Recent reports from Isfahan, Iran, have announced the possibility of potential land subsidence due to the persistence of drought, interbasin water transfer, and groundwater overexploitation. By this token, we seek to employ the persistent scatterer interferometric synthetic aperture radar technique to detect and measure the land subsidence phenomenon in the Isfahan metropolitan area. The Sentinel interferometric synthetic aperture radar dataset was used for detecting land subsidence phenomena. The corresponding data pairs were acquired and analyzed for the period from 2014 to 2019. Our prime objective is to map the spatial and temporal variations in surface deformation in Isfahan using the multitemporal persistent scatterer interferometric synthetic aperture radar (MT-PSI) technique. Results indicate that Isfahan had been undergoing subsidence throughout the observation period of 2014 to 2019 at an estimated rate of −5 to −100 mm / year. Subsidence has increased from south to north of metropolitan areas from 2014 to 2019. The MT-PSI time series during the observation period correspond well with fluctuations in the level of the groundwater. The subsidence in the Isfahan region is mainly due to human activities. The estimated subsidence rates in the city for the period from 2014 to 2019 showed no signs of high subsidence rate in the cultural-historic and economic sites of the urban regions of Isfahan.
The forest structural attributes are required information for sustainable forest management. The use of different remote sensing sources has been investigated intensively as a new potential and an alternative for the forest stand characteristics estimation during the last few years. This research purpose was to examine the phased array type L-band synthetic aperture radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) data ability in order to estimate stand volume, basal area, and tree density in the Hyrcanian forests of Iran with high composition and structure variations. The required preprocessing and processing steps were performed on the ALOS/PALSAR raw data, and the corresponding values of circular plots were extracted on all SAR data. The modeling of forest structure attributes was performed using field-collected attributes by the k-nearest neighbor (kNN), support vector machine (SVM), artificial neural network (ANN), and multiple linear regression (MLR) algorithms. The modeling validity was performed by unemployed plots and by the absolute and relative root mean square error (RMSE) and bias measures. The results of this study have shown that although the results of ANN, SVM, and kNN algorithm were not very different but compared to MLR algorithm, they had better performance. In addition, based on the results of this study, the ANN algorithm showed slightly better performance in forest attribute prediction than the other used algorithms. The results were 34.56%, 27.65%, and 31.16% in relative RMSE for stem volume, basal area, and tree density prediction.
Environmental conditions have considerable effects on synthetic aperture radar (SAR) imagery. Therefore, assessing these effects is important for obtaining accurate and reliable results. In this study, three series of RADARSAT-2 SAR images were evaluated. In each of these series, the sensor configuration was fixed, but the environmental conditions differed. The effects of variable environmental conditions were also investigated on co- and cross-polarized backscattering coefficients, Freeman–Durden scattering contributions, and the pedestal height in different classes of a forest area in Ottawa, Ontario. It was observed that the backscattering coefficient of wet snow was up to 2 dB more than that of dry snow. The absence of snow also caused a decrease of up to 3 dB in the surface scattering of ground and up to 5 dB in that of trees. In addition, the backscatter coefficients of ground vegetation, hardwood species, and softwood species were more similar at temperatures below 0°C than those at temperatures above 0°C. Moreover, the pedestal height was generally greater at temperatures above 0°C than at temperatures below 0°C. Finally, the highest class separability was observed when the temperature was at or above 0°C and there was no snow on the ground or trees.
Minerals have unique spectral signatures that can be used for their identification similar to a fingerprint. Although some minerals have extremely similar compositions thus comparable signatures, they can be differentiated through remote sensing. Therefore, a derivative spectrum was used in this study, which enhanced the subtle spectral discrepancies to help determine whether a special order of a derivative spectrum is applicable in discrimination of these mineral targets. Second, we investigated whether derivative spectra in higher orders can be applied to discriminate between mineral targets using those in five orders as an input for a similarity measure (Jeffries-Matusita distance). Results of this study have shown that the best derivative order selection for each target is a target-specific problem. The first and fourth derivative orders were the bests for alunite and quartz minerals, respectively. As spectral smoothing is a preliminary process of derivative analysis, its bandwidth influence on derivative spectra was then investigated, and a smoothing window size of five sampling points was considered. Based on the results of this study, we recommend the introduction of high-order derivative spectra as the input for many detectors or classifiers in remote sensing especially for differentiation of minerals such as phengite, muscovite, and sericite.
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