Journal of Remote Sensing & GIS (JoRSG) ISSN (Online): 2230-7990 ISSN(Print): 2321–421X Click here for complete Editorial Board Scientific Journal Impact Factor (SJIF): 6.105 Journal of Remote Sensing & GIS (JoRSG) Journal of Remote Sensing & GIS (JoRSG) is a print and e-journal which contains good quality- original research papers, review papers, short communications, and book reviews that focus on practical and theoretical issues, collection, analysis, modeling, interpretation, and display of spatial data, connections between GIS and related technologies and also new GIS applications which help to solve current issues & problems. It's a triannual journal, started in 2010. Journal DOI No: 10.37591/JoRSG Indexing: The Journal is indexed in DRJI, Citefactor, Journal TOC, Google Scholar, Index Copernicus (ICV : 54.42) Focus and Scope Covers Aerial Photography Cartography and Map Publishing Geoinformatics Geo Physics Image Compression and Image Processing Remote Sensing, Spatial Science, Surveying and Mapping Global Positioning Systems (GPS) Submission of Paper: All contributions to the journal are rigorously refereed and are selected on the basis of quality and originality of the work. The journal publishes the most significant new research papers or any other original contribution in the form of reviews and reports on new concepts in all areas pertaining to its scope and research being done in the world, thus ensuring its scientific priority and significance. Manuscripts are invited from academicians, students, research scholars and faculties for publication consideration. Papers are accepted for editorial consideration through email tanvi.singh@stmjournals.com Plagiarism: All the articles will be checked through Plagiarism Software before publication. Abbreviation: (JoRSG) Frequency: Three issues per year Peer Review Policy Editorial Board Instructions to Authors Publisher: STM Journals, an imprint of CELNET (Consortium e-Learning Network Pvt. Ltd.) Address: A-118, 1st Floor, Sector-63, Noida, Uttar Pradesh-201301, India Phone no.: (+91)120-4781-215/ Email: cete@celnet.in
Exploring the Benefits and Efficacy of Green Cloud Computing Techniques for Distributing Educational Library Data in India's Remote Regions During a Pandemic
The COVID-19 pandemic has underscored the necessity for digital infrastructure and accessibility, particularly in the education sector. This paper investigates the benefits and effectiveness of utilizing Green Cloud Computing (GCC) techniques for the dissemination of educational library data in remote regions of India during this critical period. The GCC model, known for its energy efficiency and reduced environmental impact, is proposed as a robust, scalable, and eco-friendly solution for providing remote educational access. The research utilizes a mixed-methods approach, incorporating both quantitative assessments of data reach and usage, and qualitative surveys to understand user experiences. The study reveals that GCC techniques can significantly improve educational resource distribution, thereby mitigating the educational disparities further exacerbated by the pandemic. These findings reinforce the potential of GCC techniques as a sustainable and inclusive technology in reshaping the educational landscape in remote regions.
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Effective Road Safety for Future Cities: Case Study Using GIS and Remote Sensing for Accident Analysis in Navi Mumbai
Urban transportation systems face significant challenges due to rapid population growth and development. This study delves into the enhancement of road safety in Navi Mumbai by employing Geographic Information System (GIS) and Remote Sensing (RS) techniques. The escalating rate of vehicular accidents in Navi Mumbai presents a pressing concern. This research investigates accident data and traffic patterns, identifying vulnerable areas prone to accidents and congestion. By conducting spatial analysis using GIS and RS, the study aims to uncover accident hotspots and traffic congestion zones, offering insights into underlying road safety issues. The research methodology involves a multi-stage process. Initial data collection from various sources, including police reports, live traffic data, and satellite imagery, forms the foundation. Geographic coordinates extracted and processed through GIS applications aid in plotting accident locations and creating density maps. Additionally, on-site investigations at strategically chosen locations provide invaluable insights into local conditions, traffic patterns, and contributing factors to congestion and accidents. The findings are presenting tailored solutions for each area, ranging from optimized traffic signal timings to infrastructural improvements. The findings of this study present actionable insights aimed at improving road safety and traffic management in Navi Mumbai. Recommendations encompassing signal optimizations, infrastructure enhancements, and community engagement strategies offer a holistic approach to mitigate traffic congestion and reduce accidents. The collaborative effort with relevant authorities, as highlighted in the study, serves as a crucial step towards implementing these recommendations for meaningful change. This research not only identifies critical areas for intervention but also serves as a model for leveraging GIS and RS techniques to enhance the road safety in urban areas, paving the way for safer and efficient transportation networks in the future.
Climate Change Monitoring and Solution Using Remote Sensing, Geographic Information System and Artificial Intelligence: A Review
One of the most crucial global issues of our era is climate change, whose consequences are presently being felt all over the world. As climate change keeps on forgoing, it's important that governments, organizations, and businesses take visionary actions for adapting and protecting themselves from calamities. This paper summarizes the application of Remote Sensing (RS) and Geographic Information System (GIS) in observing impact of climate change on drought, soil moisture, land degradation, food security, EHIs’ characterization and blue carbon science and review of AI based climate solutions. The integration of advanced machine learning algorithms, real-time data analysis, and other cutting-edge technologies could lead to even more effective climate change adaptation strategies. AI-activated climate change adaptation strategies have the prospective to significantly improve the resilience of infrastructure communities, and businesses to the changing climate.
Towards Renewable Energy Transition: Evaluating Wind Resources in Odisha
The urgency to transition to renewable energy sources is underscored by the environmental crises stemming from our reliance on non-renewable fuels. This study focuses on assessing wind energy potential in Odisha, India, utilizing satellite data and Geographic Information System (GIS) technologies. The research addresses the critical need for strategic planning and site selection before investing in renewable energy infrastructure. By employing a model that integrates various free satellite datasets and leverages fundamental physical principles, the study calculates wind power density (WPD) at a height of 90 meters above the surface for both onshore and offshore locations. The methodology involves acquiring and processing datasets such as temperature, wind speed, digital elevation model (DEM), pressure, air density, and land use/land cover (LULC) classifications. The model applies equations derived from physical laws to determine key parameters necessary for calculating WPD. Specifically, temperature and pressure data are used to estimate air density, while surface roughness is assigned based on LULC classes with windspeed at 10m to extrapolate wind speed at 90 meters above ground level. The method can be used at any hub height. Results reveal significant wind energy potential in Odisha, particularly along the coastal regions. Jagatsinghpur and Puri emerge as areas with high WPD onshore, while the offshore exclusive economic zone (EEZ) of Odisha exhibits substantial wind energy resources. The model outputs provide valuable insights for various studies related to renewable energy and facilitate informed decision-making in site selection analyses. Furthermore, the study emphasizes the simplicity and effectiveness of the developed model, making it a practical tool for assessing wind energy potential in other regions as well. Overall, this research contributes to the global effort towards transitioning to sustainable energy sources and combating climate change. By highlighting the renewable energy potential of Odisha, it underscores the importance of harnessing wind energy as a viable pathway towards a cleaner, greener future.
Advanced survey techniques for mapping-A case study of ratnagiri hill Udaipur
The research strives to enhance digital surveying methodologies for precise and detailed land mapping, utilizing advanced instruments like terrestrial scanners, SLAM scanners, total stations, DGPS, and drones. The survey conducted at Ratnagiri Hill in Udaipur aims to scrutinize and compare the merits and drawbacks of each surveying method. The study outlines a systematic process encompassing data collection, processing, output generation, and validation of survey techniques. It highlights the adaptability of these methods across various domains, such as building conservation, restoration, and mapping of typically inaccessible areas, emphasizing their potential for time and resource savings. The research underscores the effectiveness of a one-time data collection process for subsequent work, laying the groundwork for the advancement of digital surveying technologies. When integrated with the Total station and DGPS survey, the combination of terrestrial scanners, drones, and SLAM scanners achieves a vertical accuracy of around 32 mm.