Thuong V. Tran

Monash University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Estimating Soil Water Susceptibility to Salinization in the Mekong River Delta Using a Modified DRASTIC Model

Water (2021)

Thanh N. Le Duy X. Tran Thuong V. Tran Sangay Gyeltshen Tan Van Lam

Saltwater intrusion risk assessment is a foundational step for preventing and controlling salinization in coastal regions. The Vietnamese Mekong Delta (VMD) is highly affected by drought and salinization threats, especially severe under the impacts of global climate change and the rapid development of an upstream hydropower dam system. This study aimed to apply a modified DRASTIC model, which combines the generic DRASTIC model with hydrological and anthropogenic factors (i.e., river catchment and land use), to examine seawater intrusion vulnerability in the soil-water-bearing layer in the Ben Tre province, located in the VMD. One hundred and fifty hand-auger samples for total dissolved solids (TDS) measurements, one of the reflected salinity parameters, were used to validate the results obtained with both the DRASTIC and modified DRASTIC models. The spatial analysis tools in the ArcGIS software (i.e., Kriging and data classification tools) were used to interpolate, classify, and map the input factors and salinization susceptibility in the study area. The results show that the vulnerability index values obtained from the DRASTIC and modified DRASTIC models were 36–128 and 55–163, respectively. The vulnerable indices increased from inland districts to coastal areas. The Ba Tri and Binh Dai districts were recorded as having very high vulnerability to salinization, while the Chau Thanh and Cho Lach districts were at a low vulnerability level. From the comparative analysis of the two models, it is obvious that the modified DRASTIC model with the inclusion of a river or canal network and agricultural practices factors enables better performance than the generic DRASTIC model. This enhancement is explained by the significant impact of anthropogenic activities on the salinization of soil water content. This study’s results can be used as scientific implications for planners and decision-makers in river catchment and land-use management practices.

Saltwater intrusion

Desertification

River delta

Vulnerability

10.3390/w13121636

Cite

Citations (16)

Characterizing the spatial distribution of coral reefs in the South-Central Coast region of Viet Nam using Planetscope imagery

PeerJ (2021)

Khanh V. Nguyen Vinh Duong Kinh Thi Kieu Thuong V. Tran Cho‐ying Huang

This study aims to understand the spatial distribution of coral reefs in the central region of Viet Nam. We classified live coral cover in Son Tra Peninsula (ST) and Cu Lao Cham Island (CLC) in the South-Central Coast Region of Viet Nam using the Maximum Likelihood Classifier on 3 m Planetscope imagery. Confusion matrices and the accuracy of the classifier were assessed using field data (1,543 and 1,560 photographs in ST and CLC, respectively). The results showed that the reef's width ranged from 30 to 300 m across the study site, and we were able to detect live coral cover across a depth gradient of 2 to 6 m below the sea surface. The overall accuracies of the classifier (the Kappa coefficient) were 76.78% (0.76) and 78.08% (0.78) for ST and CLC, respectively. We found that 60.25% of coral reefs in ST were unhealthy and the live coral cover was less than 50%, while 25.75% and 11.46% of those in CLC were in good and excellent conditions, respectively. This study demonstrates the feasibility of utilizing Planetscope imagery to monitor shallow coral reefs of small islands at a high spatial resolution of 3 m. The results of this study provide valuable information for coral reef protection and conservation.

Viet nam

10.7717/peerj.12413

Cite

Citations (1)

Assessing Spatiotemporal Drought Dynamics and Its Related Environmental Issues in the Mekong River Delta

Remote Sensing (2019)

Thuong V. Tran Duy X. Tran Soe W. Myint P. Carmona Duan Dinh Ho

Drought is a major natural disaster that creates a negative impact on socio-economic development and environment. Drought indices are typically applied to characterize drought events in a meaningful way. This study aims at examining variations in agricultural drought severity based on the relationship between standardized ratio of actual and potential evapotranspiration (ET and PET), enhanced vegetation index (EVI), and land surface temperature (LST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform. A new drought index, called the enhanced drought severity index (EDSI), was developed by applying spatiotemporal regression methods and time-series biophysical data derived from remote sensing. In addition, time-series trend analysis in the 2001–2018 period, along with the Mann–Kendal (MK) significance test and the Theil Sen (TS) slope, were used to examine the spatiotemporal dynamics of environmental parameters (i.e., LST, EVI, ET, and PET), and geographically weighted regression (GWR) was subsequently applied in order to analyze the local correlations among them. Results showed that a significant correlation was discovered among LST, EVI, ET, and PET, as well as their standardized ratios (|r| > 0.8, p < 0.01). Additionally, a high performance of the new developed drought index, showing a strong correlation between EDSI and meteorological drought indices (i.e., standardized precipitation index (SPI) or the reconnaissance drought index (RDI)), measured at meteorological stations, giving r > 0.7 and a statistical significance p < 0.01. Besides, it was found that the temporal tendency of this phenomenon was the increase in intensity of drought, and that coastal areas in the study area were more vulnerable to this phenomenon. This study demonstrates the effectiveness of EDSI and the potential application of integrating spatial regression and time-series data for assessing regional drought conditions.

Moderate-resolution imaging spectroradiometer

Enhanced vegetation index

Condition index

10.3390/rs11232742

Cite

Citations (38)

Groundwater potential zones using a combination of geospatial technology and geophysical approach: case study in Dehradun, India

Hydrological Sciences Journal (2019)

Sangay Gyeltshen Thuong V. Tran Goutham Krishna Teja Gunda Suresh Kannaujiya R. S. Chatterjee

A combination of geospatial, geophysical and statistical models using satellite data, the weighted index overlay (WIO) method and two-dimensional electrical resistivity tomography (2D-ERT) is applied to generate the highest potential groundwater area and to further explore the groundwater in Dehradun, India. The results show that of 19.7 km2 total basin area, 0.26% falls under the “poor” category as a prospect zone for groundwater, 4.3% is “moderate”, 10.10% “moderately good”, 4.9% “good” and 0.17% “very good”. In addition, the demonstration of the geophysical survey is presented, in which Purkal Youth Society Division (PYSD) site is categorized as a shallow aquifer zone and the Guru Nanak Fifth Centenary School (GNFCS) site is a deeper aquifer zone. Our study emphasizes remote sensing and geographic information system integrated with a geophysical survey to support prospecting the most probable area and confirm the existence of groundwater.

Vertical electrical sounding

Electrical Resistivity Tomography

Geomatics

10.1080/02626667.2019.1688334

Cite

Citations (43)

A Review of Spectral Indices for Mangrove Remote Sensing

Remote Sensing (2022)

Thuong V. Tran Ruth Reef Xuan Zhu

Mangrove ecosystems provide critical goods and ecosystem services to coastal communities and contribute to climate change mitigation. Over four decades, remote sensing has proved its usefulness in monitoring mangrove ecosystems on a broad scale, over time, and at a lower cost than field observation. The increasing use of spectral indices has led to an expansion of the geographical context of mangrove studies from local-scale studies to intercontinental and global analyses over the past 20 years. In remote sensing, numerous spectral indices derived from multiple spectral bands of remotely sensed data have been developed and used for multiple studies on mangroves. In this paper, we review the range of spectral indices produced and utilised in mangrove remote sensing between 1996 and 2021. Our findings reveal that spectral indices have been used for a variety of mangrove aspects but excluded identification of mangrove species. The included aspects are mangrove extent, distribution, mangrove above ground parameters (e.g., carbon density, biomass, canopy height, and estimations of LAI), and changes to the aforementioned aspects over time. Normalised Difference Vegetation Index (NDVI) was found to be the most widely applied index in mangroves, used in 82% of the studies reviewed, followed by the Enhanced Vegetation Index (EVI) used in 28% of the studies. Development and application of potential indices for mangrove cover characterisation has increased (currently 6 indices are published), but NDVI remains the most popular index for mangrove remote sensing. Ultimately, we identify the limitations and gaps of current studies and suggest some future directions under the topic of spectral index application in connection to time series imagery and the fusion of optical sensors for mangrove studies in the digital era.

10.3390/rs14194868

Cite

Citations (96)

Examining spatiotemporal salinity dynamics in the Mekong River Delta using Landsat time series imagery and a spatial regression approach

The Science of The Total Environment (2019)

Thuong V. Tran Duy X. Tran Soe W. Myint Cho‐ying Huang Hoa Viet Pham

Dryland salinity

10.1016/j.scitotenv.2019.06.056

Cite

Citations (53)

Characterising spatiotemporal vegetation variations using LANDSAT time‐series and Hurst exponent index in the Mekong River Delta

Land Degradation and Development (2021)

Thuong V. Tran Duy X. Tran Ho Nguyen P. Carmona Soe W. Myint

Abstract Spatiotemporal analysis and monitoring of vegetation help us investigate ecological health and guide better forest conservation and land management practices for sustainable development. This paper proposes the use of spatial analysis approaches (i.e., ordinary least squares [OLS] and the Hurst exponent) combined with time‐series analysis using enhanced vegetation index (EVI) data, derived from LANDSAT via the Google Earth Engine, to estimate the trends and sustainability of vegetation dynamics in the Tra Vinh Province in the Mekong River Delta. We also assessed the EVI changes connected to land change issues to examine the influence of land use conversion on vegetation dynamics. Results show that a large portion of the study area was covered by abundant vegetation (over 50% of the total area), and the increased EVI area was about 5.5‐times greater than the area of EVI reduction. Additionally, vegetation sustainability was being seriously compromised (e.g., a decrease in the total area of 8,275 ha) due to several land conversion drivers such as shrimp farming, urbanisation, and industrialisation. Furthermore, results obtained from this research provide insight into the spatiotemporal dynamics of vegetation coverage and reveal the consistency of future vegetation trends. Moreover, the study also quantitatively assessed the positive impacts of Buddhist doctrines on reducing the negative trend of vegetation change in the study area. These findings can lay the ground to formulate sustainable land and environmental plans that meet the 11th, 13th and 15th Sustainable Development Goals (SDGs) (i.e., the sustainable cities and communities, the climate actions, and the life on land). Besides, the analytical procedure adopted in this study can also be applicable to any other coastal areas that require the accurate assessment of vegetation status over time.

Enhanced vegetation index

10.1002/ldr.3934

Cite

Citations (32)

Long-term changes of mangrove distribution and its response to anthropogenic impacts in the Vietnamese Southern Coastal Region

Journal of Environmental Management (2024)

Thuong V. Tran Ruth Reef Xuan Zhu

Vietnamese

10.1016/j.jenvman.2024.122658

Cite

Citations (1)

Decadal assessment of agricultural drought in the context of land use land cover change using MODIS multivariate spectral index time-series data

GIScience & Remote Sensing (2023)

Thuong V. Tran David Bruce Cho‐ying Huang Duy X. Tran Soe W. Myint

Using a multivariate drought index that incorporates important environmental variables and is suitable for a specific geographical region is essential to fully understanding the pattern and impacts of drought severity. This study applied feature scaling algorithms to MODIS time-series imagery to develop an integrated Multivariate Drought Index (iMDI). The iMDI incorporates the vegetation condition index (VCI), the temperature condition index (TCI), and the evaporative stress index (ESI). The 54,474 km2 Vietnamese Central Highlands region, which has been significantly affected by drought severity for several decades, was selected as a test site to assess the feasibility of the iMDI. Spearman correlation between the iMDI and other commonly used spectral drought indices (i.e. the Drought Severity Index (DSI–12) and the annual Vegetation Health Index (VHI–12)) and ground-based drought indices (i.e. the Standardized Precipitation Index (SPI–12) and the Reconnaissance Drought Index (RDI–12)) was employed to evaluate performance of the proposed drought index. Pixel-based linear regression together with clustering models of the iMDI time-series was applied to characterize the spatiotemporal pattern of drought from 2001 to 2020. In addition, a persistent area of LULC types (i.e. forests, croplands, and shrubland) during the 2001–2020 period was used to understand drought variation in relation to LULC. Results suggested that the iMDI outperformed the other spectral drought indices (r > 0.6; p < 0.005). The analysis revealed an increase in drought risk in some provinces of the Central Highlands including Gia Lai, Kon Tum, and Dak Lak. It was also found that changes in LULC patterns could minimize (reforestation) or exacerbate (deforestation) the impacts of drought. Our study suggests that applying a multivariate drought index enables a better understanding of drought patterns at the local scale. This provides valuable information for the development of appropriate land and environmental management practices that can affect and mitigate climate change effects.

Land Cover

Shrubland

10.1080/15481603.2022.2163070

Cite

Citations (16)

Characterising the distribution of mangroves along the southern coast of Vietnam using multi-spectral indices and a deep learning model

The Science of The Total Environment (2024)

Thuong V. Tran Ruth Reef Xuan Zhu Andrew Gunn

Mangroves are an ecologically and economically valuable ecosystem that provides a range of ecological services, including habitat for a diverse range of plant and animal species, protection of coastlines from erosion and storms, carbon sequestration, and improvement of water quality. Despite their significant ecological role, in many areas, including in Vietnam, large scale losses have occurred, although restoration efforts have been underway. Understanding the scale of the loss and the efficacy of restoration requires high resolution temporal monitoring of mangrove cover on large scales. We have produced a time series of 10-m-resolution mangrove cover maps using the Multispectral Instrument on the Sentinel 2 satellites and with this tool measured the changes in mangrove distribution on the Vietnamese Southern Coast (VSC). We extracted the annual mangrove cover ranging from 2016 to 2023 using a deep learning model with a U-Net architecture based on 17 spectral indices. Additionally, a comparison of misclassification by the model with global products was conducted, indicating that the U-Net architecture demonstrated superior performance when compared to experiments including multispectral bands of Sentinel-2 and time-series of Sentinel-1 data, as shown by the highest performing spectral indices. The generated performance metrics, including overall accuracy, precision, recall, and F1-score were above 90 % for entire years. Water indices were investigated as the most important variables for mangrove extraction. Our study revealed some misclassifications by global products such as World Cover and Global Mangrove Watch and highlighted the significance of our study for local analysis. While we did observe a loss of 34,778 ha (42.2 %) of mangrove area in the region, 47,688 ha (57.8 %) of new mangrove area appeared, resulting in a net gain of 12,910 ha (15.65 %) over the eight-year period of the study. The majority of new mangrove areas were concentrated in Ca Mau peninsulas and within estuaries undergoing recovery programs and natural recovery processes. Mangrove loss occurred in regions where industrial development, wind farm projects, reclaimed land, and shrimp pond expansion is occurring. Our study provides a theoretical framework as well as up-to-date data for mapping and monitoring mangrove cover change that can be readily applied at other sites.

Mangrove ecosystem

10.1016/j.scitotenv.2024.171367

Cite

Citations (9)