HOTSPOT VALIDATION OF THE HIMAWARI-8 SATELLITE BASED ON MULTISOURCE DATA FOR CENTRAL KALIMANTAN

Khalifah Insan Nur Rahmi, Sayidah Sulma, Indah Prasasti

Abstract

The Advanced Himawari Imager (AHI) is the sensor aboard the remote-sensing satellite Himawari-8 which records the Earth’s weather and land conditions every 10 minutes from a geostationary orbit. The imagery produced known as Himawari-8 has 16 bands which cover visible, near infrared, middle infrared and thermal infrared wavelength potentials to monitor forestry phenomena. One of these is forest/land fires, which frequently occur in Indonesia in the dry season. Himawari-8 can detect hotspots in thermal bands 5 and band 7 using absolute fire pixel (AFP) and possible fire pixel (PFP) algorithms. However, validation has not yet been conducted to assess the accuracy of this information. This study aims to validate hotspots identified from Himawari images based on information from Landsat 8 images, field surveys and burnout data. The methodology used to validate hotspots comprises AFP and PFP extraction, determining firespots from Landsat 8, buffering at 2 km from firespots, field surveys, burnout data, and calculation of accuracy. AFP and PFP hotspot validation of firespots from Landsat-8 is found to have higher accuracy than the other options. In using Himawari-8 hotspots to detect land/forest fires in Central Kalimantan, the AFP algorithm with 2km radius has accuracy of 51.33% while the PFP algorithm has accuracy of 27.62%.

Keywords

hotspot; Himawari-8; validation; Landsat 8; forest/land fire

Full Text:

PDF

References

Choi, Y. Y. & Suh, M.-S. (2018). Development of Himawari-8/ Advanced Himawari Imager (AHI) land surface temperature retrieval algorithm. Remote Sensing, 10(12). doi:10.3390/rs10122013

Fitriana, H. L., Sulma, S., Suwarsono, Zubaidah, A., & Prasasti, I. (2018). Spectral analysis of the Himawari-8 data for hotspot detection from land/forest fires in Sumatra. International Journal of Remote Sensing and Earth Sciences 15(1), 15–24.

Giglio, L., Csiszar, I., & Justice, C. O. (2006). Global distribution and seasonality of active fires as observed with the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Sensors. Journal of Geophysical Research, 111, 1–12. doi:10.1029/2005JG000142

Giglio, L., Schroeder, W. & Justice, C.O. (2016). The Collection of 6 MODIS active fire detection algorithm and fire products. Remote Sensing of Environment 178, 31–41. doi:10.1016/j.rse.2016.02.054

Guangmeng, G., & Mei, Z. (2004). Using MODIS land surface temperature to evaluate forest fire risk of Northeast China. IEEE Geoscience and Remote Sensing Letters 1(2), 98–100. doi:10.1109/LGRS.2004.826550.

Haikal, T. (2014). Analisis Normalised Difference Wetness Index (NDWI) Dengan Menggunakan Data Citra Landsat 5 TM (Studi Kasus : Provinsi Jambi Path/Row : 125/61) [Normalised difference wetness index (NDWI) analysis using Landsat 5 TM image data (Case study: Jambi Province path/row: 125/61). Undergraduate Thesis, IPB University.

Hally, B., Wallace, L., Reinke, K., & Jones, S. (2016). Assessment of the utility of the Advanced Himawari Imager to detect active fire over Australia. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 41, 65–71. doi:10.5194/isprsarchives-XLI-B8-65-2016

Hayasaka, H., Noguchi, I., Putra, E. I. Yulianti, N., & Vadrevu, K. (2014). Peat-Fire-Related Air Pollution in Central Kalimantan, Indonesia. Environmental Pollution, 195, 257–66. doi:10.1016/j.envpol.2014.06.031

Zubaidah, A., Vetrita, Y., & Rokhis Khomarudin, M. (2014). Validasi Hotspot MODIS di Wilayah Sumatera dan Kalimantan Berdasarkan Data Penginderaan Jauh SPOT-4 Tahun 2012: 1–15 [Validation of MODIS hotspots in Sumatra and Kalimantan region based on SPOT-4 remote sensing data for 2012: 1–15]. Jurnal Penginderaan Jauh 11, 1–14.

JMA (2017). Himawari Standard Data User ’s Guide. Tokyo: JMA.

Kushardono, D. (2012). Kajian Satelit Penginderaan Jauh Cuaca Generasi Baru Himawari 8 Dan 9 [The new generation of satellite weather sensing satellite studies Himawari 8 and 9]. Jurnal Inderaja, 3(5), 41–49.

LAPAN (2015). Pedoman Pemanfaatan Data Landsat-8 Untuk Deteksi Daerah Terbakar (Burned Area)[ Data utilization guidelines Landsat-8 for detection of fire region (burned area). Jakarta: Pusat Pemanfaatan Penginderaan Jauh LAPAN.

LAPAN (2016). Informasi Titik Panas (Hotspot) Kebakaran Hutan/Lahan [Information on forest/land fire hotspots]. In Parwati, A. Widipaminto, Suwarsono, A. Zubaidah, A. Indrajat, & N. D. Salyasari (Eds.) (1st ed). Jakarta: Deputi Bidang Penginderaan Jauh LAPAN.

Noor, M. (2019). Kebakaran Lahan Gambut [Peatland fire]. Yogyakarta: UGM Press.

Osaki, M., Nursyamsi, D., Noor, M., Wahyunto, & Segah, H. (2016). Peatland in Indonesia. In M. Osaki & N. Tsuji (Eds.), Tropical Peatland Ecosystems (pp. 49–58). Tokyo: Springer. doi:10.1007/978-4-431-55681-7_3

Pandjaitan, B. S. & Panjaitan, A. (2015). Pemanfaatan Data Satelit Cuaca Generasi Baru Himawari 8 Untuk Mendeteksi Asap Akibat Kebakaran

Hutan Dan Lahan Di Wilayah Indonesia (Studi Kasus : Kebakaran Hutan Dan Lahan Di Pulau Sumatera Dan Kalimantan Pada Bulan September 2015) [Utilization of new generation Himawari 8 weather satellite data to detect smoke due to forest and land fires in Indonesian territory (Case Study: forest and land fires on Sumatra and Kalimantan Island in September 2015)] Seminar Nasional Penginderaan Jauh 2015: 636–51. doi:10.13140/RG.2.2.27568.05120

Puspa, A., Sukaesih, I. and Syaufina, L. (2016). Hotspot pattern distribution in peat land area in Sumatera based on spatio-temporal clustering. Procedia Environmental Science 33, 635–45. doi: 10.1016/j.proenv.2016.03.118

Suwarsono, Sulma, S., Fitriana, H. L. Zubaidah, A. Prasasti, I. & Rokhis Khomarudin, M. (Submitted for publication). Fire Hotspot Detection Using Advance Himawari Imager Sensor in Sumatera and Kalimantan Using Multithreshold Technique. TELKOMNIKA (Telecommunication Computing Electronics and Control) 13(2), 1–8.

Tanpipat, V., Kiyoshi, H., & Prayoonyong, N. (2009). MODIS hotspot validation over Thailand. Remote Sensing 1(4), 1043–1054. doi:10.3390/rs1041043

USGS (2019). Landsat 8 (L8) Data Users Handbook Version 5.0. South Dakota: USGS.

Vlassova, L., Pérez-Cabello, F., Mimbrero, M. R., Montorio Lloveria, R. & Garcia-Martin, A. (2014). Analysis of the relationship between land surface temperature and wildfire severity in a series of Landsat Images, Remote Sensing 6, 6136–62. doi:10.3390/rs6076136

DOI: http://dx.doi.org/10.30536/j.ijreses.2019.v16.a3293

Refbacks

  • There are currently no refbacks.