PENGINDERAAN JAUH UNTUK PENDETEKSIAN AWAL POTENSI TEMBAGA DI SUMBAWA

Atriyon Julzarika

Abstract


Tembaga merupakan salah satu jenis mineral penting yang memiliki banyak fungsi dalam berbagai aplikasi. Penelitian ini bertujuan untuk pendeteksian awal tembaga menggunakan data penginderaan jauh. Lokasi penelitian terletak di Sumbawa. Data penginderaan jauh yang digunakan berupa Landsat, ALOS Palsar, X SAR, SRTM C, dan Satelit Geodesi. Landsat digunakan untuk ekstraksi parameter geologi berupa penutup lahan dan perubahannya, bentuk lahan, dan alterasi hidrotermal. ALOS PALSAR, X SAR, dan SRTM C digunakan untuk pembuatan DTM (Digital Terrain Model). Integrasi DTM berguna untuk ekstraksi parameter geologi lainnya berupa struktur dan formasi geologi. DTM yang digunakan memiliki akurasi vertikal + 1,5 m. Data Satelit Geodesi bisa digunakan untuk ekstraksi gaya berat, medan magnet, geodinamika, serta densitas batuan. Berbagai parameter geologi ini diekstraksi dengan metode VIDN, integrasi, dip and strike, interferometri, backscattering, alterasi hidrotermal, geodesi fisis, dan klasifikasi digital berbasis objek. Semua parameter geologi yang telah diekstrak dikorelasikan antar data, sehingga bisa digunakan untuk deteksi potensi tembaga. Informasi geospasial deteksi awal tembaga dan ekstraksi parameter geologinya merupakan produk yang dihasilkan dari penelitian ini. Informasi geospasial ini menggunakan referensi ketelitian ASPRS Accuracy Data for Digital Geospatial Data.

Copper is one of the essential mineral that has many functions in variety of applications. This research aimed to detect the copper potential using remote sensing data. The research location is Sumbawa. Remote sensing data used were Landsat, ALOS PALSAR, X SAR, SRTM C, and Satellite Geodesy. Landsat was used for geological parameters extraction such as land cover and its changes, geomorphology, landforms, and hydrothermal alteration. ALOS PALSAR, X SAR and SRTM C were used for height model integration (DTM). This DTM was useful for the other geological parameters extraction, such as geological structures and formations. DTM used has vertical accuracy + 1,5 m. Geodesy Satellite data can be used for the extraction of gravity, magnetic field, geodynamics, and rock densities. These various geological parameters were extracted by VIDN, integration, dip and strike, interferometry, backscattering, hydrothermal alteration, physical geodesy, and classification based digital objects. All of those parameters were then correlated for copper potential detection. The results obtained were geospatial information of copper potential and geological parameters at a scale of 1: 50.000 with reference ASPRS Accuracy Data for Digital Geospatial Data.


 


Keywords


remote sensing, Sumbawa, copper, geological parameters.

References


Amer, R., Kusky, T., & El Mezayen, A., 2012. Remote Sensing Detection of Gold Related Alteration Zones in Um Rus area, Central Eastern Desert of Egypt. Advances in Space Research, 49(1), 121-134.

ASPRS, 2014. ASPRS Accuracy Standard for Digital Geospatial Data. ASPRS. Amerika Serikat.

Bedini, E., 2011. Mineral Mapping in the Kap Simpson Complex, Central East Greenland, using HyMap and ASTER Remote Sensing Data. Advances in Space Research, 47(1), 60-73.

Canty M. J., 2010. Image Analysis, Classification and Change Detection in Remote Sensing, With Algorithms for ENVI/IDL, Second edition. Taylor & Francis, CRC Press.

Canty, M. J., & Nielsen, A. A., 2006. Visualization and Unsupervised Classification of Changes in Multispectral Satellite Imagery. International Journal of Remote Sensing, 27(18), 3961-3975.

Canty, M. J., & Nielsen, A. A., 2008. Automatic Radiometric Normalization of Multitemporal Satellite Imagery with the Iteratively Re-Weighted MAD Transformation. Remote Sensing of Environment, 112(3), 1025-1036.

Ciampalini, A., Garfagnoli, F., Antonielli, B., Moretti, S., & Righini, G., 2013. Remote Sensing Techniques using Landsat ETM+ Applied to the Detection of Iron Ore Deposits in Western Africa. Arabian Journal of Geosciences, 6(11), 4529-4546.

Desheng, Y., Gang, C., & Xiaoping, L., 2010. Application of Geological Interpretation and Mineralization Information Extracting by Remote-Sensing in mineral Resource Evaluating. Journal of Henan Polytechnic U-niversity: Natural Science, 29(2), 184-189.

Freeden, W. (Ed.), 2010. Handbook of Geomathematics. Springer Science & Business Media.

Gabr, S., Ghulam, A., & Kusky, T., 2010. Detecting Areas of High-Potential Gold Mineralization using ASTER data. Ore Geology Reviews, 38(1), 59-69.

Jin, H., Mountrakis, G., & Stehman, S. V., 2014. Assessing Integration of Intensity, Polarimetric Scattering, Interferometric Coherence and Spatial Texture Metrics in PALSAR-Derived Land Cover Classification. ISPRS Journal of Photogrammetry and Remote Sensing, 98, 70-84.

Julzarika, A., Susanto, dan Sutanto, A., 2013. Pengembangan Model Standar Pemanfaatan Data Penginderaan Jauh (Optik dan SAR) untuk Identifikasi Sumber Daya Mineral Tembaga. Laporan Penelitian Inhouse Tahun 2013. LAPAN. Jakarta.

Julzarika, A., Tjahjaningsih, A., Sutanto, A., Nugroho, U. C., 2015. Pemanfaatan data penginderaan jauh untuk identifikasi tambang emas di Geumpang Aceh. Laporan penelitian inhouse 2015. LAPAN. Jakarta.

Julzarika, A., 2015. Integration of Height Model using SRTM C, X SAR, Aster GDEM, and ALOS Palsar. Asian Conference on Remote Sensing.

KESDM, 1999. Kepmentamben no 1519.K/20/MPE/1999. KESDM, Jakarta.

KESDM, 2009. UU Minerba No.4 tahun 2009. KESDM, Jakarta.

Leverington, D. W., & Moon, W. M., 2012. Landsat-TM-Based Discrimination of Lithological Units Associated with the Purtuniq Ophiolite, Quebec, Canada. Remote Sensing, 4(5), 1208-1231.

Liu, L., Zhou, J., Yin, F., Feng, M., Zhang, B., 2013. The reconnaissance of mineral resources through ASTER data–based image processing, interpreting and ground inspection in the Jiafushaersu area, West Junggar, Xinjiang (China). J. Earth Science.

Liu, L., Zhuang, D. F., Zhou, J., & Qiu, D. S., 2011. Alteration Mineral Mapping using Masking and Crosta Technique for Mineral Exploration in Mid-Vegetated Areas: A Case Study in Areletuobie, Xinjiang (China). International Journal of Remote Sensing, 32(7), 1931-1944.

Maryono, A., Setijadji, L. D., Arif, J., Harrison, R., 2014. Gold, Silver, and Copper Metallogeny of the Eastern Sunda Magmatic Arc Indonesia. Majalah Geologi Indonesia, 29 (2), 85-99.

Nielsen A. A., 2010. Kernel Maximum Autocorrelation Factor and Minimum Noise Fraction Transformations. IEEE Transactions on Image Processing, 20 (3).

Peters, S. G., King, T. V., Mack, T. J., & Chornack, M. P., 2011. Summaries of Important Areas for Mineral Investment and Production Opportunities of Nonfuel Minerals in Afghanistan (No. 2011-1204). US Geological Survey.

Prasad, K., & Prabhu, G. K., 2011. Diag-AID: A Diagnostic Aid for Medical Image Enhancement using Colour Coding and Modified Histogram Equalisation Techniques. International Journal of Medical Engineering and Informatics, 3(3), 223-233.

Rajendran, S., Nasir, S., Kusky, T. M., Ghulam, A., Gabr, S., & El-Ghali, M. A., 2013. Detection of Hydrothermal Mineralized Zones Associated with Listwaenites in Central Oman using ASTER Data. Ore Geology Reviews, 53, 470-488.

Sabins, F. F., 1999. Remote Sensing for Mineral Exploration. Ore Geology Reviews, 14(3), 157-183.

Schimmer, R., 2008. A Remote Sensing and GIS Method for Detecting Land Surface Areas Covered by Copper Mill Tailings. Pecora 17–The Future of Land Imaging. Denver, Colorado.

Schölkopf, B., Smola, A., & Müller, K. R., 1998. Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation, 10(5), 1299-1319.

Schroeder, T. A., Cohen, W. B., Song, C., Canty, M. J., & Yang, Z., 2006. Radiometric Correction of Multi-Temporal Landsat Data for Characterization of Early Successional Forest Patterns in Western Oregon. Remote Sensing of Environment, 103(1), 16-26.

Seigel, H. O., Brcic, I., & Mistry, P., 1995. A Guide to High Precision Land Gravimeter Surveys. Scintrex Limited, 222.

Shen, P., Shen, Y. C., Liu, T. B., Pan, H. D., Meng, L., Song, G. X., & Dai, H. W., 2010. Discovery of the Xiemisitai Copper Deposit in Western Junggar, Xinjiang and its Geological Significance. Xinjiang Geology, 28(4), 413-418.

Radke, R. J., Andra, S., Al-Kofahi, O., & Roysam, B., 2005. Image Change Detection Algorithms: a Systematic Survey. IEEE Transactions on Image Processing, 14(3), 294-307.

USGS, 2010. Porphyry Copper Deposit Model. Scientific Investigations Report 2010–5070–B. USGS. Amerika Serikat.

Wu L., 2011. The Geological Structure and Mineral Resources of Thailand J. Mineral Deposits.

Youssef, A. M., Pradhan, B., Sabtan, A. A., & El-Harbi, H. M., 2012. Coupling of Remote Sensing Data Aided with Field Investigations for Geological Hazards Assessment in Jazan Area, Kingdom of Saudi Arabia. Environmental Earth Sciences, 65(1), 119-130.

QIN, Y. Z., & LIU, L. M., 2010. Extraction of Information on Structure, Rock and Alteration by ETM+ Remote Sensing at West Beishan Mountain, Gansu Province. Southern Metals, 6, 009.

Zhou, J., Liu, L., Jiang, D.,Zhuang, D., Mansaray L. R., dan Zhang B., 2013. Targeting Mineral Resources with Remote Sensing and Field Data in the Xiemisitai Area, West Junggar, Xinjiang, China. Journal Remote Sensing, 5(7), 3156-3171




DOI: http://dx.doi.org/10.14203/risetgeotam2018.v28.434

Refbacks

  • There are currently no refbacks.


Copyright (c) 2018 Jurnal RISET Geologi dan Pertambangan

Copyright of Journal RISET Geologi dan  Pertambangan (e-ISSN 2354-6638 p-ISSN 0125-9849). Powered by OJS

  

Indexed by:

   

      

 

Plagiarism checker: