Land subsidence is a common phenomenon that occurs worldwide due to natural and human-induced causes. It is a gradual sinking of the ground surface, which can result in significant damage to infrastructure, buildings, and natural resources. The application of remote sensing techniques, such as Differential Interferometric Synthetic Aperture Radar (DInSAR), has been widely used in monitoring and mapping land subsidence. DInSAR is a radar-based technique that measures the deformation of the ground surface by comparing two or more synthetic aperture radar (SAR) images acquired at different times. This technique can detect subsidence with millimeter-scale accuracy over large areas, making it a valuable tool in monitoring land subsidence. The DInSAR technique has been successfully applied to monitor various types of subsidence, such as natural subsidence caused by geological processes in areas prone to natural disasters such as earthquakes and volcanic eruptions, and human-induced subsidence caused by groundwater extraction, mining, and oil and gas extraction. In urban areas, the DInSAR technique has been used to monitor subsidence related to the construction of large buildings and infrastructure projects such as highways and bridges. The application of DInSAR in monitoring land subsidence has several advantages over traditional monitoring methods. It is a noninvasive and cost-effective technique that can provide accurate and timely data on subsidence over large areas. This makes it an ideal tool for early warning systems and decision-making processes related to land use and water planning and management. This chapter provides an introduction to DInSAR and explains the methodology, with a focus on its application for monitoring land subsidence. © 2024 Elsevier Inc. All rights reserved.
Soil organic carbon (SOC) is a critical indicator of soil health and plays a significant role in global carbon cycling. Based on the multiple roles that soil carbon plays in pasture ecosystems, such as reducing greenhouse gases, improving soil yield and fertility, reducing soil erodibility, and increasing water and food storage capacity, it is evident that carbon management is crucial to improving pasture quality. Remote sensing technologies have the potential to provide accurate and efficient predictions of SOC at large spatial scales, making it a valuable tool for monitoring and managing soil carbon stocks. Remote sensing can help inform policies and practices aimed at promoting sustainable land use by allowing for monitoring and management of soil carbon stocks at regional scales, The use of remote sensing for SOC prediction involves the integration of spectral, spatial, and temporal data. Spectral data, derived from remote sensing imagery, can provide information on vegetation cover, soil moisture, and other factors that influence SOC. Spatial data, such as topography and land use, can also be incorporated into SOC prediction models. Temporal data, including changes in vegetation cover and weather patterns, can provide insights into the dynamics of SOC. Algorithms, such as multivariate regression and factor analysis, have been successfully applied to predict SOC from remote sensing data. However, the accuracy of SOC predictions from remote sensing depends on factors such as the quality and frequency of the data, the selection of predictive variables, and the calibration/validation of the models. To improve the accuracy of SOC predictions, remote sensing can be integrated with other data sources, such as soil sampling. This integration can help to validate and calibrate the remote sensing models, ultimately leading to more accurate predictions of SOC. © 2024 Elsevier Inc. All rights reserved.
Geotechnical and Geological Engineering (09603182)39(2)pp. 1201-1222
Applications of Synthetic Aperture Sensors (SAR) and particularly Differential Interferometric Synthetic Aperture Radar (DInSAR) have provided new opportunities for detecting and monitoring of slow and even fast land deformations such as landslides and also updating their inventory maps. Employing this technique has made possible continuous detection and monitoring of small land movements with high precision over a wide spread area. In this study, two image series including 12 radar images with descending orbit acquired by ASAR sensor of ENVISAT satellite and 10 radar images with ascending orbit collected by PALAR sensor of ALOS satellite were selected and processed by the DInSAR method in order to detect landslides in the Doab-Samsami basin in Chaharmahal and Bakhtiari province, Iran. Landslides detected in the study area cover over 5959 hec according to the processing of ASAR and PALSAR images collected between 2003 and 2011, whereas landslides detected by field studies cover over 5056 hec. Based on the results of the radar processing technique for detecting and mapping of landslides, the ASAR images can provide more details of slides due to their shorter wavelengths but the PALSAR images have comparatively greater penetration and lower incoherence due to the longer wavelengths. Results of the receiver operator characteristic (ROC) method show a well agreement between the landslides map provided by the DInSAR approach and field study. The area under the curve of receiver operator characteristic (ROC) curve was estimated to be 0.95 with a standard deviation of 0.02 at 95% confidence level. The Cohen’s Kappa of 0.61 indicate relatively good conformity between classification of the detected landslide distribution in the study area based on the DInSAR method and field survey. © 2020, Springer Nature Switzerland AG.
Bulletin of Engineering Geology and the Environment (14359537)80(3)pp. 2045-2067
In this research, landslide susceptibility map of the Fahliyan sub-basin was provided employing adaptive neuro-fuzzy inference system (ANFIS) in ensemble with the ant colony optimization (ACOR) and differential evolution (DE) algorithms. Forty-three out of 61 landslides (70%) were employed to provide landslide susceptibility map and 18 landslides (30%) to validate the models. Thirteen landslide controlling factors including altitude, plan curvature, slope angle, aspect, profile curvature, distance to roads, distance to rivers, distance to faults, rainfall, TWI, SPI, land use, and lithology were employed to provide the map of landslide susceptibility. Weights of every effective factor class and effective factors were calculated based on frequency ratio of landslides relative to the class area and entropy model. The landslide susceptibility maps were generated by the GIS-based algorithms, and the resultant was validated using the training (70%) and test (30%) data of landslide locations for success and prediction rates, respectively. According to the entropy model, distance to road, rainfall, and SPI are the most effective factors on landslide occurrence in the area. The area under the curve (AUC) of ROC for the ANFIS, ANFIS-ACOR, and ANFIS-DE algorithms ranges from 0.845 to 0.946 for success rate curves and 0.793 to 0.924 for prediction rate curves, respectively. Therefore, performances of the analyzed models of landslide susceptibility are good to excellent. The success rate curves suggest that the employed algorithms have high prediction performance, but the success rate curves indicate that the ANFIS-DE algorithm has the best estimation performance (0.946) with respect to the other models. © 2021, Springer-Verlag GmbH Germany, part of Springer Nature.
Environmental Earth Sciences (18666299)78(1)
Persistent scatterer synthetic aperture radar interferometry (PSInSAR) is an applied time series technique to overcome limitations of InSAR (temporal and geometrical decorrelation and atmospheric delay anomalies) for monitoring of ground surface deformations. This method only monitors displacements on pixels with nearly constant temporal backscattering characteristics. In this study, datasets of ascending ALOS PALSAR (L-band) images recorded from 2006 to 2010 and descending ENVISAT ASAR (C-band) images acquisitioned between 2003 and 2010 were processed to detect and monitor the landslide occurred in the Noghol area, Iran. Application of the PSInSAR technique on both PALSAR and ASAR images has significantly improved monitoring of the Noghol landslide. However, the determination of vertical displacement of the landslide by the ASAR images processing is more correct compared to results of the PALSAR processing due to the descending orbital motion of ASAR. The ASAR images also overwhelm PALSAR images for determination of the landslide extent because of detection of more persistent scatterer points. The landslide displacement and aspect obtained by the Global Navigation Satellite System (GNSS) and PSInSAR techniques are in agreement (about 1.2–1.5 m westward in the period of 2003–2010). Particularly, processing results of the ASAR images are more similar to the GNSS measurements. Furthermore, assessment of the landslide type, mechanism and its displacement direction were possible by integration of the PALSAR and ASAR radar images with ascending and descending orbital motions, respectively. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
Natural Hazards (15730840)93(3)pp. 1379-1418
Landslides are natural disasters often activated by interaction of different controlling environmental factors, especially in mountainous terrains. In this research, the landslide susceptibility map was developed for the Sarkhoun catchment using Index of Entropy (IoE) and Dempster–Shafer (DS) models. For this purpose, 344 landslides were mapped in GIS environment. 241 (70%) out of the landslides were selected for the modeling and the remaining (30%) were employed for validation of the models. Afterward, 10 landslide conditioning factor layers were prepared including land use, distance to drainage, slope gradient, altitude, lithology, distance to roads, distance to faults, slope aspect, Topography Wetness Index, and Stream Power Index. The relationship between the landslide conditioning factors and landslide inventory maps was determined using the IoE and DS models. In order to verify the models, the results were compared with validation landslide data not employed in training process of the models. Accordingly, Receiver Operating Characteristic (ROC) curves were applied, and Area Under the Curve (AUC) was calculated for the obtained susceptibility maps using the success (training data) and prediction (validation data) rate curves. The land use was found to be the most important factor in the study area. The AUC are 0.82, and 0.81 for success rates of the IoE, and DS models, respectively, while the prediction rates are 0.76 and 0.75. Therefore, the results of the IoE model are more accurate than the DS model. Furthermore, a satisfactory agreement is observed between the generated susceptibility maps by the models and true location of the landslides. © 2018, Springer Science+Business Media B.V., part of Springer Nature.
Hydrological Sciences Journal (02626667)62(12)pp. 2012-2024
Proper estimation of the spatial distribution of water-table depth is highly important in most groundwater studies. Groundwater depth is measured at specific and limited points and it is estimated for other parts using spatial estimation methods. In this study, two multivariate methods, artificial neural network (ANN) and multiple linear regression (MLR), are examined to estimate water-table depth in an unconfined aquifer located in Shibkooh, Iran. The different ancillary data, including spatial coordinates, digital elevation model (DEM), aquifer bed elevation, specific resistivity and aquifer thickness were used to improve estimates based on these methods. It was proved that performance of the ANN surpasses that of the MLR method. Using the spatial coordinates, the aquifer bed elevation and aquifer thickness resulted in the optimum spatial estimation of the water-table depth. These parameters, directly or indirectly, affect the water-table depth estimation through techniques such as ANN capable of modelling of nonlinear relationships. © 2017 IAHS.
Pakzad, H.R.,
Pasandi, M.,
Yeganeh, S.,
Alizadeh ketek lahijani, H. Environmental Monitoring And Assessment (01676369)188(5)
Sampling of the offshore seabed sediments of southwestern part of the Caspian Sea was carried out by gravity corer in order to study heavy metal concentration and the physicochemical factors controlling their distribution in the fine-grained fraction. The grain size distribution, amount, and type of clay minerals, total organic carbon (TOC) content, and Eh–pH of the sediments were determined. The average concentrations of the heavy metals in ppm are Mn (563), Cu (207.5), Sr (187), Zn (94), Pb (26.3), Ni (14.5), Co (11.5), Cd (2.56), and Ag (1.04) in their order of abundances. Co and Zn mostly indicate increase in silt-size fraction of the sediments suggesting their probable detrital provenance but the Mn, Ni, Cu, Sr, Pb, Cd, and Ag concentrations show a similar trend to distribution of the clay-size fraction. The concentrations of Mn, Co, and Cd increase with increase in the TOC content but the Cu, Pb, Ni, Ag, and Sr concentrations decrease with increase of the TOC content. The amounts of Zn, Cu, Sr, Pb, Cd, and Ag increase with increase in the CaCO3 content. The calculated enrichment factor indicates that the sediments are very strong to extremely enriched in Ag, significantly enriched in Cu and Cd, and depleted to mineral for Pb, Sr, Co, Ni, and Zn. Variations of the Cu, Sr, Cd, Ag, and Pb concentrations are similar to the clay and CaCO3 distributions. © 2016, Springer International Publishing Switzerland.
Carbonates and Evaporites (08912556)30(2)pp. 135-143
The Gavkhuni playa lake consists of sand, mud, and salt flats. The salt pan covers extensive part of the playa. Its color is usually clear and white, but black, pink and green colors also occur. The black color of halite has been caused by impurities of detrital sediments. The sand detrital sediments have been derived from the aeolian sands located in the west of the playa lake. The pink to light red color of the halite is due to the existence of iron oxides or/and microbial effects. There are potentials for natural concentrations of heavy metals in the evaporite sediments of this lake especially due to the occurrence of sedimentary Pb/Zn ore deposits in its drainage basin. To study the concentration of the heavy metals in the salt pan, 18 samples were taken from the salt pan and analyzed. The results show that average concentrations (ppm) of the heavy metals in the salt pan are Ni (56.46), Sr (26.46), Pb (11.42), Ag (10.70), Mn (6.15), Co (2.86), Cd (1.98), Zn (1.48) and Cu (1.14) in their order abundances. The amounts of Zn, Mn, Sr, Cu, Cd and Pb are relatively high in samples containing calcium minerals. The concentrations of Mn and Cu in the pink and green salts are relatively higher than the white ones, because these metals tend to be adsorbed by organic matter. Manganese oxides are important factors influencing the Ni concentration in the sediments. The Mn and Sr concentrations increase in the samples containing iron silicate minerals and carbonate grains. The Co and Ni concentrations are high in the samples containing Fe/Mg-bearing clastic grains. The Ag concentration is high in the samples containing sulfide minerals. Strong adsorption of Mn2+, Co2+ and Zn2+ to clay minerals and precipitation of Cu as Cu°, Cu2S, CuS and Pb as PbCO3 and PbS in the mud sediments can be the reasons for the lower concentrations of these elements in the pure salt sediments than the mud deposits. Enrichment factor indicates that Ag is moderately enriched and other elements are weakly enriched in the evaporite deposits. © 2014, Springer-Verlag Berlin Heidelberg.
Quaternary International (10406182)345pp. 138-147
In order to study natural concentration of heavy metal in sand sediments of the Oman Sea and its relationship with composition and provenance of the deposits, the concentrations of Cd, Co, Cu, Sr, Pb, Cr, Zn, Ni, Mn and Fe were determined. Relationships between the heavy metal concentration with composition of the sediments and rocks existing in the rivers drainage basins of the area were studied. The results indicate that the Zn and Cu concentrations are closely related to biotite and muscovite contents of the sediments. The amounts of Sr, Cd and Pb change similarly to the variation of calcium carbonate content in the sand sediments. The Fe and Mn concentrations show correlations with the total amount of heavy minerals. The distribution pattern of Cr resembles the pyroxene content. The Co and Ni concentrations fluctuate similarly to the variation of the amphiboles and the intrusive igneous fragments, respectively. © 2014 Elsevier Ltd and INQUA.
Environmental Earth Sciences (18666299)71(11)pp. 4683-4692
The sedimentary basin of Gavkhuni playa lake includes two sedimentary environments of delta and playa lake. These environments consist of mud, sand and salt flats. There are potentials for concentration of heavy metals in the fine-grained sediments (silt and clay) of the playa due to existence of Pb/Zn ore deposits, industrial and agricultural regions in the water catchment of Zayandehrud River terminating to this area. In order to study the concentration of heavy metals and the controlling factors on their distribution in the fine-grained sediments, 13 samples were taken from the muddy facies and concentration of the heavy metals were determined. The results showed that the heavy metal concentrations range in the sediments (in ppm) are Mn (395.5-1,040), Sr (100.4-725.76), Pb (14.66-91.06), Zn (23.59-80.9), Ni (37-73.66), Cu (13.83-29.83), Co (5.73-13.78), Ag (3.03-4.76) and Cd (2.3-5.5) in their order of abundances. The concentration of Ag is noticeable in the sediments relative to the average concentration of this element in mud sediments. The amounts of Pb and Zn are relatively high in all the samples in comparison with the other elements. The concentration of Ni is relatively high in the oxidized samples. The distribution of Pb is directly related to organic matter content of the sediments. The concentrations of Zn, Sr, Cu, Co and Cd in the samples of the playa are lower than those in the delta. The amount of illite is another factor influencing Zn and Pb concentrations. Sr is more concentrated in the sediments with the high content of calcium carbonate. The distribution pattern of Cu, Co, Pb and Mn resembles to that of the clay content of the sediments. The clay content shows positive correlations with Co, Cu and Mn concentrations and negative correlation with Ag. The Sr and Ag concentrations are positively correlated with the amount of CaCO3. The amounts of Co, Cu, Ni and Mn show negative correlations with the calcium carbonate content. Pb and Co are noticeably correlated with Mn. © 2013 Springer-Verlag Berlin Heidelberg.
Ground Water (0017467X)47(6)pp. 762-762
Advances in Water Resources (03091708)31(2)pp. 383-398
An analytical model is presented for the analysis of constant flux tests conducted in a phreatic aquifer having a partially penetrating well with a finite thickness skin. The solution is derived in the Laplace transform domain for the drawdown in the pumping well, skin and formation regions. The time-domain solution in terms of the aquifer drawdown is then obtained from the numerical inversion of the Laplace transform and presented as dimensionless drawdown-time curves. The derived solution is used to investigate the effects of the hydraulic conductivity contrast between the skin and formation, in addition to wellbore storage, skin thickness, delayed yield, partial penetration and distance to the observation well. The results of the developed solution were compared with those from an existing solution for the case of an infinitesimally thin skin. The latter solution can never approximate that for the developed finite skin. Dimensionless drawdown-time curves were compared with the other published results for a confined aquifer. Positive skin effects are reflected in the early time and disappear in the intermediate and late time aquifer responses. But in the case of negative skin this is reversed and the negative skin also tends to disguise the wellbore storage effect. A thick negative skin lowers the overall drawdown in the aquifer and leads to more persistent delayed drainage. Partial penetration increases the drawdown in the case of a positive skin; however its effect is masked by the negative skin. The influence of a negative skin is pronounced over a broad range of radial distances. At distant observation points the influence of a positive skin is too small to be reflected in early and intermediate time pumping test data and consequently the type curve takes its asymptotic form. © 2007 Elsevier Ltd. All rights reserved.
Hydrological Sciences Journal (02626667)52(1)pp. 192-205
Considering the geological conditions of the southwest of Boroujerd and northwest of Doroud, Iran, karst development is analysed with respect to the hydrodynamic behaviour of the main draining springs of the units and the karstic aquifers are classified as either those developed in Cretaceous limestone or those developed in Oligomiocene limestone. For this purpose, the yields of the main karstic springs of the region - Absardeh and Zoorabad (Cretaceous karstic limestone aquifer), Kalamsooz and Azizabad (Oligomiocene karstic limestone aquifer) - were measured and analysed. Analysis of the recession curve is used for hydrodynamical analysis and to construct the conceptual model for estimation of karst development in the selected aquifers. Based on the results, the dynamic storage capacity of the saturated zone in Cretaceous limestone is evaluated as low to medium and that in Oligomiocene limestone as medium to high. The dynamic storage capacity of the unsaturated zone in Cretaceous limestone is evaluated as high and that in Oligomiocene limestone as low to medium. Moreover, the contribution of quickflow in karstic aquifers developed in the Cretaceous limestone drained by the Absardeh and Zoorabad springs is 23.5 and 82.2%, respectively, and that for the Kalamsooz and Azizabad springs (Oligomiocene limestone) is 5.7 and 22.5%, respectively. Flow in the Cretaceous limestone aquifer drained by the Zoorabad Spring is of concentrated type and the main flow occurs in the well-developed karstic conduits. The main flow in the Oligomiocene limestone aquifer, drained by the Kalamsooz Spring, occurs in a network of joints and fractures and the contribution of concentrated flow is very low. The transmissivity of the saturated zone in the karstic aquifer drained by the Zoorabad and Absardeh springs is medium to high and that for the Kalamsooz and Azizabad springs is found to be low. Copyright © 2007 IAHS Press.
Ground Water (0017467X)42(1)pp. 2-2
Ground Water (0017467X)42(1)pp. 2-2
Ground Water (0017467X)41(5)pp. 602-607
The Theis type curve matching method and the Cooper-Jacob semilog method are commonly used for estimation of transmissivity and storage coefficient of infinite, homogeneous, isotropic, confined aquifers from drawdown data of a constant rate pumping test. Although these methods are based on drawdown data, they are often applied indiscriminately to analyze both drawdown and recovery data. Moreover, the limitations of drawdown type curve to analyze recovery data collected after short pumping times are not well understood by the practicing engineers. This often may result in an erroneous interpretation of such recovery data. In this paper, a novel but simple method is proposed to determine the storage coefficient as well as transmissivity from recovery data measured after the pumping period of an aquifer test. The method eliminates the dependence on pumping time effects and has the advantage of employing only one single recovery type curve. The method based on the conversion of residual drawdown to recovered drawdown (buildup) data plotted versus a new equivalent time (Δt X tp/tp + Δt). The method uses the recovery data in one observation point only, and does not need the initial water level h0, which may be unknown. The accuracy of the method is checked with three sets of field data. This method appears to be complementary to the Cooper-Jacob and Theis methods, as it provides values of both storage coefficient and transmissivity from recovery data, regardless of pumping duration.
