Moslem Darvishi; Abouzar Ramezani
Abstract
Extended Abstract Introduction Due todecreased rainfall and increased groundwater harvesting, our country faces drought. With drastic decline of water levelin lakes and hydroelectric reservoirs, water scarcity is deeply felt. Thus, managers and officials shall find new ways of decreasing waterconsumption ...
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Extended Abstract Introduction Due todecreased rainfall and increased groundwater harvesting, our country faces drought. With drastic decline of water levelin lakes and hydroelectric reservoirs, water scarcity is deeply felt. Thus, managers and officials shall find new ways of decreasing waterconsumption and overcome this crisis. Due to the rising global temperatures and reportsof the World Wildlife Fund, water scarcitycrisis will dominate most countries of the world, especially in Europe and Asia in the next ten years (Sengupta, 2018). Therefore, advanced water management principles shall be applied to decrease water consumption in the agricultural sector and maintain water security. Iran is among the top five countries of the world in terms of having vast irrigated land (Bruinsma, 2017), which shows that in many parts of the country agricultural lands are irrigated. Thus, the country’s water resources reach a critical stage, and because of limited resources, no more water can be provided for agriculture. The present study primarily seeks to optimize crop cultivation using two approaches: first, reduce water consumption and increase farmers’ income and second, reduce water consumption and meet domestic demand. In order to achieve this goal, first, the type of crops and area under cultivation were determined using remote sensing and satellite imagery. Then,spatial information system was used for data analysisand optimization of crop cultivation. Materials & Methods Remotely sensed images were used to collect data about the area under cultivationin agricultural patches and crop type. Those images were then analyzed using remote sensing techniques.According to pixel-based classification ofmultitemporal satellite images using training data, a croplabel is assigned to each pixelin this method. Moreover, borders of each agricultural land are extracted from pan-chromatic images of the region with higher spatial resolution. Finally, fitting the results of pixel-based classification with the extracted bordersof each agricultural land,a final croplabel is determinedfor the total area of the agricultural landbased on the majority labels. In order to optimize the problem, two objective functions (relationships 1 and 2) are defined in which income maximization and water consumption minimization are considered. Typically, location and allocation problems include objective and constraints functionswhich are maximized or minimized based on the goal of the problem. Linear programming is used to solve the problem. Linear programming is a classical optimization method whichdevelop a deterministic algorithm tosolve the problem. This method can only be used when the relationships between variables are linear. In other words, the relationship between variables shall be perfectly proportional and directin this method. (1) (1) (2) Result &Discussion The study area consists of 198 hectares of agricultural land in vicinity of GolangTapeh village of Asadabad city. The city covers an area of 1195 km2 and constitutes 6.1% of Hamadan province. It is located between 34° 37› to34°50 ‹northern latitude and 47°9› to 47°51›eastern latitude. Its average height is 1607 meters above sea level. The city is bounded in northwest with the province of Kordestan,in west with the province of Kermanshah, in southeast with Tuyserkancity and in the northeast withBaharcity. Assad Abad consists of three plains and a mountainside, but since it mostly consists of fertile plains, it can be considered as a flat area (Fig. 1). Fig1: Case study area Figure 2 shows the results of pixel-basedclassificationusing neural network method. In this method, network is trained using ground data. After training the network on the basis of ground truth estimator data, the estimation accuracy is about 88%. Fig. 2: The results ofclassification using neural network Following the calculation of the area under cultivation in agricultural lands and the type of crops, optimization is investigated using two scenarios (Figure 3). In the first scenario, reduction of water consumption and increased farmers’ income and in the second scenario,meeting domestic demandsto prevent capital outflow is considered. Fig3: Crop type and boundaries of agricultural lands In the first scenario, our priority is to reduce water consumption and increase farmers’ income. In this scenario, the goal is to select the type of crops according to the modeling constraints so that the crop type and water consumption are optimized. Figure 4 shows the proposed crop type. Fig4: The results of thefirst scenario Conclusion The present study used a combination of remote sensing and spatial information system to find a solution for optimization ofcultivation pattern through two different scenarios. First, land boundaries and types of crops were determinedusing pan-chromatic images and artificial intelligence. Then, two objective functions were developed to minimize water consumption and maximize income. Also, constraints such as crop type, periodicity constraints and domestic demand were modeled. Considering two objective functions, an algorithm was presented to optimize the cultivation pattern and the results were implemented in this algorithm. Results indicated that the difference between the first scenario which seeks to minimize water consumption and maximize farmers’ income and the second scenario which seeks tominimize water consumption and maximizedomestically demanded crops is relatively small. In both scenarios, the water use rate inAsadabad plain have decreased by about 1000 m3. In other words, in both scenarios there was a 50% reduction in water consumption. Moreover, if priority is given to meeting domestic demand, water consumption increase by about 3% and income decrease by about 3%. In future studies, owners of each agricultural land can be determined and each farmer’s incomecan be considered to further optimize crop cultivation.
Elahe Khesali; Mohammadreza Mobasheri
Abstract
Extended Abstract Introduction Frost causes a lot of damage to the agricultural sector every year.From the meteorological point of view, when the temperature drops below a certain value, frost occurs. This threshold may vary from one crop to the other. Not much research has been done to predict frost ...
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Extended Abstract Introduction Frost causes a lot of damage to the agricultural sector every year.From the meteorological point of view, when the temperature drops below a certain value, frost occurs. This threshold may vary from one crop to the other. Not much research has been done to predict frost using remote sensing technology. Most of the models used to predict frost have been provided by climatologists, geographers and meteorologists based on data collected at meteorological stations.The measurements at meteorological stations are at a point and the number of these stations are limited. Therefore, depending on the surface coverage and texture around the station, the air temperature would only be valid in certain and limited distance from the stations. On the other hand, satellite images have relatively acceptable spatial resolution specially for using in the environmental studies.This indicates the necessity of using remote sensing data in many occasions including frost prediction.This work tried to predict areas at risk of frost using the NEAT method in the state of Georgia, USA. For this purpose, the MODIS satellite data and the data collected in meteorological stations of AEMN network are used. Materials and Methods The State of Georgia, in the southern part of the United States between latitude of 30o31’ to 35o north, and longitude of 81o to 85o53’ west with an area of 154077 square kilometers, was chosen for this case study.The reason for choosing this region was merely because of accessibility and availability of surface collected data mostly in cultivating and agricultural zones. In this study, data collected in 10 AEMN stations from 2005 to 2015 were used for modeling and evaluation. Also, data collected in 68 stations of AEMN were used for evaluation of model for two different periods. The satellite images used in this study is collected by Moderate Resolution Imaging Spectroradiometer (MODIS) on board of Terra and Aqua platforms. The MODIS products used in this study consist of LST (MOD11 and MYD11), lifted index (MOD07 and MYD07), total precipitable water (MOD05 and MYD05), and normalized differential vegetation index (MOD13). Also, in this study, to estimate air temperature in each 1 by 1 km grid box, the method developed by Mobashari et al. (2018) was used. The method offered an accuracy of 2.33 °C and a correlation coefficient of 0.94. Khesali and Mobasheri, 2019 presented Near-surface Estimated Air Temperature (NEAT) model in which extrapolation coefficients for air temperature to the next hours are calculated. To increase the accuracy of the NEAT model, it was recalculated using AEMN data at Aqua and Tera passing times. The methodology in this study consists of the following steps. • Selection of study area and collecting temperature data from AEMN meteorological stations, • Reproducing NEAT model coefficients usinga set of AEMN data, • Evaluating NEAT equation using another set of AEMN data, • Receiving and preparation of MODIS products and calculation of air temperature at the passing time of Terra and Aqua, • Applying NEAT to the MODIS images, • Producing Frost map using temperatures estimated by NEAT • Evaluation of frost prediction accuracy Results and Discussion In order to implement the model, Two periods were selected: 3–9 December 2006 and 3–11 April 2007 in which severe crop damage across the southeastern United States has happened (Prabha and Hoogenboom, 2008). First, the NEAT model coefficients are calculated using the AEMN network data, and evaluated for air temperature extrapolation to the next hours. Then, the air temperature was extracted using MODIS products for Aqua and Terra night time sensors. Finally, the NEAT model was applied to the air temperature extracted from satellite images, and the nighttime temperature was predicted from approximately 22:30 pm to 7:30 am of next day at 15 minute intervals. Then in the extracted images the air temperature was classified into two degreeintervals. Areas with temperatures below zero degrees Celsius are considered frost zones. Data from 68 AEMN network stations were used for evaluation. Statistical parameters like RMSE and variations of User Accuracy and Overall Accuracy were analyzed over the night. The RMSE value for all data, which is 13,840, is estimated to be 2.5 degrees. This parameter has an increasing trend from the satellite passing time to 6 hours and varies from 0.1 to 2.5 degrees Celsius. The results show the effectiveness of the proposed model in frost prediction. Conclusion In this study, AEMN meteorological data and MODIS satellite images were used for frost prediction. The study area is located in the Georgia state in the southeast of the US. Using the Neat model, air temperature is extrapolated during night in 15 minute intervals. Air temperature maps for two periods of time are produced. The results and accuracy assessment parameters show the ability of the proposed model in air temperature prediction and its effectivenessin frost prediction
Ghaffar Fallahtabar
Volume 21, Issue 83 , November 2012, , Pages 108-112
Abstract
Researches indicate that around one third of lands are located in arid areas and Islamic republic of Iran is also located in arid and semi-arid area of the world. Apart from aridity, a significant part of the country, i.e. around 25 million hectares are wastelands. But beyond this most permanent, seasonal ...
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Researches indicate that around one third of lands are located in arid areas and Islamic republic of Iran is also located in arid and semi-arid area of the world. Apart from aridity, a significant part of the country, i.e. around 25 million hectares are wastelands. But beyond this most permanent, seasonal and temporal rivers and many important inland lakes have saltwater which worsen this sad situation. On the other hand, more water is extracted from groundwater sources to satisfy ever increasing demands. For example, 79837 million m3 water was extracted from ground water resources in 2002-2003 water year which compared to 2001-2002 water year has increased up to 2.5 percent. Inappropriate and wasteful use of groundwater also results in salinization of freshwater resources.
Southern provinces and cities bordering the desert have lots of saltwater. Water shortage and water saltiness along with salt salinization have even reached agricultural villages and lands are no longer profitable. Most aqueduct, especially those in southern parts of the country and those bordering the desert have dried. There were around 40000 aqueduct which reached 26307 in 2004. Many lands are now barren and desolate. Irregular and unplanned extraction of water from aqueducts, springs and freshwater resources by deep and semi-deep wells have decreased freshwater resources to a great degree and have gradually increased saltwater, water shortage crisis and drought crisis. This crisis is an alarm indicating a massive crisis of water shortage. Planners and authorities should see this crisis as an important religious and divine responsibility and try to find a compassionate and responsible solution. Before it is too late, they should manage and protect water resources, try to preserve rural agriculture and avoid wasting water and polluting its resources, which are shortly discussed in the present article.
Mehrdad Hoseini; Naser Maleki; Farrokh Matlabifar
Volume 19, Issue 74 , August 2010, , Pages 86-90
Abstract
One of the basic and most fundamental factors in the structure of the planet is the climate. There is no doubt that humans, living things and manifestations of life on the Earth are affected by climatic conditions and atmospheric phenomena. Inappropriate distribution of dry land and water, different ...
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One of the basic and most fundamental factors in the structure of the planet is the climate. There is no doubt that humans, living things and manifestations of life on the Earth are affected by climatic conditions and atmospheric phenomena. Inappropriate distribution of dry land and water, different latitudes, passage of atmospheric systems, heights and elevations covered by snow and ice, dry desert without water and grass, forests covered by trees,... In different regions of the planet, has created different climatic conditions, in such a way that in the broad geography of the world, atmospheric and hydrological hazards such as floods, storms, thunder and lightning, fatal cold, overwhelming heat, etc., annihilate thousands of humans and living organisms annually, or bring about many financial and environmental losses. In this regard, today's civilized human is releasing into the atmosphere millions of tons of carbon dioxide and toxic pollutants annually in order to gain industrial agriculture, so that greenhouse gases have formed a dark blanket on the ozone layer, and this harsh and fatal contamination has seriously threatened the lives of humans and living beings. Therefore, today's violent climate and humans have caused climatic changes in micro and macro climatic levels. Therefore, it is desirable and imperative to identify and investigate climatic changes and to prepare research plans in order to overcome the hydrological atmospheric hazards and to reduce and control pollution and human industrial toxicants.
Masoud Mo'ayyeri; Ali Jowzi Khomslouei
Volume 18, Issue 71 , November 2009, , Pages 20-25
Abstract
This paper summarizes the changes in gas reaction and climate tracing in the Holocene period (about 10,000 years ago), with respect to the four glacier periods of the past. The industrial era, which usually begins in the 18th century, is associated with increase in atmospheric greenhouse gases as a result ...
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This paper summarizes the changes in gas reaction and climate tracing in the Holocene period (about 10,000 years ago), with respect to the four glacier periods of the past. The industrial era, which usually begins in the 18th century, is associated with increase in atmospheric greenhouse gases as a result of fossil fuels and land use changes, and these are linked to an increase in the average temperature of the earth's surface during the last decade of the twenty-first century. However, the analyses carried out by Ruddiman, which take the Holocene era and the urban community into consideration, have unprecedentedly compared changes in atmospheric greenhouse gases with that of glacial records of the past four hundred thousand years. During this period, carbon dioxide (CO 2) and methane (CH4) have increased, and this increase is probably due to the beginning of agricultural activities and land clearing in Eurasia. These and other changes in land use resulting from agricultural and rural activities may cause poor climatic change and prevent land’s temperature fall, or maybe due to natural forces. Although the early evidence supports the theory of Ruddiman, forestry and agricultural activities during the period of urban community may have exerted an impact on the climate equal to at least eight thousand years of the past.
