جستجوی مقالات مرتبط با کلیدواژه "modis" در نشریات گروه "عمران"

Industrialization and the escalated consumption of fossil fuels have resulted in global warming, which has caused alterations to nature, climate, and human life. The long-term monitoring and examination of parameters associated with global warming, particularly land surface temperature (LST), using advanced technologies like remote sensing, can contribute to a more comprehensive and precise understanding of this phenomenon. Solar energy during the daytime, raising its temperature, while dissipation of energy gradually occurs during the night, leading to a decrease in temperature. Here, a collection of images related to the LST was amassed over 20 years, from 2003 to 2023, for Khorasan Razavi Province in northeastern Iran, utilizing the MODIS data. These images were averaged weekly. Subsequently, the surface temperature trend encompassing all climates in the region was determined through linear regression. The findings demonstrate a general increase in the LST index during both daytime and nighttime; however, the rise in surface temperature during the daytime exhibits a more substantial increase compared to nighttime. The province is predominantly characterized by arid and semi-arid climates, with an average rise in surface temperature during the day of 0.80°C and 1.07°C per decade, respectively, and during the night of 0.55°C and 0.59°C per decade. Moreover, it was observed that there is no significant correlation between climate and surface temperature trends. The daytime LST trend in areas with agricultural land use displays a decreasing pattern, whereas the nighttime LST trend does not seem to be influenced by agricultural land use or vegetation cover.

In this study, using Modis images and wets pass model, the value of evapotranspiration for the northern Karun basin was estimated monthly from 2000 to 2014. The value of correlation coefficient for the obtained evaporations was R2 = 0.72. This model was calibrated and verified for different regions with different parameters. Data from 2000 to 2009 were used for calibration and data from 2010 to 2014 were used for validation of the model. The value calculated 0.54. In this study, with a review of climatic parameters of temperature, precipitation, evaporation during the period before the dam construction and after that, the effects on the microclimate of the area is investigated. Finally, to study the changes caused arising from the creation of a dam on the amount of evapotranspiration using the monthly images of Modis during the studied statistical period, the classification of images was done and the amount of changes were calculated. The results showed that the amount of evapotranspiration in the basin after the construction increased and its changes with distance from the dam in some months also has been increased.

Global warming and climate change affect the depletion of available water resources and limited access to new resources in recent years. Due to these issues, the essential of managing water resources feels more than ever. Water resources have been one of the most important topics in recent years, and many types of researches have been done so far (Rulin et al., 2008). Snow is one of the essential factors in maintaining and sustaining people's livelihood. The snowmelt runoff model (SRM) is a degree-day based method. The SRM was presented to calculate the snowmelt impact on watershed daily runoff (Martinec, 1975). The SRM was successfully applied in some basins to simulate melt season runoff (Zhang et al., 2014; Tekeli et al., 2005). Multi-station calibration has been used in this study that is a useful way to overcome the limitations of Single-site calibration strategy (e.g. large scale basins with much spatial variability).

Water vapor is the most important atmospheric greenhouse variable gas, both spatially and temporally. It involves a positive feedback loop in climate change. In addition, the atmospheric water vapor content is not only an important parameter to forecast precipitation and severe weather but also a central factor for studying the global water cycle. Therefore, in order to weather forecast and climate monitoring applications, study of spatio-temporal distribution of water vapor is valuable. Today, satellite sensors, due to their wide spectrum range, high -spatial resolution, and almost daily global coverage, have enabled them to observe the Earthchr('39')s atmosphere and constantly monitor its changes. One of the atmospheric parameters measured by satellite sensors is water vapor. Among the various sensors that measure water vapor, the MODerate resolution Imaging Spectroradiometer (MODIS) is one of the most famous. MODIS offers water vapor in the form of two separate products, one is the near-infrared product and the other is the infrared product. In addition to satellite sensors, water vapor can also be accessed through observations of ground-based devices such as Global Position System (GPS) and Sunphotometer. In this research, among the MODIS water products, the near-infrared product was used and the data measured at the Aerosol RObotic NETwork (AERONET) site along with the computed values from GPS observations were used to validate the satellite water vapor data. In this research, the method of Point Precise Positioning (PPP) has been used to process GPS observations. The main goal of this research is the investigation of MODIS and AERONET water vapor data quality using GPS observations at Zanjan city. In this study, standard MODIS near-infrared level-2 Integrated Water Vapor (IWV) data product and AERONET IWW retrievals from sunhpotometer at Institute for Advanced Studies in Basic Sciences (IASBS) site, were evaluated using estimated data from GPS permanent station of IASBS from 2011 to 2013. Intercomparison results showed that AERONET-IWV and GPS-IWV had high correlation (R2=0.95). The mean values of an Root Mean Square (RMSE) and Bias between AERONET-IWV and GPS-IWV (AERONET-GPS) were about 3.2 mm and 2.2 mm, respectively. The MODIS IWV product showed slightly higher correlation coefficient (R2 =0.92) with GPS-IWV compared with AERONET –IWV (R2 =0.90). In addition, MODIS-NIR IWV showed approximately 2 times larger RMSE and 2.5 times larger Bias from AERONET -IWV (RMSE =5.48 mm, Bias =4.88 mm) compared with GPS IWV (RMSE =2.76 mm, Bias =2 mm). Finally, the results of the presented study provide good information about the overall quality of the MODIS and AERONET IWV data for the areas of atmospheric researches. The results of this study were comparable to previous studies conducted in Kanpur India and Beijing, China. The results obtained in this research provide valuable information to researchers in the fields of meteorology, climate, hydrology.

Earthquake is one of the most devastating natural hazards which efforts to predict the time, location and magnitude of it have not been yet completely successful. Remote Sensing data is proved to be an effective source of information about lithospheric and atmospheric activities around the impending earthquakes which are referred to as earthquake precursors. The issue of detecting anomalies in these precursors has been interesting to many researchers. One of the precursors that has been taken into consideration by the researchers, is the chlorophyll-a (chl-a) concentration on the sea surface. Since, over %70 of the Earthchr('39')s surface is covered by water and many seismic active faults are located in coastal belts of the continents, the behavior of oceanic earthquake-related parameters such as Sea Surface Temperature (SST), surface latent heat flux, upwelling index and chl-a, is of particular importance. Elastic strain in rocks, formation of micro-cracks, gas release and other chemical or physical activities in the Earthchr('39')s crust before and during earthquakes has been reported to cause changes in oceanic parameters. Chl-a parameter is obtained through various methods including laboratory methods of spectroscopy, chlorophyll fluorescence measurement or through satellite data using Color Index (CI) and Raily band ratio (OCX) algorithms etc. Changes from time to time in plankton population in ocean surface and chl-which is the indicator of the primary productivity of phytoplankton biomass in the ocean, can be continuously monitored from space by Ocean Color sensors. In this study, MODIS on Aqua and Terra products were used to examine the pattern of variations of chl-a. By examining the chlorophyll time series of five large earthquakes produced by MODIS sensor products on Aqua and Terra platforms and using a random forest algorithm, it was observed that the release of thermal energy and ground gases due to the activity of Tectonic plates or other physical and chemical activities of the earthchr('39')s crust before, during and after coastal and near-coastal earthquakes can lead to changes in the amount of chlorophyll in the water surface and this parameter can be used and investigated as an earthquake precursor in future research. The results showed that chlorophyll-a levels exceeded the permissible limits 51, 48, 46 and 28 days before the Gujarat earthquake by 85, 45, 15 and 35%, respectively. In the 2004 Sumatra earthquake in the 20 days before and 18 days after the earthquake, the percentage of chlorophyll-a parameter crossing the upper limit was 110 and 190, respectively. In the 2006 Java earthquake, 42 days before, 15 and 16 days after the earthquake, the amount of chlorophyll-a suddenly changed to 136.84, 52.63 and 107.89% of the allowable threshold. In two other studies, this amount is equal to 199.87, 25, 150 and 190% more than allowable limit, respectively, on the 44th and 34th days before, on the day of the earthquake and 13 days after the Chile earthquake, and 321.42, 50 and 160.71% more than allowable limit 7 and 4 days before and 17 days after the earthquake in Mexico.  In addition, the clear superiority of the Random Forest (RF) algorithm in correct detection of anomalies showed that RF algorithm can be introduced as an effective tool in anomaly detection in time series.

Landsat 8, Modis and Support Vector Machine (SVM) classification algorithm are used to map soil erosion in South Khorasan province in the present study. For this purpose, three information layers of land use map, slope and vegetation are used. In the proposed method to prepare the land use map, four supervised classification algorithms of maximum likelihood, Mahalobani distance, minimum distance and artificial neural network with thermal infrared band are used. Six integration algorithms of NNDiffuse, HPF, Brovey, Gram-Schmidt, PC and CN are used to prepare the vegetation map. The ASTER satellite DEM map is used to prepare the slope map of the area. The results of the experiments show that the maximum likelihood algorithm with thermal band is the most accurate in land use mapping. The NNDiffuse algorithm is also more accurate for integrating the red and infrared bands near Landsat 8 and Modis. After preparing the land use, slope and three vegetation maps derived from Landsat 8, Modis and Landsat 8 and Modis integrated images, three erosion maps are prepared using SVM algorithm. The results show that the highest value of Kappa coefficient (69.1) is related to the erosion map obtained from Landsat 8 and the lowest value (57.1) is related to Modis image. The integration of Landsat 8 and Modis increases the kappa coefficient to 67.3 %.

Land surface temperature (LST) is one of the most important variables required in environmental and climatological studies. In order to calculate LST, accurate emissivity is needed. Recently, several methods have been developed to calculate LAST and emissivity. Some of these methods estimate LST based on a pre-known emissivity, while the others calculate LST and emissivity, simultaneously. LST mapping in urban areas can be difficult due to the high variation of the land cover and the formation of mixed pixels. Accordingly, the LST calculation based on the emissivity derived from a single method can be erroneous, especially using a low spatial resolution image in the urban areas. Integration of the emissivity values derived from different methods seems to be an effective solution in this situations. In this study, LST was calculated using Split Window and Planck Law methods for Tehran city. Three different methods including classification, normalized difference vegetation index (NDVI)-based method, and normalization emissivity method were applied to derive emissivity from MODIS images. NDVI-based method is a common method used NDVI thresholding to determine the emissivity of different pixels. In classification method, each pixel is classified into one of 14 classes for which the emissivity is known. Normalization emissivity method assumes a constant value as emissivity for a pixel in different bands to calculate temperature for these bands and then the maximum temperature derived through, is used for calculation of emissivity coefficients which are used for actual LST calculation using Planck function. In addition, MODIS emissivity product (MOD11A1) was used to compare with the emissivity derived from the other methods. In order to implement this study, the remotely sensed data including Landsat-TM data acquired in 2010, and MODIS products (MOD021KM, MOD05, MOD11A1) acquired in 2012 to 2013 were downloaded. Temperature data measured by three meteorological stations around Tehran were provided to validate the results. In order to integrate the emissivity values, averaging and median methods were used to fuse the emissivity values derived from three methods and MODIS emissivity product. The results showed that NDVI-based method produces more accurate emissivity as the LST calculated based on this emissivity was more accurate than that derived from other emissivity values. Fusing the emissivity values through mean and median methods, the fused emissivity values were used for calculating LST using Planck’s equation and Split Window methods. It was shown that the fused emissivity derived from averaging method can improve the accuracy of the LST maps derived from each emissivity method. Moreover, Planck Law performed better for calculating LST using MODIS bands 31 and 32 with error of 1.6 and 1.63 Kelvin degrees, respectively, compared to that derived from Split Window method.

Soil moisture content (SMC) is one of the most significant variables in drought assessment and climate change. Near-real time and accurate monitoring of this quantity by means of remote sensing (RS) is a useful strategy at regional scales. So far, various methods for the SMC estimation using a RS data have been developed. The use of spectral information based on a small range of electromagnetic spectrum in the form of a single index cannot be a suitable way to estimate the moisture content in the optical and thermal RS. Single use of each index has its own limitations. These limitations can include the effect of atmospheric conditions, solar illumination geometry, topographic conditions and soil characteristics. Therefore, the use of all potential of the electromagnetic spectrum (visible to short-wave infrared bands) in the form of regression combinations of spectral indices might be useful in improving the accuracy of SMC estimation. In this study, at first, the correlation of 20 indices commonly used in soil moisture estimation studies with in situ soil moisture measurements were evaluated. In the next step, based on the correlation results, the indices gradually were added into the soil moisture linear regression and the accuracy of each stage was evaluated. The best SMC estimation model was included the linear regression made of LST, VSDI, NDWI and SASI indices. This ended up with and improvement accuracy (RMSE = 0.048). Soil texture is one of the most important factors in the estimation of SMC by means of RS data especially in optical and thermal regions of spectrum. On the other hand, due to the fact that the soils have different levels of porosities, it seems that the SMC modelling should be based on soil texture. Therefore, the SMC model was evaluated for three dataset, medium textured, moderately coarse texture and coarse texture soils. The results showed that the medium texture soils have a profound relationship with in situ measured compared to coarse texture soils (RRMSE=29%…, RRMSE=0.032). On the other words, the SMC model is not generalizable for all soil types. The results showed an inverse relationship between the accuracy in the SMC estimation and the soil particle size. In other words, the accuracy of the SMC model decreased by the increase in soil particle size. In the case of medium texture soils, better response to the SMC estimation have been seen in optical bands. Coarse texture soils such as sandy soil, because of porosity, water penetrates rapidly and freely inside the soil due to the force of gravity and show a lower water content capacity. On the contrary, medium texture soils have the ability to retain more water in their textures and the length of capillary rise in these soils is greater than those of coarse texture soils. Thus, moisture variations in this type of soils have a greater effect on the soil spectral responses compared to the coarse texture soils where the results of the SMC modelling for loamy medium texture soils approves this.

Obtaining reliable estimation and information about particulate matters (PM) with aerodynamic diameter less than 10 micron is crucial. This is due to the PM10 serious negative impact on human health and environment. Therefore, studying the concentration and spatial pattern of the PM10 in urban cities, especially populated and industrial cities, has become a popular an important subject for researchers. For this purpose, several pollution stations have been established in different part of cities. These stations have the capability of measuring and recording the concentration amount of various pollutant including PM10, PM2.5, and etc. however, pollution stations can measure the concentration amount of various stations precisely, their measurements are not spatially connected and only spars point observations are provided. One of mostly used and efficient methods to solve this problem is to use remote sensing data. Aerosol optical depth (AOD) and aerosol contribution to apparent reflectance (ACR) are two mostly used remote sensing data which have been used to study the concentration and spatial pattern of PM10. In this study, we have adopted to use ACR images instead of AOD based on three reasons including their higher spatial resolution, spatially connected (without any gap), and at last the absence of aerosol robotic network (AERONET) for AOD retrieved values evaluation in our study area. ACR images can be generated though a relation using red and SWIR (2.1 µm) bands. In particular, we can estimate the surface reflectance (SR) of the Red band from top of atmosphere reflectance (TOAR) of the SWIR (2.1 µm) band. This is possible based on an assumption that the aerosol’s effect on SWIR band is negligible because of its higher wavelength. After the estimation of Red band SR, the difference between Red band SR and TOAR can be an illustration of the amount of atmospheric reflectance. In this study, moderate resolution imaging spectroradiometer (MODIS) level-1B images identified as MOD02HKM with 500 m spatial resolution for 12 days over Tehran, Iran have been utilized. Their corresponding ground measurements of PM10 concentration from 13 pollution stations spread have been used. We have used artificial neural networks to develop the model to estimate the PM10 concentration from ACR data. This has been done in two different approaches including daily modeling (each day separately) and overall modeling (using 12 days data together). Artificial neural networks with one, two, and three hidden layers and sigmoid transfer function using the levenberg-marquardt algorithm are employed to model the relation between ACR values and corresponding PM10 concentration. Also, pollution maps are generated to investigate the spatial pattern of the PM10. In daily modeling artificial neural networks with one, two, and three layer(s) achieved 0.769, 0.806, and 0.848 R2 values respectively, representing the higher capabilities of artificial neural networks with more hidden layer. However, it should be noted that the increase in the number of hidden layer will result in overfitting of the model to the training part of data. Finally, the overall modeling using artificial neural networks with one, two, and three hidden layer(s) obtained 0.412, 0.499, 0.503 R2 values.

Determination of particulate matter (PM) levels, as one of the most important pollutants, requires dense monitoring stations network in megacities. Extending monitoring network, especially in regions with sparse monitoring sites, needs a signicant economic source and may be rejected due to feasibility considerations. During the last decade, remotely sensed atmospheric data are known as a cost- eective way with appropriate and comprehensive spatial and temporal coverage to estimate ground-based PM concentrations. In this regard, aerosol optical depth (AOD), which represents the amount of aerosol in the column of atmosphere, was used as an independent satellite derived product to predict PM values in monitoring stations. Also, meteorological variables can be used as auxiliary parameters to improve model performance during validation period. In this study, a statistical model was developed using AOD along with eective meteorological parameters to estimate ground level of PM10 (particulate matters with aerodynamic diameter less than 10m). AOD was extracted from 6 collections 6 of Moderate Resolution Imaging Spectroradiometer (MODIS) by 3 km spatial resolution over Tehran during Marchof 2009. Meteorological variables can specify vertical distributions in atmospheric November column and optical properties of PM, and they are capable to improve AOD and PMs relationship. So, to improve the model performance, model it is developed by meteorological parameters. The meteorological parameters were collected from synoptic stations in Tehran, every 3 hours, during the study period. The linear mixed eect model was tted into all independent variables to examine their in uence on PM10 concentrations. The results showed that the proposed model could explain concentration accurately with relative high correlation coecient of the variation of daily PM10 ( R2 = 0.77 ). Statistical model performance was acceptable during cross validation with 0.88 (R2 = 0.61 ). The model had the best performance during correlation coe cient of 0.78 autumn with root mean square error (RMSE) of 15:4g/m3, while the worst one occurred in summer with RMSE of 19:3g/m3.

Particulate matters (PM) with an aerodynamic diameter less than 10 microns will cause serious damages to human health. Moreover, their presence can have a critical impact on climate change, global warming, and earth radiance budget. Therefore, obtaining precise information about their concentration and spatial distribution is crucial for public health and environmental studies. High concentration of PM10 can be named as a major environmental and public health problems especially for industrial and populated cities around the world. Thus, policymakers and environmental organizations have decided to establish pollution station to measure various pollutants including PM10. Obviously, it is not possible to establish many pollution stations based on economic justifications so, an only limited number of these instruments are located in every city. However these instruments can measure and record the PM10 concentration with high precision, they only provide sparse point observations. In this case, remote sensing data can be utilized to fill this gap and solve the existing discontinuity problem. Generally, two kinds of remote sensing data which have a good representation of existing pollutant in the atmosphere can be used for this purpose. Aerosol optical depth (AOD) and aerosol contributions to apparent reflectance (ACR) are two of these data. ACR images can be simply calculated from each satellite image consisting of Red and SWIR (2.1 µm) bands. This could be achieved by estimating the surface reflectance (SR) of the Red band from the top of atmosphere reflectance (TOAR) of the SWIR band. Then, the difference of SR and TOAR of the Red band can be a representation of the amount of atmosphere reflectance related to existing pollutants. In this study, we have used ACR images instead of AOD data to estimate PM10 concentrations and produce PM10 pollution maps for Tehran city in Iran based on three reasons. First, they have better spatial resolution and second, they are spatially continuous in contrast to AOD data which include much gaps in the study area due to dark target limitations for AOD value retrieval. Lastly, an aerosol robotic network (AERONET) station is not located in this area which is required to evaluate the precision of retrieved AOD values. MODIS level-1B images named MOD02HKM for 8 days in 2017 with corresponding ground measurements of PM10 concentrations from 14 pollution stations have been utilized in this area. Four different regression model including linear, exponential, logarithmic, and power regressions are employed to estimate PM10 concentrations and produce pollution map. Three criteria of R square, the correlation between estimated and observed (measured) PM10 concentrations, and root mean square error (RMSE) are employed to investigate the performance of four regression models. Based on the R square criterion, the linear regression model with 0.5912 performs better than exponential, logarithmic and power regressions with 0.5826, 0.5808, and 0.5782 R square values respectively. Since we have observed different performance from four regression model based on three evaluation criterion, we have applied a ranking method based on the evaluation criterion to determine the best regression model. Based on the ranking, we recognize that the exponential regression model performs better than linear, logarithmic and power regressions.

The Middle East is one of the hot-spot regions that has reported the highest occurrence of dust storms. In this study changes in aerosol optical and radiative properties during dust storm over the Middle East on July 2009, was analyzed using retrieved Aerosol Optical Depth (AOD) from Simplified Aerosol Retrieval Algorithm (SARA) algorithm over study regions and ground-based observations from CIMEL Sunphotometer measurements at AERONET (AEronet RObotic NETwork AERONET) site at Kuwait University. Dust plumes captured well with the SARA AOD during dusty days. Maximum AOD values occurred on the 4 July in Kuwait, Ahvaz, Iran, the Persian Gulf, Baghdad and Riyadh where values of 1.44, 2.56, 1.07, 3.0, and 1.99 were recorded, respectively. The maximum aerosol volume size distributions (VSDs) at AERONET site occurred on dusty days and minimum VSDs on non-dusty days. The single scattering albedo (SSA) obtained higher values on dusty days compared to non-dusty days, for all wavelengths (440, 675, 870, and 1020 nm) at the AERONET site. The maximum SSA value of 0.99 occurred at a wavelength of 1020 nm, which is an indication of the dust aerosol. The asymmetry parameter (AP) obtained higher values at shorter wavelengths over the study period. The AP (AP-T) for both modes (fine and coarse) values were higher in the near infrared region than in the visible spectral region on both dusty and non-dusty days. The coarse AP (AP-C) values were higher in the visible spectral region than in the near infrared region on dusty days with the reverse being true on non-dusty days. The VSD, SSA, AP, and refractive index values on dusty days suggested that dust aerosols were predominant over anthropogenic aerosols in the study area. Effect of aerosol on Downward Surface Shortwave Radiation (ARFDSSR) on both dusty and non-dusty days ranged between -51 and -160 Wm−2 (average: -90 Wm−2) at the earth's surface. Effect of aerosol on Net Surface Shortwave Radiation (ARFNSSR) on both dusty and non-dusty days ranged between -39 and -122 Wm−2 (average: -69 Wm−2) at the earth's surface. Dust-induced turbid conditions caused significant extinction of 15–20%, in DSSR, resulting in an increase of 66% in aerosol radiative forcing (ARF) at the AERONET site at Kuwait University. The HYSPLIT back-trajectory analysis revealed that the air masses reached Kuwait from the western part of the Sahara Desert in northwest Africa and Saudi Arabia over the Middle East. Aerosol vertical profiles retrieved by the space-borne Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) reveal a well-mixed dust layer occurred over the study area. The aerosol types identified by CALIPSO in the near of the study regions include both dust and polluted dust, but dust aerosols were dominant over the anthropogenic (polluted) aerosols. The CALIPSO aerosol profile indicated a layer of thick dust extending from the surface to an altitude of about 6 km. The results of radiative forcing of dust storm in the Middle East show that mineral dust cause decrease of shortwave radiation and net radiation at the surface. Additionally the presence of mineral dust cause decrease of temperature at earth’s surface. So, dust directly influence the earth’s radiative budget and cause surface cooling.

Forests have covered 31 percent of the land surface and have an important role for mankind and nature. Hundreds of fires occur annually in Iran and thousands of hectares of trees and plants make their prey. Due to the country's arid belt of the Earth and subtropical high pressure zone, climatic conditions for the occurrence of forest and ranges fires are provided. Fire has harmful effects such as destruction of the cycles of nature and the environment, global warming, loss of vegetation, soil, dry soil and leads. As a result, predict and prevent the spread of fire in these areas is more important, that in order to detect fires in these areas and study the results. This study aimed to investigate various methods of fire detection sensors in remote sensing point of view. As you know, due to the expansion in the field of remote sensing satellite, a variety of remote sensing satellites and sensors that detect possible fires provides for us. In this study, the identification of fire by sensors such as MODIS, AVHRR, HJ-IRS and Landsat has been done. The sensors in this research study cannot be cut to an absolute certainty that one sensor is suitable for fire detection studies, but depending on the method and algorithm to detect fires on various sensor data is used and due to the limitations of the region and achieved mixed results. In general, we can use all of sensors with thermal and reflective bands to detect fire.