علمی - پژوهشی
Hadi Zare khormizie; Hamid Reza Ghafarian Malamiri
Abstract
Phenology is the study of the occurrence of repeatable plant life events in relation to living and non-living factors. The phenology reflects the response and adaptability of ecosystems to climate change. Phonological study can be used to regulate livestock grazing management programs at rangeland, various ...
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Phenology is the study of the occurrence of repeatable plant life events in relation to living and non-living factors. The phenology reflects the response and adaptability of ecosystems to climate change. Phonological study can be used to regulate livestock grazing management programs at rangeland, various agricultural activities, and etc. In order to study the effect of height and temperature on plant phenological processes, harmonic analysis of time series satellite observations was used. In this study, the 8-day products of the NDVI indices of MODIS sensor (namely MOD09Q1) with a spatial resolution of 250 m was used. First, the HANTS algorithm was used to decompose the one-year NDVI MODIS products time series to thier Fourier Series components (the amplitude and phase images). Then, the correlation of each of these components with respect to height and temperature were investigated in Shirkoh area of Yazd province. According to the results, with 1 centigrade decrease in the average spring temperature, which occurs with elevation, there were a delay of 6.6 days in annual cycles and 3.9 days in the 6-month cycles of the NDVI time series, respectively. These results indicate that a delay of 6.3 days was observed in phenological processes and plant starting growth time in plant with annual growth periods and a delay of 3.9 days in plants with seasonal and six-month growth stages. Accordingly, the results of the HANTS algorithm and the Fourier series analysis can be very effective in understanding the effects of climatic factors on phenological processes and the onset of plant growth.
علمی - پژوهشی
Samira Karbasi; Hossein Malakooti; Mehdi Rahnama; Majid Azadi
Abstract
In this report, we compare data products from three different algorithms with the reference data obtained by ground-based high-resolution Fourier Transform Spectrometers (g-b FTSs) in the Total Carbon Column Observing Network (TCCON), with the 8 selected sites in five years(2011-2015). The algorithms ...
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In this report, we compare data products from three different algorithms with the reference data obtained by ground-based high-resolution Fourier Transform Spectrometers (g-b FTSs) in the Total Carbon Column Observing Network (TCCON), with the 8 selected sites in five years(2011-2015). The algorithms evaluated are NIES, ACOS and SRFP algorithms. These algorithms are focused on retrieving the column abundance of the CO2 to take advantage of the molecular amounts of dry air carbon dioxide (XCO2). To evaluate the products of each algorithm with its equivalent ground observations, statistical indices such as Bias error, root mean square error (RMSE), absolute error (MAE), standard deviation (SD), and Pearson correlation coefficient (CR) were used. By examining the values presented by each algorithm and comparing it with the ground observation values, it can be concluded that the NIES, ACOS, and RemoTeC (SRFP) algorithms have the lowest RMSE, Bias and MAE error respectively. The best agreements with TCCON measurements in the most stations were detected for NIES 02.xx. The SRFP algorithm has a significant difference in estimating CO2 retrieving rates compared to the other two algorithms. So that the lowest correlation values belong to the SRFP algorithm and the highest correlation, values belong to the NIES algorithm.
علمی - پژوهشی
Mina Moradizadeh
Abstract
Atmospheric column water vapor, which is the total atmospheric precipitable water vapor contained in a vertical air column, is one of the most important factors in all surface-atmosphere interactions (such as energy fluxes between the earth and the atmosphere) and plays a key role in wide variety of ...
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Atmospheric column water vapor, which is the total atmospheric precipitable water vapor contained in a vertical air column, is one of the most important factors in all surface-atmosphere interactions (such as energy fluxes between the earth and the atmosphere) and plays a key role in wide variety of environmental studies, ecological and agricultural applications. However, measuring this parameter at meteorological stations requires the use of radiosonde instruments, which being pointwise and costly are limitations of these observations. Therefore, remote sensing is used as an alternative to estimate this important atmospheric parameter. Compared to other atmospheric parameters, atmospheric water vapor which attenuates remotely sensed radiance is of great importance. Although this atmospheric parameter is measured by AIRS (Atmospheric Infrared Sounder) sensor, its low resolution (about 40 km) is not acceptable for many applications. Therefore, developing an algorithm to downscale the AIRS-derived column water vapor is the main goal of this study, so that its spatial resolution can be improved. To do this, using the ratio method, the AIRS-derived column water vapor is fused with the MODIS (Moderate Resolution Imaging spectroradiometer) data. Then, due to the major influence of this parameter on Land Surface Temperature (LST) estimation, the role of improved resolution atmospheric column water vapor in the estimation of LST is investigated as a secondary goal. In order to validate the estimated parameters and evaluate their accuracy, independent datasets were used. Results of the implementation indicate that proposed downscaling method has high potential to enhance the spatial resolution of AIRS-derived atmospheric column water vapor, without significant degradation of the RMSE. It was also found that the atmospheric column water vapor when moving into higher spatial resolution can dramatically increase the accuracy of the LST estimation.
علمی - پژوهشی
Behzad Mohammadi Sheikh Razi; Mohammad Sharif Molla; Ali Jafar Mousivand; Ali Shamsoddini
Abstract
< p >Vegetation biophysical and biochemical variables are key inputs to a wide range of modelling approaches for carbon, water, energy cycle, climate and agricultural applications. Leaf Area Index (LAI) is among the most important canopy variables, used by many different physiological and functional ...
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< p >Vegetation biophysical and biochemical variables are key inputs to a wide range of modelling approaches for carbon, water, energy cycle, climate and agricultural applications. Leaf Area Index (LAI) is among the most important canopy variables, used by many different physiological and functional plant models. Several approaches have been developed for vegetation properties retrieval from remotely sensed hyperspectral data. Among them, nonparametric machine learning methods have increasingly gained attention in vegetation variable retrieval due to their flexibility and efficiency while working with data of high dimensionality over the last decades. Although these methods provide reasonable accuracy at relatively high speed, they are mainly restricted to estimate values within their training domain and often perform poorly on the marginal values (i.e. outside of the training domain). The performance of these methods has not been adequately studied in retrieving LAI on the marginal values. This study employs four well-known machine learning methods including SVR, GPR, ANN, and RF to retrieve LAI from a hyperspectral CHRIS-Proba image over Barrax, Spain, in order to inspect their capability in retrieving marginal values. The results showed that although all the methods perform similarly well on retrieving LAI over the training domain values with RMSE values of less than 0.5 and relative error of less than 10%, GPR and SVR performed slightly better. However, ANN outperformed the other methods in estimating LAI on the marginal values, resulted in the generated LAI map more consistent with the NDVI map, as well as, the hyperspectral image of the region.
علمی - پژوهشی
Ghasem Javadi; Mohammad Taleai
Abstract
Public satisfaction is a multidimensional and dynamic concept that changes over time, so it must be evaluated at appropriate times. A major challenge for this evaluation, especially in large geographical areas such as one country, is the lack of regular procedures and updated relevant index values. In ...
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Public satisfaction is a multidimensional and dynamic concept that changes over time, so it must be evaluated at appropriate times. A major challenge for this evaluation, especially in large geographical areas such as one country, is the lack of regular procedures and updated relevant index values. In recent years, several indicators have been presented based on traditional methods of data collection, including the use of questionnaires, to measure public satisfaction. Since, in recent years, the use of User Generated Geo-Content (UGGC) has been widely considered, in this research, with a new perspective by using of location-based social networks (LBSNs), extraction of information and criteria that can somehow reflect public satisfaction has been done. Finally, considering the uncertainties in the input data and the definition of public satisfaction, a fuzzy inference system was used to evaluate and compare public satisfaction in Iranian provinces. The extracted indices in this study, include negative/positive tweet ratio, the ratio of joy and love tweets to all tweets, and the ratio of sadness, anger and fear tweets to all tweets. The results of the proposed method resulted in the classification of the provinces of Iran from favorable to unfavorable situations. The results of this study demonstrated the potential of UGGC for public satisfaction assessment in the role of complementary data rather than as an alternative to official data. The proposed method in this study is a step towards evaluating public satisfaction using data shared by users on location-based social networks.
علمی - پژوهشی
karim solaimani; Fatemeh Ruhani; Morteza Shabai; Mohsen Rohani
Abstract
The increase in population and the development of urbanization and, consequently, diforested areas have caused an increase in the surface temperature in urban areas, which results in an urban heat island. The heat islands of the city is one of the factors that has become important at the same time with ...
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The increase in population and the development of urbanization and, consequently, diforested areas have caused an increase in the surface temperature in urban areas, which results in an urban heat island. The heat islands of the city is one of the factors that has become important at the same time with the development of the city and today it can be calculated and evaluated using satellite images. The objectives of this study are to evaluate the points of temperature changes, land use, vegetation, traffic and soil types relationship with surface temperature in Sari and the trend of its spatial changes during the two time periods of 1988 and 2018. For this purpose, TIRS and Landsat 5 and 8 TM images in a period of 30 years (1988-2018) were used to study the heat island changes and calculate the surface temperature with a single-channel algorithm. The results showed that during a period of 30 years with a decrease of 235.3 hectares of green space and a 34% increase in land occupation in Sari, the area of heat islands increased by 21.83%. Also, considering the value of P-value less than 0.05, it showed that there is a significant relationship between vegetation index and city occupation level with land surface temperature and it can be argued that land use change, vegetation and traffic due to population growth and land use change is one of the main factors in increasing spatial changes in the heat islands of Sari.
علمی - پژوهشی
Salman Ahmadi; Reza Soodmand
Abstract
The temperature of the Earth's surface is a very important parameter in environmental studies, climate change, soil moisture content, Evapotranspiration and urban thermal islands at different scales. Currently, there is no perfect method for accurately measuring the temperature of the surface of the ...
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The temperature of the Earth's surface is a very important parameter in environmental studies, climate change, soil moisture content, Evapotranspiration and urban thermal islands at different scales. Currently, there is no perfect method for accurately measuring the temperature of the surface of the earth, but since high spectral resolution sensors prevent the vapor spectral absorption in the infrared bands, this Increases computational accuracy in determining vegetation index. The purpose of this paper is to calculate the surface temperature using satellite images of OLI and TIRS sensors of Landsat 8. In this research, the separate window algorithm has been used to calculate ground temperature. The algorithm uses spectral radiance and emissivity to calculate the surface temperature. To estimate the spectral radiance in Landsat 8, the bands of 10 and 11 have been used. Emissivity is also obtained by using the NDVI threshold technique by using the OLI bands 2, 3, 4 and 5. Also, In this paper the temperature is calculated by The algorithm has been calibrated and corrected by a two-dimensional projective mathematical model, which tried to bring the calculated temperature closer to the actual ground temperature. In the present paper, the RMSE value is equal to 0.3678°C and the correlation between Meteorological data and temperature estimated by the model is equal to 0.9791. Also, the performance of the model that used to estimate the Earth's surface temperature is equal to 0.9751.
