Integrated Remote Sensing for Geological and Mineralogical Mapping of Pb-Zn Deposits: A Case Study of Jbel Bou Dahar Region Using Multi-Sensor Imagery
Abstract
:1. Introduction
2. Materials and Methods
2.1. Geological and Mineralogical Settings
2.2. Satellite Data Preprocessing
2.3. False-Color Composite (FCC)
2.4. Band Ratio
2.5. Principal Components Analysis (PCA)
2.6. Spectral Angle Mapper Classification (SAM)
2.7. Accuracy Assessment
3. Results
3.1. Red–Green–Blue False-Color Composite Results (FCC)
3.2. Band Ratio Results
3.3. Principal Component Analysis Results (PCA)
3.4. Spectral Angle Mapper (SAM) Classification Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Ewais, M.M.M.; El Zalaky, M.A.; Selim, A.Q.; Abu Sharib, A.S.A.A. Implementation of ASTER data for lithologic and alteration zones mapping: Derhib area, South Eastern Desert, Egypt. J. Afr. Earth Sci. 2022, 196, 104725. [Google Scholar] [CrossRef]
- Mahanta, P.; Maiti, S. Regional scale demarcation of alteration zone using ASTER imageries in South Purulia Shear Zone, East India: Implication for mineral exploration in vegetated regions. Ore Geol. Rev. 2018, 102, 846–861. [Google Scholar] [CrossRef]
- Sheikhrahimi, A.; Pour, A.B.; Pradhan, B.; Zoheir, B. Mapping hydrothermal alteration zones and lineaments associated with orogenic gold mineralization using ASTER data: A case study from the Sanandaj-Sirjan Zone, Iran. Adv. Space Res. 2019, 63, 3315–3332. [Google Scholar] [CrossRef]
- Traore, M.; Çan, T.; Tekin, S. Mapping carbonate-hosted Pb-Zn mineralization zones in Yahyali Province (Eastern Taurus-Turkey) using ASTER data. Adv. Space Res. 2022, 69, 266–281. [Google Scholar] [CrossRef]
- Guglietta, D.; Belardi, G.; Passeri, D.; Salvatori, R.; Ubaldini, S.; Casentini, B.; Trapasso, F. Optimising the management of mining waste by means Sentinel-2 imagery: A case study in Joda West Iron and Manganese Mine (India). J. Sustain. Min. 2020, 19, 4. [Google Scholar] [CrossRef]
- Scotti, A.; Milia, S.; Silvani, V.; Cappai, G.; Guglietta, D.; Trapasso, F.; Tempesta, E.; Passeri, D.; Godeas, A.; Gómez, M.; et al. Sustainable Recovery of Secondary and Critical Raw Materials from Classified Mining Residues Using Mycorrhizal-Assisted Phytoextraction. Metals 2021, 11, 1163. [Google Scholar] [CrossRef]
- Guglietta, D.; Conte, A.M.; Paciucci, M.; Passeri, D.; Trapasso, F.; Salvatori, R. Mining Residues Characterization and Sentinel-2A Mapping for the Valorization and Efficient Resource Use by Multidisciplinary Strategy. Minerals 2022, 12, 617. [Google Scholar] [CrossRef]
- Yang, M.; Ren, G.; Han, L.; Yi, H.; Gao, T. Detection of Pb–Zn mineralization zones in west Kunlun using Landsat 8 and ASTER remote sensing data. J. Appl. Remote Sens. 2018, 12, 1. [Google Scholar] [CrossRef]
- Di Tommaso, I.; Rubinstein, N. Hydrothermal alteration mapping using ASTER data in the Infiernillo porphyry deposit, Argentina. Ore Geol. Rev. 2007, 32, 275–290. [Google Scholar] [CrossRef]
- Sekandari, M.; Masoumi, I.; Pour, A.B.; Muslim, A.M.; Rahmani, O.; Hashim, M.; Zoheir, B.; Pradhan, B.; Misra, A.; Aminpour, S.M. Application of Landsat-8, Sentinel-2, ASTER and Worldview-3 spectral imagery for exploration of carbonate-hosted Pb-Zn deposits in the Central Iranian Terrane (CIT). Remote Sens. 2020, 12, 1239. [Google Scholar] [CrossRef]
- Phiri, D.; Simwanda, M.; Salekin, S.; Ryirenda, V.R.; Murayama, Y.; Ranagalage, M.; Oktaviani, N.; Kusuma, H.A.; Zhang, T.; Su, J.; et al. remote sensing Sentinel-2 Data for Land Cover / Use Mapping: A Review. Remote Sens. 2019, 42, 14. [Google Scholar]
- Liu, L.; Li, Y.; Zhou, J.; Han, L.; Xu, X. Gold-copper deposits in Wushitala, Southern Tianshan, Northwest China: Application of ASTER data for mineral exploration. Geol. J. 2018, 53, 362–371. [Google Scholar] [CrossRef]
- Mehdikhani, B.; Imamalipour, A. ASTER-based remote sensing image analysis for prospection criteria of podiform chromite at the khoy ophiolite (NW Iran). Minerals 2021, 11, 960. [Google Scholar] [CrossRef]
- Ghorbani, A.; Honarmand, M.; Shahriari, H.; Hassani, M.J. Regional scale prospecting for non-sulphide zinc deposits using ASTER data and different spectral processing methods. Int. J. Remote Sens. 2019, 40, 8647–8667. [Google Scholar] [CrossRef]
- Honarmand, M.; Shahriari, H.; Hosseinjani Zadeh, M.; Ghorbani, A. Enhancing Zn-bearing gossans from GeoEye-1 and Landsat 8 OLI data for non-sulphide Zn deposit exploration. Egypt. J. Remote Sens. Space Sci. 2024, 27, 93–107. [Google Scholar] [CrossRef]
- Bouabdellah, M.; Sangster, D.F. Geology, Geochemistry, and Current Genetic Models for Major Mississippi Valley-Type Pb–Zn Deposits of Morocco. In Mineral Deposits of North Africa; Springer: Cham, Switzerland, 2016. [Google Scholar] [CrossRef]
- Rddad, L.; Bouhlel, S. The Bou Dahar Jurassic carbonate-hosted Pb-Zn-Ba deposits (Oriental High Atlas, Morocco): Fluid-inclusion and C-O-S-Pb isotope studies. Ore Geol. Rev. 2016, 72, 1072–1087. [Google Scholar] [CrossRef]
- Michard, A.; Hoepffner, C.; Soulaimani, A.; Baidder, L. The Variscan Belt. In Continental Evolution: The Geology of Morocco; Michard, A., Saddiqi, O., Chalouan, A., de Lamotte, D.F., Eds.; Springer: Cham, Switzerland, 2008; p. 116. [Google Scholar]
- Agard, J.; Dresnay, D.R. Les gîtes plombo-zincifères du jbel Bou Dahar près de Beni-Tajjite (Haut Atlas oriental). Notes Mém. Serv. Géol. Maroc 1965, 181, 135–166. [Google Scholar]
- Blomeier, D.P.G.; Reijmer, J.J.G. Facies architecture of an early jurassic carbonate platform slope (Jbel Bou Dahar, High Atlas, Morocco). J. Sediment. Res. 2002, 72, 462–475. [Google Scholar] [CrossRef]
- Verwer, K.; Oscar, M.T.; Kenter, J.A.M.; Della Porta, G. Evolution of a high-relief carbonate platform slope using 3D digital outcrop models: Lower jurassic djebel bou dahar, high atlas, Morocco. J. Sediment. Res. 2009, 79, 416–439. [Google Scholar] [CrossRef]
- Cooley, T.; Anderson, G.P.; Felde, G.W.; Hoke, M.L.; Ratkowski, A.J.; Chetwynd, J.H.; Gardner, J.A.; Adler-Golden, S.M.; Matthew, M.W.; Berk, A.; et al. FLAASH, a MODTRAN4-Based Atmospheric Correction Algorithm, Its Application and Validation. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Toronto, ON, Canada; 2002; Volume 3, pp. 1414–1418. [Google Scholar] [CrossRef]
- Drusch, M.; Del Bello, U.; Carlier, S.; Colin, O.; Fernandez, V.; Gascon, F.; Hoersch, B.; Isola, C.; Laberinti, P.; Martimort, P.; et al. Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sens. Environ. 2012, 120, 25–36. [Google Scholar] [CrossRef]
- Fujisada, H. Design and Perfonnance of ASTER Instrument. In Advanced and Next-Generation Satellites; Fujisada, H., Sweeting, M.N., Eds.; Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series; SPIE: Bellingham, WA, USA, 1995; Volume 2583, pp. 16–25. [Google Scholar] [CrossRef]
- Irons, J.R.; Dwyer, J.L.; Barsi, J.A. The next Landsat satellite: The Landsat Data Continuity Mission. Remote Sens. Environ. 2012, 122, 11–21. [Google Scholar] [CrossRef]
- Pour, A.B.; Hashim, M. Identification of hydrothermal alteration minerals for exploring of porphyry copper deposit using ASTER data, SE Iran. J. Asian Earth Sci. 2011, 42, 1309–1323. [Google Scholar] [CrossRef]
- Crowley, J.K.; Brickey, D.W.; Rowan, L.C. Airborne imaging spectrometer data of the Ruby Mountains, Montana: Mineral discrimination using relative absorption band-depth images. Remote Sens. Environ. 1989, 29, 121–134. [Google Scholar] [CrossRef]
- Hunt, G.R. Spectral signature of particulate minerals in the visible and near infrared. Geophysics 1977, 42, 501–513. [Google Scholar] [CrossRef]
- Mars, J.C.; Rowan, L.C. Regional mapping of phyllic- and argillic-altered rocks in the zagros magmatic arc, Iran, using advanced spaceborne thermal emission and reflection radiometer (ASTER) data and logical operator algorithms. Geosphere 2006, 2, 161–186. [Google Scholar] [CrossRef]
- Manuel, R.; Brito, M.d.G.; Chichorro, M.; Rosa, C. Remote sensing for mineral exploration in central Portugal. Minerals 2017, 7, 184. [Google Scholar] [CrossRef]
- Schowengerdt, R.A. Remote Sensing: Models and Methods for Image Processing, 3rd ed.; Academic Press: Orlando, FL, USA, 2007. [Google Scholar]
- Kruse, F.A.; Lefkoff, A.B.; Boardman, J.W.; Heidebrecht, K.B.; Shapiro, A.T.; Barloon, P.J.; Goetz, A.F.H. The Spectral Image Processing System (SIPS) Interactive Visualization and Analysis of Imaging Spectrometer Data. J. South Am. Earth Sci. 1993, 44, 145–163. [Google Scholar]
- Weyermann, J.; Schläpfer, D.; Hueni, A.; Kneubühler, M.; Schaepman, M. Spectral Angle Mapper (SAM) for anisotropy class indexing in imaging spectrometry data. Imaging Spectrom. XIV 2009, 7457, 74570B. [Google Scholar] [CrossRef]
- Petropoulos, G.P.; Kalivas, D.P.; Georgopoulou, I.A.; Srivastava, P.K. Urban vegetation cover extraction from hyperspectral imagery and geographic information system spatial analysis techniques: Case of Athens, Greece. J. Appl. Remote Sens. 2015, 9, 096088. [Google Scholar] [CrossRef]
- Loughlin, W.P. Principal component analysis for alteration mapping. Photogramm. Eng. Remote Sens. 1991, 57, 1163–1169. [Google Scholar]
- Rowan, L.C.; Mars, J.C. Lithologic mapping in the Mountain Pass, California area using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. Remote Sens. Environ. 2003, 84, 350–366. [Google Scholar] [CrossRef]
- Bouabdellah, M.; Boukirou, W.; Potra, A.; Melchiorre, E.; Bouzahzah, H.; Yans, J.; Zaid, K.; Idbaroud, M.; Poot, J.; Dekoninck, A.; et al. Origin of the moroccan touissit-bou beker and jbel bou dahar supergene non-sulfide biomineralization and its relevance to microbiological activity, late miocene uplift and climate changes. Minerals 2021, 11, 4001. [Google Scholar] [CrossRef]
- Merino-Tomé, Ó.; Della Porta, G.; Kenter, J.A.M.; Verwer, K.; Harris, P.M.; Adams, E.W.; Playton, T.; Corrochano, D. Sequence development in an isolated carbonate platform (Lower Jurassic, Djebel Bou Dahar, High Atlas, Morocco): Influence of tectonics, eustacy and carbonate production. Sedimentology 2012, 59, 118–155. [Google Scholar] [CrossRef]
Sensor | Spectral Region | Band Number | Spectral Range (μm) | Spatial Resolution |
---|---|---|---|---|
ASTER | VNIR | 1 | 0.520–0.600 | 15 m |
2 | 0.630–0.690 | |||
3N | 0.780–0.860 | |||
SWIR | 4 | 1.600–1.700 | 30 m | |
5 | 2.145–2.185 | |||
6 | 2.185–2.225 | |||
7 | 2.235–2.285 | |||
8 | 2.295–2.365 | |||
9 | 2.360–2.430 | |||
TIR | 10 | 8.125–8.475 | 90 m | |
11 | 8.475–8.825 | |||
12 | 8.925–9.275 | |||
13 | 10.25–10.95 | |||
14 | 10.95–11.65 | |||
Landsat-8 | VNIR | PAN (8) | 0.500–0.680 | 15 m |
Coastal aerosol (1) | 0.433–0.453 | 30 m | ||
Blue (2) | 0.450–0.515 | |||
Green (3) | 0.525–0.600 | |||
Red (4) | 0.630–0.680 | |||
NIR (3) | 0.845–0.885 | |||
SWIR | SWIR1 (6) | 1.560–1.660 | ||
SWIR2 (7) | 2.100–2.300 | |||
Cirrus (9) | 1.360–1.390 | |||
TIR | TIRS1 (10) | 10.60–11.19 | 100 m | |
TIRS2 (11) | 11.50–12.51 | |||
Sentinel-2 | VNIR | Coastal aerosol (1) | 0.433–0.453 | 60 m |
Blue (2) | 0.458–0.523 | 10 m | ||
Green (3) | 0.543–0.578 | |||
Red (4) | 0.650–0.680 | |||
Vegetation Red Edge (5) | 0.698–0.713 | 20 m | ||
Vegetation Red Edge (6) | 0.733–0.748 | |||
Vegetation Red Edge (7) | 0.773–0.793 | |||
NIR (8) | 0.785–0.900 | 10 m | ||
Water-vapor (9) | 0.935–0.955 | 60 m | ||
SWIR | SWIR-Cirrus (10) | 1.360–1.390 | ||
SWIR1 (11) | 1.565–1.655 | 20 m | ||
SWIR2 (12) | 2.100–2.280 |
Sensors | Sentinel-2 | Landsat-8 | ASTER | |||
---|---|---|---|---|---|---|
ROI Class | Pixel. No | Coverage Area (m2) | Pixel. No | Coverage Area (m2) | Pixel. No | Coverage Area (m2) |
Do | 16 | 1600 | 4 | 3600 | 8 | 1800 |
Cp | 100 | 10,000 | 22 | 19,800 | 59 | 13,275 |
Sl | 52 | 5200 | 14 | 12,600 | 34 | 7650 |
Lm | 96 | 9600 | 15 | 13,500 | 48 | 10,800 |
Ss | 439 | 43,900 | 60 | 54,000 | 201 | 45,225 |
CE | 151 | 15,100 | 27 | 24,300 | 78 | 17,550 |
Cb | 97 | 9700 | 18 | 16,200 | 51 | 11,475 |
Pc | 325 | 32,500 | 54 | 48,600 | 162 | 36,450 |
Al | 1309 | 130,900 | 164 | 147,600 | 598 | 134,550 |
GD | 17 | 1700 | 4 | 3600 | 10 | 2250 |
Or | 69 | 6900 | 13 | 11,700 | 37 | 8325 |
Bs | 35 | 3500 | 10 | 9000 | 17 | 3825 |
Ml | 47 | 4700 | 12 | 10,800 | 28 | 6300 |
Od | 12 | 1200 | 6 | 5400 | 4 | 900 |
Vg | 81 | 8100 | 16 | 14,400 | 42 | 9450 |
Eigenvectors | Band 1 | Band 2 | Band 3 | Band 4 | Band 5 | Band 6 | Band 7 | Band 8 | Band 9 | % Total Variance | Cumulative % |
---|---|---|---|---|---|---|---|---|---|---|---|
PC1 | 0.2046 | 0.2886 | 0.3060 | 0.4146 | 0.3518 | 0.3547 | 0.3652 | 0.3362 | 0.3366 | 93,381 | 93,381 |
PC2 | 0.4699 | 0.5128 | 0.5292 | −0.1975 | −0.2332 | −0.2466 | −0.1685 | −0.0508 | −0.2259 | 4.042 | 97,422 |
PC3 | 0.2929 | 0.0929 | −0.4442 | −0.4978 | −0.0691 | −0.1559 | 0.2300 | 0.5965 | 0.1505 | 1.351 | 98,774 |
PC4 | 0.4755 | 0.2369 | −0.5635 | 0.2142 | 0.2248 | 0.2116 | 0.0152 | −0.3403 | −0.3782 | 0.475 | 99,249 |
PC5 | −0.1639 | −0.1335 | 0.1015 | 0.2515 | −0.0610 | −0.1052 | 0.3002 | 0.4305 | −0.7691 | 0.375 | 99,625 |
PC6 | 0.5504 | −0.7239 | 0.2663 | −0.1403 | 0.2313 | −0.0364 | 0.1281 | −0.1026 | −0.0232 | 0.131 | 99,756 |
PC7 | −0.2529 | 0.1375 | 0.1676 | −0.6281 | 0.2201 | 0.4756 | 0.3453 | −0.2230 | −0.2258 | 0.100 | 99,856 |
PC8 | −0.1671 | 0.1678 | −0.0114 | −0.0211 | 0.5714 | −0.7038 | 0.2780 | −0.2058 | 0.0424 | 0.081 | 99,937 |
PC9 | 0.0609 | −0.0075 | −0.0449 | 0.1321 | −0.5782 | −0.1019 | 0.6943 | −0.3533 | 0.1587 | 0.063 | 100 |
Eigenvectors | Band 2 | Band 3 | Band 4 | Band 8 | Band 11 | Band 12 | % Total Variance | Cumulative % |
---|---|---|---|---|---|---|---|---|
PC1 | 0.2186 | 0.2759 | 0.3786 | 0.5075 | 0.5395 | 0.4292 | 91,654 | 91,654 |
PC2 | 0.4515 | 0.4575 | 0.3090 | 0.2270 | −0.5259 | −0.4040 | 6.224 | 97,878 |
PC3 | −0.4605 | −0.3314 | 0.0364 | 0.7309 | −0.0605 | −0.3727 | 1.597 | 99,475 |
PC4 | 0.3835 | 0.1473 | −0.8024 | 0.2569 | 0.2680 | −0.2227 | 0.308 | 99,784 |
PC5 | 0.0347 | −0.0737 | −0.2891 | 0.3004 | −0.5972 | 0.6804 | 0.184 | 99,968 |
PC6 | −0.6229 | 0.7600 | −0.1802 | −0.0228 | −0.0155 | 0.0340 | 0.032 | 100 |
Eigenvectors | Band 1 | Band 2 | Band 3 | Band 4 | Band 5 | Band 6 | Band 7 | % Total Variance | Cumulative % |
---|---|---|---|---|---|---|---|---|---|
PC1 | 0.1342 | 0.1571 | 0.2590 | 0.3674 | 0.4363 | 0.5572 | 0.5044 | 90,245 | 90,245 |
PC2 | −0.3849 | −0.4109 | −0.4896 | −0.3266 | −0.1381 | 0.4297 | 0.3646 | 6.973 | 97,218 |
PC3 | 0.2528 | 0.2294 | 0.1741 | 0.0086 | −0.8046 | 0.0073 | 0.4535 | 2.154 | 99,371 |
PC4 | 0.3447 | 0.3271 | 0.1109 | −0.7974 | 0.1487 | 0.2968 | −0.1262 | 0.360 | 99,732 |
PC5 | 0.0422 | −0.0117 | −0.0432 | −0.2674 | 0.3439 | −0.6452 | 0.6246 | 0.218 | 99,950 |
PC6 | −0.5321 | −0.2221 | 0.7834 | −0.2251 | −0.0530 | −0.0002 | 0.0185 | 0.042 | 99,992 |
PC7 | 0.6051 | −0.7729 | 0.1871 | −0.0179 | −0.0038 | 0.0224 | −0.0249 | 0.008 | 100 |
Satellite | Overall Accuracy | Kappa Coefficient |
---|---|---|
ASTER | 93.76% | 0.92 |
Landsat-8 | 83.37% | 0.79 |
Sentinel-2 | 76.21% | 0.69 |
Class | Do | Cp | Sl | Lm | Ss | CE | Cb | Pc | Al | GD | Or | Bs | Ml | Od | Vg | Total | Producer’s Accuracy (%) | User’s Accuracy (%) |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Unclassified | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ||
Do | 8 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 15 | 100 | 53.33 |
Cp | 0 | 52 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 53 | 88.14 | 98.11 |
Sl | 0 | 0 | 20 | 1 | 0 | 0 | 0 | 8 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 33 | 58.82 | 60.61 |
Lm | 0 | 0 | 0 | 45 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 53 | 93.75 | 84.91 |
Ss | 0 | 0 | 0 | 0 | 201 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 201 | 100 | 100 |
CE | 0 | 0 | 0 | 0 | 0 | 78 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 | 82 | 100 | 95.12 |
Cb | 0 | 0 | 0 | 0 | 0 | 0 | 42 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 48 | 82.35 | 87.50 |
Pc | 0 | 0 | 0 | 1 | 0 | 0 | 9 | 121 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 131 | 74.69 | 92.37 |
Al | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 598 | 0 | 0 | 0 | 0 | 0 | 0 | 598 | 100 | 100 |
GD | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 15 | 0 | 10 | 4 | 0 | 0 | 0 | 0 | 31 | 100 | 32.26 |
Or | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 29 | 0 | 0 | 0 | 0 | 42 | 78.38 | 69.05 |
Bs | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16 | 0 | 0 | 0 | 16 | 94.12 | 100 |
Ml | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 28 | 0 | 0 | 31 | 100 | 90.32 |
Od | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 4 | 100 | 100 |
Vg | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 39 | 39 | 92.86 | 100 |
Total | 8 | 59 | 34 | 48 | 201 | 78 | 51 | 162 | 598 | 10 | 37 | 17 | 28 | 4 | 42 | 1377 |
Class | Do | Cp | Sl | Lm | Ss | CE | Cb | Pc | Al | GD | Or | Bs | Ml | Od | Vg | Total | Producer’s Accuracy (%) | User’s Accuracy (%) |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Unclassified | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 5 | ||
Do | 4 | 3 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 100 | 40 |
Cp | 0 | 19 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 19 | 86.36 | 100 |
Sl | 0 | 0 | 7 | 1 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 50 | 63.64 |
Lm | 0 | 0 | 0 | 7 | 0 | 0 | 3 | 7 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 21 | 46.67 | 33.33 |
Ss | 0 | 0 | 1 | 0 | 60 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 62 | 100 | 96.77 |
CE | 0 | 0 | 0 | 0 | 0 | 27 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 27 | 100 | 100 |
Cb | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 22 | 27.78 | 22.73 |
Pc | 0 | 0 | 0 | 3 | 0 | 0 | 4 | 23 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 30 | 42.59 | 76.67 |
Al | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 164 | 0 | 0 | 0 | 0 | 0 | 0 | 164 | 100 | 100 |
GD | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 4 | 1 | 0 | 0 | 0 | 0 | 8 | 100 | 50 |
Or | 0 | 0 | 6 | 4 | 0 | 0 | 3 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 14 | 53.85 | 50 |
Bs | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 10 | 100 | 100 |
Ml | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 12 | 0 | 0 | 19 | 100 | 63.16 |
Od | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 6 | 100 | 100 |
Vg | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 11 | 68.75 | 100 |
Total | 4 | 22 | 14 | 15 | 60 | 27 | 18 | 54 | 164 | 4 | 13 | 10 | 12 | 6 | 116 | 439 |
Class | Do | Cp | Sl | Lm | Ss | CE | Cb | Pc | Al | GD | Or | Bs | Ml | Od | Vg | Total | Producer’s Accuracy (%) | User’s Accuracy (%) |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Unclassified | 0 | 0 | 3 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 35 | 42 | ||
Do | 12 | 24 | 0 | 0 | 0 | 0 | 0 | 3 | 197 | 0 | 0 | 0 | 0 | 0 | 0 | 236 | 75 | 5.08 |
Cp | 3 | 76 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 80 | 76 | 95 |
Sl | 0 | 0 | 21 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 17 | 0 | 0 | 0 | 0 | 41 | 40.38 | 51.22 |
Lm | 0 | 0 | 17 | 59 | 0 | 0 | 6 | 88 | 0 | 0 | 25 | 0 | 0 | 0 | 0 | 195 | 61.46 | 30.26 |
Ss | 0 | 0 | 0 | 0 | 439 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 439 | 100 | 100 |
CE | 0 | 0 | 0 | 0 | 0 | 151 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 152 | 100 | 99.34 |
Cb | 0 | 0 | 0 | 0 | 0 | 0 | 21 | 118 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 143 | 21.65 | 14.69 |
Pc | 0 | 0 | 0 | 10 | 0 | 0 | 4 | 102 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 116 | 31.38 | 87.93 |
Al | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1112 | 0 | 0 | 0 | 0 | 0 | 0 | 1114 | 84.95 | 99.82 |
GD | 0 | 0 | 0 | 0 | 0 | 0 | 17 | 0 | 0 | 13 | 0 | 0 | 0 | 0 | 0 | 30 | 76.47 | 43.33 |
Or | 0 | 0 | 2 | 20 | 0 | 0 | 46 | 12 | 0 | 0 | 27 | 0 | 0 | 0 | 0 | 107 | 39.13 | 25.23 |
Bs | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 35 | 0 | 0 | 0 | 35 | 100 | 100 |
Ml | 0 | 0 | 9 | 4 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 47 | 0 | 0 | 62 | 100 | 75.81 |
Od | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 8 | 66.67 | 100 |
Vg | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 46 | 46 | 56.79 | 100 |
Total | 16 | 100 | 52 | 96 | 439 | 151 | 97 | 325 | 1309 | 17 | 69 | 35 | 47 | 12 | 81 | 2846 |
Sensors | ASTER | Landsat-8 | Sentinel-2 | ||||||
---|---|---|---|---|---|---|---|---|---|
Class | No. Pixel of ROI | No. Pixel Classified | No. Pixel Misclassified | No. Pixel of ROI | No. Pixel Classified | No. Pixel Misclassified | No. Pixel of ROI | No. Pixel Classified | No. Pixel Misclassified |
Unclassified | - | - | - | - | - | 5 | - | - | 42 |
Do | 8 | 8 | 7 | 4 | 4 | 6 | 16 | 12 | 224 |
Cp | 59 | 52 | 1 | 22 | 19 | 0 | 100 | 76 | 4 |
Sl | 34 | 20 | 13 | 14 | 7 | 4 | 52 | 21 | 20 |
Lm | 48 | 45 | 8 | 15 | 7 | 14 | 96 | 59 | 136 |
Ss | 201 | 201 | 0 | 60 | 60 | 2 | 439 | 439 | 0 |
CE | 78 | 78 | 4 | 27 | 27 | 0 | 151 | 151 | 1 |
Cb | 51 | 42 | 6 | 18 | 5 | 17 | 97 | 21 | 122 |
Pc | 162 | 121 | 10 | 54 | 23 | 7 | 325 | 102 | 14 |
Al | 598 | 598 | 0 | 164 | 164 | 0 | 1309 | 1112 | 2 |
GD | 10 | 10 | 21 | 4 | 4 | 4 | 17 | 13 | 17 |
Or | 37 | 29 | 13 | 13 | 7 | 7 | 69 | 27 | 80 |
Bs | 17 | 16 | 0 | 10 | 10 | 0 | 35 | 35 | 0 |
Ml | 28 | 28 | 3 | 12 | 12 | 7 | 47 | 47 | 15 |
Od | 4 | 4 | 0 | 6 | 6 | 0 | 12 | 8 | 0 |
Vg | 42 | 39 | 0 | 16 | 11 | 0 | 81 | 46 | 0 |
Total | 1377 | 1291 | 86 | 439 | 366 | 73 | 2846 | 2169 | 677 |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Chniouar, M.; Wafik, A.; Daafi, Y.; Guglietta, D. Integrated Remote Sensing for Geological and Mineralogical Mapping of Pb-Zn Deposits: A Case Study of Jbel Bou Dahar Region Using Multi-Sensor Imagery. Mining 2024, 4, 302-325. https://doi.org/10.3390/mining4020018
Chniouar M, Wafik A, Daafi Y, Guglietta D. Integrated Remote Sensing for Geological and Mineralogical Mapping of Pb-Zn Deposits: A Case Study of Jbel Bou Dahar Region Using Multi-Sensor Imagery. Mining. 2024; 4(2):302-325. https://doi.org/10.3390/mining4020018
Chicago/Turabian StyleChniouar, Marouane, Amina Wafik, Youssef Daafi, and Daniela Guglietta. 2024. "Integrated Remote Sensing for Geological and Mineralogical Mapping of Pb-Zn Deposits: A Case Study of Jbel Bou Dahar Region Using Multi-Sensor Imagery" Mining 4, no. 2: 302-325. https://doi.org/10.3390/mining4020018