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Surface ruptures and deformation of large earthquakes are important for investigating earthquake mechanisms, fault activities and continental deformation. With the improvement of satellite techniques, op‐ tical and radar images have been widely used in earthquake studies since the 1992 Landers earthquake. However, due to a lack of pre ‐earthquake images, historical earthquakes prior to the 1990s are rarely studied. Recent declassification of American KeyHole (KH) satellite images opened up new possibilities of investi‐ gating old earthquakes back to the 1970s. Researchers have successfully applied KH ‐9 images to the 1978 Tabas‐e‐Golshan and 1979 Khuli‐Boniabad earthquakes in Iran, and gained some new insights into fault be‐ haviours. We first provided a review of the methodology and progresses of using KH ‐9 images to measure earthquake deformation, then investigated the 1976 Chaldiran, Turkey earthquake by matching the pre ‐ and post‐earthquake KH ‐9 images,and obtained an E ‐W displacement of about (3.1±0.7) m (i.e. strike‐slip), consistent with the measurements in the field. KH ‐9 imagery provides a new means of investigating histori‐ cal earthquakes in detail, but there are some limitations. These limitations are briefly discussed in the end. [ABSTRACT FROM AUTHOR]

Due to excessive harvesting of underground water resources in many important aquifers inside Iran, ground subsidence is occurring at different speeds. In present study, InSAR processing was applied to identify land displacement by using Sentinel-1A. For this target, 17 frames of images during 2015–2019 with small temporal-perpendicular baseline were allocated and using SBAS approach were analyzed. After removing unnecessary noise and phases, phase shift due to land deformation is extracted and then converted to subsidence. Besides SAR images, fluctuations of groundwater were analyzed using piezometric data for last 10 years. Then the spatial pattern relationship between subsided regions and underground water resources as well as urbanization was investigated. Throughout this period, all datasets reveal 125 km2 bowl subsidence in a maximum rate of 10 cm/year. Generally, areas with high rates of subsidence are located between the industrial town and city of Semnan over clay foundation with high rates of groundwater head decline. Moreover, surveying the piezometers and landuse change map obtained from Landsat indicates that due to the intense groundwater withdrawal as a result of industrialization and urbanization, the maximum annual decline of groundwater head at 123 cm/year is detected. Our preliminary investigation shows some spatiotemporal positive correlation between the subsidence and seismicity of the area. Considering the fragility of arid ecosystems and increasing the population of Semnan, it is recommended that the development of industries with high water consumption is prevented and the groundwater resources policies should seek to strictly reduce overusing of groundwater in agricultural lands and urban areas. [ABSTRACT FROM AUTHOR]

Land subsidence susceptibility mapping is a necessary prerequisite for land subsidence management. Proper assessment of land subsidence requires determining parameters affecting subsidence to discover the spatial relationships with subsidence. For this purpose, two frameworks were prepared, according to the land site subsidence, eight parameters affecting the subsidence in the Damaneh-Daran plain. In the first framework, eight parameters including annual groundwater level drawdown, aquifer medium, land use, discharge, aquifer saturation thickness, net recharge, fault proximity and topography called PWRSTFAL based on weighting parameters and the second model based on optimization and compatibility of eight active layers on subsidence, were prepared by AHP-fuzzy method and the final map of subsidence vulnerability obtained. Radar images and the InSAR method validated the methods. The results show that both maps show a good correlation with radar data. Still, the map prepared by the AHP-fuzzy method offers the highest correlation with radar data and subsidence in the plain. It separates subsidence vulnerability in more detail on the whole surface of the plain. According to this model, most plain areas, especially the eastern part, are subject to subsidence, and management programs should control subsidence. The results ROC diagram obtained from the performance of the PWRSTFAL framework based on the AHP-fuzzy method was more promising than the ordinary PWRSTFAL framework., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Estimation of forest height is an important parameter of stands structure that aids in the determination of forest biomass, successional stage dynamics, and the decision of the type of forest management. In addition, estimating the height of trees especially in uneven-aged, massive, and multi-storied forest stands always faces challenges in kind of inventory and accuracy of the assessment. In this research, the synthetic aperture radar (SAR) interferometry technique was used to estimate the height of trees for determining the vertical structure of forest. For this purpose, we focused on an area at the mixed and uneven-aged forest in Iran and evaluated the potential of Envisat ASAR data to characterize the tree height in the forest patches and the digital surface model (DSM) was produced via SAR interferometry. The height of trees and the vertical structure of the forest stands were estimated using produced DSM and Digital elevation Model (DEM). Furthermore, the accuracy of estimated parameters was evaluated with real ground data (11 × 1 ha (100 × 100 m) sample plots). The results indicated that the estimated height of trees was meanly 7.69 m with a 22 m STDV over the reality. Furthermore, the vertical structure in all the plots was three-storied that they are the same as ground truth, but the percentage of the share of trees in the under and middle story was different from the ground truth. In conclusion, the tree height and vertical structure of forest stands can be determined with acceptable accuracy via SAR interferometry and Envisat ASAR data.

A framework for estimating aboveground forest carbon stock (AFCS) is required for measurement, reporting, and verification (MRV) systems under the United Nations Collaborative Program on Reducing Emissions from Deforestation and Forest Degradation (REDD) in Developing Countries. Recently, methods for estimating the spatial distribution of AFCS using remotely sensed datasets and multiple prediction techniques have been found to be useful, particularly given the prohibitive costs of acquiring the necessary sample sizes for sufficiently precise pure field-based estimation of this variable. The objective of the study was to assess and compare the capabilities of airborne laser scanning (ALS) data, L-band radar data and UltraCam images in combination with four commonly used prediction techniques for estimating mean AFCS per unit area: multiple linear regression (MLR) and the nonparametric k-Nearest Neighbors (k-NN), support vector regression (SVR), and random forest (RF) algorithms. Our study area was a part of Hyrcanian deciduous forests in two managed and unmanaged stands at Shast Kalateh forest. We used a systematic sample consisting of 308 circular field plots of 0.1 ha located at the intersections of a 150 × 200 m grid with a random starting point and remote sensing-derived metrics as auxiliary data. We used 67% of the sample plots for training purposes and the remaining 33% for validation. Also, we used the model-assisted estimators to statistically rigorously estimate mean AFCS per unit area and its standard error (SE). Among the remotely sensed datasets, considered singly, the ALS data with R2⁎ = 0.34, rRMSE = 47.42% and relative efficiency (RE) = 1.51 produced the greatest accuracy and precision for AFCS estimation. RE can be interpreted as the factor by which the sample size for the pure field-based estimator would have to be increased to obtain the same precision as for the model-assisted estimator using auxiliary data. Among the remotely sensed datasets considered in combination, the ALS and PALSAR dataset with R2⁎ = 0.41, rRMSE = 44.80% and RE = 1.70 produced the greatest prediction accuracy and precision and increased the proportion of variability explained relative to ALS and PALSAR separately by 7% and 36%, respectively. Contrary to our expectation, the combination of PALSAR and UltraCam data decreased the precision of AFCS estimates. All the remotely sensed datasets, singly and in combination, with the most accurate prediction techniques for each combination produced estimates of AFCS that were within the 95% confidence interval for the field-based estimate. • ALS produced greater accuracy than PALSAR and UltraCam for AFCS estimation. • ALS and PALSAR in combination produced the greatest accuracy for AFCS estimation. • Nonparametric k-NN, SVR and RF were more accurate than multiple linear regression. • Remote sensing-assisted 95% confidence intervals included the field-based estimate. [ABSTRACT FROM AUTHOR]

The dynamic impact factor (IM) is a widely accepted parameter to account for the effect of vehicles on bridges. An accurate evaluation of this IM is of paramount importance in bridge engineering, both for designing safe and economical new bridges, and for the assessment of the existing ones. At the state of the art, the current procedure for the experimental assessment of the IM of a bridge relies upon the deployment of a sensor network. The aim of this article is to propose the use of a remote sensor, the interferometric radar, for assessing the IM without the deployment of any sensor on the bridge, with evident advantages in terms of cost, time and safety of the workers. Two different case studies of bridges in Northern Iran are reported. In both cases the interferometric radar has been demonstrated an effective and reliable measurement equipment for this kind of in-field assessment. [ABSTRACT FROM AUTHOR]

This study presents a new fusion method namely supervised cross-fusion method to improve the capability of fused thermal, radar, and optical images for classification. The proposed cross-fusion method is a combination of pixel-based and supervised feature-based fusion of thermal, radar, and optical data. The pixel-based fusion was applied to fuse optical data of Sentinel-2 and Landsat 8. According to correlation coefficient (CR) and signal to noise ratio (SNR), among the used pixel-based fusion methods, wavelet obtained the best results for fusion. Considering spectral and spatial information preservation, CR of the wavelet method is 0.97 and 0.96, respectively. The supervised feature-based fusion method is a fusion of best output of pixel-based fusion level, land surface temperature (LST) data, and Sentinel-1 radar image using a supervised approach. The supervised approach is a supervised feature selection and learning of the inputs based on linear discriminant analysis and sparse regularization (LDASR) algorithm. In the present study, the non-negative matrix factorization (NMF) was utilized for feature extraction. A comparison of the obtained results with state of the art fusion method indicated a higher accuracy of our proposed method of classification. The rotation forest (RoF) classification results improvement was 25% and the support vector machine (SVM) results improvement was 31%. The results showed that the proposed method is well classified and separated four main classes of settlements, barren land, river, river bank, and even the bridges over the river. Also, a number of unclassified pixels by SVM are very low compared to other classification methods and can be neglected. The study results showed that LST calculated using thermal data has had positive effects on improving the classification results. By comparing the results of supervised cross-fusion without using LST data to the proposed method results, SVM and RoF classifiers showed 38% and 7% of classification improvement, respectively.