Pemetaan Cepat Kawasan Terdampak Bencana Longsor dan Banjir di Kabupaten Bangli, Provinsi Bali
Theresia Retno Wulan(1*), Wiwin Ambarwulan(2), Anggara S. Putra(3), Mega D Putra(4), Dwi Maryanto(5), Ferrari Pinem(6), Edwin Maulana(7)
(1) Badan Informasi Geospasial, Cibinong Science Center, Bogor, Jawa Barat
(2) Parangtritis Geomaritime Science Park, Parangtritis, Kretek Bantul, Yogyakarta
(3) Statistika, Universitas Islam Indonesia, Yogyakarta
(4) Parangtritis Geomaritime Science Park, Parangtritis, Kretek Bantul, Yogyakarta
(5) Badan Informasi Geospasial, Cibinong Science Center, Bogor, Jawa Barat
(6) Badan Informasi Geospasial, Cibinong Science Center, Bogor, Jawa Barat
(7) Parangtritis Geomaritime Science Park, Parangtritis, Kretek Bantul, Yogyakarta
(*) Corresponding Author
Abstract
Abstrak
Teknologi penginderaan jauh mengalami perkembangan yang sangat pesat. Salah satunya adalah teknologi akuisisi data dengan menggunakan UAV (Unmanned Aerial Vehicle). Teknologi UAV dapat dipergunakan dalam berbagai bidang, salah satunya adalah bidang kebencanaan. Tujuan penelitian ini adalah untuk melakukan pemetaan secara cepat kawasan terdampak bencana banjir dan longsor di Kabupaten Bangli, Bali dengan menggunakan teknologi UAV. Metode yang digunakan adalah pemotretan udara dengan UAV, survei lapangan dan analisis laboratorium. Pemotretan udara dilakukan satu hari pasca kejadian longsor dengan ketinggian jelajah pesawat antara 100-120 meter di atas permukaan tanah. Resolusi spasial yang dihasilkan antara 4,5 - 6,5 cm. Wilayah yang berhasil dipetakan adalah wilayah yang terdampak banjir dan longsor di Desa Songan A serta Songan B, wilayah terdampak banjir bandang Yeh Mampeh di Desa Batur Selatan, serta wilayah terdampak longsor di Desa Sukawana dan Desa Awan. Berdasarkan hasil pemotretan udara, dapat diketahui luasan daerah terdampak longsor. Lebih lanjut, strategi rehabilitasi dan rekonstruksi dapat dilakukan dengan menggunakan hasil pemotretan udara.
Abstrak
Remote sensing technology is experiencing rapid developments. One of which is in the field of data acquisition that has currently adopted the use of Unmanned Aerial Vehicle (UAV). UAV technology is, for instance, employed in various studies related to disasters. This research aimed to perform a rapid mapping of flood- and landslide-affected areas in Bangli Regency, Bali using UAV technology. The applied methods included UAV-assisted aerial photography, field survey, and laboratory analysis. The aerial photography was conducted one day after the landslide event and at a recording altitude of 100-120 m above the ground. The spatial resolution produced in the photography was 4.5-6.5 cm. The mapped areas were the ones affected by floods and landslides in Songa A and Songa B Villages, flash floods in Yeh Mampeh, Batur Selatan Village, and landslides in Sukawana and Awan Villages. The aerial photography also provided the extent of the landslide-affected areas. Therefore, the post-disaster rehabilitation and reconstruction strategies can be implemented using the results of the aerial photography.
Keywords
Full Text:
PDFReferences
Amir, O., Tuvia, B., Jean, M.W., Jorge S., Daniel B., Aharon, A., & William T.A. (2017). Evaluation of remote dielectric sensing (ReDS) technology-guided therapy for decreasing heart failure re-hospitalizations. International Journal of Cardiology, 240, 279 – 284.
Andres, S., Damien A., Isabelle M., Therese L., & Laurent D. (2017). Ontology-based classification of remote sensing images using spectral rules. Journal of Computer & Geosciences, 102, 158 – 166.
Badan Pusat Statistik. (2016). Kabupaten Bangli Dalam Infografis 2016. Bangli: BPS Kabupaten Bangli.
Bemis, S.P., Steven M., Darren T., Mike R.J., Sinan A., Sam T.T., & Hasnain, A.B. (2014). Ground-based and UAV-Based photogrammetry: A multi-scale, high-resolution mapping tool for structural geology and paleoseismology. Journal of Structural Geology, 69, 163 – 178.
Djomo, A.N. & Cedric, D.C. (2017). Tree allometric equations for estimation of above, below and total biomass in a tropical moist forest: Case study with application to remote sensing. Journal of Forest Ecology and Management, 391, 184 – 193.
Guo, K., Tao Z., Dejuan J., Cheng T., & Hua Z. (2017). Variability of Yellow River turbid plume detected with satellite remote sensing during water-sediment regulation. Journal of Continental Shelf Research, 135, 74-85.
Marfai, M.A., Rosaji, F.S.C., Cahyadi, A., & Ghozali, M.R. (2014). Application of Unmanned Aerial Vehicle (UAV) for Shorline Anelysis. Paper in The 6th Indonesia Japan Joint Scientific Symposium (IJJSS) 2014.
Marfai, M.A., Rosaji, F.S.C., Cahyadi, A., & Ghozali, M.R. (2014). Analisis Dinamika Pantai di Teluk Baron Menggunakan Teknologi Pesawat Tanpa Awak. Prosiding Pekan Ilmiah Tahunan Ikatan Geograf Indonesia (PIT IGI) 2014.
Masruroh, H., Sartohadi, J., & Setiawan, A. (2016). Membangun Metode Identifikasi Longsor Berbasis Foto Udara Format Kecil di DAS Bompon, Magelang, Jawa Tengah. Majalah Geografi Indonesia, 30(2), 169 – 181.
Maulana, E., & Wulan, T.R. (2015a). Pemotretan Udara dengan UAV Untuk Mendukung Kegiatan Konservasi Kawasan Gumuk Pasir Parangtritis. Simposium Nasional Sains Geoinformasi IV 2015: Penguatan Peran Sains Informasi Geografi dalam Mendukung Penanganan Isyu-Isyu Strategis Nasional.
Maulana, E., & Wulan, T.R. (2015b). Pemetaan Multi-Rawan Kabupaten Malang Bagian Selatan dengan Menggunakan Pendekatan Bentangalam. Simposium Nasional Sains Geoinformasi IV 2015: Penguatan Peran Sains Informasi Geografi dalam Mendukung Penanganan Isyu-Isyu Strategis Nasional.
Neugirg, F., Stark, M., Kaiser, A., Vlacilova, M., Seta, M. D., Vergari, F., Schmidt, J., Becht, M., & Haas, F. (2016). Erosion processes in calanchi in the Upper Orcia Valley, Southern Tuscany, Italy based on multitemporal high - resolution terrestrial LiDAR and UAV surveys. Journal of Geomorphology, 269, 8-22.
Niethammer, U., Jamer, M.R., Rothmund, S., Travelletti, J., & Joswig, M. (2012). UAV-based remote sensing of the Super-Sauze landslide: Evaluation and results. Journal of Engienring Geology, 128, 2 – 11.
Pineux, N., LIsein, J., Swerts, G., Bielders, C.L., Lejeune, P., Colinet, G., & Degre, A. (2017). Can DEM time series produced by UAV be used to quantify diffuse erosion in an agricultural watershed?. Journal of Geomorphology, 280, 122-136.
Purwanto, T.H. (2017). Pemanfaatan Foto Udara Format Kecil untuk Ekstraksi Digital Elevation Model dengan Metode Stereoplotting. Majalah Geografi Indonesia, 31(1), 73 – 89.
Putra, A. S., Maulana, E., Rahmadana, A. D. W., Wulan, T. R., Mahendra, I. W. W. Y., & Putra, M. D.(2016). Uji Akurasi Foto Udara Dengan Menggunakan Data Uav Pada Kawasan Padat Pemukiman Penduduk (Studi Kasus: Kawasan Padat Sayidan, Daerah Istimewa Yogyakarta). Prosiding Seminar Nasional Pengindraan Jauh 2016. ISBN: 978-979-1458-99-3.
Rhee, J. & Jungho I. (2017). Meteorological drought forecasting for ungauged areas based on machine learning: Using long-range climate forecast and remote sensing data. Journal of Agricurtural and Forest Meteorology, 237, 105-122.
Rozenstein, O. & Jan A. (2017). A review of progress in identifying and characterizing biocrusts using proximal and remote sensing. International Journal of Applied Earth Observation and Geoinformation, 57, 245 – 255.
Stumpf, A., Malet, J. P., Kerle, N., Niethammer, U., & Rothmund, S. (2013). Image-based mapping of surface fissures for the investigation of landslide dynamics. Journal of Geomorphology, 186, 12-27.
Taubenbock, H., Standfus, I., Wurm, M., Krehl, A., & Siedentop, S. (2017). Measuring morphological polycentricity - A comparative analysis of urban mass concentrations using remote sensing data. Journal of Computers, Environment and Urban Systems, 64, 42-56.
Trigueros, C. R., Pedro A. N., Juan J., A., Johannes E. H., Margarita P., Sergio C., Peter D., & Emilio N. (2017). Effects of saline reclaimed waters and deficit irrigation on Citrus physiology assessed by UAV remote sensing. Journal of Agricultural Water Management, 183, 60 – 69.
Vetrivel, A., Markus G., Norman K., Francesco N., & George V. (2017). Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning. IPRS Journal of Photogrametry and Remote Sensing. https://doi.org/10.1016/j.isprsjprs.2017.03.001.
Watanabe, Y. & Kawahara, Y. (2016). UAV photogrammetry for monitoring changes in river topography and vegetation. 12th International Conference on Hydroinformatics (HIC) 2016. Procedia Enginering, 154, 317-325.
DOI: https://doi.org/10.22146/mgi.26230
Article Metrics
Abstract views : 5627 | views : 10530Refbacks
- There are currently no refbacks.
Copyright (c) 2017 Majalah Geografi Indonesia
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Volume 35 No 2 the Year 2021 for Volume 39 No 1 the Year 2025
ISSN 0215-1790 (print) ISSN 2540-945X (online)
Statistik MGI