Tạp chí của Hội Trắc địa – Bản đồ - Viễn thám Việt Nam
In this study, we employed the TELEMAC-2D hydrodynamic model coupled with the SISYPHE sediment transport module to simulate sediment dynamics and riverbed morphodynamic changes of the Hau River over the period 2014–2017. The results show that suspended sediment concentrations (SSC) during the flood season are approximately four times higher than during the low-flow season. Along the river reach, SSC declines progressively from Chau Doc toward Can Tho during the flood period, whereas in the low-flow period the change in SSC longitudinally is minimal. Notably, SSC levels from Chau Doc to the Vam Nao confluence are relatively low compared to the adjacent branch (Tien River), but abruptly increase just downstream of the junction, consistent with the transfer of about 40–50 % of the Tien River’s sediment load. Modelled bed changes indicate predominance of scour over deposition on the Hau River: an average scour depth of –0.68 m corresponds to a mean annual scour rate of –0.23 m/year. Forecasts to 2026 suggest severe bed lowering and formation of deep scour holes, many of which coincide with bank-erosion hotspots. These findings underscore that bed lowering is strongly linked to bank instability and highlight the urgent need for management measures to mitigate bank-erosion risk.
Copyright (c) 2026 Magazine of Geodesy – Cartography
This study evaluates the impact of dredging on the hydraulic regime and channel morphodynamics—erosion and deposition—along the Gianh River reach near the Mai Thanh inland port using the MIKE modeling suite. A linked modeling chain was established: MIKE NAM (rainfall–runoff) to estimate catchment inflows, MIKE 11 HD (1D hydraulics) for routing and for providing boundary conditions to MIKE 21 FM (HD+ST) for 2D hydrodynamic and sedimenttransport simulations. Model parameters were calibrated and validated against representative flood events (2013, 2016), showing high agreement (Nash–Sutcliffe ≈94–96%; mean absolute water-level error ≈0.34–0.45 m), ensuring confidence in scenario simulations. Under adverse flood-season conditions (September–November 2016), comparison of “pre-dredging” and “post-dredging” scenarios shows: (i) a slight increase in mean velocity (~0.6 cm/s), (ii) a reduction in peak flood water level of about 0.15 m, (iii) an increase in unit discharge at the outlet (e.g., at the 15 October flood peak: from 39.58 to 42.97 m³/s/m), and (iv) localized channel-bed changes, with the dredged area tending to infill rapidly after the flood season and maximum bed variations reaching ~2 m. The results imply that dredging can enhance conveyance capacity but entails localized erosion–deposition risks; adherence to approved dredging extents and techniques, together with annual monitoring of channel evolution, is necessary to inform protective measures against bank erosion and to maintain channel stability.
Study of the Earth's crustal structure in the Area of Kon Tum and It's adjacent using boundary determination method and 3D inversion from gravity data
This paper presents researching results of the application of maximum horizontal gradient of gravity anomaly and analytic signal to determining fault systems. The analytical workflow of the 2D modeling problem is used as input data for constructing an initial 3D model and performing 3D inversion to determine the depths of the crystalline basement and the Moho surface. The results have shown that: The fault system in the study area is quite complex, including the Northwest - Southeast, sub-meridian and Northeast - Southwest fault systems. Large and deep faults have been identified and recognized such as: Song Po Co, Ba To - Kon Tum, Song Ba, Hung Nhuong - Ta Vi; The depth of the crystalline basement in the area ranges from 1km to 3.4km, with the location of the basement reaching great depths in the Northwest and Southeast of the area; The depth of the Moho surface changes continuously from 27km to 35km. The deepest location of the Moho is in the Northwest and tends to gradually rise towards the East.
Evaluation and selection of an appropriate Global Geopotential Model over the Territory of Vietnam
The purpose of this study is to evaluate and select the most suitable Global Geopotential Model (GGM) for the territory of Vietnam. Over the past two decades, many GGMs have been developed worldwide. Based on comparisons with GPS/levelling data in various countries, six of the best-performing models were selected for testing. These models were compared with GPS/levelling data from 818 points across Vietnam. The results show that the deviations follow a normal distribution and that the correlation between model-derived height anomalies and GPS/levelling data is very strong. The accuracy of the six models ranges from ±0.298 m to ±0.159 m. Among them, the XGM2019e model is the most suitable for Vietnam, exhibiting the smallest standard deviation and the best correlation with GPS/levelling data. Large deviations are mainly found in the northwestern mountainous region, where levelling measurements are difficult to obtain. The findings of this study can serve as a reference for selecting an appropriate GGM for geodesy applications.
Research on assessing Sentinel 2 to estimate coastal water depth in the region of Phu Tan – Tan Phu Dong – Tien Giang
The coastal zone represents not only a geographical interface between land and sea but also plays a strategic role as a gateway for maritime trade and a catalyst for international economic integration. Among the key geospatial variables, bathymetric information is particularly significant for the development of inland waterway transport networks and the planning of aquaculture zones. Although several high precision methods such as LiDAR bathymetry and sonar have been widely applied, their operational costs remain considerably high. In this context, satellite remote sening, particulartly the use of multispectral imagery, offers a promising and cost-efective alternative for estimating shallow-ưater bathymetry in coastal areas. The availability of Sentinel-2 data, featuring 13 spectral bands, not only ehances the capabilities of natural resource monitoring and management but also opens new avenues for reasearch and application. This study utilizes Sentinel -2 data in combination with band ration and multivariate regression algorithms for bathymetric estimation. Among the tested approaches, the multivariate regression model achieved the highest performance, with a coeficient of determination R2 equals 0.9047, in the experiemental area of Phu Tan commune, Tan Phu Dong district, Tien Giang province.
Analysis of impervious surface expansion trends during urbanization in Long An ward using Sentinel-2 imagery from 2016 to 2025
The urbanization process has led to a significant increase in impervious surface area, reducing the natural water infiltration capacity. On the other hand, urbanization also serves as a driving force for socio-economic development. This study utilizes Sentinel-2 satellite imagery at three time points (2016, 2020, and 2025), combined with the Random Forest algorithm on the Google Earth Engine platform, to classify and map impervious surfaces and analyze urban expansion trends over time in Long An Ward. The results show that impervious surfaces have rapidly increased, from 5.76 km² in 2016 to 7.01 km² in 2020, reaching a peak of 10.46 km² in 2025. Notably, the trend of impervious surface expansion is mainly concentrated in the southeastern and northeastern areas of Long An Ward, as identified through the Urban Expansion Intensity Index (UEII). This approach provides an important data foundation for planning and supports sustainable urban management in the context of increasing urbanization.
Spatial Heterogeneity Analysis and Machine Learning-Based Forecasting of Land Subsidence in Ho Chi Minh City: A GWR and ConvLSTM Integrated Approach
This study develops a framework for simulating and forecasting land subsidence in Ho Chi Minh City, specifically focusing on District 12. By utilizing InSAR time-series subsidence data from 2015-2020 alongside influencing factors such as building density, distance to water bodies, and land use types, the research employs Geographically Weighted Regression (GWR) to analyze the underlying subsidence mechanisms. Experimental results demonstrate significant spatial heterogeneity in land deformation, where Land Use and Distance to Water emerge as the most dominant factors, with average regression coefficients of -0.390 and -0.344, respectively. Furthermore, the study proposes an integrated forecasting system architecture leveraging advanced Machine Learning models, including Random Forest, XGBoost, and ConvLSTM deep learning architectures to predict future surface deformation. Risk zonation results derived from K-means clustering provide effective visual tools for urban planning and early warning systems for geological hazards.
Copyright (c) 2025 Magazine of Geodesy – Cartography
Spatiotemporal Analysis of Urban Surface Cover Structure in Ho Chi Minh City from 2015 to 2025: A Big Data and Machine Learning Approach
Land-use structure transformation in megacities such as Ho Chi Minh City (HCMC) not only reflects rapid economic growth but also constitutes a fundamental driver of geohazards, particularly land subsidence caused by increasing static and dynamic loads. To quantitatively assess this process, the study developed an automated monitoring framework on the Google Earth Engine (GEE) platform, integrating the Random Forest algorithm to process multi-temporal satellite imagery from Landsat 8/9 and Sentinel-2 over 11 years (2015–2025). Accuracy assessment results indicate robust classification performance, with Kappa coefficients ranging from 0.85 to 0.96 and Overall Accuracy between 88.1% and 97.4%. The findings reveal a clear expansion of built-up impervious surfaces, increasing from 5,500.45 ha in 2015 to 6,395.12 ha in 2025. The study successfully captured the spatiotemporal dynamics of five major land-cover classes, highlighting the pronounced growth of “built-up impervious surfaces” and the complex fluctuations of “bare land,” which reflect different construction preparation stages. Statistical analysis shows a strong spatial correlation between impervious surface expansion and areas identified as subsidence-prone. The resulting dataset provides reliable input data for geotechnical models, enabling clearer differentiation between static structural loads and dynamic traffic loads in ground deformation prediction.
Prediction of daily streamflow using adaptive neuro-fuzzy inference systems and group method of data handling approaches: a case study of kone river, binh dinh province
ement, and planning. This study evaluates the performance of the Group Method of Data Handling (GMDH) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models in predicting daily streamflow. Rainfall and streamflow data, collected from rain gauges and hydrological stations in the upstream area of the Kone River in Binh Dinh Province, were used as inputs for the models and tested across various input scenarios. Model performance was assessed using three statistical metrics: the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). The results revealed that the ANFIS model consistently outperformed the GMDH model, achieving the highest R² value of 0.94 and the lowest RMSE (64.6 m³/s) and MAE (14.2 m³/s). Additionally, Scenario 1 demonstrated the best predictive performance across both models. This study successfully developed reliable approaches for daily streamflow forecasting and provided valuable insights into the influence of input variables on prediction accuracy.
Landslide susceptibity mapping using Analytical Hierarchy Process and Fuzzy- Analytical Hierarchy Process approches: A case study in Binh Dinh province, Viet Nam
Binh Dinh Province, Vietnam, has recently experienced frequent landslide events, highlighting the urgent need for effective hazard assessment. This study aims to evaluate landslide susceptibility using the Analytical Hierarchy Process (AHP) and Fuzzy Analytical Hierarchy Process (Fuzzy-AHP) models. Ten conditioning factors were considered in both models: elevation, slope, aspect, Topographic Wetness Index (TWI), Standardized Precipitation Index (SPI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), distance to roads, distance to rivers, and geological characteristics. The resulting susceptibility maps were classified into five categories: very low, low, moderate, high, and very high. Model validation was conducted using the Receiver Operating Characteristic (ROC) curve, with Area Under the Curve (AUC) values exceeding 0.80, Root Mean Square Error (RMSE) values around 0.2, and accuracy scores above 0.8 for both models—indicating excellent predictive performance. Notably, the Fuzzy-AHP model slightly outperformed the AHP model. The analysis revealed that approximately 15% of the area falls within high and very high susceptibility zones, 30% within the moderate zone, and the remaining areas within low or very low susceptibility zones. These findings confirm the effectiveness and reliability of both the AHP and Fuzzy-AHP approaches for landslide susceptibility assessment. The resulting maps provide valuable guidance for local authorities and stakeholders in implementing disaster risk reduction strategies, early warning systems, and sustainable land-use planning.
Mapping wind turbine tower locations in Ca Mau coastal region using Sentinel-1 synthetic aperture radar images
Ca Mau province is a coastal region with strong and fairly stable winds, less affected by storms. By July 2025, Ca Mau province has 13 completed and operational wind farms. The increasing number of wind energy projects shows that monitoring the development of wind energy projects is essential. The objective of this study is to detect the persistent construction structures on sea, specifically wind towers built off the shore of Ca Mau province. This study used the two dimensional polarization space method on Sentinel-1 time-series radar images to identify the locations of wind turbine towers off the shore of Ca Mau province. The results showed that the confounding effects of complex variations in incident and azimuth angles of the Sentinel-1 synthetic aperture radar (SAR) images have been completely eliminated. In addition, the method used in this study was successful in mapping persistent construction structures with Sentinel-1 time-series SAR images. The accuracy of results was evaluated by observing and collecting ground truth data in the study area. With very high accuracy, this method can be applied to map the locations of persistent construction structures at sea at other wind farm projects in the future.
Application of Google Earth Engine to detect land - cover changes in Ben Luc commune, Tay Ninh province, period 2020 - 2025
This study applies the Google Earth Engine (GEE) platform combined with Sentinel-2 imagery to derive land-cover maps and land-cover change maps for Ben Luc commune, Tay Ninh province, in the period 2020 2025. Two dry-season composites for 2020 and 2025 were generated from Sentinel-2 Level-2A data, pre processed on GEE by cloud masking, mosaicking and clipping to the administrative boundary. The images were then classified using the Random Forest (RF) algorithm into four major land-cover classes: bare land, built-up land, water bodies and vegetation. Training and validation samples were derived from high-resolution imagery and field verification. Classification accuracy was assessed using confusion matrices, overall accuracy and the Kappa coefficient. The results show an overall accuracy of 91.05% with a Kappa coefficient of 0.88 for 2020, and 87.58% with a Kappa coefficient of 0.83 for 2025, indicating a high reliability of the classification model. In 2020, bare land and vegetation together accounted for nearly 70% of the commune’s area, while built-up land and water covered smaller proportions. In 2025, bare land decreased by about 657.77 ha, whereas vegetation increased by around 402.50 ha, built-up land expanded by over 124.33 ha and water bodies increased by roughly 130.95 ha. These changes reflect a reduction in unused land, enhanced green coverage and the expansion of built-up space and surface water systems. Based on the two land-cover maps, a land-cover change map for 2020-2025 was generated using overlay analysis in ArcGIS. The resulting layers were stored in a PostgreSQL/PostGIS database and published via GeoServer to build a thematic WebGIS application. The WebGIS enables users to query change types, areas and locations directly on an interactive online map. The findings demonstrate the effectiveness of GEE and Sentinel-2 data for local-scale land cover monitoring, as well as the potential of integrating WebGIS to support land resource management and spatial planning.
Study on the urban heat island phebomenon in the craft village area in Bac Ninh, Vietnam
This study uses Landsat-9 satellite imagery data to analyze the Urban Heat Island (UHI) phenomenon in the craft village area of Bac Ninh, Vietnam. This area is experiencing severe pollution caused by human activities such as dust, water pollution, emissions, and high temperatures. The calculation of Land Surface Temperature (LST) from Landsat-9 proves useful for observing surface temperatures in the study area. LST is processed to determine the extent and scope of the urban heat island, while the study also classifies land cover based on indices such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built up Index (NDBI), and Normalized Difference Water Index (NDWI). Based on these results, the study has shown a significant surface temperature difference of up to 10.2°C between areas with abundant greenery and densely populated areas with houses, especially in production zones that are still concentrated or mixed with residential areas. In comparison, the temperature difference with other areas is 5.8°C. The research findings could provide valuable information on the impact of the UHI effect in the craft village area of Bac Ninh, Vietnam.
The Integration of GNSS/Leveling Data with Global Geopotential Models to Define the Height Reference System in Several Regions of Vietnam
Integrating GNSS and leveling data for height determination in Vietnam requires an accurate geoid model. This study uses GNSS observations and precise leveling benchmarks to adjust global geopotential models to the Vietnamese height datum (VN-2000). We selected 18 first-order leveling benchmarks in the Red River Delta, computing the actual geoid height anomaly N_TC from GNSS ellipsoidal heights (h) and orthometric heights (H). Geoid undulations N_G were computed from five global models (EGM2008, EIGEN-6C4, GECO, SGG-UGM-2, XGM2019e_2159). Differences between N_TC and N_G were minimized using a 3D Molodensky transformation and a second-degree polynomial fit. Results show that XGM2019e_2159 had the smallest initial residuals, consistent with previous findings. After adjustment, all models achieve comparable accuracy (RMSE ≈ 0.04–0.07 m) on independent check points. The adjusted XGM2019e_2159 yields the best performance (RMSE ≈ 0.047 m). This demonstrates that high-degree global models, when transformed to the VN-2000 datum, provide an effective solution for GNSS-based height determination in Vietnam. Keywords: GNSS; precise leveling; geoid; ellipsoidal height; orthometric height; model adjustment; height reference system.
Application of UAV LiDAR Data to Assess the Relationship Between Forest Canopy Structure and Understory Light Environment in the Son Tra Nature Reserve, Da Nang
The three-dimensional (3D) structure of forest canopies plays a critical role in determining habitat conditions for living organisms, in which the heterogeneous distribution of understory light is a key factor directly influencing forest regeneration and biodiversity. However, conventional field surveys and ground-based imaging methods are limited in spatial coverage, posing challenges for comprehensive ecosystem monitoring. This study introduces a methodological framework utilizing LiDAR data acquired from unmanned aerial vehicles (UAVs) to quantify canopy structural heterogeneity and model the relationship between 3D canopy structure and light distribution. The workflow includes collecting high-density LiDAR point clouds, generating digital terrain, surface, and canopy height models (DTM, DSM, CHM), and calculating canopy height and canopy cover metrics. Statistical analyses are then conducted to evaluate the relationships between canopy height, canopy surface structure, and understory light conditions. The results demonstrate that UAV-based LiDAR is an effective tool for high-resolution forest structural mapping, providing valuable potential for applications in sustainable forest management.
Application of Sentinel-1 Radar Imagery for Monitoring Surface Water Changes in Dau Tieng Reservoir (2015–2025) Using Google Earth Engine and Google Colab
The study effectively applied the Otsu algorithm to monitor surface water changes in Dau Tieng Reservoir using Sentinel-1 radar imagery during the period 2015–2025. This method allows for the automatic determination of threshold values, enabling clear separation between water bodies and land surfaces in satellite images. The results show that the water surface area fluctuated significantly over time, with distinct phases such as sharp shrinkage (2015–2016), expansion (2016–2017), rapid increase (2021–2022), decline (2023–2024), and a trend toward stabilization (2024–2025). In addition, the integration of Google Earth Engine for powerful satellite image processing and Google Colab for spatial data visualization has created a flexible, automated, and highly reproducible cloud computing environment, contributing to enhanced water resource monitoring in the context of climate change.
Estimation of Chlorophyll-a Concentration from Landsat 9 Satellite Imagery for Surface Water Quality Monitoring in Urban Management
his study presents a method for estimating chlorophyll-a (Chl-a) concentrations in surface water of the Day River basin using Landsat 9 satellite imagery. The satellite data were acquired in June 2025 and processed using a spectral band ratio between the near-infrared (NIR) and red (RED) bands. A total of 25 surface water samples were collected simultaneously during field surveys to develop and calibrate a simple linear regression model linking the spectral index to measured Chl-a concentrations. The results indicate that the regression model achieved a high coefficient of determination (R² = 0.86), with low estimation error and strong statistical significance (p < 0.001). The spatial distribution map of Chl-a concentrations shows higher values concentrated in the middle and downstream sections of the basin, reflecting nutrient accumulation processes and an increased risk of eutrophication during the early rainy season. The findings demonstrate the effectiveness of remote sensing applications for rapid surface water quality monitoring, supporting environmental management and urban water quality assessment.
Application of GNSS Reflectometry and Machine Learning in Estimation Vegetation Biomass
Biomass estimation plays a vital role in sustainable forest management and global environmental monitoring, especially under the challenges of climate change. Traditional field-based surveys are costly, labor-intensive, and difficult to implement at large scales, while optical remote sensing and synthetic aperture radar (SAR) methods, though effective, remain constrained by specific limitations. Recently, Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a promising alternative, offering continuous, low-cost, and all-weather observations. Reflected GNSS signals from vegetation provide structural and density-related information that can be used to infer biophysical parameters such as biomass. This study evaluates the potential of machine learning algorithms for estimating vegetation biomass in northern Vietnam using GNSS-R data collected by the CyGNSS satellite constellation. Results indicate a strong correlation (up to 0.99) between GNSS-R signal features and biomass density. The findings highlight the effectiveness of GNSS-R combined with machine learning for rapid, scalable, and continuous biomass monitoring, with implications for forest resource management and climate change research.
Environmental Sensitivity Zonation in the Northern Vietnam Coastal Zone
This study emphasizes Environmental Sensitivity Zonation (ESI zoning) of the Northern Vietnam coastal zone, focusing on the Thai Binh – Nam Dinh area. Baseline datasets (shoreline, mangroves, tidal flats, aquaculture, protected areas, and coastal infrastructure) were integrated within a GIS framework and segmented into 500–1,000 m shoreline units to assign Environmental Sensitivity Index (ESI) values. The results indicate that the Xuan Thuy – Giao Thuy mangroves and the Ba Lat and Tra Ly estuaries exhibit very high sensitivity (ESI = 6); tidal flats used for aquaculture and tourist beaches such as Con Vanh and Con Den are classified as high sensitivity (ESI = 5); whereas artificial shorelines and coastal infrastructure show lower to medium sensitivity levels. The ESI zoning effectively identifies priority protection areas and provides a robust scientific basis for coastal management, marine spatial planning, and oil spill contingency planning in the Northern Vietnam coast.
The development of the recurrent adjustment theory in dynamic model deformation displacement analysis
Since the 80s of the last century, Professor Markuze IU.I proposed and developed the theory of recurrent adjustment with outstanding advantages in filtering gross errors and adjusting large geodetic networks. In this paper, the results of research on the development of the theory of recurrent adjustment in the analysis of dynamic model deformation in cycles over time are presented. The scientific basis of this method is the combination of the theory of recurrent adjustment and the theoretical basis of Kalman filtering. The research results can serve to build software to solve Geodynamic problems using Geodetic methods
Integrating Landsat and Sentinel Data to Map Mangroves and Estimate Biomass and Carbon Stocks in Ca Mau, Vietnam
Wetlands are among the most important ecosystems on Earth, playing an essential role in biodiversity conservation, water purification, carbon sequestration, and oxygen production for the atmosphere. In the context of increasingly severe climate change, monitoring and assessing these areas has become critically important. Remote sensing has long been widely applied in the monitoring of natural resources and the environment, especially with the support of freely available data sources such as Landsat and Sentinel. This study focuses on the use of multispectral remote sensing data from Landsat 8 and Sentinel-2 to identify coastal mangrove forest areas in Ca Mau, Vietnam. By calculating vegetation indices such as NDVI, NDWI, etc., the research team estimated above-ground biomass, carbon storage, carbon sequestration capacity, and oxygen concentration. The results include the generation of spatial distribution maps of mangrove forests, as well as maps of carbon and oxygen reserves. These findings contribute to the effective monitoring, conservation, and management of wetland ecosystems, while also providing essential input data for climate and environmental studies in Vietnam.
Modeling Oil Spill Trajectories for Hazard and Risk Assessment in the Coastal Area of Khanh Hoa – Nha Trang
Oil spills pose significant threats to coastal ecosystems and socio-economic activities, yet integrated assessment approaches remain limited in Vietnam. This study presents a combined framework using the OpenDrift oil spill trajectory model and the Environmental Sensitivity Index (ESI – NOAA) to evaluate the potential impacts of a hypothetical spill in Nha Trang Bay. A 15-day simulation demonstrates a predominant southward drift, with oil slicks exposing sensitive habitats such as coral reefs, seagrass beds, and aquaculture areas. By overlaying the modeled spill distribution with the ESI map, we calculated a risk index (RI = CI × SI × EI) that accounts for oil contact frequency, ecosystem sensitivity, and exposure duration. The results highlight several hotspots of very high risk, notably around Hon Mun and the eastern shoreline of Nha Trang Bay. The proposed approach provides a reproducible tool for spatially explicit oil spill risk assessment, supporting contingency planning and environmental protection in Vietnam’s coastal zones.
Development of a filtering solution for outliers in satellite altimetry-derived significant wave heights in the East Sea
The objective of this study is to develop a solution for filtering out significant wave height values that do not meet accuracy requirements from satellite altimetry data in the East Sea. Satellite altimeters typically operate with two frequency bands (e.g., Ku and C), both of which can be used to derive significant significant wave heights. By comparing significant wave heights obtained from these two bands, anomalous values that deviate from statistical probability distributions can be identified. At these anomalous points, the Ku-band–derived significant wave heights are further compared with neighboring values to determine whether they should be discarded or retained. To automate the filtering process, a computer program was developed. The experiment was conducted over the East Sea using 464,324 measurements from 226 cycles of Jason-3 satellite data collected between February 12, 2016, and March 28, 2022. A detailed analysis of cycle 225 revealed 28 anomalous points, of which 6 were removed. For the entire Jason-3 dataset, 460 measurements (approximately 0.1%) were discarded. Although the number of rejected points is small, their removal is necessary. The accuracy of the significant wave heights, evaluated by comparing Ku- and Cband results from Jason-3, was found to be ±0.254 m.
Application of GIS in Compensation and Land Clearance for the Provincial Road 7 Upgrade Project through An Nhon Tay Commune, Ho Chi Minh City
Xây dựng bản đồ nguy cơ trượt lở đất tỉnh Quảng Bình bằng phương pháp phân tích đa tiêu chí (AHP) tích hợp GIS và ảnh viễn thám
WebGIS, geodesic information Ứng dụng WebGIS thành lập hệ thống thông tin hỗ trợ các công tác ngoại nghiệp trong lĩnh vực trắc địa
Foreign surveying and drawing work plays an important role in the field of geodesy, but the data collection process is still fragmented and inconsistent. This study builds and applies a WebGIS system to effectively support field work in field conditions. The system uses a PostgreSQL/PostGIS spatial database to store geographic data, combines HTML, CSS, JavaScript (leaflet) interfaces, and Node.js server processing. Tests in some field areas have shown that the system helps identify measurement points, track progress and coordinate teams flexibly, while contributing to reducing errors, saving time and manpower. The results show that WebGIS is a feasible solution in modernizing geodesy, especially in the context of increasing demand for digitization and spatial information management.
Developing hourly rainfall maps from open meteorological data sources to analyze the spatial extent and temporal evolution of flash flood events
Research on flash floods and related geological hazards has often focused on spatial hazard assessment. However, studies on the temporal evolution of such disasters remain limited, primarily due to the lack of hourly rainfall data. Conventional rain gauge stations fail to capture the full spatial extent of disaster impacts and are typically unevenly distributed, especially in mountainous regions. This paper proposes a novel approach using the R programming language to retrieve, process, and calibrate open-access meteorological datasets in order to generate historical hourly rainfall maps. The proposed method is tested on three extreme rainfall events in Northern mountainous Vietnam: (1) the flash flood on August 1, 2025 in Điện Biên and Sơn La; (2) the flash flood on May 18, 2025 in Tuyên Quang and Bắc Kạn; and (3) the flash flood on August 5, 2023 in Yên Bái and Lai Châu. Results indicate that both the intensity and spatial distribution of hourly rainfall change rapidly. Peak hourly rainfall intensities often exceed 60 mm, and the locations experiencing such rainfall levels are typically those suffering the most severe damage. Notably, the spatial extent of flash flood impacts generally overlaps with areas where 24-hour accumulated rainfall exceeds 200 mm. The workflow for generating hourly rainfall evolution maps provides a reliable basis for delineating disaster-affected areas in alignment with actual damage observations. It also serves as a foundation for developing data-driven tools to establish quantitative thresholds for flash flood early warning systems
Automated Forest Loss Detection Using Machine Learning Algorithms and Sentinel-2 Imagery on Google Earth Engine: A Study in Ca Mau Province, Vietnam
This study presents the development and testing of machine learning models for automated forest loss detection in Ca Mau Province. The entire workflow, from preprocessing Sentinel-2 satellite imagery (Surface Reflectance - L2A) to model training and evaluation, was implemented on the Google Earth Engine (GEE) cloud computing platform. The study applied cloud filtering techniques to obtain images with cloud cover below 40%, while extracting 10 spectral bands and 8 vegetation indices, with delta indices (ΔNDVI, ΔEVI, ΔNBR, ΔSAVI) serving for model training and evaluation. Three machine learning algorithms were employed including Random Forest, Support Vector Machine (SVM), and Gradient Boosting Trees (GBT). The study utilized 2,500 sample points collected with a balanced ratio of 50% forest loss and 50% non-forest loss, and after extracting features for these sample points, the data was divided into 80% for training (train and validation) and 20% for testing, maintaining the 50:50 ratio in each subset. Evaluation results showed that the Gradient Boosting Trees model achieved the highest performance with an overall accuracy of 85.40%, recall of 72.00%, precision of 98.36%, and F1-score of 83.14%. With considerable recall and very high precision, the system demonstrates high reliability when alerting forest loss while detecting the majority of actual forest loss cases. The study successfully developed an automated forest loss detection tool on GEE, providing effective information to support local forest management efforts. However, further research on integrating Sentinel-1 radar data is needed to overcome cloud cover limitations during the rainy season and further enhance detection capabilities.
Application of Persistent Scatterer Interferometric SAR (PSInSAR) Method for Processing Sentinel-1 Images to Detect Surface Deformation in Muong Lay Town, Dien Bien Province
Interferometry (InSAR) is a technique to measure ground subsidence and has ability to building ground subsidence map on a large spatial scalewith high accuracy from space. TThis study presents the Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) method to determine the surface deformation velocity of the terrain in Muong Lay town – Dien Bien province, based on Sentinel-1 ascending orbit data with 50 images acquired from 2018 to 2022. The results reveal surface deformation in the Muong Lay area with 20.493 PS points, ranging from -8.1 mm/year to 7.4 mm/year. The distribution of PS values across residential areas along the Nam Lay stream within Muong Lay town indicates zones experiencing downward subsidence (negative values) as well as uplift (positive values). These findings provide a basis for identifying the causes of terrain surface changes and for proposing preventive measures to mitigate damages in Muong Lay town, Dien Bien province, in future in-depth studies.
Urban tree extraction from UAV Imagery using deep learning techniques for urban management applications
This paper proposes a method that leverages deep learning techniques to extract urban trees from high-resolution UAV imagery. UAVs play a crucial role in capturing both nadir and oblique images with high geometric detail, facilitating effective modeling of the three-dimensional spatial characteristics of urban vegetation. The core of the proposed approach lies in its ability to automatically identify and segment trees based on learned visual features, particularly the shape and size of tree canopies, which often exhibit irregular boundaries and natural spatial distribution, distinct from man-made objects. The resulting output is a spatial dataset that accurately reflects the location and geometric structure of trees, effectively supporting urban green space management, analysis, and planning. The method demonstrates high feasibility, automation capability, and scalability, contributing to the development of intelligent and sustainable urban management systems.