Information System Environment Research Articles

Digital Analysis with the Help of an Integrated UAV System for the Surveillance of Fruit and Wine Areas

The main purpose of this study was to create a prototype of an unmanned aerial system equipped with intelligent hardware and software technologies necessary for surveillance and monitoring the health and growth of crops from orchards with vines and fruit trees. Using low-cost sensors that accurately measure ultraviolet solar radiation was an important objective. The device, which needed to be attached to the commercial DJI Mini 4 Pro drone, had to be small, portable, and have low energy consumption. For this purpose, the widely used Vishay VEML6075 digital optical sensor was selected and implemented in a prototype, alongside a Raspberry Pi Zero 2 W minicomputer. To collect data from these sensors, a program written in Python was used, containing specific blocks for data acquisition from each sensor, to facilitate the monitoring of ultraviolet (UV) radiation, or battery current. By analyzing the data obtained from the sensors, several important conclusions were drawn that may provide valuable pathways for the further development of mobile or modular equipment. Furthermore, the plantation state analysis results with proposed models in the geographic information system (GIS) environment are also presented. The visualization of maps indicating variations in vegetation conditions led to identifying problems such as hydric stress.

Land use and land cover mapping of the Saco River’s watershed, State of Maranhão, Brazil

Land use and land cover mapping benefits landscape understanding and its changes, especially those resulting from anthropogenic actions in the physical environment. The present study aimed at mapping land use and coverage of the Saco River’s watershed (located in Codó, state of Maranhão). The area comprises the coconut forest (named Mata dos Cocais), a region that encompasses the characteristics of different biomes on the same temporal and spatial scale. The methodology applied was based on remote sensing techniques developed in a Geographic Information System (GIS) environment, processing data from Landsat 8 and supervised classification. The results showed a predominance of dense vegetation in the basin under study, in addition to a reduced occupation by classes of urban area and water bodies. The overall accuracy was 79% and dense vegetation presented a higher user and producer accuracy than the general, with 91 and 87%, respectively. On the other hand, the highest commission and omission errors were those in urban areas and water bodies, which coincided with the lowest occupation classes in the Saco River basin. These results are pioneering for the coconut forest and provide data for strategic planning of environmental actions.

The INPHET- a global approach to Environmental Public Health Tracking

Abstract Environment and health monitoring and information systems are fundamental infrastructure for evidence provision for policy making. The International Network for Public Health and Environment Tracking (INPHET) was formed by a community of environmental public health practitioners to address common interests and activities that advance environmental public health surveillance (EPHS) and data systems, and their uses to inform public health actions. Its main goal is to address the vast inequalities across countries and communities in their ability to monitor, assess, inform and mitigate environmental and health issues. INPHET supports the relevant role of environmental epidemiology as the pillar science providing evidence for decision making and interventions aiming at reducing and preventing environmental related diseases and contributes to an overall vision for public health. As a “Community of Practice”, INPHET focuses on sharing experiences and expertise; addressing common challenges for implementing and enhancing the science of EPHS/tracking; improving understanding of the link between the environment and health; and providing the appropriate evidence that informs public policy. The network, initiated in conference meetings in 2012-13 and formally launched in Modena, Italy in 2014, has carried out several activities which aim at sharing expertise and learning from different models of environmental and health data integration and surveillance systems that directly inform policy. These include Twinning cooperation with Euro-Asian and Balkan countries and sharing of expertise with African Public Health Agencies. Some experiences are presented in this workshop and highlight the EPHS/EPHT model adopted by several national public health agencies. It promotes that such integration and uses of environmental and health data is key for EPHS, including their accessibility and usability for communities of public health researchers, practitioners and policy makers.

Groundwater potential mapping and its sustainable management using AHP and FR models in the Jedeb watershed, Upper Blue Nile Basin, Ethiopia

ABSTRACT Groundwater potential mapping and its sustainable development are important aspects of the Jedeb watershed due to an increase in groundwater demand and the local population. This study aimed to delineate potential groundwater zones in the Jedeb watershed, using the analytical hierarchy process (AHP) and frequency ratio (FR) model. Nine thematic layers, including lithology, geomorphology, slope, drainage density, land cover and land use, lineament density, rainfall, soil, and static water level, were considered. The thematic layers obtained were integrated into a Geographic Information Systems (GIS) environment to generate the groundwater potential map. Four classes of groundwater potential have been identified: low, moderate, high, and very high. The low classes represent 12.8% and 7.02%; moderate 25.35% and 34.84%; high 38.8% and 33.11%; and very high 23.05% and 25.03%, for the AHP and FR models, respectively. The result map was also validated by using receiver operating characteristic (ROC) curve from which result accuracy was obtained is 85.6% and 88.3% for the AHP method and FR method, respectively. The study's validation verified that the FR model produced more efficacious results than the AHP model. This study, will be helpful in providing planners, and decision-makers with rapid decision-making when it comes to managing groundwater resources.

Pengaruh Ketidakpastian Lingkungan dan Komitmen Organisasi Terhadap Kinerja Manajerial Dengan Kualitas Sistem Informasi Akuntansi Manajemen Sebagai Variabel Intervening

This study aims to directly test and analyze the Influence of Environmental Uncertainty, Organizational Commitment and Quality of Management Accounting Information Systems on Managerial Performance and indirectly to test and analyze directly the influence of Environmental Uncertainty and Organizational Commitment on Managerial Performance with the Quality of Management Accounting Information Systems as an Intervening Variable in Star Hotels in Medan City. The research approach uses quantitative associative. The population of this study is all 3 to 5 star hotels in Medan City, totaling 48 hotels with a total sample of 240 respondents. This study uses the Structural Equation Modeling (SEM) analysis method using SmartPLS.3 software. Based on the results of direct hypothesis testing, there is an influence of environmental uncertainty, organizational commitment and management accounting information systems on managerial performance. Indirectly, there is an influence of environmental uncertainty and organizational commitment on managerial performance mediated by management accounting information systems

Flow dynamics and channel changes at Yamuna River in Delhi-National Capital Region, India

ABSTRACT The present work was carried out on River Yamuna passing through Delhi-National Capital Region (Delhi-NCR) of India for a stretch of about 125 km to assess the fluvial characteristics in the highly human-dominated region for a period of 200 years with the help of historical maps, topographical maps and satellite data with integration of geographic information system (GIS) environment. Erosion, deposition and channel stability data were analyzed for 1977–1986, 1986–1996, 1996–2006, 2006–2016, and 2016–2022 period. Digital Shoreline Analysis System (DSAS) application was utilized to quantify river channel movement with average channel migration of 22.8 m/year. Westward migration is 4149.2 m maximum in length and eastward migration is about 4083.8 m. The river has migrated a total of 32.26 km2 of area during 1955-2022. The findings indicate that various human activities, such as engineering structures, sand mining, embankments, urbanization, land use/land cover changes, and canal networks, have significantly impacted the river. The DSAS application was also used to predict the position of river channel centerline in future for 2032 and 2042 with channel length of 132.5 and 141.6 km respectively.

A framework for appraising the status of disaster resilience within the multi-hazard environment of coastal Bangladesh

Fostering resilience is widely acknowledged as a crucial strategy for mitigating the escalating risks posed by climate-related disasters around the world. Resilience assessment plays a vital role in this endeavour by assessing the baseline level of resilience, identifying the key resilience factors, and monitoring progress towards building resilience with time. Given this context, and considering the evolving conceptualization of resilience, we devised a framework for appraising the status of resilience across the coastal region of Bangladesh. Our framework represents a significant scholarly contribution, as it encompasses a broad spectrum of resilience dimensions, including stability, recoverability, and transformability. To operationalize this framework, we selected thirty context-specific variables and organized them systematically around these three overarching components. Employing a simple weighted average methodology, we constructed sub-indices for each component, which were subsequently aggregated into a composite resilience index. For spatial visualization, our findings were mapped in a Geographic Information System (GIS) environment. Our analysis revealed Chattogram, Barguna, and Khulna as the most resilient districts. This manifests the potential for resilience enhancement even within areas exposed and inherently sensitive to multiple hazards through the optimal use of available resources. Notably, in the trio of districts, Barguna exhibited the highest stability index score, Chattogram showcased the highest recoverability score, and Khulna emerged with the top transformability index score. This finding underscores the significance of incorporating diverse capacities in resilience frameworks. These insights offer valuable guidance for refining resilience assessment frameworks in other developing countries of the world. Furthermore, the outcomes of our assessment can serve as a baseline for the coastal administrators of Bangladesh while developing disaster management policies and actions centering around fostering resilient communities. Thus, our study not only contributes to disaster theory, but holds practical implications for evidence-based decision-making to secure a more resilient future of the coastal communities of Bangladesh.

A review of resistance–capacitance thermal network model in urban building energy simulations

With the increasing population and urbanization promotion, energy consumption and carbon emissions have increased, and concern for inefficient energy use and deteriorating urban environments is growing. The prediction of building energy consumption and environmental evaluation, especially at large scales, are considered to be a major challenge confronting the research community. An outstanding strategy for mitigating energy consumption and carbon emissions resides in the field of energy modeling. As a simplified building energy modeling model, resistance–capacitance (RC) network model has the applications in fast predicting building energy consumption. Notably, in recent years, there has been an evident absence of thorough review endeavors related to RC model for urban building energy loads and climate. This review systematically explores the application of low-order models and reduction methods for urban or regional energy simulation starting from the typical RC model for building. In summation, the challenges associated with employing this model for urban building energy loads and climate can be succinctly summarized as follows: the absence of a unified platform for RC modeling; insufficient readily applicable urban datasets; the need for modeling tools that facilitate cross-platform analyses in conjunction with existing Geographic Information Systems (GIS) and urban thermal environment simulation research platforms, and the essential requirement for seamless integration with other complementary modules.

Deep learning-based detection of qanat underground water distribution systems using HEXAGON spy satellite imagery

Qanats are a remarkable type of ancient hydraulic structure for sustainable water distribution in arid environments that use subterranean channels to transport water from highland or mountainous areas. The presence of the qanat system is marked by a line of regularly spaced shafts visible from the surface, which can be used to detect qanats using satellite imagery. Typically, qanats have been documented by field mapping or manual digitisation within a Geographic Information System (GIS) environment. This process is time-consuming due to the numerous shafts within each qanat line. However, several automated methods for detecting qanat structures have been explored, using techniques such as morphological filters, custom convolutional neural networks (CNN) and, more recently, YOLOv5 and Mask R-CNN. These approaches used high-resolution RGB images and CORONA images. However, the use of black and white CORONA in CNNs has been limited in its applicability due to a high rate of false positives.This paper explores the potential of YOLOv9 in processing the black and white HEXAGON (KH-9) high-resolution spy satellite system launched in 1971. Two areas in Afghanistan (Maiwand) and Iran (Gorgan Plain) were selected to train the system images extracted from HEXAGON imagery and artificial synthetic data. The training dataset was augmented using the Albumentation library, which increased the number of tiles used. The model was tested using two types of HEXAGON imagery for selected areas in Afghanistan (Maiwand), Iran (Gorgan Plain) and Morocco (Rissani), and CORONA imagery in Iran (Gorgan Plain).Our study provided a model capable of predicting the location of qanat shafts with a precision of over 0.881 and a recall of 0.627 for most of the case studies tested. This is the first case study aimed at detecting qanats in different landscapes using different types of satellite imagery. Using real, augmented, and artificial data allowed us to generalise the representation of qanats into lineal groups of circular features. Thanks to applying labelling for individual qanats and their pairs as separate classes, our approach eliminated most of the isolated and clustered false positives.

From Culture to Structure: Key Factors in Refining the Functionality of Accounting Information Systems

The organizations attach a lot of importance to financial reporting, especially for nonprofits whose reports are made widely available to the public and may be a matter of giving trust by the donors. This only shows that its professional standard of its financial management should be demonstrated in all instances. Not-for-profit organizations require access to accurate and reliable accounting information. The objective of this research is to analyze the influence of organizational culture and structure on effectiveness accounting information systems. An organization with a strong foundational culture based on clear values, norms and well-defined structure is good an ideal environment for information systems to work efficiently as they will receive needed support. And these systems overall, aid in increasing the accuracy and reliability of accounting information used by decision-makers. This study employs an alternative framework and applies SEM-PLS analysis using a quantitative approach to test the association between organizational culture, structure, quality of accounting information system. The anticipated findings are expected to demonstrate that both organizational culture and structure significantly impact the successful deployment and functionality of these systems. The quality of an accounting information system is a critical factor in achieving organizational goals, as it provides the operational tools necessary for generating accurate, relevant, and timely financial information. By aligning their culture and structure, organizations can ensure that their accounting information systems contribute effectively to sound financial management and overall organizational success.

Development of an Internet of Things-based Environmental Information System for Realtime Monitoring of Air Conditions

This study aims to develop an environmental information system called the Environmental Monitoring System (EENNOS) to monitor air environmental conditions. ENNOS is an Internet of Things (IoT) based system that monitors air environmental conditions in the form of temperature, humidity and carbon dioxide. The research was conducted using the 4D development method which consisted of the Define, Design, Develop, and Disseminate stages. EENNOS was developed using a DHT 22 sensor for DHT 22 temperature and humidity and a carbon dioxide sensor with MQ-135. The controller used is Arduino uno ESP8266 with output sending using the internet to the database and presented on the dashboard and displayed on a 16x2 liquid crystal screen. The output is in the form of dashboard innovations that are registered with copyrights and scientific publications in the form of articles in the Journal of Environmental Sciences.

Analysis of the intensity of erosive processes and state of vegetation cover in the zone of influence of the Kolubara Mining Basin

Abstract Ensuring the quality protection of geospatial elements entails environmental control, a task unattainable without precise measurement results. This article aims to conduct a spatio-temporal analysis of soil degradation and vegetation status within the influence zone of the Kolubara Mining Basin in Serbia. Remote sensing is employed to assess vulnerability to erosion using the erosion potential method. A geographic information system environment is utilized to generate an erosion map, illustrating erosive processes across different time periods, particularly comparing the present situation (2022) to 1983. Results indicate that observed areas are experiencing erosion due to changes in land use. Furthermore, this study investigates the use of the normalized difference vegetation index to monitor vegetation cover changes from 1992 to 2022. The objective is to demonstrate that these methods effectively depict degradation levels and vegetation status in the area. This comprehensive overview provides insights into the changes occurring across the analyzed years. Such insights are crucial for informing future efforts to restore the region to its natural state prior to lignite mining.

Analysing space-time patterns of maternal mortality attributed to anaemia in Karnataka, India: a spatial epidemiological study

BackgroundMaternal health is a globally important health challenge and is the key indicator of healthcare services in any given region. One of the main objectives of Sustainable Development Goal 3.1...

Data processing in internet of things networks

One of the distinctive features of cloud applications in the Internet of Things (IoT) network is the real-time collection and processing of data about the objects of the physical environment. To solve this problem, with the help of sensors, data about the state of physical objects is collected to form the processing of data streams. The performance of such systems is critically dependent on the ability to securely and efficiently collect, transmit, and analyze data streams between environmental (real-world) objects and information systems. In such a case, it is relevant to study the data stream in Internet of Things networks and the operations that may occur on them. The issue of which platforms to process the operations to be carried out at the same time is the main subject of discussion.

Promoting child health and environmental equity through a children’s environmental health information system

Promoting child health and environmental equity through a children’s environmental health information system

A Methodological Benchmark in Determining the Urban Growth: Spatiotemporal Projections for Eskişehir, Türkiye

Urban growth changes spatial uses over time due to different dynamics. These processes cause many physical, environmental, and socioeconomic problems, such as climate change, pollution, and population-related events. Therefore, it is essential to predict future urban expansion to produce effective policies in sustainable urban planning and make long-term plans. Many models, such as dynamic, statistical, and Cellular Automata and Markov Chain (CA-MC) models, are used in geographic information system (GIS) environments to meet the high-performance requirements of land use modeling. This study estimated the growth of settled areas in Eskişehir city center using models developed using two different methods. In this context, settled areas in the city center were examined within the scope of 1990–2018, and the growth areas of settled areas in 2046 were predicted using the CA-Markov method in Model 1: Quantum GIS (QGIS) MOLUSCE plugin and Model 2: IDRISI Selva. While settled areas are continuously increasing, other urban areas are decreasing. Model 1 predicts an increase of 1195 ha in settled areas by 2046, while Model 2 predicts an increase of 45,022 ha. At the same time, it is concluded that settled areas will grow in a central location in Model 1, while they will spread in an east-west extension in Model 2. The study results show that QGIS-based modeling predicts more limited spatial growth than IDRISI Selva. The research interprets growth in terms of the staging of urban services, the population size of neighboring cities, distances, and income levels based on the internal and external dynamics of the city.

USING THE GEOSPATIAL MULTI-CRITERIA DECISION ANALYSIS MODEL AND METHODS FOR SOIL DEGRADATION RISK MAPPING

Modern methods of spatial analysis and modeling are increasingly being combined with decision-making methods and fuzzy set theory. The latter are actively integrated into the environment of Geographic Information Systems (GIS), such as well-known ones like ArcGIS or QGIS, in the form of separate tools, plugins, or Python scripts. Decision-making methods allow structuring the problem in geographical space, as well as taking into account the knowledge and judgments of experts and the preferences of the decision-maker in determining the priorities of alternative solutions. This paper provides a description of a geospatial multi-criteria decision analysis model, which allows addressing a wide range of ecological and socio-economic issues. An example of applying this model to map soil degradation risk in Ukraine is presented in the paper. According to the object-spatial approach, the properties of a territory are determined as the result of the action (impact) of a set of objects (processes) belonging to this territory. The territory is represented as a two-dimensional discrete grid, each point of which (local area) is an alternative. The set of local areas of the territory constitutes the set of alternatives. The representation of the territory model as a system of objects and relationships between them allows justifying the choice of a set of criteria (factors) for assessing soil degradation risk. Each criterion is a separate raster layer of the map. To build a hierarchical decision-making structure and calculate the importance coefficients of the criteria, the Analytic Hierarchy Process (AHP) method is used. To account for uncertainty in assessments and judgments of experts at the stages of standardization of alternative attributes by different criteria and aggregation of their assessments, expert membership functions for fuzzy sets and fuzzy quantifiers are applied. The particular feature of the proposed multi-criteria decision analysis model is its low computational complexity and ease of integration into the GIS environment.

International conference on environmental observations, modeling and information systems: Enviromis-2024

Announcement of the international conference on measurements, modeling and information systems for environmental studies: ENVIROMIS-2024, will be held in Tomsk on July 1-6, 2024.

Assessing Multi-Decadal Landmass Changes and River Bank Erosion in the Char Areas of Majuli River Island of Assam, India

Majuli, the biggest populated river island, which is bordered to the north by the Subansiri River and to the south by the powerful Brahmaputra River. Since ancient times, the island has been continuously eroding, which is cause for serious concern. The current method uses Remote Sensing (RS) satellite imagery covering the years 1976 to 2024 and Survey of India (SOI) toposheets to study the erosion–deposition processes in a Geographic Information System (GIS) environment. The observation has revealed a dramatic change in reduction of land area of the Majuli Island. This paper provides a comprehensive analysis of the long-term changes in landmass and erosion patterns in the char areas of Majuli Island, highlighting both the environmental challenges and the socio-economic implications for local communities. It is evident that the erosion rate on the island mass was significantly higher than that of the depositional processes.

Assessing drought in Turkish basins through satellite observations

AbstractDrought occurs when there is a sustained decrease in rainfall over an extended period, impacting the socio‐cultural and environmental aspects of humans and other living beings. The geographic distribution and timing of droughts play a crucial role in drought management and mitigation strategies. Identifying and predicting the onset of droughts in specific regions, especially in watershed areas, is a primary concern in the field of hydrology. This study focuses on how the spatiotemporal patterns of drought are developing in Turkish Basins using detailed data on Terrestrial Water Storage (TWS), precipitation, and temperature at the pixel level. GRACE (Gravity Recovery and Climate Experiment), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), and WorldClim (World Climate) data sets are employed to assess long‐term changes of drought on a basin‐scale. Spatial analyses are conducted in a Geographic Information System (GIS) environment for the derivation of basinal monthly mean, minimum, and maximum statistics of TWS, precipitation, and temperature anomalies within Turkish Basins. Time series analyses are implemented to investigate the temporal evolution of droughts in these basins, for the basinal monthly mean, minimum, and maximum statistics obtained. The Mann–Kendall trend test and Pettitt change point detection tests are used to assess the statistical significance of the calculated trends and to expose the existence of any change point therein, respectively. The findings of the study indicate that Turkiye faces a significant risk of drought development in nearly all its basins, particularly after 2016. The GRACE dataset provides realistic insights into the temporal behaviour of hydrological droughts. PERSIANN is effective in identifying years with extreme meteorological conditions, and the standardized precipitation index (SPI) shows similar effectiveness, while they are ineffective in exposing significant trends due to the nature of the precipitation data. WorldClim data proves insufficient for modelling the temporal behaviour of droughts in these basins.