جستجوی مقالات مرتبط با کلیدواژه "آنالیز مولفه های اصلی" در نشریات گروه "جغرافیا"

Erodibility, which is determined by the soil's intrinsic features, is one of the most important elements in soil erosion. This factor reflects how sensitive the particles of a particular soil are to separation and transmission by erosion causes, both quantitatively and qualitatively. For measuring soil loss, the Universal Soil Loss Equation (USLE) is very useful. Sources reveal that erodibility is influenced by a variety of physical and chemical features of soil. In several soil erosion and sedimentation models, such as USLE, RUSLE, and MUSLE, one of the essential parameters is erodibility, which is represented as K. Particle size distribution, organic matter, structure, and permeability all have a role. The goal of this research was to quantify the amount of erodibility (K) in dry and semi-arid soils, as well as the physicochemical parameters that influence it. Another purpose of this research is to develop a connection that uses principal component analysis (PCA) and linear multivariate regression to estimate the quantity of soil erodibility based on effective physicochemical parameters.

The research location is 20 kilometers from Yazd city, along the Yazd-Ardakan road, on the edge of the dunes facies, which includes bare, mantled, and covered pediments. Using the stratified random sampling approach, soil samples were gathered to a depth of 10 cm within the facies in this study. The size and form of aggregates, as well as water penetration in the soil, were used to calculate soil structure codes using Wischmeier and Smith's tables. In the desert, soil permeability was assessed using double cylinders based on the ultimate infiltration rate. The hydrometer technique was used to determine the spread of soil granulation. Wet sieving and the Walkley and black methods were used to assess the proportion of extremely fine sand and organic matter, respectively. Lime was calculated by multiplying the volume of the hydrochloric acid neutralization reaction by the quantity of neutralizing agents. Statistical indicators such as mean, minimum, maximum, and standard deviation were derived at this step after computing the soil erodibility index. Principal component analysis was performed using SPSS17.0 software, and the linear multivariate regression model was utilized to predict soil erodibility index. After selecting significant components, linear multivariate regression between these components and soil erodibility was conducted concurrently. The coefficient of determination was used to assess the equation's accuracy in this investigation (R2).

The findings of the physical and chemical features of soil study revealed that the texture of the soil is mostly light sandy to loamy, with low organic content and calcareous. In terms of structural form, the analyzed soils were extremely fine granular and spongy, and their structural code was based on USLE (2 and 1). The permeability of the soil profile was high to extremely high (18.4 cm/h), and it was often in Class 1, 2, and in some instances Class 3 according to USLE. In the three naked, mantled, and covered pediments, the estimated erodibility indexes based on Wischmeier and Smith regression relationships were 0.0385, 0.03, and 0.0199 ton.hr/MJ.mm, respectively. According to the particular values acquired from the parameters and the percentage of variance, the top three components may be picked as the major component using principal component analysis. The first, second, and third components have correlation values of 0.88, -0.04, and 0.41, respectively, with the soil erodibility index. As a result, the first component has a stronger relationship with the soil erodibility index than the second and third ones. The percentage of sand and silt, soil permeability, and percentage of clay have a higher correlation with the soil erodibility index, respectively, and the correlation of other factors (organic matter, gravel, fine sand, and lime) is low in this component, according to the values for the given loading period. The amount of sand in the soil and its permeability are negatively correlated; whereas, the percentage of silt and clay in the soil is positively correlated. The maximum load is connected to the variables of gravel and lime in the second component, and it is related to organic matter and extremely fine sand in the third component. The effect of characteristics on soil erodibility is significant (0.001>p) and its coefficient of determination (R2) is 0.88 percent, according to an investigation of the relationship between soil erodibility and principal component values obtained from PC1, PC2, and PC3 using a linear multivariate regression model.

 The quantity of erodibility in dry and semi-arid soils, as well as the physicochemical parameters that impact it, were investigated in this research. Using principal component analysis and linear multivariate regression, a link was found to estimate the quantity of soil erodibility based on the effective physicochemical parameters. Because of the high amount of sand in the region's soils, these soils are readily separated due to poor adhesion, but because they contain bigger particles, they resist runoff and hence create less sediment. This barrier to transfer reduces as the quantity of clay and silt in the soil increases, and consequently more sediment is transported. Furthermore, a considerable quantity of sand improves soil permeability and reduces runoff. However, when the amount of silt and clay in the soil increases as a result of surface sealing, permeability reduces and greater runoff occurs. Soil erodibility is additionally influenced by organic content, lime, gravel, and permeability. Lime has a negligible influence on soil erodibility since it contains calcium cation, which increases particle homogeneity and hence increases soil resilience to rain drops. Organic matter has a negative relationship with soil erodibility as well. The breakdown of aggregates is slowed by increasing the quantity of organic matter in the soil. As a result, as organic matter levels rise, the rate of aggregate decomposition in a particular soil falls by one-third. Similar research studies in other semi-arid and arid soils in Iran are required to provide a more reliable connection for forecasting erodibility of soils in semi-arid and arid locations.

Oak is a common species in Iran and the most important one in Zagros forests. Zagros forests play a crucial and effective role in water supply, soil conservation and climate modification in Iran. Unfortunately, a significant part of those forests suffer from oak decline. Oak decline (or oak mortality) is a widespread phenomenon in oak forests around the world, which has gained the attention of many researchers in forestry over the past decade. In Iran, this phenomenon was first observed in Zagros forests in 2013. Factors affecting oak decline and their mutual interactions are not clearly identified, which makes understanding and modeling of these processes challenging. Only a few studies have been performed in relation to this phenomenon in Iran. Thus, we chose to determine the most effective parameters and find the best modeling method for oak decline in Iran and especially in Lorestan province.

In order to find effective environmental variables, related literature review was thoroughly investigated. Environmental parameters including temperature, precipitation, elevation, slope, direction, soil type, and amount of aerosols were selected as basic influencing parameters. All parameters were then interpolated to produce raster data with 30-meter cell resolution. To find the optimal combination of the parameters, four operators including multiplication, logarithm, hyperbolic transformations, and principal component analysis (PCA) were used. A total 385 different combinations of the influencing parameters were produced using the above mentioned operators. The relation and weight of each parameter are unknown, thus Artificial Neural Networks were used to model oak decline process. Three feed forward artificial neural network, including Back-propagation Neural Network (BP), Probabilistic neural network (PNN) and Support Vector Neural Network (SVNN) were selected as modeling methods. Then, 385 different combinations of the influencing parameters were used in the above mentioned models. To train and evaluate each neural network, a total number of 10000 samples were randomly selected from the study area. 70 percent of these random samples were used to train, 15 percent to evaluate and 15 percent to validate the models. Also, cross-validation method was used to avoid over fitting of neural networks. Finally, 1155 created NN models were compared using R parameter to find the best configuration for modeling oak decline and identifying the most influential environmental parameters in oak decline.

Evaluating 1155 different networks indicated that Probabilistic neural network (R=0.87) with 6 inputs, including 1) elevation, 2) slope, 3) direction, 4) aerosols, 5) soil type and 6) principal component of temperature and precipitation, performed better than SVNN and BP in modeling oak decline. Moreover, using different combinations of influencing factors improved the results and increased correlation coefficient (R) of optimal inputs by 0.05 as compared to initial inputs. Thus, it can be concluded that increased number of inputs does not necessarily guarantee a better performance. Furthermore, two principle parameters of temperature and perception have a more significant role in modelling drought stress as compared to other parameters.

Oak decline is a complicated phenomenon and different factors contribute to its occurrence. The present study investigates all environmental parameters affecting oak decline through a comprehensive literature review. Results indicate appropriate performance of probabilistic neural networks in modeling oak decline. Moreover, principal component analysis is considered to be a useful tool for modeling of drought stress in oak trees. Due to different accuracy and precision of these neural networks, it is necessary to evaluate different configurations. For further researches, it is suggested to use other parameters, such as distance from population centers, water table, age of oak trees, oak tree height and characteristics of other nearby trees.

Extended Abstract
Land use/cover (LULC) change detection is one of the most important applications in the remote sensing field, providing insights that inform management, policy, and science. In the recent decade, development of remote sensing systems and accessibility to high spatial resolution images has associated with the improvement of digital image processing. The advantage of high spatial resolution remote sensing imagery further supports opportunities to apply change detection with object-based image analysis, i.e. object-based change detection – OBCD.
OBCD analysis in comparison with pixel-based techniques provides a more effective way, especially in high spatial resolution imagery to incorporate spatial, spectral, textural and geometry feature that can identify the LULC change in comparison with pixel-based technique. OBCD approach is classified into for categories: (i) image-object, (ii) class-object, (iii) multi- temporal object, and (iv) hybrid change detection. Different algorithms and features can be employed in the process of image classification for OBCD. Therefore, the choice of algorithm and optimization features are major challenges in OBCD. This paper has introduced an object- based change detection method based on the machine learning algorithm, which can overcome the traditional change detection method limitation and find the interested changed objects. In this paper, multi-temporal object approach is utilized and high spatial resolution imagery, GeoEye-1 and Quick Bird-1 satellite images were acquired during 2002 and 2015, covering a region of the Geshm Island which were used to detect the meaningful detailed change in the study area. As an essential preprocessing for change detection, multi-temporal image registration with the accuracy of less than one second of a pixel is applied. Also, radiometric correction is performed using histogram matching algorithm in ENVI Software. In the Next step, a number of texture features of images such as mean, variance, entropy, homogeneity, momentum and such are extracted from two images. To reduce the input features space, PCA algorithm is employed and the result of this process is used in the segmentation process. The two images are incorporated with PCA output and are used as inputs feature to segmentation. Segmentation is the first step in OBCD. It divides the image into larger numbers of small image objects by grouping pixels. The segmentation algorithm is a region-merging technique. It begins by considering each pixel as a separate object. Subsequently, adjacent pairs of image objects are merged to form bigger segments. The merging decision is based on local homogeneity criterion, describing the similarity between adjacent image objects. Correct image segmentation is a prerequisite to successful image classification. At the same time, this task requires explicit knowledge representation. Furthermore, optimal segmentation results are depended on not only the choice of segmentation algorithm or procedure, but are also often influenced by the choice of user-defined parameter combinations which are required inputs for many segmentation programs. The segmentation has been done using multi resolution segmentation algorithm which involves knowledge-free extraction of image objects. Multi-resolution segmentation begins with single pixel objects and employs a region-growing algorithm to merge pixels into larger objects; pixels are merged based on whether they meet user-defined homogeneity criteria. Each multi-resolution segmentation task must be parameterized by the user and involves settings of three parameters: Scale, Color-versus-Shape, and Compactness-versus-Smoothness. In this paper the process of segmentation is performed in four different levels using Ecognition software and finally, the level with better output with scale of 100 is selected to provide the change map. The scale values were determined through an iterative method. The color/shape was set to 0.6/0.4 and compactness/sharpness was set to 0.5/0.5 for the selected level. Color and shape weightage are inter-connected to each other. If color has a high value, which means it has a high influence on segmentation; Shape must have a low value with less influence. If both parameters are equal, then each will have roughly equal amount of influence on segmentation outcome. In addition, texture, spatial and geometrical features from the segmented image are extracted. Feature space Optimization (FSO) tool available in Ecognition software have been used to calculate optimum feature combination based on class samples in four classes including: ”barren to road”, ”barren to building”, barren to vegetation” and “barren with no change. It evaluates the Euclidean distance in feature space between the samples of all classes and selects a feature combination resulting in best class separation distance. In this study, the performance of the proposed RF-based OBCD method is compared with the conventional methods such as support vector machine (SVM) and KNN. The commonly used accuracy assessment elements include overall accuracy, producer’s accuracy, user’s accuracy and the Kappa coefficient. The overall accuracy of the change map produced by the RF method was 86.57%, with Kappa statistic of 0.79, whereas the overall accuracy and Kappa coefficient of that by the SVM and NN methods were 83.76%, 0.75 and 75%, 0.63, respectively. Experimental results show that overall accuracy and kappa coefficient obtained from the proposed RF-based OBCD method improve 3% and 18%, 2% and 10% respectively compared with SVM and KNN improved. The results indicated that object base change detection method can be performed more accurately and reliably in the high-density region if it uses image with high spatial resolution. Also, selection of classification algorithm has very impressive effect on the providing change map.

The purpose of the current study is to investigate the effects of some environmental factors on erosion value, to determine the most important governing factors and the relation between erosion of working units and environmental factors (soil characteristics, slope, aspect, elevation, lithology, geomorphology faces and vegetation cover percentage). We used the geomorphology method to prepare a working units map. This map was derived by overlaying slope, aspect, elevation, lithology andgeomorphology face maps.In order to study plant cover, random-systematic sampling in each working unit was conducted in ten plots.Regarding the species type and distribution, the area of each plot was determined based on the minimal area method. Canopy cover of species was determined in each plot. Furthermore, five profiles were sampled within the working units to study soil characteristics at depths of 0-50 cm. Subsequently, the texture, percent of lime, organic matter, gravel, pH and EC were measured. Erosion value was determined in each working unit by the E.P.M. method.Statistical analysis was performed using Principal Components Analysis (PCA) through the PC-ORD4 software program.The results showed that among environmental factors, the vegetation cover, lime percentage, face and organic matter were the first set of factors that determined the change in erosion value by 33.99%.The second set of factors that included loam,stone resistance and clay percentage play contributed to the change by 17.295%. These two sets of factors altogether explain 51.288% of the erosion value variation in VarkeshBasin.