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This study presents a long-term reactivation monitoring approach using multi-source satellite SAR and optical data. It also explores the relationship between landslide deformations and hydrological factors for reactivation assessment and management. More specifically, this approach is applied to investigate the long-term reactivation evolution of the Huangtupo landslide under the local engineering work conducted in four different phases. The spatial–temporal evolutions of the Huangtupo landslide were mapped by the multi-temporal optical images from the PlanetScope satellite, revealing that the building areas were gradually replaced by the vegetation covers. The long-term reactivated deformations were explored by the multi-source satellite SAR data from ALOS-2 and Sentinel-1A/B based on time-series InSAR analysis. Cross-validation between the multi-source InSAR results showed that the ALOS-2 and Sentinel-1A/B measurements are highly correlated. Long-term spatial–temporal deformations were investigated using the Sentinel-1A/B Track 11 data between 2016 and 2022. A long stack of SAR datasets was divided into four consecutive branches based on the local engineering work plan. A general slowing down deformation trend can be observed from the time series analysis, indicating the effectiveness of local measures for maintaining the slope stability. Most importantly, seasonal fluctuations caused by changes in rainfall and Three Gorges Reservoir (TGR) water level can be clearly observed from the time series evolutions of deformation during each phase. Through the gray correlation analysis and cross wavelet analysis, it is found that the lower part of the Huangtupo landslide shows a higher correlation with the TGR water level changes, while the upper slope mainly responds to rainfall. Importantly, it is found that the lowest common power was observed at P4 since August, 2020, suggesting that the upper part of the slope has been changed to be more rainfall erosion resistant due to the effort of the drainage system and vegetation restoration from the ecological restoration project. [ABSTRACT FROM AUTHOR]

M angrove forests are considered one of the most complex and dynamic ecosystems facing various challenges due to anthropogenic disturbance and climate change. The excessive harvesting and land-use change in areas covered by mangrove ecosystems is critical threats to these forests. Therefore, the continuous and regular monitoring of these forests is essential. Fortunately, remote sensing data has made it possible to regularly and frequently monitor this forest type. This study has two goals. Firstly, it combines optical data of Landsat- 8 and Sentinel-2 with Sentinel-1 radar data to improve land cover mapping accuracy. Secondly, it aims to evaluate the SVM machine learning algorithms and random forest to detection and differentiate forest cover from other land types in the Google Earth Engine system. The results show that the support vector machine (SVM) algorithm in the S2 + S1 dataset with a kappa coefficient of 0.94 performs significantly better than when used in the L8 + S1 combination dataset with a kappa coefficient of 0.88. Conversely, the kappa coefficients of 0.89 and 0.85 were estimated for the random forest algorithm in S2 + S1 and L8 + S1 datasets. This again indicates the superiority of Sentinel-2 and Sentinel-1 datasets over Landsat- 8 and Sentinel-1 datasets. In general, the support vector machine (SVM) algorithm yielded better results than the RF random forest algorithm in optical and radar datasets. The results showed that using the Google Earth engine system and machine learning algorithms accelerates the process of mapping mangrove forests and even change detection. [ABSTRACT FROM AUTHOR]

In this work, we propose a geodetic model for the seismic sequence, with doublet earthquakes, that occurred in Bandar Abbas, Iran, in November 2021. A dataset of Sentinel-1 images, processed using the InSAR (Interferometric Synthetic Aperture Radar) technique, was employed to identify the surface deformation caused by the major events of the sequence and to constrain their geometry and kinematics using seismological constraints. A Coulomb stress transfer analysis was also applied to investigate the sequence's structural evolution in space and time. A linear inversion of the InSAR data provided a non-uniform distribution of slip over the fault planes. We also performed an accurate relocation of foreshocks and aftershocks recorded by locally established seismographs, thereby allowing us to determine the compressional tectonic stress regime affecting the crustal volume. Despite the very short time span of the sequence, our results clearly suggest that distinct blind structures that were previously unknown or only suspected were the causative faults. The first Mw 6.0 earthquake occurred on an NNE-dipping, intermediate-angle, reverse-oblique plane, while the Mw 6.4 earthquake occurred on almost horizontal or very low-angle (SSE-dipping) reverse segments with top-to-the-south kinematics. The former, which cut through and displaced the Pan-African pre-Palaeozoic basement, indicates a thick-skinned tectonic style, while the latter rupture(s), which occurred within the Palaeozoic–Cenozoic sedimentary succession and likely exploited the stratigraphic mechanical discontinuities, clearly depicts a thin-skinned style. [ABSTRACT FROM AUTHOR]

Mangrove forests in Iran are among the complex and productive ecosystems because these types of forests directly and indirectly play a significant role for humans and the environment. This study developed a parallel land cover classification method to identify mangrove forests (mangrove forests) in southern Iran using high-resolution (~ 10 m) optical image of Sentinel-2 satellite and high-resolution (~ 10 m) synthetic aperture radar image that presents ALOS-2 satellite (dipolar). Therefore, in this paper, ALOS-2 bipolar (VV, VH) was used for land cover classification and Sentinel-2 multispectral data as reference data. Generally, GLCM textures in different window sizes were applied to the SAR data, and then all of them were subjected to PCA transformation; finally, the first three components were used as input to the maximum likelihood classification (MLC) algorithm to classify the two mangrove classes. Other lands in addition, the backscatter image was also included separately in the MLC algorithm for land cover classification. The obtained statistical results showed that when the texture with different windows is placed in the input of the ML algorithm, it has a kappa coefficient value of 0.52, and when the input is a single backscattered image, it has a higher kappa coefficient value of about 0.83. In general, the results show that the map prepared by the only backscattered image performs better and similar to the optical image used. In addition, the accumulation of GLCM texture in the dimensions of the windows reduces the accuracy of the mangrove cover map, which as a result causes an exaggerated prominence in the land cover, especially the mangrove. [ABSTRACT FROM AUTHOR]

T he expansion of urbanization and unbalanced urban growth has attracted the attention of many urban planners and decision makers to the issues and consequences of urban population growth. In general, monitoring how urban areas are developed on a large scale is very important in order to plan urban development. However, in most cases, the lack of basic information in this area, especially in developing countries is one of the main obstacles to achieve this. With the development of human civilization and urbanization, the demand for artificial light has increased and this growth will continue. Found. Due to its lack of direct impact on daily life, light pollution has remained largely unknown and has rarely been studied. In this regard, the role of remote sensing techniques and data in identifying changes in the physical development of cities and changes in the amount of light is clearer than other methods. Using VIIRS satellite imagery, other satellite, digital and GIS data can measure and measure the physical growth of cities as well as the spatial and temporal distribution and extent of this type of pollution, and can even manage the risk of this pollution and Reach zoning. High-risk and dangerous areas. In this study, NPP images, travel time layer of Landsat 7 and 8 images have been used, which have been analyzed with the help of remote sensing and GIS techniques. The time frame considered in this study is 2012 to 2020. [ABSTRACT FROM AUTHOR]

Land subsidence is mainly caused by excessive groundwater abstraction from aquifers. This study introduces Dynamic Subsidence Vulnerability Index (DSVI) by estimating possible land subsidence time variations by considering changes in groundwater level based on the ALPRIFT framework in Iran's Hadishahr Plain, which is summarized in three modules. (i) Module I: mapping Subsidence Vulnerability Index (SVI) utilizing the ALPRIFT framework and optimization its weights by the Multiple Artificial Intelligence Models (MAIM) strategy; (ii) Module II: predicting groundwater level by Group Method of Data Handling (GMDH); and Module III: mapping DSVI by combining the results from Modules I and II. A two-pronged strategy is employed in MAIM: In Level 1, multiple models are derived from Sugeno Fuzzy Logic (SFL) and Support Vector Machin (SVM); and in Level 2, the outcomes of Level 1 models are combined by Artificial Neural Networks (ANN). According to the results: (i) ALPRIFT exhibits a correlation coefficient (r) of about 0.55 with corresponding measurements of land subsidence; (ii) using SVM and SFL to optimize the weights, r is raised to 0.83 and 0.74, respectively; (iii) the use of multiple models at Level 2 results in better performance than that of a single model at Level 1; and (iv) on the DSVI map, the central part of the plain is vulnerable at hotspot areas where groundwater is being improperly withdrawn from the Hadishahr Plain aquifer, increasing the risk of subsidence. [ABSTRACT FROM AUTHOR]

In many parts of the world, lake drying is caused by water management failures, while the phenomenon is exacerbated by climate change. Lake Urmia in Northern Iran is drying up at such an alarming rate that it is considered to be a dying lake, which has dire consequences for the whole region. While salinization caused by a dying lake is well understood and known to influence the local and regional food production, other potential impacts by dying lakes are as yet unknown. The food production in the Urmia region is predominantly regional and relies on local water sources. To explore the current and projected impacts of the dying lake on food production, we investigated changes in the climatic conditions, land use, and land degradation for the period 1990–2020. We examined the environmental impacts of lake drought on food production using an integrated scenario-based geoinformation framework. The results show that the lake drought has significantly affected and reduced food production over the past three decades. Based on a combination of cellular automaton and Markov modeling, we project the food production for the next 30 years and predict it will reduce further. The results of this study emphasize the critical environmental impacts of the Urmia Lake drought on food production in the region. We hope that the results will encourage authorities and environmental planners to counteract these issues and take steps to support food production. As our proposed integrated geoinformation approach considers both the extensive impacts of global climate change and the factors associated with dying lakes, we consider it to be suitable to investigate the relationships between environmental degradation and scenario-based food production in other regions with dying lakes around the world. [ABSTRACT FROM AUTHOR]

Monitoring urban land through satellite images has rapidly developed with the advent of modern technologies, and the increasing number of satellites plays a contributing role. While optical images have a high capability in urban monitoring, they still have some limitations, including their dependence on climatic conditions and spectral information, which lead to difficulty in making a distinction between bare land, buildings and other features. The impossibility of optical imagery at night is another issue that can make the land cover classification difficult. Synthetic aperture radar (SAR) allows imaging in all climatic conditions and at nighttime, with an ability to detect phenomena based on their geometry, roughness, and location, making the land cover classification much easier. In the present study, radar Sentinel-1 images with polarization VV and VH were used for the land classification in Tabriz. Sentinel-2 images for the same time were applied as a reference for the calibration and accuracy assessment. Maximum likelihood (ML) and support vector machine (SVM) algorithms were also employed for supervised classification. In both algorithms, the classification was performed in windows with different sizes once by the SAR backscattering coefficient (σ0) and then by combining the backscattering coefficients with the statistical data obtained from the texture. The results showed that the use of radar images only with backscattering intensity resulted in poor performance while using the gray-level co-occurrence matrix (GLCM) of texture features increased the accuracy. The transmitted frequencies of radar images have different redistributions to different phenomena. The numerical results obtained from the radar image classification show that using only the radar image redistribution led to low accuracies at both VV and VH polarization, but the use of the textural analysis significantly increased the accuracy of the classifications. The statistical results obtained from the ML and SVM classifications for radar images at VV and VH polarization indicated that the latter performed better than the former. When texture analysis was not used in the classes, the classification accuracy was low with kappa values of 0.37 and 0.42 for VV and VH polarization, respectively. The use of texture analysis and obtaining the optimum window size is increase the classification accuracy with a better performance for VH polarization. The SVM classification method with a kappa coefficient of 0.72% showed better performance than the ML one with a kappa coefficient of 0.61%. Conclusively, in the absence of Sentinel-2 datasets, Sentinel-1 images are good alternatives if the preserved texture information is available for the land cover classification. Results of this research are of great importance for developing the remote sensing methods and their techniques can be considered as progressive research in the domain of remote sensing sciences. [ABSTRACT FROM AUTHOR]

The evaluation of buildings damage following disasters from natural hazards is a crucial step in determining the extent of the damage and measuring renovation needs. In this study, a combination of the synthetic aperture radar (SAR) and light detection and ranging (LIDAR) data before and after the earthquake were used to assess the damage to buildings caused by the Kumamoto earthquake. For damage assessment, three variables including elevation difference (ELD) and texture difference (TD) in pre- and post-event LIDAR images and coherence difference (CD) in SAR images before and after the event were considered and their results were extracted. Machine learning algorithms including random forest (RDF) and the support vector machine (SVM) were used to classify and predict the rate of damage. The results showed that ELD parameter played a key role in identifying the damaged buildings. The SVM algorithm using the ELD parameter and considering three damage rates, including D0 and D1 (Negligible to slight damages), D2, D3 and D4 (Moderate to Heavy damages) and D5 and D6 (Collapsed buildings) provided an overall accuracy of about 87.1%. In addition, for four damage rates, the overall accuracy was about 78.1%. [ABSTRACT FROM AUTHOR]

Flooding is one of the most damaging natural hazards globally. During the past three years, floods have claimed hundreds of lives and millions of dollars of damage in Iran. In this study, we detected flood locations and mapped areas susceptible to floods using time series satellite data analysis as well as a new model of bagging ensemble-based alternating decision trees, namely, bag-ADTree. We used Sentinel-1 data for flood detection and time series analysis. We employed twelve conditioning parameters of elevation, normalized difference's vegetation index, slope, topographic wetness index, aspect, curvature, stream power index, lithology, drainage density, proximities to river, soil type, and rainfall for mapping areas susceptible to floods. ADTree and bag-ADTree models were used for flood susceptibility mapping. We used software of Sentinel application platform, Waikato Environment for Knowledge Analysis, ArcGIS, and Statistical Package for the Social Sciences for preprocessing, processing, and postprocessing of the data. We extracted 199 locations as flooded areas, which were tested using a global positioning system to ensure that flooded areas were detected correctly. Root mean square error, accuracy, and the area under the ROC curve were used to validate the models. Findings showed that root mean square error was 0.31 and 0.3 for ADTree and bag-ADTree techniques, respectively. More findings illustrated that accuracy was obtained as 86.61 for bag-ADTree model, while it was 85.44 for ADTree method. Based on AUC, success and prediction rates were 0.736 and 0.786 for bag-ADTree algorithm, in order, while these proportions were 0.714 and 0.784 for ADTree. This study can be a good source of information for crisis management in the study area. [ABSTRACT FROM AUTHOR]

The synthetic aperture radar SAR system with the capability of imaging during the night, day, and the all-weather conditions has a high potential in change detection on the ground surface. In this research, we used three SAR images of ALOS-2 satellite over Sarpole-Zahab town in the west of Iran that had an earthquake with the magnitude of 7.3 on November 12, 2017. The effects of speckle noise on the accuracy of the results were assessed based on noise reduction filters. Correlation coefficient, difference of intensity (in five window sizes), and difference of coherence and texture (in six window sizes) of the pre- and post-event images were calculated, and the output parameters were extracted. Then, the damage assessment was carried out based on four machine learning classifiers, containing the random forest (RDF), the support vector machine, the naive Bayes classifier, and K-nearest neighbor. The RDF showed an overall accuracy of 86.3%. Seventy percent of the dataset was used for training, and 30% of it was used for the prediction purpose (~ 300 buildings). Based on the training dataset, the total number of structures in the study area was predicted (approximately 9200 buildings). Finally, a discriminant analysis was carried out among the damaged and undamaged buildings. [ABSTRACT FROM AUTHOR]

The August 24, 2016, M 6.2 Amatrice earthquake in central Italy was one of the most destructive earthquakes in recent years, and it caused considerable human losses. The goal of this study is to distinguish between collapsed and standing buildings in the town of Amatrice using synthetic aperture radar (SAR) data acquired from the COSMO-SkyMed satellite. Data from both ascending and descending orbits in single HH polarization were evaluated separately. Two main representative change indices, SAR intensity (SI) and SAR coherence, were combined using multitemporal discriminant and fuzzy analyses. To enhance the contribution of SI in the discriminant analysis, the aggregate analysis, in a raster-based environment for each intensity dataset, was taken into account. By integrating the ascending and descending datasets, two models based on discriminant analysis and fuzzy logic were created, and different damage proxy maps (DPM) for the region were presented. The accuracy comparison of single-path discriminant analysis and the new integrated models indicates improvement, which implies that the combination of west-to-east and east-to-west observations can reduce some limitations of SAR imagery (e.g., shapes of roofs, shadows) on building classification. In addition, topographic position index (TPI) analysis was conducted as a spatial-based scale-dependent approach, and it revealed more aspects of possible damage in the town. The results of DPM and TPI indicate that 55% of the collapsed buildings are located in valleys, which may have caused seismic wave amplification in this region. [ABSTRACT FROM AUTHOR]

Quantitative understanding of stress transfer between major fault systems can elucidate the kinematics of large-scale plate interactions. This study analyzed right-lateral strike-slip motion on the North Tabriz fault (NTF) in an area where this structure appears to transition into a thrust fault known as the North Mishu fault (NMF). These faults play an important but cryptic role in accommodating stress related to the Arabia-Eurasia plate collision. We analyzed regional velocity vectors from permanent and temporary GPS arrays to estimate changes in fault-parallel and fault-normal slip rates in the transition zone between the NTF and NMF. Independent of its compressional motion, the NMF exhibits a dextral strike-slip rate of ∼2.62 mm/yr. Along the NTF, the right-lateral slip rate decreases and the vertical slip rate on increases at rates of 0.08 and 0.38 mm/yr km, respectively, as the NTF approaches the NMF. This study also used small baseline (SBAS) PS-InSAR results to reveal a NE-SW-striking reverse fault and a developing syncline hidden beneath the Tabriz Basin. Additionally, we calculated the vertical displacement rates using horizontal vectors from the GPS data and mean line-of-sight rate estimates from the SBAS data. While the study area does not express large-scale extrusion, such as that observed in the Anatolian Plate, the transformation of strike-slip motion into thrusting and crustal shortening along the NMF-NTF fault zone accommodates most of the N–S compression affecting the northwestern Iranian Plateau. In this region, small-sized, right-lateral strike-slip faults, and other folded structures form horsetail features. These dispersed structures accommodate eastward extrusion of the northwestern Iranian Plateau. [ABSTRACT FROM PUBLISHER]

Iran as a semi-arid and arid country has a serious water challenge in the recent decades. While water demand is increasing, as a consequence, land subsidence due to excessive water extraction is happening in major basins of the country. Recently the land subsidence has been proposed as an environmental problem in the country and thus important projects on this matter have been conducted. For example several basins in the country have been studied using SAR data and their result had a great deal on water management section. This paper studies the large-scale immature land subsidence in the Tabriz basin (NW Iran) using the permanent scatterer synthetic aperture radar interferometry technique with the small baseline InSAR approach and watershed analysis. InSAR time series analysis of 17 Envisat advanced SAR images between 2003 and 2010 reveals three oval-shaped regions of subsidence. The recognized water wells in the study area are categorized into two groups (sub-basin 1 and 2) based on watershed analysis in the TB and are compared with InSAR results. InSAR time series from Kriging interpolation method are also compared with GPS time series of permanent TABZ station which suggests that the land subsidence only is growing in the basin far away from the Tabriz city. [ABSTRACT FROM AUTHOR]

We present topographic slope positions of seismic stations within four independent networks (IGUT, IIEES, GSI, and BHRC) in Iran through integrated use of digital elevation models and GIS. Since topographic amplification factor (TAF) due to ground surface irregularity could be one of the reasons of earthquake wave amplification and unexpected damage of structures located on the top of ridges in many previous studies, the ridge stations in the study area are recognized using topographic position index (TPI) as a spatial-based scale-dependent approach that helps in classification of topographic positions. We also present the correlation between local topographic positions and V along with Voronoi tiles of two networks (IGUT and IIEES). The obtained results can be profitably used in seismology to establish homogeneous subnetworks based on Voronoi tiles with precise feedback and in the formulation of new ground motion prediction equations with respect to topographic position and topographic amplification factor. [ABSTRACT FROM AUTHOR]

Burned area (BA) mapping of a forest after a fire is required for its management and the determination of the impacts on ecosystems. Different remote sensing sensors and their combinations have been used due to their individual limitations for accurate BA mapping. This study analyzes the contribution of different features derived from optical, thermal, and Synthetic Aperture Radar (SAR) images to extract BA information from the Turkish red pine (Pinus brutia Ten.) forest in a Mediterranean ecosystem. In addition to reflectance values of the optical images, Normalized Burn Ratio (NBR) and Land Surface Temperature (LST) data are produced from both Sentinel-2 and Landsat-8 data. The backscatter of C-band Sentinel-1 and L-band ALOS-2 SAR images and the coherence feature derived from the Interferometric SAR technique were also used. The pixel-based random forest image classification method is applied to classify the BA detection in 24 scenarios created using these features. The results show that the L-band data provided a better contribution than C-band data and the combination of features created from Landsat LST, NBR, and coherence of L-band ALOS-2 achieved the highest accuracy, with an overall accuracy of 96% and a Kappa coefficient of 92.62%. [ABSTRACT FROM AUTHOR]

This study aimed to classify an urban area and its surrounding objects after the destructive M7.3 Kermanshah earthquake (12 November 2017) in the west of Iran using very high-resolution (VHR) post-event WorldView-2 images and object-based image analysis (OBIA) methods. The spatial resolution of multispectral (MS) bands (~2 m) was first improved using a pan-sharpening technique that provides a solution by fusing the information of the panchromatic (PAN) and MS bands to generate pan-sharpened images with a spatial resolution of about 50 cm. After applying a segmentation procedure, the classification step was considered as the main process of extracting the aimed features. The aforementioned classification method includes applying spectral and shape indices. Then, the classes were defined as follows: type 1 (settlement area) was collapsed areas, non-collapsed areas, and camps; type 2 (vegetation area) was orchards, cultivated areas, and urban green spaces; and type 3 (miscellaneous area) was rocks, rivers, and bare lands. As OBIA results in the integration of the spatial characteristics of the image object, we also aimed to evaluate the efficiency of object-based features for damage assessment within the semi-automated approach. For this goal, image context assessment algorithms (e.g., textural parameters, shape, and compactness) together with spectral information (e.g., brightness and standard deviation) were applied within the integrated approach. The classification results were satisfactory when compared with the reference map for collapsed buildings provided by UNITAR (the United Nations Institute for Training and Research). In addition, the number of temporary camps was counted after applying OBIA, indicating that 10,249 tents or temporary shelters were established for homeless people up to 17 November 2018. Based on the total damaged population, the essential resources such as emergency equipment, canned food and water bottles can be estimated. The research makes a significant contribution to the development of remote sensing science by means of applying different object-based image-analyzing techniques and evaluating their efficiency within the semi-automated approach, which, accordingly, supports the efficient application of these methods to other worldwide case studies. [ABSTRACT FROM AUTHOR]

Tabriz is located in the northwest of Iran. Two huge earthquakes with magnitudes of 7.4 and 7.3 occurred there in 1780 and 1721. These earthquakes caused considerable damage and casualties in Tabriz. Using the method of scenario building, we aim to investigate what would happen if such earthquakes occurred in 2021. This scenario building was carried out using deterministic and GIS-oriented techniques to find the levels of damage and casualties that would occur. This procedure included two steps. In the first step, a database of factors affecting the destructive power of earthquakes was prepared. In the next step, hierarchical analysis was used to weigh the data, and then the weighted data were combined with an earthquake intensity map. The obtained results were used to predict the earthquake intensity in Tabriz. According to our results, the earthquake with a magnitude of 7.3 that occurred in 1721 caused huge destruction in the north of Tabriz, as this earthquake occurred inside the site. However, this earthquake caused minimal damage to the south of the city owing to the geological situation of this area of Tabriz. The earthquake with a magnitude of 7.3 that occurred in 1780 caused less damage because of its distance from the site. In the third step of this analysis, the vulnerability of buildings and the population were examined. According to the estimates, District 4 would experience the highest damage rate in the earthquake of 1721, with 15,477 buildings destroyed, while this area would have a lower damage rate in the earthquake that occurred in 1780. The total casualties in Tabriz would number 152,092 and 505 people in the earthquakes of 1721 and 1780, respectively. [ABSTRACT FROM AUTHOR]

On 29 December 2020, an earthquake with a magnitude of M 6.4 hit the central part of Croatia. The earthquake resulted in casualties and damaged buildings in the town of Petrinja (~6 km away from the epicenter) and surrounding areas. This study aims to characterize ground displacement and to estimate the location of damaged areas following the Petrinja earthquake using six synthetic aperture radar (SAR) images (C-band) acquired from both ascending and descending orbits of the Sentinel-1 mission. Phase information from both the ascending (Sentinel-1A) and descending (Sentinel-1B) datasets, acquired from SAR interferometry (InSAR), is used for estimation of ground displacement. For damage mapping, we use histogram information along with the RGB method to visualize the affected areas. In sparsely damaged areas, we also propose a method based on multivariate alteration detection (MAD) and naive Bayes (NB), in which pre-seismic and co-seismic coherence maps and geocoded intensity maps are the main independent variables, together with elevation and displacement maps. For training, approximately 70% of the data are employed and the rest of the data are used for validation. The results show that, despite the limitations of C-band SAR images in densely vegetated areas, the overall accuracy of MAD+NB is ~68% compared with the results from the Copernicus Emergency Management Service (CEMS). [ABSTRACT FROM AUTHOR]

In this study, we measured the in situ international roughness index (IRI) for first-degree roads spanning more than 1300 km in East Azerbaijan Province, Iran, using a quarter car (QC). Since road quality mapping with in situ measurements is a costly and time-consuming task, we also developed new equations for constructing a road quality proxy map (RQPM) using discriminant analysis and multispectral information from high-resolution Sentinel-2 images, which we calibrated using the in situ data on the basis of geographic information system (GIS) data. The developed equations using optimum index factor (OIF) and norm R provide a valuable tool for creating proxy maps and mitigating hazards at the network scale, not only for primary roads but also for secondary roads, and for reducing the costs of road quality monitoring. The overall accuracy and kappa coefficient of the norm R equation for road classification in East Azerbaijan province are 65.0% and 0.59, respectively. [ABSTRACT FROM AUTHOR]

Earth, as humans' habitat, is constantly affected by natural events, such as floods, earthquakes, thunder, and drought among which earthquakes are considered one of the deadliest and most catastrophic natural disasters. The Iran-Iraq earthquake occurred in Kermanshah Province, Iran in November 2017. It was a 7.4-magnitude seismic event that caused immense damages and loss of life. The rapid detection of damages caused by earthquakes is of great importance for disaster management. Thanks to their wide coverage, high resolution, and low cost, remote-sensing images play an important role in environmental monitoring. This study presents a new damage detection method at the unsupervised level, using multitemporal optical and radar images acquired through Sentinel imagery. The proposed method is applied in two main phases: (1) automatic built-up extraction using spectral indices and active learning framework on Sentinel-2 imagery; (2) damage detection based on the multitemporal coherence map clustering and similarity measure analysis using Sentinel-1 imagery. The main advantage of the proposed method is that it is an unsupervised method with simple usage, a low computing burden, and using medium spatial resolution imagery that has good temporal resolution and is operative at any time and in any atmospheric conditions, with high accuracy for detecting deformations in buildings. The accuracy analysis of the proposed method found it visually and numerically comparable to other state-of-the-art methods for built-up area detection. The proposed method is capable of detecting built-up areas with an accuracy of more than 96% and a kappa of about 0.89 in overall comparison to other methods. Furthermore, the proposed method is also able to detect damaged regions compared to other state-of-the-art damage detection methods with an accuracy of more than 70%. [ABSTRACT FROM AUTHOR]

Digital elevation model (DEM) plays a vital role in hydrological modelling and environmental studies. Many essential layers can be extracted from this land surface information, including slope, aspect, rivers, and curvature. Therefore, DEM quality and accuracy will affect the extracted features and the whole process of modeling. Despite freely available DEMs from various sources, many researchers generate this information for their areas from various observations. Sentinal-1 synthetic aperture radar (SAR) images are among the best Earth observations for DEM generation thanks to their availabilities, high-resolution, and C-band sensitivity to surface structure. This paper presents a comparative study, from a hydrological point of view, on the quality and reliability of the DEMs generated from Sentinel-1 data and DEMs from other sources such as AIRSAR, ALOS-PALSAR, TanDEM-X, and SRTM. To this end, pair of Sentinel-1 data were acquired and processed using the SAR interferometry technique to produce a DEM for two different study areas of a part of the Cameron Highlands, Pahang, Malaysia, a part of Sanandaj, Iran. Based on the estimated linear regression and standard errors, generating DEM from Sentinel-1 did not yield promising results. The river streams for all DEMs were extracted using geospatial analysis tool in a geographic information system (GIS) environment. The results indicated that because of the higher spatial resolution (compared to SRTM and TanDEM-X), more stream orders were delineated from AIRSAR and Sentinel-1 DEMs. Due to the shorter perpendicular baseline, the phase decorrelation in the created DEM resulted in a lot of noise. At the same time, results from ground control points (GCPs) showed that the created DEM from Sentinel-1 is not promising. Therefore, other DEMs' performance, such as 90-meters' TanDEM-X and 30-meters' SRTM, are better than Sentinel-1 DEM (with a better spatial resolution). [ABSTRACT FROM AUTHOR]

Exact land cover inventory data should be extracted for future landscape prediction and seismic hazard assessment. This paper presents a comprehensive study towards the sustainable development of Tabriz City (NW Iran) including land cover change detection, future potential landscape, seismic hazard assessment and municipal performance evaluation. Landsat data using maximum likelihood (ML) and Markov chain algorithms were used to evaluate changes in land cover in the study area. The urbanization pattern taking place in the city was also studied via synthetic aperture radar (SAR) data of Sentinel-1 ground range detected (GRD) and single look complex (SLC). The age of buildings was extracted by using built-up areas of all classified maps. The logistic regression (LR) model was used for creating a seismic hazard assessment map. From the results, it can be concluded that the land cover (especially built-up areas) has seen considerable changes from 1989 to 2020. The overall accuracy (OA) values of the produced maps for the years 1989, 2005, 2011 and 2020 are 96%, 96%, 93% and 94%, respectively. The future potential landscape of the city showed that the land cover prediction by using the Markov chain model provided a promising finding. Four images of 1989, 2005, 2011 and 2020, were employed for built-up areas' land information trends, from which it was indicated that most of the built-up areas had been constructed before 2011. The seismic hazard assessment map indicated that municipal zones of 1 and 9 were the least susceptible areas to an earthquake; conversely, municipal zones of 4, 6, 7 and 8 were located in the most susceptible regions to an earthquake in the future. More findings showed that municipal zones 1 and 4 demonstrated the best and worst performance among all zones, respectively. [ABSTRACT FROM AUTHOR]

Iran, as a semi-arid and arid country, has a water challenge in the recent decades and underground water extraction has been increased because of improper developments in the agricultural sector. Thus, detection and measurement of ground subsidence in major plains is of great importance for hazard mitigation purposes. In this study, we carried out a time series small baseline subset (SBAS) interferometric synthetic aperture radar (InSAR) analysis of 15 L-band PALSAR-2 images acquired from ascending orbits of the ALOS-2 satellite between 2015 and 2020 to investigate long-term ground displacements in East Azerbaijan Province, Iran. We found that two major parts of the study area (Tabriz and Shabestar plains) are subsiding, where the mean and maximum vertical subsidence rates are −10 and −98 mm/year, respectively. The results revealed that the visible subsidence patterns in the study area are associated with either anthropogenic activities (e.g., underground water usage) or presence of compressible soils along the Tabriz–Shabestar and Tabriz–Azarshahr railways. This implies that infrastructure such as railways and roads is vulnerable if progressive ground subsidence takes over the whole area. The SBAS results deduced from L-band PALSAR-2 data were validated with field observations and compared with C-band Sentinel-1 results for the same period. The C-band Sentinel-1 results showed good agreement with the L-band PALSAR-2 dataset, in which the mean and maximum vertical subsidence rates are −13 and −120 mm/year, respectively. For better visualization of the results, the SBAS InSAR velocity map was down-sampled and principal component analysis (PCA) was performed on ~3600 randomly selected time series of the study area, and the results are presented by two principal components (PC1 and PC2). [ABSTRACT FROM AUTHOR]

In this study, we monitor pavement and land subsidence in Tabriz city in NW Iran using X-band synthetic aperture radar (SAR) sensor of Cosmo-SkyMed (CSK) satellites (2017–2018). Fifteen CSK images with a revisit interval of ~30 days have been used. Because of traffic jams, usually cars on streets do not allow pure backscattering measurements of pavements. Thus, the major paved areas (e.g., streets, etc.) of the city are extracted from a minimum-based stacking model of high resolution (HR) SAR images. The technique can be used profitably to reduce the negative impacts of the presence of traffic jams and estimate the possible quality of pavement in the HR SAR images in which the results can be compared by in-situ road roughness measurements. In addition, a time series small baseline subset (SBAS) interferometric SAR (InSAR) analysis is applied for the acquired HR CSK images. The SBAS InSAR results show land subsidence in some parts of the city. The mean rate of line-of-sight (LOS) subsidence is 20 mm/year in district two of the city, which was confirmed by field surveying and mean vertical velocity of Sentinel-1 dataset. The SBAS InSAR results also show that 1.4 km2 of buildings and 65 km of pavement are at an immediate risk of land subsidence. [ABSTRACT FROM AUTHOR]

We used remote sensing techniques and machine learning to detect and map landslides, and landslide susceptibility in the Cameron Highlands, Malaysia. We located 152 landslides using a combination of interferometry synthetic aperture radar (InSAR), Google Earth (GE), and field surveys. Of the total slide locations, 80% (122 landslides) were utilized for training the selected algorithms, and the remaining 20% (30 landslides) were applied for validation purposes. We employed 17 conditioning factors, including slope angle, aspect, elevation, curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), lithology, soil type, land cover, normalized difference vegetation index (NDVI), distance to river, distance to fault, distance to road, river density, fault density, and road density, which were produced from satellite imageries, geological map, soil maps, and a digital elevation model (DEM). We used these factors to produce landslide susceptibility maps using logistic regression (LR), logistic model tree (LMT), and random forest (RF) models. To assess prediction accuracy of the models we employed the following statistical measures: negative predictive value (NPV), sensitivity, positive predictive value (PPV), specificity, root-mean-squared error (RMSE), accuracy, and area under the receiver operating characteristic (ROC) curve (AUC). Our results indicated that the AUC was 92%, 90%, and 88% for the LMT, LR, and RF algorithms, respectively. To assess model performance, we also applied non-parametric statistical tests of Friedman and Wilcoxon, where the results revealed that there were no practical differences among the used models in the study area. While landslide mapping in tropical environment such as Cameron Highlands remains difficult, the remote sensing (RS) along with machine learning techniques, such as the LMT model, show promise for landslide susceptibility mapping in the study area. [ABSTRACT FROM AUTHOR]

Different methods have been proposed to create seismic site condition maps. Ground-based methods are time-consuming in many places and require a lot of manual work. One method suggests topographic data as a proxy for seismic site condition of large areas. In this study, we mainly focused on the use of an ASTER 1c digital elevation model (DEM) to produce Vs30 maps throughout Iran using a GIS-based regression analysis of Vs30 measurements at 514 seismic stations. These maps were found to be comparable with those that were previously created from SRTM 30c data. The Vs30 results from ASTER 1c estimated the higher velocities better than those from SRTM 30c. In addition, a combination of ASTER 1c and SRTM 30c amplification maps can be useful for the detection of geological and geomorphological units. We also classified the terrain surface of six seismotectonic regions in Iran into 16 classes, considering three important criteria (slope, convexity and texture) to extract more information about the location and morphological characteristics of the stations. The results show that 98% of the stations are situated in six classes, 30% of which are in class 12, 27% in class 6, 17% in class 9, 16% in class 3, 4% in class 3and the rest of the stations are located in other classes. [ABSTRACT FROM AUTHOR]

Small earthquakes following a large event in the same area are typically aftershocks, which are usually less destructive than mainshocks. These aftershocks are considered mainshocks if they are larger than the previous mainshock. In this study, records of aftershocks (M > 2.5) of the Kermanshah Earthquake (M 7.3) in Iran were collected from the first second following the event to the end of September 2018. Different machine learning (ML) algorithms, including naive Bayes, k-nearest neighbors, a support vector machine, and random forests were used in conjunction with the slip distribution, Coulomb stress change on the source fault (deduced from synthetic aperture radar imagery), and orientations of neighboring active faults to predict the aftershock patterns. Seventy percent of the aftershocks were used for training based on a binary ("yes" or "no") logic to predict locations of all aftershocks. While untested on independent datasets, receiver operating characteristic results of the same dataset indicate ML methods outperform routine Coulomb maps regarding the spatial prediction of aftershock patterns, especially when details of neighboring active faults are available. Logistic regression results, however, do not show significant differences with ML methods, as hidden information is likely better discovered using logistic regression analysis. [ABSTRACT FROM AUTHOR]

We performed interferometric synthetic aperture radar (InSAR) analyses to observe ground displacements and assess damage after the M 6.6 Hokkaido Eastern Iburi earthquake in northern Japan on 6 September 2018. A multitemporal SAR coherence map is extracted from 3-m resolution ascending (track 116) and descending (track 18) ALOS-2 Stripmap datasets to cover the entire affected area. To distinguish damaged buildings associated with liquefaction, three influential parameters from the space-based InSAR results, ground-based LiquickMap (from seismic intensities in Japanese networks) and topographic slope of the study area are considered together in a weighted overlay (WO) analysis, according to prior knowledge of the study area. The WO analysis results in liquefaction potential values that agree with our field survey results. To investigate further, we conducted microtremor measurements at 14 points in Hobetsu, in which the predominant frequency showed a negative correlation with the WO values, especially when drastic coherence decay occurred. [ABSTRACT FROM AUTHOR]

In this study, we used fifty-six synthetic aperture radar (SAR) images acquired from the Sentinel-1 C-band satellite with a regular period of 12 days (except for one image) to produce sequential phase correlation (sequential coherence) maps for the town of Sarpole-Zahab in western Iran, which experienced a magnitude 7.3 earthquake on 12 November 2017. The preseismic condition of the buildings in the town was assessed based on a long sequential SAR coherence (LSSC) method, in which we considered 55 of the 56 images to produce a coherence decay model with climatic and temporal parameters. The coseismic condition of the buildings was assessed with 3 later images and normalized RGB visualization using the short sequential SAR coherence (SSSC) method. Discriminant analysis between the completely collapsed and uncollapsed buildings was also performed for approximately 700 randomly selected buildings (for each category) by considering the heights of the buildings and the SSSC results. Finally, the area and volume of debris were calculated based on a fusion of a discriminant map and a 3D vector map of the town. [ABSTRACT FROM AUTHOR]