SPECTRAL ANALYSIS OF THE HIMAWARI-8 DATA FOR HOTSPOT DETECTION FROM LAND/FOREST FIRES IN SUMATRA
Abstract
Himawari-8 is the last generation of the low spatial resolution satellite imagery that has capability to detect the thermal variation on the earth of every 10 minute. This must be very potential to be used for detecting land/forest fire. This paper has explored the spectral prospective of the Himawari-8 for detecting land/forest fire hotspot. The main objective for this study is to identify the potential use of Himawari-8 for detecting of land forest fire hotspot. The study area was performed in Ogan Komering Ilir, South of Sumatra, which on 2015 occur great forest/land fire event. The main process included in this study are image projection, training sample collection and spectral statistical analysis measured by calculate statistic, they are average values, standard deviation values from reflectance visible band value and brightness temperature value, beside that validation of data obtained from medium resolution data of Landsat 8 with the similar acquisition time. The study found that the Himawari-8 has good capacity to identify land/forest fire hotspot as expressed for high accuracy assessment using band 3 and band 7.
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Bessho K., Date K., Hayashi M., et al., (2016), An Introduction to Himawari-8/9-Japan's New Generation Geostationary Meteorological Satellites. Journal of the meteorological society of japan. Vol.94, No. 2, pp. 151-183.
Fatkhuroyan, Trinahwati, Panjaitan A., (2017), Forest Fire Detection in Indonesia using Satellite Himawari-8 (case study: Sumatera and Kalimantan on August-October 2015). IOP Conf. Series: Earth and Environment Science 54 012053. Doi:10.1088/1755-1315/54/1/ 012053.
Frankenberg E., McKee D., Thomas D., (2005), “Health Consequences of Forest Fires in Indonesia,†Demography, 42(1): 109–129.
Giglio L., Kendall JD, Justice CO, (1999), Evaluation of global fire detection algorithms using simulated AVHRR infrared data. International Journal of Remote Sensing, 20, 1947–1985.
Giglio L., Descloitres J., Justice CO, et al., (2003), An Enhanced Contextual Fire Detection Algorithm for MODIS. Remote Sensing of Environment, 273-283.
Hally B., Wallace LO, Reinke K., et al., (2016), Assessment of the Utility of the Advanced Himawari Imager to Detect Active Fire Over Australia. The International Archies of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-I-88, 2016 XXIII ISPRS Congress. Prague, Chezch Republic.
Justice CO, Giglio L., Korontzi S., et al., (2002), The MODIS Fire Product. Remote Sensing of Environment 83, 244-262.
Kaufman YJ, Tucker CJ, Fung I., (1990), Remote Sensing of Biomass burning in the tropics, J. Gephys, Res., 95, 9927-9939.
Kaufman YJ, Remer LA, (1994), Detection of forests using MID-IR reflectance: An application for aerosol studies. IEEE Transactions on Geoscience and Remote Sensing, 32, 672−683.
Kaufman YJ, Justice CO, Flynn LP, et al., (1998), Potential global fire monitoring from EOS-MODIS. Journal of Geophysical Research, 103, 32215– 32238.
Murdiyarso D., Lebel L., Gintings, et al., (2004), Policy responses to complex environment problem: Insights from a Science-Policy Activity on Trans boundary Haze from Vegetation Fires in Southeast Asia. Agriculture Ecosystems & Environment 104(1):47-56. DOI: 10.1016/j.agee.2004.01.005.
Suwarsono, Rokhmatuloh, Tarsoen W., (2013), Pengembangan Model Identifikasi Daerah Bekas Kebakaran Hutan dan Lahan (Burned Area) menggunakan citra modis di Kalimantan. Jurnal Penginderaan Jauh Vol. 10 No. 2 Desember 2013: 93-112.
Sugiyono, (2009), Metode Penelitian Kuantitatif Kualitatif dan R&D. Bandung: Alfabeta.
Tacconi L., (2003), Kebakaran Hutan di Indonesia: Penyebab, Biaya dan Implikasi Kebijakan. Center for International Forestry Research. ISSN 0854-9818.
Wickramasinghe C., Jones S., Reinke K., et al., (2016), Development of a Multi-Spatial Resolution Approach the Surveillance of Active Fire Lines Using Himawari-8. Remote Sensing, 8, 932.
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