علمی - پژوهشی
Maedeh Behifar; Hossein Aghighi; Aliakbar Matkan; Hamid Salehi shahrabi
Abstract
Leaf area index (LAI) derived from remotely sensed images is considered as an important index for spatial modelling of vegetation productivity. Traditionally, the spectral vegetation indices (VIs) derived from the red (R) and near infrared (NIR) reflectance values have been utilized to statistically ...
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Leaf area index (LAI) derived from remotely sensed images is considered as an important index for spatial modelling of vegetation productivity. Traditionally, the spectral vegetation indices (VIs) derived from the red (R) and near infrared (NIR) reflectance values have been utilized to statistically estimate LAI. However, most of these VIs saturate at some level of LAI. This limitation was over-come by using the reflectance spectra in the red-edge region. Therefore, it is necessary to evaluate the capability of different VIs derived from RS data to estimate the LAI of silage maize. For this purpose, five field sampling campaigns which were near-simultaneous with Sentinel II over-passes were conducted by the Space Research Center, Iranian Space Research Center and totally 234 samples were collected from the silage maize fields, in Magsal, Qazvin. Then, 13 VIs from the time series of Sentinel-2 imagery were computed and employed to statistically estimate the LAI values. The results showed that Enhanced vegetation index (EVI) with outperformed other VIs to estimate LAI of silage maize. Moreover, the values of non-linear regression models were higher that the liner ones.
علمی - پژوهشی
Mojtaba Akhoundi Khezrabad; Mohammad Javad Valadan Zoej; alireza safdari nezhad
Abstract
Due to the wide applications of hyperspectral images, economical and innovative imaging systems are developed to acquire such images. In order to use hyperspectral images, it is necessary to establish an accurate relation between the ground space and the image space, which needs numerous Ground Control ...
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Due to the wide applications of hyperspectral images, economical and innovative imaging systems are developed to acquire such images. In order to use hyperspectral images, it is necessary to establish an accurate relation between the ground space and the image space, which needs numerous Ground Control Points (GCPs). This fact highlights the need for developing geometric corrections methods for any camera design. BaySpec OCI-F (400-1000 nm) is one of the innovative cameras that acquires pushbroom hyperspectral images. In addition to the pushbroom sensor, the camera uses a frame sensor that acquires images at the same time as the pushbroom sensor and with the same temporal rate. In this article, a geometric correction method for pushbroom images of OCI-F camera is proposed. Based on the camera’s imaging design, the first step of the method determines a set of calibration parameters which geometrically relates the pushbroom and the frame sensors. Then using this relation and the geometric relations among consecutive frames, the pixels of the pushbroom scene are rearranged and form the corrected image. The proposed method determines the relation among the consecutive images via Least Square Matching (LSM) method. The results show that the correction method has decreased the geometric distortions of the raw pushbroom scene by 62.2% on average. Such a reduction causes the average accuracies of two-dimensional and three-dimensional generic models which relate image space and ground space together, to increase by 34.1% and 39.9% respectively.
علمی - پژوهشی
Saeid Ahmadi; Hadiseh Hasani
Abstract
Nowaday, there are wide applications for satellite images in agriculture monitoring and management. According to high spatial, spectral and temporal resolution of Sentinel-2 images, we used them for precise agriculture in Qorveh country. Proposed methd consist of five step: firstly, multi-temporal images ...
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Nowaday, there are wide applications for satellite images in agriculture monitoring and management. According to high spatial, spectral and temporal resolution of Sentinel-2 images, we used them for precise agriculture in Qorveh country. Proposed methd consist of five step: firstly, multi-temporal images are collected based on agriculture calender of crops. Then feature space is generated based on spectral reflectance and vegetation indices which consists of 70 features. According to high dimensionality of feature space, principle component analysis is applied to reduce its dimension. Four power classifiers include support vector machine, k-nearest neighbour, multi-layer perceptron and random forests classify the reduced spectral feature space. On the other hand, spatial information are extracted from multi-temporal multispectral images. For this pupose, strandard deviation (STD) maps are generated for red, NIR and SWIR bands of each epoch. Then, by averaging the STD maps, final STD map is obtained. Edge detection is performed on STD map and it improves by removing small lines, smoothing, thining, etc. Finally, crop mapping is done by fusion of four classification maps and agriculture farm boundaries. The obtained results show that classification accuracy of k-nearest neighbour, support vector machine, multi-layer perceptron and random forest classifiers are 77.78%, 79,16%, 76.41% and 76.89%, respectively. The overall accuracy of the proposed method improve up to 94.72% which proves high potential of the proposed method.
علمی - پژوهشی
Matin Shahri; Afshin Shariat Mohaymany
Abstract
Analyzing traffic conditions and suggesting traffic management methods play a critical role in evaluating the effectiveness of transportation systems. Among the methods suggested for collecting traffic data, approaches based on new technologies attracted more attention due to the ability of collecting ...
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Analyzing traffic conditions and suggesting traffic management methods play a critical role in evaluating the effectiveness of transportation systems. Among the methods suggested for collecting traffic data, approaches based on new technologies attracted more attention due to the ability of collecting large amounts of dynamic spatio-temporal data making it easy to identify trends and patterns. In this study, Tehran, the capital of Iran with socio-economic characteristics and the variety of urban trips which lead to heterogeneous traffic state will be considered. Data obtained from digital processing of Google Maps traffic images the one-month time interval (April 7th to May 7th, 2017), has been applied for the first time to evaluate the trend and overall pattern of the changes in traffic congestion in the study area. Considering the variety of trip patterns and consequently the traffic congestion, traffic congestion index (CI) has been calculated on workdays and weekends separately and was assigned to the district center and the morning and evening peak-hours were extracted using descriptive analysis. By applying Getis-Ord hot-spot and cold-spot index, the clusters of congested areas have been recognized over the study area. Also, the temporal relationship between traffic congestion indexes in different time sections was evaluated using Kruskal-Wallis statistical test and the null hypothesis of correlation between the mean values of congestion index was confirmed. Using overlay analysis of congestion maps, clusters indicating congested areas at 90% confidence intervals were extracted during morning and evening peaks on weekdays and weekends separately. The results of this study can be effective in modifying traffic congestion zones, analyzing pollution or studies relating to road pricing, and assessing the process of traffic congestion propagation during desired time intervals.
علمی - پژوهشی
Mohammad Hajeb; Saeid Hamzeh; Seyed Kazem Alavipanah; Jochem Verrelst
Abstract
Leaf Area Index (LAI) plays a critical role in the mass and energy exchanges between the earth and the atmosphere. Like of other plants, LAI of sugarcane is a good indicator of the health status and growth of this crop which is of great economic importance due to its role in the food and energy industries. ...
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Leaf Area Index (LAI) plays a critical role in the mass and energy exchanges between the earth and the atmosphere. Like of other plants, LAI of sugarcane is a good indicator of the health status and growth of this crop which is of great economic importance due to its role in the food and energy industries. Launched in 2019, the PRISMA satellite provides one of the most recent hyperspectral data sources which are applicable especially for mapping plant variables. In this study, a new kind of Artificial Neural Networks (ANN) so-called Bayesian Regularized Artificial Neural Networkk (BRANN) which applies Bayes' theorem to overcome the overfitting problem of neural networks is used. The model was implemented on a data set consisting of spectrum obtained by PRISMA satellite as an independent variable and sugarcane LAI measurements as a dependent variable. The ground measurements of sugarcane LAI were carried out in 118 elementary sampling units on the fields of Amir Kabir sugarcane cultivation and industry in Khuzestan province and on seven different dates during a sugarcane growth period in 2020. Comparing the performance of BRANN in retrieving sugarcane LAI from PRISMA spectra with that of a conventional ANN trained with the Levenberg-Marquardt algorithm (LMANN) indicates that the retrieval RMSE is reduced from 2.26 m2/m2 applying LMANN to 0.67 m2/m2 applying the BRANN method. In this study, the principle component analysis was also used dimensionality reduction. Retrieving LAI from the first 20 principle components, RMSE was also reduced from 1.41 m2/m2 applying LMANN to 0.71 m2/m2 applying BRANN. Exploiting principal components significantly reduced computational time. By implementing the calibrated BRANN model over the PRISMA image pixel by pixel, the sugarcane LAI map was generated. Evaluating this map showed that this map represents the spatial variations of sugarcane LAI well. The results of this study indicate the high performance of the BRANN method and high potential of PRISMA images to retrieve sugarcane LAI.
علمی - پژوهشی
ali reza shooreshi; hassan zoghi
Abstract
Among the network of urban roads, the network of emergency roads plays an important role in providing relief during an earthquake, especially in the crisis response phase. It is very important to maintain the function of the urban roads network in the first few hours after earthquake. Protecting and ...
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Among the network of urban roads, the network of emergency roads plays an important role in providing relief during an earthquake, especially in the crisis response phase. It is very important to maintain the function of the urban roads network in the first few hours after earthquake. Protecting and strengthening vulnerable parts of the network before the crisis (especially bridges) plays a significant effect in reducing damages and injuries. However, retrofitting all vulnerable components is practically impossible due to budget constraints. The existence of this limitation requires identifying the vulnerable components accurately. Therefore, retrofitting options are prioritized first, and the most suitable ones are finally selected. In this research, after identifying the bridges that need to be retrofitted on the emergency roads network through a five-step methodology, we also considered the financial limitations and budget allocation options, and prioritized retrofitting options based on the network of layers created in the Geographic Information systems environment (GIS) under the title of input. Examining all possible situations for the stability of bridges after a specific earthquake, designing the emergency road network for all these situations, examining different options for retrofitting bridges, evaluating the effect of this retrofitting on the length of the emergency network, and finally, the prioritization of retrofitting options according to their impact during the emergency network, are the main steps of the proposed method in this study. The efficiency of the above method was evaluated after applying it on a part of the emergency roads network of Tehran as a real network with large scale.
علمی - پژوهشی
Mahdis Yarmohamadi; Ali Asghar Alesheikh; Mohammad Sharif
Abstract
Dust storms are natural disasters that have severely affected human life and the environment. The majority of research in dust storm has been dedicated to the forecasting of storm-prone areas. However, developing models to predict the movement of these storms plays a significant role in the prevention ...
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Dust storms are natural disasters that have severely affected human life and the environment. The majority of research in dust storm has been dedicated to the forecasting of storm-prone areas. However, developing models to predict the movement of these storms plays a significant role in the prevention and management of dust storms, because they reveal the transport pathway and identify the next vulnerable areas against the storm. In this research, a hybrid convolutional neural network (CNN) model has been developed to predict the path of dust storms based on airborne optical depth (AOD) data of MERRA-2 product for the next 12 hours. 40 storm events including 2489 storm hours in a dry region in Central and South Asia have been used for training the model. The results show that the proposed model provides an accurate prediction of the storm's path, so that for the time steps of 3, 6, 9, and 12 hours, the overall accuracy values are 0.9806, 0.9810, 0.9813, and 0.9790, respectively, the F1 score values are 0.8490, 0.8524, 0.8530, and 0.8384, respectively, and the Kappa coefficient values are 0.8387, 0.8424, 0.8431, and 0.8273, respectively.
