Monitoring Of Environmental Changes Research Articles

Antimony and arsenic detection: review on electrochemical biosensors and their applications

ABSTRACT Arsenic (As) and antimony (Sb) contamination poses significant health risks, manifesting in a spectrum of ailments such as cardiovascular, respiratory, and skin diseases and the development of cancer. When toxic elements enter the body through different exposure routes, they can cause serious health issues. This highlights the critical need to reduce their presence in the environment to protect public health. When detecting As and Sb in different samples, researchers prefer to use whole-cell and optical biosensors rather than electrochemical (EC) biosensors for reliable detection. Utilizing EC biosensors can significantly enhance the detection thresholds and accuracy of monitoring environmental changes. It is particularly well suited for applications requiring high sensitivity, rapid detection, portability, and cost-effectiveness. For fast onsite environmental monitoring, its portability and quick reaction times are essential. The augmentation of EC biosensors via AI integration has the potential to transform pollution detection, healthcare diagnostics, and food safety monitoring, in addition to enhancing their sensitivity and accessibility. This review highlights the potential benefits of EC biosensors and their diverse applications in environmental monitoring. It critically evaluates strategies aimed at enhancing the efficiency of environmental monitoring processes.

Research on aquatic biological signal recognition based on mode decomposition and deep learning

Abstract As an important research content in the field of marine biology and ecology, aquatic biological signal (ABS) recognition is of great significance for understanding marine ecosystems, protecting biodiversity, and monitoring environmental changes. To improve the recognition accuracy of ABS, this paper proposes a new ABS recognition method based on mode decomposition and deep learning. First, real ABS data were obtained from the public website, and some data were selected for the simulation experiment. Secondly, the signal is decomposed using improved variational mode decomposition by human evolutionary optimization algorithm (HEOA-VMD), and the decomposed intrinsic mode function (IMF) set is classified into low complexity and high complexity IMF using improved Lempel-Ziv complexity (ILZC) and reverse permutation entropy (RPE). Then, mutual information (MI) is used to select double eigenvectors from low and high complexity IMF, respectively, and recognition is performed based on the double eigenvectors using weighted-convolutional neural network (CNN)-bidirectional gate recurrent unit (BIGRU)-Attention model. Finally, the proposed ABS recognition method is applied to both chaotic and real signals, and additional proof of universality is performed using real signals. The result of the study shows that the accuracy of the proposed method for the recognition of chaotic and real signals is as high as 97.3% and 98.0%. In conclusion, the research on ABS recognition in this paper is successful and has a broad application prospect.

Cyanobacteria respond to trophic status in shallow aquatic ecosystems

Small and shallow water bodies are particularly sensitive to adverse conditions connected with anthropogenic eutrophication. As model systems, ponds are a good object for ecological research and monitoring of global environmental changes. We examined cyanobacteria along with other groups of algae versus zooplankton and abiotic characteristics of water in 51 aquatic ecosystems exposed to anthropogenic pressure (from natural forest to highly disturbed field ponds) with 3 distinct trophic groups: meso-, eu- and hypertrophic. This study aimed to define how different levels of trophy affect pond-specific cyanobacteria assemblages and to identify species responding to particular trophic states. We demonstrated that trophic type determined the occurrence of certain cyanobacteria species. From among 78 identified taxa, shade- and turbid mixed adapted were the most numerous. Eutrophic ponds had the highest cyanobacteria species and diversity and abundance of zooplankon. Dominating species such as Chroococcus minimus, Anagnostidinema amphibium, Phormidium granulatum or Komvophoron minutum preferred mesotrophic, while e.g. Jaaginema subtilissimum, Limnolyngbya circumcreta, Limnothrix vacuolifera or Romeria leopolienis eutrophic waters and these were not grazed by filtrators. Only 3 species (Aphanizomenon flos-aquae, Dolichospermum circinale, Planktothrix agardhii) were associated with hypertrophic ponds. Therefore, we assume that cyanobacteria taxa have a high indicative potential to distinguish between trophic type of ponds. Reynolds Functional Groups also exhibit responses to changes in water quality. It was partucularly evident in the case of cyanobacteria representatives of codon M which was attributed to eutrophic ponds. Advancing our understanding about trophic preferences of cyanobacteria is crucial, especially in the era of global warming and the persistent issue of water eutrophication, when problems with harmful cyanobacterial blooms are intensifying. The research findings have ecological significance and management implications, highlighting the often-overlooked importance of pond ecosystems in maintaining overall water quality.

Microplastics in remote region of the world: Insights from the glacier of Geladandong, China

Although microplastics have been detected in different remote areas, people are still short of knowledge for environmental background of microplastics in remote area. In this work, we collected air, snow, river and lake water samples from Geladandong region (the origin of the Yangtze River) of the Tibetan Plateau, along with microscopy and Micro-FTIR analyses. Microplastics abundance (0.13–19.8 items/L) was the low as compared with other remote areas worldwide, and the morphology of fiber and film, material of PP and PET, and size of <300 μm were the main microplastics features. Due to natural aging during the transport processes, microplastics were degrading to smaller particle in Geladandong. Air mass trajectory analysis shows microplastics in Geladandong is transferred by repeated atmospheric transport from no human activity place. All these results suggested that Geladandong is an ideal research area for environmental background value and characteristic of microplastics. In conjunction with data from other remote regions, we established the background values for microplastics in remote areas. This work provides quantitative evidence for establishing environmental quality standards, and monitoring environmental changes.

Soundscape Characterization Using Autoencoders and Unsupervised Learning.

Passive acoustic monitoring (PAM) through acoustic recorder units (ARUs) shows promise in detecting early landscape changes linked to functional and structural patterns, including species richness, acoustic diversity, community interactions, and human-induced threats. However, current approaches primarily rely on supervised methods, which require prior knowledge of collected datasets. This reliance poses challenges due to the large volumes of ARU data. In this work, we propose a non-supervised framework using autoencoders to extract soundscape features. We applied this framework to a dataset from Colombian landscapes captured by 31 audiomoth recorders. Our method generates clusters based on autoencoder features and represents cluster information with prototype spectrograms using centroid features and the decoder part of the neural network. Our analysis provides valuable insights into the distribution and temporal patterns of various sound compositions within the study area. By utilizing autoencoders, we identify significant soundscape patterns characterized by recurring and intense sound types across multiple frequency ranges. This comprehensive understanding of the study area's soundscape allows us to pinpoint crucial sound sources and gain deeper insights into its acoustic environment. Our results encourage further exploration of unsupervised algorithms in soundscape analysis as a promising alternative path for understanding and monitoring environmental changes.

REVIEW OF MODERN BATHYMETRIC SURVEY TECHNIQUES AND THEIR IMPACT ON OFFSHORE ENERGY DEVELOPMENT

This paper presents a comprehensive review of modern bathymetric survey techniques and their impact on offshore energy development. Bathymetric surveys play a crucial role in mapping the seafloor and are essential for various offshore activities, including oil and gas exploration, renewable energy development, and submarine cable installation. The review synthesizes recent advancements in bathymetric surveying technologies, focusing on their role in improving the exploration and monitoring of offshore energy resources. The first section of the paper provides an overview of traditional bathymetric survey methods, such as single-beam and multi-beam echo sounders, which have been widely used for mapping the seafloor. The limitations of these methods, including limited coverage and resolution, are discussed, highlighting the need for more advanced techniques. The second section of the paper discusses the recent advancements in bathymetric survey technologies, such as airborne lidar, satellite-derived bathymetry, and unmanned surface vehicles (USVs). These technologies offer significant improvements in terms of coverage, resolution, and efficiency compared to traditional methods. For example, airborne lidar can provide high-resolution bathymetric data over large areas, making it ideal for rapid seafloor mapping in offshore energy exploration. The third section of the paper examines the impact of these advancements on offshore energy development. Improved bathymetric surveying techniques enable more accurate mapping of the seafloor, which is crucial for identifying suitable locations for offshore energy infrastructure, such as oil and gas platforms, wind farms, and wave energy converters. Furthermore, these techniques facilitate the monitoring of underwater structures and environmental changes, helping to ensure the safe and sustainable operation of offshore energy facilities. In conclusion, modern bathymetric survey techniques have revolutionized offshore energy development by providing more accurate, efficient, and cost-effective methods for mapping the seafloor. These advancements are essential for meeting the growing demand for offshore energy resources while minimizing environmental impact and ensuring operational safety.&#x0D; Keywords: Modern Bathymetric, Survey Techniques, Impact, Offshore Energy, Development.

Sentinel-2 Perspective: Land Use and Land Cover Dynamics in East and West Karbi Anglong

This study delves into the dynamics of land use and land cover (LULC) in East and West Karbi Anglong using Sentinel-2 satellite imagery. In response to escalating environmental changes, remote sensing technologies, particularly Sentinel-2, offer crucial insights for sustainable land management. Through an analysis of LULC patterns, the study aims to inform evidence-based decisions for environmental conservation and resource management in the region. The present study highlights the utility of remote sensing in LULC assessments and acknowledges the necessity for customized approaches in regions with distinctive features such as Karbi Anglong. Methodologically, temporally stratified Sentinel-2 datasets were utilized, with meticulous pre-processing and accuracy assessments ensuring reliability. Results indicate notable shifts in LULC classes, including urban expansion and vegetation decline. Despite limitations, such as potential inaccuracies in data processing, the study emphasizes the importance of monitoring environmental changes and advocating for sustainable land management strategies.

Temporal Assessment of GCM-Driven Hydroclimatic Conditions for the Alberta Oil Sands Region, Canada

Abstract Freshwater supplies in most western Canadian watersheds are threatened by the warming of temperatures because it alters the snow-dominated hydrologic patterns that characterize these cold regions. In this study, we used datasets from 12 climate simulations, which are associated with seven global climate models and four future scenarios and are participating in phase 6 of the Coupled Model Intercomparison Project, to calculate and assess the historical and future temporal patterns of 13 hydroclimate indicators relevant to water resources management. We conducted linear long-term trend and change analyses on their annual time series to provide insight into the potential regional impacts of the detected changes on water availability for all users. We implemented our framework on the Alberta oil sands region in Canada to support the monitoring of environmental changes in this region, relative to the established baseline 1985–2014. Our analysis indicates a persistent increase in the occurrence of extreme hot temperatures, fewer extreme cold temperatures, and an increase in warm spells and heatwaves, while precipitation-related indices show minor changes. Consequently, deficits in regional water availability during summer and water-year periods, as depicted by the standardized precipitation evapotranspiration indices, are expected. The combined effects of the strong climate warming signals and the small increases in precipitation annual amounts generally detected in this study suggest that drier conditions may become severe and frequent in the Alberta oil sands region. The challenging climate change risks identified for this region should therefore be continuously monitored, updated, and integrated to support a sustainable management for all water users.

Fractal representations in image processing of remote sensing of the earth

This article explores the application of fractal representations in the image processing of remote sensing data for Earth observation. Fractals, with their self-similar properties and complex patterns, offer a powerful mathematical framework for analyzing the intricate structures found in natural landscapes. The study highlights the advantages of using fractal-based methods over traditional image processing techniques, particularly in capturing the multifaceted textures and irregularities of Earth’s surface features. By leveraging fractal geometry, enhanced accuracy in the classification and interpretation of remote sensing images is achieved. This approach facilitates better monitoring and understanding of environmental changes, land use patterns, and natural disasters. The findings underscore the potential of fractal representations to significantly improve the quality and efficacy of remote sensing image analysis, providing a robust tool for Earth science research and practical applications in environmental management.

CONTENT PLANNING AND STRUCTURE DESIGN OF GLOBAL LAND COVER KNOWLEDGE ATLAS

Abstract. From 2000 to 2020, three versions of the 30-meter global land cover data products (GlobeLand30) have been made accessible to the public. The dataset with 30-meter spatial resolution and 10-year temporal resolution are widely used for monitoring environment changes and resource management at global, regional and local scales. By analysing and extracting information and knowledge from the GlobeLand30 data, we have planned and designed a global land cover knowledge atlas, which aims to expand the available knowledge base for scientific research, management decision-making, and knowledge dissemination.

Growing-season drought and nitrogen addition interactively impair grassland ecosystem stability by reducing species diversity, asynchrony, and stability

Aboveground net primary productivity controls the amount of energy available to sustain all living organisms, and its sustainable provision relies on the stability of grassland ecosystems. Human activities leading to global changes, such as increased nitrogen (N) deposition and the more frequent occurrence of extreme precipitation events, with N addition increasing the sensitivity of ecosystem production stability to changes in the precipitation regime. However, whether N addition, in combination with seasonal precipitation increases or severe drought, affects ecosystem stability remains unclear. In this study, we conducted a six-year environmental change monitoring experiment in a semiarid grassland in northern China to test the effects of N addition, seasonal drought, and precipitation increases on the temporal stability of ecosystem productivity. Our study revealed that an interaction between drought and N addition reduced species diversity, species asynchrony, species stability, and thus ecosystem stability. These environmental change drivers (except for precipitation increase) induced a positive relationship between species asynchrony and diversity, whereas N addition interactively with drought and precipitation increase led to a negative relationship between diversity and species stability. Only N addition interactively with drought induced a positive species diversity–ecosystem stability relationship because lower species stability was overcome by increased species asynchrony. Our study is great importance to illustrate that production temporal stability tends to be inhibited with drought, though interactively with nutrient N addition. These findings highlight the primary role of asynchronous dynamics among species in modulating the effects of environmental change on diversity-stability relationships.

The relationship between acoustic characteristics and landscape elements with different hydrodynamic environment in Jiuzhaigou World Heritage Site, China

A large number of studies are exploring the applicability of acoustic indices, and studies have been conducted to reveal the relationship between environmental factors (vegetation, microclimate, geology, etc.) and acoustic indices in different temperature zones and under different ecosystem conditions. On the one hand, studies in water ecosystems with different hydrodynamic characteristics are relatively lacking, and on the other hand, the accuracy of acoustic index calculation under the influence of continuous hydrodynamic features still needs to be continuously explored. This study explores whether acoustic indices can provide relatively accurate revealing results of landscape characteristics under different hydroacoustic intensities. In this paper, we collected 19 typical hydrodynamic soundscape resources including waterfalls, streams and haizi in Jiuzhaigou World Natural Heritage Site, a typical water ecosystem environment, as the object of the soundscape study. The landscape features were identified using semantic segmentation image techniques, and some specific feature factors were scored by eight people to complement. The relationships between 7 commonly used acoustic indices (i.e., Bioacoustic Index-BIO, Acoustic Entropy Index-AEI, Acoustic Diversity Index-ADI, Acoustic Evenness Index-AEI, Acoustic Complexity Index-ACI, Normalized Difference Soundscape Index-NDSI, Acoustic Richness Index-AR) and landscape elements were explored. The results show that (i) different hydrodynamic characteristics cause great differences among the acoustic indices, and (ii) the extent to which acoustic indices characterize landscape elements varies under different hydrodynamic characteristics. The study reveals the characteristics of the hydrodynamically dominated acoustic environment in Jiuzhaigou, and collects response data for subsequent monitoring of environmental changes using acoustic indices, as well as laying the foundation for conservation management.

Towards an Understanding of Microglia and Border-Associated Macrophages.

The central nervous system (CNS) plays a crucial role in regulating bodily functions by sensing and integrating environmental cues and maintaining proper physiological conditions. Recent research has revealed that CNS functions are closely coordinated with the immune system. As even minor disturbances of the immune system in the CNS can lead to various dysfunctions, diseases, or even death, it is highly specialized and segregated from that in peripheral regions. Microglia in the parenchyma and macrophages at the interface between the CNS and peripheral regions are essential immune cells in the CNS that monitor environmental changes. Recent omics analyses have revealed that these cells exhibit highly heterogeneous populations. In this review, we summarize the functions and diversity of microglia in the brain parenchyma and those of macrophages in the border regions, such as the meninges, perivascular spaces, and choroid plexus.

Use of Google Earth Engine for Teaching Coding and Monitoring of Environmental Change: A Case Study among STEM and Non-STEM Students

Computational skills are advantageous for teaching students to investigate environmental change using satellite remote sensing. This focus is especially relevant given the disproportionate underrepresentation of minorities and women in STEM fields. This study quantified the effects in both a STEM and a non-STEM class of Earth science remote sensing modules in Google Earth Engine on students’ self-efficacy in coding, understanding remote sensing, and interest in science and a career in environmental research. Additionally, the STEM students engaged in a course-based undergraduate research experience (CURE) on water quality. Satellite imagery was used to visualize water quality changes in coastal areas around the world due to the COVID-19 pandemic shutdown. Pre- and post-surveys reveal statistically significant changes in most students’ confidence to apply coding skills to investigate environmental change and understand remote sensing. The intervention was not sufficient to lead to significant changes in interest in science or a career in environmental research. There is great benefit in incorporating remote sensing labs to teach environmental concepts to STEM and non-STEM students and to bolster the confidence of underrepresented minorities and females in STEM.

Continuous remote sensing ecological index (CRSEI): A novel approach for multitemporal monitoring of eco-environmental changes on large scale

With urbanization and anthropogenic pressures, the eco-environmental quality of East and Southeast Asian coastal regions has undergone drastic changes in the 21st century. To assess these environmental variations and realize regional sustainable development goals, a quantitative monitoring of the eco-environment is necessary, but challenging. The remote sensing ecological index (RSEI) was recently proposed and applied to quantitatively monitor the eco-environmental quality, but bias during multitemporal observations is a major limitation. Thus, a novel method, Continuous Remote Sensing Ecological Index (CRSEI), was developed in this study to enable constant and objective monitoring of large-scale and long-term environmental changes in the coastal regions of East and Southeast Asia. To reduce the multitemporal bias of pixels caused by imaging conditions and sensor status, a change detection algorithm requiring limited auxiliary data was developed. High-resolution images show that the CRSEI enables adequate multitemporal monitoring of eco-environmental changes with fewer false fluctuations on the local scale. The proposed CRSEI should deepen the current understanding of environmental changes and provide a solid scientific reference for environmental protection on multiple scales. The results of this research indicate that the eco-environmental quality of the east and southeast Asian coast remained generally stable during the studied period (2001–2019), with the mean CRSEI changed slightly from 5918 to 5856. However, on a regional level, each area revealed different conditions. First, the Korean Peninsula had relatively stable and well-maintained eco-environmental quality with a CRSEI of around 5500 during 2001–2019. Second, The North China coast had lower eco-environmental quality compared to other regions, but it showed the most improvement, with the CRSEI increasing from 2719 to 2807. Third, the eco-environmental quality of South China and Myanmar's coastal region remained relatively stable, with a CRSEI of around 5730 and 6720, respectively. Fourth, the continental coast of some Southeast Asian countries, particularly Vietnam and Cambodia, experienced a significant decline in eco-environmental quality, as indicated by a dramatic decrease in the CRSEI from 6751 to 6527. Notably, the areas with declining eco-environmental quality were concentrated in the western border regions of Cambodia, where unrestricted cultivation has led to the destruction of tropical rainforests.

Applications Of Remote Sensing In The Study Of COVID-19 Pandemic – A Review

The global outbreak of the COVID-19 pandemic has spread worldwide, affecting almost all countries and territories. Various social-economic and environmental factors influence the outbreak and spread of the epidemic. Many modern techniques have been widely employed to study the COVID-19 pandemic. This paper aims to give an overview of applications offered by remote sensing techniques to study the COVID-19 pandemic through summarising a total of 55 scientific papers. Three different issues related to the COVID-19 pandemic is presented under three sub-sections; namely (1) applications of remotely sensed images on monitoring environmental changes and (2) the analysis of social and economic impacts caused by the COVID-19 pandemic, and (3) the use of remote sensing in studies of the epidemiology of SARS-CoV-2. The findings of this study provide important insights into how to apply such an advanced techniques as remote sensing in the fight against the COVID-19 pandemic. The varied applications of remote sensing affirms the value of this advanced technique to the study of small-to-large scale disasters in general and of the COVID-19 pandemic in particular.

Assessing tritium contamination in Thailand's rainwater: A study of environmental monitoring and nuclear surveillance

Tritium, whether naturally occurring or caused by human nuclear activity, can result in a large amount of tritium contamination in the environment, especially in the water cycle, causing a high concentration of tritium in rainfall. The objective of this research was to measure the level of tritium in the environment from rainfall in two different areas as a basis for monitoring tritium contamination in the environment. Rainwater samples were collected in Thailand every 24 h for a period of 1 year during 2021–2022 at the Kasetsart University Station, Sriracha Campus, Chonburi province and at the Mae Hia Agricultural Meteorological Station, Chiang Mai province. The tritium levels were measured in rainwater samples using the electrolytic enrichment method combined with liquid scintillation counting. The chemical composition of the rainwater was analyzed based on ion chromatography. The results (presented with ± combined uncertainty) showed that the tritium content in the rainwater samples at Kasetsart University Station Sriracha Campus was in the range 0.9 ± 0.2–1.6 ± 0.3 TU (0.11 ± 0.02–0.19 ± 0.03 Bq.L−1). The mean concentration was 1.0 ± 0.2 TU (0.12 ± 0.03 Bq.L−1). The most common ions found in the rainwater samples were SO42−, Ca2+, and NO3−, with mean concentrations of 1.52 ± 0.82, 1.08 ± 0.51, and 1.05 ± 0.78 mg.L−1, respectively. The tritium content in rainwater collected from the Mae Hia Agricultural Meteorological Station was in the range 1.6 ± 0.2–4.9 ± 0.4 TU (0.19 ± 0.02–0.58 ± 0.05 Bq.L−1). The mean concentration was 2.4 ± 0.4 TU (0.28 ± 0.05 Bq.L−1). The most common ions found in the rainwater were NO3−, Ca2+, and SO42−, with mean concentrations of 1.21 ± 1.02, 0.67 ± 0.43, and 0.54 ± 0.41 mg.L−1, respectively. The tritium concentration in the rainwater at both stations differed but remained at a natural level (less than 10 TU). There was no correlation between the tritium concentration and the chemical composition of the rainwater. The tritium levels obtained from this study could be used as a basis for reference and monitoring of future environmental changes due to nuclear accidents or activities, both domestically and internationally.

TAXONOMIC ANALYSIS OF THE ALGOFLORA OF THE AKDARYA RESERVOIR IN THE MIDDLE REACHES OF THE ZARAFSHAN RIVER

As a result of flora-systematic studies of the Akdarya reservoir, 71 species and varieties (63 species, 16 variations, 1 form) were identified, which belong to 4 departments, 10 orders, 14 families, 24 genera. The species composition is dominated by diatoms (Bacillariophyta), consisting of 56 species of varieties, followed by detached green (Cyanophyta) - 9 (8 species, 1 form), yellowish (Xanthophyta) - 4, green (Chlorophyta) - 2 species of algae. Hormogoniophyceae, Pennatae and Heterotrycho phyceae are the most diverse classes, representing 65 species and varieties (91.54%), consisting of 6 families, generalized by 62 different species (87.95%), and 7 genera, generalized by 53 (74, 64%) species and varieties. The flora-systematic analysis shows that the species composition of the Akdarya Reservoir’s algophore has a peculiar feature, where the data obtained in the Far East can be used in the study of the ecosystem of the Akdarya Reservoir, as biodiversity of primary producers.&#x0D; Background. The reservoir plays a major role in the formation of ecosystems of certain geographical areas. The comprehensive study of biodiversity in a reservoir makes it possible to study and monitor environmental and biological changes in environmental factors. For this purpose, a preliminary inventory of the state of the aquatic environment and its organisms is carried out. In this regard, the objective of our research is the taxonomic analysis of the Akdarya Reservoir’s algophore.&#x0D; Materials and Methods. In 2014-2022 from Akdarya reservoir-More than 150 alhologic samples have been collected and processed according to the generally accepted methods of alhology and hydrobiology [5, 6]. In the laboratory, preparations were prepared from algological samples, species compositions of algae were identified by determinants and frequency of occurrence in one lens field of view was determined. The experiments were carried out using a Carl Zeiss type light microscope, eyepiece +7 micrometer, and dyes. Species composition of algae, systematics and their taxonomy have been identified with the help of the determinant [4-11, 16] and the monograph [12, 17, 18]. The chemical composition of water is analyzed by the data of UzGMNII (Uzbek Hydrometeorological Research Institute).&#x0D; Results. Based on taxonomic analysis, 71 species and varieties of algae (63 species, 16 variations and 2 shapes) were identified in alhologic samples from the Akdarya reservoir. They belong to 4 divisions (Cyanophyta, Bacillariophyta, Xanthophyta, Chlorophyta), 8 classes, 10 orders, 14 families and 23 genera, which are given in table 1.2.&#x0D; Conclusion. The Akdarya reservoir in the middle of the Zarafshan River has a large amount of suspended substances and relatively high mineralization. This factor favourably influences the development, formation and intensive distribution of the algae division of Bacillariophyta (56), Cyanophyta (9), Xantophyta (4), Chlorophyta (2) in the ecosystem of the Akdarya reservoir. The obtained data can be used in the distant biodiversity of the primary producer of the ecosystem of the reservoir.

Use of Spatial Technology for Land Cover Change Assessment in Protected Area Management

Land cover assessment and monitoring of land cover dynamics are important to understand social and ecological processes in protected areas and it is helpful in monitoring, modeling and detection of environmental changes. Land cover is the characteristics of the physical material on the ground surface. Change in land cover is among the most important human alteration affecting the surface of earth that directly impacts on biological diversity. Such changes affect and increase the vulnerability of population, environment and ecosystem to global phenomena of climate change. This study has assessed the changes in land cover in Shivapuri Nagarjun National Park (SNNP) along with its Buffer Zone during twenty-seven years. The main objective of this study is to identify the changed scenario on land cover condition of the study area based on available topographic maps (1994), Google Earth images (2021) and Key Informant Interview (KII). The study has adopted Remote Sensing (RS), Geographic Information System (GIS) and Global Positioning System (GPS) approach supported by secondary data and focus group discussion for classification and mapping of land cover. Among the different land cover classes, forest land, sandy area, water bodies and others land particularly built up area have been increased whereas, the bush/shrub land, cultivated land and grass land have been found decreased in the park area. On the other hand, the cultivated land, grass land and water bodies have been found decreased, while the bush/ shrub land, forest land, sandy area and others land particularly built up areas have been found significantly increased during 1994 to 2021 in the BZ area. It will provide a baseline for planners and policy makers to make proper plans to sustainably manage the protected area’s land cover.

Design and Implementation of Machine Learning-Based Fault Prediction System in Cloud Infrastructure

The method for ensuring availability in an existing cloud environment is primarily a metric-based fault detection method. However, the existing fault detection method makes it difficult to configure the environment as the cloud size increases and becomes more complex, and it is necessary to accurately understand the metric in order to use the metric accurately. Furthermore, additional changes are required whenever the monitoring environment changes. In order to solve these problems, various fault detection and prediction methods based on machine learning have recently been proposed. The machine learning-based fault detection and recovery model most commonly proposed in the cloud environment is a supervised machine learning method that learns data relating to fault situations and, based on this data, detects faults. However, there is a limit to fault learning because it is difficult to obtain all of the fault situation data necessary to learn all of the fault situations that occur in a large-scale cloud environment. In addition, it is difficult to detect a fault when a fault that differs from the learned fault pattern occurs. Furthermore, it is necessary to discuss the automatic recovery architecture leading to the fault recovery procedure based on the fault detection results. Therefore, in this paper, we designed and implemented a whole system that predicts faults by detecting fault situations using the anomaly detection method.