Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

Journal Browser

Special Issue "Geological Applications of Remote Sensing and Photogrammetry"

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: 15 December 2023 | Viewed by 9420

Share This Special Issue

Special Issue Editors

Dr. Riccardo Salvini

E-Mail Website
Guest Editor

Department of Environment, Earth and Physical Sciences and Centre of Geotechnologies, University of Siena, Siena, Italy
Interests: engineering geology; geomorphology; remote sensing; UAV digital photogrammetry; GIS; GNSS; laser scanning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Alexander Parshin

E-Mail Website
Guest Editor

Vinogradov Institute of Geochemistry of the Siberian Branch of the RAS, Irkutsk, Russia
Interests: geoinformatics; remote sensing; geological prospecting; geoecology; unmanned systems

Dr. Langping Li

E-Mail Website
Guest Editor

Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences, Beijing, China
Interests: GIS; remote sensing; natural hazards

Dr. Yonghong Zhang

E-Mail Website
Guest Editor

Chinese Academy of Surveying and Mapping, Beijing, China
Interests: SAR interferometry; SAR polarimetry; geohazard monitoring; land cover mapping and change detection

Special Issue Information

Dear Colleagues,

Remote sensing and photogrammetry technology play an important role in geological survey, mapping, analysis, and interpretation, which is mainly used for geomorphology, structure, lithological mapping, mineralogical identification, alteration mapping, mineral and oil exploration, groundwater and engineering geological studies, geohazard monitoring, coal mine fire mapping, volcano monitoring, earthquake disaster investigations, soil erosion, and environmental applications. Remote sensing and photogrammetry technology not only provides the possibility for geological survey and exploration in remote or difficult-to-reach areas but also leads to savings in terms of workforce and material resources, is safe and reliable, and improves the accuracy and breadth of research.

This Special Issue welcomes academic articles or reviews on the geological applications of remote sensing and photogrammetry and invites relevant scholars to discuss various remote sensing and photogrammetry techniques (such as optical and multispectral sensing, photogrammetry, laser scanning, GNSS, UAV, InSAR/DInSAR/MT-InSAR, GPR) in the frontier applications and future development of various branches of geology. The topics mainly include but are not limited to the following:

New technology:

Hyperspectral and multispectral sensors;
InSAR processing techniques;
Multitemporal data;
Integration of multiple sensor types;
Optical and high-resolution sensors;
UAVs (unmanned aerial vehicles);

Geological application:

Geological mapping;
Geomorphology;
Tectonics;
Engineering geology;
Geohazards;
Oil and gas;
Mineral and geothermal exploration.

Dr. Riccardo Salvini
Prof. Dr. Alexander Parshin
Dr. Langping Li
Dr. Yonghong Zhang
Guest Editors

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

remote sensing
photogrammetry
InSAR
LiDAR
surface process
natural hazards

Published Papers (8 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review, Other

18 pages, 28619 KiB

Open AccessArticle

Semi-Airborne UAV-TEM System Data Inversion with S-Plane Method—Case Study over Lake Baikal

Yuriy Davidenko

Valeriya Hallbauer-Zadorozhnaya

Ayur Bashkeev

and

Alexander Parshin

Remote Sens. 2023, 15(22), 5310; https://doi.org/10.3390/rs15225310 - 10 Nov 2023

Viewed by 426

Abstract

The article presents the results of transient electromagnetic (TEM) prospecting surveys using an unmanned aerial system carried out at Lake Baikal, which is a unique geoelectrical setting where low-resistivity lacustrine sediments are located under a relatively isotropic water body. The task was to investigate the possibility of using a drone-based TEM survey to delineate the electrical stratigraphy of the subsurface at depths between 50 and 300 m, separated into layers and blocks. A new version of the SibGIS UAV-TEM unmanned system was used, significantly improved compared to the prototype previously described in the literature. The current switch providing bipolar current pulses connected to a grounded electrical line was the source of the electromagnetic field in the geological environment. The hexacopter carrying a measuring system consisting of 18-bit ADC and sensor—analog of 50 × 50 loop, was the receiving system. We measured survey data of 16 traverses over the Baikal going from the shore to the depths. Significant attention is being paid to a new approach to data inversion. For fast interpretation of the TEM data, we used the Sτ-method, which allows for tracing the change in the apparent longitudinal conductivity with depth. It is shown that thanks to the new sensor and current switch, the data quality has increased significantly; now, the UAV system can register sounding curves up to 1 ms. As a result, new data on the geological structure of the shelf zone of Lake Baikal were obtained. They had a good fundamental agreement with the predecessor data obtained from terrestrial measurements (from ice cover), allowing us to conclude that the UAV-TEM technology can already replace conventional ground-based electromagnetic surveys. Full article

(This article belongs to the Special Issue Geological Applications of Remote Sensing and Photogrammetry)

► Show Figures

Graphical abstract

24 pages, 43199 KiB

Open AccessArticle

Quantitative Characterization of Coastal Cliff Retreat and Landslide Processes at Portonovo–Trave Cliffs (Conero, Ancona, Italy) Using Multi-Source Remote Sensing Data

and

Remote Sens. 2023, 15(17), 4120; https://doi.org/10.3390/rs15174120 - 22 Aug 2023

Cited by 1 | Viewed by 833

Abstract

The integration of multiple data sources, including satellite imagery, aerial photography, and ground-based measurements, represents an important development in the study of landslide processes. The combination of different data sources can be very important in improving our understanding of geological phenomena, especially in cases of inaccessible areas. In this context, the study of coastal areas represents a real challenge for the research community, both for the inaccessibility of coastal slopes and for the numerous drivers that can control coastal processes (subaerial, marine, or endogenic). In this work, we present a case study of the Conero Regional Park (Northern Adriatic Sea, Ancona, Italy) cliff-top retreat, characterized by Neogenic soft rocks (flysch, molasse). In particular, the study is focused in the area between the beach of Portonovo and Trave (south of Ancona), which has been studied using aerial orthophoto acquired between 1978 and 2021, Unmanned Aerial Vehicle (UAV) photographs (and extracted photogrammetric model) surveyed in September 2021 and 2012 LiDAR data. Aerial orthophotos were analyzed through the United States Geological Survey’s (USGS) tool Digital Shoreline Analysis System (DSAS) to identify and estimate the top-cliff erosion. The results were supported by the analysis of wave data and rainfall from the correspondent period. It has been found that for the northernmost sector (Trave), in the examined period of 40 years, an erosion up to 40 m occurred. Furthermore, a Digital Elevation Model (DEM) of Difference (DoD) between a 2012 Digital Terrain Model (DTM) and a UAV Digital Surface Model (DSM) was implemented to corroborate the DSAS results, revealing a good agreement between the retreat areas, identified by DSAS, and the section of coast characterized by a high value of DoD. Full article

(This article belongs to the Special Issue Geological Applications of Remote Sensing and Photogrammetry)

► Show Figures

Figure 1

22 pages, 7548 KiB

Open AccessArticle

Remote Sensing and Geovisualization of Rock Slopes and Landslides

and

Remote Sens. 2023, 15(15), 3702; https://doi.org/10.3390/rs15153702 - 25 Jul 2023

Viewed by 1216

Abstract

Over the past two decades, advances in remote sensing methods and technology have enabled larger and more sophisticated datasets to be collected. Due to these advances, the need to effectively and efficiently communicate and visualize data is becoming increasingly important. We demonstrate that the use of mixed- (MR) and virtual reality (VR) systems has provided very promising results, allowing the visualization of complex datasets with unprecedented levels of detail and user experience. However, as of today, such visualization techniques have been largely used for communication purposes, and limited applications have been developed to allow for data processing and collection, particularly within the engineering–geology field. In this paper, we demonstrate the potential use of MR and VR not only for the visualization of multi-sensor remote sensing data but also for the collection and analysis of geological data. In this paper, we present a conceptual workflow showing the approach used for the processing of remote sensing datasets and the subsequent visualization using MR and VR headsets. We demonstrate the use of computer applications built in-house to visualize datasets and numerical modelling results, and to perform rock core logging (XRCoreShack) and rock mass characterization (EasyMineXR). While important limitations still exist in terms of hardware capabilities, portability, and accessibility, the expected technological advances and cost reduction will ensure this technology forms a standard mapping and data analysis tool for future engineers and geoscientists. Full article

(This article belongs to the Special Issue Geological Applications of Remote Sensing and Photogrammetry)

► Show Figures

Graphical abstract

14 pages, 4944 KiB

Open AccessCommunication

Identifying Potential Landslides in Steep Mountainous Areas Based on Improved Seasonal Interferometry Stacking-InSAR

and

Remote Sens. 2023, 15(13), 3278; https://doi.org/10.3390/rs15133278 - 26 Jun 2023

Cited by 2 | Viewed by 852

Abstract

Landslides are a major concern in the mountainous regions of southwest China, leading to significant loss of life and property damage. Therefore, it is crucial to identify potential landslides for early warning and mitigation. stacking-InSAR, a technique used for landslide identification in a wide area, has been found to be faster than conventional time-series InSAR. However, the dense vegetation in southwest China mountains has an adverse impact on the coherence of stacking-InSAR, resulting in more noise and inaccuracies in landslide identification. To address this problem, this paper proposes an improved seasonal interferometry stacking-InSAR method. It uses Sentinel-1 satellite data from 2017 to 2022. The study area is the river valley section of the G213 road from Wenchuan County to Mao County. The study reveals the characteristics of seasonal decoherence in the steep mountainous region, and identifies a total of 21 potential landslides using the improved method. Additionally, optical satellite imagery and LiDAR data were used to assist in the identification of potential landslides. The results of the conventional stacking-InSAR method and the improved seasonal interferometry stacking-InSAR method are compared, showing that the latter is more effective in noise suppression caused by low coherence. Their standard deviations were reduced by 46%, 22%, 10%, and 14%, respectively, using the quantitative statistics for the four tested areas. The proposed method provides an efficient and effective approach for detecting potential landslides in the mountainous regions of southwest China. It can serve as a valuable technical reference for future landslide identification studies in this area. Full article

(This article belongs to the Special Issue Geological Applications of Remote Sensing and Photogrammetry)

► Show Figures

Graphical abstract

23 pages, 5822 KiB

Open AccessArticle

Statistical Assessments of InSAR Tropospheric Corrections: Applicability and Limitations of Weather Model Products and Spatiotemporal Filtering

and

Remote Sens. 2023, 15(7), 1905; https://doi.org/10.3390/rs15071905 - 02 Apr 2023

Cited by 1 | Viewed by 904

Abstract

Tropospheric correction is a crucial step for interferometric synthetic aperture radar (InSAR) monitoring of small deformation magnitude. However, most of the corrections are implemented without a rigorous evaluation of their influences on InSAR measurements. In this paper, we present three statistical metrics to evaluate the correction performance. Firstly, we propose a time series decomposition method to estimate the tropospheric noise and mitigate the bias caused by ground displacement. On this basis, we calculate the root-mean-square values of tropospheric noise to assess the general performance of tropospheric corrections. Then, we propose the use of semi-variograms with model-fitted range and sill to investigate the reduction of distance-dependent signals, and Spearman’s rank correlation between phase and elevation to evaluate the mitigation of topography-correlated signals in hilly areas. The applicability and limitations were assessed on the weather model-derived corrections, a representative spatiotemporal filtering method, and the integration of the two mainstream methods. Furthermore, we notice that the persistent scatter InSAR processing resulted in two components, the primary and secondary images’ contribution to the tropospheric and orbit errors. To the best of our knowledge, this paper for the first time analyzes the respective roles of the two components in the InSAR tropospheric corrections. Full article

(This article belongs to the Special Issue Geological Applications of Remote Sensing and Photogrammetry)

► Show Figures

Figure 1

Review

Jump to: Research, Other

28 pages, 4563 KiB

Open AccessReview

Remote Sensing Precursors Analysis for Giant Landslides

and

Remote Sens. 2022, 14(17), 4399; https://doi.org/10.3390/rs14174399 - 04 Sep 2022

Cited by 5 | Viewed by 2123

Abstract

Monitoring and early warning systems for landslides are urgently needed worldwide to effectively reduce the losses of life and property caused by these natural disasters. Detecting the precursors of giant landslides constitutes the premise of landslide monitoring and early warning, and remote sensing is a powerful means to achieve this goal. In this work, we aim to summarize the basic types and evolutionary principles of giant landslide precursors, describe the remote sensing methods capable of identifying those precursors, and present typical cases of related sliding. Based on a review of the literature and an analysis of remote sensing imagery, the three main types of remote sensing techniques for capturing the geomorphological, geotechnical, and geoenvironmental precursors of giant landslides are optical, synthetic aperture radar (SAR), and thermal infrared methods, respectively. Time-series optical remote sensing data from medium-resolution satellites can be used to obtain abundant information on geomorphological changes, such as the extension of cracks and erosion ditches, and band algebraic analysis, image enhancement, and segmentation techniques are valuable for focusing on the locations of geomorphological landslide precursors. SAR sensors have the ability to monitor the slight slope deformation caused by unfavorable geological structures and can provide precursor information on imminent failure several days before a landslide; furthermore, persistent scatterer interferometric SAR has significant advantages in large-scale surface displacement monitoring. Thermal infrared imagery can identify landslide precursors by monitoring geoenvironmental information, especially in permafrost regions where glaciers are widely distributed; the reason may be that freeze–thaw cycles and snowmelt caused by increased temperatures affect the stability of the surface. Optical, SAR, and thermal remote sensing all exhibit unique advantages and play an essential role in the identification of giant landslide precursors. The combined application of these three remote sensing technologies to obtain the synthetic geomorphological, geotechnical, and geoenvironmental precursors of giant landslides would greatly promote the development of landslide early warning systems. Full article

(This article belongs to the Special Issue Geological Applications of Remote Sensing and Photogrammetry)

► Show Figures

Figure 1

Other

Jump to: Research, Review

15 pages, 3588 KiB

Open AccessTechnical Note

Fracture Electromagnetic Radiation Induced by a Seismic Active Zone (in the Vicinity of Eilat City, Southern Israel)

and

Remote Sens. 2023, 15(14), 3639; https://doi.org/10.3390/rs15143639 - 21 Jul 2023

Viewed by 438

Abstract

This paper deals with the quantitative analysis of measured fracture-induced electromagnetic radiation (FEMR) near the Dead Sea Transform using the Angel-M1 instrument, which enables the recording of FEMR signals in a 3D manner. The results showed both the possibility of estimating the sizes of micro-fractures that are the sources of radiation and assessing the direction of the fractures’ locations to the measuring device, as well as the range of magnitude (Mw) of the impending “events” (EQs) associated with the FEMR measurements. Moreover, the relation between the measured FEMR activity (the number of FEMR hits per unit of time) and the FEMR event magnitudes showed consistency with the Gutenberg–Richter relationship for the region. Such measurements could therefore constitute a preliminary ‘field reinforcement’ towards a valid EMR method for a real earthquake forecast, which would provide much earlier warnings than seismic methods. The observed FEMR measurements could only be used to assess the stress concentrations and micro-fracturing in the region since they related to the very initial nucleation phase of a “virtual” earthquake. Nonetheless, they provide the necessary feasibility test for a forecasting method since all of the lab-measured FEMR features were confirmed in the field. Full article

(This article belongs to the Special Issue Geological Applications of Remote Sensing and Photogrammetry)

► Show Figures

Figure 1

15 pages, 11355 KiB

Open AccessTechnical Note

Method on Early Identification of Low-Frequency Debris Flow Gullies along the Highways in the Chuanxi Plateau

and

Remote Sens. 2023, 15(5), 1183; https://doi.org/10.3390/rs15051183 - 21 Feb 2023

Viewed by 905

Abstract

Low-frequency debris flows are characterized by strong concealment, high potential danger, and difficulty achieving an early warning; hence early identification of low-frequency debris flow gullies is crucial to mitigation. Here, an identification system for low-frequency debris flow gullies along the traffic arteries in the Chuanxi Plateau is proposed based on the identification and stability calculation of colluvium deposits in a hollow region (CDH) and the quantitative roundness analysis for the stones in a deposit fan. At first, for the watershed without a deposit fan, the CDH is identified and analyzed using the geomorphologic change point detection method combined with high-precision remote sensing images and field investigation. The watershed can be identified as a low-frequency debris flow gully with the safety factors (F_s) of all CDHs greater than 1. Then, the roundness of stones in the deposit fan is quantitatively analyzed. The watershed can also be identified as a low-frequency debris flow gully with the average roundness of the stones ranging from 0.30 to 0.41. Lastly, the identification system was tested and verified based on another ten watersheds along three traffic arteries. It shows that the method proposed in this paper has good applicability and high accuracy. Here we try to achieve the accurate early identification of low-frequency debris flow gullies by combining remote sensing interpretation and field investigation, which can provide theoretical support for predicting and mitigating debris flows in mountainous areas. Full article

(This article belongs to the Special Issue Geological Applications of Remote Sensing and Photogrammetry)

► Show Figures

Journal Menu

Journal Browser

Special Issue "Geological Applications of Remote Sensing and Photogrammetry"

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (8 papers)

Research

Review

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI