In this study, vegetation coverage changes over a 30-year period for the Tuy Duc and Dak R’lap districts,Dak Nong province (central highland of Vietnam) were assessed using remote sensing and Geographic Information Systems (GIS) techniques. 03 Landsat satellite images,including Landsat TM February 13, 1990, Landsat TM February 22, 2005 and Landsat 8 January, 15 2020 were used to calculate the normalized difference vegetation index (NDVI), then assessed the changes in vegetation coverage density. The NDVI differencing method is also used as a change detection method and provides detailed information for monitoring changes in land cover in periods 1990 – 2005, 2005 – 2020 and 1990 – 2020. Analysis of the obtained results showed that the vegetation coverage declined sharply during 1990 – 2005 period,then the vegetation coverage has begun to recover in period 2005 – 2020. From the findings of this study, it can be easily concluded that the Tuy Duc and Dak R’lap areas has lost its valuable vegetation cover both qualitatively and quantitatively.

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Classification of built-up land and bare land on remote sensing images is a very difficult problem due to the complexity of the urban land cover. Several urban indices have been proposed to improve the accuracy in classifying urban land use/land cover from optical satellite imagery. This paper presents an development of the EBBI (Enhanced Built-up and Bareness Index) index based on the combination of Landsat 8 and Sentinel 2 multi-resolution satellite imagery. Near infrared band (band 8a), short wave infrared band (band 11) of Sentinel 2 MSI image and thermal infrared band (band 10) Landsat 8 image were used to calculate EBBI index. The results obtained show that the combination of Landsat 8 and Sentinel 2 satellite images improves the spatial resolution of EBBI index image, thereby improving the accuracy of classification of bare land and built-up land by about 5% compared with the case using only Landsat 8 images.

Despite high profits, the mining process often leads to negative effects on the quality of groundwater around the mining site. Due to the close relationship between the concentration of water quality parameters and spectral reflectance values of surface watẻ, optical remote sensing image has been used effectively in the world in assessing and monitoring surface water quality. This paper presents the results of determining some surface water quality parameters in the Tan Rai bauxite mining area (Lam Dong province) such as turbidity, water-transparency (Secchi depth), and surface temperature from Sentinel-2A and Landsat 8 images taken on January 29, 2019. The results obtained in this study show that the mining process has a great influence on the surface water quality in Tan Rai (Lam Dong), reflected in all three water quality parameters such as turbidity, Secchi depth, and water temperature.