scholarly journals Geostatistical and Remote Sensing Studies to Identify High Metallogenic Potential Regions in the Kivi Area of Iran

Minerals ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 869
Author(s):  
Adel Shirazy ◽  
Mansour Ziaii ◽  
Ardeshir Hezarkhani ◽  
Timofey Timkin
Keyword(s):  
Remote Sensing ◽  
Statistical Processing ◽  
Principal Component ◽  
Stream Sediment ◽  
Geochemical Data ◽  
Landsat 8 ◽  
Study Region ◽  
Linear Discriminant ◽  
Survey Results ◽  
East Azerbaijan

The Kivi area in the East Azerbaijan Province of Iran is one of the country’s highest-potential regions for metal element exploration. The primary goal herein was to process the data obtained from geochemical, geostatistical, and remote sensing tools (in the form of stream sediment samples and satellite images) to identify metallic mineralization anomalies in the region. After correcting the raw stream sediment geochemical data, single-variable statistical processing was performed, and Ti and Zn were identified as the elements with the highest degree of contrast. The relationship among these elements was further investigated using correlation and hierarchical clustering analyses. Principal component analysis was then applied to determine the principal components related to these elements, which were subsequently plotted on a regional geological map. Elements related to Ti and Zn were identified using threshold limits of anomalous samples determined via linear discriminant analysis. Lithological units and alteration patterns were detected through remote sensing investigations on Landsat-8 images. Stream sediment geochemical and remote sensing survey results identified anomalous areas of Ti and Zn in the eastern part of the study region. Our results indicate that Ti and Zn are good pathfinder elements for further exploratory investigation in this area.

scholarly journals Evapotranspiration Estimation Using Remote Sensing Technology Based on a SEBAL Model in the Upper Reaches of the Huaihe River Basin

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1599
Author(s):  
Linshan Tan ◽  
Kaiyuan Zheng ◽  
Qiangqiang Zhao ◽  
Yanjuan Wu
Keyword(s):  
Remote Sensing ◽  
Meteorological Data ◽  
Regional Scale ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Huaihe River ◽  
Infiltration Capacity ◽  
Mountain Area ◽  
Study Region ◽  
Sebal Model

Understanding the spatial and temporal variations of evapotranspiration (ET) is vital for water resources planning and management and drought monitoring. The development of a satellite remote sensing technique is described to provide insight into the estimation of ET at a regional scale. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) was used to calculate the actual ET on a daily scale from Landsat-8 data and daily ground-based meteorological data in the upper reaches of Huaihe River on 20 November 2013, 16 April 2015 and 23 March 2018. In order to evaluate the performance of the SEBAL model, the daily SEBAL ET (ETSEBAL) was compared against the daily reference ET (ET0) from four theoretical methods: the Penman-Monteith (P-M), Irmak-Allen (I-A), the Turc, and Jensen-Haise (J-H) method, the ETMOD16 product from the MODerate Resolution Imaging Spectrometer (MOD16) and the ETVIC from Variable Infiltration Capacity Model (VIC). A linear regression equation and statistical indices were used to model performance evaluation. The results showed that the daily ETSEBAL correlated very well with the ET0, ETMOD16, and ETVIC, and bias between the ETSEBAL with them was less than 1.5%. In general, the SEBAL model could provide good estimations in daily ET over the study region. In addition, the spatial-temporal distribution of ETSEBAL was explored. The variation of ETSEBAL was significant in seasons with high values during the growth period of vegetation in March and April and low values in November. Spatially, the daily ETSEBAL values in the mountain area were much higher than those in the plain areas over the study region. The variability of ETSEBAL in this study area was positively correlated with elevation and negatively correlated with surface reflectance, which implies that elevation and surface reflectance are the important factors for predicting ET in this study area.

scholarly journals Geological mapping and mineral prospectivity using remote sensing and GIS in parts of Hamissana, Northeast Sudan

2021 ◽  
Vol 11 (3) ◽  
pp. 1123-1138
Author(s):  
Mohamed Taha AlMakki Mohamed ◽  
Latifa Shaheen Al-Naimi ◽  
Tochukwu Innocent Mgbeojedo ◽  
Chidiebere Charles Agoha
Keyword(s):  
Remote Sensing ◽  
Gold Mineralization ◽  
Principal Component ◽  
Original Data ◽  
Wall Rock ◽  
Geological Mapping ◽  
Landsat 8 ◽  
Alteration Zones ◽  
Wall Rock Alteration ◽  
Rock Alteration

AbstractIn recent years, various geological activities and different mineral prospecting and exploration programs have been intensified along the Red Sea hills in order to elucidate the geological maps and to evaluate the mineral potentials. This study is therefore aimed at testing the viability of using remote sensing and geographic information system (GIS) techniques for geological mapping and prospecting for gold mineralization in the area. The study area is located in northeast Sudan and covers an area of about 1379 km2. Different digital image processing techniques were applied to Landsat 8 Operational Land Imager image in order to increase the discrimination between various lithological units and to delineate wall rock alteration which represents target zones for gold mineralization. Image sharpening was performed to enhance the spatial resolution of the images for more detailed information. Contrast stretching was applied after the various digital processing procedures to produce more interpretable images. The principal component analysis transformations yielded saturated images and resulted in more interpretable images than the original data. Several ratio images were prepared, combined together and displayed in RGB color composite ratio images. This process revealed the existence of alteration zones in the study area. These zones extend from the northeast to the southwest in the acid meta-volcanic and silica barite rocks. The enhanced satellite images were implemented in the GIS environment to facilitate the final production of the geological map at scale 1:400,000. X-ray fluorescence analyses prove that selected samples taken from the wall rock alteration zones are gold-bearing.

scholarly journals MONITORING AND ASSESSMENT OF ECOLOGICAL CHANGE IN COASTAL CITIES BASED ON RSEI

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 461-470
Author(s):  
X. Y. Liu ◽  
X. X. Zhang ◽  
Y. R. He ◽  
H. J. Luan
Keyword(s):  
Remote Sensing ◽  
Water Body ◽  
Principal Component ◽  
Vegetation Coverage ◽  
Weighting Coefficient ◽  
Landsat 8 ◽  
Ecological Change ◽  
Remote Sensing Images ◽  
Environment Change ◽  
Sentinel 2A

Abstract. With the speeding up of urbanization process, ecological problems, such as unsustainable land use and environmental pollution,have emerged one after another in cites. Nowadays, green development and ecological priority are the important concepts and trends of the current new urban planning in China. In this study, Pingtan County, a coastal city in Fujian Province, China, was taken as the research area. Based on two Landsat 8 remote sensing images (2016, 2017), and two Sentinel-2A remote sensing images (2016, 2017), we first adopt the modified normalized water body index (MNDWI) to mask the water body. Four indicators, including greenness, humidity, dryness and heat were extracted to synthesize the remote sensing ecological index (RSEI), which were obtained by principal component analysis method. Based on the RSEI values acquired from Landsat 8 and Sentinel-2A images, the ecological environment change trend in Pingtan County was evaluated .The experimental results show that: 1) The RSEI indicators based on Landsat 8 and sentinel data all show a downward trend, but due to due to the influence of image spatial resolution and PCA weighting coefficient, the RSEI index has different degrees of decline. 2) The main reason for the decline in RSEI is the increase in NDSI indicators. Compared with July 2016, the bare ground increased in April 2017. Although the NDVI has increased, the overall trend is still declining. Therefore, it is necessary to ecologically return farmland and improve vegetation coverage in the future development process. 3) In recent years, the ecological quality of new construction land near drinking water sources has declined, so it is necessary to strengthen monitoring of changes in the region.

scholarly journals Comparison of In Situ and Remote-Sensing Methods to Determine Turbidity and Concentration of Suspended Matter in the Estuary Zone of the Mzymta River, Black Sea

2021 ◽  
Vol 13 (1) ◽  
pp. 143
Author(s):  
Ksenia Nazirova ◽  
Yana Alferyeva ◽  
Olga Lavrova ◽  
Yuri Shur ◽  
Dmitry Soloviev ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Black Sea ◽  
Atmospheric Correction ◽  
Landsat 8 ◽  
Suspended Particulate Matter Concentration ◽  
Water Sampling ◽  
Study Region ◽  
Sentinel 2A ◽  
Remote Sensing Methods

The paper presents the results of a comparison of water turbidity and suspended particulate matter concentration (SPM) obtained from quasi-synchronous in situ and satellite remote-sensing data. Field measurements from a small boat were performed in April and May 2019, in the northeastern part of the Black Sea, in the mouth area of the Mzymta River. The measuring instruments and methods included a turbidity sensor mounted on a CTD (Conductivity, Temperature, Depth), probe, a portable turbidimeter, water sampling for further laboratory analysis and collecting meteorological information from boat and ground-based weather stations. Remote-sensing methods included turbidity and SPM estimation using the C2RCC (Case 2 Regional Coast Color) and Atmospheric correction for OLI ‘lite’ (ACOLITE) ACOLITE processors that were run on Landsat-8 Operational Land Imager (OLI) and Sentinel-2A/2B Multispectral Instrument (MSI) satellite data. The highest correlation between the satellite SPM and the water sampling SPM for the study area in conditions of spring flooding was achieved using C2RCC, but only for measurements undertaken almost synchronously with satellite imaging because of the high mobility of the Mzymta plume. Within the few hours when all the stations were completed, its boundary could shift considerably. The ACOLITE algorithms overestimated by 1.5 times the water sampling SPM in the low value range up to 15 g/m3. For SPM over 20–25 g/m3, a high correlation was observed both with the in situ measurements and the C2RCC results. It was demonstrated that quantitative turbidity and SPM values retrieved from Landsat-8 OLI and Sentinel-2A/2B MSI data can adequately reflect the real situation even using standard retrieval algorithms, not regional ones, provided the best suited algorithm is selected for the study region.

scholarly journals Utilizing landsat-8 sensor operational land image data for hydrothermal alteration mapping within Anka Schist Belt, northwestern Nigeria

2020 ◽  
pp. 127-149
Author(s):  
Ohinowi Aliyu ◽  
Kankara Aliyu
Keyword(s):  
Remote Sensing ◽  
Hydrothermal Alteration ◽  
Image Data ◽  
Principal Component ◽  
Cost Effective ◽  
Digital Data ◽  
Mineral Deposits ◽  
Landsat 8 ◽  
Schist Belt ◽  
False Colour

Exploring for mineral deposits within the Anka Schist Belt involves the use of traditional geological techniques such as geochemical and geophysical studies that are very expensive and time consuming. There is therefore need for a better alternative that will provide accurate and reliable information with cost effective and time efficient solution. This effort seeks to explore the potential of remotely sensed digital data in highlighting mineralized zones through hydrothermal alteration studies. Landsat 8 OLI data covering the investigated area was used to detect and map locations of hydrothermal alterations. Image processing methods used were spectral enhancement, false colour composites, band rationing and Principal Component Analysis. Results of false colour composites of band 5: 7: 3 highlighted generally locations of hydrothermal alterations. Band ratios of 4/2, 6/7 and 6/5 revealed the presence of ferric iron minerals, clay rich minerals and ferrous minerals respectively. Principal Components (PCs) of two sets of images (2, 4, 5, 7 H-image and 2,5,6,7 F-image) depicting iron-oxide and hydroxyl mineral deposits as bright pixels were generated. Colour composite of H, F and H+F images enhanced the location of the mineral deposits, by showing areas of mineralization in dark blue (Fe rich), bright yellow (clay rich) and white (Fe and clay rich areas) pixels. Field coordinates of mining locations were superimposed on the remote sensing generated mineral map. The results were found to be in tune. This study recommends the use of remote sensing and geospatial technology in mineral studies through hydrothermal alteration within the basement complex rocks of Nigeria.

scholarly journals Analysis of cloud condition on Sentinel-2 MSI and Landsat-8 OLI images of a public supply lake in Belém-Pará-Brazil

2019 ◽  
Vol 14 (4) ◽  
pp. 1 ◽  
Author(s):  
Vítor Abner Borges Dutra ◽  
Paulo Amador Tavares ◽  
Hebe Morganne Campos Ribeiro
Keyword(s):  
Remote Sensing ◽  
Principal Component ◽  
Economic Losses ◽  
Landsat 8 ◽  
Sensing Applications ◽  
Dissolved Organic Matters ◽  
Combined Use ◽  
Cloud Regime ◽  
Remote Sensing Techniques ◽  
Sentinel 2

The eutrophication process leads to reduced water quality and economic losses worldwide. Furthermore, it is possible to apply remote sensing techniques for monitoring of aquatic environments. In this paper, we analysed the combined use of Sentinel-2 Multispectral Instrument and Landsat-8 Operational Land Imager data to monitor a eutrophic aquatic environment under adverse cloudy conditions, from July 2016 to July 2018. Data pre-selection was performed, and then the images were acquired for further investigation. After that, we created a key to the interpretation of cloud conditions for the study area and grouped each of 125 scenes in a Principal Component Analysis (PCA). The PCA grouped months with similarities in cloud conditions, highlighting their patterns in terms of the rainy and dry seasons for the study area. Another interesting result was that, even under the inherent adverse cloud regime of the Amazon, the combined use of both free satellite imagery data could be useful for further analyses, such as measuring of chlorophyll a, coloured dissolved organic matters, total suspended solids and turbidity. However, we highlight that, firstly, studies must be made to validate the data in situ, so that monitoring programs can be built through remote sensing applications.

Mineral-resource prediction using advanced data analytics and machine learning of the QUEST-South stream-sediment geochemical data, southwestern British Columbia, Canada

2020 ◽  
Vol 21 (1) ◽  
pp. geochem2020-054 ◽  
Author(s):  
E. C. Grunsky ◽  
D. Arne
Keyword(s):  
British Columbia ◽  
Limit Of Detection ◽  
Principal Component ◽  
Mineral Deposit ◽  
Stream Sediment ◽  
Training Dataset ◽  
Geochemical Data ◽  
Posterior Probabilities ◽  
Multivariate Statistical ◽  
Log Ratio

In this study we apply multivariate statistical and predictive classification methods to interpret geochemical data from 8545 stream-sediment samples collected in southern British Columbia, Canada. Data for 35 elements were corrected for laboratory bias and adjusted for values reported below the lower limit of detection. Each sample site was attributed with the closest British Columbia MINFILE occurrence within 2.5 km. MINFILE occurrences were grouped into ‘GroupModels’ based on similarities between the British Columbia Geological Survey mineral deposit models and geochemical signatures. These data were used to create a training dataset of 474 observations, including 100 samples not attributed with a MINFILE occurrence. The training set was used to generate predictions for the mineral deposit models from which posterior probabilities were estimated for the remaining 8071 samples. The data underwent a centred log-ratio transformation and then characterization using either principal component analysis (PCA) or t-distributed stochastic neighbour embedding using 9 dimensions (t-SNE) prior to classification by random forests. The posterior probabilities generated from the t-SNE metric provide a slightly higher level of prediction accuracy compared to the posterior probabilities obtained using the PCA metric. The results are comparable to those obtained using a conventional catchment analysis approach and expert-driven model. The approach presented here provides a repeatable, consistent and defensible methodology for the identification of prospective mineralized terrains and mineral systems.

scholarly journals REGIONAL GEOLOGICAL MAPPING IN THE GRAHAM LAND OF ANTARCTIC PENINSULA USING LANDSAT-8 REMOTE SENSING DATA

2017 ◽  
Vol XLII-4/W5 ◽  
pp. 235-238
Author(s):  
A. B. Pour ◽  
M. Hashim ◽  
Y. Park
Keyword(s):  
Remote Sensing ◽  
Antarctic Peninsula ◽  
Regional Scale ◽  
Geological Mapping ◽  
Mapping Technique ◽  
Landsat 8 ◽  
Study Region ◽  
Mineral Mapping ◽  
Mapping Tool ◽  
North Eastern

Geological investigations in Antarctica confront many difficulties due to its remoteness and extreme environmental conditions. In this study, the applications of Landsat-8 data were investigated to extract geological information for lithological and alteration mineral mapping in poorly exposed lithologies in inaccessible domains such in Antarctica. The north-eastern Graham Land, Antarctic Peninsula (AP) was selected in this study to conduct a satellite-based remote sensing mapping technique. Continuum Removal (CR) spectral mapping tool and Independent Components Analysis (ICA) were applied to Landsat-8 spectral bands to map poorly exposed lithologies at regional scale. Pixels composed of distinctive absorption features of alteration mineral assemblages associated with poorly exposed lithological units were detected by applying CR mapping tool to VNIR and SWIR bands of Landsat-8.Pixels related to Si-O bond emission minima features were identified using CR mapping tool to TIR bands in poorly mapped andunmapped zones in north-eastern Graham Land at regional scale. Anomaly pixels in the ICA image maps related to spectral featuresof Al-O-H, Fe, Mg-O-H and CO3 groups and well-constrained lithological attributions from felsic to mafic rocks were detectedusing VNIR, SWIR and TIR datasets of Landsat-8. The approach used in this study performed very well for lithological andalteration mineral mapping with little available geological data or without prior information of the study region.

scholarly journals A Satellite-based Remote Sensing Technique for Surface Water Quality Estimation

2019 ◽  
Vol 9 (2) ◽  
pp. 3965-3970 ◽  
Author(s):  
M. V. Japitana ◽  
M. E. C. Burce
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Temporal Trends ◽  
Oxygen Demand ◽  
Surface Water Quality ◽  
Principal Component ◽  
Input Image ◽  
Landsat 8 ◽  
Band Ratio ◽  
Remote Sensing Technique

Remote sensing provides a synoptic view of the earth surface that can provide spatial and temporal trends necessary for comprehensive water quality (WQ) monitoring and assessment. This study explores the applicability of Landsat 8 and regression analysis in developing models for estimating WQ parameters such as pH, dissolved oxygen (DO), total dissolved solids (TDS), total suspended solids (TSS), biological oxygen demand (BOD), turbidity, and conductivity. The input image was radiometrically-calibrated using fast line-of-sight atmospheric analysis (FLAASH) and then atmospherically corrected to obtain surface reflectance (SR) bands using FLAASH and dark object subtraction (DOS) for comparison. SR bands derived using FLAASH and DOS, water indices, band ratio, and principal component analysis (PCA) images were utilized as input data. Feature vectors were then collected from the input bands and subsequently regressed together with the WQ data. Forward regression results yielded significant high R2 values for all WQ parameters except TSS and conductivity which had only 60.1% and 67.7% respectively. Results also showed that the regression models of pH, BOD, TSS, TDS, DO, and conductivity are highly significant to SR bands derived using DOS. Furthermore, the results of this study showed the promising potential of using RS-based WQ models in performing periodic WQ monitoring and assessment.

Sign in / Sign up

Export Citation Format

Share Document