{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T00:56:11Z","timestamp":1769388971584,"version":"3.49.0"},"reference-count":54,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,8,3]],"date-time":"2021-08-03T00:00:00Z","timestamp":1627948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100008783","name":"National Research Council of Science and Technology","doi-asserted-by":"publisher","award":["No. CRC-15-06-KIGAM"],"award-info":[{"award-number":["No. CRC-15-06-KIGAM"]}],"id":[{"id":"10.13039\/501100008783","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2020R1A2C2005439"],"award-info":[{"award-number":["NRF-2020R1A2C2005439"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper demonstrates an integrative 3D model of short-wave infrared (SWIR) hyperspectral mapping and unmanned aerial vehicle (UAV)-based digital elevation model (DEM) for a carbonate rock outcrop including limestone and dolostone in a field condition. The spectral characteristics in the target outcrop showed the limestone well coincided with the reference spectra, while the dolostone did not show clear absorption features compared to the reference spectra, indicating a mixture of clay minerals. The spectral indices based on SWIR hyperspectral images were derived for limestone and dolostone using aluminum hydroxide (AlOH), hydroxide (OH), iron hydroxide (FeOH), magnesium hydroxide (MgOH) and carbonate ion (CO32\u2212) absorption features based on random forest and logistic regression models with an accuracy over 87%. Given that the indices were derived from field data with consideration of commonly occurring geological units, the indices have better applicability for real world cases. The integrative 3D geological model developed by co-registration between hyperspectral map and UAV-based DEM using best matching SIFT descriptor pairs showed the 3D rock formations between limestone and dolostone. Moreover, additional geological information of the outcrop was extracted including thickness, slope, rock classification, strike, and dip.<\/jats:p>","DOI":"10.3390\/rs13153037","type":"journal-article","created":{"date-parts":[[2021,8,4]],"date-time":"2021-08-04T02:16:07Z","timestamp":1628043367000},"page":"3037","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Integrative 3D Geological Modeling Derived from SWIR Hyperspectral Imaging Techniques and UAV-Based 3D Model for Carbonate Rocks"],"prefix":"10.3390","volume":"13","author":[{"given":"Huy Hoa","family":"Huynh","sequence":"first","affiliation":[{"name":"Department of Astronomy, Space Science and Geology, Chungnam National University, Daejeon 34134, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4518-2923","authenticated-orcid":false,"given":"Jaehung","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Geological Sciences, Chungnam National University, Daejeon 31134, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1298-4839","authenticated-orcid":false,"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6005-6626","authenticated-orcid":false,"given":"Nam Hoon","family":"Kim","sequence":"additional","affiliation":[{"name":"Convergence Research Center for Development of Mineral Resources (DMR), Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8168-1244","authenticated-orcid":false,"given":"Bum Han","family":"Lee","sequence":"additional","affiliation":[{"name":"Convergence Research Center for Development of Mineral Resources (DMR), Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Korea"}]},{"given":"Sang-Mo","family":"Koh","sequence":"additional","affiliation":[{"name":"Convergence Research Center for Development of Mineral Resources (DMR), Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Korea"}]},{"given":"Sehyun","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Earth and Environmental Sciences, Korea University, Seoul 02841, Korea"}]},{"given":"Trung Hieu","family":"Pham","sequence":"additional","affiliation":[{"name":"Faculty of Geology, University of Science, VNU-HCM, Ho Chi Minh City 008428, Vietnam"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1130\/0016-7606(1975)86<1085:POEIMA>2.0.CO;2","article-title":"Proportions of Exposed Igneous, Metamorphic, and Sedimentary Rocks","volume":"86","author":"Blatt","year":"1975","journal-title":"GSA Bull."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Blatt, H., Middleton, G., and Murray, R. (1972). Origin of Limestones. Origin of Sedimentary Rocks, Prentice-Hall.","DOI":"10.1097\/00010694-197305000-00019"},{"key":"ref_3","unstructured":"Pettijohn, F.J. (1975). Limestones and Dolomite. Sedimentary Rocks, Harper & Row. [3rd ed.]."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1086\/518051","article-title":"Carbonate Preservation in Shallow Marine Environments: Unexpected Role of Tropical Siliciclastics","volume":"115","author":"Best","year":"2007","journal-title":"J. Geol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4149","DOI":"10.3390\/rs6054149","article-title":"Determination of Carbonate Rock Chemistry Using Laboratory-Based Hyperspectral Imagery","volume":"6","author":"Zaini","year":"2014","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.mineng.2016.12.013","article-title":"Discriminating ore and waste in a porphyry copper deposit using short-wavelength infrared (SWIR) hyperspectral imagery","volume":"105","author":"Dalm","year":"2017","journal-title":"Miner. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"274903","DOI":"10.1155\/2012\/274903","article-title":"The Effects of Spectral Pretreatments on Chemometric Analyses of Soil Profiles Using Laboratory Imaging Spectroscopy","volume":"2012","author":"Buddenbaum","year":"2012","journal-title":"Appl. Environ. Soil Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1306\/03021514121","article-title":"Hyperspectral imaging for the determination of bitumen content in Athabasca oil sands core samples","volume":"99","author":"Speta","year":"2015","journal-title":"AAPG Bull."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lorenz, S., Salehi, S., Kirsch, M., Zimmermann, R., Unger, G., Vest S\u00f8rensen, E., and Gloaguen, R. (2018). Radiometric Correction and 3D Integration of Long-Range Ground-Based Hyperspectral Imagery for Mineral Exploration of Vertical Outcrops. Remote Sens., 10.","DOI":"10.3390\/rs10020176"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chung, B., Yu, J., Wang, L., Kim, N.H., Lee, B.H., Koh, S., and Lee, S. (2020). Detection of Magnesite and Associated Gangue Minerals using Hyperspectral Remote Sensing\u2014A Laboratory Approach. Remote Sens., 12.","DOI":"10.3390\/rs12081325"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kirsch, M., Lorenz, S., Zimmermann, R., Tusa, L., M\u00f6ckel, R., H\u00f6dl, P., Booysen, R., Khodadadzadeh, M., and Gloaguen, R. (2018). Integration of Terrestrial and Drone-Borne Hyperspectral and Photogrammetric Sensing Methods for Exploration Mapping and Mining Monitoring. Remote Sens., 10.","DOI":"10.3390\/rs10091366"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Krupnik, D., and Khan, S.D. (2020). High-Resolution Hyperspectral Mineral Mapping: Case Studies in the Edwards Limestone, Texas, USA and Sulfide-Rich Quartz Veins from the Ladakh Batholith, Northern Pakistan. Minerals, 10.","DOI":"10.3390\/min10110967"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.sedgeo.2016.09.008","article-title":"Study of Upper Albian rudist buildups in the Edwards Formation using ground-based hyperspectral imaging and terrestrial laser scanning","volume":"345","author":"Krupnik","year":"2016","journal-title":"Sediment. Geol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.proenv.2015.03.032","article-title":"The Potential of UAV-based Remote Sensing for Supporting Precision Agriculture in Indonesia","volume":"24","author":"Rokhmana","year":"2015","journal-title":"Procedia Environ. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Teodoro, A., Santos, P., Espinha Marques, J., Ribeiro, J., Mansilha, C., Melo, A., Duarte, L., Rodrigues de Almeida, C., and Flores, D. (2021). An Integrated Multi-Approach to Environmental Monitoring of a Self-Burning Coal Waste Pile: The S\u00e3o Pedro da Cova Mine (Porto, Portugal) Study Case. Environments, 8.","DOI":"10.3390\/environments8060048"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.jseaes.2017.11.028","article-title":"Sequence stratigraphy in the middle Ordovician shale successions, mid-east Korea: Stratigraphic variations and preservation potential of organic matter within a sequence stratigraphic framework","volume":"152","author":"Byun","year":"2018","journal-title":"J. Asian Earth Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.sedgeo.2006.03.024","article-title":"Sequence stratigraphy of the Taebaek Group (Cambrian\u2013Ordovician), mideast Korea","volume":"192","author":"Kwon","year":"2006","journal-title":"Sediment. Geol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/BF02910579","article-title":"The Cambrian-Ordovician stratigraphy of the Taebaeksan Basin, Korea: A review","volume":"9","author":"Choi","year":"2005","journal-title":"Geosci. J."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/S0012-8252(00)00029-5","article-title":"Tectonic and sedimentary evolution of the Korean Peninsula: A review and new view","volume":"52","author":"Chough","year":"2000","journal-title":"Earth-Sci. Rev."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF02910269","article-title":"Cyclic tidal successions of the Middle Ordovician Maggol Formation in the Taebaeg area, Kangwondo, Korea","volume":"3","author":"Woo","year":"1999","journal-title":"Geosci. J."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Behmann, J., Acebron, K., Emin, D., Bennertz, S., Matsubara, S., Thomas, S., Bohnenkamp, D., Kuska, M.T., Jussila, J., and Salo, H. (2018). Specim IQ: Evaluation of a New, Miniaturized Handheld Hyperspectral Camera and Its Application for Plant Phenotyping and Disease Detection. Sensors, 18.","DOI":"10.3390\/s18020441"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/S0034-4257(98)00032-7","article-title":"Derivative Analysis of Hyperspectral Data","volume":"66","author":"Tsai","year":"1998","journal-title":"Remote. Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/36.3001","article-title":"A transformation for ordering multispectral data in terms of image quality with implications for noise removal","volume":"26","author":"Green","year":"1988","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Dabiri, Z., and Lang, S. (2018). Comparison of Independent Component Analysis, Principal Component Analysis, and Minimum Noise Fraction Transformation for Tree Species Classification Using APEX Hyperspectral Imagery. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7120488"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1382","DOI":"10.1016\/j.rse.2008.07.018","article-title":"Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects","volume":"113","author":"Kennedy","year":"2009","journal-title":"Remote. Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Moyano, J., Nieto-Juli\u00e1n, J.E., Ant\u00f3n, D., Cabrera, E., Bienvenido-Huertas, D., and S\u00e1nchez, N. (2020). Suitability Study of Structure-from-Motion for the Digitisation of Architectural (Heritage) Spaces to Apply Divergent Photograph Collection. Symmetry, 12.","DOI":"10.3390\/sym12121981"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cabrelles, M., Lerma, J.L., and Villaverde, V. (2020). Macro Photogrammetry & Surface Features Extraction for Paleolithic Portable Art Documentation. Appl. Sci., 10.","DOI":"10.3390\/app10196908"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"987","DOI":"10.3390\/rs4040987","article-title":"Effect of Grain Size and Mineral Mixing on Carbonate Absorption Features in the SWIR and TIR Wavelength Regions","volume":"4","author":"Zaini","year":"2012","journal-title":"Remote Sens."},{"key":"ref_29","unstructured":"Clark, R.N., Swayze, G.A., Wise, R., Livo, E., Hoefen, T., Kokaly, R., and Sutley, S.J. (2021, April 30). USGS Digital Spectral Library splib06a: U.S. Geological Survey, Digital Data Series 231, Available online: https:\/\/www.usgs.gov\/labs\/spec-lab\/capabilities\/superseded-spectral-library-versions?qt-capabilities_objects=0#qt-capabilities_objects."},{"key":"ref_30","unstructured":"Kaab, A. (2005). Remote Sensing of Mountain Glaciers and Permafrost Creep, Geographisches Institut der Universit\u00e4t Z\u00fcrich."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/S0034-4257(98)00084-4","article-title":"Spectroscopic Determination of Leaf Biochemistry Using Band-Depth Analysis of Absorption Features and Stepwise Multiple Linear Regression","volume":"67","author":"Kokaly","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1214\/aos\/1176344552","article-title":"Bootstrap Methods: Another look at the Jackknife","volume":"7","author":"Efron","year":"1979","journal-title":"Ann. Stat."},{"key":"ref_33","unstructured":"Breiman, L., and Cutler, A. Random Forests\u2014Classification Manual. Available online: http:\/\/www.math.usu.edu\/~adele\/forests\/."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn. J. Pap."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.rse.2005.10.014","article-title":"Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (Random Forest)","volume":"100","author":"Lawrence","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2838","DOI":"10.3390\/rs5062838","article-title":"The Performance of Random Forests in an Operational Setting for Large Area Sclerophyll Forest Classification","volume":"5","author":"Mellor","year":"2013","journal-title":"Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2019). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC Press Taylor Fr. Group. [3rd ed.].","DOI":"10.1201\/9780429052729"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Davoudi Kakhki, F., Freeman, S.A., and Mosher, G.A. (2019). Use of Logistic Regression to Identify Factors Influencing the Post-Incident State of Occupational Injuries in Agribusiness Operations. Appl. Sci., 9.","DOI":"10.3390\/app9173449"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Borucka, A., and Grzelak, M. (2019). Application of Logistic Regression for Production Machinery Efficiency Evaluation. Appl. Sci., 9.","DOI":"10.3390\/app9224770"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"\u0160tefko, R., Horv\u00e1thov\u00e1, J., and Mokri\u0161ov\u00e1, M. (2020). Bankruptcy Prediction with the Use of Data Envelopment Analysis: An Empirical Study of Slovak Businesses. J. Risk Financ. Manag., 13.","DOI":"10.3390\/jrfm13090212"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"D\u00edaz-P\u00e9rez, M., Carre\u00f1o-Ortega, \u00c1., G\u00f3mez-Gal\u00e1n, M., and Callej\u00f3n-Ferre, \u00c1.-J. (2018). Marketability Probability Study of Cherry Tomato Cultivars Based on Logistic Regression Models. Agronomy, 8.","DOI":"10.3390\/agronomy8090176"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Hosmer, D.W., Lemeshow, S., and Sturdivant, R.X. (2013). Applied Logistic Regression, John Wiley & Sons.","DOI":"10.1002\/9781118548387"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1080\/00031305.2000.10474502","article-title":"Coefficients of determination for multiple logistic regression analysis","volume":"54","author":"Menard","year":"2000","journal-title":"Am. Stat."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1093\/biomet\/78.3.691","article-title":"A note on a general definition of the coefficient of determination","volume":"78","author":"Nagelkerke","year":"1991","journal-title":"Biometrika"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Deng, C., Zhang, X., Li, Y., and Xiong, Q. (2020). Garch Model Test Using High-Frequency Data. Mathematics, 8.","DOI":"10.3390\/math8111922"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zizi, Y., Oudgou, M., and El Moudden, A. (2020). Determinants and Predictors of SMEs\u2019 Financial Failure: A Logistic Regression Approach. Risks, 8.","DOI":"10.3390\/risks8040107"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Lowe, D.G. (1999, January 20\u201325). Object recognition from local scale-invariant features. Proceedings of the Seventh IEEE International Conference on Computer Vision, Corfu, Greece.","DOI":"10.1109\/ICCV.1999.790410"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Dong, Y., Jiao, W., Long, T., Liu, L., He, G., Gong, C., and Guo, Y. (2019). Local Deep Descriptor for Remote Sensing Image Feature Matching. Remote Sens., 11.","DOI":"10.3390\/rs11040430"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Chen, S., Yuan, X., Yuan, W., Niu, J., Xu, F., and Zhang, Y. (2018). Matching Multi-Sensor Remote Sensing Images via an Affinity Tensor. Remote Sens., 10.","DOI":"10.3390\/rs10071104"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"12653","DOI":"10.1029\/JB095iB08p12653","article-title":"High spectral resolution reflectance spectroscopy of minerals","volume":"95","author":"Clark","year":"1990","journal-title":"J. Geophys. Res."},{"key":"ref_51","first-page":"151","article-title":"Spectral reflectance of carbonate minerals in the visible and near infrared (0.35\u20132.55 microns); calcite, aragonite, and dolomite","volume":"71","author":"Gaffey","year":"1986","journal-title":"Am. Mineral."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jafrearsci.2011.04.003","article-title":"Using HySpex SWIR-320m hyperspectral data for the identification and mapping of minerals in hand specimens of carbonate rocks from the Ankloute Formation (Agadir Basin, Western Morocco)","volume":"61","author":"Baissa","year":"2011","journal-title":"J. Afr. Earth Sci."},{"key":"ref_53","unstructured":"Clark, W., and Hoskings, P. (1986). Statistical methods for geographers. Clark Statistical Methods for Geographers, John Wiley and Sons."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.gexplo.2014.06.008","article-title":"Hydrothermal alteration mapping through multivariate logistic regression analysis of lithogeochemical data","volume":"145","author":"Mokhtari","year":"2014","journal-title":"J. Geochem. Explor."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/15\/3037\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:39:27Z","timestamp":1760164767000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/15\/3037"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,3]]},"references-count":54,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13153037"],"URL":"https:\/\/doi.org\/10.3390\/rs13153037","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,3]]}}}