An Application of Remote Sensing Data to Assess Water Shortage Areas in Chanthaburi Province
Abstract
Drought is a natural disaster having complex causes and a spatial problem in Chanthaburi province for a long time. This study aimed to assess water shortage areas by using remote sensing data from Landsat 4-5 (TM), Landsat 7 (ETM+) and Landsat 8 (OLI/TIRS) satellites from 2002 to 2018. We found that in dry season during 2002 to 2018 most of Chanthaburi province (more than 40% of the area) was in slight drought conditions. During the years 2002, 2003, 2006 and 2015, severe drought conditions covered the west side of the province and in 2015, severe drought conditions covered almost the whole province (86% of the study area). In the average wet season, Chanthaburi province has slight to moderate drought conditions (approximately 30-40%), but in 2002, 2004, 2005, and 2006, moderate to severe drought was found in the northern and middle parts of the province, covering more than 40-60% of the total area which is mostly agriculture and forestry area. During the cool dry season, it was found to be in normal conditions and slight drought with more than 80% of total area, but in 2002, 2005 and 2011 vegetation conditions were moderate to severe especially in the eastern part of the study area. The data obtained from this study are consistent with the report on drought-prone situations in Chanthaburi Province in the past ten years. Therefore, the information can be used to integrate with relevant agencies to effectively solve the problem of water shortage in Chanthaburi Province.References
Amatayakul, P. & Chomtha, T. (2016). Agricultural Meteorology to know for Chanthaburi. Agrometeorological Division,Meteorological Department Bureau. (in Thai)
Cheng, Y., Zhang, K., Chao, L., Shi, W., Feng, J. & Li, Y. (2023). A comprehensive drought index based on remote sensing data and nested copulas for monitoring meteorological and agroecological droughts: A case study on the Qinghai-Tibet Plateau. Environmental Modelling and Software, 161, 105629.
Frantz, D., Röder, A., Udelhoven, T. & Schmidt, M. (2015). Enhancing the Detectability of Clouds and Their Shadows in Multitemporal Dryland Landsat Imagery: Extending Fmask. IEEE Geoscience and Remote Sensing Letters, 12(6), 1242-1246.
Gu, Y., Brown, J,F., Verdin, J,P and Wardlow, B. (2007). A five-year analysis of MODIS NDVI and NDWI For grassland rought assessment over the central Great Plains of the United States. Geophysical Research Letter, 34 L06407.
Jongmeewasin, S., Nanuam, J., Noichaisin, L., Sriwongchai, S., & Na-U-Dom, T.(2020) The Conflict Prevention and Management in Water Consumption and Use: A Case Study of Eastern Economic Corridor. Thailand Science Research and Innovation.(in Thai)
Lin, M.-L., Wang, Q., Sun, F., Chu, T., & Shiu, Y. (2010). Quick Spatial Assessment of Drought Information Derived from MODIS Imagery Using Amplitude Analysis. International Journal of Geographical and Environmental Engineering, 4(7), 271–275.
Lorpittayakorn, P. (2017). The Influence of El Nino on Rainfall Distribution during Wet and Dry Seasons in Eastern Thailand. Thai Journal of Science and Technology, 25(6), 945-959. (in Thai)
Mishra, A.K., Singh, V.P.(2010). A review of drought concepts. J. Hydrol. 391 (1–2), 202–216.
Nanuam, J., Noichaisin, L., & Na-U-Dom, T. (2022). Assessment of Susceptible Areas and Issues of Water Shortage in Agriculture in the Area of Sa Kaeo Province. Burapha Science Journal, 27(2), 868-884. (in Thai)
Noichaisin, L., Buranapratheprat, A., Manthachitra, V. & Intarawichian, N. (2020). Drought risk area assessment using GIS in Sa Kaeo Province,Thailand. International Journal of Agricultural Technology, 16(3), 655-666.
Noichaisin, L., Na-U-Dom, T., Sriwongchai, S., & Niyomrat, S. (2021). The Influence of ENSO (El Nino/Southern Oscillation) on Rainfall Distribution in Sa Kaeo Province. Burapha Science Journal, 26(1), 1-13. (in Thai)
Parsons, D.J., Rey, D., Tanguy, M., Holman, I.P.(2019). Regional variations in the link between drought indices and reported agricultural impacts of drought. Agric. Syst. ,173, 119–129.
Peainlert, S. and Tongdeenok, P. (2018). Drought Risk Area Assessment using Remotely Sense Data and Meteorological Data in Chern Sub-watershed. KKU Research Journal (Graduate Study), 18(3), 67-83. (in Thai)
Shi, W., Huang, S., Liu, D., Huang, Q., Leng, G., Wang, H., Fang, W., Han, Z.(2020). Dry and wet combination dynamics and their possible driving forces in a changing environment. J. Hydrol., 589, 125211.
Treerittiwitaya, K., Chaiupala, A., & Rungrattanaubol, K. (2015). Effect of El nino and La nina Phenomenon on agricultural products in Chanthaburi basin. Faculty of Industrial Technology, Rambhai Barni Rajabhat University.(in Thai)
Yuvanatemiya, V., Kunjet, S., Srinil, P., Suvannasan., W. & Laongmanee, W. (2017). Study on water resource for agriculture in Chanthaburi province with GIS. Khon Kaen Agriculture Journal, 46(SUPPL. 1), 866-872. (in Thai)
Zhang, L., Jiao, W., Zhang, H., Huang, C., Tong, Q.(2017). Studying drought phenomena in the Continental United States in 2011 and 2012 using various drought indices. Remote Sense Environ., 190, 96–106.
Zhu, Z., Wang, S. & Woodcock, C.E. (2015). Improvement and Expansion of the Fmask Algorithm: Cloud, Cloud Shadow, and Snow Detection for Landsats 4-7, 8, and Sentinel 2 Images. Remote Sensing of Environment, 159, 269-277.