Analisis Permasalahan Risiko Bencana Cuaca Ekstrim Kota Cirebon
DOI:
https://doi.org/10.12962/geoid.v15i2.1660Keywords:
AWS, Landsat 8 imagery, SCA, Ground Surface TemperatureAbstract
Population density in urban areas has implications for the limited availability of green open space. Green open space has an important role in maintaining the quality of the environment and the health of the people living in the area. For this reason, an indicator in the form of Ground Surface Temperature is needed to determine the distribution and adequacy of green open space in a certain area. Continuous ground surface temperature data can then be used as the basis for the development and management of green open space. Ground Surface Temperature can be obtained by recording meteorological data using a weather station. However, the data obtained are limited in number and spatial distribution. So that the use of satellite imagery with thermal sensors becomes a solution to get Ground Surface Temperature with a wide area coverage. In this study, the analysis of temperature changes was carried out in the city of Surabaya using the Landsat 8 TIRS data from August 5 2018 to September 12 2018.The results of the Ground Surface Temperature estimation using the Single-Channel Algorithm (SCA) method were then validated with the temperature recorded at the Juanda Weather Station (BMKG Juanda) and ITS Automatic Weather Station (ITS PWS). In the period 5 August 2018 to 12 September 2018, the maximum, minimum and average of Ground Surface Temperature in Surabaya is 36oC, 20oC and 27oC. There is a difference in temperature between LST and BMKG Juanda of ± 0.11oC (11 August 2018) and +- 0.34oC (12 September 2018). While the temperature difference between LST and PWS ITS is ± 0.88oC (11 August 2018) and +- 3.22oC (12 September 2018). The results of the correlation test between SPT data with BMKG Juanda and PWS ITS showed a very strong correlation between the two data (R = 87%).
References
Hakim, A. 2018. Risma : Suhu di Surabaya Turun 2 Derajat Celcius. Antaranews Jatim.
Prasasti, I., Sambodo, K. A., & Carolita, I. 2007. Pengkajian Pemanfaatan Data TERRA-MODIS Untuk Ekstraksi Data Suhu Permukaan Lahan (SPL) Berdasarkan Beberapa Algoritma. LAPAN, 4(1), 1–8.
Rajeshwari, A., & Mani, N. D. 2014. Estimation of Land Surface Temperature of Dindigul District Using Landsat 8 Data. International Journal of Research in Engineering and Technology (IJRET), 3(5), 122–126.
Landsat 8 OLI And TIRS Calibration Notices. 2017. Retrieved June 13, 2019, From Https://www.Usgs.Gov/Land-Resources/Nli/ Landsat/Landsat-8-Oli-And-Tirs-Calibration-Notices
Walawender, J. A. P. W., Zymanowski, M. A. S., Ajto, M. O. J. H., & Okwa, A. N. B. 2014. Land Surface Temperature Patterns in the Urban Agglomeration of Krakow ( Poland ) Derived from Landsat-7 / ETM + Data, 171, 913–940. https://doi.org/10.1007/s00024-013-0685-7
Tsou, J., Zhuang, J., Li, Y., & Zhang, Y. 2017. Urban Heat Island Assessment Using The Landsat 8 Data: A Case Study In Shenzhen And Hong Kong. Urban Science, 1(1), 10.
Qudratullah, M. I., Kamus, Z., & Asrizal. 2017. Analisis Unsur-Unsur Cuaca Berdasarkan Hasil Pengukuran Automated Weather System (AWS) Tipe Vaisala Maws 201. Pillar Of Phyiscs, 9, 17–24.
Nugroho, G. A., Machfud, M. S., & Sanjaya, M. 2016. Rancang Bangun Automatic Weather Station (Aws) Menggunakan Raspberry Pi. Alhazen Journal Of Physics, II(2), 48–57.
Jaelani, L. M. 2013. Kalibrasi Radiometrik: Mengubah Digital Number (DN) ke Radiance dan/atau Reflectance. Retrieved January 16, 2019, from http://lmjaelani.com/2013/12/kalibrasi-radiometrik-mengubah-digital-number-dn-ke-radiance-danatau-reflectance/
Waas, H. J. D., & Nababan, B. 2010. Pemetaan dan analisis index vegetasi mangrove di pulau Saparua, Maluku Tengah. E-Jurnal Ilmu Dan Teknologi Kelautan Tropis, 3(2), 50–58.
Sobrino, J. A., & Raissouni, N. 2000. Toward Remote Sensing methods for land cover dynamic monitoring. Application to Morocco. International Journal of Research in Engineering and Technology (IJRET), 21, 353–366.
Sobrino, J. A., Casseles, V., & Becker, F. 1990. Significance of the remotely sensed thermal infrared measurements obtained over a citrus orchard. ISPRS J. Photogramm, 44, 343–354.
Walawender, J. P., Szymanowski, M., Hajto, M. J., & Bokwa, A. 2014. Land Surface Temperature Patterns in the Urban Agglomeration of Krakow (Poland) Derived from Landsat-7/ETM+ Data. Pure and Applied Geophysics, 171(6), 913–940. https://doi.org/10.1007/s0 0024-013-0685-7
Barsi, J. A., Barker, J. L., & Schott, J. R. 2003. An Atmospheric Correction Parameter Calculator for a Single Thermal Kanal Earth-Sensing Instrument. IEEE IGARSS. Toulouse, France.
Jimenez-Munoz, J. C., Cristobal, J., Sobrino, J. A., Soria, G., Ninyerola, M., & Pons, X. 2009. Revision of the Single- Channel Algorithm for Land Surface Temperature Retrieval From Landsat Thermal-Infrared Data. IEEE Trans. Geosci. Remote Sensing, 47, 339–349.
Sobrino, J. 2010. A Single-Channel Algorithm for Land-Surface Temperature Retrieval From ASTER Data, (February), 3–7. https://doi.org/10.11 09/LGRS.2009.2029534
Hidayat, A. 2012. Uji Pearson Product Moment dan Asumsi Klasik. Retrieved June 23, 2019, from https://www.statistikian.com/2012/07/pearson-dan-asumsi-klasik.html/amp
Sarwono, J. 2006. Metode Penelitian Kuantitatif dan Kualitatif (Edisi pert). Yogyakarta: Graha Ilmu.
Dofir, M. 2018. Sampah Warga Surabaya di TPA Benowo Dirubah Jadi Energi Listrik. Faktualnews.Co. Retrieved from https://faktualnews.co/2018/06/29/sampah-warga-surabaya-di-tpa-benowo-dirubah-jadi-energi-listrik/86411/
Istantinova, D. B., Hadiwidodo, M., & Handayani, D. S. 1995. Terhadap Konsentrasi Gas Pencemar Sulfur Dioksida ( So 2 ) Dalam Udara Ambien Di Sekitar Pt . Inti General Yaja Steel Semarang Abstrak. Tentang Konsentrasi Gas Sulfur, 10(Gas Sulfur), 1–10.
