جستجوی مقالات مرتبط با کلیدواژه « classification » در نشریات گروه « محیط زیست »

Maize (Zea mays. L) is one of the most important crops acrossthe world that ranks third in terms of acreage behind wheat and rice. As this crop can adapt to different climatic conditions, it is of great importance and has a large area under cultivation.Therefore, maize is one of the major products of temperate, warm-temperate, subtropical, and humid regions. After wheat, rice, and barley, this plant is the main crop in Iran with the largest cultivated area.There are different types of maizeseeds, so their classification is essential to ensure quality. A key component of sustainable agriculture is quality assurance. On the one hand, techniques such as drying, cooling, and edible coating must be used to maintain the quality of agricultural products. On the other hand, effective and efficient methods should be developed to evaluate and classify their quality, which is used in seed and seedling processing centers, silos, and mechanized warehouses.The detection of various varieties of crop seeds using instrumental methods has been the subject of extensive research. As a non-destructive and rapid inspection method for the recognition and classification of cereal seed varieties, the visual machine is available. Machine vision-based automated methods can have a positive impact on food processing. In other words, this tool is the process of preparing and analyzing images of a real scene using a computer to obtain information or control a process. The features of images can be extracted using this machine to recognize and identify the quality of different types of products. To identify the types of plants, their growth patterns, and the effects of the environment on them to obtain more and superior products, machine vision occupies a special place and is one of the most important research areas. Inspection and quality control of factory output products is an important application of machine vision.Advances in image processing technology have opened up a wide range of machine vision applications in agriculture. The development of powerful microcomputers and specialized software has led to the use of image processing for the inspection of fruits and agricultural products, especially for quality control and sorting. Many agricultural products sorting systems used to separate fruits or crops based on color, shape, size, the extent of damage, crushing, bursting, spotting, etc., now rely on visual machines and image processing functions.Images of products moving on the conveyor system are taken by a CCD camera, transmitted to a computer for processing, and in these systems, the necessary data are extracted from them. Depending on the information obtained, commands are then issued to activate or deactivate a mechanical part so that the product can be removed from or allowed to cross the main path. Sorting is a common practice in many industries. Compared to mechanical systems, machine vision technology offers the highest accuracy and quality at the lowest cost and with the lowest error rate, so it can be considered the most effective solution to this problem. The agricultural industry is one of the areas where sorting and grading systems based on machine vision are urgently needed.The core elements of machine vision are image processing and analysis used together with new methods and classifiers such as neural networks, backup vector machines, fuzzy logic, etc. to perform classifications and required measurements. This study aimed to identify seeds of three maize varieties using macroscopic imaging techniques, evaluate the morphological and chromatic features in maize grains, and discriminate varieties using a stepwise method and remove some variables using LDA and ANN.

Three seed varieties of single cross 703,single cross 704, and single cross 705 were provided by the Agricultural and Natural Resources Research Centre of Ardabil Province in Pars Abad Moghan. The seeds were then taken to the Biophysical Properties Laboratory of the Department of Biosystems and Mechanical Engineering, MohagheghArdabili University.Three samples (20 g) of each variety were stored in a laboratory oven at 105 °C for 24 h to determine the initial moisture content of maize grains. According to the dry weight of grains, the initial moisture content of them was calculated by 10.50%. To distinguish 3 maize varieties, 180 samples were analyzed as single seeds (30 replicates in the anterior direction and 30 replicates in the posterior direction) for each variety with 60 replicates. In addition, 9 more samples were used in bulk with 3 replicates for each variety.Thus, we imaged a total of 189 samples. In addition, a digital scale with an accuracy of 0.001 g was used to measure the weight of the grains. Computer vision systems consist of five main components: lighting chamber, camera, analogue-digital card (for digitization), computer, and computer software. Images were taken using a Canon IXY DIGITAL 510 IS digital camera. A dome-shaped chamber was used to reduce noise and control ambient light. The system was illuminated with four fluorescent lamps and two rows of LED lamps, one white and one yellow. While the camera was pointed perpendicular to the imaging surface, it provided images with a resolution of 12.1 megapixels.In this case, the images were processed using MATLAB software. First, 10 maize seeds were randomly sampled from the first variety (single cross 703) and weighed using a digital balance. Then, parameters such as the large and small diameters and thickness of each grain were measured using a caliper of 0.02 mm. Then, these grains were placed at appropriate distances from each other on a plate of red cardboard in the opposite direction to be imaged. Finally, 30 maizeseeds were imaged in both directions and 60 images were taken as single seed. In total, we obtained 180 images of all three varieties as single seeds. To prepare the mass, first, some seeds of the first variety were placed in a cylindrical container (1.5 cm high, 4.2 cm in basal diameter, and 70.62174 cm2 in volume) so that the container was filled. After weighing, the mass of grains with a certain volume was poured onto the red plate in a circular pattern. In the end, the camera was placed on the bulk sample and the image was taken, just like the single grain image.The same procedure was repeated twice more on two more bulk samples of the first variety. Similarly, three bulk samples of two more varieties were imaged. In this way, a total of nine images were obtained. After each imaging, we measured and recorded the dimensions and weight of 10 randomly selected seeds from the imaged bulk. In the end, 189 images were obtained, including 180 single-grain and 9 bulk images.In the single sample feature extraction step using the bwlabel function, all samples were labeled and the grain morphological features were extracted. Then, the set of Regionprop functions was used to determine eight parameters, including area, perimeter, major principal axis, minor principal axis, integrity, irregularity, convex area, and equivalent diameter. An artificial neural network (NAA) and a statistical linear discriminant analysis (LDA) method were used to identify maize varieties based on their morphological and color characteristics. The data were normalized before analysis. LDA is a statistical method for classifying objects based on independent variables. The analysis was carried out using SPSS software. The diagnostic analysis includes stepwise analysis, principal component analysis, and elimination of recursive features. In this study, the stepwise method was used. In the usual method, all variables are included in the analysis. However, in the stepwise method, some variables were removed and only the variables with the greatest influence on the model were included. To classify the maize varieties, a network consisting of three layers: input, output, and hidden layers was used.

We performed image processing to classify three maize varieties based on the results obtained. A linear diagnostic analysis method was used in this study. A total of 16 predictor variables were used with an accuracy of 70.6%. Some variables were eliminated by a stepwise method. In addition, eight other predictor variables were analyzed with the same accuracy of 70.6%. Thus, although the number of predictor variables was reduced, the detection accuracy remained constant. Moreover, the highest accuracy of diagnosis (80%) was associated with the first variety (single cross 703). Additionally, the accuracy of the methods of ANN with 16 and 8 predictor variables was 75.6% and 72.2%, respectively. These values were higher than that of LDA.Predictive variables included areas, perimeter, major principal diameters, minor principal diameters, irregularities, concave areas, equivalent diameters, color indices (red, green, and blue) resulting from maize grain sample processing, weight, and grain size. The following factors were the most important predictors of varietal discrimination: thickness, major principal axis, minor principal axis, irregularity, equivalent diameter, integrity, red color index, and green color index. According to the results, the length and width of individual grains had no significant effect on variety classification.Our finding demonstrated thatmachine vision technology can be used in seed and seedling processing centers, silos, mechanized warehouses, and other places where maize seed crops need to be identified and separated in a non-destructive manner.

Remote Sensing Technology is considered one of the most important sources of spatial and thematic data in the developed world of today. The objective of this work is a comparison of two different methods of change detection in forests using Landsat images. Therefore, sensor Landsat TM images of 1990 and 2011 (ETM+) satellite images have been used.

In the classification of images, the maximum likelihood algorithm, and artificial neural network to multilayer perceptron method were used.

Evaluated results showed that the algorithm approach, the maximum likelihood overall accuracy, and kappa coefficient maps classified in TM image, respectively, are 96.72 and 0.96 percent and image ETM+ 98.02 and 0.97 percent, and the method of artificial neural networks, overall accuracy and kappa coefficient map classified, TM image was 98.22 and 0.97% and ETM+ image was 98.34and 0.97 percent respectively. Following TM and ETM+ classification maps to detect the changes were marked and the map changes obtained.

The results of this study showed that using Landsat data along with data from have inventory capabilities of forest change mapping

Pepper (Capsicum annuum L.) is one of the most consumed vegetables in the world, containing a large amount of vitamins C and A, as well as minerals. Therefore, the consumption of about 60 to 80 g of pepper per day can provide 100 and 25% of the recommended daily amount of vitamin C and A, respectively. In addition, this horticultural product contains considerable levels of other health-promoting substances with antioxidant activity, including carotenoids, flavonoids, and other polyphenols.The quality of fresh pepper depends primarily on consumer acceptance, which is determined primarily by color, pungency, and aroma. Aroma plays an essential role in determining the sensory characteristics of these products. Volatile organic compounds (VOCs) are generally associated with the taste and aroma of foods and are important factors in assessing consumer acceptance or rejection. Consequently, food quality, originality, purity, and origin can be evaluated by determining VOC.Because it is important to distinguish hot peppers from sweet ones, we used an electronic nose to determine food quality in this study. Research has shown that the electronic nose is able to discriminate between products.

The variety used in this study was Padrón, a very popular species in Spain. The peppers can be harvested when they reach a length of 2.5 to 4 cm. One fruit out of 20 has a spicy flavor, while the rest has a mild taste. The green fruits showed no signs of ripening or discoloration and remained completely green.The peppers weighed an average of 12 ± 2 g when fresh. The weights for the sweet and spicy varieties were determined by weighing 30 fruits each. The fruits to be examined were evaluated by electronic nose.In this research, an electronic nose made in the Department of Biosystem Engineering of Mohaghegh Ardabili University was used. This device uses 9 low-power metal oxide (MOS) semiconductor sensors.The sample chamber was connected to the electronic nose and data collection was performed. The data collection was done by first passing clean air through the sensor chamber for 100 seconds to clear the sensors of odors and other gases. The sample odor was then sucked out of the sample chamber by the pump for 100 seconds and directed to the sensors, and finally fresh air was injected into the sensor chamber for 100 seconds to prepare the device for repetition and subsequent tests. 30 replicates were considered for each sample.The study began with the chemometrics method with principal component analysis (PCA) to detect the output response of the sensors and reduce the data dimension. In the next step, Quadratic detection analysis and Mahalanobis detection analysis (QDA and MDA) were used to classify 2 group of pepper. Principal component analysis (PCA) is one of the simplest multivariate methods and is known as an unsupervised technique for clustering data by groups. It is usually used to reduce the size of the data and the best results are obtained when the data are positively or negatively correlated with each other.Quadratic detection analysis and Mahalanobis detection analysis (QDA and MDA) are the most common monitored technique for separating samples into predetermined categories. This technique selects independent data variables to differentiate the sample that is to follow the normal distribution. The QDA and MDA are based on linear classification functions in which intergroup variance is maximized and intragroup variance is minimized.

Principal component analysis diagram shows the total variance of the data equal to PC-1 (90%) and PC-2 (6%), respectively, and the first two principal components constitute 96% of the total variance of the normalized data. When the total variance is above 90%, it means that the first two PCs are sufficient to explain the total variance of the data set. two group of pepper are well differentiated by PCA method. Therefore, it can be concluded that e-Nose has a good response to the smell of 2 group of pepper and they can be distinguished from each other, which shows the high accuracy of the electronic nose in detecting the smell of different products.The correlation loadings plot diagram can show the relationships between all variables. The loading diagram shows the relative role of the sensors for each principal component. The inner ellipse shows 50% and the outer ellipse shows 100% of the total variance of the data. The higher the loading coefficient of a sensor, the greater the role of that sensor in identifying and classifying. Therefore, the sensors located on the outer circle have a greater role in data classification and it is clear that the three sensors MQ4, MQ9 and TGS822 have played an important role in identifying 2 group of pepper.The correlation loadings plot diagram can show the relationships between all variables. The loading diagram shows the relative role of the sensors for each principal component. The inner ellipse shows 50% and the outer ellipse shows 100% of the total variance of the data. The higher the loading coefficient of a sensor, the greater the role of that sensor in identifying and classifying. Therefore, the sensors located on the outer circle have a greater role in data classification and it is clear that the three sensors MQ4, MQ9 and TGS822 have played an important role in identifying 2 group of pepper. Unlike the PCA method, the LDA method can extract multi-sensor information to optimize resolution between classes. Therefore, this method was used to detect 2 group of pepper based on the output response of sensors. The results of detection of cultivars were equal to 100%.The electronic nose has the ability to be used and exploited as a fast and non-destructive method to distinguish sweet and hot pepper from leaf odor. Using this method in identifying sweet and hot pepper will be very useful for consumers, especially processing units and food industries in order to select appropriate cultivars. Since the detection of pepper using an electronic nose has not yet been researched, the promising results of this study can be widely applied in the sorting industry.

Potato is an important vegetable that grows all over the world and is considered as an important product in developing and developed countries for human diet as a source of carbohydrates, proteins, and vitamins. This product is native to South America and its origin is from Peru, and after wheat, rice and corn, it is the fourth product in the food basket of human societies. According to the statistics of the Food and Agriculture Organization of the United Nations, the area under cultivation of this crop in Iran in 2017 was 161 thousand hectares and the crop harvested from this area is about 5.1 million tons. Traditional methods of determining potato varieties were based more on morphological features, but with the production of new products, there was a need for methods that were faster and more recognizable. Meanwhile, high-performance artificial neural network can be used to classify cultivars. Artificial neural network can classify and detect cultivars, is flexible and is used in most agricultural products. Azizi conducted a study on 120 potatoes in 10 different cultivars using a visual and image processing machine with a MATLAB R2012 software toolbox to detect texture, shape parameters and potato cultivars. First, potato cultivars were classified using LDA method, which obtained 66.7% accuracy. This method also erred in distinguishing the two cultivars Agria and Savalan and also classified the two cultivars Fontane and Satina in other classes. They also used artificial neural networks to classify potato cultivars, in which the network was 82.41% accurate with one hidden layer and 100% accurate with two hidden layers. In this study, it was found that different types of potatoes can be identified and identified with a very high level of accuracy using the three color characteristics, textural and morphological features extracted by the visual machine and the use of a non-linear classifier artificial neural network. Categorized.By determining and examining the existing relations between the force and the deformation of agricultural products up to the point of surrender, the range of forces harmful to fruit can be determined so that harvesting and transportation machines are designed in such a way that the forces from them do not exceed this range. On the other hand, one of the ways to determine the degree of ripeness of the fruit is to touch and press it with the thumb, which is an experimental way and depends on the skill of the person touching. The mechanical penetration test of the fruit can be an indicator to check the ripeness of the fruit by quantifying this diagnosis and using this diagnosis to determine the optimal harvest time.

First, 5 different potato cultivars were prepared from Ardabil Agricultural Research Center and kept at a temperature of 4-10 ° C. One day later, 21 samples of each potato cultivar were prepared using a cutting cylinder and then data were collected. To determine the toughness of the samples, the Centam device available in Mohaghegh Ardabili University was used. Each potato cultivar was subjected to compressive force at three levels of loading speed of 10, 40 and 70 mm / min with 7 repetitions. Then the amount of toughness was calculated according to Equation (1). Then linear diagnostic analysis (LDA) and artificial neural networks (ANN) were used to classify potato cultivars. LDA is a supervised method used to find the most distinctive special vectors, maximizing the ratio of variance between class and within the class, and being able to classify two or more groups of samples. ANN and pattern recognition were used to find similarities and differences in the classification of potato cultivars. For this, 1 neuron was considered for the input layer, the hidden layer with the optimal number of neurons will be considered and five output neurons with Depending on the number of output classes the target will be considered. In network training, the logarithmic sigmoid transfer function and Lunberg-Marquardt learning method were used (Figure 4), and the error value was calculated using the mean square error. For learning (70%), testing (15%) and validation (15%) all data were randomly selected. Training data was provided to the network during the training and the network was adjusted according to their error. Validation was used to measure network generalization and completion of training. Data testing had no effect on training and therefore provided an independent measurement of network performance during and after training. All of the calculations and matrix classification were performed using MATLAB R2018a and X 10.4 Unscrambler software.Toughness in 5 different potato cultivars was obtained using Centam machine and Equation 1. The values obtained for the toughness of 5 potato cultivars were analyzed using Mstatc software. The results of analysis of variance were significant for the toughness of 5 different potato cultivars at the level of 1% and its coefficient of variation was 2.28. LDA and ANN methods were used to detect potato cultivars based on the values calculated for toughness. Detection results of cultivars using LDA were equal to 70.48% (Figure 6). Also, the accuracy of ANN method according to the perturbation matrix was equal to 72.4% (Figure 7).

In this study, the amount of toughness for 5 different potato cultivars was calculated using Centam machine available in Mohaghegh Ardabili University with the help of Equation 1. Chemometrics methods including LDA and ANN were used for qualitative and quantitative analysis of data to identify and classify potato cultivars. Thus, LDA and ANN were able to identify and accurately classify different potato cultivars with an accuracy of over 70%. The obtained toughness has the ability to be used as a method to distinguish different potato cultivars. The use of this method in identifying potato cultivars will be very useful for factories such as chips factory and processing units, and it is also expected that similar methods related to mechanical properties such as crispness and stiffness with the help of chemometrics methods to optimize production and The processing of agricultural products should be used in the food industry, which has led to more customer friendliness and, in addition, can reduce agricultural waste.

National parks and wildlife shelter are the most important natural heritages; therefore, knowing of quantitative and qualitative changes in their land use plays an essential role in the quality of these areas' management. various algorithms have been developed to classify satellite imagery in remote sensing, selecting an appropriate classification algorithm is very important in achieving the accurate results. In this research, a more accurate algorithm was determined by comparing the classification accuracy of two artificial neural network and support vector machine algorithms, and it was used to examine the process of the land use changes. The present study was performed in Boujagh National Park, in the Guilan Province, during the years 2000 to 2017, using satellite imagery ETM and OLI of Landsat 7 and 8. The results of the research revealed that the support vector machine algorithm with overall accuracy and Kappa coefficient of 86.42 and 0.83 respectively for the year 2000 and, 90.65 and 0.88 for the year 2017, classified the satellite images more precisely, in comparison with the artificial neural network algorithm with overall accuracy and Kappa coefficient of 83.71 and 0.80 respectively for the year 2000 and overall accuracy and Kappa coefficient of 89.25 and 0.87 for the year 2017. Therefore, the land use maps of the support vector machine algorithm were used to determine the land use changes. The study of land use change by this method concluded that the areas of the waterbody, sea, grassland and agriculture have decreased and marshland, woody and bare lands classes showed an increase during the study period.

Wheat is one of the strategic agricultural products in Iran and the world. Having statistics data and information about the area under cultivation of this crop and estimating the amount of its production in one crop year can help the planners of agriculture and industry parts in order to manage the production and consumption of the mentioned product as effectively as possible. One of the tools that can calculate the area under wheat cultivation in the shortest time and with low cost and appropriate accuracy is the remote sensing system. In this study, the area under cultivation of rain wheat crop in Sojasroud village of Khodabandeh city of Zanjan province was estimated using multi-time satellite images of Sentinel-2 measuring satellite and its results were compared with the agricultural cadastral map of 2017-2018. Supervised classification and two methods of support vector machine and maximum likelihood were used to extract information and by comparing the two methods, the most appropriate method was selected and suggested. The error matrix was used to evaluate the accuracy of the classification. The overall accuracy of the support vector machine method was 89% with a capa coefficient of 0.80 and in the maximum llikelihood method it was 88% with a capa coefficient of 0.79. The evaluation results showed that the support sector machine classification method has a higher accuracy than the maximum likelithood, so to extract the area under cultivation in the study area, the support vector Machine classification method is proposed. Comparison of the results of the area under cultivation with the statistics of Jihad Keshavarzi showed a deviation of 18% and the amount of wheat crop using the area under cultivation obtained by classification method was compared with the statistics of the Rural Cooperative Organization which showed a deviation of 17%. The results showed that the classification of the support vector machine is an acceptable and appropriate method for identifying and separating wheat from other agricultural crops.

Since agricultural plain lands of Ardabil have been important in providing food stuffs, and due to the necessities of land usage management in this section, determination of process and change-rating of land cover I important for developers. In this regard, dynamic temporal data of remote sensing can play an important role in searching and analyzing of land usage change which is done by means of numerical change detection techniques in which the descriptive process of changes in covering variety and situation of land use is based on the temporal series image data. Basic hypothesis in using remote sensing data is on the base of these criteria that claims the ascending or descending nature of changes between two or more periods is recognizable, and by comparing the related data from different years, we can detect mutual relationships between natural, social and economic parameters. In this study, maximum likelihood supervised classification and change detection techniques were applied to Land sat images acquired in 1990 and 2007, respectively, to map land cover changes in the Ardebil city. A supervised classification was carried out on reflective bands for the four images individually with the aid of ground truth data. Ground truth information collected were used to assess the accuracy of the classification results. Using ancillary data, and expert knowledge of the area through GIS further refined the classification results. Change detection technique was used to produce change image through cross-tabulation Changes among different land cover classes were assessed. During the study period, a very severe land cover change has taken place as a result of agricultural and urban development projects. These changes in land cover led to vegetation degradation and cropland in part of the study area.

There was a possibility to study earth coverage on a large scale using remote sensing data. The support vector machines (SVM), artificial neural network ‏)ANN( and maximum likelihood )ML‏( algorithms were used to Land cover classification on OLI sensors data and 4 kernels in Guilan province.

Classifications were based on training samples of 10 different covers in the entire Guilan province. To improve the classification accuracy on OLI image data, the MODIS atmospheric products used in 6SV atmospheric correction model. The OLI atmospheric corrected image segmented to 219000 polygons based on homogeneity. In this study 2% of polygons were used to test and training samples by the random statistical method. Polygons labeled to classes by field survey.

Applying ANN, SVM and ML algorithms on the OLI images after atmospheric corrected by 6SV model, the overall accuracy of classification improved 0.11%, 0.8%, and 1.9% respectively. The results indicated that the land cover map by RBF-SVM had overall accuracy and kappa coefficient with 75.6% and 0.72 respectively. In this algorithm accuracy of agriculture, range‏ shrub land and water body classes were ‏93.16%, 72.55% and‏ 96.57% respectively. The results of this study indicated that SVM algorithm improved overall accuracy 1.67% compared to the ML algorithm.

This research indicated that in land cover classification and mapping of Guilan province, the nonparametric SVM algorithm had more accurate than the ML parametric algorithm. According to the results of this research, it is suggested that atmospheric correction models should be used especially on the large and local images.

In this study, the ecological status of Gorgan Bay is classified by using the results of biological indicators and diversity index. For this aim, data on macrobenthose and  the sedimentation characteristics were recorded at 22 sampling points during 2011 to 2012. According to the changes in the depth of the bay in different parts, the stations were classified into four groups of depth less than 1 meter, 1-2 meters, 2-3 meters and more than 3 meters. The total Range of organic matter, gravel, silt and clay in the Bay was recorded in (1.3-4.7), (47.1-57.7), (37.6-51.7) and (6.2-8.4) percent. The positive relationship between the total organic matter and the texture of bed indicates the accumulation of organic matter in the depths of the bay. By using multivariate tests the classified and distinct high depth from low depth of the bay, based on environmental conditions and parameters. The amplitudes change of Shannon index, BO2A and M-AMBI in the Bay were, calculated in range of (0.1-96.69), (0.02- 0.06) and (0.6- 0.8). Overall, according to the average of the calculated indices, Shannon weakened the whole ecological status of the bay and BO2A and M-AMBI indices the desired results were obtained. On the other hand, evaluation of the indicators showed that the condition of the shallow western part is better than the deep eastern part. This is can because of the region's hydrodynamic conditions and the adverse conditions for different benthic species due to the proximity to the River and other entrance around the bay.

In the past decade, remote sensing has been widely used to identify surface changes of different vegetation and their classification. Increasing the level of Canebrake of the Zarivar Lake and its risks for aquatic organisms living in the lake has become one of the most important issues in recent years. Therefore, the aim of this study was to identify surface changes of this Canebrake in the past three decades using Landsat TM and ETM. For this purpose, bands 3, 4, and 5 of images were geo-referenced. RMSE were less than one pixel for all bands. The supervised classification method with a maximum likelihood algorithm was also applied to detect the changes of water area on the combined images (bands 5, 4, and 3) of months with full water in the lake. NDVI index was utilized to identify the surface changes of Canebrake on the images taken in the months with low water in the lake. The results show that the rise and fall of water area and surrounding canebrake has a direct correlation with a rainfall and increase in both levels maybe occur at the same time. Study on the coastal strip of water area with GPS and combined images showed that the coastal line had not a significant change in the past three decades.

Since agricultural plain lands of Ardabil have been important in providing food stuffs, and due to the necessities of land usage management in this section, determination of process and change-rating of land cover I important for developers. In this regard, dynamic temporal data of remote sensing can play an important role in searching and analyzing of land usage change which is done by means of numerical change detection techniques in which the descriptive process of changes in covering variety and situation of land use is based on the temporal series image data. Basic hypothesis in using remote sensing data is on the base of these criteria that claims the ascending or descending nature of changes between two or more periods is recognizable, and by comparing the related data from different years, we can detect mutual relationships between natural, social and economic parameters. In this study, maximum likelihood supervised classification and change detection techniques were applied to Land sat images acquired in 1990 and 2007, respectively, to map land cover changes in the Ardebil city. A supervised classification was carried out on reflective bands for the four images individually with the aid of ground truth data. Ground truth information collected were used to assess the accuracy of the classification results. Using ancillary data, and expert knowledge of the area through GIS further refined the classification results. Change detection technique was used to produce change image through cross-tabulation Changes among different land cover classes were assessed. During the study period, a very severe land cover change has taken place as a result of agricultural and urban development projects. These changes in land cover led to vegetation degradation and cropland in part of the study area.

Despite of all the studies of aquatic invertebrates of Bahracan estuary, as yet Amphipoda inhibiting in the eastern shores muddy substrates of it, have not been studies from the point view of identification. The present investigation, aims to identify the Amphipods Bahracan shores (29˚ 50'-30˚ 15'N, 49˚ 30'-49˚ 55'E) of the Persian Gulf which was done during the seasons of spring and summer of 1392. Sampling was done by using vanveen Grab (with 0.0625m² surface areas) from stations in intertidal zone and 6-25 m depth. The samples were counted and identified through the valid identification keys. The obtained results of the study, identified six families were belonged to suborder Gammaridea, which are fallow: Ampithoidae, Ampeliscidae, Isaeidae, Gammaridae, Melitidae, Ischyroceridae. Frequency of each family was calculated throughout the study period; the families of Ampeliscidae and Ampithoidae were identified and introduced as the dominant families in Bahracan with 39.47%.

Classification of atmospheric aerosols is one of the important topics in the airborne and ground-based instruments remote sensing. Polarized sky radiance resulting from interaction between sunlight and atmospheric particles strongly depends on the presenceof aerosols in the atmosphere, and can be monitored by looking at the aerosol polarized phase function. In this paper, the ability the polarized phase function to classify atmospheric aerosols has been investigated for the atmosphere of the Zanjan. To do this, aerosol optical depth (AOD), Angstrom exponent (α), single scattering albedo (SSA), and polarized phase function have been retrieved from the measurements of SPM in the period of February 2010 to December 2012. The results show that the maximum valueof aerosol polarized phase function is strongly correlated (R = 0.95) with the Angstrom exponent. Furthermore the polarized phase function shows a moderate negative correlation with respect to the atmospheric aerosol optical depth and single-scattering albedo (R =−0.76 and−0.33, respectively). Therefore the polarized phase function can be regarded as a key parameter to characterize the atmospheric particles of the region –a populated city in the semi-arid area and surrounded by some dust sources of the Earth’s dust belt.

In this paper, has been tried to discriminate wheat_cultivated fields from those crops that have similar characteristics to this class, spectrally and phonologically, in bahar_hamedan region. So based on cultivation calendar of study region, decided to apply bitemporal or dual_season multispectral spot5 images for this aim. Both images divided to discrete objects(polygpns) using multiresoluion segmentation algorithm. The zonal attributes of segments entered to discriminant analysis as variables. After the stepwise discrimination analysis, all the segments were classified using outputted discrimination functions. ‘’discrimination accuracy’’ and ‘’ classification accuracy’’ were defined as discrete concepts. Also, with assuming the DNs of each band pixels as variables “pixel based” discriminant analysis was applied. Comparison of both methods results indicated that generally, accuracy of pixel_based classification is upper than other method.