Spatio-temporal modeling of Louisiana land subsidence using high resolution geo-spatial data

Problems caused by subsidence are very common in many areas of the world, and this kind of problems may be serious and threatening to living people in Louisiana. Adverse subsidence in Louisiana will cause serious problems, such as excessive wetland formation or land loss, if we can’t make appropriate treatments, and this topic will also be what we focus on in this research (Kent and Dokka 2012). For subsidence survey, we can use three kinds of common techniques, leveling, InSAR (Interferometric Synthetic Aperture Radar) and GPS observation (Lu, C. et al. 2012). In this research, high accuracy of subsidence data in Louisiana has been collected by GPS, and Kriged-Kalman Filter (KKF) has been used to process such subsidence data (Mardia et al. 1998). Results by KKF have shown spatio-temporal distributions of subsidence rates from 2011 to 2013, and these results have also been validated by the Bayou Corne Sinkhole knowledge in this research (Mardia et al. 1998; Cusanza 2013; Jones and Blom 2014; Jones and Blom 2015). Based on the validated KKF results in this research, we have used some geo-statistics models, such as Geographically Weighted Regression (GWR), the spatial-lag model and the spatial-error model, so as to find which main factors have caused adverse subsidence in the study site in 2013 (Mardia et al. 1998; Fotheringham et al. 2002; Baller et al. 2001; Wang 2006; Wang et al. 2014; Abdollahzadeh et al. 2013). Modeling results have shown that, either GWR or the spatial-error model may be suitable in this research, and Bayou Corne Sinkhole, sediment compaction, groundwater withdrawal and mass loading of buildings may be the significant and explainable factors causing subsidence in the study site (Fotheringham et al. 2002; Hayashi and Fumio 2000; Abdollahzadeh et al. 2013; Xu and Wang 2015; Kim et al. 2006; Kim et al. 2009; Oh and Lee 2010; Oh et al. 2011; Cusanza 2013; Jones and Blom 2014; Jones and Blom 2015; Anselin et al. 2006; Baller et al. 2001; Wang 2006; Wang et al. 2014; Shang et al. 2011; Sclater and Christie 1980). Thus, in this research, we have concluded that KKF is a valid model to generate spatio-temporal distributions of subsidence rates, by GWR the spatial heterogeneity for subsidence will be clearly found and by the spatial-lag model the main factors causing subsidence in Louisiana will also be clearly found (Mardia et al. 1998; Fotheringham et al. 2002; Hayashi and Fumio 2000; Abdollahzadeh et al. 2013; Xu and Wang 2015; Kim et al. 2006; Kim et al. 2009; Oh and Lee 2010; Oh et al. 2011; Cusanza 2013; Jones and Blom 2014; Jones and Blom 2015; Anselin et al. 2006; Baller et al. 2001; Wang 2006; Wang et al. 2014; Shang et al. 2011; Sclater and Christie 1980)

Subsidence determined by InSAR – a review

Multi-temporal interferometry or InSAR allows monitoring of a deformation phenomenon at millimetre level, via the generation of mean deformation velocity maps and displacement time series from a data set of acquired SAR satellite images. The advantages of satellite radar interferometry for displacement monitoring are demonstrated in cases of monitoring man-made structures (e.g. buildings, bridges, dams, subway lines, mines exploitation). This paper presents works in which subsidence phenomena were analyzed by InSAR technique

Land subsidence susceptibility mapping in South Korea using machine learning algorithms

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. In this study, land subsidence susceptibility was assessed for a study area in South Korea by using four machine learning models including Bayesian Logistic Regression (BLR), Support Vector Machine (SVM), Logistic Model Tree (LMT) and Alternate Decision Tree (ADTree). Eight conditioning factors were distinguished as the most important affecting factors on land subsidence of Jeong-am area, including slope angle, distance to drift, drift density, geology, distance to lineament, lineament density, land use and rock-mass rating (RMR) were applied to modelling. About 24 previously occurred land subsidence were surveyed and used as training dataset (70% of data) and validation dataset (30% of data) in the modelling process. Each studied model generated a land subsidence susceptibility map (LSSM). The maps were verified using several appropriate tools including statistical indices, the area under the receiver operating characteristic (AUROC) and success rate (SR) and prediction rate (PR) curves. The results of this study indicated that the BLR model produced LSSM with higher acceptable accuracy and reliability compared to the other applied models, even though the other models also had reasonable results

Development of Geospatial Models for Multi-Criteria Decision Making in Traffic Environmental Impacts of Heavy Vehicle Freight Transportation

Heavy vehicle freight transportation is one of the primary contributors to the socio-economic development, but it has great influence on traffic environment. To comprehensively and more accurately quantify the impacts of heavy vehicles on road infrastructure performance, a series of geospatial models are developed for both geographically global and local assessment of the impacts. The outcomes are applied in flexible multi-criteria decision making for the industrial practice of road maintenance and management

Application of InSAR and GIS Techniques to Ground Subsidence Assessment in the Nobi Plain, Central Japan

Spatial variation and temporal changes in ground subsidence over the Nobi Plain, Central Japan, are assessed using GIS techniques and ground level measurements data taken over this area since the 1970s. Notwithstanding the general slowing trend observed in ground subsidence over the plains, we have detected ground rise at some locations, more likely due to the ground expansion because of recovering groundwater levels and the tilting of the Nobi land mass. The problem of non-availability of upper-air meteorological information, especially the 3-dimensional water vapor distribution, during the JERS-1 observational period (1992–1998) was solved by applying the AWC (analog weather charts) method onto the high-precision GPV-MSM (Grid Point Value of Meso-Scale Model) water-vapor data to find the latter’s matching meteorological data. From the selected JERS-1 interferometry pair and the matching GPV-MSM meteorological data, the atmospheric path delay generated by water vapor inhomogeneity was then quantitatively evaluated. A highly uniform spatial distribution of the atmospheric delay, with a maximum deviation of approximately 38 mm in its horizontal distribution was found over the Plain. This confirms the effectiveness of using GPV-MSM data for SAR differential interferometric analysis, and sheds thus some new light on the possibility of improving InSAR analysis results for land subsidence applications