مهدی Amiri; Farzad Amiri; Mohammad Hossein Pourasad; Seyfollah Soleimani
Abstract
Clean air quality, as one of the most essential needs of living organisms, has been compromised by natural and artificial activities. Dust storms have been constantly increasing in recent years, which have resulted in countless social, economic and environmental health damages for residents of southern ...
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Clean air quality, as one of the most essential needs of living organisms, has been compromised by natural and artificial activities. Dust storms have been constantly increasing in recent years, which have resulted in countless social, economic and environmental health damages for residents of southern and southwestern regions of Iran. In this study, (MODIS) sensor data are used to investigate dust storms and detect horizontal depth of field. The advantages of MODIS sensor data include high spectral and temporal resolution. In addition, data from meteorological stations are collected according to the study period. After pre-processing data and preparing field observations, features required for modeling are derived from the MODIS sensor data through a differential method between the selected bands of each MODIS sensor image along with the features extracted from the sensor. Ground meteorological stations are used. With further studies and evaluations and using the opinions of meteorological experts 42 features are used of which36 are extracted from different bands of Moody's images and 6 features are extracted from meteorological station data. Next, best features are identified through feature selection techniques and a new method called ML-Based GMDH. which is the result of improving the GMDH neural network by changing partial functions with machine learning models, was used to detect dust concentration and horizontal visibility. In addition, to achieve the appropriate accuracy, the meta-parameters of this model are adjusted by the TLBO optimization algorithm. Furthermore, basic GMDH machine learning methods SVM, MLP, MLR, RF and their group model are implemented to compare with the main approach. Results shows that the ML-Based GMDH method adjusted with TLBO by improving on the best methods. The machine learner provides good accuracy in detecting dust concentrations.
maryam soltanikazemi; Saeid Minaei; Hossein Shafizadeh Moghadam; AliReza Mahdavian
Abstract
Sheath moisture is an important parameter during the growth period of sugarcane, which is of special importance from the perspective of water stress and field irrigation management. Remote sensing data has a high capacity to update crop growth monitoring systems. In this regard, satellite images that ...
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Sheath moisture is an important parameter during the growth period of sugarcane, which is of special importance from the perspective of water stress and field irrigation management. Remote sensing data has a high capacity to update crop growth monitoring systems. In this regard, satellite images that provide a variety of information can be used. In the crop year of 2020, with the aim of predicting the moisture content of sugarcane pods, 4 spectral indices and 7 single band sensors of Sentinel-2 satellite were evaluated. Four methods PLSR, RF, GRNN and SVR were used to model and predict pod moisture. Bayes algorithm was used to optimize the parameters in RF, GRNN and SVR models. In addition, improved sensitivity analysis was used improved stepwise was used to find the most effective input parameter in estimating pod moisture. The results showed that the SVR model provided a more acceptable estimate of sheath moisture content than the other models when the parameters NDVI, EVI, SRWI, Clgreen, B2, B3, B5, B4, B11 and B12 were used as input to the four models. According to the sensitivity analysis, SRWI parameter was considered as the most effective index in the modeling process. Therefore, it can be concluded that among the inputs given to the model, a combination of indices and bands of NDVI, EVI, SRWI, Clgreen, B2, B3, B5, B4, B11 and B12 give a better estimate of sugarcane sheath moisture content.
Fatemeh Shokrian; Karim Solaimani
Abstract
Investigating land use changes requires the integration of layers in a certain period. This research aims to investigate land use changes in Haraz Plain from 1980 to 2021. Therefore, Landsat data was used to measure the changes. By applying atmospheric, geometric and radiometric corrections, image enhancement ...
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Investigating land use changes requires the integration of layers in a certain period. This research aims to investigate land use changes in Haraz Plain from 1980 to 2021. Therefore, Landsat data was used to measure the changes. By applying atmospheric, geometric and radiometric corrections, image enhancement operations were performed and land use change maps were produced based on the supervised classification method, maximum likelihood algorithm and basis component analysis functions. The type of land use changes was determined from the difference function of the identification images and the accuracy of the maps using the overall accuracy test and the Kappa statistic. The results showed that from 1980 to 1990, the area of forest lands decreased by 4 km2. The rangeland area also decreased from 450 to 436 km2. From 2000 to 2010, the area of forest land decreased from 272 to 270 km2 and rangeland decreased from 432 to 420 km2. Finally, between 2011 and 2021, the area of forest lands decreased by 9 km2 and the rangeland area decreased by 5 km2. The results of the investigation of the changes in land use in the region indicate that the area of forest and rangeland lands decreased and the area of agricultural lands and residential areas increased. These results can help planners find the factors affecting land use changes and make correct management decisions in the future.
Elahe Akbari
Abstract
Estimation and forecast of crop yield using crop growth models is imperative to plan agricultural operations and manage crop yield. To this end, the AquaCrop model parameters were estimated and the model was calibrated with measuring and sampling different requied information of model in the crop growing ...
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Estimation and forecast of crop yield using crop growth models is imperative to plan agricultural operations and manage crop yield. To this end, the AquaCrop model parameters were estimated and the model was calibrated with measuring and sampling different requied information of model in the crop growing stages and prior to cultivation over agricultural silage maize fields at the regional scale. Field sampling of soil (prior to cultivation) and crop (during the growth season), digital hemispherical photography (DHP) and destructive method for comparison purposes were carried out for silage maize in Qhale-Nou county, South Tehran, in the summer of 2019. Remote sensing data assimilation based on forcing method, by biophysical variable of fCover extracted of remote sensing data was incorporated into the AquaCrop model. Then, the most sensitive model parameters which identified through sensitivity analysis were estimated and the obtained results were then compared with the case where assimilated data were not incorporated. As the results suggest, the output yield for the model with data assimilation was estimated with R2 values of 0.89 and 0.88 for calibration and evaluation, respectively. The superiority of RS data assimilation into the model as opposed to not its incorporating was also verified by improving the accuracy with Relative RMSE (RRMSE) values of 4.12 and 5.17 percent and RMSE of 2.5 and 2.4 ton/ha for calibration and evaluation, respectively. The overall findings allude to the advantages of incorporating remote sensing data assimilation by the forcing method as a relatively efficient tool for simulating silage maize yield under variable environmental conditions.
Mohammad Karim Sirat; Gomroki Masoomeh
Abstract
Today, land use change detection in urban areas have key role in city management, resource management and city development. So, in this research the land use change detection is investigated. The purpose of this research is to investigate the land use changes in Herat using Landsat 8 satellite images ...
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Today, land use change detection in urban areas have key role in city management, resource management and city development. So, in this research the land use change detection is investigated. The purpose of this research is to investigate the land use changes in Herat using Landsat 8 satellite images of OLI sensor during 2015 and 2022. After geometrical, radiometric and atmospheric correction in these four-use satellite images; Soil, plant, city and water were identified in the study area. Two methods of maximum likelihood and artificial neural network have been used to classify satellite images to identify land use changes. In general, the classification of the images used by the maximum likelihood method has provided better accuracy, which has a good kappa coefficient and overall accuracy. In the image of 2015, the maximum likelihood method with a kappa coefficient of 0.75, overall accuracy of 0.85 was used, and in the image of 2022, classification was done using the maximum likelihood method. which has a kappa coefficient of 0.96 and an overall accuracy of 0.97. Based on the results of the classification, during the period of 2015-2022, a decrease in the level of land and water and an increase in the level of city and plant land were observed. After classifying the number of changes in the time period of 2015-2022, the land use area has decreased by 4.00 square kilometers, water by 1.62 square kilometers, and the city has increased by 1.39 square kilometers and plants by 4.59 square kilometers.
Mohsen Ebrahimi; Zohre Ebrahimi-Khusfi
Abstract
The Central Plateau of Iran, due to climate changes and the reduction of available water resources on one hand, and the increase in population and the consequent increase in demand on the other hand, is facing a severe water crisis. The science of remote sensing and the availability of numerous satellite ...
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The Central Plateau of Iran, due to climate changes and the reduction of available water resources on one hand, and the increase in population and the consequent increase in demand on the other hand, is facing a severe water crisis. The science of remote sensing and the availability of numerous satellite products have made it possible to monitor the process of changes in various environmental parameters, especially surface and underground water sources, with appropriate accuracy. For this purpose, using the Google Earth Engine system, 16 different satellite products including different environmental parameters such as precipitation, temperature, evaporation and transpiration, soil moisture, runoff, total water storage (GRACE), vegetation cover index and water surface area were received and prepared for the time period 2000-2022. Then, using the non-parametric Mann-Kendall test and the Sen’s slope estimator, the change trend of these parameters was investigated. According to the results, the changes in earth's gravity, which indicates the level of underground water, as well as the area of water surfaces, which indicates surface water resources, and soil moisture, showed a significant decreasing trend. On the other hand, maximum temperature, minimum temperature, potential evaporation and transpiration and NDVI index have a significant increasing trend. Despite the decrease in water surface area, the vegetation cover index has increased, which indicates the increase in the area under cultivation of agricultural products and excessive harvesting of underground water resources, which is also confirmed by the decreasing trend of the GRACE satellite product. The correlation coefficients between parameters with significant trends also showed that there is a significant correlation between GRACE and NDVI parameters, minimum temperature, maximum temperature, soil moisture and area of surface water bodies.
Maryam Haghighi Khomami; Mohammad Panahandeh; Mohammad javad tajaddod; Fariborz Jamalzad Fallah; Mahsa Abdoli
Abstract
Wetlands as an integral part of the global ecosystem in flood prevention or mitigation, feeding aquifers and providing unique habitat for plants and animals and other services and benefits are key elements of a regional conservation strategy. Anzali International Wetland in Guilan Province is one of ...
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Wetlands as an integral part of the global ecosystem in flood prevention or mitigation, feeding aquifers and providing unique habitat for plants and animals and other services and benefits are key elements of a regional conservation strategy. Anzali International Wetland in Guilan Province is one of the 10 most valuable wetlands in the world, which has undergone many changes in land use and vegetation due to structural changes resulting from man-made processes, and its nature and ecological functions have been endangered. The purpose of this study is to investigate the application of remote sensing data in mapping changes in the spatial pattern of the landscape with the help of field work training areas at the bed of the wetland and to analyze the changes of territorial cohesion based on the metrics of the landscape. First, satellite data were analyzed and Sentinel -2 images from 2016 to 2020 were classified by training areas. Then, a map of land cover in 7 classes of agriculture, barren, reed, forest, rangeland, water and urban area was created for mapping and analysis of land use metrics. After extracting class-level and landscape-level metrics in Fragstats software and determining appropriate metrics using PCA method with R and Canoco software, LPI, LSI, ENN_MN, CA, TE, NP, SHAPE_MN, PARA_MN, IJN, ARE_ Applications were selected for better analysis of the area. Analysis of metrics indicates that, in general, the landscape is fragmented, more complex and irregular in form, and more discontinuous in terms of the degree of integration of structural elements.
Mohammad Mansourmoghaddam; Iman Rousta; Mohammad Sadegh Zamani; Mohammad Hossein Mokhtari; Mohammad Karimi Firozjaei; Seyed Kazem Alavipanah
Abstract
The effect of urban thermal islands due to intersections with major environmental challenges of the 21st century is one of the most important studies on environmental phenomena, and in this regard, the study of the land surface temperature gives a clear perspective of the thermal islands in cities, which, ...
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The effect of urban thermal islands due to intersections with major environmental challenges of the 21st century is one of the most important studies on environmental phenomena, and in this regard, the study of the land surface temperature gives a clear perspective of the thermal islands in cities, which, according to the warm and dry climate of Yazd, examines the status and factors affecting the land surface temperature in this city seem to be necessary. This research, using the spectrally and spatially fused image of Landsat-8, for August 2020, and using machine learning algorithms, tries to model the changes in land surface temperature by calculating different parameters related to urban land perspective. Based on the results of this study, the spectral-spatial fusion of Landsat-8 with Sentinel-2 by Pan sharpening, increased 10.7% of the overall accuracy and 16.5% of the Kappa coefficient in the classification of this image. The study also showed that most neighboring parameters associated with land cover are ranked 1 to 11 of influencing the land surface temperature of Yazd city. In this area, the proximity to bare lands in the radius of 100, 50, and 150 meters ranked 1 to 3 of the most important parameters affecting the land surface temperature respectively. This study showed that the change in land cover arrangement could affect the land surface temperature and changing the bare lands to the built-up areas, up to 1.1°C, to vegetation, up to 2.1°C, and changing 30% of bare land to vegetation, up to 1.6°C can reduce the average land surface temperature in Yazd. Also, this study showed that two different models of vegetation simulation in Yazd city showed that the "land-sparing " model could reduce the average land surface temperature in Yazd by 1.3° and the "land-sharing" model by 1.4°C.
Hamid Reza Matinfar; Aliakbar Shamsipor; Hadis Sadeghi
Abstract
Vegetation plays an important role in protecting water and soil resources, stabilizing carbon and improving air quality. In Middle Zagros, forest and pasture vegetation is very important in terms of protecting soil and water resources and sustaining economic activities. In this research, using the Google ...
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Vegetation plays an important role in protecting water and soil resources, stabilizing carbon and improving air quality. In Middle Zagros, forest and pasture vegetation is very important in terms of protecting soil and water resources and sustaining economic activities. In this research, using the Google Earth Engine platform and Landsat 7 satellite images, the drought of Middle Zagros (Lorestan province) was monitored with vegetation indices NDVI, SAVI and VCI, as well as meteorological drought index SPI for the statistical period of 2020-2000. To calculate the SPI index, the precipitation data of 9 synoptic meteorological stations with appropriate spatial distribution and the length of the statistical period (2020-2000) were used, and the processing was done in DPI software. In order to calculate the plant indices, first, all the geometrically corrected satellite images of the ETM+ sensor of the Landsat satellite were called for Lorestan province for each year. At this stage, an average of 52 images were called for each year. Then the images with less than 5% cloud cover were selected and processed. The results of the VCI index showed that mainly the studied area was affected by mild drought during the statistical period of 2020-2000. The year 2008 had the highest amount of drought related to the middle class with 5880.6 hectares among the studied years. The results of the SPI index showed that there was a moderate drought in 2010, a severe drought in 2008 and 2017, a mild drought in 2006, and a severe drought in 2019. The results of NDVI and SAVI indices also show the increase of thin vegetation classes and areas without vegetation by 1.331679 and 115164 hectares, respectively, and the decrease of normal and dense vegetation by 446160.7 and 682.4 hectares respectively per year. 2008 was compared to 2006 and 2007. Based on the results of all three investigated indicators, the favorable conditions of vegetation cover and ecological threat were obtained in 2016, 2019 and 2020. The highest level of this coordination between SPI meteorological drought and vegetation indices was observed in 2008 and 2010 and to some extent in 2019. In general, the results show that the increase or decrease of vegetation can be caused by the occurrence or absence of drought, while other factors such as land use changes should also be considered.
M Shaygan; Marzieh Mokarram
Abstract
Industrial activities and urban traffic contribute to increased air pollution in large cities, resulting in a rise in various diseases among the population. Consequently, studying and investigating polluted areas is crucial for effective city management. This study aims to examine the air pollution levels ...
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Industrial activities and urban traffic contribute to increased air pollution in large cities, resulting in a rise in various diseases among the population. Consequently, studying and investigating polluted areas is crucial for effective city management. This study aims to examine the air pollution levels in Tehran, Isfahan, and Qom cities, focusing on NO2, CO2, CO, and CH4 pollutants, during two distinct periods: pre-COVID-19 (2018-2019) and during COVID-19 (2020-2021), across all four seasons. By employing the Pearson correlation method and RBF neural networks (radial basis function neural network), the relationship between temperature and pollutants was explored. The findings reveal higher levels of air pollution in Tehran and Isfahan compared to other regions. Moreover, the study demonstrates a significant reduction in pollution during the COVID-19 era compared to the pre-COVID-19 period. Additionally, the regression analysis highlights a strong correlation between temperature increase and pollution levels (R2=0.981). Furthermore, the RBF method exhibits high accuracy in predicting air pollution levels (R2 = 0.85, RMSE = 0.08). In conclusion, this research underscores the urgent need for comprehensive measures to mitigate air pollution, particularly in highly polluted areas, and emphasizes the role of temperature as a crucial factor affecting pollution levels.
Maedeh Behifar; Hossein Aghighi; Aliakbar Matkan; Hamid Salehi shahrabi
Abstract
Leaf area index (LAI) derived from remotely sensed images is considered as an important index for spatial modelling of vegetation productivity. Traditionally, the spectral vegetation indices (VIs) derived from the red (R) and near infrared (NIR) reflectance values have been utilized to statistically ...
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Leaf area index (LAI) derived from remotely sensed images is considered as an important index for spatial modelling of vegetation productivity. Traditionally, the spectral vegetation indices (VIs) derived from the red (R) and near infrared (NIR) reflectance values have been utilized to statistically estimate LAI. However, most of these VIs saturate at some level of LAI. This limitation was over-come by using the reflectance spectra in the red-edge region. Therefore, it is necessary to evaluate the capability of different VIs derived from RS data to estimate the LAI of silage maize. For this purpose, five field sampling campaigns which were near-simultaneous with Sentinel II over-passes were conducted by the Space Research Center, Iranian Space Research Center and totally 234 samples were collected from the silage maize fields, in Magsal, Qazvin. Then, 13 VIs from the time series of Sentinel-2 imagery were computed and employed to statistically estimate the LAI values. The results showed that Enhanced vegetation index (EVI) with outperformed other VIs to estimate LAI of silage maize. Moreover, the values of non-linear regression models were higher that the liner ones.
Nastaran Nazariani; Asghar Fallah; Habibolah Ramezani Moziraji; Hamed Naghavi; Hamid Jalilvand
Abstract
Gathering accurate information for statistics requires high cost and precision. The time factor is also one of the important issues that should be seriously considered in statistics. Therefore, the use of sampling methods and satellite images will be a good alternative for this purpose. In the present ...
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Gathering accurate information for statistics requires high cost and precision. The time factor is also one of the important issues that should be seriously considered in statistics. Therefore, the use of sampling methods and satellite images will be a good alternative for this purpose. In the present study, the aim of the effect of different cluster sampling schemes in estimating the quantitative characteristics of the traditional forests of Olad Ghobad in Koohdasht township, Lorestan province using Sentinel 2 sensor images. To estimate the studied characteristics, 150 clusters in the form of six designs (triangular, square, star 1, linear, L-shaped, star 2) were implemented in the region. Then, in each subplot, the characteristics of the number and area of the tree canopy were measured. Afterimage preprocessing and appropriate image processing (principal component analysis, texture analysis, and different spectral ratios to create important plant indices), the corresponding digital values of the ground sample plots are extracted from the spectral bands and used as independent variables. Modeling was performed using nonparametric methods of random forest, support vector machine, and nearest neighbor. The results showed that the average density per hectare was 51 and the canopy area was 32.94%. The diagram of the mean squares of the error of the training and test data against the number of trees for the characteristic number per hectare and canopy showed that the optimal number of trees was obtained at approximately 75 and 350 points. The results of validation according to the percentage of squared mean squared error showed that for both density and canopy surface characteristics of random forest algorithm with linear and double star sampling designs with the squared percentage of mean squared error respectively (46.00%) and (10.44%) and Bias (-0.02%, 2.82%) along with cluster sampling designs linear and double star, respectively, had better performance in modeling. In general, the results showed that the use of different cluster sampling schemes, nonparametric modeling methods, and Sentinel2 sensor images can better performance estimate the quantitative characteristics of Zagros forests.
alireza mahmoodi; Marzieh Mokarramb
Abstract
Today, remote sensing is used for plant studies, such as determining nutrient levels, plant diseases, water deficiency or excess, weed identification, and so on. As electromagnetic waves strike the plants, they react in different ways (absorption, reflection or passage) based on the characteristics of ...
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Today, remote sensing is used for plant studies, such as determining nutrient levels, plant diseases, water deficiency or excess, weed identification, and so on. As electromagnetic waves strike the plants, they react in different ways (absorption, reflection or passage) based on the characteristics of the plants. The quantity of nutrients in a plant can be determined through measurement science in plant studies. Since the amount of nutrients in the plant can be determined, it is possible to know how much fertilizer the plant needs. On the other hand, identified the nutrients in the plant, especially rangeland plants. A spectrometer was used to measure the plant's response to electromagnetic waves in the range of 0.3 to 1.1 m. Following that, the relationship between the amount of electromagnetic waves and the amount of nutrients in these plants was determined. The results showed that in Fagonia bruguieri b1026 nm, in Peganum harmala b1040 nm, in Ziziphus spina-christi b1046 nm, in Tecurium persicum band 1030 nm, in Vitex pesedo-negundo b400 and b1038 and in Otostegia persica band They are effective in predicting the value of P. For the prediction of Zn in F. bruguieri b1026 nm band, in P.harmala b1040 nm band, in Z. spina-christi ba1045 nm band, in T. persicum pea b1030 nm band, in V. pesedo-negundo plant b1010 nm and in O. persica band They are the most effective bands. To predict Cu, it is determined using spectral band values that in F.bruguieri band is b402 nm, in P. harmala band is b410 nm, in Z. spina-christi band is b1046 nm, in T. persicum band is b1030 nm, in V.pesedo and O. persica b1038 are the most effective bands.
Reza Shahhoseini; Kamal Azizi; Arastou Zarei; Fatemeh Moradi
Abstract
Land use maps describe the spatial distribution of natural resources, cultural landscapes, and human settlements that are essential for decision-makers. Therefore, the accuracy of maps obtained from the classification of satellite images is very effective in uncertainty for urban management. Due to the ...
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Land use maps describe the spatial distribution of natural resources, cultural landscapes, and human settlements that are essential for decision-makers. Therefore, the accuracy of maps obtained from the classification of satellite images is very effective in uncertainty for urban management. Due to the uniform quality of images in large areas at regular intervals, remote sensing images are essential for land use maps. The primary purpose of this study is to present a proposed method to create an accurate land cover map in urban areas using a combination of Sentinel-1 and Sentinel-2 data. For this purpose, the features of the backscattering coefficient VV and the two parameters obtained from the H-α decomposition method (entropy, alpha) of Sentinel-1 radar images and the features of the blue, green, red band, NDVI, NDWI, MNDWI, and SWI were extracted from Sentinel-2 Multispectral images and used as influential components to classify the urban area. To separate agricultural areas from other coatings, the SWI index was used. Elevation data have also been used to optimally distinguish complex classes with different topographies. We evaluated the extraction of effective indicators from these two datasets in an object-oriented approach based on support vector machine algorithms and random forest for land use classification. The results showed that using properties extracted from radar and Multispectral images simultaneously in the object-oriented classification method could altogether determinate the object's properties in the study area. When optical and radar data were used simultaneously for both classification algorithms, the overall accuracy classification increased. For the stochastic forest method, which provided the highest accuracy, the overall accuracy for the radar and optics data combination approach increased by 13% and 5%, respectively, compared to the radar feature approach and the optics feature approach alone. There was also a significant difference in classification accuracy at all levels between the support vector machine classification algorithm and the random forest. The results showed that the random forest classification method's overall accuracy and support vector machines were 83.3 and 79.8%, respectively, and the kappa coefficient was 0.72 and 0.68%, respectively.
Eslam galehban; Saeid Hamzeh; Shadman Veysi; Seyed Kazem Alavipanah
Abstract
Determination of the Crop Water Requirement (CWR) of different crops and the value of crop water consumption is one of the problems at a large scale and in real-time to the soil and water expert. The first step to compute this variable is to determine the reference evapotranspiration (ET0). The standard ...
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Determination of the Crop Water Requirement (CWR) of different crops and the value of crop water consumption is one of the problems at a large scale and in real-time to the soil and water expert. The first step to compute this variable is to determine the reference evapotranspiration (ET0). The standard method to compute this parameter is to utilize the climate data and experimental equations. The problem with classic methods is that the meteorological station isn’t available in the agricultural lands and usually, we have data limitations. The optimized solution is to utilize remote sensing data. So with the combination of different datasets then the reference evapotranspiration and actual evapotranspiration will be estimated. The goal of the study is to an evaluation of open-source WaPOR and ERA5 to compute daily reference evapotranspiration based on the FAO-Penman Monthis equation at the meteorological stations of Sistan and Baluchestan province. The result has shown that the open-source dataset estimated the reference evapotranspiration as more than 80 percent accurate at the place of the meteorological station and in all of the stations RMSE was less than 2 mm per day. The accuracy assessment of results shown at different crop seasons that ET0 in the autumn season is better than in the spring season. So that the ERA5 combined with the GLDAS Wind data has a better correlation with in situ measurement of ET0 than to the WaPOR. All of the results shown that this dataset can be used in each place in the province to estimate ET0.Therefore, the present study is to investigate the possibility of using the products of WaPOR and ERA5 systems to calculate the amount of daily reference evapotranspiration based on the experimental method of Penman-Monteith and to evaluate and validate its outputs in Sistan and Baluchestan Province of Iran.The results showed that remote sensing systems with an accuracy of over 80% at meteorological stations estimated the amount of reference evapotranspiration and an error of less than 2 mm was reported in all stations. Also, studies during the growing season (June 15 to November 6) compared to the growing season (1 November to 15 May) showed that the reference evapotranspiration obtained from satellite data in the first growing season has a higher (R2). Also, the results of NRMSE index evaluation indicate that the reference evapotranspiration obtained from ERA-GLDAS2.1 data is appropriate.Therefore, since the estimated and validated values had acceptable accuracy, in the next step, these systems can be used anywhere in the province.
Mehran Shaygan; Marzieh Mokarram
Abstract
Due to the fact that droughts can affect both water quality and quantity, the purpose of this study is to determine the effect of droughts on water quality and quantity in Northern Fars province, Iran, based on drought indicators. The drought indices PCI, TVDI, and NDVI are used to study drought from ...
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Due to the fact that droughts can affect both water quality and quantity, the purpose of this study is to determine the effect of droughts on water quality and quantity in Northern Fars province, Iran, based on drought indicators. The drought indices PCI, TVDI, and NDVI are used to study drought from 2000 to 2020. Also, the kriging method is used to generate zoning maps of elements in water (Ca, Cl, EC, K, Na, Mg). Then, using the neural network (MLP) method, the amount of elements in the water is predicted based on drought indices. Based on the values of the drought indicators, the trend of drought changes in the region is increasing from 2000 to 2020, with the southern areas of the region experiencing a more acute drought than the rest of the region. In addition, the zoning map of the elements in water indicated that salt concentrations are higher in the southern parts than in the northern parts. Correlation between drought indices and the amounts of elements in water showed that Ca has a high correlation (R2= 0.820) with TVDI index, and also Cl, EC, K, Na, and Mg have significant correlations (R > 0.8) with the index. Using drought indicators, MLP results for predicting water quality status show that southern regions have more solutes and lower water quality. Furthermore, the R2 values of the model for predicting the elements Cl, EC, K, Na, Mg, TDS, TH using PCI index equal to 0.85 and for Ca using TVDI index equal to 0.71, which indicates high accuracy.
Rozhin Moradi; Bubak Souri; Marzieh Reisi
Abstract
The aim of this study was to estimate soil properties using Landsat 8 satellite bands in part of farmlands of Qorveh-Dehgolan plain in western Iran. Soil sampling was conducted at a total number of 107 points from 0-15cm depth throughout the study area and their physicochemical properties were measured ...
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The aim of this study was to estimate soil properties using Landsat 8 satellite bands in part of farmlands of Qorveh-Dehgolan plain in western Iran. Soil sampling was conducted at a total number of 107 points from 0-15cm depth throughout the study area and their physicochemical properties were measured in the laboratory. In order to extract information from the Landsat 8 satellite image following application of the vegetation mask; DOS values for bands 1-7 were extracted for the sampling points. Correlation Analysis, Stepwise Linear Regression and Principal Component Regression were used to determine the relationship between soil properties and digital value of Landsat 8 bands. Validation of Regression Analysis was evaluated using two parameters of Coefficient of Determination and Root Mean Square Error. The results showed that there was a positive and significant correlation between the digital value of most Landsat8 bands to the amounts of sand and free iron oxides in the soil but a negative and significant of it to amounts of clay and silt in the soil. There was no significant correlation between heavy metals concentration and digital value in visible and near infrared bands while Regression Analysis did not provide acceptable performance in estimating soil properties of the study area. According to the obtained results, it seems that Landsat 8 satellite images can be used to estimate the soil texture and the amount of free iron oxides across the study area.
Monireh Amini; Romina Sayahnia
Abstract
Development in its general sense, industrial, technological and spatial progress, especially in developing countries, has led to adverse effects on the environment not only on a regional scale but also at different regional, national and sometimes global levels which has similarly affected the ecological ...
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Development in its general sense, industrial, technological and spatial progress, especially in developing countries, has led to adverse effects on the environment not only on a regional scale but also at different regional, national and sometimes global levels which has similarly affected the ecological security of the regions. In recent decades, more attention has been paid to the issue of environmental safety in the world, and various methods have been developed to evaluate it, but to date, most research on ecological safety has been done based on the pressure- Status -response model and fewer studies have been conducted based on approach landscape ecology models have dealt with this category. There is also little research focusing on dynamic changes in ecological security, in particular simulating and predicting the future development of environmental security. The purpose of this study is to monitor and predict the environmental security situation in the period 1991 to 2035 by combining the support vector machine algorithm, landform ecology model, Markov chain combination model, and automated cells for the Nazarabad county area of the functions Alborz province. For this purpose, using the classification of Landsat satellite images in two 15-year time periods from 1991 to 2021, the trend of land use changes in the region in five land use classes; Construction lands, cultivated lands, wetlands, vegetation and barren lands were studied and CA-Markov model was used to prepare land use maps for 2035. MPS, CA, NP, PLAND, AWMSI, and PD metrics were calculated to quantify the landscape appearance patterns at the class level and LPI, CONTAG, and SHDI metrics were calculated at the landscape level. Then, the ecological safety index was modeled for the landscape metrics of the study area. The results indicate a decrease in integration and an increase in the number of spots in the cultivated land class and the development and expansion of man-made lands in these lands. On the other hand, we have witnessed the phenomenon of integration in the barren lands of the region. Therefore, the ecological security of the region during the study period affected by the above events was evaluated more intensively during the years 1991 to 2006 and more gently in the years 2006 to the forecast of 2035. It was suggested that more attention be paid to environmental considerations and principles of protection in regional development programs.For this purpose, using the Landsat satellite image classification in two fifteen-year periods between 1991, 2006, and 2021, the trend of land use change in the study area in five land use classes; Construction lands, cultivated lands, wetlands, vegetation cover, and bare lands were examined and to quantify the patterns of landscape appearance at the class level, MPS, CA, NP, PLAND, AWMSI, PD metrics and at the landscape level LPI, CONTAG and SHDI metrics were calculated. The CA-Markov chain model was used to prepare land use maps for 2035, and then the ecological security index was modeled for land use metrics in the study area. The results indicate a decrease in integration and an increase in the number of spots in the cultivated land class and the development and expansion of man-made lands in these lands. On the other hand, we have witnessed the phenomenon of integration in the barren lands of the region. Therefore, the ecological security of the region, during the period under review, affected by the above events, was assessed with more intensity during the years 1991 to 2006 and with a milder slope in the years 2006 to the forecast of 2035. Therefore, it seems necessary to pay more attention to environmental considerations and the principles of protection and protection in the development programs of the region.
Hamidreza Matinfar; foziyeh kohani; Ali Akbar Asilian mahabadi
Abstract
Soil salinity is one of the most important environmental problems, and the identification and zoningof saline soils is difficult due to the need for sampling and laboratory analysis, as well as havingtemporal and spatial variability. In recent years, the use of satellite imagery has always been ofinterest ...
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Soil salinity is one of the most important environmental problems, and the identification and zoningof saline soils is difficult due to the need for sampling and laboratory analysis, as well as havingtemporal and spatial variability. In recent years, the use of satellite imagery has always been ofinterest to experts due to its ease of use and ability to detect phenomena. Remote sensing informationgreatly aids the study of soil salinity and can be helpful in identifying salinity values. In this study,220 soil samples were collected from Meymeh area of Dehloran, south of Ilam province, according tothe type of study and physiographic types and soil units. Then, pH and EC values were measuredusing standard methods. Soil salinity values were evaluated using correlations between EC electricalconductivity values obtained from ground data and variables obtained from Landsat 8 satellite imagesincluding bands, salinity indices, vegetation indices and soil indices. Finally, the soil surface salinityestimation model was obtained using stepwise regression method. This method involves the automaticselection of independent variables, and with the availability of statistical software packages, it ispossible to do so even in models with hundreds of variables. In previous studies, indicators and bandshave been used separately and in a limited way, but in this study, an attempt has been made to use acombination of different indicators more widely, and finally to achieve the best relationship byeliminating the indicators that have the least impact on soil salinity estimation. Using significant levelanalysis and correlation between the output of models and ground data, the best model with a value of(R2 = 0.882) was selected and a soil salinity map was prepared based on it. In the study area, thehighest area belonged to non-saline class which comprises 75% of the total study area and about 1%of the soils belong to the saline class.
Manouchehr Manteghi; Yazdan Rahmatabadi
Abstract
Remote sensing is the science of obtaining information from the surface of the earth without explicitcontact with the components studied. Commercialization is a set of activities that converts aninnovation into a product or service that brings economic benefits. Given the widespread use formeasurement ...
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Remote sensing is the science of obtaining information from the surface of the earth without explicitcontact with the components studied. Commercialization is a set of activities that converts aninnovation into a product or service that brings economic benefits. Given the widespread use formeasurement and the high importance of its application in agriculture, commercialization of thistechnology in agriculture has been a top priority and investigated in this study. The target populationof this research is active and passive companies in this field to use their experience to provide suitablefield for cultivation of remote sensing technology through in-depth interviewing and snowballsampling. The catch is used. In this research, using product and technology life cycle diagrams,examining the challenges of technology and infrastructure commercialization, commercializationelements, types of software used in the world agricultural industry, remote sensing investment chartsand analysis The viability of remote sensing in agriculture as a business has been scrutinized. As aresult, the best way to commercialize the product is to reduce constraints for active companies, buildthe necessary infrastructure, especially timely data, and be independent in deploying this technologyto allow users to use a variety of business methods. Provide.
Seyed Ahmad Mousavi; milad janalipour; Nadia Abbaszadeh Tehrani
Abstract
The basis for proper planning and management is to have accurate and timely statistics and information. One of the most important statistics of the agricultural sector is the annual production rate of each crop, which also depends on the area under cultivation of crop and its efficiency. One of the tools ...
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The basis for proper planning and management is to have accurate and timely statistics and information. One of the most important statistics of the agricultural sector is the annual production rate of each crop, which also depends on the area under cultivation of crop and its efficiency. One of the tools that can calculate the area under cultivation in the least time, with low cost and with high accuracy is remote sensing science and technology. In this study, two classification methods including artificial neural networks and support vector machine with different kernels are used and the area under cultivation of major crops in the region consisting of 8 classes is estimated. According to the results, the overall accuracy of the artificial neural networks and support vector machine was 97.74% and 97.96% with a kappa coefficient of 0.97 for both methods, indicating that both methods are good for separation and classification of agricultural lands in the area. Based on the overall accuracy, it can be concluded that the two methods of classification have almost the same results in the region. Also, based on the results of the user's accuracy for the four crops including Alfalfa, Rice, Onion and Melon, the support vector machine method performs better than the neural network method and also for dry and water Wheat, Sorghum, and Pasture, the neural network method out performs the support vector machine in the region.
Maedeh Behifar; a.a Kakroodi; Majid Kiavarz; Farshad Amiraslani
Abstract
Drought is one of the most important natural disasters in the country, with devastating environmental and economic effects. Most drought studies have focused on drought severity and other drought characteristics have not been usually investigated. In this research, for the first time, the capability ...
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Drought is one of the most important natural disasters in the country, with devastating environmental and economic effects. Most drought studies have focused on drought severity and other drought characteristics have not been usually investigated. In this research, for the first time, the capability of meteorological drought indices and satellite data are combined and applied to study drought in inland and coastal basins. For this purpose, the SPI index was calculated by using TRMM satellite precipitation products and then, the drought characteristics such as severity, duration, magnitude, and extent were spatially studied. The results showed that the correlation coefficient between the SPI calculated from the image and the station data was 0.94. The maximum intensity of drought in the study area was -4.19 which occurred in December 2010. Furthermore, the frequency of extreme droughts in 6- and 12-months timescales was higher in the inland area compared with the coastal area. Moreover, in the six-month timescales, 60 percent of drought events had a magnitude of -18.3 or less. The results showed that it is possible to obtain the extent of drought by using satellite imagery which cannot be calculated by other methods. Besides, by using satellite images, drought characteristics could be studied spatially at the basin scale, which is not possible by traditional methods. The results showed the advantage of using satellite precipitation images in the drought study
Saeed Mojarad
Abstract
The study area is located in the northeast of neyriz and near the village of Ghori in Fars province. Geologically, the units of the study area are located in the zone-Sanandaj-Sirjan and with the general northwest-southeastern trend. Most of these Units calcareous units, units sericitic - chlorite schist ...
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The study area is located in the northeast of neyriz and near the village of Ghori in Fars province. Geologically, the units of the study area are located in the zone-Sanandaj-Sirjan and with the general northwest-southeastern trend. Most of these Units calcareous units, units sericitic - chlorite schist and amphibolite units up. In this research, ASTER sensor images and ground magnetometric data were used to explore and identify iron-rich regions in the study area. In this investigation, we applied methods of False Color Composite (FCC), Band Ratio (BR), Principle Component Analysis (PCA) using ASTER images and areas with severe alterations propellitic, phyllic and sericite. Using methods of ground magnetometric processing, many methods containes reduce to pole (RTP), upward continuation, Analytic Signal, Tilt Angle, Vertical Derivative were used to identify the sources and we were able to identify the edges of these anomalies. In the study area, we were able to identify four anomalies under the ground that it is very important. The results of both methods explored four anomalies. Aster imager process and magnetometric data led to primary potential mineral map of the area. For credibility of results, 52 samples were taken and analyzed by XRD methods. Five boreholes have been drilled to a depth of 140 meters and all results are consistent with each other. The methods used are important and valuable
Mir Hamed Mirloye Mousavi; Behzad Zamani Gharechamani
Abstract
Nowadays remote sensing science is widely used in the earth sciences. One of these branches is the identification, investigation and interpretation of the surface structures of the earth. The study of the fracture systems, the recognition of the geometry and spatial distribution of them, could helped ...
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Nowadays remote sensing science is widely used in the earth sciences. One of these branches is the identification, investigation and interpretation of the surface structures of the earth. The study of the fracture systems, the recognition of the geometry and spatial distribution of them, could helped to development of the oil fields. In this research, using Landsat 8 satellite imagery data, STA algorithm and remote sensing processing techniques, geometric analysis of the surface structures in the Siyahmakan oil field has been investigated. Then, the ground surface temperature (LST), calculated by Landsat project science office model for the oil field, and finally examined the relationship between the distribution of surface fractures and the surface temperature distribution pattern of the study area. The results show that the parallel axial (SA) and shear fractures(SO1), that have the N-S and NW-SE trend ordinary, have the highest frequency. According to the density map, the density of the lineaments in the mid-zone to the southeast of the field is the highest. Since underground resources are located along the tectonic lineaments near the surface, these structures have seen cooler and darker in the satellite images of the surrounding areas. Structural lineaments have shown conformity with surface temperature map so that lineaments often located in the regions with low to medium and medium – level Temperatures. Therefore low temperatures correspond to the highly fractured areas.
Soheil Radiom; hossein Aghighi; Hamid Salehi Shahrabi
Abstract
Evapotranspiration is one of the most important components of energy and water balance. The most important way to get real large-scale evapotranspiration is to utilize satellite imagery and remote sensing. Implementation of evapotranspiration calculation algorithms such as SEBAL demands calculation of ...
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Evapotranspiration is one of the most important components of energy and water balance. The most important way to get real large-scale evapotranspiration is to utilize satellite imagery and remote sensing. Implementation of evapotranspiration calculation algorithms such as SEBAL demands calculation of reference evapotranspiration and thus measuring air temperature, humidity and wind speed. Calculation of evapotranspiration is usually based on obtained information from the nearest weather stations to the study area, which can be error-prone. Therefore, in this study, IoT sensors were used to accurately measure air temperature at 2 m above the ground, as well as air humidity and wind speed in the study area. The study area is the farms of Moghan Agricultural Company in Ardabil province. In this study, 23 nodes were installed in a number of farms. The ground-based energy balance algorithm (SEBAL) was used to calculate the evapotranspiration using Landsat 8 images in 2015.
