Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 12 4 2021 01 20 Investigation of the Vegetation Effect on the Surveying and Mineral Exploration Using Hyperspectrsl Data Investigation of the Vegetation Effect on the Surveying and Mineral Exploration Using Hyperspectrsl Data 115 131 100943 10.52547/gisj.12.4.115 FA Ali Sadeghi Faculty of Geographical Sciences And Planning, University of Isfahan, Isfahan, Iran. Ali Darvishi Boloorani Faculty of Geography, Department of Remote Sensing and GIS, Tehran University, Tehran, Iran. Ataolah Abdolahi Kakroodi Faculty of Geography, Department of Remote Sensing and GIS, Tehran University, Tehran, Iran. Seyed Kazem Alaipana Faculty of Geography, Department of Remote Sensing and GIS, Tehran University, Tehran, Iran. Saeid Hamzeh Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran Journal Article 2021 04 06 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. 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. https://gisj.sbu.ac.ir/article_100943_2c4bacadc670c5c008638a2ebc3d735f.pdf