bluuebunny/crossref_metadata_2025 huggingface dataset:
Thermodynamic and Economic Analysis of a Trigenerative Adiabatic Compressed Air Energy Storage
Authors: Jenne Sunku Prasad , Li Chenghao , Zhang Xuecen , Navarro Helena , Wang Jihong , He Wei , Spencer Joe , Ding Yulong
Published: December 2024

Energy storage is indispensable for decarbonising power systems with high penetration of fluctuating renewable generation (e.g., wind, solar). In this study, an innovative trigeneration adiabatic compressed air energy storage (Tri-ACAES) system that produces heating, cooling, and electricity is proposed. The Tri-ACAES system can absorb more wind power than an ACAES system. The Tri-ACAES can work in heating and cooling mode when renewable generation is too low to charge the ACAES system due to the off-design characteristics of the compressor. The results reveal that the round-trip efficiency and turbine power output of the proposed Tri-ACAES system are enhanced by up to 5.8% and 33%, respectively than conventional ACAES system. Further, the proposed system reduced the cost of thermal energy storage by up to 60% than conventional ACAES system.
Digital Dynamic Agent (DDA) Driven Energy Contract Negotiation
Authors: Rezamand Milad , Carriveau Rupp , Stagner Jacqueline , Davison Matt
Published: December 2024

Digital Dynamic Agent (DDA) introduces the new paradigm of digital dynamic agent-driven energy contract negotiation, which is dynamic and real-time in adjustment. Long-term static contracts normally produce one-sided traditional market outcomes. On the contrary, DDA driving will be continuous in nature, at a level of rapid negotiations over the underlying transactions. This paper proposes a new generic DDA framework that negotiates energy contracts between grid agents and renewable generation sources, especially wind farms equipped with battery storage. The agents dynamically update their strategy concerning realtime market conditions, demand, and storage capacity. By simulation over one week with real data from the wind farms, it is evident that the DDA framework is better adapted in using battery storage for significantly lesser wastage of energy. Interestingly, the system secured energy at competitive prices in both live energy and capacity markets; it showed an improvement in cost savings compared to conventional static contracts. It also allows for effective energy procurement within diverse market states, which enables greater flexibility and responsiveness in energy management through the optimization of the negotiation process. The current paper highlights the capability of the framework to enhance negotiation outcomes while allowing the broader transition toward renewable energy markets.
Simulation of a decentralized floating offshore wind hydrogen production system with hydro-pneumatic energy storage and subsea isobaric hydrogen storage
Authors: Zhao Zecheng , Xiong Wei , Wang Hu , Sant Tonio , Carriveau Rupp , Ting David S-K , Wang Zhiwen
Published: December 2024

Hydrogen production using offshore wind power is a promising solution for producing clean fuels in remote areas. However, the intermittency of offshore wind power poses significant challenges to Proton exchange membrane (PEM) water electrolysis systems. Besides, there is generally an imbalance between hydrogen production and hydrogen demand. In this study, hydro-pneumatic electricity energy storage and subsea isobaric hydrogen storage are integrated into the decentralized offshore wind hydrogen production system. The hydro-pneumatic energy storage unit (HPES) is used for mitigating the intermittency and fluctuation of wind power, thereby prolonging the lifespan of PEM electrolyzers. Subsea isobaric hydrogen storage unit is used for replacing conventional isochoric hydrogen storage. In this study, simulation models of a decentralized offshore wind hydrogen production system with various configurations are established with the software Simcenter Amesim 2021.1. The results show that an 83% reduction in the on/off operation can be achieved with the help of hydro-pneumatic electricity energy storage. The isobaric and isothermal storage of compressed hydrogen can be achieved with subsea isobaric hydrogen storage, saving compression energy and facilitating thermal management. In terms of the investigated decentralized offshore wind hydrogen production system, the amount of produced hydrogen is increased by less than 1% by integrating hydro-pneumatic energy storage and subsea isobaric hydrogen storage.
Mathematical comparison of classical and quantum mechanisms in optimization under local differential privacy
Authors: Yoshida Yuuya
Published: December 2024

Differential privacy (DP) is an influential method of protecting private data, and is a condition for a conditional probability distribution. Since we can regard DP as a condition for a tuple of probability vectors, it is natural to consider a similar condition for a tuple of density matrices as a quantum version of DP. This condition is called classical-quantum DP (CQ-DP) because it is considered in converting classical data to quantum states. In the study of DP (including CQ-DP), a positive parameter ɛ represents the privacy level to be guaranteed. In this paper, we show that CQ-DP has a quantum advantage in certain optimization problems. Moreover, we compare classical DP and CQ-DP mathematically to clarify a relation between privacy level and quantum advantage.
Towards Water Hyacinth Identification using Index Vegetation from Sentinel-1 and Sentinel-2 in Selorejo Dam
Authors: Sahda Nabila Cut , Taufik Muhammad
Published: December 2024

Water hyacinths have become a significant issue at the Selorejo Dam in recent years. The invasive nature of this plant and the dam’s location, surrounded by rice fields, make predicting the growth of water hyacinths at the Selorejo Dam challenging. Several studies have applied passive remote sensing satellite technology, such as Sentinel-2A/B, to observe water hyacinth growth as a substitute for in-situ observations, which are limited by time and area coverage. However, considering that rainfall in the Selorejo Dam area can reach up to 3000 mm/year, passive satellites alone may not sufficiently accommodate the need for comprehensive water hyacinth monitoring. The first successful research utilizing Sentinel-1 for monitoring water hyacinths was conducted in 2021 at Vembanad Lake, India. This research addresses the limitations of passive satellites in identifying water hyacinths due to weather conditions. It integrates passive satellite data from Sentinel-2 and active satellite data from Sentinel-1 to provide a more complete identification of water hyacinths in the Selorejo Dam. The results of this research indicate that both Sentinel-1 and Sentinel-2 can differentiate between vegetation and water areas using DPSVIm for Sentinel-1, with an overall accuracy of 0.8, and NDVI for Sentinel-2, with an overall accuracy of 0.84. These findings represent a preliminary step in the process of identifying water hyacinths in the Selorejo Dam.
Deep Learning Mask R-CNN and Template Matching Algorithm For Tree Counting Analysis of Oil Palm Trees (Case Study: East Tanjung Jabung District, Jambi Province)
Authors: Cahyono A.B , Ristawan S.H
Published: December 2024

Indonesia is one of the largest producers and exporters of palm oil in the world. According to the Central Bureau of Statistics (BPS) in 2022 palm oil production has increased to 46.82 million tons. Palm oil has great potential; thus, technology is needed to analyze its productivity. Therefore, calculating palm tree percentages is an important aspect of land monitoring, plant maintenance, and efficient production planning. However, manual palm tree counting is time-consuming and labor-intensive and tends to be less efficient in large plantations. In addition, the lack of accuracy in estimating the number of trees and the age of crops can affect land planning and management. Algorithm development in geomatics science can automatically calculate oil palm trees using aerial photo data combined with template matching and deep learning methods, which are expected to provide efficient and accurate solutions. In this study, 620 samples of oil palm trees were trained. From the visual interpretation results, 6212 trees were obtained; from the processing results using the deep learning method, 6359 trees were obtained, and using the template matching method, 6756 trees were obtained. Through the validation test using the confusion matrix, the overall accuracy of the deep learning method was 95.15%, and the overall accuracy obtained using the template matching method was 87.17%.
Using Sentinel-2 Imagery and Machine Learning Algorithms to Estimate Deforestation (Case Study: a district in Malang Regency)
Authors: Maheswara S D A , Nurwatik Nurwatik
Published: December 2024

Forests are an inseparable element of human life. As time passes, the population of humans and other living beings will continue to increase which may induce the conversion of forest land will be widespread. This land conversion activity causes deforestation. In 2019, protected forest land in Sumbermanjing Wetan District, Malang Regency was reduced by approximately 200 Ha. To achieve Sustainable Development Goal (SDG) number 15 on sustainable forest management, a deforestation analysis is conducted to assess the rate of forest land reduction and predict forest land reduction from 2024 to 2028. This research classifies the rest land using Sentinel-2 satellite imagery from 2019 to 2023. Based on this classification, this research estimates the rate of reduction and prediction of forest area using the polynomial regression during 2024 – 2028. This research applies machine learning algorithms; random forest, SVM, and CNN as the classification method. The dataset is split into training and testing data with a ratio of 70:30. The results witness that the best algorithm for classifying forest areas is a random forest with an average accuracy of 0,957. The forest area based on the classification results using random forest from 2019 to 2023 is consecutively 14342,98 Ha, 12243,44 Ha; 13033,36 Ha; 14677,75 Ha; and 15347,88 Ha. This research concludes that the forest in Sumbermanjing Wetan District has no deforestation, with an average rate of forest addition of 438.084 Ha/year and the prediction of forest area in the period of 2024 – 2028 is consecutively 15228,73 Ha; 15666,814 Ha; 16104,898 Ha; 16542,981 Ha; and 16981,065 Ha.
Palm Oil Tree Canopy Identification Using Deep Learning Approach (Case Study: Tanjung Gusta District, North Sumatera)
Authors: Fitri Alya Nurul , Hapsari Handayani Hepi , Fuad Rachmadi Reza , Nabila Rizki Ramadhani Anisa , Ihsan Muhammad , Mangopo Mikael
Published: December 2024

The palm oil plantation industry in Indonesia has growing rapidly as demand for palm oil increases globally. This needs to be supported by technological innovation to increase palm oil production. One of them is to integrate the power of artificial intelligence technology. This research aims to develop a robust and accurate method for segmenting oil palm trees in plantation areas. Leveraging deep learning algorithms techniques, the research explores the potential of SAM in accurately delineating individual oil palm trees derived from aerial imagery data. The study also involves the development of a comprehensive and versatile labelled dataset to support the training and validation of the deep learning models for oil palm tree counting and segmentation. The performance of the proposed approach is evaluated and discussed critically. This research demonstrates the potential of deep learning algorithms for large-scale mapping and accurate counting of oil palm trees in plantation areas. The author hopes that the result and analysis of this research will give insight and improvement in detecting oil palm trees using automatic method.
Study of Orthophotos Spatial Quality from Integrated Physical Data Collection in Samarinda City
Authors: Buyung Permadi Fajar , Widjajanti Nurrohmat , Ariefiansyah Singagerda Fierman , Riswanto Totok
Published: December 2024

Integrated physical data collection as a mapping instrument and an up-to-date and comprehensive base map have been carried out on a massive scale by the Ministry of Agrarian Affairs and Spatial Planning/National Land Agency throughout Indonesia. There are facts in the field that the results of orthophoto products still have a spatial quality that could be more optimal. In Samarinda City, this activity’s subdistrict was indicated to have a geometric accuracy of above 0.5 m. This condition does not meet the accuracy tolerances stated in the Service Level Agreement (SLA) or relevant regulations. Therefore, alternative solutions are needed to improve the quality of orthophotos by paying attention to economic value. This research aims to evaluate, and improve the spatial quality of orthophotos resulting from integrated physical data collection in 2023 in Samarinda City without re-acquisition. In other words, this research aims to determine alternative low-cost and efficient solutions to improve orthophotos’ spatial data quality. Data in the form of orthophotos and a 2023 Ground Control Point (GCP) coordinates list in Samarinda City. The approach method is the addition of GCP in the reprocessing of orthophotos. The study area is hilly and flat terrain. Results analysis uses geometric accuracy (horizontal and vertical) and semantic accuracy tests. The geometric accuracy test results before reprocessing had a CE90 value of 0.438 m based on the SLA attachment. However, they exceeded the CE90 threshold of ≥ 0.5 m based on the results of field suppletion measurements. The results of the semantic accuracy test have a National Imagery Interpretability Rating Scale (NIIRS) level 6, which means that orthophotos can make it easier to interpret geographical objects in detail. reprocessing with the addition of GCP can increase geometric accuracy with a CE90 value of 0.240 m and LE90 1.01 m. The spatial quality of orthophotos before and after reprocessing has a relatively significant difference. Adding GCP followed by reprocessing can be used as an alternative solution to improve the quality of orthophotos at a low cost.
Fill Volume Calculation Analysis of The Meninting Dam Project Based on Terrestrial and Photogrammetry Measurement Data Using The Cross Section Method
Authors: Ardiansyah Muhammad , Sania Irbah Nafisatus
Published: December 2024

Meninting Dam is a construction included in National Strategic Project (PSN) to support food and water security, especially in Eastern Indonesia, and also serves as a construction project for a dam located on Lombok Island. The earthwork becomes a crucial in construction work. It is necessary to calculate the volume to monitor the progress of the work. Measurements for volume calculation can be done with terrestrial methods and photogrammetric method. Therefore, it is necessary to compare the two measurement methods. The results of the total fill volume calculation were 1,699,589.02 m3 for drone measurements, 1,695,142.13 m3 for GNSS-RTK measurements, and 1,688,170.66 m3 for total station measurements. From these results, there is a difference of 4,446.89 m3 between drone measurements and GNSS-RTK and 6,971.47 m3 between total station measurements and GNSS-RTK. Based on the paired sample t-test (statistical test), at the 95% confidence level, the difference in area’s profile per STA and volume per partition of total station and drone measurements is not very significant to the data that is considered correct, which is the GNSS-RTK measurement. ASTM standard test, related to precision with precision error test, drone measurement is more precise than total station measurement. Regarding accuracy with the volume error test, drone measurements are more accurate than total station measurements. The results of this research can be used to support SDGs activities in infrastructure development and water resources management.
Localities of ID-74 to DGN95 Coordinate Transformation Parameters
Authors: Sidiq Teguh P. , Purwanto Heri , Gumilar Irwan , Retno Atik , Prijatna Kosasih , Nurwinas Dzun , Hibatul Wafi Hudaya , Dirgaputera Trizky
Published: December 2024

As can be referred to almost all regulation in Ministry of Energy and Mineral Resources, all spatial information used in the oil and gas industry in Indonesia, should be referenced to DGN95. While it seems that the datum is now deprecated, the use of DGN95 in many data, especially seismic data collected in 1970 to 1990 are still used and maintain in many companies to avoid huge data transformation work if it is applied nationwide. Some work in finding coordinate transformation has been done since early 2000, but it produces a different set of parameters. In this paper, we collect some published ID74 to DGN95 coordinate transformation parameters and compare to our own results. We measure 20 common points on oil and gas fields using the GNSS method. However, only three points were found consistent and suitable to be the common points. Using the Molodensky-Badekas method, we generate a set of coordinate transformation parameters to be used in the fields. There are 10 parameters to be estimated, three translations, three rotations, one scale and coordinate of center if rotation parameters. Again, there are discrepancies between our results and other published parameters. This shows that the transformation parameters are usually exclusive to the field or region. Our study suggests that coordinate transformation parameters are best to calculate for each specific concession area.
Analysis of Semeru Volcano Deformation Using Static and Kinematic Solutions with GPS Data 2015-2023
Authors: Yuki Tamim Bayoumi Ilham , Maulida Putra , Haerani Nia
Published: December 2024

Semeru Volcano is the highest peak on the island of Java. Semeru is characterized by continuous volcanic and strombolian eruptions that produce eruptive material which might result in casualties and infrastructure damage. One of them is the eruption on December 4, 2021. Therefore, to reduce the impact of future disasters, deformation monitoring using GPS is needed. In this study, we utilize GPS observation data located on Semeru Volcano and process it using GAMIT/GLOBK and TRACK. Processing is done with static solutions, kinematic solutions, and strain to determine the deformation pattern before, during, and after the December 4, 2021, eruption. The processing results of statis solution shows the pattern of displacement to the southeast in accordance with the direction of movement of the Sunda Block, with a range of 21.49 mm/year to 30.96 mm/year. The inSluence of the Sunda Block and other subduction-induced deformations were removed by referenced to CLUM station located 25 km from the volcano. The value of local velocity experienced a velocity of 1.81 mm/year to 7.83 mm/year with varying directions of movement. Kinematic processing using TRACK succeeded in showing the deformation pattern that occurred in the eruption of Semeru Volcano on December 4, 2021. Time Series in the Sirst phase (07:46:59 UTC–07:51:10 UTC) shows an inSlation and extension pattern of 0.054 m at the LEKR and 0.055 m at the SAWR station, followed by a deSlation and compression pattern of −0.018 m at the LEKR and −0.031 m at the SAWR in the second phase (07:51:10 UTC–07:56:00 UTC). The third phase (07:56:00 UTC– 08:03:00 UTC) showed deSlation and compression patterns with values of −0.058 m at the LEKR and −0.003 m at the SAWR. While using strain calculations, in the phase before the eruption, the strain was dominated by compression on the entire mountain body. Meanwhile, the phase after the eruption has a pattern that is divided into two, namely compression on the east side of the mountain and compression-extension on the southeast side of the mountain. These results are expected to contribute to understand the characteristics Semeru Volcano eruption in the future based on geodetic data.
Spatio-temporal Variation of Chlorophyll-a around the Flores Sea, Java Sea, and Makassar Strait and its Relationship with Temperature and Salinity
Authors: Putri Hanansyah Megivareza , Yuli Handoko Eko , Muryono Mukhammad
Published: December 2024

The Flores Sea, Java, and Makassar Strait are areas where water masses meet as well as one of the entry points for the Indonesian Through Flow. Freshwater mass from the Pacific Ocean enters Indonesian waters through the Makassar Strait. Not only that, Indonesia’s maritime region is influenced by the Asian-Australian Monsoon, which also causes seasonal changes in temperature and salinity. This phenomenon affects primary productivity in Indonesian waters as indicated by variations in chlorophyll-a concentrations in surface waters. Therefore, it is important to study the dynamics of chlorophyll-a in time series to better understand the ecosystem and the phenomena that occur. This study aimed to analyze the spatio-temporal variation of chlorophyll-a in the waters surrounding the Flores Sea, Java, and Makassar Strait, and its correlation with temperature and salinity over a period of seven years, from 2016 to 2023. This reasearch uses chlorophyll-a data collected from sensor, named Ocean and Land Color Instrument carried by the Sentinel-3 satellite. The chlorophyll-a data used is Ocean Color 4 for MERIS data. This study found that chlorophyll-a concentrations in the Flores Sea, Java, and Makassar Strait sample areas tend to be high during the Northwest Monsoon period, along with the arrival of the rainy season which increases river water runoff. On the other hand, chlorophyll-a concentrations in the southern waters of Java to Nusa Tenggara exhibit a notable increase during the Southeast Monsoon, which is subsequently accompanied by a decline in temperature of sea surface and a rise in salinity of sea surface. In 2018 and 2019, the occurrence of the El Niño phenomenon, along with the positive phase of the IOD, led to an increase in chlorophyll-a. From 2018 to 2019, the average chlorophyll-a reached 0.71 mg/m3 in the Flores Sea and Makassar Strait and 0.73 mg/m3 in the Java Sea. This study analyzes chl-a based on satellite observation data that may have errors due to cloud cover. Further research with in situ observation methods is needed for better results and a longer temporal range so that the variability of chl-a against the global phenomena of ENSO and IOD can be seen more clearly.
Mapping The Dynamics of Settlement Areas around the tourism areas by Using Geographic Information Systems (Case Study: Prambanan and Kalasan District)
Authors: Putra Wijaya Arwan , Syadayagra Arwanto Daffa , Novrizal Ramadhani Rizky , Wahyuddin Yasser
Published: December 2024

The construction of National tourism strategic area (KSPN) Prambanan - Kalasan is planned to begin in 2016 through the Master Plan and Detail Plan of KSPN Prambanan - Kalasan and its surroundings for the 2016 fiscal year. The development of tourist areas can have an impact on increasing the land value around tourist areas, and also cause the development of residential areas. Therefore, this research was conducted to analysis changes in land values around the KSPN development and also analysis changes in residential areas and the suitability of residential areas for land values in Prambanan and Kalasan Districts. The results of the suitability of residential areas were then superimposed on the analysis with the RDTR of Prambanan and Kalasan Regencies. The results show that in 2017-2024 there was an increase in residential land area by 67 hectares in Prambanan District and 73 hectares in Kalasan Regency and the highest significant increase in land value was in zone 50, namely Jl. Raya Solo-Yogyakarta for IDR. 6,597,200 and the lowest increase in land value was in zone 49 amounting to IDR. 158,500 to the north of Tamanmartani Village, Kalasan District. The relatively small increase in residential area in the highest land value zone and a large increase in residential area in the lowest land value zone. The suitability of the dominant residential area is very suitable in the highest land value zone and the suitability of the dominant residential area is less suitable in the lowest land value zone.
The Impact of Population Density, Land Cover, and Spatial Patterns on the Geoecological Evaluation of the Surabaya Coast
Authors: Safitri D.A. , Alina A.N. , Bespalova L.A. , Suprayitno W.E. , Dhani N.Q.
Published: December 2024

Coastal areas are vulnerable to the influence of geomorphological processes from activities in the waters, so that the condition of coastal ecosystems will always change according to the dynamics that occur on the coast, both changes could be occurred in coastlines and land cover. Geoecology is the spatial interaction between natural phenomena and their relationship to phenomena in a particular ecosystem. Surabaya is a coastal city that frequently experiences changes in land cover. The construction of new settlements and changes in land use have a significant impact on the coastal environment of Surabaya. This research aims to determine the influence of population density, land cover and spatial plan on the geoecological evaluation of Surabaya’s coast using remote sensing data. The data used in the research is Landsat satellite image data from 1994, 2003 and 2018 (maximum likelihood classification method). The dynamics of changes in coastlines on the coast of Surabaya are caused by three phenomena, namely erosion, accretion and anthropogenic. Each phenomenon has a different value for land cover class, population density and spatial patterns. The research results show that geoecological evaluation is closely related to phenomena that occur on the Surabaya coast (value range 1-3). The geoecological evaluation value due to the erosion phenomenon is low (30%) and medium (70%). The geoecological evaluation value due to the accretion phenomenon is low (100%). Meanwhile, the geoecological evaluation value due to anthropogenic phenomena is low (25%) and medium (75%). Geoecological evaluation of Surabaya’s coast shows that it is in a “safe” condition, but there is requirement for coastline management. Where the coastal area planning process is carried out by identifying the dynamics of coastal areas and potential risks and consequences related to decisions that produce different future scenarios.
Waste Volume Estimation by Surface Changes of Sarimukti Landfill 2010 – 2023
Authors: Dzun Nurwinas Saepudin Muhammad , Lestari Nurmaulia Sella , Gumilar Irwan , Dirgaputera Trizky , Ibrahim Hakiem Haekal
Published: December 2024

Sarimukti landfill was established in 2006 and is located in Sarimukti Village, which is 45 km away from Bandung City. This landfill handles trash dumping from nearby areas such as Bandung City, Cimahi City, and West Bandung Regency. In 2010, this landfill underwent a rehabilitation project to optimize its trash handling capability. Several months ago, the Sarimukti landfill experienced a fire crisis which was successfully handled by the local government. After the crisis, speculations emerged regarding the rehabilitation of the landfill once again. In response to that idea, this research will explore several pieces of information that are required for the landfill to undergo another rehabilitation. Two of them are the latest topographic conditions and the latest waste volume. The latest topographic surface is determined by aerial photogrammetry, utilizing a multi-rotor UAV. The output will be used to estimate the waste volume by differentiating the latest topographic surface data with the 2010 topographic surface data, which was acquired from the previous rehabilitation project. Unfortunately, the 2010 data comes with an unknown coordinate system, so these two surfaces cannot be perfectly overlayed. Thus, the 2D coordinate transformation has to be performed. The transformation parameters, derived from multiple common points, exhibit an accuracy of 30 cm. Following this transformation, volumetric calculations reveal that several areas have a total volume increase of 2.3 million m3. Contrary to that, the remaining areas have a total volume decrease of 23,742 m3.
Discovering Hidden Pattern in COVID-19 Prevention through Exit-Entry Permit Letter (SIKM) System using Geospatial Analysis
Authors: Ismanto B , Hawaari T R , Anugraha A S , Silitonga M S , Juliendra G , Alifiyanti H , Erdiza H P , Sari P R , Apriyadi D
Published: December 2024

COVID-19 was declared as a global pandemic in 2020, and described as a highly contagious virus that spreads through direct contact. Almost all countries affected by COVID-19 implement various steps to prevent its spread, one of which is lockdown, described as a prohibition of human movement outside the home. Jakarta, the capital city of Indonesia with a population of 10,609,681, implements restrictions on human movement entering and leaving of the city using the Exit-Entry Permit Letter (SIKM) system to prevent the spread of COVID-19. Lockdown does not chosen as option to prevent the spread of COVID-19 in Jakarta due to the consideration of its large population, which could have significant economic impacts on the community. Meanwhile, the SIKM system is designed to restrict the mobility of people entering and leaving Jakarta in order to control the spread of COVID-19, with the hope of minimizing significant impacts on the community’s economy. The aim of this research is to discover hidden patterns in human movement entering Jakarta through the SIKM system, utilizing geospatial analysis with Geographic Information System (GIS). The results of this research indicate that the central business district, known as the ‘Golden Triangle’ of Jakarta, emerges as the area with an economic activity zone with the densest hotspot as the primary destination for people during the implementation of the SIKM system. Furthermore, the hidden pattern of Jakarta’s spatial plan indicates that in general the economic activity tend to located in Central Jakarta, meanwhile the residence is tend to located around the borders of Jakarta. Therefore, with the geospatial analysis, hidden patterns of human movement during COVID-19 through the SIKM system can be identified for decision support system for Jakarta government.
Examining The Impact of The Transportation, Manufacturing, and Energy Sectors on Air Quality In Jakarta Using Spatial Regression
Authors: Iqbal M. , Susilo Bowo , Hizbaron D.R
Published: December 2024

This study employs Geographically Weighted Regression (GWR) to analyze the spatial distribution of air pollutants NO2, SO2, and CO in Jakarta and its surrounding areas, focusing on variations between dry and wet months in 2023. The analysis utilizes pollution data from Sentinel-5P images, processed using Google Earth Engine and ArcGIS/QGIS software. The study area, encompassing Jakarta and a 100 km radius, includes industrial and energy sector data to understand pollution source contributions. The GWR models explored three scenarios with different predictor variables: network density, number of intersections, and industrial proportion. The results reveal significant spatial heterogeneity in pollutant concentrations, with higher emissions during dry months. Scenario 1, which includes all predictor variables, shows the highest LocalR2 values in highly industrialized zones. Scenario 2, excluding the energy sector variable, demonstrates broader model applicability, while Scenario 3, with only transportation-related variables, offers the widest coverage but reduced specificity. These findings provide critical insights for policymakers to formulate targeted strategies for air quality management, aiming to mitigate the adverse effects of air pollution on the population.
Building Footprint Extraction from Fixed-Wing UAV Imagery using Mask R-CNN and Object-based Image Analysis Methods (Case Study: Banturejo Village, Malang Regency)
Authors: Hidayat Husnul , Dhihan Irawan Reyhan , Maulana Azhali Firmansyah
Published: December 2024

As a developing area in Malang Regency, Banturejo Village has many potencies since its location near the tourism area of Selorejo Dam. To maximally the harness of potencies while maintaining efficient land use in Banturejo village, mapping the built area in large scale should be carried out. The photogrammetric techniques using fixed-wing UAV could be a good alternative for large-scale mapping in this village area because of its capability to quickly acquire high resolution image with highly customizable mission specifications. But the problem arises in interpreting these imagery into meaningful cartographic representation which often requires cautious manual digitization in much slower rate that its acquisition. In this research the automatic image analysis method for building footprint extraction using Mask R-CNN algorithm and Object-Based Image Analysis was performed. The fixed wing UAV imagery was captured in 2023 and the structure from motion algorithm was employed for photogrammetric processing which produced 10-cm resolution orthophoto. Manually digitized building polygons from the same imagery serve as the gold standard for accuracy analysis, and small proportion of the data was used as training samples for the algorithm. The results shows that 1447 buildings with total area of 180,595 m2 was generated with Mask R-CNN algorithm, while OBIA-Mask R-CNN produced 572 buildings and total area of 201,932 m2. The confusion matrices reveal precision value of 77.94%, recall 51.54%, F1 Score 62.02% by Mask R-CNN method, and precision value of 35.95%, recall 9.21%, F1 Score 14.66% by OBIA-Mask RCNN method. Mask R-CNN method generated slightly lower accuracy of total building area, but in terms of precision the OBIA-Mask RCNN method produces lower number of building polygons.
From Space to Health: The Impact of Earth Observation Research on the Smart4COV19 Telemedicine project
Authors: Filchev Lachezar , Dimitrova Maria , Trenchev Plamen , Chanev Milen , Jelev Georgi
Published: December 2024

This study investigates the potential of Earth observation research in improving air quality management and supporting telemedicine initiatives in response to the COVID-19 pandemic. Utilizing ground stations in Sofia and Burgas, we collected hourly measurements of NO2, CO, PM10, and PM2.5. Satellite data from TROPOMI-S5p and ground-based air quality observations were integrated to assess the spatial distribution of surface particulate matter concentrations. Our results demonstrate the feasibility of leveraging satellite-derived atmospheric chemistry data to enhance air pollution modeling and urban-scale air quality management. Furthermore, the technology developed for Burgas has the potential for expansion to other Bulgarian cities and replication in different urban centers. Overall, this research highlights the importance of Earth observation research in addressing critical environmental and public health challenges. Telemedicine via smartphones can help manage these risks by offering assistance to patients with mild symptoms, thereby minimizing their exposure to COVID-19 patients. The study also presents the findings of models used to convert TROPOMI – S5p aerosol data into PM concentrations in Burgas, Bulgaria.
Three-dimensional City Modeling for Microclimate Simulation of Urban Areas
Authors: Izzah Aryana K , Hepi Hapsari H , Gusti Ngurah A I , Zenda Mergita F
Published: December 2024

Increasing urbanization and building density can trigger an increase in environmental temperature, thereby influencing urban climate change. Urban management that responds to climate change is urgently needed, requiring a smarter urban design approach than before. As geospatial science and technology develop, conventional two-dimensional methods will transition to more complex three-dimensional spatial modeling. This approach has great potential in supporting climate change-responsive decision making in urban environments. Three-dimensional city modeling at the LOD 2.0 level was used for the microclimate simulation. The research was conducted in the Central Business District (CBD) of Mayjend Sungkono, Surabaya, which has special urban characteristics related to the urban climate, such as variations in the use of building functions, such as residential, village, commercial, apartment, or hotel, as well as unique types of roofs in the area. The parameters used for the simulation included air temperature (°C), relative humidity (%), wind speed (m/s), and wind direction. Three-dimensional city modeling was performed using a semi-automatic method based on LiDAR data and aerial photos. The simulation was carried out by integrating three-dimensional city modeling with the visualization of microweather parameters in Envi-Met, along with pattern indicators from building facades. In addition, a vegetation scenario was created to reduce the temperature. The second factor is a comparison between the existing conditions and the scenarios that have been created.
Utilization of SAR Data to Evaluate Industrial Locations on the North Coast of Central Java
Authors: Mispaki Syachril W , Ar Rosyid Muhammad H , Rahmawaty Mitha A
Published: December 2024

Central Java province, located on the northern coast of Java Island, holds a strategic position as a busy land transportation route, connecting cities in southern Central Java and East Java with the capital city. The growth of industrial centers and industrial zones along the northern coast of Central Java is a positive effect of this position. However, the subsidence of land along the northern coast of Central Java is its negative impact. This can be detrimental to industrial areas and settlements along the northern coast of Central Java. Therefore, spatial planning must consider this land subsidence. The utilization of SAR data to periodically monitor land subsidence is crucial in formulating spatial arrangements that can accommodate the impact of land subsidence. In this study, to obtain land subsidence, the InSAR method was used. The data processed in this study consisted of Sentinel 1 from 2015 – 2023. It was found that land subsidence in the coastal areas of northern Central Java ranges from 0 to 25 cm per year. This data on land subsidence serves as an important basis for policymakers, planners, and stakeholders to develop more effective adaptation and mitigation strategies in spatial planning in the coastal areas of northern Central Java.
Integrating Remote Sensing and GIS for Precision Agriculture: Leveraging Google Earth Engine for Enhanced Agricultural Management
Authors: Yuli Handoko Eko , Fahriza Achmad , Muryono Mukhamad
Published: December 2024

This study aims to develop a plant health monitoring platform using Google Earth Engine and Sentinel-2 satellite imagery. This platform enables real-time and accurate monitoring of plant conditions in Ponorogo Regency, supporting better decision-making in agricultural management. The platform utilizes high-resolution multispectral data such as the NDVI, Chlorophyll Vegetation Index, and Normalized Difference Built-up Index to generate vegetation indices, providing comprehensive information about plant structure and condition. The Google Earth Engine platform offers a robust platform for monitoring and analysis functions within the platform, providing valuable insights for precision agriculture applications
Optimization of GGMplus Gravity Data to Identify Sumatran Faults Segments in Kaba Stratovolcano, Bengkulu, Revealed by FHD and SVD Techniques
Authors: Murdapa Fauzan , Sumanjaya Erlan , Ikhlas Fadli Darmawan , Ridki Permana Nanda , Sari Atika , Purqan Aulia
Published: December 2024

Kaba volcano is a perilous and currently active volcanic site located near the Sumatran fault, specifically within the Kepahiyang region of Bengkulu. Given the intricate nature of its location, it is crucial to monitor the local fault zone activity in the vicinity of the Kaba volcano. meanwhile, these fault zones are typically associated with high permeability areas and are characterized by high-density contrasts. Therefore, we applied First Horizontal Derivative (FHD) and Second Vertical Derivative (SVD) methods to identify the presence of the Musi Kepahiyang segment in Bengkulu, using GGMplus high resolution gravity data. Based on the results of the FHD analysis, clear gravity anomalies are observed along the northwest (NW) and southeast (SE) regions of Kepahiyang, with Bouguer anomaly values reaching 800 mGal. The discernible patterns unveiled through FHD analysis distinctly delineate the Musi fault (NW), Kepahiyang fault (SW), and Garba fault, unveiling a rich tapestry of tectonic activity surrounding the Kepahiyang area in Bengkulu. Complementing these findings, SVD analysis reveals a consistent anomaly distribution, albeit with marginally diminished Bouguer anomaly values, affirming the robustness of the detected features. Through the fusion of FHD and SVD methodologies, our study offers an understanding of the structural complexities pervading the segment in Kaba Stratovolcano, shedding light on its dynamic geological evolution, and fortifying our comprehension of fault dynamics in the Sumatra region.
Application of Density Lineament Mapping as an Indication of CBM Potential in Tanjung Agung, Muara Enim, South Sumatera
Authors: Singgih Purwanto Moh , Navisa Siti , Khadar Kinasih Ardelia , Ramadhani Yuniar , Winda Avrilla Vicka , Ayu Nuridhiani Fathan
Published: December 2024

Coal Bed Methane (CBM) gas is formed and trapped within coal seams, offering significant economic potential as it can be converted into electricity or transported through pipelines for various purposes. Indonesia is estimated to have approximately 574 trillion cubic feet (TCF) of CBM gas reserves, capable of meeting domestic energy needs. South Sumatera is classified as a province with the largest coal reserves in Indonesia, indicating abundant potential for CBM gas resources. This research aims to identify potential CBM indicators based on structural lineament interpretation using DEMNAS data. Field research was conducted to gather necessary data, and the findings from the study indicate that the surveyed area has a high potential for Coal Bed Methane (CBM) due to the high lineament density, ranging from 570/km2 to 710/km2 indicating high permeability that supports the well development of CBM gas. Optimal permeability can enhance the quantity of trapped Coal Bed Methane (CBM) gas, given the available space for the gas.
Geodiversity Assessment of Widodaren Spring: A Treasure Trove Site for Bromo Tengger Volcano’s Future Geopark
Authors: Haris Miftakhul Fajar M. , Budi Santoso Eko , Ulumuddin Faqih , Kusuma Rahayu Helda , Wirayudhatama Mahendra , Khairia Alfany Marsha , Erfand Dzulfiqar Rafi Muhammad , Zakiyyah Zukhrufah Syabibah
Published: December 2024

The Bromo Tengger Volcano in Indonesia, a prominent geotourism spot, features the notable Widodaren Spring within its caldera, originating from the inactive Widodaren Vulcanic Cone. This study evaluated the geodiversity of Widodaren Spring through field and laboratory analyses and identified geodiversity elements by assessing their potential educational utility (PEU), potential touristic utility (PTU), and degradation risk (DR). Widodaren Spring is unique, showcasing rocks, hydrogeology, and landscape elements. This location is composed of pyroclastic fall deposits and pyroclastic surges, which characterize the products of volcanic activity on this volcano and provide a Sea of Sand Caldera landscape. Field and laboratory data indicate that this spring is genetically topographic-stratigraphic and hydrochemically calcium-chloride-type. The high levels of sulfate-chloride in the groundwater samples suggest the influence of volcanic gases from the nearby Bromo Volcanic Cone. Rainwater infiltration at the upper cone (2400-2560 meters) flows through lapilli layers serving as aquifers, with tuff and lapilli tuff layers below being impermeable to semi-permeable. Geodiversity assessments revealed a high educational utility potential (PEU) of 310 points, medium tourist utility potential (PTU) of 260 points, and low degradation risk (DR) of 135 points. The high PEU score was attributed to the site’s unique geological elements, making it valuable for education and science. The medium PTU score reflects its location in a national park, limiting accessibility but protecting it from human-caused damage. Potential natural damage may arise from weathering and volcanic eruptions on Mount Bromo. In conclusion, Widodaren Spring possesses significant geodiversity value with great potential for education and tourism, alongside a minimal risk of degradation. This site is a treasure trove for the future Bromo-Tengger Volcano geopark.
A Study of Lineament Density and Correlation Residual Gravity Anomalies to Identify Pacul Fault in Mount Pandan, East Java
Authors: Naufal Muhammad Z. , Pandu Gya Nur Rochman Juan , Triahadini Agnis , Achsan Faiz M. , Dewi Maulida K. , Ashari Dwiska Calvin , Erick Asyari M.
Published: December 2024

This study analyzes the identified Pacul Fault using lineament density and residual gravity anomalies in Mount Pandan, East Java. The study utilizes DEMNAS data, geological data, and gravity satellites. The research process includes creating hill shades from DEM data, extracting lineaments using PCI Geomatica software, and interpolating data to create a lineament density map. Further analysis correlates lineament density and residual anomaly with the fault zone around the Pacul Fault. The results show that fault zones are located in areas with moderate to high lineament density and at anomaly contrast from residual anomaly. The complete Bouguer anomaly map indicates low anomalies likely representing fault planes. This study confirms a positive correlation between lineament density and contrast anomaly according to the pacul fault presence of Mount Pandan.
Comparison of multitemporal multifrequency multibeam echosounder norbit iwbms: depth and sedimentation rate analysis of selorejo reservoir 2022-2023
Authors: Khomsin  , Nuradi Ikhsan
Published: December 2024

Selorejo Reservoir is a reservoir constructed and located in Pandansari Village, Ngantang District, Malang Regency, East Java. The Indonesian Government built this reservoir to accommodate the water flow from several rivers, including the Konto River, Kletak Lahar, Kwayangan River, and several other small rivers. The biggest challenge for a reservoir, where the primary clean water source comes directly from rivers, is siltation caused by sediment accumulation at the bottom of the water body. Therefore, severe and continuous control in the reservoir area is necessary to prevent damage and overflow and ensure the community’s safety by engaging in activities around the Selorejo Reservoir. Bathymetric surveys are essential in obtaining information related to marine fields and precise depth values with spatial references. When using a multibeam echosounder, surveyors must consider several aspects regarding the specifications of the equipment before data acquisition, one of which is the frequency used. Different frequencies result in different penetration abilities and data quality. This study aims to estimate the sedimentation rate in the Selorejo Reservoir using multitemporal data and investigate the effect of using multifrequency on the depth measurement results obtained. The results of this study indicate that the higher the frequency used, the shallower the depth value obtained. Higher frequencies provide better representation in object detection capabilities. At the same time, the sedimentation rate was relatively high, from 3.2 to 5.3 centimetres per month.