نشریه علوم و فنون نقشه برداری
سال ششم شماره 3 (پیاپی 23، زمستان 1395)

Study of local changes in wetlands is of great importance in hazard management of water resources and climate change prediction. According to the infiltration process of water on the ground surface and mixing with groundwater, there will always be a need for monitoring and quality control of surface waters to avoid horrible environmental hazards. Retreat of the coastlines can cause a hydrological concern, moreover, it can be a serious challenge to the quality of water resources followed by the consequences on living organisms.
Lake Urmia is the largest permanent salt water lake located in north-western part of Iran, between East and West Azerbaijan provinces, and the twentieth largest terminal lake in the world which stands in second place after the Great Salt Lake, located in the northern part of the U.S. state of Utah. Drought, heat, increased demand for irrigation water, construction of numerous dams and ecological changes due to construction of a causeway that divides the lake into south and north parts, have been steadily shrinking the salty Lake Urmia. By a continuation of this process, disturbance of the regional ecosystem is possible in the near future.
Sequential data over a long time interval is required to monitor fluctuations of the lake and form time series. For this reason, satellite data captured by radar and optical sensors in a 24-year interval from 1992 to 2016 were taken into account. Radar data was used to obtain surface water level fluctuations of the lake and a spectral process of the optical data was used to compute the surface water extent of the lake for the intended time interval. Surface water level (height) and surface water extent (area) are two important parameters that are directly correlated with the water balance of Lake Urmia. Normalized difference vegetation index (NDVI) was calculated from Landsat satellite imageries to obtain candidate wetland pixels followed by a multiplication of candidate wetland pixels and ground sampling distance (GSD) of Landsat scenes to calculate the area of the lake for any desired time interval. Then, classic and modern methods for modeling the time series of height and area data were studied and cross validated. Auto-regressive integrated moving average (ARIMA) and generalized auto-regressive conditional heteroscedasticity (GARCH) were selected as representatives of classic methods and Markov chain Monte Carlo (MCMC) was selected as a representative of modern methods.
A Markov chain is a discrete time stochastic process with the property that the distribution of any new value of the process, only depends on the pervious value of the chain. This chain needs to be aperiodic to stop Markov chain from oscillating between different sets of states in a regular periodic movement. Monte Carlo sampling technique was then used to avoid this problem. Periodic seasonal variation parameters were then added to the MCMC model using a combination of delayed rejection and adaptive metropolis sampling (DRAM). In this case, the solution to the problem that we are pursuing is to compute the probability of transition from current state to any other possible states. Finally, a comparison between results from classic models and MCMC based on root mean square error (RMSE) and R-squared measures was done to obtain goodness of fit in cross-validation section. For this purpose, classic and modern models were applied on the first 90 percent of input data and the efficiency of each model evaluated from comparison of predictions from the model and latter 10 percent of input data. Results from goodness of fit tests showed that ARIMA and GARCH are not able to model non-linear behavior of input data. However, Markov chain random sampling time series analysis using Monte Carlo algorithm showed good results in prediction of Lake Urmia height and area time series in comparison with classic methods.
Deployment of MCMC in monitoring and prediction of height and surface fluctuations of Lake Urmia can provide precise measurements with error intervals of about ±14 centimeter and ±1.66 square kilometer, respectively. Considering calculated error intervals and predictions from MCMC model, Lake Urmia fluctuations until 2020 were computed. Results showed a nearly stable state for drought conditions at Lake Urmia. According to the predictions from MCMC model, maximum height and surface fluctuations are limited between [-23 to ] centimeter and [-80 to ] square kilometer, respectively. Recent observations of height and surface in a six-year period from 2011 to 2016, showed a good stability in fluctuations which could be a cause of implementation of restoration policies for Lake Urmia, however, there is still a long way to full restoration of this lake. Strict plans for restoration policies are necessary in order to avoid an environmental disaster due to a possible decrease in height and surface of Lake Urmia based on future predictions.

Railway transportation system is composed of interactions among a set of equipment and operations determining the capability and capacity of the railway system in terms of cargo and passenger transportation. The structure of capacity in rail transport is different from that of other methods of transport and especially road transport. Parameters affecting the capacity of rail transport systems are very extensive, depending on type of route in terms of the route geometry and the technologies used in operation. The ability and capacity of carrying freight and passenger in a rail transport network is a function of various parameters in the path of the fleet such as route characteristics (slope, arc, electric signs system, speed limitations, tunnels, etc.), features of fleet (locomotive speed, weight, type of pulling force, type of wagons, etc.), features and characteristics of operations in stations (loading and unloading system and equipment of stations), type of operation and schedule of departure in the rail network (speed and type of trains), construction or reopening of stations, and tracking. All these factors define and determine the capacity of a rail transport system and the nodes for utilization of the network. For this purpose, capacity calculation and analysis of changes in any factor affecting it are important as the corresponding knowledge imposes large contributions in enhancing operating level of existing railway networks. There are various methods for calculating the capacity, of which one may select one depending on the network type and application. As of now, in Iran, considering the variety of applications planned for railway network (cargo, passenger transportation, or a mixed of the two), so-called practical capacity method (Scott’s formula) is used to calculate line capacity. Accordingly, current procedures followed in data gathering and obtaining effective parameters on line capacity calculation, as well as actual capacity calculation and analysis are performed manually, resulting in some discrepancies in different calculations and results. Further, there are chances that, planning based on an inappropriate estimation of line capacity, a portion of actual line capacity remains unused. As such, the present research, which is the first on the topic, aims at using capabilities of geographic information system (GIS) to design and implement a web-based GIS service to determine the state of railway network capacity via a novel approach which offers higher efficiency than conventional methods. For this purpose, a base GIS environment was designed and connected to different databases run by Islamic Republic Of Iran Railways Co., including the travel database, to observe current state of the network in terms of capacity and propose solutions to further exploit unused capacity of the network and address bottle-necks of railway network capacity usage. The use of this method not only resulted in enhanced pace and accuracy of network capacity calculations, but also brought about better managerial decision-making to use unused capacity of the network and sell this capacity to applicant companies and generate further revenue for Islamic Republic Of Iran Railways Co., to the extent that, prior to any new season or year, one could sign appropriate contracts to address the demand raised by cargo owners and passengers across the railway network.

When the molecules and atoms of the atmosphere receive enough external energy, one or more electrons are dissociated from the molecules or atoms. This process is called ionization. The solar ultraviolet (EUV) radiation and particle precipitation are the two primary energy sources in the ionization. Also cosmic radiation contributes to this ionization. This layer of atmosphere is called ionosphere. The ionosphere is that part of the atmosphere in which the number of free electrons is so high that, it significantly affects the propagation of radio waves. Ionospheric refraction is one of the main error sources on GPS signals. This effect is proportional to the total electron content (TEC). TEC is a projection of electron density along signal path extending from the satellite to the receiver on the ground. The unit of TEC is TECU and 1 TECU equals 1016 electrons/m2. Production of free electrons in the ionosphere depends on many factors, such as solar, geomagnetic, gravitational and seismic activity period.
There are many methods to obtain electron density or TEC profiles and predictions. In early time, direct measurements such as ionosonde was a kind of effective instrument to achieve this purpose. Later, some empirical and mathematical models were developed. For example, IRI (international reference ionosphere) model, PIM (the parameterized ionospheric model) are empirical models. Mathematical models divided to two categories: single-layer (2-D) and multi-layer (3-D & 4-D). The existing 2-D methods of modeling the electron density can be classified to non-grid based and grid based techniques. The former modeling techniques are based on the least squares estimation of a functional model for certain types of observables derived from the GPS carrier phase and code measurements. Polynomials and spherical harmonics are some of the base functions that are commonly in use. In grid based modeling, the spherical shell of free electrons is developed into a grid of rectangular elements. Special reconstruction algorithms are then used for estimating the electron density within the every element of the shell. Neglecting the vertical gradient of the electron density is the main deficiency of the two dimensional modeling techniques.
To study the physical properties of the ionosphere, computerized tomography (CT) demonstrated an efficient and effective manner. Due to the sparse distribution of GPS stations and viewing angle limitations, ionospheric electron density (IED) reconstruction is an ill-posed inverse problem. Usually, iterative or non- iterative algorithm used for electron density reconstruction. Non- iterative algorithms are known regularization methods. Using these methods to solve the ill posed problems will produce bias in final results. In this paper, we used hybrid regularization algorithm for solving ionosphere tomography. This method is a combination of two regularizations Tikhonov regularization and total variation (TV). Tikhonov regularization is a classical method for solving ill-posed inverse problem and total variation effectively resists noise in results. To apply the method for constructing a 3D-image of the electron density, GPS measurements of the Iranian permanent GPS network (at 3-day in 2007) have been used. The modeling region is between 240 to 400 N and 440 to 640 W. The result of hybrid regularization method has been compared to that of the zero order Tikhonov regularization method and NeQuick model outputs. The minimum relative error for hybrid method is 1.55% and the maximum relative error is 19.52%. Also, maximum and minimum absolute error is computed 1.32×1011 (ele/m3) and 6.67×1011 (ele/m3), respectively. Experiments demonstrate the effectiveness, and illustrate the validity and reliability of the proposed method.

Energy demand continuously increases due to the growth of population and industries and rising consumption of electric energy in current decade. Because of the limitation of fossil fuels reserves and environmental problems of using those energy sources, optimization of energy consumption and using new and renewable energy sources are essential. Among renewable energies, wind energy due to low power production price and less environmental pollutions, becomes more an attractive renewable energy source. Recent shortages of power and fuel have aroused interest in the possibility of utilizing wind power on a large scale for the generation of electricity in Iran. For determining, an appropriate location for a wind farm, several criteria and different factors should be considered.
Renewable energy resources exist over wide geographical areas, in contrast to other energy sources, which are concentrated in a limited number of countries. Rapid deployment of renewable energy and energy efficiency is resulting in significant energy security, climate change mitigation, and economic benefits.
For this potential to be fully realized, several aspects, related to public acceptance, and technical issues, related to the expected increase in penetration on the electricity network and the current drive towards larger wind turbines, need to be resolved. Nevertheless, these challenges will be met and wind energy will, very likely, become increasingly important over the next two decades. Wind farms consist of many individual wind turbines which are connected to the electric power transmission network. Onshore wind is an inexpensive source of electric power, competitive with or in many places cheaper than coal or gas plants.
While many renewable energy projects are large-scale, renewable technologies are also suited to rural and remote areas and developing countries, where energy is often crucial in human development. Areas where winds are stronger and more constant, such as offshore and high altitude sites are preferred locations for wind farms.
In the present study, at the first those important factors identified and localization. Besides that, the role and the influence of each factor are determined and several thematic maps were prepared by using ArcGIS and finally digital based maps integration by applying the fuzzy logic models. In order to accomplish the above-mentioned integration method, Damghan’s county of Semnan province as a windy area was selected because of its great wind potential. Damghan has the best conditions to install wind turbine compared to others in the province mentioned.
As a result, 2240 square kilometers (about 16% of the total land) of Damghan’s county were selected as suitable locations for wind power plants. These areas are located in adequate distance from access roads, transmission lines, population centers and so on and so forth. Extractable amount of the wind energy in Damghan’s county, by considering appropriate distance between wind turbines and wind farms to avoid wake effects and losses, has been estimated about 1000 Mega Watts.

Travel demand forecasting is an important topic in transportation planning. Understanding and modeling the travel demand has numerous applications in designing urban infrastructures, managing the spread of diseases, monitoring the dispersion of computer viruses, urban planning and policies, spatiotemporal analyses in GIS, and Location-Based Services (LBS). Traditionally, in order to predict the travel demand, a four step model is used, of which the second step is called trip distribution. The output of trip distribution step in this model is termed Origin-Destination (OD) matrix. The elements of this matrix indicate the amount of trips departing from origin zones to destination zones. The OD matrix is considered as an important input in various spatial analyses in Geospatial Information Systems (GIS). The most essential part of trip distribution is the model used for OD matrix estimation. Up to now, various models such as gravity have been introduced to estimate the trip distribution. Recently, some parameterized and non-parametric models of human mobility pattern prediction, also known as spatial interaction (SI) models, have been developed. Among them are the rank-based (parameterized), radiation (non-parametric), and PWO (non-parametric) models. These models can be applied to a broad range of scales, from within a house or stadium, to a city, country, or even the whole earth. The probabilistic form of these models is the same as OD estimation models. In addition, in these models, computational mechanisms of trip distribution are not limited and different behavioral and decision-making characteristics of people are also taken into account. In this paper, the applicability of PWO, radiation and rank-based models in OD matrix estimation is addressed. As a case study, the rank-based model has been applied for Manhattan, New York City (NYC) and the results have been evaluated. Manhattan is one of the most important trade centers in the world and its mobility rate is remarkable. In order to calibrate the models in which adjustable parameters are appeared, the Hyman method was employed. Hyman method is a repetitive algorithm which uses secant procedure to minimize the difference between the real trips’ average distance and the modelled trips’ average distance. Also, in order to balance the resulting matrix, another process is needed. In this paper, the Furness method has been used. For the purpose of evaluating the results, trajectory data of taxi vehicles within NYC was employed. These dataset are published monthly by NYC Taxi and Limousine Commission (TLC). To capture a more complete pattern, the trajectories of yellow- and white-colored taxis were combined together. Finally, we used Sorensen Similarity Index (SSI), Regression Analysis, and a visual measure, termed sparsity pattern, to examine the model. The results show that the rank-based model can predict trip distribution up to 67 percent according to Sorensen Similarity Index. Additionally, the r-square measure obtained from regression analysis is 0.32 that shows a good agreement between estimated and ground truth matrices. Taking the huge volume of point data being regressed into account, this value shows a good agreement between modeling results and real trips. These results show the potential of recently introduced spatial interaction models in trip distribution estimation.

One of the most important strategies for suitable management of municipal waste that has been in the spotlight as a first priority in urban waste management in many countries, is waste sorting at the sources. Waste sorting is the process by which waste is separated into different elements. Separating the different elements of waste is essential for enabling the recovery of useful materials and minimizing the amount of material sent to landfill, therefore; effective recycling relies on effective sorting, so that there is a wide range of sorting technologies nowadays.
Developed countries have the different systems of waste sorting and these can vary from town to town. In these kinds of countries, waste sorting schedules are generally distributed to every household in the cities, moreover; Ambitious household waste recycling programs have been introduced during recent decades and many different waste-sorting and collection schemes have been developed. In most Asian countries, 2 methods of waste separation play a significant role for the handling of the waste collection and recycling. The formal and the informal separation and recycling of the materials are known as applicable methods. The formal separation means the separation of the waste in the waste treatment facility after the collection of the waste. The informal separations of the waste can occur in 3 different ways, such as: direct at the source, during the collection, and at the disposal site. Unfortunately, in these kinds of countries the waste pickers separate and collect the waste, because they have private benefits by doing this. They can sell the materials which they collect. It does not happen, because of an existing or raising environmental awareness. This research aimed to assess the amount of separated waste in the 22 districts of Tehran during the years 89-92, using geographical information systems (GIS), therefore; the number of residents, statistical data of waste production and its sorting was utilized for analyzing.
Organic waste with 74.56% of weighted portion, has allocated the largest weighted portion of Tehran’s waste. District 4 with more than 885 thousand and district 22 with less than 135 thousand of residents are considered as the most populous and the least populous of municipal districts at the end of 1392, respectively. Districts 2 and 5 in the during years 89-90 and 91-92 have had the highest waste production respectively, so that, district 5 has produced the waste, more than 6 times of district 13, in the year 92. Among the 22 districts of Tehran, the amount of source separation for districts 4 and 5 was the highest, and the lowest level has obtained for districts 21 and 22. The results show that 15.23%, 14.85%, 14.38% and 15.23% of the total collected waste collected on average at the sources, in the years 89, 90, 91 and 92, respectively, in Tehran. District 12 has had the highest rate of collected separated waste in years 89, 90 and 91, moreover; district 14 with 23.73%, district 1 with 22.7%, and district 8 with 22.42% have been the best districts in this case.

Walking has a great importance in our everyday lives and it has an important role in preventing obesity and enhancing the physical health of the society, reducing the traffic, decreasing the air and noise pollution, and generally in improving the quality of our life. In the last decade, there is growing interest in understanding the relationship between characteristics of the built environment on walking. One of the reasons for people’s unwillingness to walk is the lack of suitable and attractive pathways for walking. Although, the main stated goal of the municipality of most Asian cities including Tehran is to encouraging walking, but the quality of the pathways discourages walking. A range of methodologies has been developed in the literature to assess the walkability. Two types of these indicators include Walkability-Index (WAI) and Walk Score have been used intensively in developed countries to evaluate walkability.
This study aimed to do a spatial multi-criteria evaluation of walkability of pathways based on a number of urban planning indices. For this purpose, an integrated method based on geospatial information science (GIS), remote sensing (RS) and multi-criteria analysis, specifically AHP and TOPSIS, is used. Fifth spatial criteria are considered: connectivity with other passages, access to public transport infrastructure, land use mixed, density of the residential parcels and greenness level. The importance of criteria is determined based on the preferences of both urban planning experts and citizens. In both groups, the connectivity and residential density factors had the smallest importance. The existence of varied land uses and accessibility to transportation systems received the greatest importance, respectively.
The proposed approach is implemented for the districts number 2 and 7 of region number 1 of Tehran municipality. After doing the required analysis and implementing the model in the studying area, based on experts’ view, Valiasr, Darakeh and Rashidoddin Fazlollah among the main streets and Daneshju, Karami and Alborzkuh among the second level streets were indicated as the streets with high walkability potential. Based on citizen's opinion, Valiasr, Evin and Darakeh among the main streets, Daneshju, Karami and Sharifimanesh among the second level streets have the highest walkability potential. The availability of transportation infrastructures and greenness in the neighborhoods received the highest scores in the case study area.
According to the results of this research, different passages can be evaluated from the perspective of these criteria, and improvement of passages’ walkability potential can be planned for the future; Also, according to the results, the passages which need more improvements and have the higher priority can be recognized. Based on the results of this study, urban planners can evaluate the walkability potential in street segment scale. A possible extension includes investigating the relation between actual walkability in the case study area and walkability based on the proposed method. We acknowledge the limitations of using road centerlines instead of sidewalks, but this data was not available for Tehran.

Today several datasets are available for the users where each of them demonstrates different representations of the real world. Modeling and representation of the world in the form of spatial information have been performed by the private and public organizations. Moreover, recently the spatial data is generated by VGI approach such as Google Map and OpenStreetMap projects. These different representations could create problems for the data producers or users during the processing steps like integration, data quality estimation, updates, and multi-scale analysis. Hence, it is required that objects with identical entities in different datasets be linked to each other. This process is called “data matching” or “object matching” in the literature. The matching method for different types of vector data (i.e. point, line, and polygon) is different. The subject of this article is the linear object matching. In the previous studies, the linear objects matching has been done by considering each or a combination of two geometrical and semantic properties. However, in this study, the geometrical property was used for identifying the corresponding objects; as the studies that are based on semantic matching lose their efficiency or their efficiency decreases when data such as attribute information are missing in one of the datasets.
For this problem, an approach composed of five sections was proposed for improving the matching of roads in the datasets with various scales and sources. The proposed framework was based on the graph theory considering criteria related to geometry in order to match the roads network with various scales and sources. In the proposed solution, the goal was to determine the similarity degree of objects in the datasets with various scales and sources by considering geometrical criteria such as distance, orientation, area, shape, and buffer overlapped area and by considering spatial cognition of the experts’ in determining the weight of criteria. In the first section, in addition to convert the datasets to the same format and the coordinate system, the topologic errors were eliminated. In the second section of the proposed approach, ambiguity in the definition of objects in the datasets was resolved by considering a pre-defined graph structure. In the third and fourth sections, the similarity degree of objects was calculated and the corresponding objects were determined by extracting the introduced geometrical criteria from the objects. In the final section, the proposed approach was tested through the precision, Recall and F-score indices.
The results of the study illustrated that careful and adequate matching is not possible in the data collections with various scales and sources despite the previous studies in which the matching was done with acceptable care and adequacy with one, two, or three geometrical criteria extracted from features in the data collections with various scales and same source or with same scale and various sources. Moreover, in this article, in addition to removing ambiguity in the definition of objects, the F-Score was calculated as 82.67% by considering five geometrical criteria extracted from the objects. It is worthy to note that this value was calculated in the datasets with high scales difference and with various sources without considering other criteria such as semantic and topological criteria.

More than two decades, continuous observations of permanent GPS stations used extensively for behavior of geodynamic phenomena have found such as deformation of the earth's crust, moving tectonic plates and fault. Daily position of permanent GPS stations are considered independent of each other in statistic. In other hand, some errors such as the satellite orbit modeling errors, determining parameters of rotation's earth, parameters of atmospheric modeling and etc; cause color noise or the correlation between daily positions of stations. Another important systematic error in GPS time series is offset. Offset by factors such as earthquakes, replace the GPS antenna, human and environmental errors are generated. Offset in GPS time series's functional model causes the bias estimation of unknown parameters Therefor, we require exact statistical model and function model of GPS time series to exact estimation of the speed parameter. For this purpose, multivariate noise analysis on 38 permanent GPS stations of Iran, were carried out by the period of 7 years (2006–2012), in this study. These stations are scattered throughout the country. In this analysis, statistical model of data presented of by incorporating white noise, flicker noise and random Walk noise and noise components estimation has taken place by method of " least squares variance component estimation ". Also the effect of offset in the data were examined on estimation of noise and station's speed parameters by method of "least squares". The temporal correlation with multivariate noise analysis was performed for both single-station and multi- station. The results showed that noise amounts in coordinate series distributed due to processing all stations with each other in multi-station mode. In multivariate analysis single-station, Random Walk noise amounts after offset removal get zero in some stations, but in multi- station mode any stations were not zero for Random Walk noise. So single- station mode is more realistic than multi- station mode. Noises estimation from data with offset and compare them with results from data without offset shows offset effect on the amounts of noise, especially Random Walk noise that the greater part of it consist of offset. In addition to consideration of offset effect on noise, its effect on the speed parameters were also assessed. This assessment shows changes of length and direction of speed vector, after removing offset that examine necessity of offset's studying at data . So having the correct functional model and statistical model and without offset in the exact determination of parameters such as speed is essential. The studying of spatial correlation showed that there are significant correlation for components location North - North, East - East and height - height after removing offset, but correlation results are not significant between the various components of the coordinate, North - East, North - height and East - height. After removing offset of data, difference of speed vector from its average value, was calculated. It's found, speed of stations in tectonic boundaries (Saudi Arabia in the south and Eurasia tectonic plates in the north of Iran) more than center of Iran.

Geomatics science and technology is a main source of geospatial information providers in variant forms. In producing such information, Remote Sensing and Photogrammetry with high potentials as different sensors with variant data types have an essential role. In recent years, change detection has been important for many organizations in attention to the nature of the change. Until today, many change detection methods as direct comparison, Transformations, classification based and so on have been used. Each one of these methods with considering to the final goal has been employed variant type of data and different results have been obtained. The image in the visible and near infrared part of the electromagnetic wave has the most usage as an input data for change detection. According to the nature of the change detection method, different feature spaces as textural and morphological features for increasing the accuracy of the produced results have been tested. Textural statistics extracted from the Gray Level Co-occurrence Matrix (GLCM) with high variant has different effects on the classification and change detection obtained results. But using all kinds of the textural features for increasing the accuracy of the produced results will make problems because of the correlation between classes and sometimes because of the high noise values and also in some cases with more increasing the feature spaces and so decreasing the processing speed. In this paper, comprehensive performance evaluation of each textural statistic: Mean, Variance, Homogeneity, Entropy, Dissimilarity, Second order moment, Contrast and Correlation on improving the change detection accuracy have been done. For this, firstly three spectral bands of Landsat 8: 2, 5 and 7 bands, in the visible, near infrared and mid infrared region of EM wave scince 2013 and 2015 years selected as input data. Then, mentioned textural statistics in 4 directions (0, 45, 90 and 135 degree) on the each considered bands and years are extracted. After that, for eliminating the direction effect on the features the mediocre of all the 4 directions for each statistic are estimated. Afterward, differential image is estimated by subtraction of corresponding textural bands of the 2013 and 2015 years. Also, the differential images of the spectral bands: 2, 5 and 7 are produced. Then, every differential image of the textural statistics (in each band independently) is integrated with differential image of the spectral bands and are fed to the Maximum Likelihood classifier. The obtained results are shown, in visible and near infrared region, Mean statistic increased the overall accuracy about 15 and 16 percent respectively, and improved the Kappa coefficient value about 30 and 31 correspondingly and have the most influence on increasing the accuracy of the change detection output results. Also, in the both of mentioned EM regions, Second order moment, Contrast and Correlation statistics have the least effect on the change detection accuracy improvement. In the mid infrared region, approximately all statistics have the same performance. Furthermore, all features of the GLCM are combined and fed to the Principal Component Analysis (PCA) band reduction technique and first band selected. Then, the first band of the PCA is employed as other features for change detection. Obtained results have been shown the efficiency of this strategy for accuracy improvement. The achieved results of this paper on tests data have been approved that Mean, Entropy and Homogeneity have the highest improvement performance and Variance, Correlation and Contrast have the lowest improvement performance on the change detection accuracy.