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

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: 1 September 2021.

Special Issue Editor

Dr. Francesca Ardizzone
E-Mail Website
Guest Editor
Consiglio Nazionale delle Ricerche - Itituto di Ricerca per la Protezione Idrogeologica
Interests: implementation of geodatabase of geological; geomorphological; and environmental data; detection and mapping of landslides in different climatic; geological; and morphological environments; analysis of landslide types and patterns in relation to geology and topography; temporal forecasting of landslides using multi-temporal inventory maps; landslide impact analysis on human structure; landslide risk assessment and mapping; geological and geomorphological interpretation of ground deformation measured by satellites

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

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 papers will be 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 2400 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

  • Groundwater
  • Landslide
  • Land subsidence
  • Uplift
  • Ground deformation

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Risk Factor Detection and Landslide Susceptibility Mapping Using Geo-Detector and Random Forest Models: The 2018 Hokkaido Eastern Iburi Earthquake
Remote Sens. 2021, 13(6), 1157; https://doi.org/10.3390/rs13061157 - 18 Mar 2021
Viewed by 457
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 [...] Read more.
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
Show Figures

Graphical abstract

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
Remote Sens. 2020, 12(20), 3315; https://doi.org/10.3390/rs12203315 - 12 Oct 2020
Viewed by 717
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 [...] Read more.
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
Show Figures

Graphical abstract

Back to TopTop