Optimization of GGMplus Gravity Data to Identify Sumatran Faults Segments in Kaba Stratovolcano, Bengkulu, Revealed by FHD and SVD TechniquesMurdapa, Fauzan; Sumanjaya, Erlan; Ikhlas Fadli, Darmawan; Ridki Permana, Nanda; Sari, Atika; Purqan, Aulia
doi: 10.1088/1755-1315/1418/1/012056pmid: N/A
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.
Developing WebGIS Using Google Earth Engine for Carbon Monoxide Spatial Monitoring in Surabaya Using Sentinel-5PRahma Damayanti Yusuf, Devika; Lolita Sari, Inggit; Aditiya, Sasfina
doi: 10.1088/1755-1315/1418/1/012047pmid: N/A
Geospatial information has various beneficial to support sustainable environment and human health, such as monitoring distribution of pollutant gases. Carbon Monoxide (CO) is one of harmful pollutant gases. So, preventing the increases of CO and its wider spreading in the atmosphere is very important and also can be as an initial step to mitigate the CO increases. It is often the case that CO concentrations in many big cities are higher than those in its surrounding cities. The current study was conducted in Surabaya city, which is known as one of the major big cities in Indonesia and pose high population density and urban activities. Sentinel-5P imageries were used as the primary data processed and analysed using Google Earth Engine (GEE) platform in order to estimates CO concentration and its distribution in Surabaya. WebGIS for CO monitoring was developed using GEE as the final output in this research. This allowing the results of CO concentration and distribution to be accessed by public. Temporal of Sentinel-5P imagery starting from 2018 up to present were used as the primary data for extracting the CO in Surabaya. The WebGIS developed in this study can be seen at https://ee-devikarahma28.projects.earthengine.app/view/co-distribution-of-surabaya-city. Based on the WebGIS that describes the CO distribution in Surabaya, this study showed the highest CO concentration was in 2018, with values range from 0.294 to 0.331mol/m2. While the lowest CO was in year 2022 with values ranges from 0.0261 to 0.0298 mol/m2. The highest CO concentration has frequently occurred in the central of Surabaya, while the lowest CO concentration was in the east Surabaya.
3D City Modeling for Flood Inundation Analysis in Mayjen Sungkono Area, SurabayaSrikandi, Nadhira; Hapsari Handayani, Hepi; Andiek Maulana, Mahendra
doi: 10.1088/1755-1315/1418/1/012043pmid: N/A
Floods in urban areas are primarily caused by high rainfall combined with inadequate drainage systems, inappropriate land use changes, and insufficient flood mitigation measures. Surabaya, characterized as a seasonal zone with periodic rainfall, frequently experiences flood inundation due to the loss of water retention areas and an inadequate drainage infrastructure. Three-dimensional (3D) city models provide a detailed representation of urban environments, capturing the three-dimensional geometry of objects and general urban structures. This 3D modeling approach can enhance the understanding of flood depths impacting surrounding buildings and infrastructure and assist in estimating the effects on drainage capacity and catchment areas for long-term planning. In this research, 3D flood inundation modeling was conducted using LiDAR data and aerial photography, integrated with other GIS data. The flood inundation simulation was performed using HEC-RAS, while the 3D modeling was carried out using GIS-based software employing semi-automatic methods. The results of the 3D modeling and visualization indicate potential flood inundation depths reaching up to 5 meters, affecting 1,100 buildings and 30 roads. Further research could focus on improving the accuracy of terrain and channel data used in flood modeling to enhance the precision of the results.
Integration of WebODM Platform as Photogrammetry Cloud Processing and GeoNode as Corporate Spatial Data Infrastructure to Support the Acceleration of Geospatial Data Communication Flow in PT. Waskita Karya Tbk.Yudha Wahyu Saputra, Galih; Amirrudin Ahmad, Ali; Lyhardo Sidabutar, Yosevel; Shelvira Herwieany, Agnes
doi: 10.1088/1755-1315/1418/1/012078pmid: N/A
This research explores the integration of two platforms, namely WebODM and GeoNode, with Geoserver as its connecting platform. This platform integration is a solution to support the acceleration of geospatial data communication flow in PT. Waskita Karya Tbk. The main objective of the research is to combine the advantages of Photogrammetry Cloud Processing owned by WebODM with the Spatial Data Infrastructure features owned by GeoNode, thus creating an efficient and integrated system for processing and storing geospatial data. The research methodology involves user needs analysis, system integration development, as well as testing and performance evaluation of the implemented system. The research results indicate that the integration of WebODM and GeoNode has successfully created an environment that allows users to easily perform cloud-based photogrammetry processes and efficiently store and share geospatial data. With this system, PT. Waskita Karya can improve its operational performance in terms of geospatial data processing and distribution, as well as accelerate communication flow between various involved units. The implication of this research is that platform integration like this can be an effective solution for companies managing geospatial data on a large scale.
Examining The Impact of The Transportation, Manufacturing, and Energy Sectors on Air Quality In Jakarta Using Spatial RegressionIqbal, M.; Susilo, Bowo; Hizbaron, D.R
doi: 10.1088/1755-1315/1418/1/012044pmid: N/A
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.
From Space to Health: The Impact of Earth Observation Research on the Smart4COV19 Telemedicine projectFilchev, Lachezar; Dimitrova, Maria; Trenchev, Plamen; Chanev, Milen; Jelev, Georgi
doi: 10.1088/1755-1315/1418/1/012049pmid: N/A
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.
Fill Volume Calculation Analysis of The Meninting Dam Project Based on Terrestrial and Photogrammetry Measurement Data Using The Cross Section MethodArdiansyah, Muhammad; Sania Irbah, Nafisatus
doi: 10.1088/1755-1315/1418/1/012014pmid: N/A
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.
Development of a high-resolution ionospheric VTEC model over Nigeria using spherical harmonics with orthogonal transformation solutionFaruna, Solomon O; Wijaya, Dudy D; Setyadji, Bambang; Utama, Aditya K; Bramanto, Brian; Opaluwa, Yusuf D; Okoh, Daniel
doi: 10.1088/1755-1315/1418/1/012028pmid: N/A
One important ionosphere element that impacts radio signal transmission is the Vertical Total Electron Content (VTEC). Accurate estimation of VTEC is important for diverse applications such as satellite positioning, space weather forecasting, satellite communication. In regions with a sparse network of receivers, especially Nigeria, the spatial and temporal resolutions of the Global Ionospheric Maps (GIM) regularly provided by the International GNSS Service (IGS), Center for Orbit Determination in Europe (CODE), and the International Reference Ionosphere (IRI) are limited. This limits their potential to uncover local ionospheric phenomena in such areas. To address this limitation, we have developed a VTEC model for estimating high temporal-resolution VTEC and Differential Code Bias (DCB) over Nigeria using spherical harmonic expansions with an orthogonal transformation solution. Our novel method makes use of GNSS measurements from the Global Positioning System (GPS) and Global Navigation Satellite System (GLONASS) to precisely estimate VTEC and DCB. GNSS datasets in Receiver Independent EXchange (RINEX), satellite orbit (SP3) and Ionospheric Exchange (IONEX) formats from 2011 across 9 GNSS receivers in the Nigerian Geodetic Network sampled at 30-second intervals were used for this study. For this investigation, code pseudo-range observations were also smoothed using carrier phase observations. To assure data quality, we also carried out several preprocessing procedures utilizing the Melbourne-Wubbena linear and geometry-free linear combinations using an internal ITB-GNSSTEC FORTRAN application based on batch processing and least squares approaches. To validate our model, we compared the estimates with the IGS, CODE, and IRI-2020 models. Results demonstrated strong agreement with the other models with a standard deviation between 2.80 and 6.50 TECU and a correlation coefficient of not less than 0.92 at the evaluation stations. Notably, the new model aligned more closely with CODE and IGS than the IRI model. Also, the new model enabled the detection of local ionospheric VTEC post-sunset enhancement missed by GIM models. Our model also showed a strong positive correlation with the other models for quiet and disturbed days of geomagnetic activity. In Conclusively, this research has developed a high-resolution VTEC method for areas with sparse distribution of GNSS receivers, achieving a temporal resolution of 10 minutes. The ionospheric modeling in areas like Nigeria with sparse GNSS networks has greatly benefited from this research. The approach improves the precision of GNSS-based applications, such as location and navigation, by precisely calculating VTEC and DCBs. It also addresses the issue of sparse observational data in equatorial regions, offering insightful information for atmospheric and geodetic research.
NO2 mapping of Perth bushfire utilizing Sentinel-5P TROPOMINeyrizi, Sima; Muhamad Jaelani, Lalu; Hayati, Noorlaila; Saadi, Ramin
doi: 10.1088/1755-1315/1418/1/012081pmid: N/A
In the face of escalating environmental concerns, effective management of air quality remains critical. This study focuses on Perth, Australia, a region impacted by frequent bushfires and industrial emissions, necessitating precise monitoring of atmospheric pollutants like NO2. Leveraging advanced remote sensing technologies, including Sentinel-2 and Sentinel-5P satellites, this research assesses the spatial and temporal dynamics of NO2 before, during, and after the 2021 Wooroloo bushfire. A key objective was to convert satellite-derived NO2 data from mol/m2 to μg/m3 to enable accurate environmental assessment. This conversion utilized a unit conversion method, improving accuracy metrics substantially, with a correlation coefficient (r) increasing from 0.59 to 0.82 and root mean square error (RMSE) decreasing from 7.58 μg/m3 to 3.20 μg/m3. A regression model, validated with ground-level measurements, demonstrates robust predictive capability (R2 = 0.76, RMSE = 2.58 μg/m3), aiding in the creation of NO2 distribution maps across Greater Perth. Comparison with six ground stations revealed varying accuracy (RMSE: 2.9249 to 7.2705 μg/m3), likely influenced by proximity to the fire and prevailing wind directions. Spatiotemporal analysis depicted distinct NO2 patterns: stable levels pre-fire, dramatic increases during, and gradual post-fire recovery. Maximum NO2 concentrations peaked during the fire (up to 79.227 μg/m3), exceeding air quality guidelines. Post-fire, concentrations normalized, yet sporadic peaks persisted, indicating an ongoing environmental impact. Furthermore, analysis of environmental parameters such as Land Surface Temperature (LST), precipitation, and Normalized Difference Vegetation Index (NDVI) during the study period revealed significant correlations with NO2 levels. LST showed a positive correlation (r = 0.64) with NO2 concentrations during the fire, suggesting temperature influences on atmospheric stability and pollutant dispersion. Precipitation exhibited a negative correlation (r = −0.52), indicating its role in scavenging NO2 from the atmosphere post-fire. NDVI displayed a weak negative correlation (r = −0.30), reflecting vegetation recovery trends post-fire. This comprehensive study integrates advanced remote sensing with statistical modelling to enhance air quality monitoring and inform decision-making in bushfire-prone regions. By elucidating NO2 dynamics and their environmental implications, this research contributes essential insights for mitigating air pollution and safeguarding public health amidst climate-induced challenges.
Correlation Analysis of Vegetation Index Impact on Rice Paddy Productivity Estimation using Landsat-8 and Sentinel-2A Images (Case Study: Blitar District)Rizqika Ayu, Karina; Muljo Sukojo, Bangun; Ayu Retno Mukti, Dyah
doi: 10.1088/1755-1315/1418/1/012007pmid: N/A
Rice is consumed by most of the population in Indonesia. The generative phase, the final stage of rice growth cycle, produces raw grains that are processed into rice, forming the basis of rice production. To estimate rice paddy productivity in March 2023, vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and the Optimized Soil-Adjusted Vegetation Index (OSAVI) are used. These indices are crucial as they correlate with rice paddy areas in the generative phase. Sentinel-2A imagery calculations have shown strong correlations, with NDVI at R = 0.61 and OSAVI at R = 0.75. The OSAVI index from Sentinel-2A yielded an average productivity estimate of 31.35 ton/ha with an RMSE of 5.7041, while NDVI gave an average of 63.63 ton/ha with an RMSE of 12.3224. In comparison, Landsat-8 imagery estimates using NDVI showed a moderate correlation (R = 0.54), with an average productivity of 20.75 ton/ha and an RMSE of 3.2321. The OSAVI index with Landsat-8 provided an average estimate of 64.27 ton/ha with an RMSE of 12.3338 and a moderate correlation (R = 0.41). The correlation coefficients from Sentinel-2A are higher than those from Landsat-8, indicating that the choice of imagery significantly affects rice productivity estimates.