Search Results (43)

Although Paegam Sŏngch’ong 栢庵性聰 (1631–1700) received orthodox transmission in Sŏn Buddhism in the Puhyu lineage 浮休係 (deriving from Puhyu Sŏnsu 浮休善修, 1543–1615), he is remembered as an important advocate of Huayan 華嚴 doctrinal learning in the mid-Chosŏn period. He collected Buddhist works from the Chinese Jia-xing Canon 嘉興藏 that had washed ashore on Imja Island 荏子島 in Chŏlla Province and published them in more than 190 volumes. In 1686, the first work produced in this endeavor was Precious Writings on the Pure Land (Chŏngt’o posŏ 淨土寶書), in one volume. It is a compilation, in fourteen sections (including the preface), of excerpts and summaries of Pure Land writings and stories published in the supplementary canon section 續藏 of the Jiaxing Canon. The core and longest section of the work is chapter thirteen: “Efficacy of the Fruit of the Pure Land” (Chŏngt’o kwahŏm 淨土果驗). This chapter comprises devotional narratives on cases of rebirth in the Pure Land classified according to the social or birth status of the main figures: monks, kings and ministers, nobles and commoners, nuns, women, evildoers, animals, and so forth. The primary purpose of these narratives is to underscore to virtue of chanting the name of the Buddha Amitābha (yŏmbul, Ch. nianfo 念佛) as a means of rebirth in Sukhāvatī. This work is significant because it demonstrates the value and function of Chinese Pure Land literature in the popularization of Pure Land practice in the mid and late Chosŏn period. Full article

The global shipping industry is surging ahead, and with it, a quiet revolution is taking place on the water: marine lithium-ion batteries have emerged as a crucial clean energy carrier, powering everything from ferries to container ships. When these vessels dock, they increasingly rely on shore power charging systems to refuel—essentially, plugging in instead of idling on diesel. But predicting how much power they will need is not straightforward. Think about it: different ships, varying battery sizes, mixed charging technologies, and unpredictable port stays all come into play, creating a load profile that is random, uneven, and often concentrated—a real headache for grid planners. So how do you forecast something so inherently variable? This study turned to the Monte Carlo method, a probabilistic technique that thrives on uncertainty. Instead of seeking a single fixed answer, the model embraces randomness, feeding in real-world data on supply modes, vessel types, battery capacity, and operational hours. Through repeated random sampling and load simulation, it builds up a realistic picture of potential charging demand. We ran the numbers for a simulated fleet of 400 vessels, and the results speak for themselves: load factors landed at 0.35 for conventional AC shore power, 0.39 for high-voltage DC, 0.33 for renewable-based systems, 0.64 for smart microgrids, and 0.76 when energy storage joined the mix. Notice how storage and microgrids really smooth things out? What does this mean in practice? Well, it turns out that Monte Carlo is not just academically elegant, it is practically useful. By quantifying uncertainty and delivering load factors within confidence intervals, the method offers port operators something precious: a data-backed foundation for decision-making. Whether it is sizing infrastructure, designing tariff incentives, or weighing the grid impact of different shore power setups, this approach adds clarity. In the bigger picture, that kind of insight matters. As ports worldwide strive to support cleaner shipping and align with climate goals—China’s “dual carbon” ambition being a case in point—achieving a reliable handle on charging demand is not just technical; it is strategic. Here, probabilistic modeling shifts from a simulation exercise to a tangible tool for greener, more resilient port energy management. Full article

Agaricus sinodeliciosus (A. sinodeliciosus) and Coprinus comatus (C. comatus) are precious macrofungi found in Qinghai Province, China. As decomposers, they play a crucial role in the terrestrial ecosystem. The article takes A. sinodeliciosus and C. comatus growing in the saline-alkali land of the Qaidam Basin in Qinghai Province as the research objects, and deeply analyzes the influence of the two macrofungi on soil. The results show that, compared with the control soil, the total carbon (TC) content in the soil of A. sinodeliciosus and C. comatus increased by 27.48% and 113.24%, the total nitrogen (TN) content increased by 95.16% and 108.06%, the hydrolyzable nitrogen (HN) increased by 87.36% and 97.90%, and the available potassium (AK) increased by 182.72% and 596.09%, respectively. In addition, C. comatus significantly increased the available phosphorus (AP) by 163.14%. This proves that both macrofungi can enhance soil fertility, and C. comatus has a stronger fertilization effect. In terms of soil microorganisms, A. sinodeliciosus significantly influenced the distribution of soil bacteria and fungi, increasing the abundance of Streptomyces and reducing alpha diversity. C. comatus had a greater impact on bacteria, significantly increasing the relative abundance of Pseudomonas in the soil, but had no significant effect on fungi. Additionally, there was a close relationship between soil microbial abundance and physicochemical properties. pH, AP, TC, and AK were the main factors influencing bacteria, while total salt was the main factor affecting fungi. These findings reveal that A. sinodeliciosus and C. comatus influence the soil microenvironment by regulating soil physicochemical properties and microbial communities. Full article

The abandonment of farmland in Europe is a significant issue due to its environmental, socio-economic, and landscape consequences. This tendency mainly impacts marginal and inner areas, located far from large urban districts, because of biophysical and/or socio-economic factors. Although European and national regulations try to turn the fragility of these territories into an opportunity for sustainable development, many of these areas, especially in southern Europe, continue to suffer socio-economic disparities. For this reason, it is necessary to consider regional and district-wide initiatives that can economically revitalize marginal areas while safeguarding their natural capital. Alternative cropping systems, capable of optimizing the quality of some food crops, can play an essential role in the economic development of populations living in marginal areas. These areas, represented by inland zones often abandoned due to the difficulty of applying mechanized agriculture, can represent an opportunity to rediscover sustainable and profitable practices. Among the high-value crops, saffron (Crocus sativus L.), “red gold” and “king of spices”, stands out for its potential. Indeed, thanks to the use of tuff tubs, a more eco-sustainable choice compared to the plastic pots already mentioned in the literature, it is possible to improve the quality of this spice. Furthermore, Crocus sativus L. not only lends itself to multiple uses but also represents a valid opportunity to supplement agricultural income. This is made possible by its high profitability and beneficial properties for human health, offering a way to diversify agricultural production with positive economic and social impacts. It is known that the saffron market in Italy suffers from competition from developing countries (Iran, Morocco, India) capable of producing saffron at lower costs than European countries, thanks to the lower cost of labor. Therefore, this study seeks to identify marginal areas that can be recovered and valorized through an eco-sustainable cultivation system with the potential to enhance the quality of this spice, making it unique and resilient to competition. Specifically, this paper is organized on a dual scale of investigation: (a) at the local level to demonstrate the economic-ecological feasibility of saffron cultivation through the adoption of an alternative farming technique on an experimental site located in Tricarico (Basilicata—Southern Italy, 40°37′ N, 16°09′ E; 472 m. a.s.l.) that, although fertile, is not suitable for mechanized cropping systems; (b) at the regional level through a spatially explicit land suitability analysis to indicate the possible location where to export saffron cultivation. The final map, obtained by combining geo-environmental variables, can be considered a precious tool to support policymakers and farmers to foster a broad agricultural strategy founded on new crop management systems. The adoption of this alternative agroecological system could optimize the use of land resources in the perspective of increasing crop productivity and profitability in marginal agricultural areas. Full article

As a precious non-renewable resource, the rational utilization of land resources is crucial for global sustainable development, with urban land development scenario prediction and analysis serving as key methodologies to achieve this goal. Although previous studies have extensively explored urban land expansion simulation and scenario forecasting, further investigation is still required to simultaneously address spatial functional zoning differentiation and urban expansion mode diversity while simulating development trends under various expansion modes. In this study, we integrated major functional zones and ecological redlines to delineate urban spatial functional units and define development coefficients for construction land within each unit. Based on the spatial heterogeneity of expansion modes, the scopes of infill, sprawl, and leapfrog expansion modes were determined. Combining functional zoning and expansion mode zoning, we employed cellular automata model principles to design land conversion rules and simulate the evolution of land use under different expansion modes. Using Jiangyin City, China, as a case study, the model achieved a high simulation accuracy (kappa coefficient of 0.959), significantly outperforming comparative models. By predicting land-use patterns under different expansion scenarios and aligning with Jiangyin’s territorial planning goals, we recommend implementing infill–sprawl–leapfrog and infill–leapfrog–sprawl expansion modes. The results demonstrate that the model effectively supports the refined simulation of urban land expansion, providing a scientific basis for optimizing land resource allocation and balancing ecological protection with urban development. Future research could integrate multiple types of territorial control elements, refine land-use categories, and optimize prediction scenarios to enhance the model’s practicality and applicability. Full article

Chinese Caterpillar Fungus (CCF) is a fungal–insect complex formed by the underground larvae of certain species in the family Hepialidae parasitized by Ophiocordyceps sinensis (Berk.) (G.H.Sung, J.M.Sung, Hywel-Jones & Spatafora). It is a precious Chinese herbal medicine with significant medicinal value. This study aimed to identify the key environmental factors influencing the distribution of CCFs using the MaxEnt model. First, in the MaxEnt model optimized using the ENMeval package, the most suitable combinations of feature classes and regularization parameters were selected. Second, 22 environmental variables were used to construct distribution models for O. sinensis, host insects, and CCFs. Then, the distribution areas of O. sinensis and host insects were overlapped to identify highly suitable habitats where both coexist. Finally, these highly suitable habitats were compared to analyze the differences in the distribution areas of O. sinensis and host insects and their contributions to the formation of the CCF distribution area. The results showed that elevation, bio18, and bio09 were the primary environmental factors influencing the distributions of O. sinensis, host insects, and CCFs. Considering the present, 2050s, and 2070s, the highly suitable areas for all three entities overlapped to a large extent. When we superimposed the high-suitability zones of O. sinensis and host insects, the overlapping area was found to be 56.87 × 10⁴ km², which accounted for 5.92% of China’s total land area. The high-suitability area for CCFs was 64.06 × 10⁴ km², accounting for 6.67% of China’s total land area. The findings of this study provide valuable insights into the mechanisms behind the combination of O. sinensis and host insects in forming CCFs. Full article

Black soil is a precious soil resource, yet it is severely affected by gully erosion, which is one of the most serious manifestations of land degradation. The determination of the location and shape of gullies is crucial for the work of gully erosion control. Traditional field measurement methods consume a large amount of human resources, so it is of great significance to use artificial intelligence techniques to automatically extract gullies from satellite remote sensing images. This study obtained the gully distribution map of the southwestern region of the Dahe Bay Farm in Inner Mongolia through field investigation and measurement and created a gully remote sensing dataset. We designed a multi-scale content structure feature extraction network to analyze remote sensing images and achieve automatic gully extraction. The multi-layer information obtained through the resnet34 network is input into the multi-scale structure extraction module and the multi-scale content extraction module designed by us, respectively, obtained richer intrinsic information about the image. We designed a structure content fusion network to further fuse structural features and content features and improve the depth of the model’s understanding of the image. Finally, we designed a muti-scale feature fusion module to further fuse low-level and high-level information, enhance the comprehensive understanding of the model, and improve the ability to extract gullies. The experimental results show that the multi-scale content structure feature extraction network can effectively avoid the interference of complex backgrounds in satellite remote sensing images. Compared with the classic semantic segmentation models, DeepLabV3+, PSPNet, and UNet, our model achieved the best results in several evaluation metrics, the F1 score, recall rate, and intersection over union (IoU), with an F1 score of 0.745, a recall of 0.777, and an IoU of 0.586. These results proved that our method is a highly automated and reliable method for extracting gullies from satellite remote sensing images, which simplifies the process of gully extraction and provides us with an accurate guide to locate the location of gullies, analyze the shape of gullies, and then provide accurate guidance for gully management. Full article

The paper demonstrates the effectiveness of the SNOWED dataset, specifically designed for identifying water bodies in Sentinel-2 images, in developing a remote sensing system based on deep neural networks. For this purpose, a system is implemented for monitoring the Po River, Italy’s most important watercourse. By leveraging the SNOWED dataset, a simple U-Net neural model is trained to segment satellite images and distinguish, in general, water and land regions. After verifying its performance in segmenting the SNOWED validation set, the trained neural network is employed to measure the area of water regions along the Po River, a task that involves segmenting a large number of images that are quite different from those in SNOWED. It is clearly shown that SNOWED-based water area measurements describe the river status, in terms of flood or drought periods, with a surprisingly good accordance with water level measurements provided by 23 in situ gauge stations (official measurements managed by the Interregional Agency for the Po). Consequently, the sensing system is used to take measurements at 100 “virtual” gauge stations along the Po River, over the 10-year period (2015–2024) covered by the Sentinel-2 satellites of the Copernicus Programme. In this way, an overall space-time monitoring of the Po River is obtained, with a spatial resolution unattainable, in a cost-effective way, by local physical sensors. Altogether, the obtained results demonstrate not only the usefulness of the SNOWED dataset for deep learning-based satellite sensing, but also the ability of such sensing systems to effectively complement traditional in situ sensing stations, providing precious tools for environmental monitoring, especially of locations difficult to reach, and permitting the reconstruction of historical data related to floods and draughts. Although physical monitoring stations are designed for rapid monitoring and prevention of flood or other disasters, the developed tool for remote sensing of water bodies could help decision makers to define long-term policies to reduce specific risks in areas not covered by physical monitoring or to define medium- to long-term strategies such as dam construction or infrastructure design. Full article

As a precious natural resource, forests are being destroyed. In previous studies, there is a lack of an interactive assessment of their distribution that comprehensively considers multiple external disturbances. This paper takes the Barcelona Metropolitan Region as an example. Based on remote sensing, it analyzes the development process of the forest from 2006 to 2018 through multiple landscape indicators, and OLS models were established to analyze variables that have direct and indirect effects on forest distribution. In addition, the ecological structure of the forest was analyzed based on NDVI. It was found that the forest area is the largest area but has been decreasing, becoming more complex in distribution structure. Much of the forest was converted to agricultural land and grassland. The green quality of the forests has been increasing, and the broad-leaved forest, the second largest area, contributes the most. NDVI is the most important positively correlated variable, and daytime surface temperature is an important inverse factor related to NDVI. In addition, NDBI is also a negative condition that inhibits forest development. In conclusion: The BMR forest area is decreasing and becoming more fragmented. NDVI and daytime LST are the two most significant factors. Climate warming may lead to worse forest development. Full article

This study presents the environmental mapping of the Chilika Lake coastal lagoon, India, using satellite images Landsat 8-9 OLI/TIRS processed using machine learning (ML) methods. The largest brackish water coastal lagoon in Asia, Chilika Lake, is a wetland of international importance included in the Ramsar site due to its rich biodiversity, productivity, and precious habitat for migrating birds and rare species. The vulnerable ecosystems of the Chilika Lagoon are subject to climate effects (monsoon effects) and anthropogenic activities (overexploitation through fishing and pollution by microplastics). Such environmental pressure results in the eutrophication of the lake, coastal erosion, fluctuations in size, and changes in land cover types in the surrounding landscapes. The habitat monitoring of the coastal lagoons is complex and difficult to implement with conventional Geographic Information System (GIS) methods. In particular, landscape variability, patch fragmentation, and landscape dynamics play a crucial role in environmental dynamics along the eastern coasts of the Bay of Bengal, which is strongly affected by the Indian monsoon system, which controls the precipitation pattern and ecosystem structure. To improve methods of environmental monitoring of coastal areas, this study employs the methods of ML and Artificial Neural Networks (ANNs), which present a powerful tool for computer vision, image classification, and analysis of Earth Observation (EO) data. Multispectral satellite data were processed by several ML image classification methods, including Random Forest (RF), Support Vector Machine (SVM), and the ANN-based MultiLayer Perceptron (MLP) Classifier. The results are compared and discussed. The ANN-based approach outperformed the other methods in terms of accuracy and precision of mapping. Ten land cover classes around the Chilika coastal lagoon were identified via spatio-temporal variations in land cover types from 2019 until 2024. This study provides ML-based maps implemented using Geographic Resources Analysis Support System (GRASS) GIS image analysis software and aims to support ML-based mapping approach of environmental processes over the Chilika Lake coastal lagoon, India. Full article

Saline–alkaline soil is a severe threat to Sustainable Development Goals (SDGs), but it can also be a precious land resource if properly utilized according to its properties. This research takes the Songnen Plain as the study area. The aim is to figure out the saline–alkaline status and mechanisms for its scientific utilization. Sentinel-2 multispectral imagery is used, and a 3D spectral space optimization method is proposed according to the restrictive relationships among the surface soil salinity index (SSSI), vegetation index (VI), and surface soil wetness index (SSWI) to construct a surface soil salinization–alkalization index (SSSAI) for estimation of the surface soil salinity (SSS). It is testified that SSS can be precisely estimated using the SSSAI (R² = 0.74) with field verification of 50 surface salinized soil samples. Surface water and groundwater investigations, as well as deep soil exploration, indicate that the salt ions come from groundwater, and alkalinization is a primary problem in the deep soils. Fine-textured clay soils act as interrupted aquifers to prevent salt ions from penetrating and diluting downward with water, which is the cause of the salinization–alkalization problem in the study area. Finally, a sustainable solution for the saline–alkaline land resource is proposed according to the deep soil properties. Full article

Unmanned aerial vehicle technology is the most advanced and helpful in almost every area of interest in human work. These devices become autonomous and can fulfil a variety of tasks, from simple imaging and obtaining data to search and rescue operations. The most challenging environment for search and rescue operations is the mountainous area. This article is devoted to the theoretical description and simulation tests of a prototype method of landing the light and the medium-weight UAVs used as supplementary devices for SAR (search and rescue) and HEMS (helicopter emergency medical service) in hard-to-reach mountainous terrains. The autonomous flight of a UAV in mountainous terrain has many specifics, and it is usually performed according to predetermined map points (pins) uploaded directly into the control software of the UAV. It is necessary to characterise each point flown on the chosen flight route line in advance and therefore to know its exact geographical coordinates (longitude, latitude and height of the point above the terrain), and the control system of UAV must react to the change in the weather and other conditions in real time. Usually, it is difficult to make this forecast with sufficient time in advance, mainly when UAVs are used as supplementary devices for the needs of HEMS or MRS (mountain rescue service). The most challenging phase is the final approach and landing of the UAV, especially if a loss of GNSS (global navigation satellite system) signal occurs, like in the determined area of the Little Cold Valley in the Slovak High Tatras—which is infamous for the widespread loss of GNSS signals or communication/controlling connection between the UAV and the pilot-operator at the operational station. To solve the loss of guidance, a new method for guiding and controlling the UAV in its final approach and landing in a determined area is tested. An alternative landing navigation system for UAVs in a specific mountainous environment—the authors’ designed frequency Doppler landing system (FDLS)—is briefly described but thoroughly tested with the help of artificial intelligence. An estimation of dynamic stability is used based on the time recording of the current position of the UAV, with the help of a frequency-modulated or amplitude-modulated signal based on the author’s prototype of a precision landing system designed for mountainous terrain. This solution could overcome the problems of GNSS signal loss. The presented research primarily evaluates the success of the simulation flights for the supplementary UAV. The success of navigating the UAV to land in the mountainous environment at an exact landing point using the navigation signals from the FDLS was evaluated at more than 95%. Full article

The application of non-excavation construction technology, such as the pipe jacking method, has obvious advantages in building urban underground space engineering projects, which can effectively reduce the occupation of ground surfaces and the migration of obstacles above or below the ground. However, pipe jacking machines with a rectangular cross-section can easily encounter great difficulty due to the significantly increased jacking resistance while it is jacked in hard rock strata, which are often influenced by large blind spots on the working face of pipe jacking machines with a rectangular cross-section. The aforementioned blind spots belong to areas that cannot be cut by the cutter heads due to the circular cutterhead and rectangular outer frame of pipe jacking machines with a rectangular cross-section. Therefore, the effective pretreatment of the aforementioned blind spots should be implemented prior to operating pipe jacking machines with a rectangular cross-section in hard rock strata. This paper presents a case study of employing horizontal-directional drilling as a multi-pilot heading pretreatment for breaking large blind spots on the working face of pipe jacking machines with a rectangular cross-section, which was implemented prior to operating a pipe jacking machine with a rectangular cross-section in shallow buried rock strata. In particular, this multi-pilot heading pretreatment is expected to be used to safely construct a rectangular comprehensive pipe gallery using pipe jacking machines with a rectangular cross-section in shallow buried rock strata and when passing underneath existing light rail lines, which can effectively save the precious land resources required for sustainable development. The study was implemented by employing a numerical simulation, focusing on the safety of the adjacent existing light rail line and the stability of the surrounding rocks, which are influenced by the variation in the distribution positions and sizes of the drilling holes used when implementing the horizontal-directional drilling. The results demonstrate that the horizontal-directional drilling applied for the multi-pilot heading pretreatment could effectively break the blind spots on the working face of the pipe jacking machine with a rectangular cross-section, in which the safety of the adjacent existing infrastructure was significantly influenced by the distribution positions and sizes of the drilling holes used when implementing the horizontal-directional drilling. This study can provide a reference for carrying out pipe jacking construction using pipe jacking machines with a rectangular cross-section, in which horizontal-directional drilling is employed as the multi-pilot heading pretreatment for breaking the large blind spots on the working face. Moreover, the distribution positions and sizes of the drilling holes used when implementing the horizontal-directional drilling could be appropriately optimized by utilizing the method of numerical analysis. Meanwhile, the study is also expected to eliminate the hazards of safely running the aforementioned adjacent existing light rail line during implementing the multi-pilot heading pretreatment of horizontal-directional drilling. Full article

In arid and hyper-arid areas, groundwater is a precious and rare resource. The need for water supply has grown over the past few decades as a result of population growth, urbanization, and agricultural endeavors. This research aims to locate groundwater recharge potential zones (GWPZs) using multi-criteria evaluation (MCE) in the Wadi Qena Basin, Eastern Desert of Egypt, which represents one of the most promising valleys on which the government depends for land reclamations and developments. These approaches have been used to integrate and delineate the locations of high groundwater recharge and the potential of the Quaternary aquifer in the Wadi Qena basin. After allocating weight factors to identify features in each case based on infiltration, land use/land cover, slope, geology, topology, soil, drainage density, lineament density, rainfall, flow accumulation, and flow direction, these thematic maps were combined. The results of the GIS modeling led to the division of the area’s groundwater recharge potential into five groups, ranging from very high (in the western part) to very low (in the eastern part of the basin). The zones with the best prospects for groundwater exploration turned out to be the alluvial and flood plains, with their thick strata of sand and gravel. The groundwater recharge potential map was validated using data from the field and earlier investigations. The promising recharging areas show high suitability for soil cultivation. The results overall reveal that RS and GIS methodologies offer insightful instruments for more precise assessment, planning, and monitoring of water resources in arid regions and anywhere with similar setups for groundwater prospecting and management. Full article

Forests and forestry-related industries and ecosystem services play a critical role in the daily life of all societies, including in cultural, ecological, social, economic, and environmental aspects. Globally, there are about 4.1 billion hectares of forestland. In the United States, there are about 304 million hectares of forestland, covering about 34% of the total land area, and the forest product industry produces over USD 200 billion worth of forestry products annually. Evidence suggests these precious resources may be negatively impacted by climate change via direct and indirect processes, including wildfires, insect/pest pressure, drought, extreme storm events, increased air temperature, solar radiation, vapor pressure deficit, and other factors and variables that can be detrimental. All these can not only cause significant changes in the health and productivity of the forests, but can also cause the extinction, migration, and/or re-distribution of different tree species. Thus, humankind has the paramount responsibility to take policy, technologic, economic, environmental, and management decisions and actions to protect this vital resource for current and future generations, plants, and animals. This paper provides an overview of some of the important characteristics of forest environmental services, climate–environment–forest interactions with respect to forest health and productivity, climate change’s impacts on forest species, and the utilization of forest biomass for high-value products. Full article