F Foroughnia; S Nemati; Y Maghsoudi
Volume 10, Issue 1 , June 2018, , Pages 57-72
Abstract
Tehran is subject to high-rate subsidence because of extravagant water extraction. Groundwater extraction in Tehran plain due to agricultural or industrial activities has made it always be at risk and probable incoming damages. Large spatial baselines and temporal de-correlation have always limits the ...
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Tehran is subject to high-rate subsidence because of extravagant water extraction. Groundwater extraction in Tehran plain due to agricultural or industrial activities has made it always be at risk and probable incoming damages. Large spatial baselines and temporal de-correlation have always limits the use of the conventional SAR interferometry for the purpose of subsidence monitoring in regions with high deformations velocity. Therefore, in this research, the InSAR technique based on persistent scatterer (PS) is carried out to analyze Tehran subsidence. The main objective of this paper is to determine the average annual subsidence rate of some urban regions in Tehran using a time series of Sentinel-1A (S-1A) and ENVISAT-ASAR data. PS pixels remain coherent in long spatio-temporal intervals and thus less affected by the lack of radar images correlation. However, inappropriate temporal distribution of data in this technique makes it difficult to derive the absolute value of the phase due to an integer ambiguity. Therefore, the use of S-1A dataset with short temporal baselines would help identify the phase ambiguity. Results prove considerable subsidence in southern part of the case study area for all-time series analysis which further proves the arrival of subsidence to urban parts. Results are cross-validated using the different image tracks and besides, absolute validation are employed on subsidence velocity maps based on precise leveling and GPS observations.
T Managhebi; Y Maghsoudi; M.J Valadan Zoej
Volume 9, Issue 4 , May 2017, , Pages 59-72
Abstract
This paper provides an advanced method to improve results of three stage inversion algorithm using polarimetric synthetic aperture radar interferometry (PolInSAR) technique based on Random Volume over Ground model. In conventional three stage method, the ground phase, extinction coefficient and volume ...
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This paper provides an advanced method to improve results of three stage inversion algorithm using polarimetric synthetic aperture radar interferometry (PolInSAR) technique based on Random Volume over Ground model. In conventional three stage method, the ground phase, extinction coefficient and volume layer is estimated in a geometrical way without the need for a prior information or separate reference DEM. The extinction and volume height estimation is done in the third stage by searching in the two dimension area. In the proposed algorithm, defining a new geometrical index, based on signal penetration in the forest, imposes a limited range for the extinction coefficient. The new index, as an axillary data, help search in a more appropriate space. The proposed algorithm was applied on L-band ESAR single baseline single frequency polarimetric SAR interferometry data. As a result of applying this restriction in the extinction range, a 2.5 meter improvement was observed in the RMSE of proposed algorithm compared to the three stage method.
H Heydari; M.J Valadan Zouj; Y Maghsoudi; M.R Beheshtifar
Volume 8, Issue 2 , November 2016, , Pages 101-112
Abstract
Iran as one of the countrieslocated in arid and semi-arid regions of the world, has been in drought danger. Shortage information about long-term weather conditions in many regions of the country, is one of the most important problems in drought monitoring. In this article, spectral vegetation indices ...
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Iran as one of the countrieslocated in arid and semi-arid regions of the world, has been in drought danger. Shortage information about long-term weather conditions in many regions of the country, is one of the most important problems in drought monitoring. In this article, spectral vegetation indices (SVIs) have been employed in order to drought modeling and its forecast. To this end, SPI drought indicator (standardized precipitation index) used to represent period of drought and its intensity. Some broad band spectral vegetation indices including Normalized Difference Vegetation Index (NDVI), Temperature Condition Index (TCI) and Vegetation Condition Index (VCI) were extracted by using NOAA-AVHRR satellite imagery. These indices entered to SVM classifier model to gain the SPI index as its result. After comparing the results, TCI was diagnosed as the best index to predict drought condition via 3 months SPI (trimester SPI).
Volume 6, Issue 1 , April 2014
Abstract
Timely and accurate detection of changes in land use/ cover is important for land planning and management. Remote sensing images have been primary sources for change detection in recent decades. Due to its simplicity, thresholding of difference image is a popular method for change detection. The traditional ...
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Timely and accurate detection of changes in land use/ cover is important for land planning and management. Remote sensing images have been primary sources for change detection in recent decades. Due to its simplicity, thresholding of difference image is a popular method for change detection. The traditional thresholding methods such as Otsu are based on exhaustive search, so that they are time consuming. Since these methods are mainly developed for one-dimensional problems, the computation time grows exponentially with the number of thresholds when these methods are extended to be used for multi-dimensional problems. If thresholding is supposed to be as an optimization problem, optimization methods can potentially decrease the computation time. In this paper, a fast, simple and effective multi-dimensional image thresholding technique based on Particle Swarm Optimization (PSO) method is presented. This technique calculates the optimal threshold values by maximizing the Otsu objective function and minimizing the inter-class variance objective function. The proposed method has been implemented on two multispectral and multi-temporal datasets. The first dataset includes a couple of images acquired by the TM sensor taken form south islands of Aurmia Lake (Iran) in Jun 1984 and July 2010, respectively. The second dataset is obtained from a couple of images acquired by the same sensor on the Khodafarin dam (Iran) in July 2000 and July 2009, respectively. In order to evaluate the proposed method, the computational time and change detection accuracy were computed. In addition, statistical test was carried out in order to evaluate the robustness of the developed method. The experimental results show that the proposed PSO-based multi-dimensional thresholding method could provide optimum thresholds values by decreasing 98% and 15% of the time complexity compared with the most widely used Otsu and inter-class variance-based thresholding methods.
