Coastal areas along the Mekong Delta have faced a massive reduction of mangroves and have been slightly returned to their natural states due to the impact of nature-based solutions, especially the vital role of wooden fences. In this study, the role of low-frequency wave-induced flows and shear stress is assessed by the open-source model, SWASH (Simulating WAve till SHore, developed at Delft University of Technology). Modeling results are simulated using the data records in two monsoons, the southwest and the northwest, to assess firstly the model accuracy. This result shows a good agreement between the SWASH model and field data, which is presented for incoming wave heights. However, there is a slight mismatch between the simulated results and transmission records due to the higher density of fences in SWASH in comparison to the field. Last but not least, the longwave-induced flow results indicate the potential for increasing the sedimentation rates behind the fences since longer waves are less damped than shorter waves. As a result, the flow driven by longwaves has enough energy to bring sediments over the fences.

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This study aims at using Sentinel-5P satellite data for analyzing of Nitrogen Dioxide (₂) concentrations in Binh Duong province, Vietnam. Google Earth Engine (GEE) which is a cloud-based geospatial analysis platform has been used to collect the satellite data and extract information on NO₂ concentrations in Binh Duong in the period from 2018 to 2023. Applying GEE code editor, the information on NO₂ extracted from Sentinel-5P satellite data has been utiized to evaluate the air quality in Binh Duong province. Based on the analysis over time, NO₂ dispersion is dependent on seasonal variation and spatially distribution. The average NO₂ density is ranging from 17.2 to 41.86 µmol/m² in the period from 2018 to 2023. The average NO₂ concentration in the areas with high density of industrial factories is higher than other regions in Binh Duong province. Based on the spatial analysis of NO₂ concentration in Binh Duong, NO₂ index is highest in Di An City and lowest in Phu Giao district. Results from this study is valuable for air pollution monitoring in Binh Duong as well as other industrial provinces in Viet Nam.

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Nowadays, drought is considered as one of the most destructive natural disasters that negatively affects societies around the globe. Especially, in Binh Thuan Province - Vietnam, the drought tends to increase in both extent and intensity but is more difficult to predict. In recent years, with the development of remote sensing technology, its products have been effectively used in studying, monitoring, and responding to drought. Thus, in this study, we aim to determine the progress of drought through the years in Bac Binh District - Binh Thuan Province by using remote sensing images. In detail, we use images from Landsat 7 ETM+ (2002, 2005, 2010) and Landsat 8 OLI (2014 and 2017) to estimate dryness indices: temperature vegetation dryness index (TVDI) and improved temperature vegetation dryness index. These two dryness indices are based on normalized difference vegetation index (NDVI) and land surface temperature (LST) for TVDI and temperature gradient (Ts-Ta) for iTVDI. The results show that Bac Binh's area is estimated to have medium and high drought risk, and the severe drought areas increased rapidly in 2014 and 2017. Areas with high drought risk are mostly found in agricultural or non-vegetated areas in the center of Bac Binh.

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Monitoring rapid forest loss using optical remote sensing presents significant challenges, particularly in tropical regions due to persistent cloud cover. Therefore, the use of Synthetic Aperture Radar (SAR) imagery offers a promising alternative. To provide timely deforestation information, improvements in monitoring frequency and the integration of various synthetic aperture radar (SAR) imagery have been implemented, enhancing effectiveness compared to single sensors. The wavelengths of SAR data influence their ability to penetrate canopies, leading to different scattering mechanisms and varied information about forest cover. Specifically, the C-band wavelength (~5 cm) captures signals from the canopy and small branches, while the L-band wavelength (~23 cm) captures signals from tree branches. This paper focuses on combining two different types of SAR data (C-band and L-band) along with HV and HH polarizations to detect clear-cut and forest fires. The method employed involves comparing backscatter values before and after deforestation to identify forest loss. Sentinel-1 time series data has been analysed using the Radar Change Ratio (RCR) method, while three ALOS-2 image scenes have been processed using the RGB composite method. It has been demonstrated that C-band SAR data (Sentinel-1) can detect deforestation due to clear-cutting in Ha Long with a monthly frequency, although its effectiveness in identifying forest fire areas has been limited. In contrast, L-band data (ALOS-2) has been shown to be capable of detecting various types of deforestation, including clear-cutting and forest fires. Within the C-band data, HV polarization yields better results than HH, whereas the L-band data provides similar outcomes irrespective of whether HV or HH polarization is applied. The integration of C-band and L-band SAR data provides more comprehensive information on deforested areas, improving the accuracy of backscatter-based detection methods.

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