Irrigation
A. Noori; J. Omidvar; F. Modaresi; K. Davary; S. Nouri; A. Asadi
Abstract
IntroductionLimited fresh water resources and access to these resources as well as providing food security for the growing world population have led researchers to make extensive efforts in the field of optimal management of water consumption and determining the cultivation pattern in different regions. ...
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IntroductionLimited fresh water resources and access to these resources as well as providing food security for the growing world population have led researchers to make extensive efforts in the field of optimal management of water consumption and determining the cultivation pattern in different regions. Therefore, identifying cultivated crops in a region and determining their area can be very effective in land management and water allocation in these regions. With the growth and advancement of technology in the field of satellite and remote sensing in recent decades, the use of satellite images in order to identify types of land use and types of cultivated products has expanded greatly. Sentinel-1 (radar) and Sentinel-2 (multi-spectral) satellites have been very popular in agriculture due to their improved spatial resolution (10 meters) and appropriate time resolution (5 days for Sentinel 2 and 12 days for Sentinel 1).Materials and MethodsThe studied area is located downstream of the Fariman dam in an area of 22.51 square kilometers (5122 hectares) and the central coordinates are 35 degrees 41 minutes and 59 seconds north latitude and 59 degrees 50 minutes and 49 seconds east longitude. In order to classify satellite images and produce crop maps, ground observation data is needed to train the classification model and also evaluate the accuracy of the results. For this purpose, sample points were taken from different land uses in the region, using GPS. Since it was not possible to take enough samples for all land uses and crops in the determined border, a larger sampling area was selected. Then, all collected data were sorted and for each class, 70% of the data was randomly used to train the classification model and 30% was used to validate the obtained classification results. In the present study, Sentinel 2 satellite images for the first 6 months (crop season) of 2021 and 2022 and digital elevation image (DEM) of the study area were considered. According to the surveys conducted and the reports of the agricultural jihad of Fariman city, the main crops cultivated in the region include maize, tomato, sugar beet, wheat and barley. Therefore, according to the phenological stages of these products in the region, the appropriate time series of images was selected. The accuracy of the classified map was evaluated using the Kappa coefficient and overall accuracy.Results and DiscussionIn order to identify and separate the land use in the study area according to the major cultivated crops, first the agricultural calendar of the crops was determined. Then, satellite images were selected based on crop cultivation period. Based on the evaluation indexes of commission error, omission error, overall accuracy as well as the Kappa coefficient, it was observed that the identification of classes and land use was done well and with high accuracy, so that the overall accuracy for the classification map of 2022 is equal to 0.97 and the kappa coefficient value was 0.94. In order to compare land use changes during the two years 2022 and 2021, classification was also done for the images of the crop year 2021. Since the training samples of agricultural crops were not available separately and in sufficient numbers in the crop year of 2021, the classification map of this year was produced only based on the type of land use, and all crops in one class entered the classification model training process. The values of overall accuracy and kappa coefficient in 2021 were obtained as 0.97 and 0.95 respectively. According to the obtained results, the area of the orchard class has increased since 2021 compared to 2022. After repeated field visits to the study area and investigation of some land uses that had been changed and turned into orchard use, it was found that in some areas in 2022 there was the growth of villa gardens and in some areas the farmers have converted cropland to orchard (construction of an orchard). Even in some cases, the old orchard in the region was destroyed by the farmers and the land was fallow for 2 to 3 years (2021, fallow). In 2022, the farmer built a new orchard. It is also necessary to mention that fallow lands are included in the soil class depending on whether they are newly plowed or have no vegetation, and if weeds have grown on these lands, they are included in the rangeland class. ConclusionThe effective management of water resources from dams for agricultural purposes necessitates the identification of land use downstream of the dams, along with determining the types of crops and their respective areas. In this study, Sentinel 2 satellite images were employed to classify and delineate land use associated with agricultural cultivation downstream of the Fariman dam in Razavi Khorasan Province, spanning the crop years of 2021 and 2022. The results indicate that the Sentinel 2 satellite demonstrates a high capacity to differentiate between various types of land use and crops. The generated map depicting changes in land use and crop cultivation areas can be instrumental in water use planning and the allocation of water resources.
E. Soleimani Sardoo; M.H. Farpoor
Abstract
Introduction: Several archaeologists believe that there is a relationship between cultural residuals, human beings, and soil. Soil related factors such as age index, climate change, and paleoclimate are important in archaeology. Soils could be accounted as records of invaluable information. Appropriate ...
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Introduction: Several archaeologists believe that there is a relationship between cultural residuals, human beings, and soil. Soil related factors such as age index, climate change, and paleoclimate are important in archaeology. Soils could be accounted as records of invaluable information. Appropriate compiling of these data cause better understanding of soil and landscape genesis, and human activities in the past. There are two distinguished archeological sites of Daqyanous (Islamic Era) and Konarsandal (before Islamic Era) in Jiroft area. Besides, Konarsandal site is surrounded by old and new Halilrood channels. Since no data about the comparison of soil evolution in the mentioned archeological sites were available, the present research was conducted to compare soil evolution of archaeological sites using soil classification, clay mineralogy, and micromorphology in Jiroft area.
Materials and Methods: soil samples were collected from three different archaeological sites including new channel of Halilrood (pedon 1), old channel of Halilrood (pedon 2) and, Daqyanous (pedon 3). The samples were air-dried and sieved (2 mm). Routine soil physical and chemical analyses including pH, EC, soil textural class, soluble sodium, calcium, and magnesium, and gypsum and calcite contents were performed. The studied pedons were classified using Soil Taxonomy system according to morphology, laboratorial results, and field observations. The clay minerals were determined by X-ray diffraction (XRD) method after carbonates, organic matter, and Fe were removed using Jakson (1965) and Kittrik and Hope (1963) procedures. Ten undisturbed samples were selected for micromorphology studies and thin section preparation.
Results and Discussion: Pedon 1 is affected by Halilrood River sediments, that is why an old soil together with a young soil was formed. Salinity and SAR in the old soil were higher than the upper young soil. A textural discontinuity was found between the old and the young soils. Natric, calcic, and gypsic horizons were found in pedon 1 and caused a Typic Natrargid to be formed in new Halilrood channel. Natric horizon due to high Na cation was formed in pedons 1 and 2. On the other hand, salic, natric, and cambic horizons formed a Typic Haplosalid in pedon 2 (old Halilrood channel). High salinity and SAR in the upper layers caused salic and natric horizons to be formed. Pedon 3 with argillic horizon is an old polygenetic soil. Available humidity in the past caused removal of carbonates from upper layers that followed by clay illuviation and argillic horizon formation. Salinity and SAR in this soil were low and a heavy texture was found in pedon 3. Since pedon 3 showed cambic, argillic, and calcic horizons, it was classified as Arenic Haplargids. Calcium carbonate, gypsum, Fe oxides, and clay coatings were among dominant micromorphological features observed in the studied pedons. Konarsandal archeological site is located in the lowlands of Jiroft plain downward Rabor and Baft elevations. Lenticular gypsum crystals could be attributed to the solution of upward Neogene formations and groundwater close to the surface which evaporates due to capillary. Powdery calcite, Fe-oxides, and clay coating and infilling of gypsum in pore spaces of pedon 1 were observed by micromorphological investigations. Diffused clay coating around pore spaces is explainable by high sodium content and Natric horizon formation. Lenticular, interlocked plates, and infillings of gypsum were observed in pedon 1. However, gypsum with irregular shapes and low content was investigated in pedon 2. This is due to location of this pedon in Halilrood old channel. That is why pedon 2 affected by Halilrood during long periods of time is unstable and shows less evolution compared to pedon 1. Irregular and lenticular forms of gypsum show weak soil development due to low rainfall, high evaporation, and excess salt. High NaCl is reported as a requirement for lenticular gypsum formation. This form of gypsum is supported by high salinity in pedons 1 and 2. High Na and natric horizon formation in pedons 1 and 2 caused dispersion of clay and ceased formation of clay films around pore spaces. Gypsum was not found in pedon 3 during filed and laboratory studies. Besides, gypsum was not observed by micromorphological observations. Clay and calcite coatings and calcite infillings were among the micromorphological features observed in pedon 3. Calcite coating on clay coating in this pedon could be attributed to the climate with more available humidity in the past followed by an arid climate. Carophyte algae fossil was only observed in pedon 3. Kaolinite, illite, chlorite, smectite, and palygorskite clay minerals were determined by X-ray diffraction. Palygorskite is highly related to the parent material and climate. Pedogenic palygorskite formation from transformation of 2:1 clay minerals and/or neoformation is reported by several studies.
Due to the impact of paleoclimate with more available humidity, palygorskite was not found in Daqyanous archeological site. It seems that higher humidity in the past did not allow palygorskite formation or transformed it into smectite. Chlorite and illite are originated from parent material. Evidences of pedogenic mica minerals in arid and semi-arid environments were also found which is due to K fixation among smectite layers. Smectite with pedogenic origin is also reported by Sanjari et al. (29) in the study area. Chlorite, illite, and kaolinite clay minerals seem to be originated from parent material in the present study.
Conclusion: Laboratories analyses and micromorphology observations clearly showed weak development in Konarsandal pedons compared to high evolution of soils in Daqyanous archaeological site. The same results were also found for unstable surfaces of pedons 1 and 2 compared to stable surface of pedon 3. The stable surface provided the accumulation of clay and calcite coatings around the cavities and the formation of argillic and calcic horizons indicating high soil development. Results of the study showed polygenetic formation in soils. Soils in old Halilrood channel show high salinity and Na adsorption ratio compared to other two pedons under study.
N. Hasanalizadeh; A. Mosaedi; Abdolreza Zahiri; M. Babanezhad
Abstract
Characteristics of precipitation and the regionalization major role in the efficient use of water resources and soil and management of environmental hazards. Regionalization of rainfall can help to better use of water resources and to correct manage of environmental hazards. According to the analysis ...
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Characteristics of precipitation and the regionalization major role in the efficient use of water resources and soil and management of environmental hazards. Regionalization of rainfall can help to better use of water resources and to correct manage of environmental hazards. According to the analysis of climate phenomena such as precipitation, all data should be related to a homogeneous region, on the basis in this study, homogenous regions using data from long-term annual precipitation in Golestan province and the appropriate number of stations determined using the newer methods. Precipitation monthly data from 29 rain-gauge stations and evaporation poll in Golestan province from 1361 to 1391 were used to testing of homogeneity, the random and outlier data that 25 stations remained. Then using Wards hierarchicalclustering and with different variables was evaluated segmentation varies. Clustering in two clusters have higher average silhouette 0.48, accordingly, the province was divided into two regions. Homogeneity investigated by heterogeneity test for each region. according to investigations was performed by L- moments coefficient of skewness (τ_3^R) was smaller 0.23, The result Hosking and Wallis test was used to examine the homogeneity region. For this two region, the test statistic H11>, which is confirmed by the homogeneity of the two areas, Finally was divided into two regions. The high correlation coefficient between stations in each cluster and low correlation coefficient between two different cluster is another reason for separation of areas from each other.Characteristics of precipitation and the regionalization major role in the efficient use of water resources and soil and management of environmental hazards. Regionalization of rainfall can help to better use of water resources and to correct manage of environmental hazards. According to the analysis of climate phenomena such as precipitation, all data should be related to a homogeneous region, on the basis in this study, homogenous regions using data from long-term annual precipitation in Golestan province and the appropriate number of stations determined using the newer methods. Precipitation monthly data from 29 rain-gauge stations and evaporation poll in Golestan province from 1361 to 1391 were used to testing of homogeneity, the random and outlier data that 25 stations remained. Then using Wards hierarchicalclustering and with different variables was evaluated segmentation varies. Clustering in two clusters have higher average silhouette 0.48, accordingly, the province was divided into two regions. Homogeneity investigated by heterogeneity test for each region. according to investigations was performed by L- moments coefficient of skewness (τ_3^R) was smaller 0.23, The result Hosking and Wallis test was used to examine the homogeneity region. For this two region, the test statistic H11>, which is confirmed by the homogeneity of the two areas, Finally was divided into two regions. The high correlation coefficient between stations in each cluster and low correlation coefficient between two different cluster is another reason for separation of areas from each other.
H. Ghorbani; A. Roohani; N. Hafezi Moghaddas
Abstract
In this research, a learning vector quantization neural network (LVQ) model was developed to predict and classify the spatial distribution of cadmium in soil in Golestan province. The cadmium data were obtained from soils measuring total Cd contents in soil samples. Some statistical tests, such as means ...
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In this research, a learning vector quantization neural network (LVQ) model was developed to predict and classify the spatial distribution of cadmium in soil in Golestan province. The cadmium data were obtained from soils measuring total Cd contents in soil samples. Some statistical tests, such as means comparision, variance and statistical distribution were performed between the observed points samples data and the estimated cadmium values to evaluate the performance of the pattern recognition method. The Results showed that in training and test phase, there were no significant differences, with the confidence level of 95%, between the statsitcal parameters such as average, variance, statistical distribution and also coefficient of determination in the observed and the estimated cadmium concentrations. The results suggest that learning vector quantization (LVQ) neural network can learn cadmium cocentration model precisely. In addition the results also indicated that trained LVQ neural network had a high capability in predicting cadmium concentrations for non-sampled points. The technique showed that the LVQNN could predict and map the spatial cadmium concentrations variability. Our results indicated that it is possible to discriminate different cadmium levels in soil, using LVQNN.
