Study on the Geochemical Anomaly of Copper Element Based on Hyperspectral Indices

2020 ◽  
Author(s):  
Wang Shanshan ◽  
Zhou Kefa
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Remote Sensing ◽  
Coefficient Of Determination ◽  
Geochemical Data ◽  
Geochemical Anomaly ◽  
Spectral Indices ◽  
Absorption Bands ◽  
Important Indicator ◽  
Cu Content ◽  
Geochemical Anomalies

<p>Geochemical anomalies are an important indicator in prospecting. In particular, geochemical anomalies of Cu play a very important role in geological prospecting of minerals. Geochemical anomalies of Cu are mainly related to mafic-ultramafic rocks and porphyry bodies, which are also associated with ore-forming elements of the Co-Zn-Cr-Ni-Cu combination. The conventional technique of geochemical prospecting involves superimposition of element symbols (Au, Fe, Cu, Al, Ca, etc.) on the geological map of an area by analysing geochemical anomalies using geochemical data. However, this technique is not suitable for regions where geochemical anomaly data are limited. The development of hyperspectral remote sensing has enabled the mapping of spectral features related to characteristic absorption bands of elements in minerals at high spatial resolution, providing a means for precise and detailed reconstructions of geochemical anomalies facies (surface). Compared to conventional techniques for identifying elements, reflectance spectroscopy offers a rapid, inexpensive, and non-destructive tool for determining the mineralogy of rock and soil samples. Hyperspectral remote sensing also provides data for prospecting in areas without sufficient geochemical data, and thus is of vital significance in prospecting for ores in such regions. However, approaches for remotely sensing elements are still lacking, particularly for element content. In this study, a level analysis of Cu content via spectral indices in the northwestern Junggar region, Xinjiang, was conducted. Based on four levels (0–100 ppm, 100–1000 ppm, 1000–10000 ppm, and >10000 ppm) of Cu content and corresponding spectral reflectance, simple and useful spectral indices for estimating Cu content at different levels were explored. The best wavelength domains for a given type of index were determined from four types of spectral indices by screening all combinations using correlation analysis. The coefficient of determination (R2) for Cu was calculated for all indices derived from the spectra of rock samples and was found to range from 0.02–0.75. With sensitive wavelengths and a significant correlation coefficient (R2 = 0.63, P < 0.005), the Normalized Difference (ND)-type index was the most sensitive to Cu content exceeding 10000 ppm. Although the ND-type index has a few limitations, it is a useful, simple, and robust indicator for determining Cu at high concentrations. With the advent of new platforms and satellites in the future, such relationships with other elements are required to enable the widespread use of this index in broad-scale surveys of mineral elements in the field.</p>

scholarly journals Coupling Relationship Analysis of Gold Content Using Gaofen-5 (GF-5) Satellite Hyperspectral Remote Sensing Data: A Potential Method in Chahuazhai Gold Mining Area, Qiubei County, SW China

2021 ◽  
Vol 14 (1) ◽  
pp. 109
Author(s):  
Yuehan Qin ◽  
Xinle Zhang ◽  
Zhifang Zhao ◽  
Ziyang Li ◽  
Changbi Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Mining Area ◽  
Potential Method ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Matched Filtering ◽  
Geochemical Anomaly ◽  
Sw China ◽  
Coupling Relationship ◽  
Geochemical Anomalies

The gold (Au) geochemical anomaly is an important indicator of gold mineralization. While the traditional field geochemical exploration method is time-consuming and expensive, the hyperspectral remote sensing technique serves as a robust technique for the delineation and mapping of hydrothermally altered and weathered mineral deposits. Nonetheless, mineralization element anomaly detection was still seldomly used in previous hyperspectral remote sensing applications in mineralization. This study explored the coupling relationship between Gaofen-5 (GF-5) hyperspectral data and Au geochemical anomalies through several models. The Au geochemical anomalies in the Chahuazhai mining area, Qiubei County, Yunnan Province, SW China, was studied in detail. First, several noise reduction methods including radiometric calibration, Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), Savitzky–Golay filter, and endmember choosing methods including Minimum Noise Fraction (MNF) transformation, matched filtering, and Fast Fourier Transform (FFT) transformation were applied to the Gaofen-5 (GF-5) hyperspectral data processing. The Spectrum-Area (S-A) method was introduced to build an FFT filter to highlight the spectral abnormal characteristics associated with Au geochemical anomaly information. Specifically, the Matched Filtering (MF) technique was applied to the dataset to find the Au geochemical anomaly abundances of endmembers with innovative large-sample learning. Then, Multiple Linear Regression (MLR), Partial Least Squares (PLS) regression, a Back Propagation (BP) network, and Geographically Weighted Regression (GWR) were used to reveal the coupling relationship between the spectra of the processed hyperspectral data and the Au geochemical anomalies. The results show that the GWR analysis has a much higher coefficient of determination, which implies that the Au geochemical anomalies and the spectral information are highly related to spatial locations. GWR works especially well for showing the regional Au geochemical anomaly trend and simulating the Au concentrated areas. The GWR model with application of the S-A method is applicable to the detection of Au geochemical anomalies, which could provide a potential method for Au deposit exploration using GF-5 hyperspectral data.

Hyperspectral remote sensing monitoring of cyanobacteria blooms in a large South American reservoir: high- and medium-spatial resolution satellite algorithm simulation

2020 ◽  
Vol 71 (5) ◽  
pp. 593 ◽  
Author(s):  
A. Drozd ◽  
P. de Tezanos Pinto ◽  
V. Fernández ◽  
M. Bazzalo ◽  
F. Bordet ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Near Infrared ◽  
Monitoring Program ◽  
Hyperspectral Remote Sensing ◽  
Coefficient Of Determination ◽  
South American ◽  
Cell Abundance ◽  
Hyperspectral Signatures ◽  
Sentinel 2

We used hyperspectral remote sensing with the aim of establishing a monitoring program for cyanobacteria in a South American reservoir. We sampled at a wide temporal (2012–16; 10 seasons) and spatial (30km) gradient, and retrieved 111 field hyperspectral signatures, chlorophyll-a, cyanobacteria densities and total suspended solids. The hyperspectral signatures for cyanobacteria-dominated situations (n=75) were used to select the most suitable spectral bands in seven high- and medium-spatial resolution satellites (Sentinel 2, Landsat 5, 7 and 8, SPOT-4/5 and -6/7, WorldView 2), and for the development of chlorophyll and cyanobacteria cell abundance algorithms (λ550 – λ650+λ800) ÷ (λ550+λ650+λ800). The best-performing chlorophyll algorithm was Sentinel 2 ((λ560 – λ660+λ703) ÷ (λ560+λ660+λ703); R2=0.80), followed by WorldView 2 ((λ550 – λ660+λ720) ÷ (λ550+λ660+λ720); R2=0.78), Landsat and the SPOT series ((λ550 – λ650+λ800) ÷ (λ550+λ650+λ800); R2=0.67–0.74). When these models were run for cyanobacteria abundance, the coefficient of determination remained similar, but the root mean square error increased. This could affect the estimate of cyanobacteria cell abundance by ~20%, yet it still enable assessment of the alert level categories for risk assessment. The results of this study highlight the importance of the red and near-infrared region for identifying cyanobacteria in hypereutrophic waters, demonstrating coherence with field cyanobacteria abundance and enabling assessment of bloom distribution in this ecosystem.

Evaluation of geochemical anomaly classification models based on the relevant uncertainties and error propagation per class to select the most robust model(s) for the follow-up exploration 

2021 ◽  
Author(s):  
Behnam Sadeghi
Keyword(s):  
Decision Making ◽  
Error Propagation ◽  
Massive Sulfide ◽  
Geochemical Data ◽  
Geochemical Background ◽  
Geochemical Anomaly ◽  
Classification Models ◽  
Geochemical Anomalies ◽  
Anomaly Classification ◽  
Highly Correlated

<p>A significant issue in all geochemical anomaly classification methods is uncertainty in the identification of different populations and allocation of samples to those populations, including the critical category of geochemical anomalies or patterns that are associated with the effects of mineralisation. This is a major challenge where the effects of mineralisation are subtle. There are various possible sources of such uncertainty, such as (i) gaps in coverage of geochemical sampling within a study area; (ii) errors in geochemical data analysis, spatial measurement, interpolation; (iii) misunderstanding of geological and geochemical processes; (iv) fuzziness or vagueness of the threshold between geochemical background and geochemical anomalies. In this research, the well-established concentration-area (C-A) and the newly established concentration-concentration (C-C) fractal models were applied to centered-logratio (clr) transformed data, and highly correlated elements of Cu-Te, respectively. Such models were applied to the available till samples (2578 samples) collected by the Geological Survey of Sweden (SGU) from 75% of the country area, to generate the Cu volcanic massive sulfide (VMS) geochemical anomaly classified map and define the highly promising areas for further exploration. However, to be confident more about the robustness of each class recognised by the thresholds obtained from the C-A and C-C log-log plots, Monte Carlo simulation (MCSIM) was applied to each class to simulate a higher number of values per class and consider the relevant error propagation. Under the MCSIM approach, the P50 value (the average 50<sup>th</sup> percentile of the multiple simulated distributions represents a neutral probability in decision-making) is defined as the expected ‘return’. The uncertainty is calculated, in this approach, as 1/(P90-P10) for which P10 (lower decile) and P90 (upper decile) are the average 10<sup>th</sup> and 90<sup>th</sup> percentiles of the multiple simulated values, associated with each class. The most reliable classes are those with high returns and low risks. Based on the results obtained, C-A could not provide robust enough results since in the defined classes, the risk was almost equal or even higher than the return, however, the C-C model provided high returns and very low uncertainties, demonstrating the robustness of C-C compared to C-A. This approach can improve the quality of the decision-making in choosing the most robust classification models, and consequently getting more reliable results.</p>

scholarly journals Integrating Geochemical Anomaly and Remote Sensing Methods to Predict Oil- and Gas-Bearing Areas in the Yanchang Oil Field, Ordos Basin, China

2019 ◽  
Vol 23 (1) ◽  
pp. 79-86
Author(s):  
Jianming Guo ◽  
Hailong Fan ◽  
Xiangzeng Wang ◽  
Lixia Zhang ◽  
Laiyi Ren ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Oil And Gas ◽  
Ordos Basin ◽  
Mutual Authentication ◽  
Continuous Distribution ◽  
Hyperspectral Remote Sensing ◽  
Oil Field ◽  
Geochemical Data ◽  
Oil Seepage ◽  
Gas Bearing

Oil seepage is one of the most important characteristics of hydrocarbon formation, and understanding oil seepage is crucial for oil-gas exploration and the assessment of petroleum resources. Remote sensing and geochemical methods have the same material and theoretical bases for extracting oil and gas information from underlying strata and the identification of media features. As an emerging exploration method, hyperspectral remote sensing is efficient compared with traditional geochemistry because it is a finer, and sometimes more directly quantitative method for determining the specific mineral anomaly content. Hence, the use of both methods together is important. This paper describes the analysis of hyperspectral remote sensing data and the extraction of abnormal index information, including the level of carbonate alteration and the content of acidolytical hydrocarbons, pyrolysis hydrocarbons, headspace gas, and ferric and ferrous ions. The two methods have mutual authentication, and they are complementary and are useful in oil-bearing areas. When these methods are integrated, the acidolytical hydrocarbon index is the most effective geochemical index and is better at characterizing the oil field distribution than other indices. Also, hydrocarbon geochemical anomalies occurring in oil fields generally show continuous distribution points and are consistent with oil reservoirs. Consequently, a 3D model was established to comprehensively utilize hyperspectral remote sensing and geochemical data to determine the distribution of petroleum reservoirs efficiently as well as to delineate oil- and gas-bearing prospects. There is great potential for determining oil- and gas-bearing fields through the integration of hyperspectral and geochemical data.

scholarly journals Inland Waters Suspended Solids Concentration Retrieval Based on PSO-LSSVM for UAV-Borne Hyperspectral Remote Sensing Imagery

2019 ◽  
Vol 11 (12) ◽  
pp. 1455 ◽  
Author(s):  
Lifei Wei ◽  
Can Huang ◽  
Yanfei Zhong ◽  
Zhou Wang ◽  
Xin Hu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Least Squares ◽  
Empirical Model ◽  
Suspended Solids ◽  
Hyperspectral Remote Sensing ◽  
Support Vector ◽  
Inland Waters ◽  
Important Indicator ◽  
Solids Concentration ◽  
Semi Empirical

Suspended solids concentration (SSC) is an important indicator of the degree of water pollution. However, when using an empirical or semi-empirical model adapted to some of the inland waters to estimate SSC on unmanned aerial vehicle (UAV)-borne hyperspectral images, the accuracy is often not sufficient. Thus, in this study, we attempted to use the particle swarm optimization (PSO) algorithm to find the optimal parameters of the least-squares support vector machine (LSSVM) model for the quantitative inversion of SSC. A reservoir and a polluted riverway were selected as the study areas. The spectral data of the 36-point and 29-point 400–900 nm wavelength range on the UAV-borne images were extracted. Compared with the semi-empirical model, the random forest (RF) algorithm and the competitive adaptive reweighted sampling (CARS) algorithm combined with partial least squares (PLS), the accuracy of the PSO-LSSVM algorithm in predicting the SSC was significantly improved. The training samples had a coefficient of determination ( R 2 ) of 0.98, a root mean square error (RMSE) of 0.68 mg/L, and a mean absolute percentage error (MAPE) of 12.66% at the reservoir. For the polluted riverway, PSO-LSSVM also performed well. Finally, the established SSC inversion model was applied to UAV-borne hyperspectral remote sensing (HRS) images. The results confirmed that the distribution of the predicted SSC was consistent with the observed results in the field, which proves that PSO-LSSVM is a feasible approach for the SSC inversion of UAV-borne HRS images.

scholarly journals A Tool for Analysis of Spectral Indices for Remote Sensing of Vegetation and Crops Using Hyperspectral Images

2019 ◽  
pp. 51-58
Author(s):  
David Ruiz Hidalgo ◽  
Bladimir Bacca Cortés ◽  
Eduardo Caicedo Bravo
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Infrared Imaging ◽  
Software Tool ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Images ◽  
Spectral Indices ◽  
Imaging Spectrometer ◽  
Software Application ◽  
Erdas Imagine

Food requirements in the world have increased, evidencing the necessity to improve standard techniques of agricultural production. To do so, one option is through technological elements like hyperspectral remote sensing of vegetation and crops. Remote sensing and hyperspectral imagery are not invasive methods. They allow covering large land space in a reduced amount of time. These features have done the hyper-spectral remote sensing a powerful tool used in precision agriculture. This paper presents a software application to process hyperspectral images and generating pseudo-color images computed using spectral indices. This work uses the hyperspectral images were taken by Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) sensor, which was designed by the NASA. The software application aims to show different elements associated with the hyperspectral remote sensing of vegetation and crops. Functional tests are presented to verify the software requirements. Finally, quantitative results are reported comparing the results of the software proposes in this work with the ERDAS Imagine software tool.

scholarly journals RETRIVAL OF BIO-PHYSICAL PARAMETERS IN SUNFLOWER CROP (<i>HELIANTHUS ANNUUS</i>) USING FIELD BASED HYPERSPECTRAL REMOTE SENSING

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 623-629
Author(s):  
P. K. Routh ◽  
N. C. Sarkar ◽  
P. K. Das ◽  
D. Debnath ◽  
S. Bandyopadhyay ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chlorophyll Content ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Health Study ◽  
Physical Parameters ◽  
Absorption Bands ◽  
Area Index ◽  
Sunflower Crop ◽  
Gas Exchange System

<p><strong>Abstract.</strong> Information on several crop bio-physical parameters is important as inputs for crop growth modelling, leaf stress analysis, crop health study and productivity point of view. Conventionally, biophysical parameters are measured in laboratory methods which are time consuming, laborious and destructive in nature. With the advent of remote sensing technology, the limitations of conventional methods can be overcome. Moreover, due to its narrow absorption bands at different wavelength, use of hyperspectral remote sensing becomes very useful in retrieving several bio-physical parameters. In the present study, field as well as laboratory based spectro-radiometer observations were carried out at Agronomy Department of VisvaBharati University, West Bengal, on Sunflower crop at its peak vegetation stage towards retrieving different bio-physical parameters, specifically leaf area index (LAI), chlorophyll content index (CCI), fluorescence etc. Different foliar boron (no boron, 0.15% and 0.20%) and irrigation (4&amp;ndash;6 irrigations) treatments, i.e. total nine treatments with three replications, were applied on sunflower crop during different phenological stages to achievemaximum ranges of the bio-physical parameters. The LAI, CCI and fluorescence parameters were collected using canopy analyzer,chlorophyll content meter and portable gas exchange system, respectively. In each of the treatments, total four hyperspectral measurements were collected, which were further corrected for noise and smoothened using Savitzky-Golay filtering. Total thirty-four narrow band indices were computed based on the hyperspectral data, and the regression analysis was carried out among the indices and bio-physical parameters. The regression parameters were further deployed on the hyperspectral indices to retrieve the bio-physical parameters. The Gitelson &amp; Merzylak-1 (GM-1) and Carter Indices-1 (CI-1) were found to the best indices for retrieving the LAI and CCI, respectively with correlation correlation (r) values of 0.87 and 0.80. On the other hand, Normalized Phaenophytinization Index (NPQI) and GM-1 were found to best for retrieving the Fv/Fm (dark) and Fvˈ/Fmˈ (light) with correlation(r)values of 0.92 and 0.76, respectively. Hence, the hyperspectral remote sensing be successfully utilized for retrieving several bio-physical parameters both at field (canopy level) and laboratory (leaf level) conditions.</p>

Sign in / Sign up

Export Citation Format

Share Document