Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Earth Sciences
Title: Spectral indices for lithologic discrimination and mapping by using the ASTER SWIR bands
Abstract: The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a research facility instrument launched on NASA's Terra spacecraft in December 1999. Spectral indices, a kind of orthogonal transformation in the five-dimensional space formed by the five ASTER short-wave-infrared (SWIR) bands, were proposed for discrimination and mapping of surface rock types. These include Alunite Index, Kaolinite Index, Calcite Index, and Montmorillonite Index, and can be calculated by linear combination of reflectance values of the five SWIR bands. The transform coefficients were determined so as to direct transform axes to the average spectral pattern of the typical minerals. The spectral indices were applied to the simulated ASTER dataset of Cuprite, Nevada, USA after converting its digital numbers to surface reflectance. The resultant spectral index images were useful for lithologic mapping and were easy to interpret geologically. An advantage of this method is that we can use the pre-determined transform coefficients, as long as image data are converted to surface reflectance.
Extracted statement — Analysis: Calcite Index
Research field: Earth Sciences
Title: Automated Seasonal Detection of Coal Surface Mine Regions from Landsat 8 OLI Images
Abstract: Detection, and monitoring of surface mining region have various research aspects. Coal surface mining has severe social, ecological, environmental adverse effects. In the past, semisupervised and supervised clustering techniques have been used to detect such regions. Coal has lower reflectance values in short wave infrared I (SWIR-I) than short wave infrared II (SWIR-II). The proposed method presents a novel approach to detect coal mine regions without manual intervention using this cue. Clay mineral ratio is defined as a ratio of SWIR-I to SWIR-II. Here, unsupervised K-Means clustering has been used in a hierarchical fashion over a variant of clay mineral ratio to detect opencast coal mine regions in the Jharia coal field (JCF), India. The proposed method has average precision, and recall of 76.43%, and 62.75%, respectively.
Extracted statement — Supplementary information: Coal mine region
Research field: Earth Sciences
Title: Humanitarian applications of machine learning with remote-sensing data: review and case study in refugee settlement mapping
Abstract: The coordination of humanitarian relief, e.g. in a natural disaster or a conflict situation, is often complicated by a scarcity of data to inform planning. Remote sensing imagery, from satellites or drones, can give important insights into conditions on the ground, including in areas which are difficult to access. Applications include situation awareness after natural disasters, structural damage assessment in conflict, monitoring human rights violations or population estimation in settlements. We review machine learning approaches for automating these problems, and discuss their potential and limitations. We also provide a case study of experiments using deep learning methods to count the numbers of structures in multiple refugee settlements in Africa and the Middle East. We find that while high levels of accuracy are possible, there is considerable variation in the characteristics of imagery collected from different sensors and regions. In this, as in the other applications discussed in the paper, critical inferences must be made from a relatively small amount of pixel data. We, therefore, consider that using machine learning systems as an augmentation of human analysts is a reasonable strategy to transition from current fully manual operational pipelines to ones which are both more efficient and have the necessary levels of quality control. This article is part of a discussion meeting issue ‘The growing ubiquity of algorithms in society: implications, impacts and innovations’.
Extracted statement — Output/Application: Damage assessment
Research field: Earth Sciences
Title: Pegmatite spectral behavior considering ASTER and Landsat 8 OLI data in Naipa and Muiane mines (Alto Ligonha, Mozambique)
Abstract: The Naipa and Muiane mines are located on the Nampula complex, a stratigraphic tectonic subdivision of the Mozambique Belt, in the Alto Ligonha region. The pegmatites are of the Li-Cs-Ta type, intrude a chlorite phyllite and gneisses with amphibole and biotite. The mines are still active. The main objective of this work was to analyze the pegmatite’s spectral behavior considering ASTER and Landsat 8 OLI data. An ASTER image from 27/05/2005, and an image Landsat OLI image from 02/02/2018 were considered. The data were radiometric calibrated and after atmospheric corrected considered the Dark Object Subtraction algorithm available in the Semi-Automatic Classification Plugin accessible in QGIS software. In the field, samples were collected from lepidolite waste pile in Naipa and Muaine mines. A spectroadiometer was used in order to analyze the spectral behavior of several pegmatite’s samples collected in the field in Alto Ligonha (Naipa and Muiane mines). In addition, QGIS software was also used for the spectral mapping of the hypothetical hydrothermal alterations associated with occurrences of basic metals, beryl gemstones, tourmalines, columbite-tantalites, and lithium minerals. A supervised classification algorithm was employed - Spectral Angle Mapper for the data processing, and the overall accuracy achieved was 80%. The integration of ASTER and Landsat 8 OLI data have proved very useful for pegmatite’s mapping. From the results obtained, we can conclude that: (i) the combination of ASTER and Landsat 8 OLI data allows us to obtain more information about mineral composition than just one sensor, i.e., these two sensors are complementary; (ii) the alteration spots identified in the mines area are composed of clay minerals. In the future, more data and others image processing algorithms can be applied in order to identify the different Lithium minerals, as spodumene, petalite, amblygonite and lepidolite.
Extracted statement — Preprocesing: Dark Object Subtraction
Research field: Earth Sciences
Title: The Performance of Maximum Likelihood, Spectral Angle Mapper, Neural Network and Decision Tree Classifiers in Hyperspectral Image Analysis
Abstract: Several classification algorithms for pattern recognition had been tested in the mapping of tropical forest cover using airborne hyperspectral data. Results from the use of Maximum Likelihood (ML), Spectral Angle Mapper (SAM), Artificial Neural Network (ANN) and Decision Tree (DT) classifiers were compared and evaluated. It was found that ML performed the best followed by ANN, DT and SAM with accuracies of 86%, 84%, 51% and 49% respectively.
Extracted statement — Techniques/Methods: Decision Tree
Research field: Earth Sciences
Title: A short survey of hyperspectral remote sensing applications in agriculture
Abstract: Hyperspectral sensors are devices that acquire images over hundreds of spectral bands, thereby enabling the extraction of spectral signatures for objects or materials observed. Hyperspectral remote sensing has been used over a wide range of applications, such as agriculture, forestry, geology, ecological monitoring and disaster monitoring. In this paper, the specific application of hyperspectral remote sensing to agriculture is examined. The technological development of agricultural methods is of critical importance as the world's population is anticipated to continuously rise much beyond the current number of 7 billion. One area upon which hyperspectral sensing can yield considerable impact is that of precision agriculture - the use of observations to optimize the use of resources and management of farming practices. For example, hyperspectral image processing is used in the monitoring of plant diseases, insect pests and invasive plant species; the estimation of crop yield; and the fine classification of crop distributions. This paper also presents a detailed overview of hyperspectral data processing techniques and suggestions for advancing the agricultural applications of hyperspectral technologies in Turkey.
Extracted statement — Application: Estimation of Crop Yield
Research field: Earth Sciences
Title: Mapping hydrothermal alteration minerals using high-resolution AVIRIS-NG hyperspectral data in the Hutti-Maski gold deposit area, India
Abstract: ABSTRACT The present study exploits high-resolution hyperspectral imagery acquired by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor from the Hutti-Maski gold deposit area, India, to map hydrothermal alteration minerals. The study area is a volcanic-dominated late Archean greenstone belt that hosts major gold mineralization in the Eastern Dharwar Craton of southern India. The study encompasses pre-processing, spectral and spatial image reduction using Minimum Noise Fraction (MNF) and Fast Pixel Purity Index (FPPI), followed by endmember extraction using n-dimensional visualizer and the United States Geological Survey (USGS) mineral spectral library. Image derived endmembers such as goethite, chlorite, chlorite at the mine site (chlorite mixed with mined materials), kaolinite, and muscovite were subsequently used in spectral mapping methods such as Spectral Angle Mapper (SAM), Spectral Information Divergence (SID) and its hybrid, i.e. SIDSAMtan. Spectral similarity matrix of the target and non-target-based method has been proposed to find the possible optimum threshold needed to obtain mineral map using spectral mapping methods. Relative Spectral Discrimination Power (RSDPW) and Confusion Matrix (CM) have been used to evaluate the performance of SAM, SID, and SIDSAMtan. The RSDPW and CM illustrate that the SIDSAMtan benefits from the unique characteristics of SAM and SID to achieve better discrimination capability. The Overall Accuracy (OA) and kappa coefficient (ҡ) of SAM, SID, and SIDSAMtan were computed using 900 random validation points and obtained 90% (OA) and 0.88 (ҡ), 91.4% and 0.90, and 94.4% and 0.93, respectively. Obtained mineral map demonstrates that the northern portion of the area mainly consists of muscovite whereas the southern part is marked by chlorite, goethite, muscovite and kaolinite, indicating the propylitic alteration. Most of these minerals are associated with altered metavolcanic rocks and migmatite.
Extracted statement — yields: Fast Pixel Purity Index (FPPI)
Research field: Earth Sciences
Title: Characterization and mapping of hematite ore mineral classes using hyperspectral remote sensing technique: a case study from Bailadila iron ore mining region
Abstract: Abstract The study demonstrates a methodology for mapping various hematite ore classes based on their reflectance and absorption spectra, using Hyperion satellite imagery. Substantial validation is carried out, using the spectral feature fitting technique, with the field spectra measured over the Bailadila hill range in Chhattisgarh State in India. The results of the study showed a good correlation between the concentration of iron oxide with the depth of the near-infrared absorption feature (R 2 = 0.843) and the width of the near-infrared absorption feature (R 2 = 0.812) through different empirical models, with a root-mean-square error (RMSE) between &lt; 0.317 and &lt; 0.409. The overall accuracy of the study is 88.2% with a Kappa coefficient value of 0.81. Geochemical analysis and X-ray fluorescence (XRF) of field ore samples are performed to ensure different classes of hematite ore minerals. Results showed a high content of Fe &gt; 60 wt% in most of the hematite ore samples, except banded hematite quartzite (BHQ) (&lt; 47 wt%).
Extracted statement — Analysis: Geochemical analysis
Research field: Earth Sciences
Title: Raman spectroscopy of the borosilicate mineral ferroaxinite
Abstract: Raman spectroscopy, complemented by infrared spectroscopy has been used to characterise the ferroaxinite minerals of theoretical formula Ca2Fe2+Al2BSi4O15(OH), a ferrous aluminium borosilicate. The Raman spectra are complex but are subdivided into sections based upon the vibrating units. The Raman spectra are interpreted in terms of the addition of borate and silicate spectra. Three characteristic bands of ferroaxinite are observed at 1082, 1056 and 1025 cm-1 and are attributed to BO4 stretching vibrations. Bands at 1003, 991, 980 and 963 cm-1 are assigned to SiO4 stretching vibrations. Bands are found in these positions for each of the ferroaxinites studied. No Raman bands were found above 1100 cm-1 showing that ferroaxinites contain only tetrahedral boron. The hydroxyl stretching region of ferroaxinites is characterised by a single Raman band between 3368 and 3376 cm-1, the position of which is sample dependent. Bands for ferroaxinite at 678, 643, 618, 609, 588, 572, 546 cm-1 may be attributed to the ν4 bending modes and the three bands at 484, 444 and 428 cm-1 may be attributed to the ν2 bending modes of the (SiO4)2-.
Extracted statement — Techniques: Infrared spectroscopy
Research field: Earth Sciences
Title: ASTER Data Analyses for Lithological Discrimination of Sittampundi Anorthositic Complex, Southern India
Abstract: ASTER is an advanced Thermal Emission and Reflection Radiometer, a multispectral sensor, which measures reflected and emitted electromagnetic radiation of earth surface with 14 bands. The present study aims to delineate different rock types in the Sittampundi Anorthositic Complex (SAC), Tamil Nadu using Visible (VIS), near-infrared (NIR) and short wave infrared (SWIR) reflectance data of ASTER 9 band data. We used different band ratioing, band combinations in the VNIR and SWIR region for discriminating lithological boundaries. SAC is also considered as a lunar highland analog rock. Anorthosite is a plagioclase-rich igneous rock with subordinate amounts of pyroxenes, olivine and other minerals. A methodology has been applied to correct the cross talk effect and radiance to reflectance. Principal Component Analysis (PCA) has been realized on the 9 ASTER bands in order to reduce the redundancy information in highly correlated bands. PCA derived FCC results enable the validation and support to demarcate the different lithological boundaries defined on previous geological map. The image derived spectral profiles for anorthosite are compared with the ASTER resampled laboratory spectra, JHU spectral library spectra and Apollo 14 lunar anorthosites spectra. The Spectral Angle Mapping imaging spectroscopy technique has been practiced to classify the ASTER image of the study area and found that, the processing of ASTER remote sensing data set can be used as a powerful tool for mapping the terrestrial Anorthositic regions and similar kind of process could be applied to map the planetary surfaces (E.g. Moon).
Extracted statement — reference: JHU spectral library spectra
Research field: Earth Sciences
Title: Spectral indices for lithologic discrimination and mapping by using the ASTER SWIR bands
Abstract: The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a research facility instrument launched on NASA's Terra spacecraft in December 1999. Spectral indices, a kind of orthogonal transformation in the five-dimensional space formed by the five ASTER short-wave-infrared (SWIR) bands, were proposed for discrimination and mapping of surface rock types. These include Alunite Index, Kaolinite Index, Calcite Index, and Montmorillonite Index, and can be calculated by linear combination of reflectance values of the five SWIR bands. The transform coefficients were determined so as to direct transform axes to the average spectral pattern of the typical minerals. The spectral indices were applied to the simulated ASTER dataset of Cuprite, Nevada, USA after converting its digital numbers to surface reflectance. The resultant spectral index images were useful for lithologic mapping and were easy to interpret geologically. An advantage of this method is that we can use the pre-determined transform coefficients, as long as image data are converted to surface reflectance.
Extracted statement — Analysis: Kaolinite Index
Research field: Earth Sciences
Title: Characterization and mapping of hematite ore mineral classes using hyperspectral remote sensing technique: a case study from Bailadila iron ore mining region
Abstract: Abstract The study demonstrates a methodology for mapping various hematite ore classes based on their reflectance and absorption spectra, using Hyperion satellite imagery. Substantial validation is carried out, using the spectral feature fitting technique, with the field spectra measured over the Bailadila hill range in Chhattisgarh State in India. The results of the study showed a good correlation between the concentration of iron oxide with the depth of the near-infrared absorption feature (R 2 = 0.843) and the width of the near-infrared absorption feature (R 2 = 0.812) through different empirical models, with a root-mean-square error (RMSE) between &lt; 0.317 and &lt; 0.409. The overall accuracy of the study is 88.2% with a Kappa coefficient value of 0.81. Geochemical analysis and X-ray fluorescence (XRF) of field ore samples are performed to ensure different classes of hematite ore minerals. Results showed a high content of Fe &gt; 60 wt% in most of the hematite ore samples, except banded hematite quartzite (BHQ) (&lt; 47 wt%).
Extracted statement — Analysis: Kappa coefficient
Research field: Earth Sciences
Title: Application of remote sensing methods to hydrology and water resources
Abstract: Abstract A brief review of research in remote sensing of water resources indicates that there are many positive results, and some techniques have been applied operationally. Currently, remote sensing data are being used operationally in precipitation estimates, soil moisture measurements for irrigation scheduling, snow water equivalent and snow cover extent assessments, seasonal and short term snowmelt runoff forecasts, and surface water inventories. In the next decade other operational applications are likely using remote measurements of land cover, sediment loads, erosion, groundwater, and areal inputs to hydrological models. Many research challenges remain, and significant progress is expected in areas like albedo measurements, energy budgets, and evapotranspiration estimation. The research in remote sensing and water resources also has much relevance for related studies of climate change and global habitability.
Extracted statement — Application: Land cover
Research field: Earth Sciences
Title: An image processing approach for converging ASTER-derived spectral maps for mapping Kolhan limestone, Jharkhand, India
Abstract: In the present study, we have attempted the delineation of limestone using different spectral mapping algorithms in ASTER data. Each spectral mapping algorithm derives limestone exposure map independently. Although these spectral maps are broadly similar to each other, they are also different at places in terms of spatial disposition of limestone pixels. Therefore, an attempt is made to integrate the results of these spectral maps to derive an integrated map using minimum noise fraction (MNF) method. The first MNF image is the result of two cascaded principal component methods suitable for preserving complementary information derived from each spectral map. While implementing MNF, noise or non-coherent pixels occurring within a homogeneous patch of limestone are removed first using shift difference method, before attempting principal component analysis on input spectral maps for deriving composite spectral map of limestone exposures. The limestone exposure map is further validated based on spectral data and ancillary geological data.
Extracted statement — Analysis: Minimum Noise Fraction (MNF)
Research field: Earth Sciences
Title: Mapping hydrothermal alteration minerals using high-resolution AVIRIS-NG hyperspectral data in the Hutti-Maski gold deposit area, India
Abstract: ABSTRACT The present study exploits high-resolution hyperspectral imagery acquired by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor from the Hutti-Maski gold deposit area, India, to map hydrothermal alteration minerals. The study area is a volcanic-dominated late Archean greenstone belt that hosts major gold mineralization in the Eastern Dharwar Craton of southern India. The study encompasses pre-processing, spectral and spatial image reduction using Minimum Noise Fraction (MNF) and Fast Pixel Purity Index (FPPI), followed by endmember extraction using n-dimensional visualizer and the United States Geological Survey (USGS) mineral spectral library. Image derived endmembers such as goethite, chlorite, chlorite at the mine site (chlorite mixed with mined materials), kaolinite, and muscovite were subsequently used in spectral mapping methods such as Spectral Angle Mapper (SAM), Spectral Information Divergence (SID) and its hybrid, i.e. SIDSAMtan. Spectral similarity matrix of the target and non-target-based method has been proposed to find the possible optimum threshold needed to obtain mineral map using spectral mapping methods. Relative Spectral Discrimination Power (RSDPW) and Confusion Matrix (CM) have been used to evaluate the performance of SAM, SID, and SIDSAMtan. The RSDPW and CM illustrate that the SIDSAMtan benefits from the unique characteristics of SAM and SID to achieve better discrimination capability. The Overall Accuracy (OA) and kappa coefficient (ҡ) of SAM, SID, and SIDSAMtan were computed using 900 random validation points and obtained 90% (OA) and 0.88 (ҡ), 91.4% and 0.90, and 94.4% and 0.93, respectively. Obtained mineral map demonstrates that the northern portion of the area mainly consists of muscovite whereas the southern part is marked by chlorite, goethite, muscovite and kaolinite, indicating the propylitic alteration. Most of these minerals are associated with altered metavolcanic rocks and migmatite.
Extracted statement — Techniques/Methods: Minimum Noise Fraction (MNF)
Research field: Earth Sciences
Title: Spectral indices for lithologic discrimination and mapping by using the ASTER SWIR bands
Abstract: The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a research facility instrument launched on NASA's Terra spacecraft in December 1999. Spectral indices, a kind of orthogonal transformation in the five-dimensional space formed by the five ASTER short-wave-infrared (SWIR) bands, were proposed for discrimination and mapping of surface rock types. These include Alunite Index, Kaolinite Index, Calcite Index, and Montmorillonite Index, and can be calculated by linear combination of reflectance values of the five SWIR bands. The transform coefficients were determined so as to direct transform axes to the average spectral pattern of the typical minerals. The spectral indices were applied to the simulated ASTER dataset of Cuprite, Nevada, USA after converting its digital numbers to surface reflectance. The resultant spectral index images were useful for lithologic mapping and were easy to interpret geologically. An advantage of this method is that we can use the pre-determined transform coefficients, as long as image data are converted to surface reflectance.
Extracted statement — Analysis: Montmorillonite Index