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Application of Remote Sensing in Hydrogeology: Landslides, Land Subsidence and Uplift

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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 (1 September 2021) | Viewed by 16716

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Special Issue Editor

Dr. Francesca Ardizzone

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National Research Council of Italy, Research Institute of Geo-Hydrological Protection (CNR IRPI), Via della Madonna Alta 126, 06128 Perugia, Italy
Interests: landslide; landslide hazard; landslide risk; remote sensing; geodatabase
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Special Issue Information

Dear Colleagues,

Hydrogeology requires a multidisciplinary approach and interdisciplinary research aimed at investigating the interaction between water and geological systems. Various hydrological, geological, and geomorphological factors play a major role in the occurrence and movement of groundwater and have consequences for a wide range of geomorphological processes. Rainfall precipitation, infiltration, and groundwater are some of the most important landslide triggering factors, increasing the pore water pressure and decreasing the shear strength of the soil. Groundwater deficits may trigger compaction of aquifers resulting in land subsidence. Uplift can also be related to the groundwater level changes following the interruption of water pumping, or climatic drivers.

Remote sensing for earth observation, including synthetic aperture radar (SAR), optical, multi/hyper-spectral, thermal imagery, aerial photography, and unmanned aerial vehicles (UAVs), are useful tools for investigating groundwater level change impacts at the local and global scales with different spatial and temporal resolution.

The goal of this Special Issue of Remote Sensing (Section Remote Sensing in Geology, Geomorphology, and Hydrology) is to gather original research or case studies on the detection, characterization, and modelling of landslides, land subsidence, and uplift due to groundwater level changes.

We invite you to submit articles about your recent research including, but not limited to, the following topics:

landslide detection using remote sensing;
landslide modelling with remote sensing data;
land subsidence detection using remote sensing;
land subsidence modelling with remote sensing data;
detection and analysis of uplift based on remote sensing data;
applications; and
case studies.

Calò, F., Ardizzone, F., Castaldo, R., Lollino, P., Tizzani, P., Guzzetti, F., ... & Manunta, M. (2014). Enhanced landslide investigations through advanced DInSAR techniques: The Ivancich case study, Assisi, Italy. Remote Sensing of Environment, 142, 69-82.

Galloway, D. L. (2010). The complex future of hydrogeology. Hydrogeology Journal, 18(4), 807-810.

Higgins, S. A. (2016). Advances in delta-subsidence research using satellite methods. Hydrogeology Journal, 24(3), 587-600.

Manconi, A., Casu, F., Ardizzone, F., Bonano, M., Cardinali, M., De Luca, C., ... & Lanari, R. (2014). Brief communication: Rapid mapping of landslide events: The 3 December 2013 Montescaglioso landslide, Italy. Natural Hazards and Earth System Sciences, 14(7), 1835.

Pirotti, A. Guarnieri, A. Masiero & A. Vettore (2015) Preface to the special issue: the role of geomatics in hydrogeological risk, Geomatics, Natural Hazards and Risk, 6:5-7, 357-361, DOI: 10.1080/19475705.2014.984248

Dr. Francesca Ardizzone
Guest Editor

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Keywords

Groundwater
Landslide
Land subsidence
Uplift
Ground deformation

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Published Papers (4 papers)

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Research

30 pages, 7672 KiB

Open AccessArticle

A Meta-Learning Approach of Optimisation for Spatial Prediction of Landslides

by Biswajeet Pradhan, Maher Ibrahim Sameen, Husam A. H. Al-Najjar, Daichao Sheng, Abdullah M. Alamri and Hyuck-Jin Park

Remote Sens. 2021, 13(22), 4521; https://doi.org/10.3390/rs13224521 - 10 Nov 2021

Cited by 25 | Viewed by 3315

Abstract

Optimisation plays a key role in the application of machine learning in the spatial prediction of landslides. The common practice in optimising landslide prediction models is to search for optimal/suboptimal hyperparameter values in a number of predetermined hyperparameter configurations based on an objective function, i.e., k-fold cross-validation accuracy. However, the overhead of hyperparameter optimisation can be prohibitive, especially for computationally expensive algorithms. This paper introduces an optimisation approach based on meta-learning for the spatial prediction of landslides. The proposed approach is tested in a dense tropical forested area of Cameron Highlands, Malaysia. Instead of optimising prediction models with a large number of hyperparameter configurations, the proposed approach begins with promising configurations based on several basic and statistical meta-features. The proposed meta-learning approach was tested based on Bayesian optimisation as a hyperparameter tuning algorithm and random forest (RF) as a prediction model. The spatial database was established with a total of 63 historical landslides and 15 conditioning factors. Three RF models were constructed based on (1) default parameters as suggested by the sklearn library, (2) parameters suggested by the Bayesian optimisation (BO), and (3) parameters suggested by the proposed meta-learning approach (BO-ML). Based on five-fold cross-validation accuracy, the Bayesian method achieved the best performance for both the training (0.810) and test (0.802) datasets. The meta-learning approach achieved slightly lower accuracies than the Bayesian method for the training (0.769) and test (0.800) datasets. Similarly, based on F1-score and area under the receiving operating characteristic curves (AUROC), the models with optimised parameters either by the Bayesian or meta-learning methods produced more accurate landslide susceptibility assessment than the model with the default parameters. In the present approach, instead of learning from scratch, the meta-learning would begin with hyperparameter configurations optimal for the most similar previous datasets, which can be considerably helpful and time-saving for landslide modelings. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Hydrogeology: Landslides, Land Subsidence and Uplift)

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24 pages, 105003 KiB

Open AccessArticle

Combined GRACE and MT-InSAR to Assess the Relationship between Groundwater Storage Change and Land Subsidence in the Beijing-Tianjin-Hebei Region

by Wen Yu, Huili Gong, Beibei Chen, Chaofan Zhou and Qingquan Zhang

Remote Sens. 2021, 13(18), 3773; https://doi.org/10.3390/rs13183773 - 20 Sep 2021

Cited by 13 | Viewed by 3624

Abstract

Beijing-Tianjin-Hebei (BTH) has been suffering from severe groundwater storage (GWS) consumption and land subsidence (LS) for a long period. The overexploitation of groundwater brings about severe land subsidence, which affects the safety and development of BTH. In this paper, we utilized multi-frame synthetic aperture radar datasets obtained by the Rardarsat-2 satellite to monitor land subsidence’s temporal and spatial distribution in the BTH from 2012 to 2016 based on multi-temporal interferometric synthetic aperture radar (MT-InSAR). In addition, we also employed the Gravity Recovery and Climate Experiment (GRACE) mascon datasets acquired by the Center for Space Research (CSR) and Jet Propulsion Laboratory (JPL) to obtain the GWS anomalies (GWSA) of BTH from 2003 to 2016. Then we evaluate the accuracy of the results obtained. Furthermore, we explored the relationship between the regional GWSA and the average cumulative subsidence in the BTH. The total volume change of subsidence is 59.46% of the total volume change of groundwater storage. Moreover, the long-term decreasing trend of the GWSA (14.221 mm/year) and average cumulative subsidence (17.382 mm/year) show a relatively high consistency. Finally, we analyze the heterogeneity of GWS change (GWSC) and LS change (LSC) in the four typical areas by the Lorenz curve model. The implementation of the South-to-North Water Diversion Project (MSWDP) affects the heterogeneity of GWSC and LSC. It can be seen that the largest heterogeneity of LSC lags behind the GWSC in the Tianjin-Langfang-Hengshui-Baoding area. The largest uneven subsidence in Beijing and Tianjin occurred in 2015, and the largest uneven subsidence in Hengshui-Baoding occurred in 2014. After that, the heterogeneity of subsidence gradually tends to stable. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Hydrogeology: Landslides, Land Subsidence and Uplift)

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17 pages, 11842 KiB

Open AccessArticle

Risk Factor Detection and Landslide Susceptibility Mapping Using Geo-Detector and Random Forest Models: The 2018 Hokkaido Eastern Iburi Earthquake

by Yimo Liu, Wanchang Zhang, Zhijie Zhang, Qiang Xu and Weile Li

Remote Sens. 2021, 13(6), 1157; https://doi.org/10.3390/rs13061157 - 18 Mar 2021

Cited by 40 | Viewed by 4031

Abstract

Landslide susceptibility mapping is an effective approach for landslide risk prevention and assessments. The occurrence of slope instability is highly correlated with intrinsic variables that contribute to the occurrence of landslides, such as geology, geomorphology, climate, hydrology, etc. However, feature selection of those conditioning factors to constitute datasets with optimal predictive capability effectively and accurately is still an open question. The present study aims to examine further the integration of the selected landslide conditioning factors with Q-statistic in Geo-detector for determining stratification and selection of landslide conditioning factors in landslide risk analysis as to ultimately optimize landslide susceptibility model prediction. The location chosen for the study was Atsuma Town, which suffered from landslides following the Eastern Iburi Earthquake in 2018 in Hokkaido, Japan. A total of 13 conditioning factors were obtained from different sources belonging to six categories: geology, geomorphology, seismology, hydrology, land cover/use and human activity; these were selected to generate the datasets for landslide susceptibility mapping. The original datasets of landslide conditioning factors were analyzed with Q-statistic in Geo-detector to examine their explanatory powers regarding the occurrence of landslides. A Random Forest (RF) model was adopted for landslide susceptibility mapping. Subsequently, four subsets, including the Manually delineated landslide Points with 9 features Dataset (MPD9), the Randomly delineated landslide Points with 9 features Dataset (RPD9), the Manually delineated landslide Points with 13 features Dataset (MPD13), and the Randomly delineated landslide Points with 13 features Dataset (RPD13), were selected by an analysis of Q-statistic for training and validating the Geo-detector-RF- integrated model. Overall, using dataset MPD9, the Geo-detector-RF-integrated model yielded the highest prediction accuracy (89.90%), followed by using dataset MPD13 (89.53%), dataset RPD13 (88.63%) and dataset RPD9 (87.07%), which implied that optimized conditioning factors can effectively improve the prediction accuracy of landslide susceptibility mapping. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Hydrogeology: Landslides, Land Subsidence and Uplift)

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20 pages, 6551 KiB

Open AccessArticle

The Relationship between Surface Displacement and Groundwater Level Change and Its Hydrogeological Implications in an Alluvial Fan: Case Study of the Choshui River, Taiwan

by Chiao-Yin Lu, Jyr-Ching Hu, Yu-Chang Chan, Yuan-Fong Su and Chih-Hsin Chang

Remote Sens. 2020, 12(20), 3315; https://doi.org/10.3390/rs12203315 - 12 Oct 2020

Cited by 16 | Viewed by 4115

Abstract

Balancing the demand of groundwater resources and the mitigation of land subsidence is particularly important, yet challenging, in populated alluvial fan areas. In this study, we combine multiple monitoring data derived from Multi-Temporal InSAR (MTI), GNSS (Global Navigation Satellite System), precise leveling, groundwater level, and compaction monitoring wells, in order to analyze the relationship between surface displacement and groundwater level change within the alluvial fan of the Choshui River in Taiwan. Our combined time-series analyses suggest, in a yearly time scale, that groundwater level increases with the vertical surface displacement when the effect of pore water pressure dominates. Conversely, this relationship is negative when the effect of water-mass loading predominates over pore water pressure. However, the correlation between the vertical surface displacement and the groundwater level change is consistently positive over the time scale of two decades. It is interpreted that the alluvial fan sequence in the subsurface is not fully elastic, and compaction is greater than rebound in this process. These findings were not well reported and discussed by previous studies because of insufficient monitoring data and analyses. Understanding the combined effect of groundwater level change and vertical surface displacement is very helpful for management of land subsidence and usage of groundwater resources. The spatial and temporal integration of multi-sensors can be applied to overcome the limitations associated with the single technique and provides further insights into land surface changes, particularly in highly populated alluvial fan areas. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Hydrogeology: Landslides, Land Subsidence and Uplift)

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