GEOID

Detection of River Change in Modeling Flood Vulnerability using Support Vector Machine (SVM) Methods in Tallo River Makassar City

2025-09-30T18:59:29+07:00

The transformation of river morphology and the rising frequency of flooding in urban environments have emerged as increasingly concerning environmental challenges, particularly in Makassar City. The Tallo River, one of the primary waterways traversing the city, exhibits notable dynamic changes driven by both natural processes. In the contemporary era, flooding stands as one of the most recurrent natural disasters, occurring unpredictably and posing serious risks, especially in major metropolitan areas. Such events frequently disrupt daily activities, leading to traffic congestion and obstructing ground transportation. Residential zones situated near riverbanks are particularly vulnerable to its impacts. Moreover, climate change exacerbates these conditions by contributing to increasing environmental unpredictability and need through a monitoring. The purpose of this research is to analyze river morphology changes and assess flood susceptibility in the Tallo River, Makassar City, using Support Vector Machine (SVM) classification methods. Approximately, there are 20% of the area experienced significant changes during 2018 in Tallo River. As water discharge continues to increase, the volume of water mass also rises accordingly. To detect the spatial distribution of flood vulnerability along the Tallo River, which flows through Makassar City, this study utilizes Land Use and Land Cover (LULC) data from 2017 and 2024. These datasets were classified using the Random Forest model, achieving accuracies of 0.89 and 0.87, respectively values that meet the standards for land use change accuracy. Flood vulnerability is also influenced by low elevation values, particularly areas below 0 meters, which are classified as wetland zones. In the Tallo River area, which is part of the Jeneberang Watershed, the dominant class is moderate flood vulnerability, covering approximately 138.48 hectares. Remote sensing technology combined with machine learning approaches especially supervised classification techniques widely used for both binary and multivariate classification tasks, demonstrating high accuracy in detecting and classifying flood vulnerability.

Study of 3D Cadastral Mapping in the Teaching Factory Building of The Vocational School, Diponegoro University Using SLAM (Simultaneous Localization and Mapping) Method

2025-03-11T14:47:06+07:00

Cadastre is a land information system based on land parcels. The growth in the number of land parcels is influenced by the increasing conversion of land into residential areas, which in turn is driven by several factors, one of which is population growth. The demand for housing initially expanded horizontally; however, due to limited land availability, it has now shifted toward vertical development. Vertical housing types such as flats or apartments are emerging, which introduce complexity into the cadastral system due to the partitioning of internal spaces. Cadastre requires high-accuracy measurements; hence, the increase in measured areas leads to a higher workload. The SLAM (Simultaneous Localization and Mapping) method offers a breakthrough in fast and accurate measurements using laser-based technology, which can be implemented in cadastral mapping to update spatial data precisely and efficiently. This method combines the flexibility of handheld operation with high data precision by employing dense laser scanning. This study utilized the SLAM method, resulting in a polygon area processing of 0.3558 m², with an average center-point distance deviation of 0.0658 m, a polygon circularity ratio of -0.002, and a regression value of less than 10%. When this model is applied with a tolerance of up to 10% spatial error, it can achieve vertical measurements up to the 43rd floor, in accordance with the Directorate General of Taxation Circular and tested based on ISO 19113:2011 standards.

Simulation of Tidal Inundation along the Northern Coast of Central Java (Pantura) Using GIS-Based Analysis

2025-08-04T01:10:10+07:00

The northern coast of Java Island (locally known as Pantura), is a strategically important area, particularly in the distribution sector. However, its topographical characteristics and proximity to the Java Sea make it vulnerable to the threat of tidal inundation. Moreover, environmental factors such as sea level rise, land subsidence, and coastal abrasion—which causes shoreline retreat—further exacerbate the region’s susceptibility to flooding. The rob phenomenon significantly impacts the socio-economic conditions of coastal communities, disrupting daily activities and damaging critical infrastructure such as residential housing and road networks. This study aims to simulate the impact of tidal flooding in terms of inundation depth and spatial extent, using the assumption of the Highest High Water Level (HHWL). The simulation results are intended to serve as an initial reference for the development of coastal flood mitigation strategies. The methodology follows the Technical Guidelines for Disaster Risk Assessment issued by Indonesia’s National Disaster Management Agency (BNPB) and integrates various spatial datasets, including land cover data from Sentinel Land Cover by ESRI, topographic data from the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and maximum tidal height data processed using the Admiralty method. The analysis shows that, assuming a Highest High Water Level of 1.2 meters, Kendal Regency, Brebes Regency, and Semarang City are the most affected areas in terms of both flood depth and extent. The inundated areas are estimated at 3,744.91 hectares in Kendal Regency, 2,880.58 hectares in Brebes Regency, and 513.17 hectares in Semarang City. This situation could become more severe in the event of storm surge, extreme weather, or climate anomalies if timely and effective mitigation measures are not implemented. These findings are expected to provide a strong foundation for policymakers to formulate targeted, data-driven, and sustainable mitigation strategies to protect communities and infrastructure along Java’s northern coastal region.

Change Detection of Topographic Features using Iteratively Reweighted Multivariate Alteration Detection and Random Forest Classification for Partial Updating of Indonesian Topographic Map

2025-07-29T03:55:40+07:00

The current demand for geospatial information is increasingly urgent across various sectors, making the provision of base maps a top priority that is currently being accelerated. However, a major challenges faced today is the outdated nature of the Indonesian Topographic Map (Peta Rupabumi Indonesia/RBI), many of which were produced several years ago and are now considered obsolete. Updating the data is essential to ensure the validity of geospatial information in accordance with current conditions. At present, the detection of change in topographic feature is still largely conducted manually, thereby necessitating the exploration of methods to accelerate partial map updating processes. This study implements a change detection approach using Iteratively Reweighted Multivariate Alteration Detection (iMAD) method, in combination with Random Forest (RF) and Rule-based Classification. The iMAD technique is relatively insensitive to radiometric differences between acquisition times and simultaneously considers all spectral bands. Its iterative process improves accuracy, making it suitable for change detection in partial mas updates. Random Forest Classification supports the interpretation of iMAD results by providing information on changes in land cover types. The iMAD results indicate that the majority of detected changes fall under the ambiguous category (49,18%), followed by unchanged pixels (24,78%), significant changes (20,91%), and agricultural changes (5,13%). Overall accuracy of Random Forest Classification reached 90,45 % in 2019 and 93,20 % in 2023. The Kappa coefficient was 0,8920 and 0,8936 for 2019 and 2023, respectively. The final change detection results, after applying rule-based classification show that 19,70% of the study area experienced change, while 80,30% remained unchanged. Therefore, this approach presents an effective and efficient alternative for conducting partial updates of the Indonesian Topographic Map (RBI).

Land Cover Projection of Jember Irrigation Area Using MOLUSCE QGIS

2025-08-22T03:36:04+07:00

Jember Regency has the third largest agricultural area in East Java Province. However, the agricultural area has decreased due to the expansion of built-up areas in line with population growth. This indicates the need for special attention to controlling the expansion of built-up land in Jember Regency. This study focuses on predicting agricultural land loss and the increase in built-up land in Jember Regency. It examines land cover changes in the regency from 2017 to 2021. Sentinel-2 imagery was used to obtain land cover data for Jember Regency in 2017 and 2021. The 2017 and 2021 land cover maps will serve as reference maps to determine the 2025 land cover using the MOLUSCE plugin in QGIS. The obtained 2025 land cover map will be used to validate the model's accuracy by comparing it with the actual 2025 land cover using Kappa Accuracy. This model's Kappa Accuracy is 91%. The validated model will then be used to predict land cover for 2045. The analysis indicates a predicted reduction in agricultural area of 5.675 hectares and a predicted increase in built-up area in irrigated areas of 6.348 hectares during the 2025–2045 period. Over the next 20 years, irrigation areas under the authority of the regency are predicted to experience the highest growth in built-up land, at 46.1%. This is followed by areas under provincial authority, which are predicted to grow by 34.6%, and areas under central authority, which are predicted to grow by 110% of the total agricultural area in Jember Regency. These findings are important for local governments and stakeholders in land management and urban planning. They also contribute to the monitoring of agricultural land use and the development of effective policy strategies.

Development of Three JS-based 3D Scene with Seamless Visualization of Gaussian Splatting and Transformation to Global Coordinates

2025-09-07T09:47:15+07:00

Existing scholarly literature on the Gaussian Splatting algorithm has predominantly concentrated on improving the rendering and reconstruction of three-dimensional objects, as well as exploring its applications in various academic disciplines, such as medicine, robotics, and mapping, while being limited to local coordinate systems. This study describes the development of a 3D scene modeled using the Gaussian Splatting algorithm, featuring accurate distance and position geometry based on Three JS. The developed 3D scene was then evaluated with precise position and distance coordinates in the field and compared to the established SfM-MVS (Structure from Motion-Multi View Stereo) algorithm. The findings demonstrate that the proposed development successfully generated Three JS-based 3D scenes with global coordinate compatibility utilizing the Gaussian Splatting algorithm, achieving the same level of position and distance accuracy as the SfM-MVS algorithm, with a 95% confidence interval using T-Test. This research concludes that the developed approach is successful and can be further expanded for various scientific fields that require accurate position and distance information using Gaussian Splatting Algorithm.

Refining the Indonesian Geoid Model: A Comparative Study of Global Geopotential Models in East Kalimantan

2025-06-13T02:46:08+07:00

Gravity field along with its derivative, geoid, is one of the important pillars of Geodesy. The geoid is utilized in many countries as the vertical reference system, Indonesia as well. However, Indonesia is unique in topography, made the computation of geoid model throughout the archipelago a challenge. The development of geoid model in Indonesia has 4 phases, with the latest in 2020 and 2023. INAGEOID2020 is the Indonesian geoid model used as vertical reference frame for vertical control in Indonesia, updated to version 2.0 in 2023. However, it has not achieved the target accuracy of 5 cm throughout the country. INAGEOID2020 v2.0 is based on the EGM2008 global geopotential model (GGM) with order and degree 360, which is now nearly 20 years old. The implementation of EGM2008 into the regional model also lacked a fitting process, relying solely on functional calculations. This study proposes using modern GGMs, namely EGM2008, XGM2019e, and SGG-UGM-2, along with a fitting process to improve geoid modeling, to optimize the future iteration of Indonesian Geoid Model. The research compares the gravimetric undulations of these models to geometric undulations at 264 validation points, both with and without fitting in East Kalimantan. The fitting improved the accuracy of EGM2008 and XGM2019e, but SGG-UGM-2 performed worse due to elevation discrepancies both before and after the fitting, mainly due to difference on the starting point close to the coast. XGM2019e at degree 2190, truncated to 720 and 360 showed the best results after the fitting, achieving standard deviation and root mean square error (RMSE) values of 0.061 m and 0.064 m, respectively. The performance of EGM2008 is not far behind XGM2019e. This finding indicates that the XGM2019e is the best out the trio, making it a promising alternative to be utilized for future geoid modeling in Indonesia.

A Look at the Time Series of NDVI and NDWI at a Wildfire Site in California, USA

2025-04-27T13:14:58+07:00

California has experienced some of the deadliest wildfires in the recent years. Thomas Fire is the eighth largest wildfire in the state of California. In this paper, the correlation between vegetation health and canopy water content and wildfire occurrences is analyzed using Thomas Fire site as the case study site. Time series of Landsat 8 OLI and TIRS Level -1 GeoTIFF data during the period of 2013 to was processed in Esri ArcMap 10.7, and Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were computed. The results of the case study demonstrate the potential of Landsat 8 surface reflectance - derived Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) to monitor and identify areas susceptible to wildfire triggers and occurrences.