and glaciated regions are becoming increasingly frequent because of global climate warming and human activities, posing significant threats to infrastructure stability and environmental sustainability. However, despite these risks, comprehensive investigations of thaw-hazard susceptibility in permafrost regions remain limited. Here, this gap is addressed by a systematic and long-term investigation of thaw hazards in China's Qinghai Province as a representative permafrost area. A detailed inventory of 534 thawhazard sites was developed based on remote sensing, field verification, and surveys by a UAV, providing critical data for susceptibility analysis. Eleven environmental factors influencing thaw hazards were identified and analyzed using information gain and Shapley additive explanation. By using the random forest model, a susceptibility map was generated, categorizing the study area into five susceptibility classes: very low, low, moderate, high, and very high. The key influencing factors include precipitation, permafrost type, temperature change rate, and human activity. The results reveal that 17.5 % of the permafrost region within the study area is classified as high to very high susceptibility, concentrated primarily near critical infrastructure such as the Qinghai-Tibet Railway, potentially posing significant risks to its structural stability. The random forest model shows robust predictive capability, achieving an accuracy of 0.906 and an area under the receiver operating characteristic curve of 0.965. These findings underscore the critical role of advanced modeling in understanding the spatial distribution and drivers of thaw hazards, offering actionable insights for hazard mitigation and infrastructure protection in permafrost regions under a changing climate.

Arctic ecosystems are highly vulnerable to ongoing and projected climate change. Rapid warming and growing anthropogenic pressure are driving a profound transformation of these regions, increasingly positioning the Arctic as a persistent, globally significant source of greenhouse gases. In the Russian Arctic-a critical zone for national economic growth and transport infrastructure-intensive development is replacing natural ecosystems with anthropogenically modified ones. In this context, Nature-based Solutions (NbS) represent a vital tool for climate change adaptation and mitigation. However, many NbS successfully applied globally have limited applicability in the Arctic due to its inaccessibility, short growing season, low temperatures, and permafrost. This review demonstrates the potential for adapting existing NbS and developing new ones tailored to the Arctic's environmental and socioeconomic conditions. We analyze five key NbS pathways: forest management, sustainable grazing, rewilding, wetland conservation, and ecosystem restoration. Our findings indicate that protective and restorative measures are the most promising; these can deliver measurable benefits for both climate, biodiversity and traditional land-use. Combining NbS with biodiversity offset mechanisms appears optimal for preserving ecosystems while enhancing carbon sequestration in biomass and soil organic matter and reducing soil emissions. The study identifies critical knowledge gaps and proposes priority research areas to advance Arctic-specific NbS, emphasizing the need for multidisciplinary carbon cycle studies, integrated field and remote sensing data, and predictive modeling under various land-use scenarios.

Forests are increasingly impacted by climate change, affecting tree growth and carbon sequestration. Tree-ring width, closely related to tree growth, is a key climate proxy, yet models describing ring width or growth often lack comprehensive environmental data. This study assesses ERA5-Land data for tree-ring width prediction compared to automatic weather station observations, emphasizing the value of extended and global climate data. We analyzed 723 site-averaged and detrended tree-ring chronologies from two broadleaved and two gymnosperm species across Europe, integrating them with ERA5-Land climate data, CO2 concentration, and a drought index (SPEI12). A subset was compared with weather station data. For modelling interannual variations of tree-ring width we used linear models to assess parameter importance. ERA5-Land and weather-station-based models performed similarly, maintaining stable correlations and consistent errors. Models based on meteorological data from weather stations highlighted SPEI12, sunshine duration, and temperature extremes, while ERA5-Land models emphasized SPEI12, dew-point temperature (humidity), and total precipitation. CO2 positively influenced the growth of gymnosperm species. ERA5-Land facilitated broader spatial analysis and incorporated additional factors like evaporation, snow cover, and soil moisture. Monthly assessments revealed the importance of parameters for each species. Our findings confirm that ERA5-Land is a reliable alternative for modeling tree growth, offering new insights into climate-vegetation interactions. The ready availability of underutilized parameters, such as air humidity, soil moisture and temperature, and runoff, enables their inclusion in future growth models. Using ERA5-Land can therefore deepen our understanding of forest responses to diverse environmental drivers on a global scale.

Tree destruction induced by heavy rainfall, an overlooked type of forest degradation, has been exacerbated along with global climate change. On the Chinese Loess Plateau, especially in afforested gully catchments dominated by Robinia pseudoacacia, destructive rainfall events have increasingly led to widespread forest damage. Previous study has manifested the severity of heavy rainfall-induced tree destruction and its association with topographic change, yet the contributions of tree structure and forest structure remain poorly understood. In this study, we quantified the destroyed trees induced by heavy rainfall using light detection and ranging (LiDAR) techniques. We assessed the influence of tree structure (tree height, crown diameter, and crown area), forest structure (tree density, gap fraction, leaf area index, and canopy cover), and terrain parameters (elevation, slope, and terrain relief) using machine learning models (random forest and logistic regression). Based on these, we aimed to clarify the respective and combined contributions of structural and topographic factors to rainfall-induced tree destruction. Key findings revealed that when considered in isolation, greater tree height, crown diameter, crown area, leaf area index (LAI), and canopy cover suppressed tree destruction, whereas higher gap fractions increased the probability of tree destruction. However, the synergistic increases of tree structural factors (tree height, crown diameter, and crown area) and forest structural factors (LAI and canopy cover) significantly promoted tree destruction, which can counteract the inhibitory effect of terrain on destruction. In addition, increases in tree structure or canopy density (LAI and canopy cover) also increased the probability of tree destruction at the same elevation. Our findings challenge conventional assumptions in forest management by demonstrating the interaction of tree structure and canopy density can significantly promote tree destruction during heavy rainfall. This highlights the need to avoid overly dense afforestation in vulnerable landscapes and supports more adaptive, climate-resilient restoration strategies.

Malan loess is widely distributed on the Chinese Loess Plateau and poses great challenges to geotechnical, ecological, and agricultural practices due to its unique structure and collapsibility. It is essential to understand the evolution of these properties with depth to assess soil stability and reduce engineering risks in the area. This study investigates the mechanical properties and microstructural evolution of Malan loess with depth and employs multivariate statistical methods to explore their complex interrelationships. Oedometer-collapse tests reveal a 94.2 % reduction in collapsibility coefficient (delta s) from 0.0722 at 1 m to 0.0042 at 9 m, indicating a significant reduction in collapsibility with increasing depth. According to the results of the direct shear test, it showed that the shear strength initially decreases and then increases due to the combined effect of the water content and dry density. Scanning electron microscopy (SEM) images reveal the densification of the loess structure, with changes in particle contact from point to face contact and the evolution from macropores to mesopores and small pores as depth increases. Quantitative analysis by Avzio showed a decrease of 61.5 % in macropores area and an increase of 62.5 % in small pores area. The results obtained by Pearson's correlation analysis and random forest model showed that among these microstructural characteristics, the total pore area (%IncMSE = 22.77 %) is the most important factor influencing the collapsibility properties of loess and water content (%IncMSE = 17.72 %) acts a key role in controlling shear strength. Additionally, compared to traditional methods, the random forest model offers a more insightful understanding of nonlinear relationships and multifactorial coupling effects. These findings provide scientific guidance for geotechnical engineering in loess regions, aiding in risk mitigation and promoting sustainable construction.

Use of forest biomass may induce changes in the aerosol emissions, with subsequent impacts on the direct and indirect climate effects of these short-lived climate forcers. We studied how alternative wood use scenarios affected the aerosol emissions and consequent radiative forcing in Finland. In all alternative scenarios, the harvest level of forest biomass was increased by 10 million m3 compared to the baseline. The increased biomass harvest was assigned to four different uses: (i) to sawn wood, (ii) to pulp-based products, (iii) to energy biomass combusted in small-scale appliances or (iv) to energy biomass combusted in medium-to-large scale boilers. Aerosol emissions (black carbon (BC), organic carbon (OC) and sulphur dioxide (SO2)) under these scenarios were estimated using displacement factors (DFs). The global aerosol-climate model ECHAM-HAMMOZ was used to study instantaneous radiative forcing due to aerosol-radiation interactions (IRFARI) and effective radiative forcing (ERF), based on the differences in aerosol emissions between the alternative wood use scenarios and the baseline scenario. The results indicated that the use of sawn wood and energy biomass combusted in medium- to large-scale boilers decreased radiative forcings, implying climate cooling, whereas the increased use of pulpwood increased them. Energy biomass combustion in small-scale appliances increased IRFARI by 0.004 W m-2 but decreased ERF by -0.260 W m-2, specifically due to a strong increase in carbonaceous aerosols. Alternative use of forest biomass notably influenced aerosol emissions and their climate impacts, and it can be concluded that increased forest biomass use requires a comprehensive assessment of aerosol emissions alongside greenhouse gases (GHGs). Given the consequent reduction in radiative forcing from aerosol emissions, we conclude that the greatest overall climate benefits could be achieved by prioritising the production of long-lived wood-based products.

The Land Surface Temperature (LST) is well suited to monitor biosphere-atmosphere interactions in forests, as it depends on water availability and atmospheric/meteorological conditions above and below the canopy. Satellite-based LST has proven integral in observing evapotranspiration, estimating surface heat fluxes and characterising vegetation properties. Since the radiative regime of forests is complex, driven by canopy structure, components radiation properties and their arrangement, forest radiative temperatures are subject to strong angular effects. However, this depends on the scale of observation, where scattering mechanisms from canopy-to satellite-scales influence anisotropy with varying orders of magnitude. Given the heterogeneous and complex nature of forests, multi-angular data collection is particularly difficult, necessitating instrumentation distant enough from the canopy to obtain significant canopy brightness temperature and concurrent observations to exclude turbulence/atmospheric effects. Accordingly, current research and understanding on forest anisotropy at varying scales (from local validation level to satellite footprint) remain insufficient to provide practical solutions for addressing angular effects for upcoming thermal satellite sensors and associated validation schemes. This study presents a novel method founded in the optical remote sensing domain to explore the use of microcanopies that represent forests at different scales in the footprint of a multi-angular goniometer observing system. Both Geometric Optical (GO) and volumetric scattering dominated canopies are constructed to simulate impacts of anisotropy in heterogeneous and homogeneous canopies, and observed using a thermal infrared radiometer. Results show that heterogeneous canopies dominated by GO scattering are subject to much higher magnitudes of anisotropy, reaching maximum temperature differences of 3 degrees C off-nadir. Magnitudes of anisotropy are higher in sparse forests, where the gap fraction and crown arrangement (inducing sunlit/shaded portions of soil and vegetation) drive larger off-nadir differences. In dense forests, anisotropy is driven by viewing the maximum portion of sunlit vegetation (hotspot), where the soil is mostly obscured. Canopy structural metrics such as the fractional cover and gap fraction were found to have significant correlation with off-nadir differences. In more homogeneous canopies, anisotropy reaches a lower magnitude with temperature differences up to 1 degrees C, driven largely by volumetric scattering and components radiation properties. Optimal placement of instrumentation at the canopy-scale (more heterogeneous behaviour due to proximity to the canopy and small pixel size) used to validate satellite observations (more homogeneous behaviour due to larger pixel size) was found to be in cases of viewing maximum sunlit vegetation, for dense canopies. Given upcoming high spatial resolution sensors and associated validation schemes needed to benchmark LST and downstream products such as evapotranspiration, a better understanding of anisotropy over forests is critical to provide accurate, long-term and multi-sensor products.