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Remote Sensing
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Surface and Sub-surface Geological Remote Sensing at Regional Mapping Scales
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Surface and Sub-surface Geological Remote Sensing at Regional Mapping Scales

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (28 February 2024) | Viewed by 8545

Special Issue Editor

Dr. Robert Hewson

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Guest Editor
Department of Earth Systems Analysis (ESA), University of Twente, 7500 AE Enschede, The Netherlands
Interests: geological remote sensing; mineral spectroscopy; thermal inertia

Special Issue Information

Dear Colleagues,

Surface geological mapping using remote sensing imagery has been attempted since the launch of the Landsat satellite sensors in the 1970s. Since then, the application of optical, thermal and SAR radar has proven useful for describing mineral and physical properties and structural characteristics. Likewise, airborne regional geophysical surveying has been used by the exploration industry for the mapping of magnetic and geochemical properties, relevant for lithological boundaries and alteration anomalies. The application of a range of such sensors and their potential data integration, can enable a focus of particular targets and reduce the number of potential ambiguous solutions to a unique geological interpretation. In addition to the established ASTER, WorldView-3 and Sentinel-2’s multi-spectral/SAR instruments, the recently deployed hyperspectral Italian PRISMA, Japanese HISUI, German EnMAP, American EMIT and Chinese ZY1-02D satellite sensors greatly expand the choice of imagery for geological mapping. The utilisation of such globally acquired hyperspectral optical (e.g., visible, near-infrared to shortwave infrared wavelengths, VNIR-SWIR) sensors will make high-resolution information available, in addition to the already acquired extensive archive of multi-spectral and SAR satellite data.  

In summary, the overall aim of this Special Issue is to provide a cross-section of case studies and relevant modelled applications for the generation of geological map products, using archived and recently acquired optical imagery, geophysical datasets and/or their integration. Examples dealing with areas of variable vegetation cover, environments and demonstrations of remote sensing and geophysical multi-data integration approaches will be of particular interest for this Special Issue.

Dr. Robert Hewson
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • satellite remote sensing
  • multispectral
  • hyperspectral
  • SAR
  • regional geophysics
  • data integration
  • geological mapping

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Published Papers (5 papers)

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Research

14 pages, 9313 KiB  
Article
Remote Detection of Geothermal Alteration Using Airborne Light Detection and Ranging Return Intensity
by Yan Restu Freski, Christoph Hecker, Mark van der Meijde and Agung Setianto
Remote Sens. 2024, 16(9), 1646; https://doi.org/10.3390/rs16091646 - 5 May 2024
Viewed by 1407
Abstract
The remote detection of hydrothermally altered grounds in geothermal exploration demands datasets capable of reliably detecting key outcrops with fine spatial resolution. While optical thermal or radar-based datasets have resolution limitations, airborne LiDAR offers point-based detection through its LiDAR return intensity (LRI) values, [...] Read more.
The remote detection of hydrothermally altered grounds in geothermal exploration demands datasets capable of reliably detecting key outcrops with fine spatial resolution. While optical thermal or radar-based datasets have resolution limitations, airborne LiDAR offers point-based detection through its LiDAR return intensity (LRI) values, serving as a proxy for surface reflectivity. Despite this potential, few studies have explored LRI value variations in the context of hydrothermal alteration and their utility in distinguishing altered from unaltered rocks. Although the link between alteration degree and LRI values has been established under laboratory conditions, this relationship has yet to be demonstrated in airborne data. This study investigates the applicability of laboratory results to airborne LRI data for alteration detection. Utilising LRI data from an airborne LiDAR point cloud (wavelength 1064 nm, density 12 points per square metre) acquired over a prospective geothermal area in Bajawa, Indonesia, where rock sampling for a related laboratory study took place, we compare the airborne LRI values within each ground sampling area of a 3 m radius (due to hand-held GPS uncertainty) with laboratory LRI values of corresponding rock samples. Our findings reveal distinguishable differences between strongly altered and unaltered samples, with LRI discrepancies of approximately ~28 for airborne data and ~12 for laboratory data. Furthermore, the relative trends of airborne and laboratory-based LRI data concerning alteration degree exhibit striking similarity. These consistent results for alteration degree in laboratory and airborne data mark a significant step towards LRI-based alteration mapping from airborne platforms. Full article
(This article belongs to the Special Issue Surface and Sub-surface Geological Remote Sensing at Regional Mapping Scales)
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31 pages, 23132 KiB  
Article
Improving Rock Classification with 1D Discrete Wavelet Transform Based on Laboratory Reflectance Spectra and Gaofen-5 Hyperspectral Data
by Senmiao Guo and Qigang Jiang
Remote Sens. 2023, 15(22), 5334; https://doi.org/10.3390/rs15225334 - 13 Nov 2023
Cited by 2 | Viewed by 1050
Abstract
The high intra-class variability of rock spectra is an important factor affecting classification accuracy. The discrete wavelet transform (DWT) can capture abrupt changes in the signal and obtain subtle differences between the spectra of different rocks. Taking laboratory spectra and hyperspectral data as [...] Read more.
The high intra-class variability of rock spectra is an important factor affecting classification accuracy. The discrete wavelet transform (DWT) can capture abrupt changes in the signal and obtain subtle differences between the spectra of different rocks. Taking laboratory spectra and hyperspectral data as examples, high-frequency features after DWT were used to improve the discrimination accuracy of rocks. Various decomposition levels, mother wavelet functions, and reconstruction methods were used to compare the accuracy. The intra-class variability was measured using the intra-class Spectral Angle Mapper (SAM). Our results show that the high-frequency features could improve the discrimination accuracy of laboratory spectra by 13.4% (from 46.5% to 59.9%), compared to the original spectral features. The accuracy of image spectra in two study areas increased by 8.6% (from 68.3% to 76.9%) and 7.2% (from 81.3% to 88.5%), respectively. Haar wavelets highlighted the spectral differences between different rocks. After DWT, intra-class SAM reduced and intra-class variability of rocks decreased. The Pearson correlation coefficient indicated a negative correlation between intra-class variability and overall accuracy. It suggested that improving classification accuracy by reducing intra-class variability was feasible. Though the result of lithological mapping still leaves room for improvement, this study provides a new approach to reduce intra-class variability, whether using laboratory spectra or hyperspectral data. Full article
(This article belongs to the Special Issue Surface and Sub-surface Geological Remote Sensing at Regional Mapping Scales)
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30 pages, 70593 KiB  
Article
GF-2 Data for Lithological Classification Using Texture Features and PCA/ICA Methods in Jixi, Heilongjiang, China
by Tianyi Chen, Changbao Yang, Liguo Han and Senmiao Guo
Remote Sens. 2023, 15(19), 4676; https://doi.org/10.3390/rs15194676 - 24 Sep 2023
Cited by 1 | Viewed by 1165
Abstract
Lithological classification is a pivotal aspect in the field of geology, and traditional field surveys are inefficient and challenging in certain areas. Remote sensing technology offers advantages such as high efficiency and wide coverage, providing a solution to the aforementioned issues. The aim [...] Read more.
Lithological classification is a pivotal aspect in the field of geology, and traditional field surveys are inefficient and challenging in certain areas. Remote sensing technology offers advantages such as high efficiency and wide coverage, providing a solution to the aforementioned issues. The aim of this study is to apply remote sensing technology for lithological classification and attempt to enhance the accuracy of classification. Taking a study area in Jixi, Heilongjiang Province, China, as an example, lithological classification is conducted using high-resolution satellite remote sensing data from GF-2 and texture data based on gray-level co-occurrence matrix (GLCM). By comparing the accuracy of lithological classification using different methods, the support vector machine (SVM) method with the highest overall accuracy is selected for further investigation. Subsequently, this study compares the effects of combining GF-2 data with different texture data, and the results indicate that combining textures can effectively improve the accuracy of lithological classification. In particular, the combination of GF-2 and the Dissimilarity index performs the best among single-texture combinations, with an overall accuracy improvement of 7.8630% (increasing from 74.6681% to 82.5311%) compared to using only GF-2 data. In the multi-texture combination dataset, the Mean index is crucial for enhancing classification accuracy. Selecting appropriate textures for combination can effectively improve classification accuracy, but it is important to note that excessive overlaying of textures may lead to a decrease in accuracy. Furthermore, this study employs principal component analysis (PCA) and independent component analysis (ICA) to process the GF-2 data and combines the resulting PCA and ICA datasets with different texture data for lithological classification. The results demonstrate that combining PCA and ICA with texture data further enhances classification accuracy. In conclusion, this study demonstrates the application of remote sensing technology in lithological classification, with a focus on exploring the application value of different combinations of multispectral data, texture data, PCA data, and ICA data. These findings provide valuable insights for future research in this field. Full article
(This article belongs to the Special Issue Surface and Sub-surface Geological Remote Sensing at Regional Mapping Scales)
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16 pages, 9237 KiB  
Article
How Weather Affects over Time the Repeatability of Spectral Indices Used for Geological Remote Sensing
by Harald van der Werff, Janneke Ettema, Akhil Sampatirao and Robert Hewson
Remote Sens. 2022, 14(24), 6303; https://doi.org/10.3390/rs14246303 - 13 Dec 2022
Cited by 2 | Viewed by 1827
Abstract
Geologic remote sensing studies often targets surface cover that is supposed to be invariant or only changing on a geological timescale. In terms of surface material characteristics, this holds for rocks and minerals, but only to a lesser degree for soils (including alluvium, [...] Read more.
Geologic remote sensing studies often targets surface cover that is supposed to be invariant or only changing on a geological timescale. In terms of surface material characteristics, this holds for rocks and minerals, but only to a lesser degree for soils (including alluvium, colluvium, regolith or weathered outcrop) and not for vegetation cover, for example. A view unobstructed by clouds, vegetation or fire scars is essential for a persistent observation of surface mineralogy. Sensors with a continuous multi-temporal operation (e.g., Landsat 8 OLI and Sentinel-2 MSI) can provide the data volume needed to come to an optimal seasonal acquisition and the application of data fusion approaches to create an unobstructed view. However, the acquisition environment always changes over time, driven by seasonal changes, illumination changes and the weather. Consequently, the creation of an unobstructed view does not necessarily lead to a repeatable measurement. In this paper, we evaluate the influence of weather and resulting soil moisture conditions over a 3-year period, with alternating dry and wet periods, on the variance of several “geological” spectral indices in a semi-arid area. Sentinel-2 MSI data are chosen to calculate band ratios for green vegetation, ferric and ferrous iron oxide mineralogy and hydroxyl bearing alteration (clay) mineralogy. The data were used “as provided”, meaning that the performance of the atmospheric correction and geometric accuracy is not changed. The results are shown as time-series for selected areas that include solid rock, beach sand, bare soil and natural vegetation surfaces. Results show that spectral index values vary not only between dry and wet periods, but also within dry periods longer than 45 days, as a result of changing soil moisture conditions long after a last rain event has passed. In terms of repeatability of measurements, an overall low soil-moisture level is more important for long-term stability of spectral index values than the occurrence of minor rain events. In terms of creating an unobstructed view, we found that thresholds for NDVI should not be higher than 0.1 when masking vegetation in geological remote sensing, which is lower than what usually is indicated in literature. In conclusion, multi-temporal data are not only important to study dynamic Earth processes, but also to improve mapping of surfaces that are seemingly invariant. As this work is based on a few selected pixels, the obtained results should be considered only indicative and not as a numerical truth. We conclude that multi-temporal data can be used to create an unobstructed view, but also to select the data that give the most repeatability of measurements. Images selection should not be based on a certain number of days without rain in the days preceding data acquisition but aim for the lowest soil moisture conditions. Consequently, weather data should be incorporated to come to an optimal selection of remote sensing imagery, and also when analyzing multi-temporal data. Full article
(This article belongs to the Special Issue Surface and Sub-surface Geological Remote Sensing at Regional Mapping Scales)
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20 pages, 7902 KiB  
Article
Improvement of Lithological Mapping Using Discrete Wavelet Transformation from Sentinel-1 SAR Data
by Senmiao Guo, Changbao Yang, Rizheng He and Yanqi Li
Remote Sens. 2022, 14(22), 5824; https://doi.org/10.3390/rs14225824 - 17 Nov 2022
Cited by 6 | Viewed by 1600
Abstract
Lithological mapping using dual-polarization synthetic aperture radar (SAR) data is limited by the low classification accuracy. In this study, we extract ten parameters (backscatter coefficients and polarization decomposition parameters) from the Sentinel-1 dual-pol SAR data. Using 94 mother wavelet functions (MF), a one-level [...] Read more.
Lithological mapping using dual-polarization synthetic aperture radar (SAR) data is limited by the low classification accuracy. In this study, we extract ten parameters (backscatter coefficients and polarization decomposition parameters) from the Sentinel-1 dual-pol SAR data. Using 94 mother wavelet functions (MF), a one-level two-dimensional discrete wavelet transform (DWT) is applied to all the parameters, and the suitable MF is screened by comparing the overall accuracy and F1 score. Finally, the lithological mapping of the study area is performed. According to the cross-validation results, DWT can improve the overall accuracy for all MF. Db13 improved the overall accuracy by 6.1% (from 49.5% to 55.6%). The F1 score of granitoids improved by 0.223. Among the five rock units, Grantoids and Quaternary alluvium and sediment with finer gravel can be better differentiated than the other three rock units. The overall accuracy of effusive rocks (marine basic volcanic rocks) is not improved by DWT, but this study confirms the great potential of DWT in lithology classification. Full article
(This article belongs to the Special Issue Surface and Sub-surface Geological Remote Sensing at Regional Mapping Scales)
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