Geoinformatics for Spatial-Infrastructure Development in Earth and Allied Sciences

Currently, in the context of climate change and socio-economic development, the issues of water scarcity and the increasing salinization of groundwater in regions with limited water resources are of substantial concern. In response, the Prime Minister has authorized a program focused on investigating and exploring groundwater sources to fulfill domestic water requirements in these regions, intending to expand its implementation scope. Among the areas profoundly affected by these challenges lies the Mekong Delta (MKD). Internationally and domestically, a range of solutions have been proposed and examined in research settings to tackle these problems. Nevertheless, specific solutions have proven to be costly, technically intricate, or incompatible with local customs, resulting in the inefficient utilization of resources. Consequently, the primary objective of this study is to suggest suitable and sustainable approaches to alleviate salinization in groundwater extraction within water-scarce regions of the MKD. The research methodology encompasses comprehensive data collection and supplementary surveys to evaluate groundwater salinization’s present status and root causes in water-scarce areas. One such solution is the implementation of Aquifer Storage and Recovery (ASR) technology, involving the recharge of abundant freshwater from sand dunes along the Mekong Deltaic coast during the rainy season. This freshwater is then stored in deeper, saline upper Pleistocene aquifers and subsequently extracted during the dry season to meet water supply needs. Based on available data from prior research, additional investigations and a thorough analysis of influencing factors have been conducted to delineate potential areas for the ASR solution using GIS overlay mapping techniques, which have been delineated into 03 areas with low, medium, and high applicable. A pilot project at the household level in My Chanh village, Chau Thanh district, Tra Vinh province, has been tested and validated as a potential area for this solution. The results from the pilot project and simulation outcomes affirm the promise of the identified potential areas for the ASR solution, warranting further investment.

In recent years, economic growth has led to massive urban sprawl, intensive agricultural production, and environmental degradation and losses in Vietnam. In the Lower Mekong Basin, including a part of Central Highland of Vietnam, agricultural production, urban growth, unsustainable resources management (water and soil), and industrial activities are the potential polluters to the regional environment. Environmental taxes and fees based on the ‘Polluter Pays Principles” (PPP) are the key to regulating human interference in the environment. However, PPP’s efficiencies are dependent on the ability to track the pollutants and polluters using spaceborne/airborne monitoring sensors, ground stations, and field sampling. In Vietnam’s rural and mountainous areas, monitoring networks and data are scarce despite efforts to invest in environmental monitoring system for years. In this research, we examined the use of bio-optical models (i.e., Ocean Colour (OC) and Case 2 Regional Coast Colour (C2RCC) methods) and remotely sensed analyses (i.e., Landsat 5, 7, 8 & 9 data) for the case of the environmental incidents in the Yali hydropower dam, Chu Pa, Gia Lai, Vietnam in 2022 and 2009. The research’s outputs emphasized the need to revise the approach for environmental monitoring and environmental protection in Viet Nam and transboundary data sharing for water governance in the Mekong Region.

Municipal solid waste (MSW) incinerators that generate electricity are one of the most effective solutions for solid waste treatment in urban areas of Vietnam. However, the flue gas exhausted from waste-to-energy (WtE) plants may affect ambient air quality and public health. This study evaluated the emissions from the Soc Son WtE plant in Vietnam during the trial operation period using the AERMOD model and field measurements. Air samples were collected at six locations, 200–2500 m from the plant’s stack, in four different directions to measure total suspended particles (TSP), nitrogen dioxide (NO₂), carbon monoxide (CO), and sulfur dioxide (SO₂) levels. The concentrations of TSP, NO₂, CO, and SO₂ extracted from the model were well correlated with those collected from the field measurements, with correlation coefficients above 0.80 for all pollutants. This indicates that the AERMOD model is appropriate for modelling point sources in WtE plants. The concentrations of TSP and gases were highest at a distance of 200–300 m from the plant’s stack, exceeding national and international standards. The diffuse pollution of the gases was mapped with the scenario of the maximum operating capacity of the plant. The assessment of the human health risk using the estimated concentration from the model provided a scientific basis for air quality management and public health. The low health risk of pollutants at a distance of 1000 m from the factory stack indicates that the plant’s exhaust gas treatment system was operating efficiently.

Groundwater loss is one of the primary factors contributing to the degradation of water resources in terms of both quantity and quality. This phenomenon is of particular concern in coastal sandy areas where groundwater in sand dunes is a main source of fresh water for human activities and production. This study includes surveys and field measurements conducted throughout 2022 in the coastal sandy area of An Hoa Hai commune, Tuy An district, Phu Yen province, to identify types of groundwater loss in sand dunes and the level of loss of each type. The results based on the analysis of hydrogeological structures, groundwater monitoring, seepage measurements using seepage meters, and groundwater evaporation measurements using lysimeters over the course of a year (2022), it was found that freshwater for domestic use and production in the study area is obtained from a Holocene unconfined aquifer (qh) in coastal sand dunes. The natural recharge source for this aquifer is mainly rainwater. Specifically, the total amount of natural recharge in 2022 for this aquifer is approximately 1,406,261 m³. There are two main types of loss: loss to the sea and loss due to groundwater evaporation. The previous amounts to approximately 413,097 m³, accounting for 29.4% of the natural recharge, and the latter accounts for 537,264 m³, representing 38.2% of the natural recharge. Total groundwater loss in the study year 2022 took over 67.6% of the natural recharge.

Drought is a naturally occurring event associated with a significant decrease in water availability over a region. Changes in hydrological conditions in the area, such as climatic changes, especially rainfall, can lead to droughts and floods, which have many negative impacts on life and nature. Adaptation to natural disasters, such as drought, can be effective through an improved understanding of disasters. Understanding their effects is of widespread concern and a great challenge to researchers and policymakers. Comprehensive hazard analysis will deepen the understanding of disasters. Many factors influence drought hazards, such as precipitation, temperature, flow and water balance, soil structure, and land cover. In the Cai-Phan Rang River basin, South Central Coast of Vietnam, the changes in water availability are significant concerns since they often lead to drought, forest fires, reduced agricultural productivity, poverty, and food insecurity. This study uses the Standardized precipitation index (SPI) to analyze the drought characteristics, including Probability, Intensity, and Duration; then apply the analytical hierarchy method (AHP), a multi-parameter modelling, to evaluate drought hazard in the Cai - Phan Rang River basin. The drought hazard zoning map was made based on the weight of the selected indicator using GIS (geographic information system). The results showed that drought has expanded and been prolonged in recent years, although the frequency is not significantly changed compared to the past. In general, low rainfall and high temperatures in the southeast regions of the Cai-Phan Rang River basin pose a high drought risk. Due to topographical characteristics, although located in the vicinity of low-elevation streams, the narrow delta area in the central valley of the basin is the area with the most elevated hazard. Besides, the local livelihood is closely dependent on agriculture and aquaculture, so this region should be prioritized in drought response strategies.

Drought is one of the most damaging natural phenomena. Due to their extensive geographic coverage, monitoring droughts using conventional systems can be challenging. In recent years, Kon Tum province, Central Highland of Vietnam, has been experiencing severe droughts and water shortages during the dry season, causing great damage to agriculture, daily life, and the local economy. This paper presents the method of using Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) from band 4, band 5 and band 10 of LANDSAT 8 in the exhausted period from February to April in the period 2014 - 2023 to calculate the Water Supplying Vegetation Index (SWVI) and Temperature Vegetation Dryness Index (TVDI). The processing and analysis of remote sensing images were conducted on a cloud-based platform, specifically Google Earth Engine (GEE), in conjunction with ArcGIS software. The research results have shown that drought-prone areas correspond to regions with increased LST and decreased NDVI value. In addition, based on the scale of drought severity, SWVI and TVDI have demonstrated that the majority of communes within two districts experienced drought conditions during the severe drought years of 2018 and 2020. Among these, Dak To Lung, Dak To Re and Dak Ruong communes in Kon Ray district are the three communes most severely affected by drought.

The primary emphasis of this investigation centres on the application of Sentinel-2 satellite imagery to evaluate water quality indicators, with a focus on the concentration of suspended particulate matter (SPM) and turbidity. This analysis is based on optical models and is conducted within the context of Lake Varese. The research methodology encompasses the initial processing of satellite images utilizing ACOLITE software to perform atmospheric correction. The analysis of the results involves a detailed inspection of the spatial patterns and trends detected within the maps illustrating SPM concentration and turbidity. Furthermore, the study delves into the investigation of relationships between the derived parameters and local precipitation patterns in the region. In summary, the integration of Sentinel-2 satellite imagery, atmospheric correction through ACOLITE, and the application of bio-optical models for assessing SPM concentration and turbidity has demonstrated effectiveness in the evaluation of water quality parameters within Lake Varese. The spatial distribution maps, temporal patterns, and correlation analyses yield valuable insights into the evolving dynamics of water quality in this lake. These findings enhance our comprehension of the ecological condition of Lake Varese and offer support for informed decision-making in the realm of water quality management.

Submerged reefs are among the most unique environments for wave reduction globally due to their capacity to generate high bed drag forces. However, constructions on offshore coral reefs, such as sea dikes and sea walls, which serve to protect national sovereignty and foster economic development, have recently altered the wave dynamics of these reefs. This study examines changes in wave characteristics on a coral reef before and after a dike’s construction. A total of 24 experimental scenarios were conducted in a wave flume at Thuy Loi University, Hanoi (Vietnam), including cases with the presence of a dike. The results indicate a significant increase in wave heights attributable to the dike’s presence on the reef, with increases ranging from 10% to 30% due to structural obstacles and the effects of relative shallowness. Additionally, the study provides an empirical formula to predict incoming wave heights, which is intended to aid in the design of structures on submerged reefs.

The coastlines from Bac Lieu to Ca Mau are the gateway to the Mekong Delta region and are essential for the country’s economic development. However, this area is currently facing increasingly complex erosion threats. Developing shoreline maps is necessary for environmental monitoring in the area. This study applies remote sensing methodology, GIS tools and ENVI software to extract the shoreline over the years and then analyze the accreted/eroded areas. The results show that erosion along the coasts of Bac Lieu and Ca Mau in three years, 2021– 2023, has become increasingly complicated. Among the investigated coastal areas, the most erosional area occurred in the Ngoc Hien district and surrounding areas in the Ca Mau province. While the coasts of Bac Lieu province tend to accrete, such as Dong Hai district and Bac Lieu city, erosion occurs in Dat Mui commune (Ngoc Hien district). The application of remote sensing and GIS methods contributes informatively to the local authorities in finding proper solutions to prevent and limit coastal erosion as well as to regulate work for coastal zone planning and management.

Assessing the Above Ground Biomass (AGB) is vital for a better awareness of forest carbon sequestration potential. Traditional field-based methods for measuring the AGB are often long-lasting, expensive, and limited in their spatial scope. Recent studies have revealed that using a universal regression model to quantify AGB at a global or country extent is not feasible. Instead, it is necessary to create local regression models that can accurately estimate AGB on a provincial scale. However, the pattern of AGB in a deciduous broadleaf forest in Dak Lak province is currently unknown. Therefore, this study aims to develop regression analysis-based predictive models specifically for evaluating the spatial pattern of AGB in a deciduous broadleaf forest in Dak Lak province. The multiple bands and vegetation indices derived from Landsat 8 imagery were employed as independent variables, while AGB measurements were defined as the dependent variable. The statistical analysis results indicate a strong correlation between vegetation indices and measured AGB data, confirming the effectiveness of using Landsat 8 imagery for assessing AGB. The models show reasonably good performance, achieving R² values varying from 0.61 to 0.62 and RMSE values varying from 30.68 to 31.54. The estimated AGB in the forests averages 100.80 Mg/ha with a standard deviation of 44.27. The regression model-derived spatial distribution of AGB in Dak Lak reveals the variation in AGB across the forest area, highlighting areas with high and low AGB in the province. This map can serve as baseline information for future AGB estimates in the area, contribute to carbon sequestration monitoring efforts, and support improved local forest management practices.

Land-cover change, particularly habitat loss and fragmentation, poses significant threats to ecosystem services and biodiversity conservation. In the context of northwestern Vietnam, major land-cover changes occurred in the late 20th century, but their impacts on forests have not been quantified comprehensively. This study aimed to address this gap by selecting an appropriate landscape metric index for monitoring forest fragmentation and characterizing forest transitions over time, focusing on the case study in Muong La district, Son La Province, from 1990 to 2018. The study utilized satellite images and an aggregation metric at the class level to detect land-cover change and examine forest distribution patterns over the specified period. The results revealed a significant decrease in forested areas in Muong La, with forest cover declining from 77% to 64%. The main drivers of forest loss were identified as the expansion of agricultural land and the construction of the Son La Dam water reservoir. The analysis of forest transitions indicated that forest areas in Muong La became more isolated and less compact from 1990 to 2008. However, by 2018, there was a trend towards increased aggregation of forested areas. These findings provide valuable insights into the dynamics of forest fragmentation and highlight the importance of monitoring and understanding such changes over time. The research results contribute to identifying the most threatened forested areas and informing prioritization efforts for natural conservation and land-use planning by the local government. By utilizing the selected landscape metric index and analyzing forest transitions, this study provides a technique for assessing the extent and location of forest threats, supporting targeted conservation efforts and sustainable land management practices.

The Mekong Delta region is important to Vietnam’s economy, agriculture, and ecosystem. Salinity has significantly impacted the region’s living environment, livelihoods, and productive activities. Therefore, salinity prediction maps are important in agriculture, environmental management, water resource planning, and ecosystem conservation since they can provide information about the spatial distribution and variability of salinity levels in a specific area. Landsat 8 satellite image data allows for the extraction of many salinity indicators. However, not all these indicators are necessary for salinity prediction studies. This study aims to contribute to selecting suitable input indicators to enhance the accuracy and precision of salinity prediction modelling. In paper utilize advanced machine learning techniques, including Bayesian Model Averaging, Extreme Gradient Boosting, Bagging, and Random Forest, to select the salinity prediction model and evaluate the relative importance of salinity indicators. The results obtained from the XGBoost model indicate that 18 out of the 20 input variables in the first optimal model made a significant contribution. These variables include two coordinate variables, 12 time-related variables, and four variables derived from the Landsat 8 OLI images. The performance evaluation of the selected salinity prediction model was conducted using statistical indicators of Root Mean Square Error (RMSE), determination coefficient (R²), and Mean Absolute Error (MAE) for both Random Forest and Bagging models. The salinity intrusion map for the lower Mekong Delta was provided using the Random Forest model.

Surface displacement in major cities occurs for various reasons, including groundwater extraction and urbanization. The changes in groundwater levels and load construction on the land have affected geological structures, causing surface displacement. Many studies have been studying and utilizing the InSAR technique to monitor and measure the velocity of surface displacement on the Earth’s surface across a wide range. In this article, the authors used Sentinel-1A image time series and the PSInSAR (Persistent Scatterer Interferometric Synthetic Aperture Radar) approach to monitor surface displacement in Hanoi’s inner city from 2018 to 2019. The results indicate that the study area experiences surface displacement velocities ranging from −15.3 mm to +18.5 mm per year. The accuracy of the method is assessed by utilizing 55 levelling measurement points across two distinct areas: 43 of these points are located at buildings, and the 12 remaining are situated at groundwater stations. The correlation coefficient (R²) between persistent scatterer (PS) points and levelling points is greater than 0.8, with a value of 0.847 in the building area and 0.859 in groundwater stations. Simultaneously, the study results indicate that there is the minimal surface displacement at building locations in the central districts. Surface displacement is particularly high in Hanoi’s western and southern districts, including Thanh Xuan and Hoang Mai.

The rapid urban expansion and development of Hanoi, the capital of Vietnam, in recent times have presented challenges for managers in monitoring, evaluating, and rationally managing natural resources. This study monitors urban expansion in the centre of Hanoi using GIS and remote sensing techniques based on machine learning algorithms. Satellite images, including Landsat-8 and Sentinel-2, are employed as data for the study. Additionally, the study utilizes the Support Vector Machine (SVM) algorithm to classify land use/land cover and monitor their changes during the period from 2013 to 2023. The results of this study show that the built-up area expanded in different directions during each period. Especially in the last 5-year period, the built-up area expanded rapidly, continuously, and mainly to the East of the centre of Hanoi, while in the previous period, it expanded much to the West. In the past 10 years, the built-up area has increased by approximately 11.56 square kilometres, of which 5.36 and 6.20 square kilometres were increased in the period 2013–2018 and 2018–2023, respectively. The results effectively contribute to urban planning and management, monitoring of environmental protection management, and sustainable development.

The precise and prompt categorization of land cover types holds significant importance in the realm of land resource planning and management, as well as in risk reduction. The utilization of hyperspectral satellite imagery, such as the imagery delivered by PRISMA, plays a vital role in analyzing environmental changes. Even though PRISMA products are distributed at Preprocessing Level 2D (radiometrically and geometrically calibrated), the images may exhibit registration errors on the order of a few hundred meters. Therefore, co-registration is a crucial preprocessing step before their utilization. This study utilized a local co-registration method based on the optical flow estimation technique to co-register the PRISMA images using Sentinel-2/Landsat 8–9 as references. The results showed that a careful selection of an appropriate reference image holds immense importance in the co-registration process, and the closer the acquisition time of the reference image is to the acquisition time of the image to be co-registered, the higher the quality of the co-registration results. By integrating cutting-edge machine learning techniques, the proposed co-registration approach further enhances the usability and accuracy of PRISMA products for land cover classification, and makes them a valuable source of information for applications in land management and thematic hazard studies, including scenarios such as flood monitoring and landslide analysis.

Air quality, human health, industry activity, and regional sustainable development are all at risk from Aerosol Optical Depth (AOD), which is a reflection of optical attenuation. Understanding these driving factors is essential to the decrease in AOD. The purpose of this paper is to investigate the relative importance of driving factors of AOD in Hanoi city (Vietnam) using remotely sensed imagery, remote sensing and multilayer perceptron (MLP) neural networks. For this purpose, a total of nine driving factors, including natural factors (Digital Elevation Model-DEM, slope, aspect, the Modified Normalized Difference Water Index-MNDWI, and the Soil Adjusted Vegetation Index-SAVI), social factors (population density, distances to roads, and Normalized Difference Built-up Index-NDBI) and meteorological factors (Column Water Vapour-CWV) were used to assess their effects on the AOD variation. The AOD and CWV variables were first retrieved from the MODIS product. Landsat-9 OLI images were used to derive SAVI, NDBI, and MNDWI. The importance of driving factors of AOD variation was finally investigated using the MLP neural networks and Garson’s algorithm. Results show the high importance of population density and DEM in the AOD variation, followed by CWV, slope, distances to roads, MNDWI, and NDBI. The importance of vegetation, approached by the SAVI, appears to be less influential. The results of this investigation provide important insights into how to control the factors that influence AOD variation in urban areas.

As the real estate market expands, the necessity for swift and accurate land appraisal within specific market conditions becomes increasingly pertinent and socially significant. Among various methods for land appraisal, machine learning emerges as a novel approach, and the number of machine learning algorithms has rapidly increased over the past decade. Consequently, it is necessary to conduct comparative studies on the performance of various machine learning algorithms for a mass appraisal of land. The objective of this study is to evaluate and compare five machine learning algorithms, such as Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting (GB), and Extreme Gradient Boosting (XGBoost). The dataset for training and testing comprises 1082 observations spanning 12 districts of Hanoi City, encompassing 20 features related to physical, locational, legal, and social properties of the land. The results show that RF and GB are, overall, the best algorithms in terms of accuracy metrics and correspondence of feature importance for land price with correlation analysis. The locational features of land parcels (Location, Zone, CityCenter) and street features (RoadWidth, RoadType) have the biggest impact on land price. These findings underscore the potential of machine learning applications in the mass appraisal of urban residential land, primarily when supported by a substantial training dataset.

Choosing a waste disposal site has a great impact on urban planning in terms of its economy, environment, and residents’ health. Hanoi is the capital of Vietnam, a city with a large area and a dense population. However, currently, there are only two waste disposal sites: Nam Son and Xuan Son landfills. As a result, the amount of waste dumped into these two landfills is excessively high, leading to an overload situation. To address this issue, the Hanoi authority plans to construct 17 solid waste treatment areas covering an area of 430 hectares. However, there are conflicting opinions about the proposed locations for waste treatment areas, as some believe that those sites are not yet optimal. To determine the most suitable location for the solid waste landfill in Hanoi, the study combined Geographic Information System (GIS) and Multi-Criteria Analysis (MCA) to assess the importance level of each criterion for the location selection. The research results indicated that the Nam Son waste treatment area was evaluated as the most optimal location. Among the remaining 16 locations proposed by the city’s authorities in the planning document, 6 locations were deemed suitable, while the rest were considered less suitable according to the evaluation.

This paper introduces the characterization of changes in topographic parameters occurring due to slope failure by using Digital Elevation Models (DEMs) of different periods. The topographical parameters of the slope failure sites were calculated based on the DEM collected before and after the slope failure. The slope failures were triggered by heavy rainfall that occurred on the 16^th and 17^th August 2014. The difference in DEMs, pre-event and post-event of the slope failure revealed areas of erosion and sedimentation of less than 5 m that occurred frequently and can be classified as shallow landslides. The slope angle showed an overall tendency to become steeper, both in the scarp and the main body of the landslide. A comparison of landforms of failure sites before and after slope failure suggests that the flat slopes have changed to hollow or spur-type terrain forms. Further, the terrain Convergence Index (CI) was higher due to the occurrence of slope failure and the valley topography becoming more developed. Change Vector Analysis (CVA) of geomorphological parameters revealed that it has become easier not only to identify the slope failure location but also to detect changes in the slope morphology. The results obtained in this study can aid in the automatic detection of slope failure. Further, the studies also show good potential to generate a more detailed and descriptive slope failure inventory, including information on the location, shape, morphology, and flow direction, which will be useful for understanding and predicting future failures.

Deformation monitoring is crucial for structural health monitoring and early warning of natural hazards such as landslides and tsunamis. Given the requisite accuracy at the millimiter or centimeter level, networks of Global Navigation Satellite System (GNSS) receivers with static surveying have been widely adopted for deformation monitoring. However, the primary limitation of static networks is that they typically require post-processing and involve complex adjustment procedures. This study introduces a solution employing a clustered GNSS RTK network that utilizes continuous operating reference stations (CORSs) to offer an alternative strategy for real-time deformation monitoring. In this approach, virtual reference stations are generated and combined with physical monitoring stations to form triangulations, thereby enabling redundancy for cluster adjustment even when fewer than three physical GNSS receivers are used. Data processing is conducted using the Extended Kalman Filter (EKF) and cluster adjustment techniques. Two experiments were conducted to evaluate the performance under different testing conditions and data processing strategies. The results indicate that the cluster adjustment enables the output solution at a 1 Hz frequency, with significantly improved performance compared to traditional GNSS RTK and EKF methods.