Assessment of Susceptible Areas and Issues of Water Shortage in Agriculture in the Area of Sa Kaeo Province
Abstract
This research aimed to assess areas susceptible to water shortage and identify the root causes for planning solutions in agricultural sector in Sa Kaeo Province. The areas were assessed from the Normalize difference vegetation index (NDVI) and Normalize difference moisture index (NDMI) values using remote sensing data from Landsat 4-5TM, 7ETM satellites, and 8OLI from 2002 to 2018. Susceptible area was separately evaluated into 3 seasons: hot dry season, cool dry season and wet season. To identify causes of the problems, we organized group discussion which participants including government officials who being involved in water management and representatives of the people or farmers who use water in the province. The results indicated that in hot dry season most of the area (60 to 80% of the area) had low drought conditions, except 2002, 2009, 2017 and 2018 which had extreme drought conditions, covering 30% of the province. On average during the wet season, Sa Kaeo Province has mild to moderate drought conditions (more than 70% of the area) while in the cool dry season there is mostly less drought (approximately 48 to 70 % of the area). Identifying root causes by group discussion pointed out that the most susceptible areas were Ta Phraya, Khok Sung and Watthana Nakhon District where is in the north and northeast of the Province, in line with the results of the evaluation of areas susceptible to water shortages by using remote sensing technique. From the data obtained in this study, both of these methods can be used to identify areas and plan to manage water shortages in agriculture in Sa Kaeo Province. Keywords : susceptible area ; water shortage ; agriculture ; Sa Kaeo ProvinceReferences
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Office of National Water Resources. (2019). Documentation for the workshop project to formulate the Water Resource Management Master Plan and the Action Plan under the Salween River Basin Master Plan
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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.
Carlos, D.-M., & Gerardo, B. (2021). Social and environmental dimensions of drought in Mexico: An integrative review. International Journal of Disaster of Drought Risk Reduction, 55, 102067.
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.
Giovanni, G., Giuseppe, T., Federico, M., Vittorio, F., & Riccardo, L. B. (2020). Water Deficit Affects the Growth and Leaf Metabolite Composition of Young Loquat Plants, Plants, 9, 1-15.
Guoyong, L., & Jim. H. (2019). Crop yield sensitivity of global major agricultural countries to droughts
and the projected changes in the future. Science of the Total Environment, 654, 811-821.
Jannifer, I., Luis. A.J.M., Paul, R.H.R., & Maurice, B. (2013). Physiological and growth responses to water deficit in the bioenergy crop Miscanthus x giganteus. Frontiers in Plant Science, 4, 1-12.
Kwanyeun, B., Wongpetch, J., & Tepprasit, C. (2020). Administration and syntheses the study of research project on water balance and water saving for sustainable development in Eastern Economic Corridor (EEC). Research Report, Bangkok. (in Thai)
Land Development Department. (2020). Report of survey results / improvement of land use maps. Retrieved March 31, 2020, from http://www1.ldd.go.th/web_OLP/index.html. (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.
Lkhagvadorj, N., Zhang, J.-H., Tuvdendorj, B., Nabil, M., Zhang, S., & Y, B. (2019). NDVI anomaly for drought monitoring and its correlation with climate factors over Mongolia from 2000 to 2016. Journal of Arid Environments, 164, 69-77.
Meteorological Development Division. (2016). Agricultural Meteorology to know for Sa Kaeo. Retrieved March 31, 2020, from http://www.arcims.tmd.go.th>Agromettoknow.pdf. (in Thai)
National Statistical Office. (2020). Statistical data by survey / census project. Retrieved March 31, 2020, from http://www.nso.go.th/sites/2014/Pages/Statistics%20from%20major%20Survey.aspx. (in Thai)
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, 1-13. (in Thai)
Office of National Water Resources. (2019). Documentation for the workshop project to formulate the Water Resource Management Master Plan and the Action Plan under the Salween River Basin Master Plan
Fiscal Year 2020 - 2022. Retrieved February 1, 2021, from
http://www.mnre.go.th/reo01/th/news/detail/44917 (in Thai)
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.
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Published
2022-05-18
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