Search Results (8)

Glaciers are one of the most important water resources in the arid regions of Xinjiang, making it crucial to accurately monitor glacier changes for the region’s sustainable development. However, due to their typical distribution in remote, high-altitude areas, large-scale and long-term field observations are often constrained by the high costs of manpower, resources, and finances. Globally, fewer than 40 glaciers have been monitored for more than 20 years, and, in China, only Glacier No. 1 at the headwaters of the Urumqi River has monitoring records exceeding 50 years. To address these challenges, this study analyzed glacier changes in the Tomur Peak region of the Tianshan Mountains over the past 35 years using Landsat satellite imagery. Through experiments with deep learning models, the results show that the 3-4-5 band combination performed best for glacier boundary extraction. The DeepLabV3+ model, with MobileNetV2 as the backbone, achieved an overall accuracy of 90.44%, a recall rate of 82.75%, and a mean Intersection over Union (IoU) that was 1.6 to 5.94 percentage points higher than other models. Based on these findings, the study further analyzed glacier changes in the Tomur Peak region, revealing an average annual glacier reduction rate of 0.18% and a retreat rate of 6.97 km²·a⁻¹ over the past 35 years. This research provides a more precise and comprehensive scientific reference for understanding glacier changes in arid regions, with significant implications for enhancing our understanding of the impacts of climate change on glaciers, optimizing water resource management, and promoting regional sustainable development. Full article

Antarctica has attracted increasing interest in understanding its microbial communities, metabolic potential, and as a source of microbial hydrolytic enzymes with industrial applications, for which advances in next-generation sequencing technologies have greatly facilitated the study of unculturable microorganisms. In this work, soils from seven sub-Antarctic islands and Union Glacier were studied using a whole-genome shotgun metagenomic approach. The main findings were that the microbial community at all sites was predominantly composed of the bacterial phyla Actinobacteria and Cyanobacteria, and the families Streptomycetaceae and Pseudonocardiaceae. Regarding the xenobiotic biodegradation and metabolism pathway, genes associated with benzoate, chloroalkane, chloroalkene, and styrene degradation were predominant. In addition, putative genes encoding industrial enzymes with predicted structural properties associated with improved activity at low temperatures were found, with catalases and malto-oligosyltrehalose trehalohydrolase being the most abundant. Overall, our results show similarities between soils from different Antarctic sites with respect to more abundant bacteria and metabolic pathways, especially at higher classification levels, regardless of their geographic location. Furthermore, our results strengthen the potential of Antarctic soils as a source of industrially relevant enzymes. Full article

Understanding the distribution of rock glaciers provides key information for investigating and recognizing the status and changes of the cryosphere environment. Deep learning algorithms and red–green–blue (RGB) bands from high-resolution satellite images have been extensively employed to map rock glaciers. However, the near-infrared (NIR) band offers rich spectral information and sharp edge features that could significantly contribute to semantic segmentation tasks, but it is rarely utilized in constructing rock glacier identification models due to the limitation of three input bands for classical semantic segmentation networks, like DeeplabV3+. In this study, a dual-encoder DeeplabV3+ network (DEDNet) was designed to overcome the flaws of the classical DeeplabV3+ network (CDNet) when identifying rock glaciers using multispectral remote sensing images by extracting spatial and spectral features from RGB and NIR bands, respectively. This network, trained with manually labeled rock glacier samples from the Qilian Mountains, established a model with accuracy, precision, recall, specificity, and mIoU (mean intersection over union) of 0.9131, 0.9130, 0.9270, 0.9195, and 0.8601, respectively. The well-trained model was applied to identify new rock glaciers in a test region, achieving a producer’s accuracy of 93.68% and a user’s accuracy of 94.18%. Furthermore, the model was employed in two study areas in northern Tien Shan (Kazakhstan) and Daxue Shan (Hengduan Shan, China) with high accuracy, which proved that the DEDNet offers an innovative solution to more accurately map rock glaciers on a larger scale due to its robustness across diverse geographic regions. Full article

Glacier inventories are fundamental in understanding glacier dynamics and glacier-related environmental processes. High-resolution mapping of glacier outlines is lacking, although high-resolution satellite images have become available in recent decades. Challenges in development of glacier inventories have always included accurate delineation of boundaries of debris-covered glaciers, which is particularly true for high-resolution satellite images due to their limited spectral bands. To address this issue, we introduced an automated, high-precision method in this study for mapping debris-covered glaciers based on 1 m resolution Gaofen-2 (GF-2) imagery. By integrating GF-2 reflectance, topographic features, and land surface temperature (LST), we used an attention mechanism to improve the performance of several deep learning network models (the U-Net network, a fully convolutional neural network (FCNN), and DeepLabV3+). The trained models were then applied to map the outlines of debris-covered glaciers, at 1 m resolution, in the central Karakoram regions. The results indicated that the U-Net model enhanced with the Convolutional Block Attention Module (CBAM) outperforms other deep learning models (e.g., FCNN, DeepLabV3+, and U-Net model without CBAM) in terms of precision for supraglacial debris identification. On the testing dataset, the CBAM-enhanced U-Net model achieved notable performance metrics, with its accuracy, F1 score, mean intersection over union (MIoU), and kappa coefficient reaching 0.93, 0.74, 0.79, and 0.88. When applied at the regional scale, the model even exhibits heightened precision (accuracies = 0.94, F1 = 0.94, MIoU = 0.86, kappa = 0.91) in mapping debris-covered glaciers. The experimental glacier outlines were accurately extracted, enabling the distinction of supraglacial debris, clean ice, and other features on glaciers in central Karakoram using this trained model. The results for our method revealed differences of 0.14% for bare ice and 10.36% against the manually interpreted glacier boundary for supraglacial debris. Comparison with previous glacier inventories revealed raised precisions of 8.74% and 4.78% in extracting clean ice and with supraglacial debris, respectively. Additionally, our model demonstrates exceptionally high exclusion for bare rock outside glaciers and could reduce the influence of non-glacial snow on glacier delineation, showing substantial promise in mapping debris-covered glaciers. Full article

In recent years, the utilization of extremophile microorganisms for the synthesis of metal nanoparticles, featuring enhanced properties and diverse compositions, has emerged as a sustainable strategy to generate high-quality nanomaterials with unique characteristics. Our study focuses on the biosynthesis of Cu-In-S (CIS) nanoparticles, which has garnered considerable attention in the past decade due to their low toxicity and versatile applications in biomedicine and solar cells. Despite this interest, there is a notable absence of reports on biological methods for CIS nanoparticle synthesis. In this research, three yeast species were isolated from soil samples in an extreme Antarctic environment—Union Glacier, Ellsworth Mountains. Among these isolates, Filobasidium stepposum demonstrated the capability to biosynthesize CIS nanoparticles when exposed to copper sulfate, indium chloride, glutathione, and cysteine. Subsequent purification and spectroscopic characterization confirmed the presence of characteristic absorbance and fluorescence peaks for CIS nanoparticles at 500 and 650 nm, respectively. Transmission electron microscopy analysis revealed the synthesis of monodisperse nanoparticles with a size range of 3–5 nm. Energy dispersive X-ray spectroscopy confirmed the composition of the nanoparticles, revealing the presence of copper, indium, and sulfur. The copper/indium ratio ranged from 0.15 to 0.27, depending on the reaction time. The biosynthesized CIS nanoparticles showed higher photostability than biomimetic nanoparticles and demonstrated successful application as photosensitizers in quantum dot-sensitized solar cells (QDSSC), achieving a conversion efficiency of up to 0.0247%. In summary, this work presents a cost-effective, straightforward, and environmentally friendly method for CIS nanoparticle synthesis. Furthermore, it constitutes the first documented instance of a biological procedure for producing these nanoparticles, opening avenues for the development of environmentally sustainable solar cells. Full article

Recently, reported long-term climate change consequences, such as rising temperatures and melting glaciers, have emphasized mitigation and adaptation actions. While moderating the severity of climate changes, precautionary human actions can also protect the natural environment and human societies. Furthermore, public and private collaboration can leverage resources and expertise, resulting in more impactful mitigation and adaptation actions for effective climate change responses. A coordinated and strategic approach is necessary in order to prioritize these actions across different scales, enabling us to maximize the benefits of climate action and ensure a coordinated response to this global challenge. This study examines the interplay between climate mitigation and adaptation actions in Greece and the European Union (EU). We conducted a literature search using relevant keywords. The search results were systematically approached in alignment with two pairs of thematic homologous entities, enabling the review of these literature findings to be organized and holistically investigated. In this respect, the three fields of agriculture, energy, and multi-parametric determinants of climate neutrality have emerged and been discussed. Our analysis also focused on the key implemented and planned mitigation and adaptation climate actions. Through this review, we identified the most important motives and challenges related to joint adaptation and mitigation actions. Our findings underscore the need for a comprehensive approach to climate action planning that incorporates both adaptation and mitigation measures. Full article

Antarctica plays a key role in the hydrological cycle of the Earth’s climate system, with an ice sheet that is the largest block of ice that reserves Earth’s 90% of total ice volume and 70% of fresh water. Furthermore, the sustainability of the region is an important concern due to the challenges posed by melting glaciers that preserve the Earth’s heat balance by interacting with the Southern Ocean. Therefore, the monitoring of glaciers based on advanced deep learning approaches offers vital outcomes that are of great importance in revealing the effects of global warming. In this study, recent deep learning approaches were investigated in terms of their accuracy for the segmentation of glacier landforms in the Antarctic Peninsula. For this purpose, high-resolution orthophotos were generated based on UAV photogrammetry within the Sixth Turkish Antarctic Expedition in 2022. Segformer, DeepLabv3+ and K-Net deep learning methods were comparatively analyzed in terms of their accuracy. The results showed that K-Net provided efficient results with 99.62% accuracy, 99.58% intersection over union, 99.82% precision, 99.76% recall and 99.79% F1-score. Visual inspections also revealed that K-Net was able to preserve the fine details around the edges of the glaciers. Our proposed deep-learning-based method provides an accurate and sustainable solution for automatic glacier segmentation and monitoring. Full article

With rising temperatures, developing countries are exposed to the horrors of climate change more than ever. The poor infrastructure and low adaptation capabilities of these nations are the prime concern of current studies. Pakistan is vulnerable to climate-induced hazards including floods, droughts, water shortages, shifts in weather patterns, loss of biodiversity, melting of glaciers, and more in the coming years. For marginal societies dependent on natural resources, adaptation becomes a challenge and the utmost priority. Within the above context, this study was designed to fill the existing research gap concerning public knowledge of climate vulnerabilities and respective adaptation strategies in the northern Hindukush–Himalayan region of Pakistan. Using the stratified sampling technique, 25 union councils (wards) were selected from the nine tehsils (sub-districts) of the study area. Using the quantitative method approach, structured questionnaires were employed to collect data from 396 respondents. The study reveals varying public perceptions about different factors contributing to the causes and impacts of climate change and the sources of information in the three zones of the study area. The primary causes of climate change are deforestation, industrial waste, anthropogenic impurities, natural causes, and the burning of fossil fuels exacerbated by increased population. Changes in temperature, erratic rainfalls, floods, droughts, receding glaciers, and extreme weather events are some of the impacts observed over the past decades. While limiting the indiscriminate use of fossil fuels combined with government-assisted rehabilitation of forests can help combat climate change, the lack of proper education and economic, social, and governance barriers are hindering the local adaptation strategies. In addition, reduce environmental pollution (air, water, soil, etc.) and plantation polluted areas with suitable plants, are the two main actions in combating climate change. This study recommends policy interventions to enhance local adaptation efforts through building capacity, equipping local environmental institutions, discouraging deforestation, and ensuring sustainable use of natural resources. Full article