علمی - پژوهشی
farzaneh hadadi; Davod Ashourloo; Alireza Shakiba; Aliakbar Matkan
Abstract
Climate change is one of the most important challenges facing mankind. This phenomenon has already had significant impacts on agricultural products in most parts of the world, especially arid and semiarid regions. Also, average temperature has risen in many regions in recent decades. Nowadays, in various ...
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Climate change is one of the most important challenges facing mankind. This phenomenon has already had significant impacts on agricultural products in most parts of the world, especially arid and semiarid regions. Also, average temperature has risen in many regions in recent decades. Nowadays, in various researches, remote sensing indices are used as one of the new methods in identifying climate change. One of the important indices of remote sensing is the phonological characteristics of vegetation, which in recent studies has shown great potential in identification and estimation of vegetation. In the present study, using the 5-day normalized vegetation index (NDVI) time series of NOAA-AVHRR images and plant phenology parameters, vegetation changes in rangelands and dryland areas of Lake Urmia Basin during 1984-2013 were investigated. Climatic temperature and precipitation data was obtained from the meteorological stations of Lake Urmia basin and was compared with the results of satellite images. The results of time series analysis over thirty years of statistical period in Lake Urmia basin showed that the beginning of the growing season in Oshnavieh, Saghez and Sarab started earlier in 2013 than in 1984. But in the Maragheh area it began later. The end of the growing season in Oshnaviyeh, Saghez and Takab has ended earlier. Also, the peak growth parameter in the above mentioned vegetation reached its maximum value earlier. The length of the growing season has been decreased in the cities of Oshnavieh, Maragheh and Saghez, respectively. The results of statistical analysis obtained from satellite images and climatic data showed that changes in phonological parameters are location dependent and also decreased and increased in cold nights and hot days at the beginning of the growing season, respectively. But at the end of the growing season, the warm days have increased. These changes increased the slope of the plant growth phenology curve at the time of plant aging and ultimately reduced the length of the growing season.
علمی - پژوهشی
Bhareh gharedaghy; amir ghasemzadeh
Abstract
Due to its high environmental diversity, Iran has a high rank in crises caused by natural disasters. Flood as one of the natural disasters, following the rapid growth of cities and climate changes in many regions, has caused severe social and economic, health and environmental damage. For this reason, ...
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Due to its high environmental diversity, Iran has a high rank in crises caused by natural disasters. Flood as one of the natural disasters, following the rapid growth of cities and climate changes in many regions, has caused severe social and economic, health and environmental damage. For this reason, predict of flood susceptibility is so essential that failure to identify flood susceptibility may increase its destructive effects. Recently, with the advancement of remote sensing tools, geographic information, machine learning and statistical models, it is possible to create a more accurate flood susceptibility map. For this purpose, in this research, by using Sentinel satellite images and using the Ensemble approach with six machine learning models, flood susceptibility was predicted in the Karun watershed. Individual models include Generalized Linear Model (GLM), Boosted Regression Tree (BRT), Support Vector Machine (SVM), Random Forest (RF), Multivariate Adaptive Regression Splines (MARS), Maximum Entropy (MAXENT). The results of this study show that the northeast of Aligudarz city, parts of Durud and Azna in Lorestan province, Khademmirza, Shahrekord and Kiyar in Chaharmahal Bakhtiari province, Dana and Boyer Ahmad in Kohkiloye and Boyer Ahmad province, Semirom city in Isfahan province and the southern border areas of Karun River in Khuzestan province has the highest flood potential in this basin. The results of this research are effective for managers and planners and will prevent development in vulnerable areas and reduce financial and economic losses in the future.
علمی - پژوهشی
Fatemeh Ahmadi; yasser Ebrahimian Ghajari; Abbas Kiani
Abstract
Abstract: Remote sensing provides a powerful data source for mapping urban areas and monitoring urban dynamics on various scales. Among remote sensing data, images taken at night provide an effective way to monitor human activities on a global scale. Because the features and capabilities of these images ...
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Abstract: Remote sensing provides a powerful data source for mapping urban areas and monitoring urban dynamics on various scales. Among remote sensing data, images taken at night provide an effective way to monitor human activities on a global scale. Because the features and capabilities of these images can enable separating urban areas and other human activities, the main feature of which is the use of light at night by accurately measuring the location, from the background without light. Via providing uninterrupted and continuous monitoring from the night world perspective, these images provide valuable source and results of human activities from the past to the present; the time series analysis of this data is highly valuable for discovering, estimating and monitoring social and economic dynamics in countries, especially sub-regions where there are no official statistics. With the development of night data satellite sensors in recent years and new research conducted in the field of night-time data, this study aims to review the advances in night-time sensors, introduce the existing data and products, review and express the advantages and disadvantages of each one and review methods and solutions presented in previous research for solving the existing problems and limitations in order to improve these images. Therefore, according to the reviews of 225 articles on night light from various credible journals, the results demonstrated that 65% of the available articles were from night light data in the urban field (59% related to urban dynamics extracting and studying and 6% for population surveys) and 35% for non-urban field (17% for energy consumption, 13% for economic issues and 5% for other issues such as pollution) in a more detailed look. Articles that have tried to provide methods for correcting night images can be categorized as follows: in the DMSP-OLS data, 44% of the articles were urban surveys, 14% were for economic purposes, 38% for the general purpose of correction and 4% for other purposes. Visible and infrared imaging radiometer suite (VIIRS) data were 57% for economic purposes, 36% for urban surveys and 7٪ for other purposes. The results and findings of this study can provide a general overview for researchers to familiarize and understand the trends of various studies in the use of night light data. It can also help researchers choose the right data and algorithm according to their purpose and study field.
علمی - پژوهشی
morteza Sharif; S Attarchi
Abstract
The phenological cycle of plants plays an important role in the global carbon cycle. Considering the importance of the role of plants in the urban ecosystem and its role in the health of socity, it is necessary to study and monitor the phonological cycle of plants in different seasons of the year in ...
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The phenological cycle of plants plays an important role in the global carbon cycle. Considering the importance of the role of plants in the urban ecosystem and its role in the health of socity, it is necessary to study and monitor the phonological cycle of plants in different seasons of the year in urban areas at different spatio-temporal scales In this study, the two widely used NDVI and EVI indices calculated from OLI sensor of landsat satellite and MOD13Q1 product of MODIS sensor images were used to investigate the plant phenology cycle in Ahvaz metropolitan area from the period 2015 to December 2019. In this research, satellite images were retrieved and prepared through the Google Earth Engine platform. Then, according to the type of vegetation, the phenological cycle of the plants was obtained based on the vegetation indices and compared with the phenological cycle obtained from the ground surveys. Due to the possibility of noise and pixels with spectral mixing, Savitzky-Golay filter was used to smooth the phenological cycle of plants. The results show the increasing trend in the values of both NDVI and EVI indices by 0.03 and 0.04 in the OLI sensor and 0.01 in the MOD13Q1 product (annually), respectively. These changes were positive in January, March, October, November and December on both sensors. Differences were observed in both sensors during plant phenology phases. The largest difference between two sensors was observed in 2018 and 2019. This shows, in case the weather provides better condition, the plant chlorophyll content will increase. This will lead to the difference between the results of both sensors. The growing season transition periods obtained from the OLI sensor showed more detail than the MODIS medium resolution dataset. The MODIS sensor shows the growing season period start earlier than the OLI sensor. In general, according to the MODIS product, the duration of the growing season (between the beginning of the growing season (mid-winter) and the end of growing season (EOS) (early summer) is four-month. The lowest difference between the periods of the growing season of plants with ground observations in OLI and MODIS sensors, was 7 and 10 daye for Start of growing season (SOS), respectively. The biggest defference was observed at the peak of the growing season with 20 and 35 days, and for the end of growing season (EOS), 20 days later and 20 days earlier, respectively, according to ground observations. However, the length of growing season (LOS) in the OLI sensor is about five months. That the results of OLI sensor are closer to ground observations.This difference is due to the increase in heterogeneous conditions in the target phenomena and/or the spatial resolution of the MODIS sensor images. It is concluded that, the results of the OLI sensor improve our understanding of human interactions with natural environment in urban areas. Therefore, addressing them in future studies can mitigate many environmental challenges and provide more realistic information for planning.
علمی - پژوهشی
مهدی Amiri; Farzad Amiri; Mohammad Hossein Pourasad; Seyfollah Soleimani
Abstract
Clean air quality, as one of the most essential needs of living organisms, has been compromised by natural and artificial activities. Dust storms have been constantly increasing in recent years, which have resulted in countless social, economic and environmental health damages for residents of southern ...
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Clean air quality, as one of the most essential needs of living organisms, has been compromised by natural and artificial activities. Dust storms have been constantly increasing in recent years, which have resulted in countless social, economic and environmental health damages for residents of southern and southwestern regions of Iran. In this study, (MODIS) sensor data are used to investigate dust storms and detect horizontal depth of field. The advantages of MODIS sensor data include high spectral and temporal resolution. In addition, data from meteorological stations are collected according to the study period. After pre-processing data and preparing field observations, features required for modeling are derived from the MODIS sensor data through a differential method between the selected bands of each MODIS sensor image along with the features extracted from the sensor. Ground meteorological stations are used. With further studies and evaluations and using the opinions of meteorological experts 42 features are used of which36 are extracted from different bands of Moody's images and 6 features are extracted from meteorological station data. Next, best features are identified through feature selection techniques and a new method called ML-Based GMDH. which is the result of improving the GMDH neural network by changing partial functions with machine learning models, was used to detect dust concentration and horizontal visibility. In addition, to achieve the appropriate accuracy, the meta-parameters of this model are adjusted by the TLBO optimization algorithm. Furthermore, basic GMDH machine learning methods SVM, MLP, MLR, RF and their group model are implemented to compare with the main approach. Results shows that the ML-Based GMDH method adjusted with TLBO by improving on the best methods. The machine learner provides good accuracy in detecting dust concentrations.
علمی - پژوهشی
Somayeh Rafati alashti; Abozar Ramezani; Alireza Sadeghinia
Abstract
The Covid-19 pandemic is considered as a geographical phenomenon, so spatial analysis and its geographical impact on decision-making are very important. Geographic information system and spatial analysis can play an important role in analysis of the spread of Covid-19 at the global level. This study ...
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The Covid-19 pandemic is considered as a geographical phenomenon, so spatial analysis and its geographical impact on decision-making are very important. Geographic information system and spatial analysis can play an important role in analysis of the spread of Covid-19 at the global level. This study investigated the factors affecting Covid-19 outbreak using global and local spatial regression methods. Altitude, population density, mean age, ratio of population over 55 years to the total population as well as meteorological parameters including humidity, temperature, pressure and wind speed were selected as predictor variables. Population density, air pressure, mean age and wind speed were determined as significant predictors based on the stepwise regression and entered into the OLS. The results showed that the OLS model is statistically significant, but the explanatory variables in the model have an inconsistent relationship to the dependent variable both in geographic space and in data space. Therefore, the GWR was used. To solve the problem of local multicollinearity and increase spatial variability, principal component analysis was used. Finally population density, meteorological and age factors were calculated and used as predictor variables in the GWR. Due to the relative improvement of the performance of this model compared to the general OLS model, it can be concluded that the ability of local models to explain the relationships between these variables is higher than global models. The results of Moran’s I test and hot spot analysis showed that there is at least one variable affecting this disease that has not been considered in this study. However, the results of this study have highlighted the importance of demographic and meteorological factors on the Covid 19 outbreak.
علمی - پژوهشی
Behnam Asghari Beirami; Mehdi Mokhtarzade
Abstract
The use of spatial features to improve the classification accuracy of hyperspectral images has become popular in recent years. Various methods for spectral-spatial classification of hyperspectral images have been introduced to date, and relevant research is being conducted to develop methods with a more ...
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The use of spatial features to improve the classification accuracy of hyperspectral images has become popular in recent years. Various methods for spectral-spatial classification of hyperspectral images have been introduced to date, and relevant research is being conducted to develop methods with a more straightforward structure and higher accuracy. This paper introduces a new method for producing efficient features for classifying hyperspectral images based on combining extracted features from the weighted local kernel matrix of spectral and fractal features. One of the main advantages of weighted local kernel matrices is that they model nonlinear dependencies between features that are not taken into account by traditional feature generation methods. In this study's proposed method, the weighted kernel local matrix method is used in order to generate new features from spectral features and directional fractal dimension features. Then these two feature vectors are joined together for each pixel and form a vector rich in spectral-spatial information. Finally, to determine each pixel's label, the obtained feature vector is classified by the support vector machine (SVM) algorithm. The results obtained from two real hyperspectral images of Indian Pine and Pavia University show that the accuracies in the proposed method are above 98% on average in both data sets, which is more than 5% higher than the average accuracy of several other hyperspectral image classification methods.
علمی - پژوهشی
Ali shamsoddini; Bahar Asadi
Abstract
Identifying and mapping crops provides important information for managing agricultural lands and estimating the area under cultivation of crops. This study investigates the importance of red edge bands for segregation of crops including wheat, barley, alfalfa, beans, broad beans, flax, corn, sugar beet ...
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Identifying and mapping crops provides important information for managing agricultural lands and estimating the area under cultivation of crops. This study investigates the importance of red edge bands for segregation of crops including wheat, barley, alfalfa, beans, broad beans, flax, corn, sugar beet and potatoes using random forest method and support vector machine. For this purpose, the time series of Sentinel-1 and 2 images in 2019 from the northwest of Ardabil was called in the Google Earth Engine (GEE) platform. In order to study the performance of spectral and temporal information, plant indices and backscatter information on the crop mapping, different band combinations were examined. Using the random forest feature selection method, important features were identified and introduced as the input of the random forest and the support vector machine classifiers. Random forest provided the best results for all scenarios. The results showed that the addition of red edge wavelengths and red edge-based vegetation indices are more useful than other bands and vegetation indices for mapping barley, beans, broad beans and flax. The best result among different combinations of features was related to the time series of spectral features of Sentinel-2 images fused with the time series of Sentinel-1 images for that the overall accuracy and the kappa coefficient were 84.67% and 82.31%, respectively. Moreover, the results showed that red edge bands and red edge-based vegetation indices are efficient to identify crops from each other.
Original Article
Hanieh Zhendeh Khatibi; Afshin Shariat Mohaymany; Matin Shahri
Abstract
Recently, the use of big data from mobile devices has received considerable attention in transportation studies. The need to do activities is the main inducement for urban trip generation. In addition, urban activities and their patterns vary over space and time. Mobile phone data, as a kind of continuous ...
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Recently, the use of big data from mobile devices has received considerable attention in transportation studies. The need to do activities is the main inducement for urban trip generation. In addition, urban activities and their patterns vary over space and time. Mobile phone data, as a kind of continuous spatiotemporal data, records the location of people at different times. Therefore, such data is appropriate for urban activity level estimation, and its pattern detection. In the present study, mobile phone data was applied to estimate the density of activities (standardized by area) in Shiraz metropolitan area. To examine the spatial dependency of the variable of interest (density of activities), global and local Moran’s I indices were applied on density of activities aggregated over 321 traffic analyses zone in Shiraz on workdays, semi-workdays, and weekends. The results not only confirmed significant positive spatial autocorrelation of density of activities (P_Value<0.001), but also detected the hotspots in the central parts of study areas. Using exploratory analysis of time series and time-series heterogeneity tests, the study identified the trend of activity level, intensity change by time, and change-point of activity in time series. The study also extracted the start time of activities (8 a.m. for workdays and semi-workdays and 9 a.m. for weekends), mid-day peak (12-14), evening peak of trips (20-22), and the minimum activity time (3-6 a.m.). Results of these analyses could be beneficial for appropriate transportation planning, policy-making, demand management, management of population density at hotspots at any time of the day, as well as urban transportation environmental impacts analysis.
علمی - پژوهشی
Salman Goodarzdashti; Mohamad Seifi; Mahshid Kohandel; Davoud Ashourloo; Hossein Aghighi
Abstract
Potatoes are the fourth most cultivated crop worldwide. Regarding the strategic role of this crop in food security, accurate potato mapping provides essential information for national crop censuses and potato yield estimation /prediction at any scale. Although remote sensing (RS) approaches based on ...
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Potatoes are the fourth most cultivated crop worldwide. Regarding the strategic role of this crop in food security, accurate potato mapping provides essential information for national crop censuses and potato yield estimation /prediction at any scale. Although remote sensing (RS) approaches based on optical and/or microwave sensors have been widely employed to monitor cultivated land (including crop area, type, condition, and yield forecasting), the identification of potato planting areas using RS data has not been much addressed. Hence this study addresses the literature gap by suggesting an effective potato mapping approach that uses the time series of the Sentinel-2 (S2) images, Google Earth Engine (GEE) platform and machine learning methods. Since most crops have specific spectral and temporal characteristics during the growing season, this research has presented a method to discriminate potato fields from other crops using time series images without explicit thresholding. We employed 1648 ground truth data to optimize, train, and evaluate the model at the study site, which includes potatoes and other fields. A handheld GPS receiver was used to collect these data. The performance of this approach is evaluated by conducting a set of experiments in Hamedan and Bahar cities, as the regions grow more potatoes than any other places in Iran. Accurate identification of potato fields was completed by extracting the required features, namely the potato phenology feature and NDVI medians, from the time series of the S2 satellite bands. After that, these features were utilized as the input parameters to Support Vector Machine (SVM) technique. In order to train the most optimal SVM model using RBF kernel, Gamma and C values were optimized with the help of the 5-fold cross-validation method. These values were then employed during the algorithm's implementation on GEE platform. The estimated overall accuracy and Kappa coefficient are 90.9% and 0.82 for Hamedan and 93.3% and 0.87 for Bahar, respectively. The results of this research indicate the efficiency of SVM technique in potato acreage mapping. Moreover, the selected features such as potato phenology feature can be considered as discriminating features for improved identifying of crop farms.
علمی - پژوهشی
maryam soltanikazemi; Saeid Minaei; Hossein Shafizadeh Moghadam; AliReza Mahdavian
Abstract
Sheath moisture is an important parameter during the growth period of sugarcane, which is of special importance from the perspective of water stress and field irrigation management. Remote sensing data has a high capacity to update crop growth monitoring systems. In this regard, satellite images that ...
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Sheath moisture is an important parameter during the growth period of sugarcane, which is of special importance from the perspective of water stress and field irrigation management. Remote sensing data has a high capacity to update crop growth monitoring systems. In this regard, satellite images that provide a variety of information can be used. In the crop year of 2020, with the aim of predicting the moisture content of sugarcane pods, 4 spectral indices and 7 single band sensors of Sentinel-2 satellite were evaluated. Four methods PLSR, RF, GRNN and SVR were used to model and predict pod moisture. Bayes algorithm was used to optimize the parameters in RF, GRNN and SVR models. In addition, improved sensitivity analysis was used improved stepwise was used to find the most effective input parameter in estimating pod moisture. The results showed that the SVR model provided a more acceptable estimate of sheath moisture content than the other models when the parameters NDVI, EVI, SRWI, Clgreen, B2, B3, B5, B4, B11 and B12 were used as input to the four models. According to the sensitivity analysis, SRWI parameter was considered as the most effective index in the modeling process. Therefore, it can be concluded that among the inputs given to the model, a combination of indices and bands of NDVI, EVI, SRWI, Clgreen, B2, B3, B5, B4, B11 and B12 give a better estimate of sugarcane sheath moisture content.
علمی - پژوهشی
Mohammad Karimi; Sajjad Haghi; Ahmad ali Hanafi Bojd
Abstract
Dengue fever is Chickenpox is a contagious and viral disease that is transmitted by two species of Aedes Egyptian mosquitoes and Aedes albopicus mosquitoes and is spreading rapidly in the world. The main purpose of this study is to model the spatial distribution of carriers of this disease in Iran. Due ...
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Dengue fever is Chickenpox is a contagious and viral disease that is transmitted by two species of Aedes Egyptian mosquitoes and Aedes albopicus mosquitoes and is spreading rapidly in the world. The main purpose of this study is to model the spatial distribution of carriers of this disease in Iran. Due to the lack of sufficient carrier data in the country, carrier data available worldwide and also in Asia, in two different scales were used. Among the most important aspects of this research, we can name the use of heterogeneous layer as an auxiliary factor to analyze the presence points and reduce spatial autocorrelation and the use and comparison of two species distribution models based on presence data for Selecting the optimal modeling method. In this regard, first, using the maximum entropy method (MaxEnt) and a type of genetic algorithm called GARP (GARP), the level of habitat suitability in the world with a spatial resolution of 5 km for both species was modeled. To evaluate the mentioned models, the variables of population density, climate, vegetation density, altitude and soil organic carbon were considered. Due to the high accuracy of the MaxEnt method, using this method, the habitat suitability of the Asian continent with a resolution of 900 m was modeled for both species. The values under the curve (AUC) for the two types of carriers were calculated to be 0.9. The results showed that the northern and southern provinces of the country have higher habitat suitability for both species, with the difference that Aedes aegypti species in the southern to eastern parts of the Oman Sea coast has a higher probability of distribution. In implementing the MaxEnt method for Aedes albopicus, the provinces in western Iran were also identified as desirable, which was not properly modeled on a smaller scale. In February 2016, unfortunately, a small number of mosquitoes and AIDS mosquito eggs were discovered in Bandar Lengeh, which was exactly what this research had predicted. The results of this study can be used in line with planning for population management of these vector insects to control the disease at the same time as monitoring populations during epidemic seasons.
علمی - پژوهشی
samad khosravi yeganeh; mostafa karampour; Behrouz Nasiri
Abstract
In the coming years, drought is one of the potential threats to low rainfall countries, especially Iran. Iran has always faced big and small droughts. The geographical location and natural conditions of the country are such that severe and weak droughts have occurred in it. Evaluating the effects of ...
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In the coming years, drought is one of the potential threats to low rainfall countries, especially Iran. Iran has always faced big and small droughts. The geographical location and natural conditions of the country are such that severe and weak droughts have occurred in it. Evaluating the effects of drought on vegetation is very important. In order to reduce damages and consequences caused by drought, drought monitoring using remote sensing method is considered in most countries because of its advantages. In this study, in order to reveal the effects of precipitation on vegetation in Iran, the precipitation data of 143 synoptic meteorological stations of the country were used to calculate the monthly average of the Standard Precipitation Index (SPI) in the period 2000-2021. Then, using visible infrared images (VIIRS) as a weekly average in the period of 2013-2021 (April 1st to the end of July every year) obtained from the Suomi NPP sensor, the drought condition of the vegetation was investigated. Based on the obtained results, it can be said that the vegetation cover in 2015 and 2021 was more affected by drought than the other investigated years. The most severe vegetation drought occurred in 2021. In 2016, 2019 and 2020, vegetation was in more favorable conditions. The correlation of SPI with NDVI, VCI, TCI and VHI was calculated as 0.1906, 0.038, 0.016 and 0.002 respectively.
علمی - پژوهشی
Davood Akbari; Ali Ashrafi; Mostafa Yaghoobzadeh
Abstract
The Hyperspectral remote sensing technology has many applications in classifying land covers and studying their changes. With recent developments and the creation of images with high spatial resolution, the simultaneous use of spectral and spatial information in the classification of hyperspectral images ...
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The Hyperspectral remote sensing technology has many applications in classifying land covers and studying their changes. With recent developments and the creation of images with high spatial resolution, the simultaneous use of spectral and spatial information in the classification of hyperspectral images is necessary. In this research, a new method for the classification of hyperspectral images is introduced with the help of dimensionality reduction techniques and spatial feature extraction and neural network algorithm. In the proposed method, first, the dimensions of the hyperspectral image are reduced with the help of the principal components analysis algorithm. Then ten spatial features, mean, standard deviation, contrast, homogeneity, correlation, dissimilarity, energy, entropy, wavelet transform and Gabor filter, are extracted and then the weighted genetic algorithm is applied on the spectral and spatial features obtained. In the weighted genetic algorithm, according to the information available in the features, it gives them a weight between zero and one. Finally, a multilayer perceptron neural network classification algorithm was applied to the existing features. The proposed method was implemented on two hyperspectral images of Pavia and Berlin. The results of the obtained experiments show the superiority of the proposed method compared to the support vector machines, multilayer perceptron neural network and minimum spanning forest classification methods. This increase is about 13, 6, and 5% for the Pavia image and about 8, 6, and 5% for the Berlin image in the overall accuracy parameter and in comparison with the mentioned methods, respectively.
علمی - پژوهشی
Davood Akbari
Abstract
The complexity and large volume of data from hyperspectral sensors have led to the consideration of more specialized and advanced methods of data analysis in order to extract information. One of the analyzes performed on hyperspectral images is target detection. With recent developments and the creation ...
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The complexity and large volume of data from hyperspectral sensors have led to the consideration of more specialized and advanced methods of data analysis in order to extract information. One of the analyzes performed on hyperspectral images is target detection. With recent developments and the creation of images with high spatial resolution, it is necessary to use both spectral and spatial information to detect hyperspectral images. This research introduces a new method for building detection in hyperspectral images based on the marker-based hierarchical segmentation algorithm. In the proposed method, first, multilayer perceptron neural network (MLP) and support vector machine (SVM) classification algorithms were implemented and their results were combined. Then, the resulting map was used to select the markers and combine them with the marker-based hierarchical segmentation algorithm using the majority voting decision law. The above techniques were applied to a series of CASI image data taken from the urban area of Toulouse in southern France. The results of quantitative and qualitative evaluations show that the proposed method has improved the kappa coefficient by 33, 28, 19, and 17% compared to the spectral correlation similarity (SCS), spectral information divergence (SID), SVM, and MLP algorithms, respectively.
علمی - پژوهشی
Sajedeh Morady; Majid Rezaeibanafsheh
Abstract
In this article, night temperature changes in relationship with CO2 emissions in Iran were studied using AIRS satellite images and time series graphs, regression, correlation and Mann-Kendall tests and maps. This research was conducted for the statistical period of 2003-2016 and the months of January, ...
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In this article, night temperature changes in relationship with CO2 emissions in Iran were studied using AIRS satellite images and time series graphs, regression, correlation and Mann-Kendall tests and maps. This research was conducted for the statistical period of 2003-2016 and the months of January, May, July, and November. The statistical analysis of the distribution of the average night temperature in Iran showed that the average of 2016 was higher than the average of the entire study period by 0.42 degrees Kelvin. The highest fluctuations in night temperature were related to the winter season and the months of November and January, and the lowest fluctuations were related to the summer season and the month of July. The trend of the seasonal average of CO2 in all four seasons was completely increasing and had little fluctuation. Based on the distribution of the average seasonal night temperature, the minimum night temperature were visible in the northwest, Alborz, and Zagros mountain ranges and North Khorasan in all f seasons. The southern coasts and southern coastal regions, including Khuzestan province, Bushehr province, Hormozgan and Sistan and Baluchistan provinces, as well as Kavir desert and Lut desert, have the maximum night temperature in all seasons. According to the results of the Mann-Kendall test, only the trend of night temperature in July was significant, and the trend of Co2 was upward and significant in all months. According to the results of Pearson's correlation test, night temperature in July has a high correlation (0.66) with CO2. The results of the non-linear regression model between the two variables indicated that with a coefficient of determination of 0.44, the maximum CO2 had the greatest impact on the maximum night temperature in July in the study period of 2003-2016.
علمی - پژوهشی
Abolfazl Habibitabar; sahar Alian; Davoud Soleimanian
Abstract
The energy injection point from the transmission network to the distribution network is the 63.20 kV substations. Determining the location of the substation is technically and economically important for regional electricity companies and electricity distribution companies. The purpose of this research ...
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The energy injection point from the transmission network to the distribution network is the 63.20 kV substations. Determining the location of the substation is technically and economically important for regional electricity companies and electricity distribution companies. The purpose of this research is to determine the optimal range of construction of 63.20 kV substation and the difference between the research method of this research and other similar studies is the use of criteria related to the electricity distribution company and considering the influence and internal communication of the criteria with each other. In this model, the method of network analysis process and fuzzy functions are used in the context of GIS. By reviewing sources and surveying expert experts, 13 criteria were determined as the main and influential factors in determining the location of post construction, and then using ArcGIS software, criteria zoning maps were prepared. In order to homogenize the information layers, the data were set between 0 and 1 using fuzzy techniques. The final weight of the criteria was determined using the ANP method and applied in its fuzzy map. For the final analysis of the research topic, the gamma operator with thresholds of 0.7, 0.8 and 0.9 was used. In order to choose the optimal fuzzy gamma, the data were analyzed in SPSS software and gamma correlation coefficient test, and as a result, the value of correlation coefficient and standard deviation was calculated. The results of calculating the standard deviation showed that the 0.7 gamma has a higher accuracy than the other two gammas. In the final map obtained, the area of Kohak city is the most optimal area for the construction of 63.20 kV substation in Qom province.
علمی - پژوهشی
Mohammad Khaledi; Ghasem Zarei
Abstract
In order to be able to make decisions and manage in the field of sustainable development of natural resources, knowledge of climatic parameters is one of the most important and effective topics of this matter. Meanwhile, the temperature parameter, as one of the most influential climate parameters, plays ...
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In order to be able to make decisions and manage in the field of sustainable development of natural resources, knowledge of climatic parameters is one of the most important and effective topics of this matter. Meanwhile, the temperature parameter, as one of the most influential climate parameters, plays a fundamental role in related studies and research. It is very important to have consistent, high-quality and error-free information available, Because the existence of a statistical gap or an incorrect assessment of information leads to wrong decisions and a deviation from the goal and reality. Daily temperature information is among the most important and useful climate data used in industrial, agricultural and social fields. Since time series inevitably always have problems and statistical discontinuities, in this investigation, for the first time and using classical statistical methods, in relation to the temperature data at the country scale including geographic (graphical) coordinates, normal ratios, the weighted correlation coefficient and the arithmetic mean, which commonly used in completing meteorological and climatic statistical information, were used to evaluate the methods and to determine the most reliable method to solve and estimate the missing daily-scale temperature data. According to the average values obtained from the evaluation of the results, the normal ratio method, the weighted correlation coefficient, the geographic coordinates and the arithmetic mean are prioritized with the RMSE value of 3.05, 3.28, 3.30 and 3.51 degrees Celsius, respectively. Therefore, the normal ratio method is more acceptable among other studied methods and this method can be used to solve problems such as the lack of information, the error in the data and as well as the extension of the study period.
علمی - پژوهشی
Amir Hadian; Mina Moradizadeh
Abstract
Air pollution is one of the most important crises that most countries are facing today due to the progress of industry and technology. The country of Iran and especially the city of Tehran is not exempt from this phenomenon. Air pollution measurement stations at city level, in spite of the high accuracy ...
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Air pollution is one of the most important crises that most countries are facing today due to the progress of industry and technology. The country of Iran and especially the city of Tehran is not exempt from this phenomenon. Air pollution measurement stations at city level, in spite of the high accuracy of the measurement of pollutants, are not generalizable due to time and place limits and point measurement. A complementary and sometimes alternative solution is the use of remote sensing and satellite data, which is a suitable method for monitoring air pollution due to the optimal cost and wide coverage. Nitrogen dioxide (NO2) and ozone (O3) pollutants are among the most important indicators of air pollution. In this research, an effort will be made to model the distribution of their concentration in the city of Tehran with the same spatial resolution (almost one kilometer) and higher accuracy than satellite data. For this purpose, the concentration distribution of these two pollutants has been modeled by using an innovative method based on the kriging interpolation method and simultaneous use of pollution measurement station data and high spatial resolution of Sentinel 5P satellite data. In order to evaluate the results, air pollution measurement station data were used, and the average monthly error of this model has decreased from 16.8 to 1.73% for NO2 pollutants and from 21.9 to 2.53% for O3 pollutants compared to the data of the Sentinel 5P satellite. Also, the root mean square error (RMSE) of this model for NO2 and O3 pollutants is equal to 2.79 ppb and 0.86 ppb, respectively, which shows the proper performance of this model in modeling the concentration distribution of pollutants.
علمی - پژوهشی
Sedigheh Lotfi; Toheed Alizadeh
Abstract
Land is a vital natural resource for human survival and the basis of all ecosystem services. However, land degradation in the form of land use and land cover changes has been a severe problem in the world. Rural-to-urban migration in developing countries, as well as the conversion of many nearby villages ...
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Land is a vital natural resource for human survival and the basis of all ecosystem services. However, land degradation in the form of land use and land cover changes has been a severe problem in the world. Rural-to-urban migration in developing countries, as well as the conversion of many nearby villages into urban areas, has largely caused a rapid increase in the size of urban settlements. The evidence shows that the North of Iran is experiencing rapid changes in land use, which has endangered the stability of the region during the last three decades. This article aimed to identify and compare the changes in the growth of the city and the expansion of the city for the study case of Amol located in Mazandaran province in three decades from 1986 to 2016. Therefore, in order to produce land cover changes images Using Landsat time-series image data (TM, ETM+ and OLI/TIRS sensors), the Linear spectral unmixing model has used with the extraction of endmembers and two classes of urban and non-urban lands were identified for each time section. Spectral unmixing method as a new method in the analysis of land cover changes has been less discussed in research related to urban issues. Accuracy assessment of land cover classification, using the confusion error matrix with the calculation of overall accuracy above 90% and kappa coefficient above 80% for the obtained images, shows high and very good accuracy. The findings showed that the doubling of the city's population in these four decades has not only been accompanied by a horizontal expansion of 60% in the surrounding green and open lands, but has also resulted in an increase in building density and the number of floors. In these four decades, the expansion of Amol city has generally been from the center to the periphery, and the least changes have occurred in the central parts of the city and the most changes have occurred in the northern and eastern parts of the city.
علمی - پژوهشی
Elaheٍ Akbari; M Hajeb; Mehrdad Jeihouni; Saeid Hamzeh
Abstract
To determine the effect of the leaf biochemical contents on its spectral reflectance behavior via remote sensing (RS) can help to understand the process of the ecosystem and its parameters such as plant water stress. The present study aimed to do a quantitative analysis of the effect of leaf parameters, ...
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To determine the effect of the leaf biochemical contents on its spectral reflectance behavior via remote sensing (RS) can help to understand the process of the ecosystem and its parameters such as plant water stress. The present study aimed to do a quantitative analysis of the effect of leaf parameters, including the amount of leaf chlorophyll, leaf structure, and leaf water content, on the leaf spectral reflectance. To this end, the PROSPECT radiative transfer model which developed to simulate the spectral behavior of plant leaves, was employed. The research results showed that the increase of chlorophyll with the effect of reducing the leaf spectral reflectance leads to the increase of Triangular Vegetation Indices (TVIs). In the visible light spectrum, it is possible to distinguish monocotyledons (monocots), dicotyledons (dicots), and old plants. Also, in the near-infrared (NIR) light spectrum, the amount of reflection decreases in old and unstructured plants, dicotyledonous plants, and monocotyledonous plants, respectively. The drying of the plant does not have much effect on the reflection, but drying more than a certain amount causes a significant increase in the reflection, especially outside the water absorption spectra. Therefore, finding the critical points of the reflectance curve against the water content can contribute to detecting severe water stress in plants. By examining the graphs, it can be observed that the critical point occurs about the water content of 0.03 to 0.04 g⁄〖cm〗^2 . In the PROSPECT radiative model, the effect of soil on the spectral reflectance of plants is not considered. Therefore, it is recommended to use models such as SAIL and SLC that have been upgraded for this purpose.
علمی - پژوهشی
Mir Saeed Moosavi; Aida Ghafoori; Mahsa Faramarzi Asl
Abstract
The problems in urban life, such as the diminution of the role of the environment as a place for the presence of citizens and their physical activity, the increase in vehicles and as a result inactivity and the increase in non-communicable diseases, have caused the concern of the world community about ...
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The problems in urban life, such as the diminution of the role of the environment as a place for the presence of citizens and their physical activity, the increase in vehicles and as a result inactivity and the increase in non-communicable diseases, have caused the concern of the world community about public health. The aim of the research is to evaluate subjectively and objectively the environmental criteria affecting the general health of the residents in three physical, psychological and social dimensions in Agha Zaman neighborhood of Sanandaj city. The collection of subjective and objective research data was done sequentially in terms of time, in this regard, four phases of the research were defined. The first phase is the subjective qualitative assessment of the residents regarding their mental and social health, the second phase is the collection of quantitative subjective data of the residents regarding their physical health, the third phase is the quantitative objective assessment of the environment, and the fourth phase is the examination of the correlation between the research variables. Subjective data was obtained using a questionnaire and objective data was obtained using a geographic information system. To analyze the relationship between neighborhood environmental criteria and physical activities and to determine the relationship between variables, regression analysis was used in SPSS software. Objective data such as the type of block arrangement and the example of the spatial pattern of the neighborhood, the land use layer and the road network, etc., were calculated by the geographic information system and entered into the index of space syntax to determine the pedestrianization of the neighborhood. Finally, the relationships and effects of environmental criteria on public health have been identified using regression analysis in Lisrel software. The results showed that the two measures of comfort and tranquility of the environment with a score of 23 and social interactions and neighborhood culture with a score of 21 respectively have the greatest impact on mental and social health. Also, environmental criteria such as mix of uses with a score of 5.671, visual and aesthetic qualities with a score of 7.961, and special infrastructure for pedestrians and bicycles with a score of 8.475 have the greatest impact on physical work, leisure and sports activities, respectively. As a result, they have physical health. According to the obtained data, Namaki Street with a score of 21 has the highest level of connection and interconnection, depth and control with the whole neighborhood and has the highest level of pedestrian circulation in the neighborhood. The general results show that due to the direct impact and relationship of environmental criteria of urban design on physical activities and on the other hand, the existence of a positive and meaningful relationship between physical activities and general health, it was found that the general health of residents is related to the environmental criteria of urban design. Yes, but this relationship does not happen directly but with the intervention of physical activities.
علمی - پژوهشی
کمال امیدوار; massumeh nabavi zadeh
Abstract
Due to the statistical limitations of the data of the Grace satellite, a common time period of 16 years was chosen from 2002 to 2017. Two indices VHI and TSDI were obtained a high correlation in the south, center and some areas of the north. Finally, results of the research were validated by GLDAS ...
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Due to the statistical limitations of the data of the Grace satellite, a common time period of 16 years was chosen from 2002 to 2017. Two indices VHI and TSDI were obtained a high correlation in the south, center and some areas of the north. Finally, results of the research were validated by GLDAS data of moisture of soil and SPEI standardized precipitation-evaporation and transpiration index. Then, graph changes of the SPEI index was calculated in a long-term period of thirty years (1987-2018) and the studied time period was separated from 2002 to 2018. The results showed that the SPEI index and data of moisture GLDAS have a high agreement with the VHI and TSDI indices. Also, the maps and charts showed that this province is facing severe meteorological, agricultural and hydrological droughts, and the deficit trend of the total storage of underground water, especially since 2012, has been very dry and exceptional. This point doubles the planning spaicially in the restoration of underground water.
Original Article
Fatemeh Shokrian; Karim Solaimani
Abstract
Investigating land use changes requires the integration of layers in a certain period. This research aims to investigate land use changes in Haraz Plain from 1980 to 2021. Therefore, Landsat data was used to measure the changes. By applying atmospheric, geometric and radiometric corrections, image enhancement ...
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Investigating land use changes requires the integration of layers in a certain period. This research aims to investigate land use changes in Haraz Plain from 1980 to 2021. Therefore, Landsat data was used to measure the changes. By applying atmospheric, geometric and radiometric corrections, image enhancement operations were performed and land use change maps were produced based on the supervised classification method, maximum likelihood algorithm and basis component analysis functions. The type of land use changes was determined from the difference function of the identification images and the accuracy of the maps using the overall accuracy test and the Kappa statistic. The results showed that from 1980 to 1990, the area of forest lands decreased by 4 km2. The rangeland area also decreased from 450 to 436 km2. From 2000 to 2010, the area of forest land decreased from 272 to 270 km2 and rangeland decreased from 432 to 420 km2. Finally, between 2011 and 2021, the area of forest lands decreased by 9 km2 and the rangeland area decreased by 5 km2. The results of the investigation of the changes in land use in the region indicate that the area of forest and rangeland lands decreased and the area of agricultural lands and residential areas increased. These results can help planners find the factors affecting land use changes and make correct management decisions in the future.
علمی - پژوهشی
sasan motaghed; amin nakhlian; lotfolla emadali; nasrolla eftekhari; heshmatalla mahmoudian
Abstract
In this study, an integrated geographic information system (GIS) has been used to assess the seismic hazard in the southwestern region of Iran. In data preparing, the important factors in earthquake hazard were identified and relevant thematic data layers including digital elevation model (DEM), the ...
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In this study, an integrated geographic information system (GIS) has been used to assess the seismic hazard in the southwestern region of Iran. In data preparing, the important factors in earthquake hazard were identified and relevant thematic data layers including digital elevation model (DEM), the focus of past earthquakes and active faults were created for the region. Numerical ranking based on AHP has been used to weight and rank effective parameters in seismic hazard assessment for spatial data analysis using GIS. Using the weighted arithmetic overlay method, an EPI map was prepared for an area of 400 × 400 square kilometers in the southwest of Iran, and the area was divided into 4 relative hazard classes, i.e., high, medium, low, and very low classes. Finally, its maps are provided. For more transparent, the hazard values of the cities of Khuzestan province is given in comparison with the standard 2800 earthquake hazard map. The comparison of the results shows that the standard 2800 zoning in this area is reliable. Also, based on the results, the use of the EPI, as a non-ergodic hazard metric, is suggested in areas with a rich data background.
علمی - پژوهشی
yazdan yarahmadi; hojatolah younesi; Ahmad Godarzi; saeed rostami
Abstract
Determining the value of the runoff coefficient is one of the biggest problems and the main source of uncertainty in many water resources projects. The aim of the current research is to estimate the runoff coefficient by combining Arc CN-Runoff, SCS-CN and ICAR experimental relationship in the Selseleh ...
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Determining the value of the runoff coefficient is one of the biggest problems and the main source of uncertainty in many water resources projects. The aim of the current research is to estimate the runoff coefficient by combining Arc CN-Runoff, SCS-CN and ICAR experimental relationship in the Selseleh watershed. Selseleh Study area is located in the north of Lorestan province and is one of the sub-watersheds of Kashkan. In order to carry out this research, data including digital elevation model information, land use classes, soil texture and meteorological and hydrological statistics (rainfall and runoff) related to the research area were used. The map of the land use layer and soil hydrological groups was entered into the Arc CN-Runoff tool environment, and Intersect was applied on two layers the Land soil layer was prepared, and the runoff coefficient map was prepared based on this layer. Finally, the runoff coefficient was estimated in three conditions dry, medium and wet moisture conditions and a comparison was made. The results of the research showed that the runoff coefficient (CR) in the Selseleh Study area is 0.26, 0.53, and 0.77, respectively, in dry, medium, and wet conditions. Therefore, the dry humid state has decreased by 68% compared to the average, and the more humid state has increased by 37% compared to the average. Investigating the correlation between the runoff coefficient and watershed characteristics showed that the runoff coefficient is influenced by the six physiographic features of the Study area: area, slope, length of the waterway and Gravelius coefficient, the maximum height and density of the waterway, which has a significant effect on the value of the runoff coefficient in this Study area. The value of the runoff coefficient in the ICAR method for the entire Study area was 0.48. In the Selseleh Study area, the runoff coefficient has a decreasing trend from April to September. The type of land and soil use in the basin under study is one of the influential factors that affect the runoff coefficient and, consequently, the peak discharge of the Study area. Also, in early spring, the flood potential is high in the said Study area. Among the measures to increase water infiltration are the establishment of a rainwater collection system and the operation of the Farrow meter along with the increase of plant cover with seeding and planting and intercropping of pasture plants.
علمی - پژوهشی
mahdi naderi
Abstract
In recent decades, land use and land cover changes information has been successfully derived from remote sensing data at various levels, from local to global scale. Accurate and frequent monitoring of these changes is required for urban planning and sustainable management of land resources. In this study, ...
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In recent decades, land use and land cover changes information has been successfully derived from remote sensing data at various levels, from local to global scale. Accurate and frequent monitoring of these changes is required for urban planning and sustainable management of land resources. In this study, an object-oriented approach using a combination of GLCM, SNIC, and machine learning algorithms is presented to classify the LULC of a part of the lands of North Mahabad, West Azerbaijan, in 2019 using satellite images in Google Earth Engine. For this purpose, after preparing the initial dataset, which contains the bands of Sentinel-1 and Sentinel-2 images, the ALOS digital surface model, and NDVI, BSI, SAVI, and total scattering power indices, two pixel-based and object-oriented approaches, as well as the random forest algorithm, were used to classify land use and land cover, and their results were compared to explain the best approach in terms of the accuracy of the various classes. In the object-oriented approach, textural measures were extracted by applying the GLCM matrix to the initial dataset. Due to the increase in the number of bands, the PCA method was used to reduce the dimensions of the image. Finally, by combining the segmentation layer obtained from the SNIC algorithm and the PC1 layer, the random forest algorithm was considered to produce land use and land cover maps of the study area. According to the research findings, the object-oriented approach performed better than the pixel-based approach in classifying various land use classes in the study area, with an overall accuracy and kappa coefficient of 86.40% and 0.8307, respectively, compared to 82.73% and 0.8028. The results of the accuracy evaluation criteria showed that the producer accuracy of most of the classes except for corn, fall irrigated vegetables, and wheat, and barley irrigated in the object-oriented approach was higher than the pixel-based method, and their classification accuracy was more than 90%. Additionally, water, build-up, corn, and sugar beet classes have the highest user accuracy in the object-oriented LULC map. The findings showed that the appropriate determination of the super-pixel size of the SNIC clustering algorithm and the use of GLCM texture criteria effectively improved the performance of the proposed approach in land use and land cover classification.
علمی - پژوهشی
Masoud minaei; sadegh boulaghi; hanieh afsahi
Abstract
In recent years, the global population growth and urban expansion have led to significant changes in land use and land cover. This process has numerous detrimental consequences, such as increasing surface temperature, deforestation, desertification, degradation of ecosystem services, biodiversity loss, ...
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In recent years, the global population growth and urban expansion have led to significant changes in land use and land cover. This process has numerous detrimental consequences, such as increasing surface temperature, deforestation, desertification, degradation of ecosystem services, biodiversity loss, and threats to food security. Therefore, monitoring and modeling these changes are essential to enable optimal land management and sustainable utilization of natural resources. Considering that the Aras River Basin has undergone significant transformations over time, particularly in terms of human-made land developments, the focus of this research is on modeling land use/land cover changes in this area. Initially, land use maps for the region were extracted for the years 2000 and 2020 from the Globeland30 project of the China National Geomatics Center. Subsequently, two maps were prepared to illustrate the potential growth of human-made land based on the scenario of land development. This was achieved using advanced decision-making analysis methods based on GIS, including BWM (Best-Worst Method) and MEREC (Multi-objective Evaluation of the Regional Ecosystem Carrying Capacity). Finally, these two maps, along with the land use maps, were combined to form the input for the CA-Markov model. The modeling process was carried out once using the BWM + CA-Markov combination, and again using the CA-Markov + MEREC combination for the year 2040. The examination of the results demonstrated that in the output of the combined BWM + CA-Markov model, the extent of human-made land increased from 603 square kilometers in 2020 to over 930 square kilometers in 2040. Meanwhile, this figure was approximately 829 square kilometers in the output of the MEREC + CA-Markov model. Furthermore, the final results obtained from the intersection of these combined models also indicated an increase in the extent of this land from 603 square kilometers in 2020 to 930 square kilometers in 2040. The continuous growth of human-made land in this basin can lead to the destruction of environmental resources and ecosystem threats. The findings of this study provide relevant managers with valuable insights for optimal management of future conditions and the provision of necessary infrastructure.
علمی - پژوهشی
Mina Moradizadeh; Mohamad Reza Talari
Abstract
Atmospheric water vapor is a key parameter in modeling the energy balance on the earth's surface and plays a major role in keeping the temperature of the earth's atmosphere balanced. Retrieving of this parameter, as the most influential atmospheric parameter on the sensors received radiance, ...
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Atmospheric water vapor is a key parameter in modeling the energy balance on the earth's surface and plays a major role in keeping the temperature of the earth's atmosphere balanced. Retrieving of this parameter, as the most influential atmospheric parameter on the sensors received radiance, is of great importance. Since the atmospheric water vapor content in the near of surface is more and its temporal and spatial changes are more intense, the measurements of ground meteorological stations, despite their high accuracy, are not generalizable due to temporal and spatial limitations and point measurements. Therefore, it seems necessary to provide practical satellite-based methods to accurate and continuous retrieval of this parameter with appropriate spatial distribution. Therefore, retrieving the near surface water vapor content with accuracy and appropriate spatial resolution is very important, and the purpose of this research is to provide four innovative and accurate methods to estimate the mass mixing ratio of near surface water vapor in Isfahan province in 1 km resolution. Different sensors measure water vapor with different resolution and sensitivities to this parameter. Thus, providing methods based on the integration of different sensor's and ground observations data is essential to simultaneously improve the spatial resolution and accuracy of water vapor retrievals. In this research, the combination of MODIS and AIRS data and ground station observations have been used. Also, the band ratio method, IDW interpolation and scaling have been used along with the proposed methods. Correcting the bias of AIRS-derieved water vapor during the scaling stage and interpolation error is on the agenda. Validation results of proposed methods show that the method based on the generalization of accurate ground-basedwater vapor observations and removing interpolation error, through integration with MODIS-derieved water vapor values, has the best performance (R2=0.55, RMSE=1.05 Gr/Kr).
علمی - پژوهشی
Matin Shahri; Mohammad Amin Ghannadi
Abstract
Pedestrians are considered as one of the most vulnerable road users due to many reasons including the lack of protective shells or being less visible to drivers, especially in low-light conditions or adverse weather and their accidents lead to many casualties or irreparable injuries. Accordingly, in ...
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Pedestrians are considered as one of the most vulnerable road users due to many reasons including the lack of protective shells or being less visible to drivers, especially in low-light conditions or adverse weather and their accidents lead to many casualties or irreparable injuries. Accordingly, in recent years, efforts have been carried out to provide methods to investigate the presence of organized patterns of pedestrian accidents and to evaluate and organize them. When dealing with spatio-temporal data such as traffic accidents, the effect of events located in the spatial and temporal neighborhood of the studied event cannot be neglected. In this research, pedestrian accidents during a five-year period of 2014-2018 in Mashhad have been examined. By aggregating traffic data over 253 traffic analyses zone (TAZ), the temporal pattern and the presence of temporal autocorrelation among monthly and hourly pedestrian accidents has been confirmed using time series analyses. Using Buishand’s homogeneity test, a sudden change of accident occurrence at different hours of the day (7:00-8:00 A.M.) and months of year (July and September) was identified. Spatio-temporal differential Moran's I index was applied for the first time in safety analyses to evaluate the spatial correlation of changes of pedestrian accidents between the two periods of the beginning (2014) and end of the analysis period (2018), and the locations with significant changes were identified. As an exploratory method, this research can provide traffic engineers and planners of urban transportation with a tool to identify areas showing high rate of accidents over a period of time and therefore, require more attention in terms of allocating budget or providing strategic approaches to effectively improve the pedestrian safety.
علمی - پژوهشی
hossein aghamohammadi; reyhaneh saeedi; Ali Asghar Alesheikh; Alireza Vafaeinejad
Abstract
Intelligent emergency response during crises refers to the effort to improve the performance of emergency response units. This type of assistance is created using modern technologies, especially the Internet of Things, to enhance service quality, reduce costs, and increase supervision over the emergency ...
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Intelligent emergency response during crises refers to the effort to improve the performance of emergency response units. This type of assistance is created using modern technologies, especially the Internet of Things, to enhance service quality, reduce costs, and increase supervision over the emergency response process. In a new approach to intelligent emergency response, IoT-based routing models are used. These models optimize the emergency response route through the communication between objects and the collection of spatial data, improving the user experience. In this study, a spatial data infrastructure is designed to integrate the system and improve emergency response. The designed portal displays the optimal route from the incident location to the medical center on a map using the desired service and transfers sensor information (such as heart rate, blood oxygen level, blood pressure, thermometer, glucose meter, electroencephalography, and pulmonary function) to the doctor's mobile phone in the ambulance via Bluetooth and shares this information on the portal. Medical centers also determine their priority using an online hierarchical weighting model. In this system, real-time health information of the injured is received through the submission and confirmation from medical centers, and based on this information, the appropriate location for treatment and transfer of the injured person is determined. In a test conducted for this model, an injured person in Tehran Zone 5 on 4/24/1400 at 12:00 noon was accepted. The emergency team arrived at the scene in less than 8 minutes and, after providing initial assistance, sent the real-time health status of the patient to the surrounding medical centers, receiving a confirmation from Omid hospital in the same area, and transferred the injured person to the hospital in 5 minutes via the optimal route. In contrast, in the traditional system, due to the lack of simultaneous use of IoT and spatial data infrastructure, suitable routing algorithms and weighting models, the emergency response process becomes time-consuming, and medical centers are not informed of real-time health information of the injured. Consequently, this research improves the performance of the emergency response system and addresses some of the issues in the traditional system, such as delays in dispatching emergency personnel and patient non-acceptance. Overall, the use of intelligent emergency response methods reduces human, financial, and time costs, provides quicker and better responses to different crises, and allows emergency organizations to make more intelligent decisions regarding resource distribution and human resource allocation using the data collected by geographic information systems and the Internet of Things.
علمی - پژوهشی
Mohammad Soleymani lamyani; Amer Nikpour; Sedigheh Lotfi; hammed Abbasi
Abstract
The form of the city is very important because it forms the basis of urban movements within itself. One of the key components of treating the quality of the urban form is access to urban activities and services. The expansion of the horizontal growth of the metropolis of Tehran in the last few decades ...
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The form of the city is very important because it forms the basis of urban movements within itself. One of the key components of treating the quality of the urban form is access to urban activities and services. The expansion of the horizontal growth of the metropolis of Tehran in the last few decades has caused the fabric and structure of this city to move towards instability, which results in unequal and unbalanced access to different parts of the city. Therefore, the structuring of the city in such a way as to meet the needs of the citizens in the shortest distance from the residential area and easily, is very important in urban planning. The present research was conducted with the aim of investigating the condition of the form and accessibility of the neighborhoods of Tehran. The necessary data and information were extracted from the statistical blocks of the Statistics Center, the land use map of the city, and the Open Street Map website, and GIS and Excel software were used to analyze the data. 3 main indicators of density, connection and mixing were used to evaluate the urban form, and Hansen's gravity model was used to measure accessibility. The results of the research showed that the neighborhoods in the central part of Tehran have a more stable form compared to other neighborhoods, and the western neighborhoods of Tehran have an unstable form. The access factor in Tehran also showed that the central and northern parts of the city are in a better condition than other parts of the city in terms of access. The results obtained from the correlation test also showed that there is a direct and significant relationship between urban form and accessibility; But the degree of correlation between them is not strong. Meanwhile, among the urban form indices, the building density index has the highest correlation with urban accessibility.
Keywords: urban form, accessibility, density, urban neighborhoods, Tehran.
علمی - پژوهشی
Elahe Akbari
Abstract
Estimation and forecast of crop yield using crop growth models is imperative to plan agricultural operations and manage crop yield. To this end, the AquaCrop model parameters were estimated and the model was calibrated with measuring and sampling different requied information of model in the crop growing ...
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Estimation and forecast of crop yield using crop growth models is imperative to plan agricultural operations and manage crop yield. To this end, the AquaCrop model parameters were estimated and the model was calibrated with measuring and sampling different requied information of model in the crop growing stages and prior to cultivation over agricultural silage maize fields at the regional scale. Field sampling of soil (prior to cultivation) and crop (during the growth season), digital hemispherical photography (DHP) and destructive method for comparison purposes were carried out for silage maize in Qhale-Nou county, South Tehran, in the summer of 2019. Remote sensing data assimilation based on forcing method, by biophysical variable of fCover extracted of remote sensing data was incorporated into the AquaCrop model. Then, the most sensitive model parameters which identified through sensitivity analysis were estimated and the obtained results were then compared with the case where assimilated data were not incorporated. As the results suggest, the output yield for the model with data assimilation was estimated with R2 values of 0.89 and 0.88 for calibration and evaluation, respectively. The superiority of RS data assimilation into the model as opposed to not its incorporating was also verified by improving the accuracy with Relative RMSE (RRMSE) values of 4.12 and 5.17 percent and RMSE of 2.5 and 2.4 ton/ha for calibration and evaluation, respectively. The overall findings allude to the advantages of incorporating remote sensing data assimilation by the forcing method as a relatively efficient tool for simulating silage maize yield under variable environmental conditions.
علمی - پژوهشی
Parinaz Abdoli; Sohayla Hashemi
Abstract
One of the benefits of remote sensing and Vis–NIR are rapidity, simplicity, and low cost of analysis compared with traditional methods. The aim of this research, is to use Landsat 8 satellite sensors and near infrared spectrum in agricultural and forestry uses in Gyan Nahavand plain, Hamadan province ...
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One of the benefits of remote sensing and Vis–NIR are rapidity, simplicity, and low cost of analysis compared with traditional methods. The aim of this research, is to use Landsat 8 satellite sensors and near infrared spectrum in agricultural and forestry uses in Gyan Nahavand plain, Hamadan province to estimate soil calcium carbonate. 48 soil samples were collected from Gyan randomly and some physico-chemical characteristics of soils were analyzed. The correlation between the value of the main bands, composition of the bands, calcite indices with the amount of soil calcite was done. Spectral analysis of the desired soils was done using a fieldSpec3 with a wavelength range of 350-2500 nm. Laboratory results showed that the average of soil calcium carbonate in agriculture and forest use are 30 and 22.22%, respectively. The results showed that the bands 10 and 11 had a significant correlation with soil calcite in forest use (p<0.05). 12 band compositions at the 5% level and 6 band compositions at the 1% level showed a significant correlation with the amount of soil calcite. Also, R1 index ((Band5/Band4)/(Band5/Band2)) with soil calcite had significant correlation (p<0.05). The correlation between the measured calcite in laboratory and the equation achieved from satellite image was found to be equal (r2= 0.45) for agriculture use. In the spectroscopic method, the highest correlation was observed at the wavelength of 612 nm (r2=0.85**). Among the models fitted by multivariate regression, in satellite images, the Stepwise Multivariate Linear Regression (SMLR) model is suggested as a suitable model for calcite estimation. The partial least square regression (PLSR) model has been an almost suitable for estimating calcite by spectroscopic method. It can be concluded that Vis-NIR spectroscopy method has more accuracy than remote sensing and titration methods, but it requires more.
علمی - پژوهشی
Imam Baharloo
Abstract
Public transportation and easy commuting in the city are one of the main aspects of urban life, and taking care of a complete, regular and developed transportation system is one of the basic needs of the city. In this respect, it is important to pay attention to the principles of "spatial justice" ...
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Public transportation and easy commuting in the city are one of the main aspects of urban life, and taking care of a complete, regular and developed transportation system is one of the basic needs of the city. In this respect, it is important to pay attention to the principles of "spatial justice" that have been included in urban planning discussions in order to solve the inadequacies of urban management. In recent years, the city of Isfahan has experienced significant population growth and physical development in different directions. On the other hand, the results of comprehensive transportation studies of Isfahan City indicate that to provide citizens with balanced access to public transportation lines, the creation of 21 high-speed bus lines has been proposed. Therefore, the purpose of this study is to prioritize the development of high-speed bus lines by combining the Shannon, Kopras and entropy models with an emphasis on the concepts of spatial justice up to the horizon of 1410. The distinctive feature of this research is the integration of a geographic information system with spatial justice indicators in order to prioritize bus express routes for implementation. Therefore, in the first step, the status of high-speed bus lines was calculated in terms of permeability, proximity, and accessibility indicators, and the Gini coefficient and Lorenz curve of the current situation was calculated. In the second step, by calculating the Gini coefficient of each of the proposed lines separately and comparing it with the current value, the effectiveness of the proposed lines was evaluated. Secondly, the importance of the desired criteria was weighed by the Shannon entropy method. In the end, using the Kopras method, 21 proposed bus routes were prioritized for development to the 1410 time horizon. The results showed that although the Ayatollah Ghaffari terminal line to the end of Sheikh Sadouq with a length of 14.9 km is one of the medium length lines, it should be implemented considering other criteria as the first priority.
Keywords: Spatial Equity, Public Transport, Shannon Entropy, Kopras, Lorenz Curve
علمی - پژوهشی
Mohammad Karim Sirat; Gomroki Masoomeh
Abstract
Today, land use change detection in urban areas have key role in city management, resource management and city development. So, in this research the land use change detection is investigated. The purpose of this research is to investigate the land use changes in Herat using Landsat 8 satellite images ...
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Today, land use change detection in urban areas have key role in city management, resource management and city development. So, in this research the land use change detection is investigated. The purpose of this research is to investigate the land use changes in Herat using Landsat 8 satellite images of OLI sensor during 2015 and 2022. After geometrical, radiometric and atmospheric correction in these four-use satellite images; Soil, plant, city and water were identified in the study area. Two methods of maximum likelihood and artificial neural network have been used to classify satellite images to identify land use changes. In general, the classification of the images used by the maximum likelihood method has provided better accuracy, which has a good kappa coefficient and overall accuracy. In the image of 2015, the maximum likelihood method with a kappa coefficient of 0.75, overall accuracy of 0.85 was used, and in the image of 2022, classification was done using the maximum likelihood method. which has a kappa coefficient of 0.96 and an overall accuracy of 0.97. Based on the results of the classification, during the period of 2015-2022, a decrease in the level of land and water and an increase in the level of city and plant land were observed. After classifying the number of changes in the time period of 2015-2022, the land use area has decreased by 4.00 square kilometers, water by 1.62 square kilometers, and the city has increased by 1.39 square kilometers and plants by 4.59 square kilometers.
علمی - پژوهشی
sara Beheshtifar; Siamak Bakhshali pour gavgani
Abstract
Earthquake is one of the natural hazards that, due to its unexpected nature, often causes a lot of human and financial losses. Currently, there is no way to prevent earthquakes; But with strategies such as identifying vulnerable areas, the resulting damages can be reduced to some extent. In this research, ...
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Earthquake is one of the natural hazards that, due to its unexpected nature, often causes a lot of human and financial losses. Currently, there is no way to prevent earthquakes; But with strategies such as identifying vulnerable areas, the resulting damages can be reduced to some extent. In this research, the level of vulnerability of a part of the 2nd zone of Tabriz city against this event has been investigated. For this purpose, first, effective indices (criteria) such as building materials, quality of buildings, age of buildings and number of floors, were specified and related information was collected. Then the weights of these criteria were calculated using the best-worst method (BWM), which is one of the new methods of multi-criteria decision making. In this research, the simplex linear programming method was used to solve the mentioned model, and the inconsistency rate was about 0.06. In each factor map, the best and worst areas were determined by spatial analysis and through calculations based on the condition of the parcels inside each area, instead of the expert opinions (unlike the usual BWM). Finally vulnerability map was produced in GIS environment. Also, the allowable change for the weight of each criterion was specified. The weight of building quality and distance from open spaces have the most and least flexibility against change, respectively. According to the results of the research, area No. 2 is the most vulnerable to earthquakes, which accounts for 16% of the studied area and includes 1815 parcels.
علمی - پژوهشی
Zahra Barkhordari; Ali Shamsoddini
Abstract
Due to the limitations of real precipitation measurement and the lack of proper spatial and temporal coverage of rainfall measurement in the country, remote sensing technology with high temporal and spatial resolution is considered as a useful tool for estimating the amount of precipitation phenomenon ...
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Due to the limitations of real precipitation measurement and the lack of proper spatial and temporal coverage of rainfall measurement in the country, remote sensing technology with high temporal and spatial resolution is considered as a useful tool for estimating the amount of precipitation phenomenon and its temporal and spatial changes; however, satellite data are contaminated to the various errors such as uncertainty in sampling, retrieval errors and inherent errors. Therefore, it is necessary to evaluate the accuracy of their precipitation products before using them. In this study, the effectiveness and accuracy of the precipitation products of CHIRPS, TRMM and MERRA satellites were evaluated using statistical methods against the measured data of 222 synoptic stations located in IRAN on a monthly time scale, for the years 2005-2019. The results showed that the TRMM product with RMSE of 23.84 mm performed better compared to other precipitation products, and after that, the MERRA product with RMSE of 30.57 mm has shown a suitable performance compared to the CHIRPS product with RMSE of 35 mm. Also, the examination of POD, FAR and CSI indices showed that the performance difference of three precipitation products is not considerable and all three have a good performance. In general, the results showed that TRMM satellite products can be used as a good substitute for measured data in where there is no synoptic station. It has also matched in terms of indicators.
علمی - پژوهشی
mostafa karampour; samad khosravi yegane; hamed heidri
Abstract
IntroductionIn each region, drought conditions vary from moderate to severe and with different durations, which require continuous and operational monitoring. The longer a drought occurs, the greater its effects on vegetation and water resources, and the more severe the drought, which can limit human ...
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IntroductionIn each region, drought conditions vary from moderate to severe and with different durations, which require continuous and operational monitoring. The longer a drought occurs, the greater its effects on vegetation and water resources, and the more severe the drought, which can limit human services and alter natural systems. The effects of drought include habitat destruction for wildlife and water quality, reduced access to water resources, etc. and as a result, disruptions such as fire incidents and other natural disasters increase. ). Vegetation in each region, especially in different regions of Lorestan province, is at risk of numerous fires every year due to the lack of rain and dryness of the environment. For this reason, the issue of revealing and identifying fire-prone areas in relation to the most important climatic element (rainfall) has been selected and carried out, which can facilitate appropriate and preventive measures to protect vegetation areas. In this research, a combined method has been used.Material and methods:In this study, an attempt has been made to investigate the drought condition of vegetation in Lorestan province by using Suomi NPP infrared images using NDVI, VCI and TCI indices. The studied period of 2013-2021 corresponds to the first of April to the end of July (week of 13-26 AD) as a weekly average. The monthly average of Standard Precipitation Index (SPI) using precipitation data, the use of monthly precipitation data from Aligoderz, Durood, Khorramabad, Borujerd, Noorabad, Kohdasht and Azna weather stations was done to analyze the precipitation situation well and separate dry and wet months from each other. become Then the correlation coefficient of SPI index with each vegetation index (NDVI, VCI and TCI) is calculated.Results and discussion:Based on the rainfall data recorded in the meteorological stations of Lorestan province, it can be said that there is no rainfall in the study area in the summer season (July, August and September) and only in the autumn, winter and spring seasons. Therefore, the water year in Lorestan province starts approximately from the third decade of September and continues until the second and third decade of June every year. This indicates the very dry air and lack of humidity. Dry air or lack of humidity and increase in temperature provide the necessary conditions for causing fire in the province. In this article, they put a dry season in the summer season of Lorestan province and August is the driest month of the year.ConclusionThe results of this research showed that the vegetation in Lorestan province is always facing the risk of fire and this is very high in the years when there is a lack of rainfall, in different months. It was proved that if there is a lack of rainfall in the first months of the water year, there is a risk of vegetation fire even in the cold months of the year, and this risk increases significantly in the hot months of the year, which is the case in 2021. there have been. SPI calculations showed that the months of July, August and September are negative in Lorestan province. The results show that the best indicator is based on satellite images for monitoring vegetation drought and fire risk in the study area (TCI). In the years 2013 and 2015, the highest fire risk occurred in the western and central regions of Lorestan province. In 2021, the most severe fire risk has occurred in vegetation in the studied area. Due to the large changes and dispersion of vegetation indicators effective in the occurrence of fires in terms of time and place, Spearman's non-parametric correlation has been used.Keywords: Vegetation fire, drought , precipitation, infrared images, Lorestan province
علمی - پژوهشی
fatemeh ahmadloo; Saeedh Eskandari; mahmood Bayat; Mehrdad Mirzaei; Shahryar Sobh Zahedi; Amin Nasrollahian
Abstract
Monitoring the current status of existing afforestation’s in management decisions is very important for the development of afforestation’s in the future. This study was conducted in order to monitor the area, distribution and health of afforestation’s in Langroud county, Guilan province. ...
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Monitoring the current status of existing afforestation’s in management decisions is very important for the development of afforestation’s in the future. This study was conducted in order to monitor the area, distribution and health of afforestation’s in Langroud county, Guilan province. For this purpose, first, field surveys were done in the form of land points from the existing afforestation, and the distribution map of afforestation was prepared using the land surveys, GPS Fields Area Measure PRO application (GFAMP), and Google Earth system. Then the Sentinel 2 satellite image related to the growing season in Langroud county was prepared from the Copernicus site. From the Sentinel 2 satellite images, various vegetation indices such as NDVI, TNDVI, SAVI and RVI related to the growing season were extracted and their maps were prepared in the area of afforestation. In the following, the amount of each vegetation index studied was extracted at the points of land harvesting and the correlation of the values of each vegetation index (resulting from satellite images of the growing season of Sentinel 2) with the health of afforestation (resulting from field harvesting) was investigated. For this purpose, Pearson's correlation coefficient was used. Then, the index that showed the highest correlation with the health of afforestation in Langroud county (NDVI) was selected as the most important index to estimate the health of afforestation and its regression relationship with the health of afforestation was obtained. In the following, using the map of the most favorable vegetation index, information of field harvests and the relationship between these two cases, a health map of afforestation in Langroud county was prepared. The results showed that the area of afforestation in this county is 746.2 ha, which are mainly distributed in the southwest of the county. In addition, the results showed that the Normalized Difference Vegetation Index (NDVI) is the most favorable vegetation index for estimating the health of afforestation in Langroud county. Of the current total area of afforestation, 400.56 ha are in full health status, 305.60 ha are in medium health status, and 40.04 ha are in unhealthy status. The results of this research will help the forest managers for quantitative and qualitative monitoring of afforestation and its continuity in certain time periods as well as future afforestation development plans.The results showed that the area of afforestation in this county is 746.2 ha, which are mainly distributed in the southwest of the county. In addition, the results showed that the Normalized Difference Vegetation Index (NDVI) is the most favorable vegetation index for estimating the health of afforestation in Langroud county. Of the current total area of afforestation, 400.56 ha are in full health status, 305.60 ha are in medium health status, and 40.04 ha are in unhealthy status. The results of this research will help the forest managers for quantitative and qualitative monitoring of afforestation and its continuity in certain time periods as well as future afforestation development plans.n addition, the results showed that the Normalized Difference Vegetation Index (NDVI) is the most favorable vegetation index for estimating the health of afforestation in Langroud county. Of the current total area of afforestation, 400.56 ha are in full health status, 305.60 ha are in medium health status, and 40.04 ha are in unhealthy status. The results of this research will help the forest managers for quantitative and qualitative monitoring of afforestation and its continuity in certain time periods as well as future afforestation development plans.Of the current total area of afforestation, 400.56 ha are in full health status, 305.60 ha are in medium health status, and 40.04 ha are in unhealthy status. The results of this research will help the forest managers for quantitative and qualitative monitoring of afforestation and its continuity in certain time periods as well as future afforestation development plans.n addition, the results showed that the Normalized Difference Vegetation Index (NDVI) is the most favorable vegetation index for estimating the health of afforestation in Langroud county. Of the current total area of afforestation, 400.56 ha are in full health status, 305.60 ha are in medium health status, and 40.04 ha are in unhealthy status. The results of this research will help the forest managers for quantitative and qualitative monitoring of afforestation and its continuity in certain time periods as well as future afforestation development plans.
علمی - پژوهشی
Seyedeh Narges Sadati; Mahsa Fahmi; gholamreza Ahmadzadeh
Abstract
The studied area is located in the structural zone of Alborz-Azerbaijan and on the sheet 1:100000 of Lahrud. In some areas, intrusive bodies with a combination of granodiorite and quartzdiorite with Oligocene age have penetrated into the volcanic rocks, and in the central parts around the village of ...
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The studied area is located in the structural zone of Alborz-Azerbaijan and on the sheet 1:100000 of Lahrud. In some areas, intrusive bodies with a combination of granodiorite and quartzdiorite with Oligocene age have penetrated into the volcanic rocks, and in the central parts around the village of Qaragol and in the southwest of the region, in the area of Sahib Divan and Doust Biglo, they are the agent of extensive hydrothermal alteration (kaolinite), siliceous and alunite in upper Eocene latitic rocks. In order to introduce training samples after geological surveys, 13 samples from the Sahib Divan analyzed by XRD method. The areas that were representative of the major alterations and the pixels corresponding to them in satellite images were selected as training samples for the base spectrum methods. In the study area, the MNF results were extracted from Aster and sentinel 2A data and used in the classification of the base spectrum and using the software spectral library and training samples from the altered areas of the three minerals kaolinite, muscovite and chlorite respectively was used as indicators for argillic, phyllic and propylitic alteration. The interesting result obtained from this research was highlighting the alteration in the images of the Aster of Sahib Divan mine, which indicated the simultaneous presence of argillic and phyllic alteration in the center and its enclosure with propylitic alteration, which is consistent with the conventional pattern of porphyry deposits. Also, the analysis of Sentinel images showed that hematite, jarosite, goethite and limonite show strong absorption characteristics in the VNIR region and this sensor has been more successful in identifying them. The resulting products were also compared with the published geological map of the studied area and the findings have shown that the resulting maps support the conceptual geological model in the porphyry copper deposit.In the present study, the Aster and Sentinel-2A MSI datasets from the 1:100,000 sheet of Lahrud have been examined with a special attitude to Sahib Divan mine. The main goal of this study is to create a new method by considering remote sensing data, especially Aster and Sentinel 2, in identifying alterations associated with porphyry copper deposits. This identification was based on two approaches: identification of hydrothermal alteration zones associated with porphyry copper deposits (through spectral library) and direct identification of alteration zones and copper-bearing minerals (through direct sampling of the deposit site and using it as a training pixel). In this research, taking into account that the purpose of exploring porphyry copper deposits, acidic to intermediate igneous rocks in which extensive argillic, phyllic, and propylitic alteration had occurred, special attention was paid to alunitic, silicic, and iron oxide alteration. Recent technological advances in the field of spectral/spatial resolution of satellite data in classification algorithms have created interesting opportunities and solutions for geological mapping. Therefore, after taking the geology and conducting XRD studies, base spectrum mineralogical mapping methods such as BE, LSU, CEM were used, among which the BE method gave the best result for separating the target pixels, especially for detecting different types of changes related to porphyry copper deposits. It was shown using training pixels on Aster's images. Although Sentinel images did not achieve satisfactory results for clay minerals due to low spectral resolution in the short infrared range, they were favorable for separating iron-containing minerals such as hematite, jarosite, and goethite, and by using a color combination, it was concluded that it shows the presence of hematite and jarosite at the same time. The results of the investigation of the intrusive masses of the 100000 sheet of Lahrud, siliceous veins and the spread of faults and deformations show that, in general, the extensive altreation in the central part (Salavat and Niaz Qoli-Mashiran) and the southwest of the study area (Saheb Divan and Dust Biglo) is significant. The products obtained from the processing were compared with the published geological map of the studied area and it was shown that the resulting maps correspond to the conceptual geological model of the porphyry copper deposit and the alterations are related to the host rock of this type of deposit.