Mining land identification in Wetar Island using remote sensing data
DOI:
https://doi.org/10.15243/jdmlm.2018.061.1513Keywords:
mining land identification, landsat, Vegetation Index Differencing (VIDN), Wetar IslandAbstract
Wetar Island is one of the 92 outer islands of Indonesia. On this island, there is a variety of geological potential that can be seen from the structure, formation and geological folds including mine geology potential energy and mineral resources. This makes the island having mining activities. Remote sensing data in the form of optical images, Synthetic Aperture Radar, microwave, laser, and others can be used to determine the mining activities in Wetar Island. This research was focused on mining land identification in Wetar Island. This study aimed to identify the mining land in Wetar Island using remote sensing data. The method used was the Vegetation Index Differencing, which calculated difference value of vegetation index temporally. Landsat satellite images of 1975, 1990, 2000 and 2005 were used for mining land identification. First Landsat satellite image must have had a geometric and radiometric correction. The results obtained were in the form of mining land identification and non- mining land area. These results are useful for monitoring the mining activities carried out on Wetar Island. The methods used may also be applied to monitor, identify, and evaluate various mining operations in other parts of Indonesia. Mining region that has been identified can be used for management and planning of maritime space.
References
Abrams, M.J., Rothery, D.A. and Pontual, A. 1988. A Mapping in the Oman ophiolite using enhanced Landsat Thematic Mapper images. Tectonophysics 151(1-4): 387-401.
Boesche, N.K., Mielke, C., Segl, K., Chabrillat, S., Rogass, C., Thomson, D., Lundeen, S., Brell, M. and Guanter, L. 2016. EnGeoMAP Test Data: Simulated EnMAP Satellite Data for Mountain Pass, USA and Rodalquilar, Spain. GFZ Data Services. http://doi.org/10.5880/enmap.2016.001,
BPS, 2010. Statistik Indonesia. Badan Pusat Statistik. Jakarta.
Chang, S.H. and William, C. 1983. Confirmation of the airborne biogeophysical mineral exploration technique using laboratory methods. Economic Geology 78(4): 723–736.
Clark, R.N. 2007. USGS Digital Spectral Library splib06a, US Geology Survey, Denver.
Cudahy, T., Hewson, R., Caccetta, M., Roache, A., Whitbourn, L., Connor, P., Coward, D., Mason, P., Yang, K., Huntington, J., and Quigley, M. 2009. Drill core logging of plagioclase feldspar composition and other minerals associated with Archean gold mineralization at Kambalda, Western Australia, using a bidirectional thermal infrared reflectance system. Reviews in Economic Geology 16: 223–235.
Dill, H.G. 2010. The “chessboard†classification scheme of mineral deposits - mineralogy and geology from aluminium to zirconium. Earth Science Reviews 100(1): 1–420.
Harris, J.R., Wickert, L., Lynds, T., Behnia, P., Rainbird, R., Grunsky, E., McGregor, R. and Schetselaar, E. 2011. Remote predictive mapping 3. Optical remote sensing - a review for remote predictive geological mapping in northern Canada. Geoscience Canada 38(2): 49-83.
Julien, Y., Sobrino, J.A., Mattar, C., Ruescas, A.B., Jimenez-Munoz, J.C., Soria, G., Hidalgo, V., Atitar, M., Franch, B. and Cuenca, J. 2011. Temporal analysis of NDVI and LST parameters to detect changes in the Iberian land cover between 1981 and 2001. International Journal of Remote Sensing 32(7): 2057–2068.
Mielke, C., Boesche, N.K., Rogass, C., Kaufmann, H., Gauert, C. and de Wit, M., 2014. Spaceborne mine waste mineralogy monitoring in South Africa, applications for modern push-broom missions: Hyperion/OLI and EnMAP/Sentinel-2. Remote Sensing 6(8): 6790–6816.
Noomen, M.F., Smith, K.L., Colls, J.J., Steven, M.D., Skidmore, A.K. and Van der Meer, F.D. 2008. Hyperspectral indices for detecting changes in canopy reflectance as a result of underground natural gas leakage. International Journal of Remote Sensing 29(20): 5987–6008.
Rajendran, S., al-Khirbash, S., Pracejus, B., Nasir, S., Al-Abri, A.H., Kusky, T.M. and Ghulam, A. 2012. ASTER detection of chromite bearing mineralized zones in semail ophiolite massifs of the northern Oman Mountains. Exploration strategy. Ore Geology Review 44: 121-135.
Randolph, K., Wilson, J., Tedesco, L., Li, L., Lani Pascual, D. and Soyeux, E. 2008. Hyperspectral remote sensing of cyanobacteria in turbid productive water using optically active pigments, chlorophyll a and phycocyanin. Remote Sensing Environment 112: 4009–4019.
Ramachandran, B., Justice, C.O. and Abrams, M.J. 2011. Eds.; Remote Sensing and Digital Image Processing 11, Springer Science and Business Media: Berlin, Germany, pp. 807-834.
Riaza, A., Buzzi, J., Garcia-Meléndez, E., Carrère, V. and Müller, A. 2011. A monitoring the extent of contamination from acid mine drainage in the Iberian Pyrite Belt (SW Spain) using hyperspectral imagery. Remote Sensing 3(10): 2166-2186.
Taranik, J.V. and Aslett, Z.L. 2009. Development of hyperspectral imaging for mineral exploration. Reviews in Economic Geology 16: 83–95.
Thompson, A.J.B., Hauff, P.L. and Robitaille, A.J. 1999. Alteration mapping in exploration: Application of short-wave infrared (SWIR) Spectroscopy. Society of Economic Geologists, SEG Newsletter 39(1): 16–27. Reprinted here as p. 11–23.
Van der Meer, F. and de Jong, S.M. 2000. Improving the results of spectral unmixing of Landsat Thematic Mapper imagery by enhancing the orthogonality of end-members. International Journal of Remote Sensing 21(15): 2781-2797.
Van der Meer, F.D., van der Werff, H.M.A., van Ruitenbeek, F.J.A., Hecker, C.A., Bakker, W.H., Noomen, M.F. and Woldai, T. 2012. Multi- and hyperspectral geologic remote sensing: A review. International Journal of Applied Earth Observation and Geoinformation 14(1): 112–128.
Downloads
Submitted
Accepted
Published
How to Cite
Issue
Section
License
Submission of a manuscript implies: that the work described has not been published before (except in the form of an abstract or as part of a published lecture, or thesis) that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher. 
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Scientific Journal by Eko Handayanto is licensed under a Creative Commons Attribution 4.0 International License.
Based on a work at https://ub.ac.id.
Permissions beyond the scope of this license may be available at https://ircmedmind.ub.ac.id/.
This work is licensed under a 