Remote Sensing (RS)
Nastaran Nazariani; Asghar Fallah; Hava Hasanvand; Hassan Akbari
Abstract
Extended Abstract
Introduction
The traditional method of chemical analysis has high accuracy and precision. However, it is time-consuming and laborious, and it is not possible to obtain continuous information about the pollutant status over a large area. Therefore, there is an urgent need for a reliable ...
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Extended Abstract
Introduction
The traditional method of chemical analysis has high accuracy and precision. However, it is time-consuming and laborious, and it is not possible to obtain continuous information about the pollutant status over a large area. Therefore, there is an urgent need for a reliable and environmentally friendly method to quickly identify and investigate the distribution of heavy metals in soil and thus identify suspected contaminated areas (Scheuber & Köhl, 2003:33). Remote sensing is one of the ways that can provide a cost-effective and quick solution to investigate the distribution of heavy metals on a large scale using spectroscopic techniques (Bi et al., 2009:16). Habibi et al. (2023:4) also measured and evaluated the concentration of heavy metals in the aerial parts and soil of the tree species of Bandar Abbas city and also identified the species that has the highest potential for absorbing heavy metals. The results showed that the pattern of heavy metals in soil and leaves of tree species was Mn>Zn>Pb>Cd. (Nikolaevich, 2023:30) they addressed the modeling of heavy metal pollution in Central Russia based on satellite images and machine learning. Al, Fe, and Sb contamination were predicted for 3000 and 12100 grid nodes in an area of 500 km2 for the Central Russian region for 2019 and 2020. Estimating the amount of this pollution requires time and high cost. Considering the traffic on the Aleshtar -Khorramabad highway near Kakareza forests and the effect of heavy metal concentration in the soil and leaves of the oak species which can be caused by natural and human pollution, the accumulation of heavy metals in the species Iranian oak is a serious threat to this forest. Therefore, it is necessary to study and discuss pollutants and their effects on the environmental cycle. In this regard, considering the cost and time-consuming nature of traditional methods and since remote sensing methods are a suitable complement to traditional methods; the aim of the present research is to use remote sensing techniques and spectral analyses to evaluate and model the accumulation of heavy metals in Iranian oak species.
Materials and Methods
The present study is located on the road of Aleshtar -Khorramabad, 20 kilometers northwest of Khorramabad. For this purpose, five transects were created at distances adjacent to the road, 500 and 1000 meters on both sides of the road, and 10 x 10 m sample pieces were planted. Inside the sample plots, 30 soil samples were randomly collected and 30 leaf samples were collected from trees in all directions of the crown. To extract heavy metals from soil samples and plant samples, the acid digestion method was used and the physical characteristics of the soil were measured using standard methods. After preparing the samples, the concentration of Pb, Cu, and zinc heavy metals in soil and leaves was measured and the index of biological concentration of heavy metals from soil to leaves was calculated. Then the relationship between the concentration of heavy elements measured and the reflectance in different bands or band ratios at the corresponding sampling points was obtained. Non-parametric methods and generalized multiple linear regression models were used in order to model quantitative variables and spectral values corresponding to sample parts in satellite data. ArcGIS software was used to implement sample parts on the image, ENVI software was used for image processing, and STATISTICA software was used for modeling.
Results and Discussion
Cu and Pb in Iranian oak leaves had significant differences at different distances at the 0.05 level, but Cu did not have significant differences at different distances at the 0.05 level. Cu and Pb did not have significant differences in different soil intervals at the 0.05 level, but Cu had significant differences in different soil intervals at the 0.05 level. The bioconcentration factor was obtained as (0.2, 0.5, 0.2) mg/kg. The study of modeling of non-parametric methods using Sentinel-2 satellite data showed that the highest explanatory coefficient values (0.85, 0.88, and 0.97) were obtained for the three metals Cu, Pb, and Cu, respectively. The artificial neural network (ANN) algorithm obtained the highest accuracy. Also, according to the results of the random forest algorithm, for the three mentioned metals, PSRI, HMSSI, and PSRI indices are the most important in modeling.
Based on the findings, the concentration values of Cu and zinc were significantly different at different distances, but the Cu values were not significantly different at different distances. In this regard, Mansour concluded in 2014 that there is a significant difference between the concentration of Cu and zinc in the leaves of the species, which can be attributed to traffic density and human activities, and the high amount of zinc metal in this study is the wear of car tires؛ and stated that the concentration of Cu is caused by the production of greenhouse gases and the use of vehicles using Cu gasoline. Based on the findings, the values of Cu and zinc concentrations at different distances did not have significant differences, but the Cu values had significant differences at different distances. Sources of input of Cu element to the soil are urban, industrial, and agricultural waste, fertilizers, and chemicals that add it to the soil through liquid, solid, or mineral fertilizers. These findings are with the results of some researchers including Wu and colleagues (2010:38), Botsou et al. (2016:17) are consistent. Based on the findings obtained from the calculation of the bioconcentration index and their comparison with the classification proposed by Ma et al. (2001:25) for Iranian oak species plants in relation to Cu, zinc, and Cu metals from soil to leaves, it acts as an accumulating plant. In accordance with the results of this research, in the study of Khodakarmi et al. (2009:15), Iranian oak was included in the category of superabsorbent plants in relation to the accumulation of Cu pollutants, which has a high capacity in terms of root absorption. Also, Madejón et al. (2006:25) stated that oak leaves are more resistant than olive leaves. The concentrations of elements in leaves and fruits decrease with time and the risk of toxicity in the food web is reduced. The review and comparison of five algorithms showed that (ANN) the highest explanatory coefficient values (0.85, 0.88, and 0.97) were obtained for three metals, Cu, Zn, and Cu, respectively. Considering the importance of the PSRI synthetic band in increasing the accuracy of modeling with satellite images and the influence of the visible and near-infrared bands, the amount of reflection measured by the spectroscopic method showed that with the increase in the concentration of heavy elements, the amount of reflection in the visible and infrared range decreases (Liu et al., 2011:24).
Conclusion
The results showed that Sentinel-2 images along with artificial intelligence techniques have a relatively good ability to model the level of biological pollution index in the region. In line with the obtained results, it is suggested that the Iranian oak species is used to reduce pollution on highways because it accumulates heavy metals.
Remote Sensing (RS)
Keyvan Mokhtari; Hooshang Asadi Harouni; Mohammad Ali Aliabadi; Somayeh Beiranvand
Abstract
Extended Abstract 1- IntroductionAlteration is the simplest, cheapest and most suitable means of mineral exploration. The best way to find changes is to use satellite data processing.Asadi and Tabatabaei (2007) have used band ratio processing methods and false color images by using selected principal ...
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Extended Abstract 1- IntroductionAlteration is the simplest, cheapest and most suitable means of mineral exploration. The best way to find changes is to use satellite data processing.Asadi and Tabatabaei (2007) have used band ratio processing methods and false color images by using selected principal component processing (PCA) to identify the range of variations in different regions on Aster images. Gomez et al. (2005) visualized the lithological units of Namibian using the PCA algorithm on Aster data.The exposed rock units in Muteh mining area include a series of sedimentary, volcanic, and volcanic-clastic metamorphic rocks that extends from the green schist facies to the border of green schist and amphibolites along the northeast-southwest direction. These units have been repeatedly penetrated by alkaline intrusions, especially acid and granite (Rashidenjad, Omran et al., 2002).In general, the controlling elements of mineralization in Muteh area include structural factors (faults and fractures), alteration, and deformation. Field observations indicate the occurrence of vein mineralization and gold sulfide deposits in mylonite shear zones and fault zones in felsic to mafic metavolcanic host rocks.Gold mineralization is mainly concentrated in highly altered metariolites containing iron and copper sulfides and within fractures as veins and deposits. Alterations in silica, sericite, and carbonation are also observed along with these sediments, which are studied as exploration keys (Moritz et al., 2006).In this area, according to the lithology and distribution of alteration zones and the type of mineralization in Muteh gold mine, gold orogeny-type mineralizations are expected, which can be indirectly identified by recognizing the above alteration.2- Materials and methodsIn this study, Aster satellite images have been used to identify, discover and separate alteration zones in ENVI 5.3 software. Also, Landsat 8 satellite images have been utilized for general investigation and identification of hydrothermal alteration zones and expansion of iron oxide minerals, and Sentinel 2 satellite data due to better spatial and radiometric resolution than the above data has been applied to increase the spatial resolution of these data and the spatial accuracy of the map from the extracted changes.In order to validate between the field observations and spectral analysis, 24 rock samples were taken from the place of alteration, especially siliceous, argillic, and sercitic alteration around Senjedeh and Chah Khatoon deposits. 11 samples were sent to Zarazma laboratory for XRD analysis, and five samples were sent to Zarkavan Alborz Company’s laboratory for chemical analysis of 41 elements by ICP-MS method and gold element by Fire Assay method.3- ResultsConsidering the relationship between alteration zones and metal mineralization, it is very important to know and map these areas in the exploration of these deposits.The results and images show that the methods used in determining and separating the altered areas in Muteh exploratory area are acceptable and the optimal and effective methods in this research, SAM and MF, have been introduced.According to the field observations and surface sampling around Chah Khatoon and Senjedeh mineral deposits, as well as the investigation of changes, it was found that the most important changes in the region are: silicification, kaolinization, sericization, chlorination, alonation, pyrite, carbonation and so forth. This wide range shows the difference in intensity of alteration in different parts of the mineral reserve, which can be attributed to the system of joints, fractures and faults in the region.According to the available evidence, the metariolite rock is highly silicified in the tensile zones or in places with dense seams, and the pyrite particles in the context of these rocks have turned into iron hydroxide.4- DiscussionBy using satellite data processing, various data and information can be identified and extracted. Satellite data processing is done in two ways: visual and digital processing. By combining these two methods, the desired effects can be detected more accurately than the accuracy of satellite images. The visual method consists of preparing images of different color combinations by placing spectral bands in the red, green, and blue channels. Digital satellite image processing methods include band ratio, principal component analysis, least square regression method (Ls-Fit), spectral analysis, spectral angle mapping (SAM), and adaptive MF filter. The selection of the above methods was based on the type of information requested to extract data from images.Aster sensor images have no blue band (spectral range 0.4-0.5 µm) and the color composition of its VNIR bands is a standard RGB (1,2,3) false color composition. In this color combination, vegetation is seen in red. Since the study area is located in a relatively arid environment without vegetation, vegetation cover was avoided in the spectral analysis.The use and processing of Aster satellite data is one of the main features of this sensor; the use of unique spectral reflectance curves of alteration indicator minerals helped to identify and highlight these altered areas as well as finding the potential of areas prone to metal mineralization. Due to the high ability of Sentinel-2A images in identifying gossan and iron oxide ranges, the processing of these data was used to highlight these areas better.5- ConclusionAccording to the agreement of the results of geochemical and XRD studies with the distribution map of the alteration zones identified from the reference spectrum (USGS) and the spectral library (JPL), with the distribution map of lines and structural fractures of Muteh exploratory zone outside the pre-identified areas, new alteration zones were also introduced that require field research to confirm the results of stereo data processing.
Remote Sensing (RS)
Mahsa Jahanbakhsh; Ali Esmaeily
Abstract
Extended AbstractIntroduction In recent years, we have seen the importance and high demand of lithium (Li) due to its many applications, for example in the production of rechargeable lithium batteries, which are mainly related to the global markets of electric vehicle manufacturing ...
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Extended AbstractIntroduction In recent years, we have seen the importance and high demand of lithium (Li) due to its many applications, for example in the production of rechargeable lithium batteries, which are mainly related to the global markets of electric vehicle manufacturing to achieve a healthy environment and more suitable transportation. Due to this high demand, the identification of new lithium reserves is very important and the investigation of its identification and zoning methods has been the focus of many researchers, and the use of remote sensing data and image processing techniques in the detection of lithium due to cost reduction of earth exploration has increased, greatly.In this research, using modern methods, a general and intelligent approach was presented, so that with the least time and cost, after selecting the bands of the desired satellite images and zoning the area of Degh Ptergan, in Zirkoh city, South Khorasan province, as a possible area for the existence of lithium reserves, modeling was done by the supervised machine learning method, and the relative importance of the variables was determined using the trained model.Also, the relative importance of the variables was determined by the trained model, and the ability of each of the remote sensing techniques to achieve this goal has been challenged.Materials& Methods Here, 13 bands of Sentinel-2 images and the region of 12 known lithium mines around the world were used as lithium presence areas, so that, by going through steps, suitable data for modeling were produced. In this way, by using the boundaries of these mines, samples were produced that can be used as input for modeling algorithms. The maximum entropy algorithm was used to model the distribution of lithium samples. Since the correlation between the input variables reduces the performance of the model and makes it difficult to interpret the results of the modeling, first, the correlation between the input variables was calculated and those with a high correlation were discarded. So that, 16 variables were used as input in the maximum entropy algorithm and finally a suitable model was obtained with the AUC (Area Under the Curve) criterion of 0.706 and by it, the study area of Degh Patregan, located in the province South Khorasan, Iran was zoned and two possible areas containing lithium resources were identified.To determine the relative importance and contribution of the input variables in the prediction map of lithium minerals, the Jacknife method was implemented. According to this method, the variables B10, B06/B08, B06/B07 and B01/B10 have a high relative importance, which shows that they have more information than the other variables. Then classic remote sensing techniques including color composition, band ratio, principal component analysis and SAM was done to zone the study area, too. The results of maximum entropy modeling were compared with these techniques and the high ability of the maximum entropy algorithm was determined.Results & Discussion According to the results and prediction maps related to the classical methods, it showed that although some of these methods approximately identified the areas specified by the maximum entropy algorithm, but they had problems that is emphasized on the development of more suitable remote sensing algorithms to describe the changes associated with lithium minerals. The maximum entropy algorithm with its unique options is a powerful tool for extracting the features of satellite images and expresses their hidden information more clearly. The accuracy of this method was compared with classical techniques and it was able to provide a more appropriate classification with a low noise and with a Kappa coefficient of 0.8775 and an overall accuracy of 0.9435, and identified two areas with the possibility of the presence of lithium minerals in the study area.Conclusion & SuggestionsIn the present research, the study area of Degh Patergan, located in South Khorasan province, Iran, was zoned, whereby two possible areas containing lithium resources were identified and the ability of classical remote sensing methods and maximum entropy algorithm was challenged. The method discussed in the research may be used as a cost-effective and technological solution with priority over field mapping for mineral exploration in remote border areas with difficult access, also an automatic approach with the maximum entropy algorithm was presented for the exploration of different mineral resources, which can be used for other exploration as well. Therefore, it is suggested to be used in different areas and to explore different sources.
