Mohammad Mahdi Khoshgoftar; Mehdi Akhoondzadeh Hanzaei; Iman Khosravi
Abstract
Introduction
Drought is a critical climate condition affecting many places on Earth. Drought severity is often measured using a combination of different variables including rainfall, temperature, humidity, wind, soil moisture, and steam flow. During the last decades, Iran has suffered from drought conditions ...
Read More
Introduction
Drought is a critical climate condition affecting many places on Earth. Drought severity is often measured using a combination of different variables including rainfall, temperature, humidity, wind, soil moisture, and steam flow. During the last decades, Iran has suffered from drought conditions and it may suffer more in future. The frequent occurrence of drought in Iran is mainly due to lack of sufficient precipitation and improper water management system. Drought is often categorized into three types: meteorological, agricultural, and hydrological. There are various methods for measuring and quantifying drought severity. The most commonly used ones are Palmer Drought Severity Index (PDSI) and Standardized Precipitation Index (SPI). Remotely sensed data can also be used for monitoring drought condition. The most widely used ones are Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Vegetation Condition Index (VCI), Temperature Vegetation Index (TVX) and NDVI deviation Index (DEV). Neural Network (NN) and Autoregressive Integrated Moving Average (ARIMA) are two of the most widely applied methods for modeling and monitoring drought severity indices.
In this paper, monthly time series data (2000 to 2014) of three remotely sensed indices (i.e., NDVI, VCI, and TVX) and one meteorological index (i.e., SPI) were applied for modeling drought severity. In addition, the NN and ARIMA were developed for modeling these indices.
Materials & Methods
Data used in this paper were the time series of NDVI, VCI, TVX, and SPI. The study area in this paper was Arak, center of Markazi province. It has cold and wet winters with warm and dry summers. ARIMA and NN were employed for modeling indices.
ARIMA model is generally derived from three basic time series models: Autoregressive (AR), Moving Average (MA), and Autoregressive Moving Average (ARMA). These basic models are used with static time series, i.e., they have constant mean and covariance in relation to time.
Usually, NN method has three layers. The first layer or the input layer introduces data to network. Input data is processed in the second layer or the hidden layer. Finally, the output layer produces the results of the input data. In this paper, single hidden layer feed forward network, which is the most widely utilized NN form, was employed for modeling indices.
Results & Discussion
After implementing NN and ARIMA models on the time series data, the performance of the models was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The RMSE obtained by NN and used for modeling NDVI, VCI, TVX, and SPI indices of Arak were 0.1944, 0.2191, 0.1295, and 0.2990, respectively. In addition, RMSE obtained from ARMIA, and used for modeling these indices were 0.0770, 37.2318, 0.2658, and 1.3370. In another experiment, the correlation between remotely sensed indices and SPI was studied. Among the remotely sensed indices, TVX shows the most powerful correlation with SPI.
Conclusion
In the present study, drought condition in the central region of Markazi province was studied during the 2000 to 2014 period. We used the time series of remotely sensed data (such as LST and NDVI) and meteorological data (such as SPI). Then TVX, VCI, and DEV indices were extracted from NDVI and LST data. NN and ARIMA were applied for modeling time series data. Based on the findings, it is concluded that NN is more successful and efficient than ARIMA for this study area. In addition, TVX, which is built based on NDVI and LST, had the most powerful correlation with SPI. This issue implies that both vegetation index and temperature index had an important role in modeling and monitoring drought condition.
faeze Soleimani vosta kolaei; Mehdi Akhoondzadeh Hanzaei
Abstract
Extended abstract
Land Surface Temperature (LST) and Emissivity are two significant physical features of the Earth’s surface and atmosphere. The calculation of land surface temperaturehas a great significance in environmental studies, meteorology, evapotranspiration study, interactions between ...
Read More
Extended abstract
Land Surface Temperature (LST) and Emissivity are two significant physical features of the Earth’s surface and atmosphere. The calculation of land surface temperaturehas a great significance in environmental studies, meteorology, evapotranspiration study, interactions between land surface and the atmosphere, detection of earthquake- related thermal anomalies, monitoring the drought, fire and energy balance models on the surface of the earth on a regional and global scale.The use of remote sensing technology and types of satellite images as one of the most important sources of data collection to study and monitor the land and environmental resources has attracted the attention of many experts and specialists of various sciences including environment, meteorology, hydrology, etc. in recent years.In recent years, hyperspectralthermal images have become a powerful tool for estimation of the land surface temperature due to the large number of thermal bands. The main purpose of this research is to obtain land surface temperature and emissivity using two distinct methods of TES (Temperature/Emissivity Separation Algorithm) and ARTEMISS (Automatic Retrieval of Temperature and emissivity using Spectral Smoothness) from the HyTES thermal hyperspectral images. The HyTES (Hyperspectral Thermal Emission Spectrometer) is an airborne thermal hyperspectral sensor with 256 spectral channels within the range of 7.5 and 12 micrometers in the range of thermal infrared of the electromagnetic spectrum designed by NASA.
The scope of this study was to retrieve land surface temperature, emissivity and atmospheric parameters from the HyTES sensor in two different methods: ARTEMISS and TES. We used the ISAC method that estimates the transmission and upwelling radiance of the atmosphere. In ISAC method, it is necessary to fit a straightforward line to optimize upper boundary of data. We used the smoothness of the spectral emissivity in the ARTEMISS algorithm in order to retrieve temperature and emissivity. Atmospheric parameters that were obtained from ISAC were used in ARTEMISS and TES. In the next step, the TES algorithm was applied to derive surface emissivity and LST. This method is designed to reduce systematic errors in LST and LSE and also to limit errors in the amplitude and shape of emissivity spectra. This algorithm first estimates the normalized emissivity and then, calculates emissivity band ratios. Next, anempirical relationship predicts the minimum emissivity from the spectral contrast (MMD) of the normalized values, permits recovery of the emissivity spectrum with improved accuracy by using an empirical relationship between emissivity contrast and minimum emissivity, the nondeterministic problem of TES was solved. The basic problemof TES is, as indicated by Realmuto 1990 that we obtain spectral measurements of radiance and need to find unknowns ( emissivities and one temperature). This is a nondeterministic problem, so at least one additional constraint must be considered. Several methods have been developed to resolve these problems such as Normalized Emissivity Method (NEM), RATIO and Minimum-Maximum emissivity Difference (MMD). In the NEM module of TES, we guessed preliminary values of temperature and LSE assuming a value for the maximum local emissivity (for blackbodies). Then, in RATIO module, we estimated emissivity normalized spectrum (). In order to scale the spectrum to actual emissivity values, we used the MMD module of TES. After applying NEM, RATION and MMD module, TES estimates and reports pixel-by-pixel precisions for LST and LSE. Finally, we compared the results of LST and LSEs derived from these algorithms with products of HyTES. The results shown in this study prove the feasibility of retrieving accurate estimates of atmospheric parameters, surface temperature and emissivity with HyTESdata.It should be noted that the noise and water vapor absorption bands of HyTEShyperspectral image were removed, therefore, 202 optimal bands were selected. Then, TES algorithm consists of modules NEM, MMD and RATIO was applied. ARTEMISS method is based on (1) in-scene atmospheric transmission estimation, (2) matching of the transmission to a database and (3) retrieving a spectrally smooth emissivity by an iterative method used on hyperspectral data. The ARTEMISS algorithm was applied. The final outputs of these two algorithms include thermal and emissivity images. In order to evaluate these two methods and quality assessment, we used the satellite products that have been prepared by NASA. The results of the quality assessment show that temperature RMSE for TES and ARTEMISS methods are 0.6 and 1.2 kelvin respectively, and also emissivity RMSE for band 171 are 0.01 and 0.02 respectively. Therefore, TES algorithm is a more accurate method than ARTEMISS which was implemented for the first time on this type of data.The obtained results show that the thermal hyperspectral data are suitable for accurate retrieval of emissivity and land surface temperature in any kind of land cover.
narges fatholahi; Mehdi Akhoondzadeh Hanzaei; Abbas Bahroudi
Abstract
Extended Abstract
Land subsidence is a vertical movement of the earth surface relative to a stable reference level. It occurs as a result of plate tectonic and human activities. The common causes of subsidence from human activities are pumping under-ground water, oil and gas from overlying reservoirs. ...
Read More
Extended Abstract
Land subsidence is a vertical movement of the earth surface relative to a stable reference level. It occurs as a result of plate tectonic and human activities. The common causes of subsidence from human activities are pumping under-ground water, oil and gas from overlying reservoirs. Withdrawal of fluids from hydrocarbon reservoirs causes their pressure to decrease. This pressure reduction rises the stress of reservoir’s overburden sediments which was previously controlled by the pressure of inside fluids before exploitation, and consequently increases the density of their porous surroundings. If the reservoir’s density exceeds a specific threshold, overburden rocks start to subside because of their weight. Therefore pressure drawdown leads to reservoir compaction, movement of the overburden and subsidence over the reservoir. This subsidence can prove costly for production and surface facilities. So study of the subsidence caused by hydrocarbon exploitation is an important task which needs precise considerations. Several methods are available to monitor land subsidence. Classical surveying such as Leveling and global positioning system (GPS) can produce some related data whereas they are expensive and cannot also produce the needed map at a particular period of time. Recent advances in satellite and Radar technology have made it possible to measure very small movements of the earth surface. Interferometric Synthetic Aperture Radar (InSAR) is a novel technology for measuring the surface deformation. Using the InSAR technique at relatively large subsidence areas can be monitored. The pros of InSAR are that it is not necessary to physically access the deformation areas and also the high spatial and temporal resolution of its data. Sub-centimeter accuracy has been reported for InSAR derived surface deformations. Interferometric Synthetic Aperture Radar relies on repeated imaging of a given geographic location by space-borne radar platforms. Synthetic Aperture Radar sensors measure both magnitude and phase of the transmitted electromagnetic signal that is backscattered from the earth surface. The phase measurement is used to derive information on heights and deformations of the terrain. This phase represents a combination of the distance scattering effect. If a second SAR data set is collected then from comparing the phase of the second image with the phase of the first, an interferogram can be formed. The basic principle of interferometric SAR is that if the surface characteristics are identical for both images, the phase differences are sensitive to topography and any intrinsic change in position of a given ground reflector. The interferogram can be corrected for topographic information using an external digital elevation model (DEM). The change in distance is along the line of sight to the satellite, preventing it from directly distinguishing vertical and horizontal movement. As geometrical and temporal baseline de-correlations and atmospheric noise are limitation factors to assess slow movements in subsidence areas, recent developments in multi temporal InSAR (MTI) algorithms have enabled the detection and monitoring of the slow deformation with millimetric precision. In this paper, Marun oil field; the second-largest oil field which is located in the south west of Iran has been studied. The Small Base Line Subset (SBAS) approach that is an (InSAR) algorithm has been performed for generating mean deformation velocity map and displacement time series from a data set of subsequently acquired SAR images. SBAS technique identifies coherent pixels with phase stability over a specific observation period which has been implemented in StaMPS software. This method which is based on multiple master interferograms, works with interferograms with small spatial baselines and short temporal intervals to overcome de-correlations by increasing spatial and temporal sampling and coherent areas. For this study, we have used 10 ASAR images acquired by the ENVISAT satellite from European Space Agency (ESA) during 2003 to 2006 and have generated 22 interferograms by the SBAS method. All interferometric processing were implemented using DORIS software. A SRTM Digital Elevation Model (DEM) with 3-arcsecond geographical resolution has been used to remove the topographic phase. SBAS processing was then implemented using the Stanford Method for Persistent Scatterers (StaMPS) software. As a result, the mean velocity map obtained through InSAR time series analysis which is in the Line-Of-Sight (LOS) direction of satellite to the ground. The time series analysis results of InSAR have been then compared with field production data. This sampled data allows us to evaluate potential of non-tectonic effects such as petroleum extraction on surface displacements and the relationship between both deformation and oil production rate. The results of InSAR analysis reveal the maximum subsidence on order of 13/5 mm per year over this field due to the extraction and geological characteristics in the time period of 2003-2006.
Fatemeh Jahani Cherebargh; Mehdi Akhoondzadeh Hanzaei
Abstract
Extended Abstract Aerosols are small (sub-micron to several microns) suspended particles in the solid or liquid phase in the atmosphere. The main origins of aerosols are natural and anthropogenic. They can be directly emitted as particles (primary aerosols) into the atmosphere namely, mineral aerosol, ...
Read More
Extended Abstract Aerosols are small (sub-micron to several microns) suspended particles in the solid or liquid phase in the atmosphere. The main origins of aerosols are natural and anthropogenic. They can be directly emitted as particles (primary aerosols) into the atmosphere namely, mineral aerosol, sea salt, volcanic eruptions, organic aerosols, industrial dust, soot, biomass burning, etc. They can also be the result of chemical reactions (secondary aerosols) namely, sulfates from biogenic gases or volcanic and nitrates from transportation and diffusion of aerosol particles from the source region depend on wind vector and wind strength. Aerosols are ever present and highly varying constituents of our atmosphere. They play roles in many physical and chemical processes that shape the composition of the atmosphere and thereby affect cloud formation, visibility, and air quality. They interact both directly and indirectly with radiation and thus affect the amount of radiative energy reaching the surface and reflected to space. The shortwave part of the radiative energy at the surface (insolation) is an important component of the surface energy budget, and a necessary input to models of land-surface processes. Aerosol Optical Thickness (AOT) is calculated by measuring light absorption at specific wavelengths of the visible spectrum. For the most widely used AOT data product, the absorption at 550 nm is the preferred wavelength for measurement (In the visible spectrum, humans perceive a light wavelength measuring 550 nm as a shade of green). AOT is a dimensionless quantity, expressing the negative logarithm of the fraction of radiation (e.g., light) that is not scattered or absorbed on a path. High AOT indicates a large quantity of aerosols, and thus a significant amount of absorption and scattering of radiation (i.e., light). Low AOT indicates clearer air with fewer aerosols and increased transmission of radiation. Increasing aerosol concentrations can thus affect global temperature and the radiation balance of the globe by reducing the amount of radiation reaching the Earth’s surface, and that reduction can result in lower air temperatures. Penetration of the large particles into the atmosphere in certain cases leads to decreasing the particles mobility and then dropping the conductivity, which will increase the electric field but aerosol measurements in the seismically active zones are more complicated due to the mosaic character of the gas emanation in the seismic zones and the uncertainty of aerosol origin in gas probes. Some remote sensing satellites due to their suitable temporal, spatial and spectral resolutions provide useful information of time and spatial distributions of Aerosols. This leads to creating an appropriate database for statistical study of the seismic atmospheric effects. The AOD measurement is taken by the MODIS sun-synchronous instrument onboard Terra and Aqua satellites every day. The satellites provide more continuous coverage nearer to the poles but there are more gaps in the coverage of the satellite nearer to the equator. AOT can be determined by implementing different methods on satellite images, but it is a difficult task to achieve it because solar lights are reflected by the atmosphere and the whole solar lights do not hit the ground. The most famous methods used to derive aerosol parameters are Dark Dense Vegetation (DDV), deep blue algorithm and synergy of Terra and Aqua MODIS (SYNTAM). SYNTAM approach can remove limitations in deriving AOT by combining data from two sensors of MODIS of TERRA and AQUA satellites and this method gives the right results. In this study, SYNTAM method has been applied over a region of Iran to produce an AOT map. The comparison between our results and NASA AOT products for the same time and location shows a good agreement. The result of comparing NASA data and SYNTAM approach with Newton iteration algorithm for the wavelength of 0.55 µm, gives the RMSE equal to 0.253. Therefore SYNTAM could be a robust method to derive AOT map over regions without AERONET ground stations. In the next section, SYNTAM method was combined with nonlinear parametric adjustment model. In this case, the results are more accurate than implementation of SYNTAM method alone. The result of comparing NASA data and SYNTAM approach with nonlinear parametric adjustment model for the wavelength of 0.55 µm, gives the RMSE equal to 0.207.
Farideh Sabzehee; Mohammad Ali Sharifi; Mehdi Akhoondzadeh hanzaee
Abstract
Extended Abstract
Electrondensity is one of the significant parameters for monitoring and describing the ionosphere.The ionosphere is a consequential source of errors for the GPS signals that traverse through the ionosphere on their ways to the ground-based receivers, because there is a high concentration ...
Read More
Extended Abstract
Electrondensity is one of the significant parameters for monitoring and describing the ionosphere.The ionosphere is a consequential source of errors for the GPS signals that traverse through the ionosphere on their ways to the ground-based receivers, because there is a high concentration of free electrons and ionsreleased by the ionizingaction of solar X-ray and ultraviolet radiation on atmospheric formers. Radio Occultation(RO) is one of the most modern satellite techniques to study on vertical profiles of neutral density, temperature, pressure and water vapor in the stratosphere and troposphere and ionospheric electron density profiles with high vertical resolutions.Since the RO technique using the GPS signals was employed for the first time by the Global Positioning System Meteorology (GPS/MET), the low-earth-orbit-based GPS RO technique has been proven as a successful method in exploring the earth’s lower atmosphere and ionosphere.
Abel transformation is the basic hypothesis made in the retrieval of radio-occulted ionospheric parameters.The Abel inversion is a powerful tool to retrieve high-resolution vertical profiles of electron density from GPS radio occultation collected by satellites into Low Earth Orbit(LEO).
COSMIC satellite records measurements during the whole day and is not limited to the specific times and special atmospheric conditions.It should be noted that the GPS radio occultation techniques provide continuous and useful ionospheric layers information and are not obtained from the point wise measurements by other satellites.
COSMIC satellite also records the altitude for the measurements of the electron density profile. COSMIC satellite provides more than1000 electron density profiles per day with approximately global coverage and also parts of them cover IRAN .In this approach, the LEO-GPS line of sight is occulted by the Earth’s limb with the setting(or rising) motion of the LEO satellite. The GPS-LEO radio connection successively records the atmospheric layers at different altitudes. The ionosphere is highly variable in space and time. Thus, for modeling the electrondensity profile the time changes(diurnaland seasonal) and location changes(geographical position of station), must be considered. In this research, the input space includes the day number (seasonal variation), hour (diurnal variation), latitude, longitude, height and F10.7 index (measure of the solar activity). The output of the model is the ionospheric electron density profile(Ne).The COSMIC observations and IRI-2007-based data of electron density profiles were also analyzed during the solar minimum period. In this research, we used a feedforward Artificial Neural Network (ANN) with 55 neurons in hidden layer for modeling profiles of electron density of COSMIC satellite performance of the ANN models was evaluated using correlation coefficient (R=92%),R-Squared(0.83). It was found that the ANN model could be applied successfully in estimating the electron density profiles retrieved from the FORMOSAT-3/COSMIC.The comparison of the IRI model electron density profile with the COSMIC RO measurements during each month of the year 2007 over IRAN is performed.The electron density profile from all three International Reference Ionosphere (IRI) models, namely IRI-NEQ,IRI-2001, and IRI-01-Corr are used.
The results showed that the results of the IRI2007 model electron density is not satisfactory over IRAN and ANN model electron density profile is in very good agreement with COSMIC RO measurements. It was concluded that IRI-NEQ model is more appropriate thanthe other two models.
The results showed that the differences between the modeled profile electron density and theobserved profile electron density are very lower than the differences between the IRI-2007 models.Maximum changes occurred in January and December at analtitude of about 450 km and minimum changes were recorded in November at the height of 250 Km and in April at the height of 450 Km. The differences also decreased in the summer at higher altitudes and in winter at lower altitudes.
Monir Darestani Farahani; Mahdi Akhondzadeh Hanzaei; Farhang Ahmadi Qivi
Abstract
Abstract
Water salinity is one of the important environmental factors of the sea and plays a significant role in the study and prediction of the oceanic surface currents, location analysis of the fish aggregation, density determination and studying its changes, and also in ecological properties. This ...
Read More
Abstract
Water salinity is one of the important environmental factors of the sea and plays a significant role in the study and prediction of the oceanic surface currents, location analysis of the fish aggregation, density determination and studying its changes, and also in ecological properties. This parameter changes greatly with time and location, and proper recognition of it requires measurements at short time intervals (monthly) of multiple points in the study area.
In traditional ways, the assessment and evaluation of one or several specific factors of water quality is often costly and time-consuming, and cannot be a good indication for the entire area of a vast region. But in recent years, satellite and remote sensing technology have been considered as an appropriate tool for evaluating some water quality parameters because, given the digitality of these data, their wide availability, regular measurements, their repetition in short periods of time, Less cost and time, a wide range of projects can be achieved. The purpose of this study is mapping sea surface salinity of the Persian Gulf in Iran and the Gulf of St. Lawrence in Canada using MODIS satellite imagery. In this regard, a software has been produced in Iran for the first time that can prepare salinity, temperature and density maps of the sea surface in three different models with proper accuracy by entering the MODIS satellite imagery and CTD field data. High capability and flexibility of the Artificial Neural Network in approximation of nonlinear and linear continuous functions in hybrid space, led this study to provide a new method based on using this network in which salinity map is determined by a multilayer perceptron network.
Monireh Shamshiri; Mahdi Akhondzadeh Hanzaei
Abstract
Discussion about earthquake to reduce its casualties and damages is very important, especially in a seismic area like Iran where the occurrence of this natural phenomenon is seen annually. Anomaly detection prior to earthquake plays an important role in earthquake prediction. Ionosphere changes which ...
Read More
Discussion about earthquake to reduce its casualties and damages is very important, especially in a seismic area like Iran where the occurrence of this natural phenomenon is seen annually. Anomaly detection prior to earthquake plays an important role in earthquake prediction. Ionosphere changes which are recognizable by remote measurements (such as using Global Positioning System) are known as earthquake ionospheric precursors. In this study, two data sets from the ionospheric Total Electron Content (TEC) derived from the GPS data processing by Bernese software were used for two studies, Ahar earthquake, East Azerbaijan (2012/08/11) and Kaki earthquake,Bushehr (2013/4/9), and the results were compared with data obtained from the global stations. Because of the nonlinear behavior of TEC changes, in order to predict and detect its changes, integration of neural network (using multilayer Perceptron (MLP)) with particle swarm optimization algorithm (PSO) was used. Particle Swarm Optimization algorithm with a performance based on the population can be effective in improving estimatedweight by artificial neural network. By analyzing the causes of ionospheric anomalies including the geomagnetic fields and solar activities and their removal from the processes, the results indicate that some of this anomalies caused by the earthquake and using intelligent algorithms were able to have appropriate efficiency for the prediction of nonlinear time series. The output resulted from the integration of artificial neural network and PSO shows that both positive and negative anomalies occur. The anomalies before earthquakes often occur close to the epicenter of the earthquake and are visible 3 days before the Ahar earthquake and 2 to 6 days before the Kaki earthquake are.
