Patterns of oceanographic factor distribution and tuna fishing potential: Spatial and temporal analysis

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

Utilization of remote sensing in monitoring terrorism threats in the border areas of Indonesia: In the context of relocating the capital city

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

Application of machine learning and remote sensing in monitoring land use dynamics in 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

Automated control design in a sensor and AI-based intelligence monitoring system for suspicious activity detection

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

Utilization of remote sensing in post-disaster recovery for environmental damage assessment

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

Land cover change analysis based on spectral indices and deep learning convolutional neural network model

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

The role of maritime geospatial in navigating uncharted waters mapped

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

The use of satellite imagery in supporting non-military operations: a geospatial intelligence perspective

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

The use of remote sensing in monitoring shoreline change: Implications for maritime area security

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

Dynamics of seasonal impacts on Total Suspended Solid (TSS) concentrations in coastal Semarang City using landsat 8

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