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Showing 14 results for Modis
Volume 3, Issue 4 (12-2015)
Abstract
There are many reports of serious problems of dust storm events in the western parts of Iran. Based on many researches, Iraq is one of the main sources of dust storms in western parts of Iran. The Radial Basis Function Network (RBFN) model has been used to assess wind erosion hazard in Iraq as a main source area of dust storms over several western cities of Iran. The percentage of vegetation is the only changeable factor of RBFN model. The wind erosion hazard map in two time periods (2003 and 2012) verified the vegetation changes over time. The results showed that the vegetation percentage index in all land use types of 2003 was higher than those of 2013. In addition to drought as a natural parameter, overgrazing, dam construction on Tigris and Euphrates Rivers (in Turkey) and high amount of water for crop production are human and policy factors causing loss of vegetation cover in source area and wind erosion exacerbation.
Volume 10, Issue 3 (10-2022)
Abstract
Aims: This research investigates the impact of land cover on dust distribution in the southern part of Khuzestan province in the period of 2000 to 2018.
Material and Method: We used the Landsat 7 and 8 satellite data in 2000, 2009, and 2018 to extract land cover. The land cover map was prepared using the decision tree classification. Aerosol data was extracted using the aerosol optical depth index from the Modis Terra and Aqua sensors. Finally, the relationship between land cover changes and dust index was analyzed.
Findings: The results of land cover maps showed a 5% decrease in rangeland cover; a 4.3% increase in salt marshes area; and, a 0.2% decrease in water bodies. The results also showed that the maximum aerosol index in 74% for Hindijan, Ahvaz, and Bandar Mahshahr. The maximum value of this index has increased in recent years. The highest percentage of land-use changes between 2000 and 2018 are bare lands to saline lands, rangelands to bare lands, and bare lands to croplands, respectively. We believe that salt lands by an increase in area by 68195 ha are the main cause of the increase in dust storms in the study area.
Conclusion: Our results confirm the need to reconsider land use management and restore the basic functionality of the region's ecosystems to prevent the occurrence of grave consequences of aerosol accumulation in the atmosphere.
Volume 11, Issue 1 (3-2007)
Abstract
Assessment of Evapo-Transpiration (ET) in the cases such as Irrigation programming, water basin evaporation determination, water balance calculation, water runoff estimation and climatological studies are important. It is possible to determine ET by field measurements. However these methods only can determine ET for the regions with the limited areas. This limitation has made the use of remote sensing techniques reliable for assessment of ET in a vast area. In this work, the amount of ET has been evaluated in an army wheat field located in the Golestan Province (North of the Iran) for May 5th and June 7th, 2003 using MODIS images. Surface albedo affects in the outcome of SEBAL that we estimate it using two methods, one using 1 and 2 bands of MODIS image (old method) and the other using 1 to 5 and 7 bands of MODIS image (new method). The comparison of the results of SEBAL to the results of other works showed the accuracy of the estimation of surface albedo using the new method is better than the old method. Also, the accuracy of SEBAL outcomes are relatively satisfactory and can be improved by further detailed studies.
Maryam Karimian, Omid Beyraghdar, Reza Modarres, Saeid Pourmanafi,
Volume 11, Issue 3 (8-2022)
Volume 11, Issue 3 (8-2022)
Abstract
Chl a is the main pigment of phytoplankton, which is an indicator of phytoplankton biomass and reflects the primary production in the marine environment. In this study, level 3 (4 km) data of Chl a concentration of Persian Gulf and Oman Sea for the period of 2003- 2018 were used. The data was converted to raster format in ArcGIS10.5 environment and then the numerical values of each pixel were extracted in R (version 4.0.2). Missing data were observed in Chl a data, to solve this problem, DINEOF algorithm was applied and non-parametric Mann-Kendall and Sen’s Stimulator tests were used to analyze Chl a concentration trends. The results showed that the maximum concentration of Chl a is in September (0.09 to 18.75 mg / m3) and October (0.23 to 18.03 mg / m3) and the minimum concentration of Chl a in May (0.22 to 5.74 mg / m3) and June (0.20 to 5.12 mg / m3). The trend of Chl a concentration variability over the study period was negative in most areas and not significant. These analyses provide an overall description of Chl a concentration variability in the Persian Gulf and Oman Sea based on satellite observations; however, further investigations based on in situ observations are needed to achieve better understanding of the patterns of of Chl a concentration alterations.
Shahnaz Kaleji, Mohammad Akbarinasab, Abbas Einali,
Volume 12, Issue 4 (12-2023)
Volume 12, Issue 4 (12-2023)
Abstract
This study investigated the Caspian Sea surface temperature fronts from 2015 until 2019 using satellite images (MODIS). The sea surface temperature front, a narrow-width area with a high-temperature gradient, plays a crucial role in various biological, chemical, physical, and agricultural parameters and climate change issues. Detecting sea surface temperature fronts also helps understand other phenomena such as upwelling, eddy, and biological accumulation. The Canny algorithm was used in the MATLAB environment for detecting the fronts, and the results were compared spatially and temporarily. It was observed that the fronts in the north of the Caspian Sea are permanent from September until November and March until May, while they are impermanent and dashed at other times of the year. In the east of the south Caspian Sea, the only detected fronts in March repeat annually, whereas in the west of the south Caspian Sea, fronts were detected in all months except for August, consistently over the five years. The pattern of temperature fronts in the east and west of the Middle Caspian Sea differs. The fronts are clustered in winter but become coherent and clear in summer. Additionally, the southern Caspian Sea temperature fronts are less abundant in the pre-summer period than in other periods. The Caspian Sea surface temperature fronts were mostly detected in the location of internal waves, steep areas near the coast, and eddies.
Volume 14, Issue 1 (3-2010)
Abstract
The ability in assessment of Total Precipitable Water (TPW) is useful in the prediction of the amount of raining, dam over-flooding and the flood. To extract TPW, the algorithm of infra- red bands near the MODIS sensor images were used. The satellite TPW, was validated using radiosonde data. Due to the limitation of the algorithm implementation to the cloud free sky and stable atmosphere, the general atmospheric conditions in the satellite passing date were investigated using auxiliary curves produced by synoptic and higher level meteorological data. In this way, the calm and eddy free atmosphere were selected. Then MODIS images were supplied from Iran Space Agency for this satellite passage. Then the TPW data were estimated using radiosonde and thermodynamics equation. Then regarding the stability and lack of new air masses in the region for the selected days (using analysis of the ground data and atmospheric profiles), the TPW for the time of satellite passage was interpolated.For determination of and in the aforementioned algorithm, EVI and ENDVI indices were deployed. At the end, a regression between the TPW produced by satellite and the one calculated from the radiosonde. showed that for the Mehrabad weather conditions, the MODIS channels 18 and 19 are suitable. Using the ratio of the apparent reflectance in the water vapor absorption bands to the one in the non-absorbing band, the atmospheric water vapor transparency for each one of the water vapor bands was calculated. The TPW in the earth-sensor path was calculated by implementing MODIS infrared bands under different atmospheric conditions, taking into account sensor and zenith angles, and the water vapor transparency using band ratio technique.
Volume 15, Issue 2 (7-2011)
Abstract
Usefulness and authenticity of satellite data are strongly related to weather conditions. Dust storm, atmospheric gases and especially the presence of clouds can considerably affect on the reflected energy from surface and encounter the reading of optical sensors with error. Cloud contaminated pixels usually increase the reflection of land covers and show their temperature less than the real one. The clouds smaller than pixels are not observable and cause an increase in pixel reflection and error. Considering that different clouds have diverse interactions with each other, it is possible to determine the amount of cloudiness of pixel by combining the analysis of different spectral bands of MODIS. By using this method, it is possible to determine the the cloud polluted pixels faster. For the first time, MODIS Cloud Mask algorithm was declared by Ackerman and his colleagues in 2006. Firstly the cloudy pixels were identified in MODIS image by the help of the presented five- step model in MODIS Cloud. Than for upgrading the model and determining the present percentage cloud in one pixel for those clouds smaller than pixel dimensions, brightness temperature of the pixels in band 14 of ASTER and band 31 of MODIS was compared and the cloud coverage percentage of every pixel was calculated. Model evaluation demonstrates more than 93% correlation between the real amounts of distracted cloud coverage in ASTER and the measured amount by the model, which is promising regarding local resolution.
Volume 18, Issue 3 (12-2014)
Abstract
One of the most important pollutants that its surveillance in the atmosphere by remote sensing is possible is the suspended particles density using the MODIS sensor images. In the analyzing related to pollution study, the studies of distribution and relation between variables have an important role that the regression analyzing applications in these studies is inevitable. The main goals of this paper is preparing the particulate matter less than 10 micron distribution in Khuzestan province in both hourly/daily periods using the linear regression models the AOD product, the MODIS sensor also the ground stations data of the atmosphere pollution measurement and lateral insight of Ahvaz city in 2009 in order to estimating the pm10 were used. The results showed MODIS data have high accuracy to estimate the atmosphere pollutions and results indicates that the hourly period with R square 90% against daily period with R square 76% have a higher coefficient. After the model estimating correction by interpolating the produced plots by using the resultant relation in both time periods the suspended particles distribution maps were prepared. Key Word: ,MODIS,Ahvaz, suspended particles;linear regression Key Word: ,MODIS,Ahvaz, suspended particles;linear regression Key Word: ,MODIS,Ahvaz, suspended particles;linear regression Key Word: ,MODIS,Ahvaz, suspended particles;linear regression
Volume 20, Issue 2 (4-2013)
Abstract
Total Perceptible Water (TPW) is an important parameter in climatology and weather forecasting and is directly related to any climate process. There are three approaches to estimate this parameter i.e. using radiosonde, using GPS and calculating from satellite images where the first two are localized and the last one can give an instant view of TPW in a vast region. The algorithm used for the TPW calculation from MODIS images is related to the ratio of the reflectance in a water vapor absorbing channel and the reflectance in a non-absorbing channel. Due to strong horizontal variation in the surface reflectance in non-absorbing channels, the retrieved TPW varies strongly from one pixel to its neighboring pixels while it is believed that the horizontal gradient of TPW is very weak. To solve this problem, a damping coefficient was added to the non-absorbing channel reflectance. It is found that this coefficient differs for different surface covers. The current work presents a procedure for calculating these coefficients. The results of a comparison between modified TPW and those extracted from GPS data showed a R2 of 0.81 whilst this was about 0.67 for non-modified MODIS TPW.
Volume 23, Issue 4 (12-2019)
Abstract
Abstract
Satellite time series data play a key role in characterizing land surface change and monitoring of short and long-term land cover change processes over time. While coarse spatial resolution optical sensors (e.g. MODIS) can provide appropriate time series data, the temporal resolution of high to intermediate spatial resolutions sensors (1-100 m e.g. Landsat) does not allow for having temporally frequent measurements because of the orbital configuration of such sensors and cloud contamination. A promising approach for addressing this challenge and producing Landsat-like imageries is the blending of data from coarse spatial resolution sensors like MODIS. Among different approaches proposed in the literature, the ESTARFM model has been reported to outperform other models in generating Landsat-like imageries with reasonable accuracy over heterogeneous areas. Despite the large body of studies implementing ESTRAFM for downscaling MODIS data, quantitative evaluation of the model under different conditions has not yet been investigated. This study quantitatively evaluates model performance over different land cover types, resampling methods and time-difference analysis between input and synthetic images. The results demonstrated that employing bilinear resampling in the ESTARFM produces results slightly better than nearest neighbor and cubic resampling methods. Moreover, the ESTARFM model accurately predicts Landsat-like surface reflectance images with RMSE better than 0.02 and correlation more than 90% over different land cover types. However, the model performance significantly degrades as the time difference between the input and synthetic images increases.
Volume 23, Issue 4 (12-2019)
Abstract
Introduction: According to the Intergovernmental Panel on Climate Change (IPCC) in 2012, globally, a large number of climatic events have increased in recent decades such as extreme temperatures, floods and etc. That’s the number of warm days and nights has increased, and climate models predict extreme temperature by the end of the 21st century (IPCC, 2012). Ecosystems, the global economy and public health are highly vulnerable to these extreme events, especially extreme temperatures (Kunkel et al., 1999). Generally, in Iran, the regionalization of extreme temperatures has been studied. For example, Rezaei et al. (2015) examined the extreme temperatures in two months with extreme temperature and identified different areas for Iran. Masoudian and Darand(2008) also studied extreme cold temperature in Iran and regionalized six areas for Iran. Considering the studies that indicates the occurrence of extreme temperatures for different parts of the world, it is interesting to note the role of these extreme temperatures on evapotranspiration difference between extreme cold and warm temperatures. Evapotranspiration is the water loss from the ground to the atmosphere and defined as a key process in the water cycle (Wang and Dikeson, 2012), which is related to plant growth (Alberto et al., 2014), drought (Anderson et al, 2011), greenhouse gas (Balogh et al., 2015) and climate change (Abtew and Melesse, 2012). The purpose of this study is to answer the question of what is the changes in evapotranspiration under extreme temperature conditions in Iran.
Methodology: For answer the research’s question it found clearly that January 2008 and July 2010 had recorded extreme cold and warm temperatures during the period of 30 years. For these two months, 55- air temperature stations data, soil temperature from NCEP / NCAR reanalysis database, land surface temperature (LST), vegetation cover, and evapotranspiration from Moderate Resolution Imaging Spectroradiometer (MODIS) were utilized in five kilometer or 0.05 degree resolution. At first, the risk of occurrence of the extreme temperatures was determined by the distribution of the cumulative risk and the Gumbel distribution during these two months. The land surface temperature data product (LST) namely MOD11C3, which has 0.05 degrees (approximately 5 kilometers or 5600 meters) and with a monthly and global time scale was used. To investigate the changes in evapotranspiration, the MODIS evapotranspiration product namely MOD16 was utilized (Mu et al., 2012). The data is available on an annual, eight-day and monthly basis. In this process, evapotranspiration is provided globally and with a resolution of one kilometer covering 109 million square kilometers of the land’s surface. The algorithm used the Penman-Monteith equation to produce this product (Monteith, 1965). Then for the analysis Pearson’s correlation and coefficient of determination were used.
Results and discussion: The results showed that the occurrence of extreme temperatures above 50 degrees Celsius is 0.06 in July and temperatures higher than 22 degrees Celsius is 0.008 in January. Also, the probability of temperature higher than 5 degrees Celsius is 0.50 in January. Correlations results indicated that the two factors of energy (air temperature) and soil moisture are the main controller of the relationship between these parameters (LST and evapotranspiration), so that when the air temperature was above 5 degrees Celsius, a significant negative correlation was observed (-0.24 in January and -0.64 In July) and when the air temperature is below than 5 degrees, it will be positive (0.23 in January). Generally, regardless of the threshold, a negative correlation was obtained for every two months, but a weakest negative correlation (close to zero) was observed in January, due to the recording of temperatures exceeding 5 ° C with an incidence of 50%. The humidity factor shows that every two months have suffered from a certain moisture threshold due to extreme cold and warm temperatures, and if there is a moisture limit, this relation will be negative, thus it’s a determination factor for the overall negative relationship (regardless of the temperature threshold) in January.
Conclusion: The extreme temperatures showed the highest impact on evapotranspiration so that air temperature was identified as a trigger for the relationship between LST and evapotranspiration.
Volume 24, Issue 4 (12-2020)
Abstract
Introduction
Particulate matter is any liquid or solid component (except pure water) that is dispersed in the Earth's atmosphere and is microscopic or sub-microscopic but larger than the molecular size. These particles play an important role in the Earth's climate. Suspended particles are created by various natural or anthropogenic processes and are among the deadliest types of air pollution, especially smaller particles less than 10 micrometers in diameter. Since the number of pollution station is very low, Satellite measurements have been widely used to estimate particulate matters (PMs) on the ground and their effects on human health.
Methodology
In this research, we tried to estimate PM10 using the regression model based on the Aerosols optical depth. Because the AOD value recorded by satellite sensors is affected by the weather conditions, to increase the accuracy of the PM10 estimation, meteorological parameters were also used in the AOD to PM10 conversion model. The used meteorological parameters include surface wind speed, surface temperature, relative humidity, visibility, and planetary boundary layer height.
Since the data used were extracted from four different sources with different temporal and spatial resolution, it is necessary to apply a method for integration and synchronization in space and time. To generate AOD and PM10 data from the Aqua satellite, pollution stations were mapped onto satellite images and AOD values for the nearest neighbor pixels as well as values for a 3 by 3 window were extracted. So, two pairs of AOD and PM10 were formed, one with the nearest neighbor values and the other with the weighted average AOD values in a 3 by 3 window. Because the PM10 values were more closely related to the AOD values than the nearest neighbor, the pair of the weighted average was excluded from the calculations. There were extracted 100 samples in warm season (June, July, August and September) for model development (Shokoufa station data) and 65 samples for validation (Cheshmeh and Atisaz station data) and 140 samples in cold season (November, December, February and March) for model development, and 50 samples for validation. In the last step, the accuracy of the model was evaluated using indices such as coefficient of determination, mean error deviation (Bias), and mean square error (RMSE).
Results and Discussion
The results showed that AOD and PM10 have a better relationship with each other in the warm season than in the cold season. Only two variables of AOD and wind speed were included as independent variables in the best model presented for the warm season; both of which have a direct relationship with PM10, that is, with increasing of both variables the value of PM10 increases. The results showed that the regression model of warm season can only predict 16% of the PM10 variations correctly, which is not a satisfactory result.
In the multivariate cold season regression model, only the visibility remained, and other variables that had no significant effect on model improvement were excluded from the regression model. Multivariate correlation coefficient of this model was estimated to be 0.59. Therefore, the cold season regression model, at best can predict 35% of the PM10 variations correctly. By deleting the visibility variable, it was attempted to measure the impact of other variables such as AOD on PM10 estimation. In this model, the boundary layer height, AOD and temperature variables were retained. The boundary layer height variable has a negative relationship and the other two variables have a positive relationship with PM10. The maximum effect of temperature on PM10 is justified by the increase in boundary layer and the relationship of these two dependent variables which decreases PM10 density, but since this role of temperature element is represented by the same boundary layer height variable, what remains is the secondary role. Temperature is in the PM10 particle production. However, the latter model is weaker than the previous model and its multivariate correlation coefficient is 0.45 and accounts for 20% of the PM10 variations.
In the evaluation of the model in the warm season, the root mean square error at the Cheshmeh station was 31.76 µg / m3 and at the Atisaz station was 33.56 µg / m3. In the cold season, the root mean square error was estimated to be 47.10 µg / m3 at the Cheshmeh station and 49.81 µg / m3 at the Atisaz station, respectively. However, using the model with independent variables AOD, boundary layer height and temperature, the root mean square error was estimated to be 38.42 μg / m3 at the Cheshmeh station and 39.11 μg / m3 at the Atisaz station. The former shows a decrease of approximately 10 micrograms per cubic meter. Therefore, although the latter model with independent variables of boundary layer height, AOD and temperature had less multivariate correlation coefficients and determination coefficients than the model with independent observational variables in cold season, it yielded better results based on evaluation of the model for different locations and days from modeling location and days.
Conclusion
Generally, based on the results, it can be stated that regression models of warm and cold seasons are statistically acceptable at a confidence level of 99%. Therefore, the amount of PM10 fluctuations that is justifiable by the model is not accidental, although the modifications justified by models are low. The calculated errors in the evaluation section showed that the proposed models are not very accurate. It was also found that in the warm season, the wind speed can improve the results of the regression model of the relationship between AOD and PM10, and in the cold season, the variables of the boundary layer height and temperature in the regression model are statistically acceptable and improve the results of the model.
Volume 25, Issue 4 (12-2021)
Abstract
Introduction
Due to technical and financial limitations, it is not possible to simultaneously provide high spatial and temporal resolution by a sensor. There is always a trade-off between the spatial and temporal resolution of the sensors. For studies such as estimating evapotranspiration, land surface temperature with high temporal and spatial resolution is required; however, estimating actual evapotranspiration with high temporal and spatial resolution by a single sensor is not possible. Since high spatial and temporal resolution together increase the reliability of analyzing and extracting information from the image, so the best way to overcome this problem is to downscale images to high temporal and spatial resolutions. Downscaling is the process of converting images with low spatial resolution to images with high spatial resolution. So far, several methods have been proposed for downscaling. These methods differ for downscaling of the reflectance and thermal bands. Many studies that have been conducted so far on the actual evapotranspiration estimation, indicate the efficiency of SEBAL algorithm for this purpose. Therefore, in this study, in order to calculate the actual evapotranspiration, the SEBAL model was used and the products of different downscaling methods were given as input to this model. Assessing the accuracy of actual evapotranspiration calculated using remote sensing data indicates the efficiency of products obtained from different methods. According to the studies conducted in this field, so far no study has been done on the combination of downscaled bands obtained from different downscaling methods applied on thermal data and non-thermal data in order to calculate the actual evapotranspiration. In this study, STARFM, ESTARFM and Regression algorithms were used to downscale the reflectance bands and SADFAT, Regression and Cokriging algorithms were used to downscale the thermal bands. Then the accuracy of the results was evaluated.
Methodology
The study area is Amirkabir agro-industry located in the south of Khuzestan province, one of the seven companies for the development of sugarcane cultivation and ancillary industries (longitude 48.287100, and latitude 31.029696 degrees). The gross land area of this agro-industry is 15000 hectares and its net area is 12000 hectares which is divided into several 25-hectare plots. In this research, the images of MODIS located on Terra satellite and the images of OLI and TIRS sensors of Landsat 8 satellite were used. It is worth noting that the Landsat image for time 2 was used to evaluate the simulation results. The downscaling algorithms used in this research included STARFM, ESTARFM, and REGRESSION algorithms were applied on reflectance bands and SADFAT, Regression and Cokriging algorithms were used for thermal band downscaling. In order to conduct this research, first, various downscaling methods were applied on MODIS images to be downscaled to the images with Landsat spatial resolution. Then, using MODIS downscaled images, evapotranspiration values were calculated for different combinations of downscaled data using SEBAL method and the results were compared and evaluated with evapotranspiration obtained from Landsat images acquired at the same date as MODIS data.
Results and discussion
In order to evaluate the results, the downscaled bands were visually and quantitatively compared with the corresponding bands of the Landsat image acquired on the same date. In order to compare these data quantitatively, the root mean square error (RMSE) and the coefficient of determination (R2) were used. According to the RMSEs, it can be concluded that the STARFM, ESTARFM, Regression, SADFAT and Cokriging downscaling algorithms all perform well. Among the methods applied to the reflectance bands, STARFM with the RMSE of 0.0180 had the best performance, followed by ESTARFM with the RMSE of 0.0186 and Regression with the RMSE of 0.0479. Among the methods applied to thermal bands, the SADFAT algorithm with the RMSE of 0.0224 had the best performance, followed by Cokriging with the RMSE of 0.0234 and Regression with the RMSE of 0.0464. It should be noted that the difference in outputs is very small, and given that the study area of this study is a homogeneous area of agricultural land cover including a single sugarcane crop. This issue can be the main reason for the close performance of downscaling methods and the high accuracy of their outputs. Moreover, according to the results obtained for evapotranspiration, ESTARFM / Regression, ESTARFM / SADFAT, STARFM / Regression and STARFM / SADFAT had the best performance with the lowest difference and the Regression / Cokriging method had the weakest performance, respectively.
Conclusion
This study can be concluded as follows:
Due to technical and financial limitations, it is not possible to simultaneously provide high spatial and temporal resolution by a sensor. There is always a trade-off between the spatial and temporal resolution of the sensors. For studies such as estimating evapotranspiration, land surface temperature with high temporal and spatial resolution is required; however, estimating actual evapotranspiration with high temporal and spatial resolution by a single sensor is not possible. Since high spatial and temporal resolution together increase the reliability of analyzing and extracting information from the image, so the best way to overcome this problem is to downscale images to high temporal and spatial resolutions. Downscaling is the process of converting images with low spatial resolution to images with high spatial resolution. So far, several methods have been proposed for downscaling. These methods differ for downscaling of the reflectance and thermal bands. Many studies that have been conducted so far on the actual evapotranspiration estimation, indicate the efficiency of SEBAL algorithm for this purpose. Therefore, in this study, in order to calculate the actual evapotranspiration, the SEBAL model was used and the products of different downscaling methods were given as input to this model. Assessing the accuracy of actual evapotranspiration calculated using remote sensing data indicates the efficiency of products obtained from different methods. According to the studies conducted in this field, so far no study has been done on the combination of downscaled bands obtained from different downscaling methods applied on thermal data and non-thermal data in order to calculate the actual evapotranspiration. In this study, STARFM, ESTARFM and Regression algorithms were used to downscale the reflectance bands and SADFAT, Regression and Cokriging algorithms were used to downscale the thermal bands. Then the accuracy of the results was evaluated.
Methodology
The study area is Amirkabir agro-industry located in the south of Khuzestan province, one of the seven companies for the development of sugarcane cultivation and ancillary industries (longitude 48.287100, and latitude 31.029696 degrees). The gross land area of this agro-industry is 15000 hectares and its net area is 12000 hectares which is divided into several 25-hectare plots. In this research, the images of MODIS located on Terra satellite and the images of OLI and TIRS sensors of Landsat 8 satellite were used. It is worth noting that the Landsat image for time 2 was used to evaluate the simulation results. The downscaling algorithms used in this research included STARFM, ESTARFM, and REGRESSION algorithms were applied on reflectance bands and SADFAT, Regression and Cokriging algorithms were used for thermal band downscaling. In order to conduct this research, first, various downscaling methods were applied on MODIS images to be downscaled to the images with Landsat spatial resolution. Then, using MODIS downscaled images, evapotranspiration values were calculated for different combinations of downscaled data using SEBAL method and the results were compared and evaluated with evapotranspiration obtained from Landsat images acquired at the same date as MODIS data.
Results and discussion
In order to evaluate the results, the downscaled bands were visually and quantitatively compared with the corresponding bands of the Landsat image acquired on the same date. In order to compare these data quantitatively, the root mean square error (RMSE) and the coefficient of determination (R2) were used. According to the RMSEs, it can be concluded that the STARFM, ESTARFM, Regression, SADFAT and Cokriging downscaling algorithms all perform well. Among the methods applied to the reflectance bands, STARFM with the RMSE of 0.0180 had the best performance, followed by ESTARFM with the RMSE of 0.0186 and Regression with the RMSE of 0.0479. Among the methods applied to thermal bands, the SADFAT algorithm with the RMSE of 0.0224 had the best performance, followed by Cokriging with the RMSE of 0.0234 and Regression with the RMSE of 0.0464. It should be noted that the difference in outputs is very small, and given that the study area of this study is a homogeneous area of agricultural land cover including a single sugarcane crop. This issue can be the main reason for the close performance of downscaling methods and the high accuracy of their outputs. Moreover, according to the results obtained for evapotranspiration, ESTARFM / Regression, ESTARFM / SADFAT, STARFM / Regression and STARFM / SADFAT had the best performance with the lowest difference and the Regression / Cokriging method had the weakest performance, respectively.
Conclusion
This study can be concluded as follows:
- All downscaling algorithms used in this research had an acceptable performance in simulating Landsat bands.
- Among the reflectance band-related downscaling methods, STARFM had the best performance, followed by ESTARFM and Regression, respectively.
- Among the thermal band-related downscaling methods, the SADFAT algorithm performed best, followed by Cokriging and Regression.
- The use of STARFM algorithm for reflectance bands and SADFAT algorithm for thermal bands in homogeneous areas is recommended.
- The difference between the different combinations of methods for estimating actual evapotranspiration is small.
Keywords: Downscaling; Landsat-8; MODIS; Evapotranspiration; Cokriging; STARFM
Volume 26, Issue 1 (5-2022)
Abstract
Introduction
Due to the growth of urbanization and migration to cities, housing supply has become an acute problem. To meet the demands for housing, in addition to providing land, capital, and building materials, a strong economic management is needed. One of the ways that has been implemented in Iran for housing and helping people to own a house is the formation of housing cooperatives, on which, with reference to Article 44 of the Constitution, more emphasis is placed in recent years to reduce government holdings and provide opportunities for participation in all economic bases.
Methodology
The research method of the present article is applied in terms of purpose and descriptive-analytical in nature. Data collection was done through library and field data gathering (interviews with experts of housing cooperatives, practitioners, and university professors through a semi-structured questionnaire using non-probabilistic sampling method (snowball)). In the present paper, the number of the informants, 30, was determined based on the cooperation conditions and the corona limitation. In this study, due to the nature of the issue based on the strategic interpretation of the drivers affecting the quality of housing cooperatives in the production of urban housing, strategy-based approaches have been used. Therefore, two important techniques, namely cross-structure analysis approach and interaction analysis approach with Mikomak software have been used.
Results and discussion
In the analysis of the strength of the relationship in the direct impact of variables, it is clear from the measurable relationships between them that there are very strong relationships among the components of "satisfaction with the efficiency of housing profits, participation of housing investment institutions, mass builders, and cooperatives in housing production, and formulating a coherent and dynamic policy of access to cheap housing of suitable quality,”and are mutually related with the component of "formulating a coherent and dynamic policy of access to cheap housing of suitable quality for cooperative members." Out of 37 factors studied in this study, 13 factors were finally extracted as key variables and drivers in the optimal performance of the cooperatives. All 13 factors were repeated in both direct and indirect methods.
Conclusion
The results show that the drivers of housing cooperatives' performance including "coherent measures to get the housing market out of recession; involvement of housing investment institutions, mass builders, and cooperatives in housing production; and satisfaction with housing efficiency" are marginalized. This trend shows that these indicators do not tend to improve, and with the current trend, the situation in the organization will continue to be unfavorable. As a result, the necessary condition for improving performance of cooperatives in the physical and spatial changes of Tabriz city based on decentralized planning and meeting the needs of all citizens in the future, is the promotion of the above indicators.
Due to the growth of urbanization and migration to cities, housing supply has become an acute problem. To meet the demands for housing, in addition to providing land, capital, and building materials, a strong economic management is needed. One of the ways that has been implemented in Iran for housing and helping people to own a house is the formation of housing cooperatives, on which, with reference to Article 44 of the Constitution, more emphasis is placed in recent years to reduce government holdings and provide opportunities for participation in all economic bases.
Methodology
The research method of the present article is applied in terms of purpose and descriptive-analytical in nature. Data collection was done through library and field data gathering (interviews with experts of housing cooperatives, practitioners, and university professors through a semi-structured questionnaire using non-probabilistic sampling method (snowball)). In the present paper, the number of the informants, 30, was determined based on the cooperation conditions and the corona limitation. In this study, due to the nature of the issue based on the strategic interpretation of the drivers affecting the quality of housing cooperatives in the production of urban housing, strategy-based approaches have been used. Therefore, two important techniques, namely cross-structure analysis approach and interaction analysis approach with Mikomak software have been used.
Results and discussion
In the analysis of the strength of the relationship in the direct impact of variables, it is clear from the measurable relationships between them that there are very strong relationships among the components of "satisfaction with the efficiency of housing profits, participation of housing investment institutions, mass builders, and cooperatives in housing production, and formulating a coherent and dynamic policy of access to cheap housing of suitable quality,”and are mutually related with the component of "formulating a coherent and dynamic policy of access to cheap housing of suitable quality for cooperative members." Out of 37 factors studied in this study, 13 factors were finally extracted as key variables and drivers in the optimal performance of the cooperatives. All 13 factors were repeated in both direct and indirect methods.
Conclusion
The results show that the drivers of housing cooperatives' performance including "coherent measures to get the housing market out of recession; involvement of housing investment institutions, mass builders, and cooperatives in housing production; and satisfaction with housing efficiency" are marginalized. This trend shows that these indicators do not tend to improve, and with the current trend, the situation in the organization will continue to be unfavorable. As a result, the necessary condition for improving performance of cooperatives in the physical and spatial changes of Tabriz city based on decentralized planning and meeting the needs of all citizens in the future, is the promotion of the above indicators.
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