Ramin Mokhtari Dehkordi; Reza Shahhoseini
Abstract
Extended Abstract Introduction Nowadays, studying urban expansion is very importantin developing countries. Rapid growth of cities has devastating environmental impacts,and irreparable economic and social consequences. Moreover, studying urban expansion is of great importance for managers and planners ...
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Extended Abstract Introduction Nowadays, studying urban expansion is very importantin developing countries. Rapid growth of cities has devastating environmental impacts,and irreparable economic and social consequences. Moreover, studying urban expansion is of great importance for managers and planners of a society. Land surface temperature (LST) is one of the important parameters in urban-regional planning.Urban heat, which is usually referred to as urban heat island, can affect human health, theecosystem, surrounding air, air pollution, urban planning, and energy management. The phenomenon of urban heat island (UHI) is closely related toland-use changes in urban areas, especially when natural surfaces turn intoimpermeable urban surfaces, and increases heat flux and reduces latent heat. Materials & Methods In this study, a collection of Landsat-5 multi-temporal satellite images received in 1986, 1989, 1993, 1998, 2001, 2008, and Landsat 8 multi-temporal satellite images received in 2013, 2015 and 2017, was used along with night images of the MODIS sensor recieved in 2001, 2008, 2013, 2015, 2017 (on the same day Landsat-5 and Landsat-8 satellite images were received). In order to classify land cover and calculate land surface temperature usingLandsat 5, Landsat 8 and MODIS sensorsatellite images, initial pre-processing (radiometric and geometric corrections)was performed.In order to classifyland cover in the study area, training areas were selected using Google Earth andthen, land cover classification was carried outusing Neural Network Algorithm. Since, classifying urban areas wasthe priority ofthe present study, Normalized Difference Built-up Index (NDBI) was also used.Ultimately, pixelidentified by classification algorithm and NDBI index was allocated tourban areas. A simple relationship suggested by the United States Geological Survey (USGS) was used to estimate land surface temperature from Landsat-5 imageries.Split-window algorithm was also used to estimate land surface temperature from Landsat-8 and MODIS imageries. Since, Landsat-8 and MODIS imageries were collectedwith only afew hours (or less than that)time difference, and their thermal bands’spectral rangeswere close to each other, Landsat-8 thermal bands’emissivity coefficient with a higher spatial resolution (30 m) was used to calculate land surface temperature from MODIS images. Results & Discussion Classifying land cover in Shahr-e Kordusing Landsat-5 and Landsat-8 imageries received in 1986, 1989, 1993, 1998, 2001, 2008, 2013, 2015, and 2017 indicated that in this31-year time period,residential areas were approximately duplicatedand reached from 1004 hectares to 2112 hectares. Analysis of land surface temperature maps using Landsat 5, and Landsat 8 imageries indicated that urban areas and areas with dense vegetation had lower surface temperatures compared to areas with thin vegetation cover. Therefore, land surface temperature of urban areas is lower than the surrounding areas. However, land surface temperature obtained from MODIS imageries indicated that land surface temperature of urban areas is higher at nights. Therefore, urban heat islands in this city occur at nights. Results indicated that with increasingexpansion of urban areas, urban heat islands also intensifyat nights. Conclusion Although, Shahr-ekordis a less developed urban area as compared to other Iranian metropolises,expansion of its constructed areas can stillhave negative effects on the environment and climate of the region. The present study investigates urban growth, and itsinfluence on land surface temperature and occurrence of urban heat island. Thermal maps produced in the present study indicated that daytime air temperature of this city was relatively lower than other regions. But this is not the case at nights: compared to other areas,residential areas have a higher temperature at nights. This indicates the existence of a heat island in the city, and possibly have adverse and devastating effects on humidity, reduces precipitation, changes local winds and the climate. Results also indicate that urban expansion have directlyaffected urban heat islands. Thus, urban heat islandshave intensified and expanded during this time period. Therefore, it is concluded that there is a direct relationship between land surface temperature and land use type.
Marziyeh Deiravi pour; Hossein mohammadasgari; saeid Farhadi; Iman Najafi
Abstract
Extended Abstract Introduction One of the important features of desert areas (arid and semi-arid) is dust phenomena that occurs in most days of the year. Dust phenomena occur especially in tropical areas. In some parts of the world, including Africa, Australia and the Middle East, the annual sediment ...
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Extended Abstract Introduction One of the important features of desert areas (arid and semi-arid) is dust phenomena that occurs in most days of the year. Dust phenomena occur especially in tropical areas. In some parts of the world, including Africa, Australia and the Middle East, the annual sediment volume carried by the flow of the wind is greater than the sediment volume carried by the rivers. Today, the dust phenomena are among the most important environmental hazards which have put human and environmental health at serious risk. Based on the country’s comprehensive water plan, the size of the real deserts of Iran has increased to 4.7 million hectares or 35.5 percent of the country’s land area. Materials & Methods The study area was the southwest of Iran including Khuzestan and the Persian Gulf regions. In recent years, these regions have strongly been affected by the dust with internal source and especially with external sources such as dust sources in Iraq, Syria, and Saudi Arabia. In this research, we employed the library method and also determined the days of the dust storm using the weather data of the province. We used satellite data, MODIS sensor data and several algorithms based on the image processing to detect dust. In order to evaluate the different methods of dust detection, it is necessary to compare the results of the algorithms with another independent source. This source can be a natural color images, aerosol sensor products, MODIS dust indicators or other sensors products. In this research, we first introduced the HDF file of MOD021k MODIS images into the ENVI5.2 software to visualize the dust. After preprocessing the satellite images, we employed different methods such as creating False Color images, BTD and NDDI algorithms, and the neural network method to detect dust on satellite imagery. In this regard, we stored the required bands for the NDDI and BTD algorithms as a single band in the ENVI software, and entered it into MATLAB software to apply the detection algorithms. Due to the importance of remote sensing and satellite images and also the efficiency of the artificial neural networks method we decided to classify the images of the MODIS sensor by using the methods of the Artificial Neural Network and dust detection indexes. In general, the bands 20, 23, 31 and 32 of MODIS sensor and the infrared thermal bands were used more to detect dust storms. The Brightness Temperature Difference between these bands can detect dust storms from other phenomena. In this study, a Feed Forward Neural Network (FFNN) was used to detect dust storm in Khuzestan and the north of the Persian Gulf, using 20 data sets for the day and 11 data sets for the night. To categorize different pixels in the neural network based on BTD values, BTD of the bands 20-31, BTD of the bands 23-31, BTD of the bands 31-32 and bands 1, 3 and 4 were used. MODIS bands 1, 3 and 4 were used to create realistic color images to for the better detection of the Earth’s surface phenomena. These three bands were used only for MODIS’s daily images. Discussion The results show that the emissivity of sand in band 31 (0.96) is slightly lower than the band 32 (0.98), while the soil emissivity for these two bands was (0.97) and water emissivity (0.99). Also, the emissivity value of band 31 for the cloud was (0.98) and for band 32 was (0.95). There was a difference between the emissivity value of bands 23 and 31 for soil, sand, and water, which can be used to distinguish dust from other surfaces. The brightness temperature of dust storm (K298/4) and cloud (K276) in the band 23 (4.6 µm) was higher than the brightness temperature of dust storm (K287) and cloud (K271) in the band 31 (11.02 micrometers), while the brightness temperature of water (K285), ground (K310) and vegetation (K295) in the band 23 was lower than that in band 31 for the same items (Water (286K), ground (310K) and vegetation (296K). For these reasons, the difference in brightness temperature between bands 23 and 31 is useful for detecting dust from the ground, vegetation, cloud and water. In the artificial neural network, the correlation coefficient of the training, evaluation, test and total data was equal to R = 0.996, R = 0.99505, R = 0.99559 and R = 0.9958, respectively. These results show the good capability of the neural network in detecting dust. The data was divided into two classes of dust (0.9) and no dust (0.1). In fact, various inputs entered the network and were divided into two classes of dust and no dust. The results showed that the error started from a large amount and gradually decreased. Epoch is referred to as every step of the data correction. In other words, when an input passes through the network and generates an overall error, the weight factors are corrected with the help of that error, a process which is called the number of repetitions or the Epoch. Thus, as itis shown in the figure, the training ends after 151 repetitions. Given the results of the neural network output images, it is observed that dust is well distinguished in both the aquatic and terrestrial ecosystems and a better differentiation will be done with higher dust concentration. The ACC parameter indicates that the neural network method has had a good accuracy and performance. Results show that neural network is a more appropriate method than the BTD index in dust detection, and the neural network does not need to determine the threshold for examining each image. Conclusions The results of the NDDI index show that this parameter alone, is not able to distinguish dust pixels existing in the atmosphere from the pixels of sand and other than dust, and has poor accuracy in images with cloud or water. It seems that this low efficiency is related to the features of the earth’s surface such as land use, land cover, topographical differences, as well as chemical properties of dust minerals in the region. According to the results of this study, the results of applying the BTD index have suitable performance for the detection of dust. In the present research, the artificial neural network shows a fairly good accuracy and performance for the daytime images with an accuracy of 60%.
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 ...
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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.
Mohsen Ahmadkhani; Mohammad Reza Malek
Abstract
Extended Abstract
Despite of widespread usage of Global Positioning System (GPS), this system is considered inefficient for indoor areas. Although the most prominent positioning system is Global Positioning System, this system uses some electromagnetic waves which are unable to pass thick obstacles ...
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Extended Abstract
Despite of widespread usage of Global Positioning System (GPS), this system is considered inefficient for indoor areas. Although the most prominent positioning system is Global Positioning System, this system uses some electromagnetic waves which are unable to pass thick obstacles such as concrete roofs and trees [1]. Thus, it cannot be considered as a robust infra-structure for indoor positioning purposes. Since, other signal networks like Wireless Local Area Network (WLAN) can be an appropriate alternative for indoor spaces. In addition, widespread usage of mobile smart instruments has provided the possibility of ubiquitous system’s development.
Several methods have been proposed to obtain indoor positions which are generally based on received radio waves from fixed points. Time of Arrival (TOA), Time Difference of Arrival (TDOA), Angle of Arrival (AOA) and Location fingerprinting can be used in this case. It is noteworthy that some of these methods are not really appropriate for indoor areas which maybe contain complex structure [2]. Time of Arrival, Time Difference of Arrival and Angle of Arrival methods use triangulation techniques so direct lines of sight are desired for them. And also acquisition of accurate time and angle of received signal without professional instruments, which are usually expensive, sounds almost impossible. Furthermore, for most of indoor areas such as commercial centers and museums direct line of sight is rarely available and signals are likely to be affected by multipath phenomena [3].
In recent years methods based on Inertial Measurement Units (IMU) have been proposed and programmed [4], [5]. These methods which are usually called Pedestrian Dead Reckoning (PDR) often employ sensors such as Gyroscope, Accelerometer and Magnetic sensors to obtain the position of the client [6]. It can be regarded as an important limitation along the objectives of the Ubiquitous systems. Such systems are restricted to clients equipped by platforms having these expensive modern sensors. Therefore, the methods using WLAN signals are usually preferred for location based services.
WLAN Fingerprinting can be regarded as a most appropriate technique that uses signal strength as an identification parameter, which can be simply obtained. Furthermore, fingerprinting does not have any special infrastructure to establish and it can be conveniently laid out. In order to apply this method there are several ways to recognize the pattern of signals received from active transmitters. Stochastic method, Artificial Neural Network and K-Nearest Neighbor methods are some of classic pattern recognition techniques [7] that were investigated in this study. In this article these three methods were scrutinized and relatively compared, eventually an enhanced method has been offered. After using several data sets in order to assess the pattern recognition techniques, the proposed method got the first rank of the accuracy and also other techniques were ranked based on the accuracy.
One of the most important differences between indoor positioning systems might be utilizing of various algorithms to recognize the spatial pattern. In this study, three popular classic methods including Probabilistic algorithm, Nearest Neighbor and Artificial Neural Network were investigated. The flowchart presented in Figure 1 has depicted the major steps of the study.
Figure 1. The flowchart of the study.
This study focuses on Nearest Neighbor in Signal Space method as the most accurate method among all and tries to enhance the output accuracy of the method. NNSS Method computes the difference between received signal strength in a point from each transmitter and the received strength of that signal in the rest of the sample points (Equation 1).
(1)
Where Sij be jth sample point of the database from ith transmitter and Si received signal strength from ith hotspot in online phase and also for m hotspots and n sample points, i= 1,2,…,m and j=1,2,…,n [8].
By applying this formula, the most likely sample point as the location of the observer can be obtained. Since the number of sample points in the design of the model in offline phase is limited and the distance between two adjacent sample points is constant in the whole model, the accuracy might be affected. Regarding these limitations, in order to increase the output accuracy of the system, the medium of first and second candidate location points was proposed as the position of the user. After applying this change, the highest accuracy was acquired (Figure 4). The study area was the third floor of the building of Geomatics faculty of K.N.Toosi university of Technology (Figure 2). For this building with dimensions of 70×14 meters, totally 6 hostspots with reasonable distribution, covering the whole area, were taken into account. The best distance between each adjacent pair was 0.9 m and for each sample point four directions were observed and recorded in to the database and also JAVA programming language was chosen to develop the user friend software. Figure 3 depicts an instance of the database.
Figure 2. Plan of the study area.
Figure 3. A part of the produced database.
In order to evaluate the accuracy of each method, observations in the online phase were categorized in 6 separate classes containing 10, 20, to 60 obser-vation in each class. Based on the output results of the system, although the accuracy of Artificial Neural Network raised up to 2.7 m by increase in the number of observations, it showed the worse accuracy among all methods. Probabilistic and KNN methods with final accuracy of 1.8 and 0.9 meters respectively were more accurate than ANN. Our extended Nearest Neighbor method was the most accurate method almost in all sets of observations. In the first observation class, ANN with 3.6 m, KNN and Probabilistic methods with 2.7 m were not really reliable to locate the position of the user, however, extended KNN with 1.5 m seemed more acceptable than the rest of methods (See Figure 4).
Figure 4. The behavior of accuracy trend in all methods in the considered sets of observation.