فصلنامه سنجش از دور و GIS ایران
سال هفتم شماره 2 (پیاپی 26، تابستان 1394)

Due to the absence of either satellite ephemeris information or camera model for various high resolution satellite images, rational functions models (RFMs) are widely used by photogrammetric and remote sensing communities. This method has various disadvantages such as: The dependency of this method on many ground control points (GCPs), numerical complexity and particularly terms selection. As there is no physical meaning for the terms of RFM, in traditional solution all of them are involved in the computational process which causes over-parameterization. In this letter, a modified Ant Colony Optimization is applied to identify the optimal terms for RFMs. For this purpose this method is tested on three images with different geometric correction levels, different coordinate systems (UTM, CT & Geodetic) and different combination of Ground Control Points (GCPs) and Independent Check Points (ICPs), without normalization of the image and ground coordinates. Experimental results demonstrate how well the proposed algorithm can determine an RFM, which is optimal in both the total number of terms and the positional accuracy. The results have showed that the CT coordinate system has the better capability in accuracy and convergence’s speed. As a conclusion, ACO when using for RFM optimization, can achieve subpixel accuracy even with just four GCPs.

The availability ofinformation about roads has great importanceinvariousapplicationssuch as transportation,traffic controlsystems, automatic navigation system, etc. In recent years, designing new road extraction algorithms has become the target of many studies by researchers. Despite the achieved progress, there are some defects in this field. The gaps in detected roads are one the most important of them. The gaps are appeared due to getting placed under trees, shadow or any other reason. Since the continuity of roads is a momentous topological trait, so filling the gaps seems necessary. The main aim of this paper is to provide a method to automatic find and fill the existing gaps in the extracted road net. Our algorithm first applies the Radon transformation to find the source and destination endpoints of the gaps, then connect these points together using Spline interpolation. This algorithm is implemented on a real detected road which has 4 gaps in straight roads and 2 gaps in junctions. The experiment shows that the proposed algorithm can correctly fill all of the gaps in straight roads, but it is not able to fill the gaps in junctions. So, regardless of the location of the gap, straight road or junction, it can be said that about 66.7% of the existing gaps was filled by the algorithm. This gap filling algorithm is implemented in MATLAB software.

Increase of temperature with height in the troposphere is called temperature inversion. Parameters such as strength and depth are characteristics of temperature inversion. Inversion strength is defined as the temperature difference between the surface and the top of the inversion and the depth of inversion is defined as the height of the inversion from the surface. The common approach in determination of these parameters is field measurements by Radiosonde. On the other hand the Radiosonde data are too sparse, so using satellite images is essential for modeling the temperature inversion. Necessary condition for the temperature inversion modeling using satellite images, examine the relationship between the brightness temperature difference with the temperature inversion strength and depth of the resulting data is Radiosonde. Temperature inversion phenomenon is common in Tehran. So Mehrabad airport weather station was selected as the 1st study area. Then correlation coefficients between Brightness temperature differences of different band pairs and the inversion depth and strength collected by Radiosonde were calculated. The results showed weak linear correlation. This could be due to the change of the atmospheric water vapor content and the relatively weak temperature inversion strength and depth occurred in Tehran. Proving this hypothesis is an innovation in the present work, in continuation of this research, the factors increasing the linear correlation coefficient was investigated. Due to the presence of deeper and stronger temperature inversion in Kermanshah, this region was chosen as the second studied region. The calculated correlation coefficients increased for Kermanshah all due to increase in the strength and depth of the temperature inversion in this region. Knowing that the amount of water vapor in the atmosphere in winter is less than warm seasons, Tehran and Kermanshah data were divided into two all seasons and cold seasons.Increase of correlation coefficients for both Tehran and Kermanshah in the cold season verifies the effect of atmospheric water content. For instance, the correlation coefficient between BT7.2-BT11 with strength and depth of inversion for Kermanshah for all season are 0.51 and 0.70 respectively. This for cold season was boosted to 0.78 and 0.85.

Determination of land use and land cover changes is necessary for monitoring of urban growth and responsible urban planning. Remote sensing can be used as a powerful technology in land use and land cover change detection. One of the challenges in this area is to developing efficient methods for accurate and highly automated change detection which can produce accurate and precise information about position and content of the changes. In this study two GeoEye images from Tehran 17th region related to 2004 and 2010 years were used. This study Proposed a method based on image context spatial features, neural networks and genetic algorithm. Six cases with direct multi-date classification approach and post classification approach were implemented and compared in the viewpoints of accuracy and runtime. Direct multi-date classification was superior in all six cases. Between six implemented cases, sixth case (proposed method of this research) was superior in the classification accuracy point of view. In this case after selecting optimized features, ANN classification was executed based on determining architecture and several times execution. Though runtime of sixth case was the highest, if accuracy is prior, it’s highly recommended.

Investigation of various types of vegetation’s characteristics as an effective parameter in the energy exchange between the atmosphere and Earth's surface is very important in environmental, natural resources and agriculture studies. Nowadays, using remote sensing techniques with a wide range of valuable spectral information facilitate the study of vegetation, especially in estimation of the biophysical parameters. One of the most important biophysical parameters used in the various analyses related to the study of vegetation is Leaf Area Index (LAI). In this study, in addition to the analyzing and modeling of the relationship between LAI and vegetation indices (VIs)via spectrometry observations, the limitations of the mathematical model for estimation of LAI has been explored, some practical guidelines have been provided to improve the accuracy of the model as well as a new vegetation index has been designed. Finally, the results showed that through the conventional vegetation index, Simple Ratio (SR) and Second Soil Adjusted Vegetation Index (SAVI-2) have the minimum RMSE (about 0.08 in LAI unit) and the fitted models using their formulas in comparison with the other indices have the minimum rate of saturation. In other words, these indices are more efficient to estimation of the LAI; especially in high density vegetation area and can be used with high reliability in linear models for LAI estimation.

Remote sensing (RS) data is widely applied for estimation of actual evapotranspiration (ETa) and different methods are also developed in this regard. Among them, SEBAL and METRIC as energy balance models and TVT as vegetation-based have received more attentions. However, they are based on some parameters that need to evaluate while being used. For this, 12 MODIS images for the 2008-2009 water year period that covers the Northern Ahwaz study region (based on the classification of Ministry of Energy), were prepared to be applied for estimation of ETa using the RS models. In order to evaluate and compare the results, estimation of actual evapotranspiration using the conventional water balance model was also used for the same period. Furthermore, the best estimations of ETa using RS models for alfalfa were compared with the respected values in the national water document. The results showed better performance of SEBAL and then METRIC and TVT. While, the water balance model showed 341 mm/yr of ETa, SEBAL showed it 347.24 mm/yr for the same period. However, evaluation of these parameters revealed that application of calibrated soil heat flux andMomentum roughness length equations have significant effect of its performance and reducing of the uncertainties.

Groundwater quality index (GWQI) is a one of important index to assessment of groundwater quality. The aim of this study was to assess the drinking quality of Lordegan aquifer using a GWQI (groundwater quality index) within a GIS framework. To do this, 97 wells were sampled and pH, Electrical Conductance (EC), Total Dissolved Solids (TDS), Total Suspended Solids (TSS), Turbidity (Tu), Ca2, Mg2,Na,K, Cl-HCO3- , SO42- , NO32- and PO42- were measured. Each parameter was mapped using inverse distance weight method. Each map was difference-normalized and then converted to a rank map. Mean value of a rank map was assumed to be the weight of the corresponding parameter which subsequently were employed in a mathematical expression to obtain GWQI. Mean GWQI of 83 indicates a relatively good quality of water in Lordegan aquifer. GWQI map indicates drinking quality decreases southwest towards the north that may be attributed to the existence of agricultural activities, municipal effluent and gypsum formations present in north of the plain. Map removal sensitivity analysis indicated that TSS, turbidity and to some extent Na were important water parameters, which therefore, must be monitored with greater accuracy and more frequently.