Lam Le * , & Thinh V. Tran

* Correspondence: Lam Le Ngoc (email:

Main Article Content


Ben Tre is a coastal province in the Mekong Delta heavily affected by negative impacts of climate change and sea level rise, such as freshwater shortage and increased salinity intrusion during the dry season. This research aimed to develop a remote sensing approach, using time series data to assess drought development for the coastal districts (Ba Tri, Binh Dai, and Thanh Phu) in Ben Tre province. The Temperature Vegetation Dryness Index (TVDI) was analyzed based on the time-series Landsat 8 OLI data, which were obtained continuously from 2009 - 2019 to evaluate drought changes over time. The drought maps of 2009 and 2019 were established and the results showed that there were four levels of drought, including non-drought, slight drought, moderate drought and severe drought. Areas with non-drought and slight drought were reported at 5.65% and 35.34% (about 6,098 ha and 38,146 ha), respectively; while about 53.14% and 5.87% of the study areas were classified as moderate and severe drought (about 57,354 ha and 6,332 ha), respectively. The assessment of fluctuations in the period 2009-2019 showed that the areas of non-drought and slight drought tended to decrease while the areas of moderate and severe drought increased. The drought was positively related to agricultural land-use change as shown by the following formula loge(Pi/(1 - Pi)) = 7.985 * TVDI - 6.746. Drought tended to decrease in the areas where the bare land was changed to lands for perennial crops, rice crops and aquaculture, while drought tended to increase in land-use types of rice and annual crops.

Keywords: Ben Tre province, Drought, Landsat, Land use change, Remote sensing

Article Details


Belal, A. A., Mohamed, E. S., El-Ramady, H. R., & Saled, A. M. (2014). Drought risk assessment using remote sensing and GIS techniques. Arabian Journal of Geosciences 7, 35-53.

Bui, T. K. T., Nguyen, P. Q., & Nguyen, C. M. (2019). Drought monitoring and warning using geographic information system and remote Sensing. Retrieved May 21, 2020, from

Han, P., Wang, P. X., Zhang, S. Y., & Zhu, D. H. (2010). Drought forecasting based on the remote sensing data using ARIMA models. Mathematical and Computer Modelling in Agriculture 51(11-12), 1398-1403.

Hayes, M. J., Svoboda, M. D., Wardlow, B. D., Anderson, M. C., & Kogan, F. (2012). Drought monitoring: Historical and current perspectives. In Wardlow, B. D., Anderson, M. C., & Verdin, J. P. (Eds.). Remote sensing of drought: Innovative monitoring approaches. Florida, USA: CRC Press/Taylor & Francis.

Jain, S. K., Keshri, R., Goswami, A., & Sarkar, A. (2010). Application of meteorological and vegetation indices for evaluation of drought impact: a case study for Rajasthan, India. Natural Hazards 54, 643-656.

Raja, R. G., Visweswara, R., B; Tammi, N, G., & Hema, M. B. (2013). Impact of drought on land use/land cover changes in Srikakulam district of Andhra Pradesh A study through remote sensing and GIS. International Journal of Multidisciplinary Educational Research 2(1), 88-103.

Trinh, H. L. (2014). Application of Landsat thermal infrared data to study soil moisture using temperature vegetation dryness index. Vietnam Jounal of Earth Sciences 36(3), 262-270.

Trinh, H. L., & Dao, H. K. (2015). Drought risk evaluation using remote sensing: a case study in Bac Binh district, Binh Thuan province. Scientific Journal of Ho Chi Minh City Educational University 5(70), 128-139.

Wang, P. X., Wan, Z. M., Gong, J. Y., Li, X. W., & Wang, J. D. (2003). Advances in drought monitoring by using remotely sensed normalized difference vegetation index and land surface temperature products. Advance in Earth Science 18(4), 527-533.

Wilhite, D. A., & Glantz, M. H. (1985). Understanding the drought phenomenon: The role of definitions. Water International 10(3), 111-120.