Risk Analysis of Hotspots in Mueang Phayao District, Phayao Province
Abstract
The aim of this research is to study the land moisture thresholds in order to classify the risk hotspots during the dry season between December 2017 and April 2018 and predict the risk areas in the future by using Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), Suomi National Polar-orbiting Partnership (Suomi NPP), Visible Infrared Imaging Radiometer Suite (VIIRS) and the satellite image Sentinel-2B. The Normalized Difference Moisture Index (NDMI) is analyzed by Sentinel-2B satellite image. The land moisture thresholds of burning risk areas are produced by the pixel of NDMI from satellite images and hotspots in the study area. In addition, daily temperature data are also explored. The result demonstrates that the land moisture thresholds from the risk hotspot areas by NDMI. The results are as follows: 0.149330 and 30.5 °C, 0.155472 and 31.0 °C, 0.147282 and 32.2 °C, 0.12724 and 33.0 °C and 0.181794 and 37.0 °C, respectively.For the results of checking the hotspots according to land use type, the number of hotspots in the urban and built-up land is 3.03%, agricultural land 29.79%, forest land 63.64% and miscellaneous land 4.54%. For testing the moisture threshold from NDMI, which chose to use 60 hotspots with high confidence in February 2019, found that there are 60 hotspots in the risk area and not found outside the area. Therefore, it can be concluded that the threshold value can predict the risk area efficiently. Keywords : hotspot, threshold, NDMI, PhayaoReferences
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Giglio, L. (2015). MODIS collection 6 active fire product user’s guide revision A. Department of Geographical Sciences. University of Maryland.
Giglio, L., Schroeder, W., & Justice, C. O. (2016). The collection 6 MODIS active fire detection algorithm and fire products. Remote Sensing of Environment, 178, 31-41.
Hantson, S., Padilla, M., Corti, D., & Chuvieco, E. (2013). Strengths and weaknesses of MODIS hotspots to characterize global fire occurrence. Remote Sensing of Environment, 131, 152-159.
Hunt, E. R., Wang, L., Qu, J. J., & Hao, X. (2012). Remote sensing of fuel moisture content from canopy water indices and normalized dry matter index. Journal of Applied Remote Sensing, 6(1), 061705.
Jin, S., & Sader, S. A. (2005). Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances. Remote sensing of Environment, 94(3), 364-372.
Mallick, J., Singh, C. K., Shashtri, S., Rahman, A., & Mukherjee, S. (2012). Land surface emissivity retrieval based on moisture index from LANDSAT TM satellite data over heterogeneous surfaces of Delhi city. International Journal of Applied Earth Observation and Geoinformation, 19, 348-358.
Pollution Control Department. (2015). Smog and weather problems. (Type 1). Pollution Control Department.
Public Relations Office District3Chiang Mai (2019). Announcement 60 days. Danger. No burn. (Announcement
No 1). Public Relations Office District 3 Chiang Mai. (in Thai)
Qian, L. X., and Cui, H. S. (2008). Relationship between normalized difference moisture index and land surface temperature [J]. Geographical Research, 6.
Schroeder, W. (2017). Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m & 750 m Active Fire Detection Data Sets Based on NASA VIIRS Land Science Investigator Processing System (SIPS) Reprocessed Data-Version 1.
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2020-01-08
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บทความวิจัยจากการประชุมวิชาการระดับชาติ"วิทยาศาสตร์วิจัย"ครั้งที่ 11
