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

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 23185

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Special Issue Editors

Prof. Dr. Stuart Marsh

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Guest Editor

Nottingham Geospatial Institute, University of Nottingham, Nottingham, UK
Interests: earth observation; geoscience; geological hazards; mineral resources; mapping

Dr. Stephen Grebby

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Guest Editor

Nottingham Geospatial Institute, University of Nottingham, Nottingham NG7 2TU, UK
Interests: earth observation; geohazards; mineral exploration; geological remote sensing; ground deformation; InSAR; LiDAR; hyperspectral; geophysics
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Special Issue Information

Dear Colleagues,

There are few more pressing concerns than the use of our planet’s limited natural resources. Whilst we strive to reuse, reduce and recycle, this cannot meet society’s demands for materials and so sustainable exploitation of natural resources remains a key element of development. At the same time, geological hazards, such as earthquakes, volcanos and landslides, claim an ever-increasing number of lives and livelihoods, as more and more people live in exposed places in the developing world. Both these issues are critical for the planet and demand urgent solutions.

This Special Issue will explore the unparalleled opportunities that satellite and airborne Earth Observation (EO) now offer to measure, map, monitor and model the natural environment. Whether applied to resource exploration, monitoring mining operations and measuring their impacts, or to hazard mapping, damage assessment and recovery activities, EO has a huge role to play. The range of data has never been greater, from optical through thermal to LiDAR and radar systems, as well as unconventional data from such sources as social media and citizen science. These data are increasingly being applied to a growing range of issues across the environmental sciences. Their use is formalised through the Intergovernmental Group on Earth Observations, which prioritises the use of EO to address the Paris Agreement on Climate, Sendai Framework for Disaster Risk Reduction and delivery of the UN’s Sustainable Development Goals. Papers on these themes would be especially welcome, but papers are invited on EO applications to any aspect of natural resources and geohazards.

Prof. Dr. Stuart Marsh
Dr. Stephen Grebby
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

earth observation
natural resources
geohazards
Paris Agreement
Sendai Framework
Sustainable Development Goals

Published Papers (9 papers)

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Research

22 pages, 17337 KiB

Open AccessArticle

An Automated Snow Mapper Powered by Machine Learning

by Haojie Wang, Limin Zhang, Lin Wang, Jian He and Hongyu Luo

Remote Sens. 2021, 13(23), 4826; https://doi.org/10.3390/rs13234826 - 27 Nov 2021

Cited by 9 | Viewed by 2510

Abstract

Snow preserves fresh water and impacts regional climate and the environment. Enabled by modern satellite Earth observations, fast and accurate automated snow mapping is now possible. In this study, we developed the Automated Snow Mapper Powered by Machine Learning (AutoSMILE), which is the first machine learning-based open-source system for snow mapping. It is built in a Python environment based on object-based analysis. AutoSMILE was first applied in a mountainous area of 1002 km² in Bome County, eastern Tibetan Plateau. A multispectral image from Sentinel-2B, a digital elevation model, and machine learning algorithms such as random forest and convolutional neural network, were utilized. Taking only 5% of the study area as the training zone, AutoSMILE yielded an extraordinarily satisfactory result over the rest of the study area: the producer’s accuracy, user’s accuracy, intersection over union and overall accuracy reached 99.42%, 98.78%, 98.21% and 98.76%, respectively, at object level, corresponding to 98.84%, 98.35%, 97.23% and 98.07%, respectively, at pixel level. The model trained in Bome County was subsequently used to map snow at the Qimantag Mountain region in the northern Tibetan Plateau, and a high overall accuracy of 97.22% was achieved. AutoSMILE outperformed threshold-based methods at both sites and exhibited superior performance especially in handling complex land covers. The outstanding performance and robustness of AutoSMILE in the case studies suggest that AutoSMILE is a fast and reliable tool for large-scale high-accuracy snow mapping and monitoring. Full article

(This article belongs to the Special Issue EO for Mapping Natural Resources and Geohazards)

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21 pages, 10843 KiB

Open AccessEditor’s ChoiceArticle

Comparative Study of Groundwater-Induced Subsidence for London and Delhi Using PSInSAR

by Vivek Agarwal, Amit Kumar, David Gee, Stephen Grebby, Rachel L. Gomes and Stuart Marsh

Remote Sens. 2021, 13(23), 4741; https://doi.org/10.3390/rs13234741 - 23 Nov 2021

Cited by 18 | Viewed by 4289

Abstract

Groundwater variation can cause land-surface movement, which in turn can cause significant and recurrent harm to infrastructure and the water storage capacity of aquifers. The capital cities in the England (London) and India (Delhi) are witnessing an ever-increasing population that has resulted in excess pressure on groundwater resources. Thus, monitoring groundwater-induced land movement in both these cities is very important in terms of understanding the risk posed to assets. Here, Sentinel-1 C-band radar images and the persistent scatterer interferometric synthetic aperture radar (PSInSAR) methodology are used to study land movement for London and National Capital Territory (NCT)-Delhi from October 2016 to December 2020. The land movement velocities were found to vary between −24 and +24 mm/year for London and between −18 and +30 mm/year for NCT-Delhi. This land movement was compared with observed groundwater levels, and spatio-temporal variation of groundwater and land movement was studied in conjunction. It was broadly observed that the extraction of a large quantity of groundwater leads to land subsidence, whereas groundwater recharge leads to uplift. A mathematical model was used to quantify land subsidence/uplift which occurred due to groundwater depletion/rebound. This is the first study that compares C-band PSInSAR-derived land subsidence response to observed groundwater change for London and NCT-Delhi during this time-period. The results of this study could be helpful to examine the potential implications of ground-level movement on the resource management, safety, and economics of both these cities. Full article

(This article belongs to the Special Issue EO for Mapping Natural Resources and Geohazards)

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17 pages, 17280 KiB

Open AccessArticle

Monitoring the Effects of Slope Hazard Mitigation and Weather on Rockfall along a Colorado Highway Using Terrestrial Laser Scanning

by Luke Weidner and Gabriel Walton

Remote Sens. 2021, 13(22), 4584; https://doi.org/10.3390/rs13224584 - 15 Nov 2021

Cited by 12 | Viewed by 1954

Abstract

Rockfall is a frequent hazard in mountainous areas, but risks can be mitigated by the construction of protection structures and slope modification. In this study, two rock slopes along a highway in western Colorado were monitored monthly using Terrestrial Laser Scanning (TLS) before, during, and after mitigation activities were performed to observe the influence of construction and weather variables on rockfall activity. Between September 2020 and February 2021, the slopes were mechanically scaled and reinforced using rock bolts, wire mesh, and polyurethane resin injection. We used a state-of-the-art TLS monitoring workflow to process the acquired point clouds, including semi-automated algorithms for alignment, change detection, clustering, and rockfall-volume calculation. Our initial hypotheses were that the slope-construction activities would have an immediate effect on the rockfall rate post-construction and would exhibit a decreased correlation with weather-related triggering factors, such as precipitation and freeze-thaw cycles. However, our observations did not confirm this, and instead an increase in post-construction rockfall was recorded, with strong correlation to weather-related triggering factors. While this does not suggest that the overall mitigation efforts were ineffective in reducing rockfall hazard and risk of large blocks, we did not find evidence that mitigation efforts influenced the rockfall hazard associated with the release of small- to medium-sized blocks (<1 m³). These results can be used to develop improved and tailored mitigation methods for rock slopes in the future. Full article

(This article belongs to the Special Issue EO for Mapping Natural Resources and Geohazards)

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20 pages, 4899 KiB

Open AccessArticle

Improving the Robustness of the MTI-Estimated Mining-Induced 3D Time-Series Displacements with a Logistic Model

by Jiancun Shi, Zefa Yang, Lixin Wu and Siyu Qiao

Remote Sens. 2021, 13(18), 3782; https://doi.org/10.3390/rs13183782 - 21 Sep 2021

Cited by 1 | Viewed by 1464

Abstract

The previous multi-track InSAR (MTI) method can be used to retrieve mining-induced three-dimensional (3D) surface displacements with high spatial–temporal resolution by incorporating multi-track interferometric synthetic aperture radar (InSAR) observations with a prior model. However, due to the track-by-track strategy used in the previous MTI method, no redundant observations are provided to estimate 3D displacements, causing poor robustness and further degrading the accuracy of the 3D displacement estimation. This study presents an improved MTI method to significantly improve the robustness of the 3D mining displacements derived by the previous MTI method. In this new method, a fused-track strategy, instead of the previous track-by-track one, is proposed to process the multi-track InSAR measurements by introducing a logistic model. In doing so, redundant observations are generated and further incorporated into the prior model to solve 3D displacements. The improved MTI method was tested on the Datong coal mining area, China, with Sentinel-1 InSAR datasets from three tracks. The results show that the 3D mining displacements estimated by the improved MTI method had the same spatial–temporal resolution as those estimated by the previous MTI method and about 33.5% better accuracy. The more accurate 3D displacements retrieved from the improved MTI method can offer better data for scientifically understanding the mechanism of mining deformation and assessing mining-related geohazards. Full article

(This article belongs to the Special Issue EO for Mapping Natural Resources and Geohazards)

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19 pages, 6600 KiB

Open AccessArticle

Landslide Susceptibility Assessment Based on Different MaChine Learning Methods in Zhaoping County of Eastern Guangxi

by Chunfang Kong, Yiping Tian, Xiaogang Ma, Zhengping Weng, Zhiting Zhang and Kai Xu

Remote Sens. 2021, 13(18), 3573; https://doi.org/10.3390/rs13183573 - 08 Sep 2021

Cited by 5 | Viewed by 2130

Abstract

Regarding the ever increasing and frequent occurrence of serious landslide disaster in eastern Guangxi, the current study was implemented to adopt support vector machines (SVM), particle swarm optimization support vector machines (PSO-SVM), random forest (RF), and particle swarm optimization random forest (PSO-RF) methods to assess landslide susceptibility in Zhaoping County. To this end, 10 landslide disaster-related variables including digital elevation model (DEM)-derived, meteorology-derived, Landsat8-derived, geology-derived, and human activities factors were provided. Of 345 landslide disaster locations found, 70% were used to train the models, and the rest of them were performed for model verification. The aforementioned four models were run, and landslide susceptibility evaluation maps were produced. Then, receiver operating characteristics (ROC) curves, statistical analysis, and field investigation were performed to test and verify the efficiency of these models. Analysis and comparison of the results denoted that all four landslide models performed well for the landslide susceptibility evaluation as indicated by the area under curve (AUC) values of ROC curves from 0.863 to 0.934. Among them, it has been shown that the PSO-RF model has the highest accuracy in comparison to other landslide models, followed by the PSO-SVM model, the RF model, and the SVM model. Moreover, the results also showed that the PSO algorithm has a good effect on SVM and RF models. Furthermore, the landslide models devolved in the present study are promising methods that could be transferred to other regions for landslide susceptibility evaluation. In addition, the evaluation results can provide suggestions for disaster reduction and prevention in Zhaoping County of eastern Guangxi. Full article

(This article belongs to the Special Issue EO for Mapping Natural Resources and Geohazards)

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20 pages, 12454 KiB

Open AccessArticle

An Adaptive Offset-Tracking Method Based on Deformation Gradients and Image Noises for Mining Deformation Monitoring

by Gang Zhao, Liuyu Wang, Kazhong Deng, Maomei Wang, Yi Xu, Meinan Zheng and Qing Luo

Remote Sens. 2021, 13(15), 2958; https://doi.org/10.3390/rs13152958 - 27 Jul 2021

Cited by 8 | Viewed by 1776

Abstract

The offset-tracking method (OTM) utilizing SAR image intensity can detect large deformations, which makes up for the inability of interferometric synthetic aperture radar (InSAR) technology in large mining deformation monitoring, and has been widely used. Through lots of simulation experiments, it was found that the accuracy of OTM is associated with deformation gradients and image noises in the cross-correlation window (CCW), so CCW sizes should be selected reasonably according to deformation gradients and noise levels. Based on the above conclusions, this paper proposes an adaptive CCW selection method based on deformation gradients and image noises for mining deformation monitoring, and this method considers influences of deformation gradients and image noises on deformations to select adaptive CCWs. In consideration of noise influences on offset-tracking results, smaller CCWs are selected for large deformation gradient areas, and larger CCWs are selected for small deformation gradient areas. For some special areas, special CCWs are selected for offset-tracking. The proposed method is implemented to simulation and real experiments, and the experiment results demonstrate that the proposed method with high reliability and effectiveness can significantly improve the accuracy of OTM in mining deformation monitoring. Full article

(This article belongs to the Special Issue EO for Mapping Natural Resources and Geohazards)

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16 pages, 8988 KiB

Open AccessArticle

Active-Learning Approaches for Landslide Mapping Using Support Vector Machines

by Zhihao Wang and Alexander Brenning

Remote Sens. 2021, 13(13), 2588; https://doi.org/10.3390/rs13132588 - 01 Jul 2021

Cited by 23 | Viewed by 3473

Abstract

Ex post landslide mapping for emergency response and ex ante landslide susceptibility modelling for hazard mitigation are two important application scenarios that require the development of accurate, yet cost-effective spatial landslide models. However, the manual labelling of instances for training machine learning models is time-consuming given the data requirements of flexible data-driven algorithms and the small percentage of area covered by landslides. Active learning aims to reduce labelling costs by selecting more informative instances. In this study, two common active-learning strategies, uncertainty sampling and query by committee, are combined with the support vector machine (SVM), a state-of-the-art machine-learning technique, in a landslide mapping case study in order to assess their possible benefits compared to simple random sampling of training locations. By selecting more “informative” instances, the SVMs with active learning based on uncertainty sampling outperformed both random sampling and query-by-committee strategies when considering mean AUROC (area under the receiver operating characteristic curve) as performance measure. Uncertainty sampling also produced more stable performances with a smaller AUROC standard deviation across repetitions. In conclusion, under limited data conditions, uncertainty sampling reduces the amount of expert time needed by selecting more informative instances for SVM training. We therefore recommend incorporating active learning with uncertainty sampling into interactive landslide modelling workflows, especially in emergency response settings, but also in landslide susceptibility modelling. Full article

(This article belongs to the Special Issue EO for Mapping Natural Resources and Geohazards)

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16 pages, 20377 KiB

Open AccessArticle

Decades of Ground Deformation in the Weihe Graben, Shaanxi Province, China, in Response to Various Land Processes, Observed by Radar Interferometry and Levelling

by Jianlong Chen, Yu Zhou, Gan Chen and Ming Hao

Remote Sens. 2021, 13(12), 2374; https://doi.org/10.3390/rs13122374 - 17 Jun 2021

Cited by 6 | Viewed by 2058

Abstract

Ground deformation is usually used as direct evidence for early warning of geological hazards. The Weihe Graben, located in the southern margin of the Ordos Plateau, is surrounded by many active faults. Earthquakes (e.g., the 1556 Huaxian M 8 earthquake), mine accidents and ground fissures are the major hazards that pose great threats to this densely populated region. In order to characterise both tectonic and anthropogenic activities in the Weihe Graben, we use Envisat data from 2003 to 2010 and Sentinel-1 data from 2014 to 2021, combined with levelling data from 1970 to 2014, to investigate the long-term ground deformation. We generate four InSAR rate maps using the small-baseline subset (SBAS) algorithm. The uncertainties of the InSAR rates are 1–2 mm/year by calculating the differences between the InSAR and levelling measurements. From the deformation time series, we found that most of the faults surrounding the Weihe Graben move at a relatively slow rate (<3 mm/year). Elastic dislocation modelling based on the InSAR and levelling data yields a slip rate of 2.3 ± 0.3 mm/year for the Huashan Fault, the seismogenic fault for the 1556 Huaxian earthquake. Anthropogenic deformation is much stronger than the tectonic deformation. We identified localised subsidence of 12 mines with a deformation rate ranging from 5 to 17 mm/year. The cities of Xi’an and Xianyang also show evident subsidence, which is likely to be caused by groundwater extraction. Land subsidence in Xi’an has slowed down from an average rate of 10–20 mm/year between 2003 and 2010 to about 5–10 mm/year between 2017 and 2020, but in Xianyang, subsidence has increased dramatically in the past five years from 1 mm/year to 7 mm/year. This is because new industrial and urban development centres have gradually moved from Xi’an to Xianyang. We identified a region bounded by the Kouzhen-Guanshan and Fufeng-Liquan Faults with strong subsidence, as a result of excessive extraction of groundwater. To quantify the effects of crustal groundwater unloading on faults, we calculated the static Coulomb stress changes on the two faults and found that Coulomb stress changes are localised in the upper 5 km with a magnitude of 0.01–0.02 bar/year. The Coulomb stress changes might be large enough (0.1 bar) to affect local seismicity if such excessive extraction of groundwater continued for 10 years. Full article

(This article belongs to the Special Issue EO for Mapping Natural Resources and Geohazards)

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21 pages, 3778 KiB

Open AccessArticle

A Spectra Classification Methodology of Hyperspectral Infrared Images for Near Real-Time Estimation of the SO₂ Emission Flux from Mount Etna with LARA Radiative Transfer Retrieval Model

by Charlotte Segonne, Nathalie Huret, Sébastien Payan, Mathieu Gouhier and Valéry Catoire

Remote Sens. 2020, 12(24), 4107; https://doi.org/10.3390/rs12244107 - 16 Dec 2020

Viewed by 2069

Abstract

Fast and accurate quantification of gas fluxes emitted by volcanoes is essential for the risk mitigation of explosive eruption, and for the fundamental understanding of shallow eruptive processes. Sulphur dioxide (SO₂), in particular, is a reliable indicator to predict upcoming eruptions, and its systemic characterization allows the rapid assessment of sudden changes in eruptive dynamics. In this regard, infrared (IR) hyperspectral imaging is a promising new technology for accurately measure SO₂ fluxes day and night at a frame rate down to 1 image per second. The thermal infrared region is not very sensitive to particle scattering, which is an asset for the study of volcanic plume. A ground based infrared hyperspectral imager was deployed during the IMAGETNA campaign in 2015 and provided high spectral resolution images of the Mount Etna (Sicily, Italy) plume from the North East Crater (NEC), mainly. The LongWave InfraRed (LWIR) hyperspectral imager, hereafter name Hyper-Cam, ranges between 850–1300 cm⁻¹ (7.7–11.8 µm). The LATMOS (Laboratoire Atmosphères Milieux Observations Spatiales) Atmospheric Retrieval Algorithm (LARA), which is used to retrieve the slant column densities (SCD) of SO₂, is a robust and a complete radiative transfer model, well adapted to the inversion of ground-based remote measurements. However, the calculation time to process the raw data and retrieve the infrared spectra, which is about seven days for the retrieval of one image of SO₂ SCD, remains too high to infer near real-time (NRT) SO₂ emission fluxes. A spectral image classification methodology based on two parameters extracting spectral features in the O₃ and SO₂ emission bands was developed to create a library. The relevance is evaluated in detail through tests. From data acquisition to the generation of SO₂ SCD images, this method requires only ~40 s per image, which opens the possibility to infer NRT estimation of SO₂ emission fluxes from IR hyperspectral imager measurements. Full article

(This article belongs to the Special Issue EO for Mapping Natural Resources and Geohazards)

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