A5035614978- Coastal wetland ecosystem dynamics
- Advanced Photonic Communication Systems
- Flood Risk Assessment and Management
- Remote Sensing in Agriculture
- Marine and coastal plant biology
- Remote Sensing and LiDAR Applications
- Coral and Marine Ecosystems Studies
- Infrastructure Maintenance and Monitoring
- Topology Optimization in Engineering
- Tropical and Extratropical Cyclones Research
- Optical Wireless Communication Technologies
- Hydrological Forecasting Using AI
- Millimeter-Wave Propagation and Modeling
- Structural Health Monitoring Techniques
- Plant and Fungal Species Descriptions
- Structural Load-Bearing Analysis
- Isotope Analysis in Ecology
- Structural Behavior of Reinforced Concrete
- Hydrology and Watershed Management Studies
- Hydrology and Drought Analysis
- Optical Network Technologies
- Conservation, Biodiversity, and Resource Management
- Land Use and Ecosystem Services
- Concrete Corrosion and Durability
- Soil and Land Suitability Analysis
Kyung Hee University
2021-2025
Macquarie University
2021-2025
Western Sydney University
2025
National Institute of Information and Communications Technology
2013-2024
Hanoi University
2024
National University of Civil Engineering
2024
Florida International University
2021
University of Miami
2021
Le Quy Don Technical University
2017-2020
RIKEN Center for Advanced Intelligence Project
2019-2020
The mangrove ecosystem plays a vital role in the global carbon cycle, by reducing greenhouse gas emissions and mitigating impacts of climate change. However, mangroves have been lost worldwide, resulting substantial stock losses. Additionally, some aspects remain poorly characterized compared to other forest ecosystems due practical difficulties measuring monitoring biomass their stocks. Without quantitative method for effectively biophysical parameters stocks mangroves, robust policies...
The main objective of this research is to investigate the potential combination Sentinel-2A and ALOS-2 PALSAR-2 (Advanced Land Observing Satellite -2 Phased Array type L-band Synthetic Aperture Radar-2) imagery for improving accuracy Aboveground Biomass (AGB) measurement. According current literature, kind investigation has rarely been conducted. Hyrcanian forest area (Iran) selected as case study. For purpose, a total 149 sample plots study were documented through fieldwork. Using imagery,...
Grassland ecosystems provide a range of services in semi-arid and arid regions. However, they have significantly declined due to overgrazing desertification. In the current study, we employed Landsat time series data (TM, OLI, OLI-2) spanning from 1990 2024, combined with vegetation indices such as NDVI SAVI, along NDWI digital elevation models (DEMs), analyze land cover dynamics Ugii Lake watershed area, Mongolia. By integrating multisource remote sensing into advanced XGBoost (extreme...
Soil salinity caused by climate change associated with rising sea level is considered as one of the most severe natural hazards that has a negative effect on agricultural activities in coastal areas tropical climates. This issue become more and increasingly occurred Mekong River Delta Vietnam. The main objective this work to map soil intrusion Ben Tre province located Vietnam using Sentinel-1 Synthetic Aperture Radar (SAR) C-band data combined five state-of-the-art machine learning models,...
This study investigates the effectiveness of gradient boosting decision trees techniques in estimating mangrove above-ground biomass (AGB) at Can Gio biosphere reserve (Vietnam). For this purpose, we employed a novel gradient-boosting regression technique called extreme (XGBR) algorithm implemented and verified AGB model using data from field survey 121 sampling plots conducted during dry season. The dataset fuses Sentinel-2 multispectral instrument (MSI) dual polarimetric (HH, HV) ALOS-2...
This study proposes a hybrid intelligence approach based on an extreme gradient boosting regression and genetic algorithm, namely, the XGBR-GA model, incorporating Sentinel-2, Sentinel-1, ALOS-2 PALSAR-2 data to estimate mangrove above-ground biomass (AGB), including small shrub patches in Red River Delta biosphere reserve across northern coast of Vietnam. We used novel decision tree (XGBR) technique together with algorithm (GA) optimization for feature selection construct verify AGB model...
Seagrass has been acknowledged as a productive blue carbon ecosystem that is in significant decline across much of the world. A first step toward conservation mapping and monitoring extant seagrass meadows. Several methods are currently use, but resource from satellite images using machine learning not widely applied, despite its successful use various comparable applications. This research aimed to develop novel approach for state-of-the-art with data Sentinel–2 imagery. We used Tauranga...
Groundwater salinization is considered as a major environmental problem in worldwide coastal areas, influencing ecosystems and human health. However, an accurate prediction of salinity concentration groundwater remains challenge due to the complexity processes its factors. In this study, we evaluate state-of-the-art machine learning (ML) algorithms for predicting identify We conducted study multi-layer aquifers Mekong River Delta (Vietnam), using geodatabase 216 samples 14 conditioning...
Mountainous areas are highly prone to a variety of nature-triggered disasters, which often cause disabling harm, death, destruction, and damage. In this work, an attempt was made develop accurate multi-hazard exposure map for mountainous area (Asara watershed, Iran), based on state-of-the art machine learning techniques. Hazard modeling avalanches, rockfalls, floods performed using three state-of-the-art models—support vector (SVM), boosted regression tree (BRT), generalized additive model...
Quantifying total carbon (TC) stocks in soil across various mangrove ecosystems is key to understanding the global cycle reduce greenhouse gas emissions. Estimating TC at a large scale remains challenging due difficulty and high cost of measurements when number samples high. In present study, we investigated capability Sentinel-2 multispectral data together with state-of-the-art machine learning (ML) technique, which combination CatBoost regression (CBR) genetic algorithm (GA) for feature...
Blue carbon ecosystems (mangroves, seagrasses and saltmarshes) are highly productive coastal habitats, considered some of the most carbon-dense on earth. They an important nature-based solution for both climate change mitigation adaptation. Quantifying blue stocks assessing their dynamics at large scales through remote sensing remains challenging due to difficulties cloud coverage, spectral, spatial temporal limitations multispectral sensors speckle noise synthetic aperture radar (SAR)....
Aboveground biomass (AGB) of mangrove forest plays a crucial role in global carbon cycle by reducing greenhouse gas emissions and mitigating climate change impacts. Monitoring forests accurately still remains challenging compared to other ecosystems. We investigated the usability machine learning techniques for estimation AGB plantation at coastal area Hai Phong city (Vietnam). The study employed GIS database support vector regression (SVR) build verify model AGB, drawing upon data from...
This study tested the use of machine learning techniques for estimation above-ground biomass (AGB) Sonneratia caseolaris in a coastal area Hai Phong city, Vietnam. We employed GIS database and multi-layer perceptron neural networks (MLPNN) to build verify an AGB model, drawing upon data from survey 1508 mangrove trees 18 sampling plots ALOS-2 PALSAR imagery. assessed model's performance using root-mean-square error, mean absolute coefficient determination (R2), leave-one-out...
Flash flood is one of the most dangerous natural phenomena because its high magnitudes and sudden occurrence, resulting in huge damages for people properties. Our work aims to propose a state-of-the-art model susceptibility mapping flash using decision tree random subspace ensemble optimized by hybrid firefly–particle swarm optimization (HFPS), namely HFPS-RSTree model. In this work, we used data from inventory map consisting 1866 polygons derived Sentinel-1 C-band synthetic aperture radar...
Mangrove forests provide numerous valuable ecosystem services and can sequester a large volume of carbon that help mitigate climate change impacts. Modeling mangrove with robust valid approaches is crucial to better understanding existing conditions. The study aims estimate Above-Ground Carbon (AGC) at Loh Buaya located in the Komodo National Park (Indonesia) using novel Extreme Gradient Boosting (XGB) Genetic Algorithm (GA) analyses integrating multiple sources remote sensing (optical,...
This research examined mangrove management in Hai Phong city, Vietnam. A combination of logistic regression model data and field survey were used to investigate the driving forces changes. The results indicate that implementation investigated by authorities, community or local people has affected change. main force loss is over expansion shrimp aquaculture. poorer families would like participate conservation activities more than richer households. Mangrove rehabilitation programs have been...
Flash floods induced by torrential rainfalls are considered one of the most dangerous natural hazards, due to their sudden occurrence and high magnitudes, which may cause huge damage people properties. This study proposed a novel modeling approach for spatial prediction flash based on tree intelligence-based CHAID (Chi-square Automatic Interaction Detector)random subspace, optimized biogeography-based optimization (the CHAID-RS-BBO model), using remote sensing geospatial data. In this...