2026-05-26T16:26:46Z https://journal-iasssf.com/index.php/RSTDE/oai

oai:ojs2.journal-iasssf.com:article/840 2025-12-02T02:52:26Z RSTDE:ART

Leveraging drone technology for advancements in photogrammetry, remote sensing, and military intelligence: A review Marzaki, Ismail Supriyadi, Asep Adang Arief, Syachrul drone intelligence military remote sensing Background: In the modern technological era, drones have become one of the leading innovations in aviation. This study aims to present a comprehensive literature review on the development and application of drone technology in various contexts, including remote sensing, photogrammetry, military, and intelligence. Methods: A literature review was used to collect, evaluate, and analyse relevant literature from various sources, such as scientific journals, conferences, and reference books. This literature review identifies the development of drone technology from its initial form to their more sophisticated use in military and intelligence operations. Results: The review presents an overview of the role and contribution of drones in information gathering, earth surface mapping, surveillance, reconnaissance, as well as battlefield attacks. The implications of drone technology for future military operations are also discussed, including the integration of sensors, the development of communication systems, and improvements in the decision-making process. Conclusion: This research provides an in-depth understanding of the potential of drone technology and the challenges and opportunities associated with its application in various fields. Institute for Advanced Science, Social, and Sustainable Future 2024-02-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal-iasssf.com/index.php/RSTDE/article/view/840 10.61511/rstde.v1i1.2024.840 Remote Sensing Technology in Defense and Environment; Vol. 1 No. 1: (February) 2024; 1-9 3062-8970 10.61511/rstde.v1i1.2024 eng https://journal-iasssf.com/index.php/RSTDE/article/view/840/520 Copyright (c) 2024 Remote Sensing Technology in Defense and Environment https://creativecommons.org/licenses/by/4.0/

oai:ojs2.journal-iasssf.com:article/841 2025-12-02T02:52:26Z RSTDE:ART

The use of remote sensing in monitoring shoreline change: Implications for maritime area security Sadewa, Annisa Harum Supriyadi, Asep Adang maritime security remote sensing shoreline change Background: Remote sensing has become an important technology in monitoring coastline change and maritime security. In this context, the literature highlights the history and understanding of remote sensing, its benefits in defense and security, and its applications in disaster mitigation and environmental management. Shoreline change analysis methods such as Digital Shoreline Analysis System (DSAS) and COASTSAT are also the focus of study to understand effective approaches in shoreline monitoring. Methods: This study used a literature review method to collect and evaluate journal articles, research reports, and official documentation related to remote sensing, maritime defense and security, and shoreline change analysis. The collected data were analyzed to provide a comprehensive understanding of the concepts, applications, and methods related to the research topic. Results: The results of the literature review show that remote sensing plays a crucial role in monitoring shoreline change and maritime security. The benefits include monitoring military activities, disaster mitigation, and coastal environmental management. Moreover, the analysis of shoreline change using the DSAS and COASTSAT methods offers a different yet effective approach in measuring and understanding shoreline change. Conclusion: In order to maintain maritime security and effectively manage shoreline change, collaboration between countries and the utilization of remote sensing technologies are key. This research provides an in-depth understanding of the concepts, benefits and methods related to the topic, and encourages further exploration of the potential of remote sensing in supporting environmental sustainability and regional peace. Institute for Advanced Science, Social, and Sustainable Future 2024-02-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal-iasssf.com/index.php/RSTDE/article/view/841 10.61511/rstde.v1i1.2024.841 Remote Sensing Technology in Defense and Environment; Vol. 1 No. 1: (February) 2024; 28-35 3062-8970 10.61511/rstde.v1i1.2024 eng https://journal-iasssf.com/index.php/RSTDE/article/view/841/523 Copyright (c) 2024 Remote Sensing Technology in Defense and Environment https://creativecommons.org/licenses/by/4.0/

oai:ojs2.journal-iasssf.com:article/842 2025-12-02T02:52:26Z RSTDE:ART

Utilization of satellite technology in communication systems, disaster monitoring, border surveillance, and military intelligence: A literature review Haloho, Luwis Suryani Supriyadi, Asep Adang Background: The utilization of satellite technology has become a critical aspect in many fields, including communications, disaster monitoring, border surveillance, and military intelligence. The ability of satellites to provide real-time, high-resolution data offers significant benefits in supporting these activities. This study aims to explore the contributions and benefits of satellites in this context through a literature review approach. Methods: This study used the literature review method, which involves collecting, analyzing, and synthesizing relevant scientific studies. The literature search was conducted through scientific databases with relevant keywords. The selected literature was categorized based on the main topics of communication systems, disaster monitoring, border surveillance, and military intelligence. Analysis was conducted to identify key findings and research gaps. Results: The review research shows that communication satellites enable fast and reliable transmission of information, both domestically and internationally, without significant time delay. In disaster monitoring, satellites such as ASTER and Sentinel-2 have proven effective in detecting environmental changes and supporting rescue operations. For border surveillance, ESA's Sentinel-2 satellite with high spatial resolution is able to effectively monitor borders. In the context of military intelligence, the use of global satellite navigation systems (GPS, GLONASS, BeiDou, Galileo) enables more accurate and real-time threat tracking and detection. Conclusion: This research confirms that satellites play a vital role in communications, disaster monitoring, surveillance, and military intelligence. The ability of satellites to provide high-resolution, real-time data is essential in supporting these critical applications. As satellite technology continues to evolve, these benefits are expected to increase, contributing even more to global security and prosperity. Institute for Advanced Science, Social, and Sustainable Future 2024-02-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal-iasssf.com/index.php/RSTDE/article/view/842 10.61511/rstde.v1i1.2024.842 Remote Sensing Technology in Defense and Environment; Vol. 1 No. 1: (February) 2024; 36-44 3062-8970 10.61511/rstde.v1i1.2024 eng https://journal-iasssf.com/index.php/RSTDE/article/view/842/524 Copyright (c) 2024 Remote Sensing Technology in Defense and Environment https://creativecommons.org/licenses/by/4.0/

oai:ojs2.journal-iasssf.com:article/843 2025-12-02T02:52:26Z RSTDE:ART

Analysis of land use dynamics in Jatinom District, Klaten Regency: 2015-2020 Maghriza, Jihad Alam Taryono geoeye GIS land use change remote sensing Background: Land use change is a significant phenomenon in the context of regional development. Jatinom District, as part of Klaten Regency, experienced a notable change in land use from 2015 to 2020. The aim is to identify patterns of land use change. Methods: This research method includes visual interpretation and digitization of GeoEye imagery, accuracy test, overlay, and field checking. The use of Remote Sensing and Geographic Information System (GIS) is key in spatial analysis of land use change in Jatinom District. Results: Land use change in Jatinom District predominantly occurs in the form of land conversion into settlements. Factors such as strategic location, land price, labor availability, and infrastructure support this change. In addition, there are also other changes in land use types, such as the growth of industrial and trade land. Conclusion: In the last five years, Jatinom District has experienced significant changes in land use, with the most notable growth occurring in residential land. Factors such as location, land price, and infrastructure are the main drivers of this change. This shows the importance of wise spatial planning in the face of dynamic regional development. Institute for Advanced Science, Social, and Sustainable Future 2024-02-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal-iasssf.com/index.php/RSTDE/article/view/843 10.61511/rstde.v1i1.2024.843 Remote Sensing Technology in Defense and Environment; Vol. 1 No. 1: (February) 2024; 10-18 3062-8970 10.61511/rstde.v1i1.2024 eng https://journal-iasssf.com/index.php/RSTDE/article/view/843/521 Copyright (c) 2024 Remote Sensing Technology in Defense and Environment https://creativecommons.org/licenses/by/4.0/

oai:ojs2.journal-iasssf.com:article/844 2025-12-02T02:52:26Z RSTDE:ART

The role of maritime geospatial in navigating uncharted waters mapped Ariputro, Aryobimo Bhardian Supriyadi, Asep Adang geospatial maritime geospatial remote sensing Background: Modern maritime navigation, especially in uncharted waters, faces major challenges that require innovative solutions. Geospatial technologies play a key role in providing effective solutions for mapping and navigation. This study aims to explore the role of geospatial technologies in improving the safety and efficiency of maritime navigation, as well as supporting sustainable management of marine resources. Methods: This study used both qualitative and quantitative approaches. Data was obtained through secondary collection from journals, books and other documents. Results: Data analysis revealed that geospatial technology plays an important role in identifying safe navigation routes, monitoring sea conditions, and sustainably managing marine resources. The integration of geospatial data from various sources enables more effective decision-making in maritime spatial planning and safe navigation. Conclusion: This research concludes that geospatial technology is a critical aspect of modern maritime navigation. With an integrated and collaborative approach, these technologies can improve navigation efficiency and safety, and support sustainable management of marine resources. Awareness and education on geospatial technology in the maritime industry is considered essential to maximize its potential in maintaining the balance of marine ecosystems and the sustainability of the maritime industry. Institute for Advanced Science, Social, and Sustainable Future 2024-02-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal-iasssf.com/index.php/RSTDE/article/view/844 10.61511/rstde.v1i1.2024.844 Remote Sensing Technology in Defense and Environment; Vol. 1 No. 1: (February) 2024; 19-27 3062-8970 10.61511/rstde.v1i1.2024 eng https://journal-iasssf.com/index.php/RSTDE/article/view/844/522 Copyright (c) 2024 Remote Sensing Technology in Defense and Environment https://creativecommons.org/licenses/by/4.0/

oai:ojs2.journal-iasssf.com:article/934 2025-12-02T02:52:52Z RSTDE:ART

Study of the implementation of geoint and remote sensing in climate change Heningtiyas, Hesti Supriyadi, Asep Adang climate change geospatial intelligence remote sensing Background: This research highlights the critical role of geospatial technologies, including remote sensing and Geospatial Intelligence (GEOINT), in addressing climate change and its impacts. These technologies extend beyond defense and security applications, proving valuable across sectors such as health, social, economic, and environmental fields. By providing real-time data, they enhance the understanding and mitigation of climate change-related issues. Methods: A systematic literature review was conducted by searching databases using relevant keywords. Peer-reviewed articles from the past 10 years were selected. Data were collected through a data extraction form, and the articles were categorized based on themes including geospatial technology applications, benefits, challenges, and recommendations. Findings: The study found that geospatial technologies significantly enhance the understanding of regional environmental conditions, aid in natural disaster mitigation, and support environmental conservation efforts through real-time monitoring of weather and climate change. Despite the high costs and data format challenges, these technologies offer indispensable tools for analyzing climate impacts and formulating effective mitigation strategies. Conclusion: The benefits of geospatial technologies in climate change mitigation are clear, though challenges such as implementation costs and data compatibility remain. These technologies provide policymakers with essential insights for crafting more informed and effective decisions in combating climate change. Novelty/Originality of this article: This study offers a comprehensive review of the diverse applications of geospatial technologies in the context of climate change. It uniquely integrates insights from multiple sectors, showcasing the broader potential of these technologies beyond traditional fields, and provides recommendations for improving data processing and analysis for climate-related decision-making. Institute for Advanced Science, Social, and Sustainable Future 2024-08-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal-iasssf.com/index.php/RSTDE/article/view/934 Remote Sensing Technology in Defense and Environment; Vol. 1 No. 2: (August) 2024; 45-55 3062-8970 10.61511/rstde.v1i2.2024 eng https://journal-iasssf.com/index.php/RSTDE/article/view/934/810 Copyright (c) 2024 Remote Sensing Technology in Defense and Environment https://creativecommons.org/licenses/by/4.0/

oai:ojs2.journal-iasssf.com:article/935 2025-12-02T02:52:52Z RSTDE:ART

The use of satellite imagery in supporting non-military operations: a geospatial intelligence perspective Kariani, Rika Supriyadi, Asep Adang geospatial intelligence remote sensing satellite imagery Background: Satellite imagery technology, initially developed for military purposes, has expanded into a critical tool in non-military applications, including environmental monitoring, disaster mitigation, infrastructure development, and humanitarian aid. This shift highlights the evolving role of satellite technology from military functions to addressing sustainability and global well-being challenges. Methods: A literature review approach was employed to examine the use of satellite imagery in non-military settings. Peer-reviewed articles were identified, selected, and analyzed from databases such as Google Scholar and ScienceDirect. The focus was on articles discussing applications in environmental monitoring, disaster management, infrastructure planning, and humanitarian assistance. Relevant literature was categorized and synthesized to identify emerging trends and implications of satellite imagery technology. Findings: Satellite imagery has proven to be invaluable in providing essential geospatial data for non-military purposes. It facilitates monitoring of environmental changes, supports infrastructure planning and evaluation, enhances disaster mitigation through risk analysis, and improves coordination of humanitarian aid during emergencies. The integration of platforms like Google Earth Engine and artificial intelligence significantly increases its utility, especially in object detection, climate change monitoring, and disaster impact assessments. Conclusion: Satellite imagery has evolved into an indispensable tool for a wide range of non-military applications, offering sustainable and efficient solutions to global challenges. It significantly enhances environmental monitoring, infrastructure development, disaster response, and humanitarian operations. The study emphasizes the need for continued innovation in satellite technology and interdisciplinary collaboration to meet future global sustainability goals. Novelty/Originality of this article: This study provides a comprehensive analysis of satellite imagery's growing role in non-military applications, emphasizing its potential in addressing global challenges. By synthesizing insights across multiple fields, the research highlights the transformative power of satellite technology in supporting sustainable development and disaster resilience. Institute for Advanced Science, Social, and Sustainable Future 2024-08-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal-iasssf.com/index.php/RSTDE/article/view/935 Remote Sensing Technology in Defense and Environment; Vol. 1 No. 2: (August) 2024; 56-66 3062-8970 10.61511/rstde.v1i2.2024 eng https://journal-iasssf.com/index.php/RSTDE/article/view/935/809 Copyright (c) 2024 Remote Sensing Technology in Defense and Environment https://creativecommons.org/licenses/by/4.0/

oai:ojs2.journal-iasssf.com:article/936 2025-12-02T02:52:52Z RSTDE:ART

Geospatial intelligent analysis to support Indonesian airspace defense Rahmandhala, Ilvan Dino Supriyadi, Asep Adang airspace defense geospatial intelligence remote sensing satellite imagery unmanned aerial vehicles Background: In the modern era, air defense plays a critical role in national security strategies, especially for Indonesia, the world's largest archipelagic nation, which faces complex challenges in safeguarding its vast airspace. Geospatial Intelligence (GEOINT) offers advanced capabilities for enhancing detection, mapping, and planning in air defense operations. By utilizing data from satellites, UAVs, and sensor technologies, GEOINT has the potential to significantly improve air defense through real-time data analysis and situational modeling. Methods: This study employed a literature review method to examine the role of GEOINT in Indonesia's air defense. Literature from academic sources and official reports was analyzed, focusing on peer-reviewed studies that discuss GEOINT technologies such as UAVs, radar systems, and topographic mapping. The review covered the application of these technologies in threat detection, mission planning, and the surveillance of adversarial activities. Findings: The analysis revealed that GEOINT is vital in supporting Indonesia's air defense operations. It enables early threat detection, efficient mission planning, and constant monitoring of adversarial movements using data from satellites and UAVs. Additionally, GEOINT facilitates accurate terrain mapping, optimal flight route planning, and the surveillance of strategic areas, enhancing defense strategies and enabling timely preventive actions against threats. Conclusion: GEOINT is an essential strategic tool for enhancing Indonesia’s air defense capabilities. To maximize its potential, investments in advanced technologies, geospatial data infrastructure, and personnel training are crucial. Furthermore, international collaboration and the development of monitoring satellites will be pivotal for the future success of Indonesia’s air defense strategy. Novelty/Originality of this article: This study provides a focused exploration of the integration of GEOINT in Indonesia's air defense strategy, highlighting its transformative impact on threat detection and defense planning. The study underscores the need for technological advancement and international partnerships in strengthening national air defense capabilities. Institute for Advanced Science, Social, and Sustainable Future 2024-08-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal-iasssf.com/index.php/RSTDE/article/view/936 Remote Sensing Technology in Defense and Environment; Vol. 1 No. 2: (August) 2024; 67-75 3062-8970 10.61511/rstde.v1i2.2024 eng https://journal-iasssf.com/index.php/RSTDE/article/view/936/808 Copyright (c) 2024 Remote Sensing Technology in Defense and Environment https://creativecommons.org/licenses/by/4.0/

oai:ojs2.journal-iasssf.com:article/1055 2025-12-02T02:52:52Z RSTDE:ART

Dynamics of seasonal impacts on Total Suspended Solid (TSS) concentrations in coastal Semarang City using landsat 8 Timurani, Erlina Candra Anna, Alif Noor total suspended solid (TSS) landsat 8 OLI/TIRS Semarang City coast Syarif Budiman algorithm remote sensing Background: Semarang City, located in Central Java, faces significant water quality challenges in its coastal areas due to various activities such as industrial operations, trade, fisheries, and infrastructure development. One major concern is the concentration of Total Suspended Solids (TSS) in the coastal waters, which negatively impacts the marine ecosystem and the fisheries sector. This study aims to measure and analyze the distribution of TSS concentrations in the coastal waters of Semarang City from March to August 2018. Methods: Landsat 8 OLI/TIRS satellite image data, obtained from the USGS, was used for this study. The images were captured on March 18, April 3, May 17, June 6, July 24, and August 25, 2018. The data processing included radiometric correction (TOA), image cropping, land-sea masking, and the application of the Syarif Budiman algorithm to calculate TSS concentrations. TSS concentration classification followed Alabaster and Lloyd’s (1982) categorization. Findings: TSS concentrations in the coastal waters of Semarang City varied between 36-220 mg/L. During the rainy season (March-May), concentrations ranged from 111-210 mg/L, while in the dry season (June-August), concentrations were lower, between 105-108 mg/L. Higher TSS concentrations were observed near estuaries and industrial areas, particularly in Genuk and Tugu sub-districts. Conclusion: TSS concentrations along the coast of Semarang City from March to August 2018 fell within class II and III of the Alabaster and Lloyd classification, indicating negative impacts on the fisheries sector. The increased TSS levels during the rainy season resulted from accumulated waste carried by water flow from human activities along the coast. Effective effluent management is essential to improve water quality and sustain the fisheries sector. Novelty/Originality of this article: This study provides a detailed spatial and temporal analysis of TSS distribution using satellite imagery, offering critical insights into the seasonal impacts of human activities on coastal water quality in Semarang City. The findings emphasize the need for targeted environmental management strategies to support sustainable coastal development. Institute for Advanced Science, Social, and Sustainable Future 2024-08-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal-iasssf.com/index.php/RSTDE/article/view/1055 Remote Sensing Technology in Defense and Environment; Vol. 1 No. 2: (August) 2024; 76-85 3062-8970 10.61511/rstde.v1i2.2024 eng https://journal-iasssf.com/index.php/RSTDE/article/view/1055/807 Copyright (c) 2024 Remote Sensing Technology in Defense and Environment https://creativecommons.org/licenses/by/4.0/

oai:ojs2.journal-iasssf.com:article/1196 2025-12-02T03:32:56Z RSTDE:ART

Patterns of oceanographic factor distribution and tuna fishing potential: Spatial and temporal analysis Debiyanti, Debiyanti Gunawan, Dadang Budiyanto, Setyo chlorophyll-a concentration mackerel fishing potential oceanographic factors sea surface temperature (SST) Background: The North Natuna Sea, located south of the South China Sea, is renowned for its rich marine biodiversity and significant role in regional fisheries. Oceanographic factors such as sea surface temperature (SST), chlorophyll-a concentration, and salinity are key influences on fish distribution and abundance in this area. While previous studies have highlighted the relationship between these factors and fishing patterns, the connection between oceanographic conditions and mackerel fishing potential remains insufficiently explored. This study aims to analyze the spatial and temporal variation of these oceanographic factors and their impact on mackerel fishing potential in the North Natuna Sea. Methods: The study utilized Aqua-MODIS satellite imagery data from 2017 to 2021 for spatial and temporal analysis of oceanographic factors. Results: Significant variations were observed in sea surface temperature, chlorophyll-a concentration, and salinity across different seasons. Higher mackerel fishing potential was identified during the Western Season and Transitional Season II, which were characterized by lower sea surface temperatures and higher chlorophyll-a concentrations. Conclusion: Understanding the seasonal variations in oceanographic factors is crucial for optimizing sustainable fishing practices in the North Natuna Sea. Novelty/Originality of this Research: This study offers new insights into the interplay between oceanographic conditions and mackerel fishing potential, providing valuable information for the sustainable management with a focus on the seasonal dynamics of marine environments. Institute for Advanced Science, Social, and Sustainable Future 2025-02-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal-iasssf.com/index.php/RSTDE/article/view/1196 10.61511/rstde.v2i1.2025.1196 Remote Sensing Technology in Defense and Environment; Vol. 2 No. 1: (February) 2025; 46-63 3062-8970 10.61511/rstde.v2i1.2025 eng https://journal-iasssf.com/index.php/RSTDE/article/view/1196/1312 Copyright (c) 2025 Debiyanti Debiyanti, Dadang Gunawan, Setyo Budiyanto https://creativecommons.org/licenses/by/4.0/

oai:ojs2.journal-iasssf.com:article/1289 2025-12-02T02:52:52Z RSTDE:ART

Anticipating the impact of artificial intelligence to increase national vigilance against terrorism attacks in Indonesia Bahriansyah, Ilmiawan Muhammad Supriyadi, Asep Adang Nurisnaeny, Poppy Setiawati artificial intelligence cyber terrorism cyber security kamikaze drones national security Background: The rapid advancement of artificial intelligence (AI) poses significant challenges to national security, particularly in the context of cyber terrorism. Indonesia, as a country with a large Muslim population and a history of terrorist activities, faces unique threats that could exploit AI technologies for malicious purposes. The increasing frequency of cyber attacks, including botnet attacks and malware incidents, highlights the urgent need for comprehensive strategies to counter these threats. Methods: This study employs a literature review methodology, analyzing relevant academic articles, policy documents, and case studies on AI and cyber terrorism. The analysis focuses on the intersection of AI technologies and terrorism, exploring the vulnerabilities within Indonesia's cyber security landscape and examining international cooperation frameworks aimed at combating cyber threats. Data sources include scholarly journals, government reports, and publications from international organizations. Findings: The findings reveal that Indonesia's current cyber security infrastructure is inadequate to handle the evolving threats posed by AI-driven cyber terrorism. Notable vulnerabilities were identified in critical sectors, including government and financial institutions, exacerbated by previous cyber breaches. Furthermore, the study highlights the potential use of AI in advanced weaponry, such as kamikaze drones, which could significantly impact national security. Conclusion: To mitigate the risks associated with AI-based cyber terrorism, Indonesia must enhance its legal frameworks and foster international cooperation. Effective measures include harmonizing national laws with international standards and strengthening collaborative efforts with regional partners. Such initiatives are crucial for developing a robust defense against the multifaceted challenges of cyber threats. Novelty/Originality of this article: This study contributes to the existing literature by providing a comprehensive analysis of the implications of AI for cyber terrorism in Indonesia. It underscores the importance of integrating international legal instruments with national policies, offering a novel perspective on addressing the vulnerabilities within Indonesia's cyber security framework. The emphasis on regional cooperation and the exploration of innovative counter-terrorism strategies further enhance the originality of this research. Institute for Advanced Science, Social, and Sustainable Future 2024-08-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal-iasssf.com/index.php/RSTDE/article/view/1289 Remote Sensing Technology in Defense and Environment; Vol. 1 No. 2: (August) 2024; 86-97 3062-8970 10.61511/rstde.v1i2.2024 eng https://journal-iasssf.com/index.php/RSTDE/article/view/1289/806 Copyright (c) 2024 Remote Sensing Technology in Defense and Environment https://creativecommons.org/licenses/by/4.0/

oai:ojs2.journal-iasssf.com:article/1761 2025-12-02T03:27:43Z RSTDE:ART

Application of machine learning and remote sensing in monitoring land use dynamics in tourism area Muafiroh, Salsa Adi Pradono, Kuncoro Sunandar, Priyo Manurung, Parluhutan land use and land cover (LULC) machine learning tourism area Background: This study uses remote sensing and machine learning techniques to investigate the spatial-temporal changes in land use and land cover (LULC) within the Lake Toba tourism area over the past 35 years. Increasing tourism activities have significantly altered the region's landscape, particularly leading to a reduction in forest cover and an expansion of built-up areas. Method: By applying the Random Forest algorithm to satellite imagery data from Landsat 5, 8, and 9, and integrating Geographic Information System (GIS) technology, we analyzed and accurately predicted these changes. Additionally, indices such as NDVI and SAVI were used to monitor ecosystem health in detail, particularly for tracking the growth of invasive species like water hyacinths. Findings: LULC analysis of the Lake Toba tourism area reveals significant changes, including an increase in built-up areas, a decrease in vegetation, and the potential growth of water hyacinths. Surface temperature analysis indicates higher temperatures in built-up areas and cooler temperatures in natural vegetation. Using NDVI, SAVI, and MDWI indices also helped in monitoring water hyacinth growth, supporting improved ecosystem management for sustainability. Conclusion: This study highlights the environmental impacts of tourism and emphasizes the need for sustainable land management practices to balance development with ecological preservation. Novelty/Originality of this Research: This research demonstrates the effectiveness of combining machine learning with spatial technologies to support informed decision-making in land use planning. Institute for Advanced Science, Social, and Sustainable Future 2025-02-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal-iasssf.com/index.php/RSTDE/article/view/1761 10.61511/rstde.v2i1.2025.1761 Remote Sensing Technology in Defense and Environment; Vol. 2 No. 1: (February) 2025; 17-31 3062-8970 10.61511/rstde.v2i1.2025 eng https://journal-iasssf.com/index.php/RSTDE/article/view/1761/1227 Copyright (c) 2025 Salsa Muafiroh, Kuncoro Adi Pradono, Priyo Sunandar, Parluhutan Manurung https://creativecommons.org/licenses/by/4.0/

oai:ojs2.journal-iasssf.com:article/1777 2025-12-02T03:30:08Z RSTDE:ART

Utilization of remote sensing in monitoring terrorism threats in the border areas of Indonesia: In the context of relocating the capital city Andimuharrom, Dipo border security capital city development geopolitical geospatial intelligence remote sensing terrorism threat Background: The relocation of the capital city of Indonesia (Ibu Kota Negara/IKN) to the island of Kalimantan presents new challenges concerning national security, particularly in addressing terrorism threats in border regions. These threats can potentially disrupt national stability and development, necessitating serious attention from the government and stakeholders. This research aims to explore the application of remote sensing technology in monitoring terrorism threats in Indonesia's border regions and to formulate more effective prevention strategies. Method: This research explores the application of remote sensing technology in monitoring terrorism threats in Indonesia's border regions and formulates more effective prevention strategies. The study employed brainstorming analysis to gather diverse perspectives based on relevant references and scientific journals, organizing these into an analytical framework. Combining brainstorming methods with scientific journal references enriched the research process while enhancing the validity and reliability of findings, allowing for comprehensive, evidence-based recommendations that contribute significantly to policy development and field practices. Findings: Findings indicate that the geographical location of the new capital, being close to the border, may increase security risks. The application of remote sensing technology can significantly enhance early detection capabilities for suspicious activities along borders. By providing more efficient real-time monitoring, these systems facilitate timely interventions and aid in predicting potential terrorism risks through advanced geospatial analysis. Conclusion: The results of this study have significant implications for national security strategic planning, emphasizing the need for technology integration into defense systems and counter-terrorism efforts, as well as enhancing international cooperation in maintaining security in border areas.  Novelty/Originality of this article: This study provides a novel approach to national security in the context of Indonesia's capital relocation by examining the potential of remote sensing technology for terrorism threat monitoring in border regions. Institute for Advanced Science, Social, and Sustainable Future 2025-02-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal-iasssf.com/index.php/RSTDE/article/view/1777 10.61511/rstde.v2i1.2025.1777 Remote Sensing Technology in Defense and Environment; Vol. 2 No. 1: (February) 2025; 32-45 3062-8970 10.61511/rstde.v2i1.2025 eng https://journal-iasssf.com/index.php/RSTDE/article/view/1777/1218 Copyright (c) 2025 Dipo Andimuharrom https://creativecommons.org/licenses/by/4.0/

oai:ojs2.journal-iasssf.com:article/1778 2025-12-02T02:54:39Z RSTDE:ART

Utilization of remote sensing in post-disaster recovery for environmental damage assessment Andrianto, Dimas Supriyadi, Asep Adang drones environmental damage assessment post-disaster recovery radar remote sensing satellite imagery Background: Remote sensing techniques have become one of the important methods in post-disaster recovery for assessing environmental damage. They offer the ability to quickly and accurately identify and map damage at a wide scale, which is particularly useful in dynamic and often unpredictable post-disaster situations. Methods: This research aims to explore the use of various remote sensing technologies, such as satellite imagery, radar and drones, in assessing environmental damage after natural disasters. In this study, brainstorming focused on how remote sensing technologies can be optimally applied in post-disaster recovery, with an emphasis on environmental damage assessment. Findings: The results showed that remote sensing technology enables the identification of structural and environmental damage more efficiently than traditional methods. Satellite imagery provides an overview of the extent of the affected area, while radar and LiDAR technologies can be used to measure physical damage in greater detail. Drones, with their high resolution and flexibility, serve as an additional tool for detailed surveys in areas that are difficult to access. However, the application of this technology is not free from challenges, such as access to high-resolution data that is often expensive, the need for field validation to ensure accuracy, and infrastructure limitations in some disaster-prone developing countries. Conclusion: This research recommends increasing access to remote sensing data at affordable costs or for free for developing countries, integration of multi-source technologies to improve assessment accuracy. In addition, policy development based on remote sensing data for disaster risk mitigation. Thus, remote sensing is very useful for long-term disaster mitigation and adaptation planning and for post-disaster assessment. Novelty/Originality of this article: This article integrative exploration of multi-source remote sensing technologies—satellite imagery, radar, LiDAR, and drones—for comprehensive environmental damage assessment in post-disaster recovery, with a specific emphasis on challenges and policy implications in developing countries. Institute for Advanced Science, Social, and Sustainable Future 2025-02-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal-iasssf.com/index.php/RSTDE/article/view/1778 10.61511/rstde.v2i1.2025.1778 Remote Sensing Technology in Defense and Environment; Vol. 2 No. 1: (February) 2025; 1-16 3062-8970 10.61511/rstde.v2i1.2025 eng https://journal-iasssf.com/index.php/RSTDE/article/view/1778/1217 Copyright (c) 2025 Dimas Andrianto, Asep Adang Supriyadi https://creativecommons.org/licenses/by/4.0/

oai:ojs2.journal-iasssf.com:article/1878 2025-12-03T08:47:50Z RSTDE:ART

Land cover change analysis based on spectral indices and deep learning convolutional neural network model Hikmah, Nur Izzatul Manurung, Parluhutan coastal zone convolutional neural network (CNN) land cover change remote sensing spectral indices Background: Coastal zones are undergoing rapid land cover changes due to urban development, land reclamation, and environmental degradation, threatening ecological stability and sustainable coastal management. This study investigates land cover changes in the coastal districts of Tangerang Regency, Indonesia, from 2020 to 2024 using an integrated remote sensing and deep learning approach. Methods: Three spectral indices—Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI)—were applied to Sentinel-2A imagery to monitor vegetation, water bodies, and built-up areas. Additionally, a Convolutional Neural Network (CNN) model was trained to enhance classification accuracy. Findings: The results showed a sharp decline in vegetated areas from 195.91 km² in 2020 to 118.32 km² in 2023, followed by a partial recovery to 157.81 km² in 2024. Water bodies consistently decreased from 122.35 km² in 2020 to 110.62 km² in 2024, suggesting intensified coastal modifications. Built-up areas displayed fluctuating patterns, with a peak of 8.88 km² in 2021 and a significant drop to 1.01 km² in 2024. The CNN model achieved a 60% validation accuracy, indicating its capacity to detect complex land cover features, despite challenges related to data imbalance and class similarity. Conclusion: This study demonstrates that combining spectral indices and deep learning provides a robust framework for detecting and analyzing coastal land cover change. The findings highlight the need for integrated methods in environmental monitoring and support sustainable planning efforts in dynamic coastal regions. Novelty/Originality of this article: This study uniquely integrates spectral indices with deep learning to accurately detect and analyze dynamic coastal land cover changes, providing a robust tool for sustainable coastal management. Institute for Advanced Science, Social, and Sustainable Future 2025-08-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article https://journal-iasssf.com/index.php/RSTDE/article/view/1878 10.61511/rstde.v2i2.2025.1878 Remote Sensing Technology in Defense and Environment; Vol. 2 No. 2: (August) 2025 3062-8970 10.61511/rstde.v2i2.2025 Copyright (c) 2025 Nur Izzatul Hikmah, Parluhutan Manurung https://creativecommons.org/licenses/by/4.0/

oai:ojs2.journal-iasssf.com:article/2035 2026-05-22T09:58:22Z RSTDE:ART

Developing an AI-enhanced maritime threat detection model: Predictive security framework for illegal fishing and piracy Novitasari, Yussie artificial intelligence exclusive economic zone illegal fishing maritime security Background: The North Natuna Sea, as a strategic area of Indonesia, faces increasingly complex maritime security threats, particularly illegal fishing activities and piracy that threaten economic sovereignty and the stability of aquatic ecosystems. This research develops a maritime threat detection model based on Artificial Intelligence that is capable of predicting and preventing illegal activities through a preventive and proactive approach. Methods: Using a qualitative research method based on documentary studies, this research analyzes patterns of maritime threats, regional geopolitical dynamics, and the limitations of conventional surveillance systems that have historically relied on a reactive approach. The developed predictive model integrates three layers of technology: multi-source data integration (Sentinel-1 satellites, VIIRS, AIS, oceanographic data), predictive analytics utilizing Long Short-Term Memory (LSTM) and Random Forest algorithms, as well as an automated operational response system. Findings: Data analysis indicates that illegal fishing incurs economic losses of USD 25 billion annually on a national scale, with 112 vessels confiscated in the first half of 2024. Meanwhile, piracy incidents have increased from 10 incidents in 2022 to 18 incidents in 2023, affecting 126 crew members in 2024. The research results indicate that the predictive AI model is capable of increasing the detection rate of foreign vessels from 40% to 85% and reducing response time from 8 hours to 45 minutes. This system generates an updated maritime threat heatmap every 15 minutes, enabling the optimization of patrols and saving fuel consumption of up to 30%. Conclusion: This research contributes to the transformation of Indonesia's maritime security paradigm from a reactive approach to a predictive-preventive one, supporting the enforcement of economic sovereignty in Indonesia's Exclusive Economic Zone (EEZ), and providing high-tech solutions to address non-traditional security challenges in the contemporary era. This model can be adapted as a best practice for other island countries facing similar maritime threats, while also reinforcing collective maritime security mechanisms within the framework of regional and global cooperation. Novelty/Originality of this article: The preventive defense framework specifically developed for the conditions adopts a proactive approach that takes into account the unique geographical characteristics, regional maritime traffic patterns, and the geopolitical dynamics of the area. Institute for Advanced Science, Social, and Sustainable Future 2026-02-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article https://journal-iasssf.com/index.php/RSTDE/article/view/2035 10.61511/rstde.v3i1.2026.2035 Remote Sensing Technology in Defense and Environment; Vol. 3 No. 1: (February) 2026 3062-8970 10.61511/rstde.v3i1.2026 Copyright (c) 2026 Yussie Novitasari https://creativecommons.org/licenses/by/4.0

oai:ojs2.journal-iasssf.com:article/2063 2026-05-26T07:59:31Z RSTDE:ART

Geospatial-Driven Maritime Border Security: Integrating AIS, Remote Sensing, and Naval Response Systems for Indonesia’s Strategic Archipelagic Sea Lanes (ALKI) Wuryantari, Benedicta automatic identification system Indonesian archipelagic sea lanes integrated maritime surveillance geospatial intelligence transnational maritime threats Background: As a strategic archipelagic nation with Indonesian Archipelagic Sea Lanes (ALKI) that serve as vital global trade routes while remaining vulnerable to commplex maritime threats (including illegal fishing, smuggling, and maritime terrorisme), Indonesia requires an integrated Geospatial Intelligence (GEOINT)-based maritime surveillance system. This system must combine Automatic Identification System (AIS), satellite imagery (SAR/optical), and rapid response capabilities from the Indonesian Navy (TNI AL) to address infrastructure limitations, inter-agency coordination fragmentation, and increasingly sophisticated transnational threat dynamics.  Methods: This study employs a descriptive qualitative method with a systematic literatur review approach. Data were collected from Scopus-indexed international journals (Q1-Q2), national policy documents, reports from international organizations, and technology whitepapers through academic databese using keywords related to GEOINT, maritime surveillance, and maritime threat detection. The collected data were then thematically analyzed and synthesized into a conceptual model of an  ALKI surveillance system. The analyzes is grounded in GEOINT and Maritime Domain Awareness (MDA) theories, with a specific focus on technology integration (AIS, SAR/optical satellite imagery) and maritime strategies. Findings: This study reveals that the integration of Artifcial Intelligence (AI)-based GEOINT through a combination of AIS, satellite imagery (SAR/optical), and GIS significantly enhances maritime threat detection (dark vessels and spoofing) and tactical response capabilities in the ALKI. However, its effectiveness depends on cross-agency data interoperability and the strengthening of national satellite infrastructure, necessitating maritime security governance reforms to address challenges such as IAS blind spots, jurisdictional overlaps, and limitations in realistic scenario to achieve a predictive and integrated surveillance system. Conclusion: This study introduces a transformational GEOINT-based maritime surveillance system that integrates AI, multi-sensor technologies (AIS, satellite, SAR radar), and spatiotemporal data fusion to enable real-time anomaly detection while generating rapid and predictive operational decision in the ALKI. Novelty/Originality of this article: Integrating Automatic Identification System (AIS), remote sensing, and sea-based rapid-response patrol systems to strengthen surveillance in the ALKI. This study highlights the application of geospatial technology in addressing surveillance blind spots and potential sovereignty violations along strategic national shipping routes. Institute for Advanced Science, Social, and Sustainable Future 2026-05-26 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal-iasssf.com/index.php/RSTDE/article/view/2063 10.61511/rstde.v2i1.2025.2063 Remote Sensing Technology in Defense and Environment; Vol. 2 No. 1: (February) 2025; 64-80 3062-8970 10.61511/rstde.v2i1.2025 eng https://journal-iasssf.com/index.php/RSTDE/article/view/2063/1894 Copyright (c) 2025 Benedicta Wuryantari https://creativecommons.org/licenses/by/4.0

oai:ojs2.journal-iasssf.com:article/2248 2025-12-02T05:37:01Z RSTDE:ART

Automated control design in a sensor and AI-based intelligence monitoring system for suspicious activity detection Alfarizi, Fahreza Nurisnaeny, Poppy Setiawati artificial intelligence intelligence monitoring internet of things Background: In the modern digital landscape, intelligence monitoring systems integrating advanced sensor technology and artificial intelligence (AI) have become essential for enhancing public safety. These systems aim to not only observe but also recognize and respond to suspicious activities effectively and efficiently. Current literature highlights the transformative impact of IoT and AI in various sectors, offering significant improvements over traditional methods. Methods: This study explores the integration of sensor networks, AI-driven algorithms, and Internet of Things platforms. Data collection involves real-time inputs from devices such as cameras, PIR sensors, and microphones, analyzed through machine learning techniques to enhance detection precision. Findings: The systems demonstrate improved monitoring efficiency and have the capacity to operate autonomously, ensuring security across both public and private sectors. They offer long-term cost savings and overcome the limitations inherent in human-operated systems. Conclusion: These systems represent a significant advancement toward proactive and intelligent surveillance, enhancing public safety and security. Novelty/Originality of this article: The research underscores the novel integration of cutting-edge technologies in intelligence monitoring, establishing new benchmarks in adaptability and responsiveness, and setting the foundation for future advancements in cohesive and sustainable surveillance frameworks. Institute for Advanced Science, Social, and Sustainable Future 2025-08-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal-iasssf.com/index.php/RSTDE/article/view/2248 10.61511/rstde.v2i2.2025.2248 Remote Sensing Technology in Defense and Environment; Vol. 2 No. 2: (August) 2025; 101-116 3062-8970 10.61511/rstde.v2i2.2025 eng https://journal-iasssf.com/index.php/RSTDE/article/view/2248/1580 Copyright (c) 2025 Fahreza Alfarizi, Poppy Setiawati Nurisnaeny https://creativecommons.org/licenses/by/4.0/

oai:ojs2.journal-iasssf.com:article/2249 2025-12-03T08:47:50Z RSTDE:ART

Utilization of deep learning in PTZ (pan-tilt-zoom) camera control systems for geospatial-based intelligence surveillance Bashir, Farhat Arief, Syachrul geospatial intelligence intelligent surveillance PTZ cameras Background: The rising complexity of threats to public safety and critical infrastructure has highlighted the limitations of conventional human-operated surveillance systems, creating the need for adaptive, intelligent, and real-time monitoring solutions. Advances in artificial intelligence (AI), computer vision, and geospatial technologies provide opportunities to enhance surveillance through automated detection, analysis, and response. This article examines the integration of pan-tilt-zoom (PTZ) cameras with deep learning models, geospatial data, and distributed computing frameworks as the foundation for next-generation intelligent surveillance systems. Methods: The study employs a narrative review approach, synthesizing recent developments in PTZ camera calibration, convolutional neural networks (CNN), reinforcement learning for autonomous control, and fog computing for distributed video analysis. Research spanning dual-mode fisheye-PTZ systems, lightweight CNN architectures, geospatial data integration, and Internet of Robotic Things (IoRT) frameworks is analyzed to demonstrate practical applications in smart city, industrial, and defense contexts. Findings: Findings reveal that PTZ cameras, when coupled with deep learning and geospatial intelligence, achieve high accuracy in real-time object tracking, small-object recognition, and anomaly detection, with minimal latency under dynamic conditions. Experimental evidence shows error margins below 2% in calibration models and near-perfect accuracy in long-range facial recognition. Integration with fog computing and IoRT enhances responsiveness, scalability, and contextual awareness, while reinforcement learning enables autonomous decision-making for robots and camera networks. Conclusion: The article concludes that combining PTZ hardware precision, AI-based visual analysis, and spatial data intelligence transforms surveillance systems from passive observers into proactive, adaptive, and collaborative agents. However, challenges remain in ensuring robustness under real-world conditions, minimizing latency, and addressing operational usability. Novelty/Originality of this article: This work presents a holistic synthesis of AI-driven vision, PTZ camera control, geospatial intelligence, and distributed architectures, offering an integrated framework for developing adaptive and context-aware surveillance systems in the digital era. Institute for Advanced Science, Social, and Sustainable Future 2025-08-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal-iasssf.com/index.php/RSTDE/article/view/2249 10.61511/rstde.v2i2.2025.2249 Remote Sensing Technology in Defense and Environment; Vol. 2 No. 2: (August) 2025; 117-131 3062-8970 10.61511/rstde.v2i2.2025 eng https://journal-iasssf.com/index.php/RSTDE/article/view/2249/1586 Copyright (c) 2025 Farhat Bashir, Syachrul Arief https://creativecommons.org/licenses/by/4.0/

oai:ojs2.journal-iasssf.com:article/2409 2025-12-02T04:08:09Z RSTDE:ART

EcoRisk-AI: A multimodal artificial intelligence framework for early prediction of mining environmental risks in Indonesia Ramadhan, Muhammad Ilham artificial intelligence environment mining prediction sustainability Background: Mining contributes significantly to Indonesia’s economy but simultaneously generates major ecological risks such as land degradation, acid mine drainage, and landslides, which threaten ecosystems and local communities. Conventional monitoring systems remain fragmented and reactive, creating an urgent need for a preventive and predictive solution tailored to local conditions. Methods: This study introduces EcoRisk-AI, a multimodal artificial intelligence framework designed for early prediction of mining-related environmental risks, with a conceptual application focus on high-risk regions such as Kalimantan and Sulawesi. The system integrates diverse data sources, including satellite imagery, ground-based Internet of Things (IoT) sensors, meteorological datasets, and field inspection reports. EcoRisk-AI consists of four components: data aggregation, a detailed spatio-temporal preprocessing unit, a hybrid machine learning engine, and a decision-support interface. The analytical process sequentially processes data, using Convolutional Neural Networks (CNNs) for spatial features, Long Short-Term Memory (LSTM) for temporal trends, and decision tree-based models for final risk classification. Findings: EcoRisk-AI demonstrates the capacity to provide adaptive, location-specific predictions of ecological hazards in mining regions. The integration of multimodal data enhances sensitivity and accuracy, while the cloud-based visualization dashboard allows stakeholders to access interactive risk maps and automated alerts. The framework's validity is conceptually demonstrated through quantitative "what-if" scenarios, supported by Digital Twin simulations, to test system resilience. This paper details the system architecture and its proposed validation metrics such as Accuracy, Precision, Recall, F1-Score. Conclusion: EcoRisk-AI offers a proactive solution for sustainable mining risk management in Indonesia, enabling early warning and preventive measures against ecological disasters. Novelty/Originality of this article: This work introduces a unique integration of multimodal environmental data and hybrid artificial intelligence techniques specifically adapted to the Indonesian mining context. EcoRisk-AI contributes an innovative predictive framework that bridges technological capability with sustainable development goals, offering new insights into disaster mitigation and environmental governance. The framework is designed for scalability and replicability, offering a model adaptable to other developing contexts. Institute for Advanced Science, Social, and Sustainable Future 2025-08-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal-iasssf.com/index.php/RSTDE/article/view/2409 10.61511/rstde.v2i2.2025.2409 Remote Sensing Technology in Defense and Environment; Vol. 2 No. 2: (August) 2025; 81-100 3062-8970 10.61511/rstde.v2i2.2025 eng https://journal-iasssf.com/index.php/RSTDE/article/view/2409/1579 Copyright (c) 2025 Muhammad Ilham Ramadhan https://creativecommons.org/licenses/by/4.0/

oai:ojs2.journal-iasssf.com:article/2620 2026-05-12T09:34:49Z RSTDE:ART

A python-based application for automated very low frequency-electromagnetic data processing and subsurface interpretation Riko, Riko Sinambela, Marzuki automated data processing python-based application geophysic very low frequency Background: Very Low Frequency Electromagnetic (VLF-EM) method is widely applied in near-surface geophysical investigations for identifying subsurface structures such as fractures, faults, and conductive zones. However, the interpretation of VLF-EM data often requires complex processing steps and specialized software, which may limit efficiency and accessibility for field-based analysis. This study presents the development of a Python-based application designed for automated processing and interpretation of VLF-EM data to support subsurface structure identification. Methods: The application integrates several essential VLF-EM data processing stages, including data input, signal filtering, Fraser and Karous–Hjelt transformations, profile visualization, and subsurface pseudo-section generation. The system was developed using Python programming language and graphical user interface (GUI) components to enable user-friendly interaction and efficient data handling. Field VLF-EM data collected from Neheun area, Aceh Besar, were used to evaluate the performance of the proposed application. The processed data were analyzed to identify subsurface conductive anomalies associated with geological structures. Findings: The results demonstrate that the developed application is capable of producing clear and interpretable VLF-EM profiles and pseudo-sections, allowing effective identification of subsurface conductive zones. Automated processing significantly reduces manual interpretation time while maintaining consistency and reliability of results. The visualization outputs enhance the understanding of subsurface structures and support preliminary geological interpretation. Conclusion: In conclusion, the proposed Python-based application provides an effective and accessible tool for automated VLF-EM data processing and subsurface interpretation. Its flexibility, open-source environment, and integrated visualization features make it suitable for geophysical surveys and educational purposes. Novelty/Originality of this article: The novelty of this study lies in the integration of automated VLF-EM data processing and interpretation within a standalone Python-based application that simplifies analysis while preserving essential geophysical principles. Institute for Advanced Science, Social, and Sustainable Future 2026-02-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal-iasssf.com/index.php/RSTDE/article/view/2620 10.61511/rstde.v3i1.2026.2620 Remote Sensing Technology in Defense and Environment; Vol. 3 No. 1: (February) 2026; 1-17 3062-8970 10.61511/rstde.v3i1.2026 eng https://journal-iasssf.com/index.php/RSTDE/article/view/2620/1857 Copyright (c) 2026 Riko Riko, Marzuki Sinambela https://creativecommons.org/licenses/by/4.0

oai:ojs2.journal-iasssf.com:article/3100 2026-05-22T09:58:22Z RSTDE:ART

Comparative analysis of burn area between google earth engine and manual digitization using the NBR algorithm Saputri, Hanum Resti burn saverity peatland fires remote sensing Background: Indonesia, as the third-largest tropical forest country in the world, is experiencing significant forest degradation driven by illegal logging, land-use conversion, and recurrent wildfires. Peatland ecosystems, particularly in Kubu Raya District, West Kalimantan, are highly susceptible to fire due to their organic-rich composition and seasonal desiccation. This study aims to assess the spatial distribution and severity of forest and land fires in Kubu Raya from 2019 to 2023 using remote sensing and geographic information system (GIS) techniques. Methods: Hotspot data from the Fire Information for Resource Management System (FIRMS) MODIS were analyzed to determine fire occurrences, while Sentinel-2 imagery was utilized to calculate the Normalized Burn Ratio (NBR) index for burn severity estimation. Image analysis was conducted using both manual digitization and the Google Earth Engine (GEE) platform to compare accuracy, efficiency, and spatial representation of burned-area detection. Findings: The findings indicated that 2023 recorded the largest burned area, covering 832,188.98 ha, predominantly within peatland zones. Accuracy assessment demonstrated that the GEE-based method achieved higher reliability, with overall accuracy and kappa statistic values of 86% and 74%, respectively, outperforming the manual approach. The spatial distribution of fire hotspots revealed that peat-dominated areas were more vulnerable to large-scale fires due to their hydrological characteristics. Conclusion: The results highlight that GEE provides a rapid, consistent, and accurate technique for burn area detection and fire severity analysis. Integrating cloud-based remote sensing with conventional GIS enhances monitoring capabilities for sustainable peatland management. Novelty/Originality of this article: The novelty of this research lies in its comparative accuracy evaluation between automated and manual burn area mapping. This study provides new methodological insights for fire monitoring across Indonesia’s tropical peatlands, demonstrating the advantages of cloud-based platforms for large-scale environmental assessments. Institute for Advanced Science, Social, and Sustainable Future 2026-02-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal-iasssf.com/index.php/RSTDE/article/view/3100 10.61511/rstde.v3i1.2026.3100 Remote Sensing Technology in Defense and Environment; Vol. 3 No. 1: (February) 2026; 18-32 3062-8970 10.61511/rstde.v3i1.2026 eng https://journal-iasssf.com/index.php/RSTDE/article/view/3100/1883 Copyright (c) 2026 Hanum Resti Saputri https://creativecommons.org/licenses/by/4.0