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Hyperspectral Remote Sensing from Spaceborne and Low Altitude Aerial/Drone-Based Platforms — Differences in Approaches, Data Processing Methods, and Applications

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (15 October 2023) | Viewed by 1219930

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Special Issue Editors


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Guest Editor

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Guest Editor
Mineral Exploration and Geoenvironment Division (Geosciences Group), National Remote Sensing Centre, Indian Space Research Organisation, Balanagar, Hyderabad 500010, Telengana, India
Interests: airborne hyperspectral; spaceborne hyperspectral satellite; hyperspectral data processing; geoenvironmental; mineral prospectivity; target detection

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Guest Editor
DISTAV, University of Genova, Corso Europa 26, 16132 Genova, Italy
Interests: structural geology; tectonics; remote sensing; geological mapping; exploration geology; minerals

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Guest Editor
Assistant Professor of Mining and Geological Engineering, Department of Geological and Mining Engineering and Sciences, Michigan Technological University, 1400 Townsend Drive, 601 Dow Building, Houghton, MI 49931, USA
Interests: hyperspectral; multi-point and multi-scale geostatistics; mathematical and artificial intelligence modeling; AVIRIS-NG data processing; digital image analysis; mineral exploration

Special Issue Information

Dear Colleagues,

In the last two decades, several important space-borne hyperspectral sensors have been launched by different space agencies. However, since the time of Hyperion (in 1999) to the latest launch of the Hyperspectral Imager Suite (HISUI) (in December 2019), no hyperspectral sensors have had global coverage. Despite this, these sensors have made significant use of hyperspectral data and also led to innovative approaches to data processing (from noise removal to spectral mapping). Previous studies have highlighted the limitations of these space-borne sensors in identifying a pure target and also in identifying spectral targets with subdued spectral signatures as these hyperspectral sensors had coarse spatial resolution (in general 20 meters to 30 meters) and poor signal to noise ratio (e.g., Hyperion has poor SNR in the shortwave electromagnetic domain). However, these spaceborne sensors have had encouraging results in environmental monitoring, for example, in improved forest cover classification, detection of phonological changes in forest, land use/land cover mapping, agriculture land cover characterization, crop stress estimation, mapping of rock types, minerals, etc. Due to the lack of global coverage of space-borne hyperspectral sensors; routine aircraft-based and drone-based hyperspectral surveys are carried out in different countries using different advanced hyperspectral sensors like advanced visible infrared spectrometer (AVIRIS) and its latest version AVIRIS-next generation (AVIRIS-NG); HyMap, DAIS, etc. These sensors, capable of collecting high spatial and spectral resolution data with optimum spectral fidelity, have led to new applications, such as soil geochemistry, water quality, forest species mapping, agricultural stress, and exploration scale mineral alteration mapping, etc. These applications have not been explored using hyperspectral data from spaceborne platforms. Machine or artificial intelligence can be used to understand and utilize the higher-order variation of field grade spectral data collected using these low-altitude airborne sensors to automate spectral feature-based target detection. It is now important to capitalize on the comparative the potential of spaceborne and airborne hyperspectral remote sensing datasets based on analyzing different applications that have been addressed by hyperspectral data from different platforms to identify the specificity of each of these two platforms.

Dr. Amin Beiranvand Pour
Dr. Arindam Guha
Prof. Dr. Laura Crispini
Dr. Snehamoy Chatterjee
Guest Editors

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Keywords

  • artificial intelligence
  • airborne and spaceborne hyperspectral sensors
  • global coverage
  • spectral mapping
  • environmental monitoring

Published Papers (12 papers)

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Editorial

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6 pages, 233 KiB  
Editorial
Editorial for the Special Issue Entitled Hyperspectral Remote Sensing from Spaceborne and Low-Altitude Aerial/Drone-Based Platforms—Differences in Approaches, Data Processing Methods, and Applications
by Amin Beiranvand Pour, Arindam Guha, Laura Crispini and Snehamoy Chatterjee
Remote Sens. 2023, 15(21), 5119; https://doi.org/10.3390/rs15215119 - 26 Oct 2023
Viewed by 1169
Abstract
Nowadays, several hyperspectral remote sensing sensors from spaceborne and low-altitude aerial/drone-based platforms with a variety of spectral and spatial resolutions are available for geoscientific applications [...] Full article

Research

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29 pages, 7123 KiB  
Article
Machine Learning (ML)-Based Copper Mineralization Prospectivity Mapping (MPM) Using Mining Geochemistry Method and Remote Sensing Satellite Data
by Mahnaz Abedini, Mansour Ziaii, Timofey Timkin and Amin Beiranvand Pour
Remote Sens. 2023, 15(15), 3708; https://doi.org/10.3390/rs15153708 - 25 Jul 2023
Cited by 5 | Viewed by 2364
Abstract
The exploration of buried mineral deposits is required to generate innovative approaches and the integration of multi-source geoscientific datasets. Mining geochemistry methods have been generated based on the theory of multi-formational geochemical dispersion haloes. Satellite remote sensing data is a form of surficial [...] Read more.
The exploration of buried mineral deposits is required to generate innovative approaches and the integration of multi-source geoscientific datasets. Mining geochemistry methods have been generated based on the theory of multi-formational geochemical dispersion haloes. Satellite remote sensing data is a form of surficial geoscience datasets and can be considered as big data in terms of veracity and volume. The different alteration zones extracted using remote sensing methods have not been yet categorized based on the mineralogical and geochemical types (MGT) of anomalies and cannot discriminate blind mineralization (BM) from zone dispersed mineralization (ZDM). In this research, an innovative approach was developed to optimize remote sensing-based evidential variables using some constructed mining geochemistry models for a machine learning (ML)-based copper prospectivity mapping. Accordingly, several main steps were implemented and analyzed. Initially, the MGT model was executed by studying the distribution of indicator elements of lithogeochemical data extracted from 50 copper deposits from Commonwealth of Independent States (CIS) countries to identify the MGT of geochemical anomalies associated with copper mineralization. Then, the geochemical zonality model was constructed using the database of the porphyry copper deposits of Iran and Kazakhstan to evaluate the geochemical anomalies related to porphyry copper mineralization (e.g., the Saghari deposit located around the Chah-Musa deposit, Toroud-Chah Shirin belt, central north Iran). Subsequently, the results of mining geochemistry models were used to produce the geochemical evidential variable by vertical geochemical zonality (Vz) (Pb × Zn/Cu × Mo) and to optimize the remote sensing-based evidential variables. Finally, a random forest algorithm was applied to integrate the evidential variables for generating a provincial-scale prospectivity mapping of porphyry copper deposits in the Toroud-Chah Shirin belt. The results of this investigation substantiated that the machine learning (ML)-based integration of multi-source geoscientific datasets, such as mining geochemistry techniques and satellite remote sensing data, is an innovative and applicable approach for copper mineralization prospectivity mapping in metallogenic provinces. Full article
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26 pages, 4586 KiB  
Article
Analysis of Water Yield Changes in the Johor River Basin, Peninsular Malaysia Using Remote Sensing Satellite Imagery
by Mazlan Hashim, Babangida Baiya, Mohd Rizaludin Mahmud, Dalhatu Aliyu Sani, Musa Muhammad Chindo, Tan Mou Leong and Amin Beiranvand Pour
Remote Sens. 2023, 15(13), 3432; https://doi.org/10.3390/rs15133432 - 6 Jul 2023
Cited by 2 | Viewed by 1974
Abstract
Changes in land-use–land-cover (LULC) affect the water balance of a region by influencing the water yield (WY) along with variations in rainfall and evapotranspiration (ET). Remote sensing satellite imagery offers a comprehensive spatiotemporal distribution of LULC to analyse changes in WY over a [...] Read more.
Changes in land-use–land-cover (LULC) affect the water balance of a region by influencing the water yield (WY) along with variations in rainfall and evapotranspiration (ET). Remote sensing satellite imagery offers a comprehensive spatiotemporal distribution of LULC to analyse changes in WY over a large area. Hence, this study mapped and analyse successive changes in LULC and WY between 2000 and 2015 in the Johor River Basin (JRB) by specifically comparing satellite-based and in-situ-derived WY and characterising changes in WY in relation to LULC change magnitudes within watersheds. The WY was calculated using the water balance equation, which determines the WY from the equilibrium of precipitation minus ET. The precipitation and ET information were derived from the Tropical Rainfall Measuring Mission (TRMM) and moderate-resolution imaging spectroradiometer (MODIS) satellite data, respectively. The LULC maps were extracted from Landsat-Enhanced Thematic Mapper Plus (ETM+) and Landsat Operational Land Imager (OLI). The results demonstrate a good agreement between satellite-based derived quantities and in situ measurements, with an average bias of ±20.04 mm and ±43 mm for precipitation and ET, respectively. LULC changes between 2000 and 2015 indicated an increase in agriculture land other than oil palm to 11.07%, reduction in forest to 32.15%, increase in oil palm to 11.88%, and increase in urban land to 9.82%, resulting in an increase of 15.76% WY. The finding can serve as a critical initiative for satellite-based WY and LULC changes to achieve targets 6.1 and 6.2 of the United Nations Sustainable Development Goal (UNSDG) 6. Full article
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24 pages, 24001 KiB  
Article
UAV-Based Hyperspectral Imaging for River Algae Pigment Estimation
by Riley D. Logan, Madison A. Torrey, Rafael Feijó-Lima, Benjamin P. Colman, H. Maurice Valett and Joseph A. Shaw
Remote Sens. 2023, 15(12), 3148; https://doi.org/10.3390/rs15123148 - 16 Jun 2023
Cited by 11 | Viewed by 1858
Abstract
Harmful and nuisance algal blooms are becoming a greater concern to public health, riverine ecosystems, and recreational uses of inland waterways. Algal bloom proliferation has increased in the Upper Clark Fork River due to a combination of warming water temperatures, naturally high phosphorus [...] Read more.
Harmful and nuisance algal blooms are becoming a greater concern to public health, riverine ecosystems, and recreational uses of inland waterways. Algal bloom proliferation has increased in the Upper Clark Fork River due to a combination of warming water temperatures, naturally high phosphorus levels, and an influx of nitrogen from various sources. To improve understanding of bloom dynamics and how they affect water quality, often measured as algal biomass measured through pigment standing crops, a UAV-based hyperspectral imaging system was deployed to monitor several locations along the Upper Clark Fork River in western Montana. Image data were collected across the spectral range of 400–1000 nm with 2.1 nm spectral resolution during two field sampling campaigns in 2021. Included are methods to estimate chl a and phycocyanin standing crops using regression analysis of salient wavelength bands, before and after separating the pigments according to their growth form. Estimates of chl a and phycocyanin standing crops generated through a linear regression analysis are compared to in situ data, resulting in a maximum R2 of 0.96 for estimating fila/epip chl-a and 0.94 when estimating epiphytic phycocyanin. Estimates of pigment standing crops from total abundance, epiphytic, and the sum of filamentous and epiphytic sources are also included, resulting in a promising method for remotely estimating algal standing crops. This method addresses the shortcomings of current monitoring techniques, which are limited in spatial and temporal scale, by proposing a method for rapid collection of high-spatial-resolution pigment abundance estimates. Full article
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30 pages, 12086 KiB  
Article
Implementation of Ground-Based Lightning Locating System Using Particle Swarm Optimization Algorithm for Lightning Mapping and Monitoring
by Kamyar Mehranzamir, Amin Beiranvand Pour, Zulkurnain Abdul-Malek, Hadi Nabipour Afrouzi, Seyed Morteza Alizadeh and Mazlan Hashim
Remote Sens. 2023, 15(9), 2306; https://doi.org/10.3390/rs15092306 - 27 Apr 2023
Cited by 1 | Viewed by 2071
Abstract
Cloud-to-ground (CG) lightning is a natural phenomenon that poses significant threats to human safety, infrastructure, and equipment. The destructive impacts of lightning strikes on humans and their property have been a longstanding concern for both society and industry. Countries with high thunderstorm frequencies, [...] Read more.
Cloud-to-ground (CG) lightning is a natural phenomenon that poses significant threats to human safety, infrastructure, and equipment. The destructive impacts of lightning strikes on humans and their property have been a longstanding concern for both society and industry. Countries with high thunderstorm frequencies, such as Malaysia, experience significant fatalities and damage due to lightning strikes. To this end, a lightning locating system (LLS) was developed and deployed in a 400 km2 study area at the University Technology Malaysia (UTM), Johor, Malaysia for detecting cloud-to-ground lightning discharges. The study utilized a particle swarm optimization (PSO) algorithm as a mediator to identify the best location for a lightning strike. The algorithm was initiated with 30 particles, considering the outcomes of the MDF and TDOA techniques. The effectiveness of the PSO algorithm was found to be dependent on how the search process was arranged. The results of the detected lightning strikes by the PSO-based LLS were compared with an industrial lightning detection system installed in Malaysia. From the experimental data, the mean distance differences between the PSO-based LLS and the industrial LLS inside the study area was up to 573 m. Therefore, the proposed PSO-based LLS would be efficient and accurate to detect and map the lightning discharges occurring within the coverage area. This study is significant for researchers, insurance companies, and the public seeking to be informed about the impacts of lightning discharges. Full article
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17 pages, 51143 KiB  
Article
Research on Scale Improvement of Geochemical Exploration Based on Remote Sensing Image Fusion
by Haifeng Ding, Linhai Jing, Mingjie Xi, Shi Bai, Chunyan Yao and Lu Li
Remote Sens. 2023, 15(8), 1993; https://doi.org/10.3390/rs15081993 - 10 Apr 2023
Cited by 4 | Viewed by 1617
Abstract
Both remote sensing and geochemical exploration technologies are effective tools for detecting target objects. Although information on anomalous geochemical elemental abundances differs in terms of professional attributes from remote sensing data, both are based on geological bodies or phenomena on the Earth’s surface. [...] Read more.
Both remote sensing and geochemical exploration technologies are effective tools for detecting target objects. Although information on anomalous geochemical elemental abundances differs in terms of professional attributes from remote sensing data, both are based on geological bodies or phenomena on the Earth’s surface. Therefore, exploring the use of remote sensing data with high spatial resolution to improve the accuracy of small-scale geochemical data, and fusing them to obtain large-scale geochemical layers could provide new data for geological and mineral exploration through inversion. This study provides a method of fusing remote sensing images with small-scale geochemical data based on a linear regression model that improves the resolution of geochemical elemental layers and provides reference data for mineral exploration in areas lacking large-scale geochemical data. In the Xianshuigou area of Northwest China, a fusion study was conducted using 200,000 geochemical and remote sensing data. The method provides fused large-scale regional chemical data in well-exposed areas where large-scale geochemical data are lacking and could provide potential data sources for regional mineral exploration. Full article
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17 pages, 4829 KiB  
Article
Mapping of Greenhouse Gas Concentration in Peninsular Malaysia Industrial Areas Using Unmanned Aerial Vehicle-Based Sniffer Sensor
by Mazlan Hashim, Hui Lin Ng, Dahiru Mohammed Zakari, Dalhatu Aliyu Sani, Musa Muhammad Chindo, Noordyana Hassan, Muna Maryam Azmy and Amin Beiranvand Pour
Remote Sens. 2023, 15(1), 255; https://doi.org/10.3390/rs15010255 - 1 Jan 2023
Cited by 2 | Viewed by 2925
Abstract
The increasing concentration of greenhouse gas (GHG) emissions due to increased fossil fuel consumption for manufacturing activities to support population growth is worrisome. Carbon dioxide (CO2) and methane (CH4) remain the two GHGs that contribute to the impact of [...] Read more.
The increasing concentration of greenhouse gas (GHG) emissions due to increased fossil fuel consumption for manufacturing activities to support population growth is worrisome. Carbon dioxide (CO2) and methane (CH4) remain the two GHGs that contribute to the impact of global warming, and inventorying their concentrations is important for monitoring their changes, which can be used to infer their emissions over time. Hence, this article highlights sniffer4D, an unmanned aerial vehicle (UAV)-based air pollutant mapping system that visualise and analyse three-dimensional (3D) air pollution data in real time, for mapping GHGs concentrations within industrial areas. Consequently, GHGs concentrations for two industrial and adjacent residential areas in Johor, Peninsular Malaysia were mapped. The GHGs concentrations were validated using a ground-based portable gas detector. The results revealed that CO2 has the highest concentration mean of 625.235 mg/m3, followed by CH4 with a mean of 249.239 mg/m3. The mapped UAV GHG concentration also reported good agreement with the in situ observations with an RMSE of 7 and 6 mg/m3 for CO2 and CH4 concentration, respectively. Ozone and nitrogen dioxide mixture (O3 + NO2) with a mean concentration of 249 μg/m3 and an RMSE of 9 μg/m3 are the remaining significant concentrations reported. This approach shall assist in fast-tracking the United Nations climate change mitigation agenda. Full article
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21 pages, 10984 KiB  
Article
Neuro-Fuzzy-AHP (NFAHP) Technique for Copper Exploration Using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Geological Datasets in the Sahlabad Mining Area, East Iran
by Aref Shirazi, Ardeshir Hezarkhani, Amin Beiranvand Pour, Adel Shirazy and Mazlan Hashim
Remote Sens. 2022, 14(21), 5562; https://doi.org/10.3390/rs14215562 - 4 Nov 2022
Cited by 25 | Viewed by 1185884
Abstract
Fusion and analysis of thematic information layers using machine learning algorithms provide an important step toward achieving accurate mineral potential maps in the reconnaissance stage of mineral exploration. This study developed the Neuro-Fuzzy-AHP (NFAHP) technique for fusing remote sensing (i.e., ASTER alteration mineral [...] Read more.
Fusion and analysis of thematic information layers using machine learning algorithms provide an important step toward achieving accurate mineral potential maps in the reconnaissance stage of mineral exploration. This study developed the Neuro-Fuzzy-AHP (NFAHP) technique for fusing remote sensing (i.e., ASTER alteration mineral image-maps) and geological datasets (i.e., lithological map, geochronological map, structural map, and geochemical map) to identify high potential zones of volcanic massive sulfide (VMS) copper mineralization in the Sahlabad mining area, east Iran. Argillic, phyllic, propylitic and gossan alteration zones were identified in the study area using band ratio and Selective Principal Components Analysis (SPCA) methods implemented to ASTER VNIR and SWIR bands. For each of the copper deposits, old mines and mineralization indices in the study area, information related to exploration factors such as ore mineralization, host-rock lithology, alterations, geochronological, geochemistry, and distance from high intensity lineament factor communities were investigated. Subsequently, the predictive power of these factors in identifying copper occurrences was evaluated using Back Propagation Neural Network (BPNN) technique. The BPNN results demonstrated that using the exploration factors, copper mineralizations in Sahlabad mining area could be identified with high accuracy. Lastly, using the Fuzzy-Analytic Hierarchy Process (Fuzzy-AHP) method, information layers were weighted and fused. As a result, a potential map of copper mineralization was generated, which pinpointed several high potential zones in the study area. For verification of the results, the documented copper deposits, old mines, and mineralization indices in the study area were plotted on the potential map, which is particularly appearing in high favorability parts of the potential map. In conclusion, the Neuro-Fuzzy-AHP (NFAHP) technique shows great reliability for copper exploration in the Sahlabad mining area, and it can be extrapolated to other metallogenic provinces in Iran and other regions for the reconnaissance stage of mineral exploration. Full article
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23 pages, 8579 KiB  
Article
Recognition of Sago Palm Trees Based on Transfer Learning
by Sri Murniani Angelina Letsoin, Ratna Chrismiari Purwestri, Fajar Rahmawan and David Herak
Remote Sens. 2022, 14(19), 4932; https://doi.org/10.3390/rs14194932 - 2 Oct 2022
Cited by 4 | Viewed by 2523
Abstract
Sago palm tree, known as Metroxylon Sagu Rottb, is one of the priority commodities in Indonesia. Based on our previous research, the potential habitat of the plant has been decreasing. On the other hand, while the use of remote sensing is now [...] Read more.
Sago palm tree, known as Metroxylon Sagu Rottb, is one of the priority commodities in Indonesia. Based on our previous research, the potential habitat of the plant has been decreasing. On the other hand, while the use of remote sensing is now widely developed, it is rarely applied for detection and classification purposes, specifically in Indonesia. Considering the potential use of the plant, local farmers identify the harvest time by using human inspection, i.e., by identifying the bloom of the flower. Therefore, this study aims to detect sago palms based on their physical morphology from Unmanned Aerial Vehicle (UAV) RGB imagery. Specifically, this paper endeavors to apply the transfer learning approach using three deep pre-trained networks in sago palm tree detection, namely, SqueezeNet, AlexNet, and ResNet-50. The dataset was collected from nine different groups of plants based on the dominant physical features, i.e., leaves, flowers, fruits, and trunks by using a UAV. Typical classes of plants are randomly selected, like coconut and oil palm trees. As a result, the experiment shows that the ResNet-50 model becomes a preferred base model for sago palm classifiers, with a precision of 75%, 78%, and 83% for sago flowers (SF), sago leaves (SL), and sago trunk (ST), respectively. Generally, all of the models perform well for coconut trees, but they still tend to perform less effectively for sago palm and oil palm detection, which is explained by the similarity of the physical appearance of these two palms. Therefore, based our findings, we recommend improving the optimized parameters, thereby providing more varied sago datasets with the same substituted layers designed in this study. Full article
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19 pages, 36482 KiB  
Article
Retrieval of Water Quality from UAV-Borne Hyperspectral Imagery: A Comparative Study of Machine Learning Algorithms
by Qikai Lu, Wei Si, Lifei Wei, Zhongqiang Li, Zhihong Xia, Song Ye and Yu Xia
Remote Sens. 2021, 13(19), 3928; https://doi.org/10.3390/rs13193928 - 30 Sep 2021
Cited by 32 | Viewed by 4486
Abstract
The rapidly increasing world population and human activities accelerate the crisis of the limited freshwater resources. Water quality must be monitored for the sustainability of freshwater resources. Unmanned aerial vehicle (UAV)-borne hyperspectral data can capture fine features of water bodies, which have been [...] Read more.
The rapidly increasing world population and human activities accelerate the crisis of the limited freshwater resources. Water quality must be monitored for the sustainability of freshwater resources. Unmanned aerial vehicle (UAV)-borne hyperspectral data can capture fine features of water bodies, which have been widely used for monitoring water quality. In this study, nine machine learning algorithms are systematically evaluated for the inversion of water quality parameters including chlorophyll-a (Chl-a) and suspended solids (SS) with UAV-borne hyperspectral data. In comparing the experimental results of the machine learning model on the water quality parameters, we can observe that the prediction performance of the Catboost regression (CBR) model is the best. However, the prediction performances of the Multi-layer Perceptron regression (MLPR) and Elastic net (EN) models are very unsatisfactory, indicating that the MLPR and EN models are not suitable for the inversion of water quality parameters. In addition, the water quality distribution map is generated, which can be used to identify polluted areas of water bodies. Full article
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26 pages, 28063 KiB  
Article
Potentials of Airborne Hyperspectral AVIRIS-NG Data in the Exploration of Base Metal Deposit—A Study in the Parts of Bhilwara, Rajasthan
by Arindam Guha, Uday Kumar Ghosh, Joyasree Sinha, Amin Beiranvand Pour, Ratnakar Bhaisal, Snehamoy Chatterjee, Nikhil Kumar Baranval, Nisha Rani, K. Vinod Kumar and Pamaraju V. N. Rao
Remote Sens. 2021, 13(11), 2101; https://doi.org/10.3390/rs13112101 - 27 May 2021
Cited by 19 | Viewed by 3421
Abstract
In this study, we have processed the spectral bands of airborne hyperspectral data of Advanced Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) data for delineating the surface signatures associated with the base metal mineralization in the Pur-Banera area in the Bhilwara district, Rajasthan, India.The [...] Read more.
In this study, we have processed the spectral bands of airborne hyperspectral data of Advanced Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) data for delineating the surface signatures associated with the base metal mineralization in the Pur-Banera area in the Bhilwara district, Rajasthan, India.The primaryhost rocks of the Cu, Pb, Zn mineralization in the area are Banded Magnetite Quartzite (BMQ), unclassified calcareous silicates, and quartzite. We used ratio images derived from the scale and root mean squares (RMS) error imagesusing the multi-range spectral feature fitting (MRSFF) methodto delineate host rocks from the AVIRIS-NG image. The False Color Composites (FCCs) of different relative band depth images, derived from AVIRIS-NG spectral bands, were also used for delineating few minerals. These minerals areeither associated with the surface alteration resulting from the ore-bearing fluid migration orassociated with the redox-controlled supergene enrichments of the ore deposit.The results show that the AVIRIS-NG image products derived in this study can delineate surface signatures of mineralization in 1:10000 to 1:15000 scales to narrow down the targets for detailed exploration.This study alsoidentified the possible structural control over the knownsurface distribution of alteration and lithocap minerals of base metal mineralizationusing the ground-based residual magnetic anomaly map. This observationstrengthens the importance of the identified surface proxiesas an indicator of mineralization. X-ray fluorescence analysis of samples collectedfromselected locations within the study area confirms the Cu-Pb-Zn enrichment. The sulfide minerals were also identified in the microphotographs of polished sections of rock samples collected from the places where surface proxies of mineralization were observed in the field. This study justified the investigation to utilize surface signatures of mineralization identified using AVIRIS-NG dataand validated using field observations, geophysical, geochemical, and petrographical data. Full article
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26 pages, 9655 KiB  
Article
Airborne Hyperspectral Data Acquisition and Processing in the Arctic: A Pilot Study Using the Hyspex Imaging Spectrometer for Wetland Mapping
by Jordi Cristóbal, Patrick Graham, Anupma Prakash, Marcel Buchhorn, Rudi Gens, Nikki Guldager and Mark Bertram
Remote Sens. 2021, 13(6), 1178; https://doi.org/10.3390/rs13061178 - 19 Mar 2021
Cited by 19 | Viewed by 4362
Abstract
A pilot study for mapping the Arctic wetlands was conducted in the Yukon Flats National Wildlife Refuge (Refuge), Alaska. It included commissioning the HySpex VNIR-1800 and the HySpex SWIR-384 imaging spectrometers in a single-engine Found Bush Hawk aircraft, planning the flight times, direction, [...] Read more.
A pilot study for mapping the Arctic wetlands was conducted in the Yukon Flats National Wildlife Refuge (Refuge), Alaska. It included commissioning the HySpex VNIR-1800 and the HySpex SWIR-384 imaging spectrometers in a single-engine Found Bush Hawk aircraft, planning the flight times, direction, and speed to minimize the strong bidirectional reflectance distribution function (BRDF) effects present at high latitudes and establishing improved data processing workflows for the high-latitude environments. Hyperspectral images were acquired on two clear-sky days in early September, 2018, over three pilot study areas that together represented a wide variety of vegetation and wetland environments. Steps to further minimize BRDF effects and achieve a higher geometric accuracy were added to adapt and improve the Hyspex data processing workflow, developed by the German Aerospace Center (DLR), for high-latitude environments. One-meter spatial resolution hyperspectral images, that included a subset of only 120 selected spectral bands, were used for wetland mapping. A six-category legend was established based on previous U.S. Geological Survey (USGS) and U.S. Fish and Wildlife Service (USFWS) information and maps, and three different classification methods—hybrid classification, spectral angle mapper, and maximum likelihood—were used at two selected sites. The best classification performance occurred when using the maximum likelihood classifier with an averaged Kappa index of 0.95; followed by the spectral angle mapper (SAM) classifier with a Kappa index of 0.62; and, lastly, by the hybrid classifier showing lower performance with a Kappa index of 0.51. Recommendations for improvements of future work include the concurrent acquisition of LiDAR or RGB photo-derived digital surface models as well as detailed spectra collection for Alaska wetland cover to improve classification efforts. Full article
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