فصلنامه سنجش از دور و GIS ایران
سال ششم شماره 1 (پیاپی 21، بهار 1393)

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.

Field spectrometry as a field of remote sensing, dealing with determination of spectral characteristics tries to provide the spectral libraries for different objects. The first objective of this study was to prepare and investigate the significant differences between the spectral signature of water samples with different amount of chlorophyll a (Chl a) of Anzali wetland in 15 cm depth. This was carried out using a full range spectrometer during the spring 2013. The second objective of this study was to discriminate the spectral signature of water samples with different amount of chlorophyll a (Chl a) of Anzali wetland in 30 cm depth. A total of 500 water sample spectral curves of illuminated and shaded samples were acquired of 80 water samples with different amount of chlorophyll between 2.07 and 23.9 (mg/lit. Following the measurements, chlorophyll and total phosphorus of the samples were extracted in laboratory. After quality control and noise remove, the spectral fingerprint of the samples was prepared along 400-900 nm. In order to investigate the spectral reflectance differences, one important index related to chlorophyll a of water were calculated and statistically analyzed. We conclude that three band model in 15 cm depth of water samples has the most relation (r=0.963) with chlorophyll a content. This result has been proved by statistical results obtained by chlorophyll and total phosphorus data in lab. We could conclude that the best wavelength region for spectral separately of eutrophication of turbid water is depend on different factors such as depth of water and amount of sediments of water.

Nowadays, urban land use and land cover information at the micro and macro levels of planning have a particular importance. So many researches have not been done in land use information extraction. Remote sensing as an inexpensive and fast method, and particular with appearance of object-based analysis, has an appropriate potential for this. In this paper, the aim is land use information extraction on a dense and complicated urban area. For this purpose, a hierarchical system inclusive land cover and land use levels has been used. After the implementation of a step by step land cover classification approach, land use unites extraction are done. In the next stage, feature space inclusive more than 50 conceptual features based on land cover information is designed and extracted. After this stage, optimized features among these features with high separability using SFFS are extracted. Finally a fuzzy nearest neighbor classification for land use classification based on optimized is implemented. Land use classification is performed on two combined and uncombined class system that combined class is recognized as most appropriate class system. In the present approach without considering area criteria of land use object, 82% overall accuracy and with this criteria 85% overall accuracy is achieved.

Secchi disc is an indicator of the clarity of the water bodies. In this study, new univariate and multivariate linear regression models are developed for monitoring of Secchi disc depth (SD) in the Caspian Sea using MERIS images and unlike of previous studies, the developed models are tested to determine the real accuracy of developed models for the Secchi depth monitoring. In situ measurements of Secchi disc depth was performed in the southern part of Caspian Sea between July and October 2005 and consequently, 25 training and 12 testing data were acquired. In this study, 25 Level 1B MERIS images of the Caspian Sea, acquired concurrent with in-situ measurements, were employed. In univariate regression, the correlation between Secchi depth and Spectral reflectance data (Rrs) and the ratio of Rrs data were investigated and then the Secchi depth and the Rrs parameters with high correlation coefficient were selected and some univariate models were fitted using the training data on them. In addition, the appropriate multivariate regression models were developed using Cp mallow’s statistics and the best one was selected by the test data. The results showed that the developed multivariate model presents better results than univariate models and it has higher correlation coefficient than the previous studies. The variables of the best multivariate model were the reflectance in 412, 510, 560, 681, 779 nm and the correlation coefficient and percentage error of the best model were about 0.7 and 37.7 %, respectively. Finally the maps of Secchi depth in the Caspian Sea were retrieved using the developed multivariate model.

The Motrabad epithermal system 30 km southwest of Bajestan is located in an assemblage of intermediate to silicic volcanic rocks. The mineralization occurs as irregular veins, veinlets and hydrothermal breccias. Hydrothermal alteration is developed around the veins and consists of silicic (