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Drought Detection by Application of Remote Sensing Technology and Vegetation Phenology

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Drought is a natural phenomenon as it often occurs in the area of Yasothon province, northeastern of Thailand. It causes effects on vegetation condition in the area. Drought information might be useful for local government to prepare for prevention and mitigation plan in the future. For this reason, the primary objective of the research was to conduct the examination of this province to find severe drought years. Firstly, the researcher needed to find Normalized Differences Vegetation Index (NDVI) and Vegetation Condition Index (VCI) by analyzing monthly Landsat data acquired at different periods of time from January to December 2014, 2015, and 2016, covering 4,096 km2. Then, the researcher needed to find relationship between VCI and monthly rainfall represented in term of space and time. As results, VCI and its relationship with monthly rainfall were congruent. VCI showed that the drought area was 33.87% or 1,387.32 km2 of studied area in 2014, 16.24% or 665.19 km2 of studied area in 2015 and 27.95% or 1,144.83 km2 of studied area in 2016. Therefore, the most and the least severe drought years were 2014, 2016, and 2015 respectively.
Rocznik
Strony
115--121
Opis fizyczny
Bibliogr. 23 poz., rys., tab.
Twórcy
autor
  • Faculty of Science, Mahasarakham University, Katarawichai, Mahasarakham, 44150, Thailand
autor
  • Faculty of Science, Mahasarakham University, Katarawichai, Mahasarakham, 44150, Thailand, teerawong@msu.ac.th
  • Space Technology and Geoinformatics Research Unit, Faculty of Science, Mahasarakham University, Mahasarakham, 44150 Thailand
Bibliografia
  • 1. Domenikiotis, C., Spiliotopoulos, M., Tsiros, E., and Dalezios, N. R. (2004). Early Cotton Yield Assessment by The Use Of The NOAA/AVHRR Derived Drought Vegetation Condition Index In Greece. Int. J. Remote Sens., 25, pp. 2807-2819.
  • 2. Dušan Húska, Ľuboš Jurík, Lucia Tátošová, Karol Šinka, Johana Jakabovičová. (2017). Cultural Landscape, Floods and Remote Sensing. Journal of Ecological Engineering. 18 (3), pp. 31-36.
  • 3. Fisher, J. I., Mustard, J. F., & Vadeboncoeur, M. A. (2006). Green leaf phenology at Landsat resolution: Scaling from the field to the satellite. Remote Sensing of Environment, 100, pp. 265-279.
  • 4. Geerken, R., Batikha, N., Celis, D., Depauw, E. (2005). Differentiation of rangeland vegetation and assessment of its status: field investigations and MODIS and SPOT VEGETATION data analyses, International Journal of Remote Sensing, 26:20, pp. 4499-4526.
  • 5. Gitay H., Suárez A., Watson R.T., and Dokken D.J., Eds., (2002). Climate Change and Biodiversity, IPCC Technical Paper V, IPCC, Geneva, 85 p,
  • 6. Gomasathit, T., Laosuwan, T., Sangpradit, S., and Rotjanakusol, T., (2015). Assessment of Drought Risk Area in Thung Kula Rong Hai using Geographic Information System and Analytical Hierarchy Process. International Journal of Geoinformatics, 11 (2), pp. 21-27.
  • 7. Hui Fan, Xiaohua Fu, Zheng Zhang and Qiong Wu. (2015). Phenology-Based Vegetation Index Differencing for Mapping of Rubber Plantations Using Landsat OLI Data. Remote Sensing. 7, pp. 6041-6058
  • 8. Kogan, F.N. (1997). Global drought watch from space. Bull. Am. Meteorol. Soc. 78, pp. 621-636.
  • 9. Kogan, F. N., (2001). Operational space technology for global Vegetation Assessment. Bull. Amer. Meteor. Soc., 82(9), pp. 1949-1964.
  • 10. Laosuwan T., and Rotjanakusol T., (2013). The Observation and Monitoring of Water Situation by Using Remote Sensing Technology and GIS, J. Sci. Technol. MSU., 32(2), pp. 246-256
  • 11. Laosuwan T., and Uttaruk P., (2014). Estimating Tree Biomass via Remote Sensing, MSAVI 2, and Fractional Cover Model, IETE Tech.Rev., 31(5), pp. 362-368.
  • 12. Laosuwan, T., Sangpradit, S., Gomasathit, T., and Rotjanakusol, T., (2016). Application of Remote Sensing Technology for Drought Monitoring in Mahasarakham Province, Thailand. International Journal of Geoinformatics, 12 (3), pp. 17-25.
  • 13. Mushtaq Ahmad Ganie and Asima Nusrath. (2016). Determining the Vegetation Indices (NDVI) from Landsat 8 Satellite Data. International Journal of Advanced Research, 4(8), pp. 1459-1463.
  • 14. Per Skougaard Kaspersen, Rasmus Fensholt, and Martin Drews. (2015). Using Landsat Vegetation Indices to Estimate Impervious Surface Fractions for European Cities. Remote Sens. 7, pp. 8224-8249.
  • 15. Quiring, S. M., and S. Ganesh. (2010). Evaluating the utility of the Vegetation Condition Index (VCI) for monitoring meteorological drought in Texas, Agric. For. Meteorol., 150(3), pp. 330-339.
  • 16. Teerawong Laosuwan, Yannawut Uttaruk. (2017). Application of Remote Sensing for Temperature Monitoring: The Technique for Land Surface Temperature Analysis. Journal of Ecological Engineering. 18 (3), pp. 53-60.
  • 17. Toshihiro Sakamoto, Masayuki Yokozawa, Hitoshi Toritani, Michio Shibayama, Naoki Ishitsuka, Hiroyuki Ohno.(2005). A crop phenology detection method using time-series MODIS data. Remote Sensing of Environment, 96 (3),pp. 366-374.
  • 18. Tucker, C. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8, pp. 127-150.
  • 19. Uttaruk Y., Laosuwan T., (2016). Remote sensing based vegetation indices for estimating above ground carbon sequestration in orchards. Agriculture and Forestry, 62 (4). pp 193-201
  • 20. Wattanakij N., Mongkolsawat C., (2008). Drought Detection in Northeast Thailand using Standardized Vegetation Index of Multi-Temporal Satellite Data,” In: Proc. 4th Environment Naresuan Conference, Naresuan Univeristy,Thailand, pp. 206-215.
  • 21. Wenzhe Jiao, Lifu Zhang , Qing Chang , Dongjie Fu, Yi Cen and Qingxi Tong. (2016). Evaluating an Enhanced Vegetation Condition Index (VCI) Based on VIUPD for Drought Monitoring in the Continental United States. Remote Sens., 8(3), pp. 2-21.
  • 22. Xiaoyang Zhang, Mark A. Friedl, Crystal B. Schaaf, Alan H. Strahler, John C.F. Hodges, Feng Gao, Bradley C. Reed, Alfredo Huete. (2003). Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 84 (3), pp. 471-475.
  • 23. Yannawut Uttaruk, Teerawong Laosuwan. 2017. Carbon Sequestration Assessment of the Orchards using Satellite Data. Journal of Ecological Engineering. 18 (1), pp. 11-17.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-92374648-6133-4bc9-b7d5-3d4b6d02c68c
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