Search Results (778)

Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) represents a major advancement in remote sensing for terrestrial observation, substantially improving the capability to map vegetation structural parameters. However, spatial heterogeneity poses significant challenges to data accuracy. To evaluate the performance of ICESat-2 and improve its inversion accuracy, this study used airborne LiDAR data to validate ICESat-2 terrain and canopy height measurements in boreal forests of Alberta, Canada, and in three tropical rainforest regions—Costa Rica, French Guiana, and Gabon. Machine-learning approaches were further applied to calibrate ICESat-2 canopy height estimates. Our results show that the uncalibrated ICESat-2 data exhibit strong consistency in boreal forests, with higher accuracy under snow-covered nighttime conditions (terrain error < 1 m, canopy height error of 3.19 m). In contrast, the uncertainties in tropical rainforests are considerably larger, with terrain errors of 3–7 m and canopy height errors of 5–7 m. After calibration, XGBoost reduced canopy height error by 0.84 m in boreal forests, whereas Random Forest calibration improved canopy height accuracy by 1.09 m in tropical regions. Overall, our findings provide additional scientific evidence supporting the reliability of ICESat-2 measurements and substantially enhance the accuracy of satellite-based canopy height estimation. Full article

Ice and snow tourism (IST) is a significant global trend, offering Russia opportunities for tourism growth and seasonal diversification. This study investigates the potential of ice and snow art as a distinct subcategory of IST on Lake Baikal. Our research is based on an analysis of academic publications and official policy documents, field surveys conducted in winter 2025, and stakeholder consultations, with the “Olkhon Ice Fest” serving as a case study. The findings indicate a clear shift toward IST, with the number of winter tourists on Olkhon Island increasing by 70% between 2021 and 2024. The festival’s key features—its use of the natural ice landscape, a unique artistic technique, an explicit ecological focus, and strong entrepreneurial initiative—support the development of a conceptual model of IST on Lake Baikal grounded in ecotourism principles. Ensuring the long-term sustainable development of IST in the region requires improved governance, infrastructure, and transport systems, as well as support for green businesses and increased environmental awareness among tourists. This study contributes to the ongoing discourse on sustainable winter tourism by demonstrating the interconnections among environmental sustainability, socioeconomic benefits, and cultural innovation, thereby situating local IST practices within the broader framework of the United Nations Sustainable Development Goals (SDGs). Full article

Recent studies have shown that lidar observations from the Ice, Clouds, and Land Elevation Satellite-2 (ICESat-2) enable seasonal snow depth retrieval over land through two primary approaches. The snow-on–off method estimates snow depth by differencing surface elevations acquired during snow-covered and snow-free periods, while the pathlength method derives it from multiple-scattering photon distributions within the snowpack. In this study, we cross-validate ICESat-2-derived surface elevations and snow depths against in situ measurements from SnowEx field campaigns. ICESat-2 surface elevations agree closely with SnowEx data, which we consider closest to the truth, achieving centimeter-level accuracy (e.g., 1 cm) over flat, sparsely vegetated terrain, with larger biases in vegetated and steep areas. Snow depth estimates from both methods show comparable performance in the tundra area, with typical errors on the order of tens of centimeters; however, in vegetated or steep terrain, the pathlength method yields more reliable snow depth results, being less affected by slope and vegetation than the snow-on–off method. These findings show that ICESat-2 is a reliable tool for measuring snow depth from space. Full article

Ice-and-snow tourism cities face pronounced seasonal fluctuations that place strong pressure on urban accommodation systems. Understanding the spatial distribution, accessibility, and service capacity of hotels is therefore critical for sustainable tourism management in cold-region cities. Taking Harbin, China, as a representative winter tourism destination, this study develops a GIS-based spatial analytical framework to examine the spatial organization and service performance of star-rated hotels. Using data from 553 three-star and above hotels, combined with questionnaire survey data (N = 224), we apply the Nearest Neighbor Index (NNI), Kernel Density Estimation (KDE), and raster-based cost-distance accessibility analysis to identify spatial clustering patterns, accessibility differentiation, and mismatches between hotel supply and peak seasonal demand. We find that available hotel rooms can only meet about 60% of peak-season demand, indicating a severe capacity deficit. The results reveal a clear core–periphery spatial structure of star-rated hotels, significant accessibility disparities among hotel categories, and a pronounced mismatch between accommodation capacity and tourism demand during peak winter seasons. Peripheral areas exhibit limited accessibility and insufficient service capacity, while central districts experience high concentration and pressure. These findings highlight the importance of integrating spatial equity and seasonal demand considerations into accommodation planning and infrastructure optimization, providing policy-relevant insights for sustainable tourism development in cold-region cities. Full article

CLARA-A3 currently provides the longest temporal coverage among available albedo products, with improvements in both retrieval algorithms and product coverage compared to earlier versions. This study first evaluates the performance of the CLARA-A3-SAL product over Greenland Ice Sheet (GrIS) and subsequently applies it to investigate spatiotemporal trends in summer albedo from 1979 to 2024. Validation against 32 in situ observation sites indicates negligible bias in the interior regions, with RMSE values ranging from 0.01 to 0.07. Although larger errors exist in the coastal ablation zone due to unresolved sub-grid surface heterogeneity, the product successfully captures observed spatiotemporal variability and long-term trends, demonstrating that CLARA-A3-SAL provides a generally reliable representation of surface albedo. Since 1979, the summer surface albedo averaged over the entire ice sheet has decreased at a rate of −0.24% decade⁻¹. Albedo in the dry snow area has remained relatively stable and showed no significant correlation with most climate variables, except for the North Atlantic Oscillation (NAO) and the Greenland Blocking Index (GBI). Conversely, the marginal zone has undergone substantial darkening (−0.66% decade⁻¹), which is strongly correlated with temperature, snowfall and melt, with meltwater showing the highest correlation (r = −0.90, p < 0.01). This suggests that meltwater-driven grain growth and exposure of bare ice are the primary drivers of albedo reduction over the non-dry snow zone. Large-scale atmospheric circulation also plays a key role: the GBI exhibits the strongest association with albedo (r = −0.63, p < 0.05), underscoring the importance of persistent blocking in amplifying surface warming and darkening. Furthermore, decadal-scale variability associated with the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO) modulates both the magnitude and spatial pattern of albedo changes across GrIS, with AMO+ generally linked to reduced albedo and PDO+ tending to enhance it. Full article

The Loess Plateau is highly susceptible to landslides due to its fragile geological structure and frequent human activities, particularly old landslides with historical structural damage. The features of these landslides in remote sensing images become blurred over time, leading to huge challenges in detection. Considering that old landslides exhibit obvious shape characteristics, we propose ResU-SPMNet, a deep learning model that integrates shape characteristics into the baseline ResU-Net. The proposed model consists of three components: ResU-Net, shape prior module (SPM), and the atrous spatial pyramid pooling (ASPP) module, which jointly enhance segmentation performance from the perspectives of shape constraints and multi-scale feature representation. To validate the effectiveness of the proposed approach, old landslides in representative regions of the Loess Plateau were selected as the study targets. Results show that the proposed model outperforms ResU-Net, SegNet, MultiResUnet, and DeepLabv3+ in old landslide segmentation, achieving an F1-score of 0.6669 and an MCC of 0.6167. Moreover, generalization tests conducted in independent regions indicate that the model exhibits strong robustness across different seasons. The best performance is achieved in summer, whereas performance declines in winter due to adverse factors such as reduced illumination and snow or ice cover. Full article

Microplastic pollution of ecosystems is considered a modern problem. Freshwater ecosystems, despite the interest shown in their study, remain poorly understood. Lake Baikal (Russia) is one of the least studied freshwater ecosystems in this regard. This large lake is distinguished from others by its high level of biodiversity and clean drinking water. The aim of this study is a multi-matrix investigation of microplastic pollution in one of the lake’s bay. The following matrices are used: surface water, water column, sediment, macrophytes, macroinvertebrates, and fish, as well as ice and snow during the winter. The results show that certain locations exhibit high concentrations of microplastic particles. In some cases, this was due to the properties or characteristics of these locations (littoral zones near the water’s edge, macrophytes with mucus sheaths, ice and snow (potentially, the near-surface water layer after ice melt)), while in others, it was due to localized pollution (pier and ship mooring areas). An analysis of the polymer types of the detected microplastic particles reveals the presence of both common (polypropylene, polyethylene terephthalate, polystyrene, polyethylene, polyvinyl chloride) and rare (polyvinyl alcohol and alkyd resin). Moreover, in some locations, the latter two polymers predominate, a phenomenon rarely observed in other studies. Further research was recommended to focus on the chronic effects of microplastic particles on organisms associated with areas of elevated particle concentrations. Full article

Accurate weather classification plays a vital role in disaster management and minimizing economic losses. However, satellite-based weather classification remains challenging due to high inter-class similarity; the computational complexity of existing deep learning models, which limits real-time deployment on resource-constrained edge devices; and the limited interpretability of model decisions in practical environments. To address these challenges, this study proposes SatNet-B3, a quantized, lightweight deep learning framework that integrates an EfficientNetB3 backbone with custom classification layers to enable accurate and edge-deployable weather event recognition from satellite imagery. SatNet-B3 is evaluated on the LSCIDMR dataset and demonstrates high-precision performance, achieving 98.20% accuracy and surpassing existing benchmarks. Ten CNN models, including SatNet-B3, were experimented with to classify eight weather conditions, Tropical Cyclone, Extratropical Cyclone, Snow, Low Water Cloud, High Ice Cloud, Vegetation, Desert, and Ocean, with SatNet-B3 yielding the best results. The model addresses class imbalance and inter-class similarity through extensive preprocessing and augmentation, and the pipeline supports the efficient handling of high-resolution geospatial imagery. Post-training quantization reduced the model size by 90.98% while retaining accuracy, and deployment on a Raspberry Pi 4 achieved a 0.3 s inference time. Integrating explainable AI tools such as LIME and CAM enhances interpretability for intelligent climate monitoring. Full article

Natural hazards exert a detrimental influence on human survival, environmental conditions and society. Historical hazard events have generated a broad corpus of literature addressing the spatiotemporal extent, dissemination or social responses. With regard to quantitative analysis based on information locked within verbose text, the release of such information from the narrative format is encouraging. Natural Language Processing (NLP), a technique demonstrated to be capable of automated data extraction, provides a useful tool in establishing a structured dataset on hazard occurrences. In our study, we utilize scattered textual records of historical natural hazard events to create a novel dataset and explore the applicability of NLP in parallel. We put forward a standard list of toponyms based on manual annotation of a compilation of disaster-related texts, all of which were references in an authoritative publication in the field. The final natural hazards dataset comprised location data, which referred to a specific hazard report in Europe during 1301–1500, together with its geocoding result, year of occurrence and detailed event(s). We evaluated the performance of four pre-trained geoparsing tools (Flair, Stanford CoreNLP, spaCy and Irchel Geoparser) for automated toponym extraction in comparion with the standard list. All four tested methods showed a high precision (above 0.99). Flair had the best overall performance (F1 score 0.89), followed by Stanford CoreNLP (F1 score 0.83) and Irchel Geoparser (F1 score 0.82), while spaCy had a poor recall (0.5). Then we divided natural hazards into six categories: extreme heat, snow and ice, wind and hails, rainstorms and floods, droughts, and earthquakes. Finally, we compared our newly digitized natural hazard dataset to a geocoded version of the dataset provided by Harvard University, thus providing a comprehensive overview of the spatial–temporal characteristics of European hazard observations. The statistical outcomes of the present investigation demonstrate the efficacy of NLP techniques in text information extraction and hazard dataset generation, offering references for collaborative and interdisciplinary efforts. Full article

Global glacier models (GGMs) are effective tools for assessing changes in water resources in mountainous regions and studying glacier degradation. Moreover, with the rapid development and increasing complexity of Earth System Models (ESMs), the incorporation of mountain glaciation parametrizations into ESMs is only a matter of time. GGMs, being computationally efficient and physically well-founded, provide a solid basis for such parametrizations. In this study, we present a new global glacier model, IGRICE. Its dynamic core is based on the Oerlemans minimal model, and surface mass balance (SMB) is explicitly simulated, accounting for orographic precipitation, radiation redistribution on the glacier surface, turbulent heat fluxes, and snow cover evolution on ice. The model is tested on glaciers situated in climatically and topographically contrasting regions—the Caucasus and Svalbard—using observational data for validation. The model is forced with ERA5 reanalysis data and employs morphometric glacial and topographic parameters. The simulated components of the surface energy and mass balance, as well as glacier dynamics over the period of 1984–2021, are presented. The model results demonstrate good agreement with observations, with correlation coefficients for accumulation, ablation, and total SMB ranging from 0.6 to 0.9. The primary driver of glacier retreat in the Caucasus is identified as an increase in net shortwave radiation balance caused by reduced cloudiness and albedo. In contrast, rapid glacier degradation in Svalbard is linked to an increased fraction of liquid precipitation and an extended snow-free period, leading to a sharp decrease in albedo. Full article

Accurate estimation of young forest height is essential for assessing the carbon sequestration potential of vast Siberian boreal forests recovering from wildfires. Satellite radar interferometry, particularly PolInSAR, is a promising tool for this task. However, its application in winter conditions and over sparse young forests remains underexplored. This study proposes a novel method for estimating the height of sparse young pine (Pinus sylvestris) stands using fully polarimetric bistatic TerraSAR-X/TanDEM-X data acquired in winter. The method is based on an analysis of the multimodal distribution of the unwrapped interferometric phase of the surface scattering component, which was isolated via PolInSAR decomposition. We hypothesize that the phase centers correspond to the snow-covered ground (located between tree groups) and the rough surface formed by the upper layer of branches and needles (of the tree groups). The results demonstrate that the difference between the dominant modes of the surface scattering phase distribution correlates with the height of young trees. However, the measurable height difference is limited by the interferometric height of ambiguity. Furthermore, a temporal analysis of the phase and meteorological data revealed a strong correlation between sudden phase shifts and daytime temperature rises around 0 °C. This is interpreted as the formation of a layered snowpack structure with a dense ice crust. This study confirms the potential of X-band PolInSAR for monitoring the structure of young Siberian forests in winter but also highlights a significant limitation: the critical impact of snowpack metamorphism, particularly melt-freeze cycles, on the interferometric phase. The proposed method is only applicable to certain forest regeneration stages where tree height does not exceed the ambiguity limit and snow conditions are stable. Full article

Accurate equilibrium-line altitude (ELA) estimates are a valuable proxy for evaluating glacier mass balance conditions and interpreting climate-driven change in the Canadian high Arctic, where sustained in situ observations are limited. A scalable remote-sensing framework is evaluated to extract the snow cover ratio (SCR) and snowline altitude (SLA) on White Glacier (Axel Heiberg Island, Nunavut) and to assess the agreement with in situ ELA measurements. Ten-metre Sentinel-2 imagery (2019–2024) is processed with a hybrid pipeline comprising the principal component analysis (PCA) of four bands (B2, B3, B4, and B8), unsupervised K-means for pseudo-label generation, and a Random Forest (RF) classifier for snow/ice/ground mapping. SLA is defined based on the date of seasonal minimum SCR using (i) a snowline pixel elevation histogram (SPEH; mode) and (ii) elevation binning with SCR thresholds (0.5 and 0.8). Validation against field-derived ELAs (2019–2023) is performed; formal SLA precision from DEM and binning is quantified ($\pm 4.7$ m), and associations with positive degree days (PDDs) at Eureka are examined. The RF classifier reproduces the spectral clustering structure with >99.9% fidelity. Elevation binning at $\mathrm{SCR}\ge 0.8$ yields SLAs closely matching field ELAs (Pearson $r=0.994$, $p=0.0006$; RMSE $=30$ m), whereas SPEH and lower-threshold binning are less accurate. Interannual variability is pronounced as follows: minimum SCR spans 0.46–0.76 and co-varies with SLA; correlations with PDDs are positive but modest. Results indicate that high-threshold elevation-bin filtering with machine learning provides a reliable proxy for ELA in clean-ice settings, with potential transferability to other data-sparse Arctic sites, while underscoring the importance of image timing and mixed-pixel effects in residual SLA–ELA differences. Full article

The decline in Arctic summer sea ice alters air–sea gas exchange. Because the Arctic Ocean accounts for 5%–14% of global oceanic carbon uptake, understanding how sea ice melt impacts the ocean’s carbon sink capacity is central to constraining future fluxes. In this study, we focus on Young Sound-Tyrolerfjord in Northeast Greenland to examine the sea ice−ocean interaction during the transition from melt onset to melt pond drainage. High-frequency measurements of partial pressure of CO₂ (pCO₂) and seawater physical properties were taken 2.5 m below the sea ice. Our results reveal that pCO₂ in the seawater was undersaturated (248–354 μatm) compared to the atmosphere (401 μatm), showing that the seawater has the potential to take up atmospheric CO₂ as the sea ice breaks up. The pCO₂ undersaturation was attributed to dilution resulting from mixing meltwater from snow and sea ice with the under-ice seawater. Additionally, the drainage of melt pond water that had been in contact with the atmosphere into the under-ice seawater further lowered pCO₂. Melt pond drainage represents an initial connection between the atmosphere and under-ice seawater through meter-thick sea ice during the summer thaw. Our study demonstrates that snow and sea ice melt reduce pCO₂ in under-ice seawater, enhancing its potential for atmospheric CO₂ uptake during sea ice breakup. Full article

Sediment connectivity constitutes a valuable metric to assess the most likely areas of sediment transport, providing a preliminary estimate of the areas to be prioritized for sediment control interventions. Assessment spatio-temporal variability in sediment connectivity can help decrease uncertainties in interpreting sediment transport and sediment yield within a catchment. In this regard, we evaluated variations in the index of sediment connectivity (IC) based on a well-established approach in the Gunt River catchment. To achieve a more effective assessment of the temporal variations in IC, we considered changes in surface soil moisture (SSM) along with normalized difference vegetation index (NDVI) in July 2015 and 2024. Also, to better represent and more accurately assess IC within this large catchment (13,700 km²), we applied weighted mean IC values (as a novel metric) based on iso-IC lines. Our results indicate that among the environmental factors affecting IC, including SSM, slope gradient, elevation, and NDVI, SSM is the most influential in such cold, dry mountainous catchments. Also, the findings demonstrated a 38.5% increase in the extent of the medium-high and high categories of IC from 2015 to 2024. Temporal monitoring of IC revealed pronounced variations in the western (close to the outlet) and eastern portions of the catchment, likely associated with the effects of climate warming on sediment connectivity. These results emphasize that SSM is a key parameter for assessing IC in the snow- and ice-melt-dominated dry mountainous catchment. Accordingly, temporal and spatial monitoring of SSM can allow implementation of more effective measures for reducing sediment transfer at the catchment scale. Full article