Investigation of the Vegetation Effect on the Surveying and Mineral Exploration Using Hyperspectrsl Data

Document Type : Original Article

Authors

1 Faculty of Geographical Sciences And Planning, University of Isfahan, Isfahan, Iran.

2 Faculty of Geography, Department of Remote Sensing and GIS, Tehran University, Tehran, Iran.

3 Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran

Abstract

The presence of dry and green vegetation in pixels containing spectral information is essential in geological and mineralogical studies. Thus, retrieving sub-pixel information, including estimation of a mineral’s quantity in a single hyperspectral RS image pixel is very important. In this study, the vegetation corrected continuum depth (VCCD) method was trained and its results were validated using spectrometry, laboratory mineralogy, and Hyperion image to reduce the effect of vegetation on the estimation of minerals. The study was conducted in Oghlansar region located in northwestern Iran. SAVI and absorption depth (2102 μm) were used for the estimation of the green and dry vegetation, respectively. Meanwhile, the trained models do not have a high sensitivity to the presence of noise in the spectrum and vegetation type changes. The correction of continuum removed band depth (CRBD) analysis was possible up to 60% for maximum green vegetation cover threshold, 56-60% for dry vegetation, and 72-76% for both dry and green vegetation. Effect of noise and different vegetation types on model capability was examined and the result shows that VCCD is not highly sensitive to random noise and changes in vegetation types. After correction of the coefficients and confirmation of its efficiency, the model was used to correct CRBD and reduce the effect of vegetation on Hyperion image. In the estimation of kaolinite and muscovite, the presence of green and dry vegetation led to the underestimation of the minerals present in the study area. The results showed that VCCD was able to increase the prediction accuracy (R2) by 0.25 and 0.13 and reduce RMSE by 0.0108 and 0.125 for kaolinite and muscovite, respectively.

Keywords

References

Bartholomeus, H., 2009, The Influence of Vegetation on the Spectroscopic Estimation of Soil Properties, PhD thesis, Wageningen University.
Ben-Dor, E., Taylor, R.G., Hill, J., Demattê, J.A.M., Whiting, M.L., Chabrillat, S. & Sommer, S., 2008, Imaging Spectro-metry for Soil Applications, Advances in Agronomy, 97, PP. 321-392.
Bierwirth, P., Huston, D. & Blewett, R., 2002, Hyperspectral Mapping of Mineral Assemblages Associated with Gold Mineralization in the Central Pilbara, Western Australia, Economic Geology, 97(4), PP. 819-826.
Canasveras, J.C., Barron, V., Del Campillo, M.C., Torrent, J. & Gomez, J.A., 2010, Estimation of Aggregate Stability Indices in Mediterranean Soils by Diffuse Reflectance Spectroscopy, Geoderma, 158(1-2), PP. 78–84.
Clark, R.N., 1999, Spectroscopy of Rocks and Minerals and Principles of Spectroscopy, Manual of Remote Sensing, (A.N. Rencz, ed.) John Wiley and Sons, New York, PP. 3-58, (Invited book chapter) Online at: http://speclab.cr.usgs.gov.
Clark, R., Swayze, G., Gallagher, A., King, T. & Calvin, W., 1993, The USGS Digital Spectral Library: Version 1: 0.2 to 3.0 μm, US Geol. Surv. Open File Rep, PP. 93-592.
Crowley, J.K., Bickey, D.W. & Rowan, L.C., 1989, Airborne Imaging Spectrometer Data of the Ruby Mountains, Montana: Mineral Discrimination Using Relative Absorption Band-Depth Images, Remote Sensing of Environment, 29(2), PP. 121-134.
Cudahy, T., Caccetta, M., Thomas, M., Hewson, R., Abrams, M., Kato, M., Kashimura, O., Ninomiya, Y., Yamaguchi, Y., Collings, S., Laukamp, C., Ong, C., Lau, J., Rodger, A., Chia, J., Warren, P., Woodcock, R., Fraser, R., Rankine, T., Vote, J., de Caritat, P., English, P., Meyer, D., Doescher, C., Fu, B., Shi, P. & Mitchel, R., 2016, Satellite-Derived Mineral Mapping and Monitoring of Weathering, Deposition and Erosion, Scientific Reports, 6(23702), PP. 1-12.
Franceschini, M.H.D., Demattêa, J.A.M., Silva Terraa, F., Vicente, L.E., Bartholomeus, H. & de Souza Filho, C.R., 2015, Prediction of Soil Properties Using Imaging Spectroscopy: Considering Fractional Vegetation Cover to Improve Accuracy, International Journal of Applied Earth Observation and Geoinformation, 38, PP. 358-370.
Grebby, S., Cunningham, D., Tansey, K. & Naden, J., 2014, The Impact of Vegetation on Lithological Mapping Using Airborne Multispectral Data: A Case Study for the North Troodos Region, Cyprus, Remote Sensing, 6(11), PP. 10860-10887.
Haest, M., Cudahy, T., Rodger, A., Laukam, C., Martens, E. & Caccetta, M., 2013, Unmixing the Effects of Vegetation in Airborne Hyperspectral Mineral Maps over the Rocklea Dome Iron-Rich Palaeochannel System (Western Australia), Remote Sensing of Environment, 129(15), PP. 17-31.
Karnieli, A., Kaufman, Y.J., Remer, L. & Wald, A., 2001, AFRI-Aerosol Free Vegetation Index, Remote Sensing of Environment, 77(1), PP. 10-21.
Kaufman, Y., Wald, A., Remer, L., Gao, B., Li, R. & Luke, F., 1997, The MODIS 2.1-mm Band Correlation with Visible Reflectance for Use in Remote Sensing of Aerosol, IEEE Transactions on Geoscience and Remote Sensing, 35, PP. 1286-1298.
Kobayashi, C., Kashimura, O., Maruyama, T., Oyanagi, M., Lau, I.C., Cudahy, T., Wheaton, B. & Carter, D., 2011, The Effect of Spectral Unmixing of Hyperspectral Imagery for Mapping of Soil Properties, 34th International Symposium on Remote Sensing of Environment, At Sydney, New South Wales, Australia, April 15. DOI: 10.13140/2.1.3857.3443.
Kokaly, R. & Clark, R., 1999, Spectroscopic Determination of Leaf Biochemistry Using Band-Depth Analysis of Features and Stepwise Multiple Linear Regression, Remote Sensing of Environment, 67(3), PP. 267-287.
Kruse, F.A., 1988, Use of Airborne Imaging Spectrometer Data to Map Minerals Associated with Hydrothermally Altered Rocks in the Northern Grapevine Mountains, Nevada, and California, Remote Sensing of Environment, 24(1), PP. 31-51.
Murphy, R., 1995, The Effects of Surficial Vegetation Cover on Mineral Absorp-tion Feature Parameters, International Journal of Remote Sensing, 16(12), PP. 2153-2164.
Murphy, R. & Wadge, G., 1994, The Effects of Vegetation on the Ability to Map Soils Using Imaging Spectrometer Data, Remote Sensing, 15(1), PP. 63-86.
Nanni, M.R. & Damette, J.A.M., 2006, Spectral Reflectance Methodology in Comparison to Traditional Soil Analysis, Soil Science Society of America Journal, 70(2), PP. 393-407.
Refahi, D., Khakzad, N., Nezafati, N., Bahar Firozi, K. & Bayatani, A., 2014, Altration Zone Studies North of Sarab by Sateillite Data, Airborne Geophisic Data and Sampling Analysis, Geosciences, 24(93), PP. 221-234.
Rodger, A. & Cudahy, T., 2009, Vegetation Corrected Continuum Depths at 2.20 μm: An Approach for Hyperspectral Sensors, Remote Sensing of Environment, 113(10), PP. 2243-2257.
Siegal, B.S. & Goetz, A.F.H., 1977, Effect of Vegetation on Rock and Soil Type Discrimination, Photogrammetric Engineering and Remote Sensing, 43(2), PP. 191-196.
Staenz, K., Nadeau, C., Secker, J. & Budkewitsch, P., 2000, Spectral Unmixing Applied to Vegetated Environments in the Canadian Arctic for Mineral Mapping, XIXth ISPRS Congress and Exhibition, Amsterdam, July 15-23.
van der Meer, F., 2004, Analysis of spectral absorption features in hyperspectral imagery, International Journal of Applied Earth Observation and Geoinformation 5: 55–68.
Wester, K., Lunden, B., and Box, G., 1990, Analytically processed Landsat TM images for visual geological interpreta-tion in the Northern Scandinavian Caledonides, ISPRS Journal of Photoqrammetry and Remote Sensing, 45: 442-460.
Wray, R.A., 2011, Alunite Formation within Silica Stalactites from the Sydney Region, South-Eastern Australia, International Journal of Speleology, 40(2), PP. 109-116.
Iranian Journal of Remote Sensing & GIS
Volume 12, Issue 4 - Serial Number 48
February 2021
Pages 115-131

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