نشریه کاربرد سنجش از دور و سیستم اطلاعات جغرافیایی در علوم محیطی
پیاپی 3 (تابستان 1401)

With the development of urbanization and excessive interference of humans in the environment, large amounts of agricultural and forest areas have been replaced by houses, industrial areas, and other infrastructures. One of the negative effects of industrialization is the sharp increase in the temperature of cities and the creation of temperature differences between urban areas and the surrounding areas, which is known as the heat island. The main goal of this research is to estimate the temperature of the earth''s surface and investigate the effects of urbanization on the temperature of the earth''s surface in the city of Tabriz. For this purpose, satellite data and remote sensing because of their wide coverage; are Used. In this method, the temperature of the earth''s surface has been calculated using the information of a thermal band (Landsat 8 satellite) and after conforming to the land use map, the temperature of different uses of Tabriz city has been estimated. The results of the research show that there is a direct relationship between the spatial-temporal changes in the cities (including the change in land use and the reduction of vegetation mass) in the city of Tabriz and the increase in the temperature of the earth''s surface, and satellite images (remote sensing) are a very suitable tool for evaluating heat and temperature. They are the surface of the earth.

Investigating land use change is one of the most important aspects of natural resource management and reviewing environmental changes. With the increasing need for food supply, the growth of urban areas and the necessities of human life, changes occur at the earth's surface that can lead to the destruction of land and resources. These changes are caused by the confrontation of the constant needs of human and environmental societies with the earth. In the present study, using the remote sensing technique, land use changes in Kermanshah county in the period between 2013 and 2021 were studied using Landsat 8 images. Radiometric and atmospheric corrections were performed by Flaash algorithm in ENVI 5.3 software and ML and SVM classification methods for generating land use maps in six classes (including urban and rural areas, forests and gardens, rocky area, irrigation and agriculture, flats). Barren land) was used. The results obtained using the error matrix for land use maps prepared for the years 2013 and 2021, with an overall accuracy of 90% and 89% and kappa coefficient of 86% and 85%, respectively, which is within acceptable range. During this period, land use changes, urban and residential areas with 11.6 percent, forest and garden cover 119.7 percent, irrigated area 293 percent, and barren land 4.6 percent positive growth, and agricultural areas and agricultural land in this nine-year period 5.5 Percentage and 2.5% rocky area recorded negative growth.

One of the important issues in water resources management is the estimation of rainfall in areas where pregnancy has not been done. Due to the lack of complete coverage of the rain measuring stations, it is necessary to estimate the rainfall in the areas between the stations. . Estimation of unknown data at different points is done using interpolation methods on the measured data in the studied area. For this purpose, different methods are used to analyze spatial dataIn this research, a variety of common methods of interpolation and geostatistics have been used to estimate the average annual rainfall parameter of Chahar Mahal and Bakhtiari province. The statistics used are the statistics of 44 rain gauge stations for the period from 1980 to 2008. . Due to the extreme changes in the topography of the study area, the effect of altitude on the distribution of precipitation as a barrier against the spread of clouds has also been investigated. For this purpose, interpolation was calculated with various models and a prediction map was drawn. Then, using the mutual evaluation method, the square root of the errors and the error of the maps were estimated and one map was selected as the appropriate map The obtained results show that for the interpolation of the average annual rainfall methods that considered the heights as obstaclesThey show higher accuracies in the studied area, among which the fourth and fifth degree polynomial models showed the most suitable information for interpolation of average annual rainfall values

  Over time, patterns of land cover and land use change and subsequent changes are fundamental and human factor plays a most important role in this process. Ever, scientists have attempted to identify factors that cause land use changes and their impact on the environment. Therefore, in previous decades, researchers have different views collected from the field, as well as aerial photographs to detect land use changes resulting from the imposition of natural and human processes have been analyzed. Today, however, based on technological advances made in the field of remote sensing, satellite imagery can be used to more accurately evaluate the environmental changes during the process and the final results of the illustrated model. The main purpose of the ongoing monitoring of land use changes in river basins liqvan is 1985-2006-2013. Accordingly, to explore the changes occurring in the study area, Landsat TM and ETM + Landsat images of the years 1985-2006-2013 were analyzed. Accordingly, after applying atmospheric and geometric correction, image enhancement operations performed using the maximum likelihood method of supervised classification algorithms similar actions and thematic maps of land use of the basin has been designed to liqvan. In general, the overall accuracy of SVM method calculated in 1985 (96.20) and in 2006 (96.26) and in 2013 (99.64) by Mdkh highest accuracy than other methods. Finally moorland in the first place and then irrigated gardens and residential areas in the study area are eventually.

Wheat stripe rust has a severe effect on the yield and quality of wheat. An effective forecasting method is essential for food security. In this study, firstly, optimal vegetation indices related to belt rust stress are extracted as candidate features for disease prediction from 2-Sentinel time series images. Then, optimal VI combinations are selected using sequential forward selection (SFS). Finally, the occurrence of wheat stripe rust in different time intervals is predicted using Support Vector Machine (SVM) method. The results of the selected traits show that, before the binding period, the optimal VIs are related to biomass, pigment and moisture of wheat. After the connection period, the red-edge VIs related to the health status of the product play an important role. The overall accuracy and kappa coefficient of the prediction model, which is based on SVM, is generally higher than K-nearest neighbor (KNN) and back-propagation neural network (BPNN) methods. SVM method is more suitable for predicting time series of wheat rust. The model achieved accuracy based on optimal VI combinations and the SVM increased over time. The highest accuracy was 86.2%. These results show that the prediction model can provide guidance and suggestions for early disease prevention in the study area, and this method combines Sentinel-2 time series images and SVM, which can be used to predict wheat stripe rust.

Network construction is an acceptable approach for better understanding the behavior of complex system which can be used to reveal the pattern of collective dynamics for realizing physical interactions in the dynamical system. In this case, characterizing functional connectivity of complex networks for studying a broad class of natural and artificial systems from the measures of correlation and causality is of utmost importance to correctly unravel physical phenomena of the system. Many network reconstruction approaches are based on heuristically thresholding the correlation matrices resulting from pairwise correlation analysis according to experimental methods. Other approaches compare the observed correlations against null models in the statistical analyses, obtaining results which are statistically robust. Different methods were used, including cross-correlation (CC), spectral coherence (SpeCoh), mutual information (MI), transfer entropy (TE), Spearman's rank correlation (SC) and convergent cross-mapping (CCM). The methods were applied to linear and nonlinear collective dynamics by autoregressive moving average (ARMA) and Logistic map (LOG) models, respectively. The dynamics of interconnected units was simulated from different complex topologies widely observed in empirical systems with well-known network models. The methods of MI and CCM were chosen after examining on the artificial cases consisting of desirable features of the real systems. The results show that network reconstruction dramatically depends on the interplay between the (unknown) underlying structure and the (unknown) underlying dynamics and high values of the area under curve of receiver operating characteristic around 72% obtain for the selected network topologies.