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Land-subsidence susceptibility zonation using remote sensing, GIS, and probability models in a Google Earth Engine platform

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Abstract

Land subsidence causes many problems, including faulting of Earth's surface, damage irrigation systems, reduced soil fertility, misalignment and failure of the walls of wells, changes of river courses and roadways, and even damages in residential areas and agricultural lands. This study assesses groundwater changes using the Google Earth Engine (GEE) and land-subsidence susceptibility (LSS) on the Gharabolagh Plain in Fars Province, Iran, using two probabilistic models, evidential belief function (EBF) and Bayesian theory (BT). In extensive field surveys, the land-subsidence points identified in the study area were recorded by handheld GPS. A land-subsidence location map (LSIM) was prepared in ArcGIS 10.6.1. The sites were divided into two sets: 70% of the subsidence points locations to be used for modeling (calibration) and 30% to be used for testing (validation) of the models. Effective factors that promote land-subsidence occurrence (LSO) were selected and maps for each were prepared from several sources—topographical and geological maps, and satellite images. Changing groundwater level was determined from the GEE platform using GRACE satellite images from a 14-year period. Three methods—CSR (Center for Space Research at the University of Texas), JPL (Jet Propulsion Laboratory), and GFZ (German Research Centre for Geosciences)—were employed and show that the greatest changes in water depth in the study area are equal to − 16.55, − 17.77, and − 15.09 cm from CSR, GFZ, and JPL, respectively. The spatial relationships between land subsidence and the ten effective factors were assessed using the EBF algorithm and BT. Also, analysis of variables importance was done using the random forest model. Prioritizing these factors, it was revealed that excessive withdrawal of groundwater, elevation, and distance from rivers were the most important variables promoting subsidence. Finally, the LSS maps were prepared in GIS. The validation analysis of the two probability models by ROC curve revealed that EBF had 93.2% accuracy and BT was 99.8% accurate. The maps generated with both models can effectively indicate LSS for managers of groundwater and can be used to prevent fatalities, financial losses, and damage to property caused by this phenomenon.

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Acknowledgements

The study was supported by the College of Agriculture, Shiraz University, and Grant No. 98GRC1M271143. Also, the authors would like to thank the Editor-in-Chief (Prof. Dr. Olaf Kolditz) and the five anonymous reviewers for their positive comments.

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Authors and Affiliations

  1. Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran

    Zeynab Najafi, Hamid Reza Pourghasemi, Gholamabbas Ghanbarian & Seyed Rashid Fallah Shamsi

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  1. Zeynab Najafi
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  2. Hamid Reza Pourghasemi
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  3. Gholamabbas Ghanbarian
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  4. Seyed Rashid Fallah Shamsi
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Correspondence to Hamid Reza Pourghasemi.

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Najafi, Z., Pourghasemi, H.R., Ghanbarian, G. et al. Land-subsidence susceptibility zonation using remote sensing, GIS, and probability models in a Google Earth Engine platform. Environ Earth Sci 79, 491 (2020). https://doi.org/10.1007/s12665-020-09238-2

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