farzaneh hadadi; Davod Ashourloo; Alireza Shakiba; Aliakbar Matkan
Abstract
Climate change is one of the most important challenges facing mankind. This phenomenon has already had significant impacts on agricultural products in most parts of the world, especially arid and semiarid regions. Also, average temperature has risen in many regions in recent decades. Nowadays, in various ...
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Climate change is one of the most important challenges facing mankind. This phenomenon has already had significant impacts on agricultural products in most parts of the world, especially arid and semiarid regions. Also, average temperature has risen in many regions in recent decades. Nowadays, in various researches, remote sensing indices are used as one of the new methods in identifying climate change. One of the important indices of remote sensing is the phonological characteristics of vegetation, which in recent studies has shown great potential in identification and estimation of vegetation. In the present study, using the 5-day normalized vegetation index (NDVI) time series of NOAA-AVHRR images and plant phenology parameters, vegetation changes in rangelands and dryland areas of Lake Urmia Basin during 1984-2013 were investigated. Climatic temperature and precipitation data was obtained from the meteorological stations of Lake Urmia basin and was compared with the results of satellite images. The results of time series analysis over thirty years of statistical period in Lake Urmia basin showed that the beginning of the growing season in Oshnavieh, Saghez and Sarab started earlier in 2013 than in 1984. But in the Maragheh area it began later. The end of the growing season in Oshnaviyeh, Saghez and Takab has ended earlier. Also, the peak growth parameter in the above mentioned vegetation reached its maximum value earlier. The length of the growing season has been decreased in the cities of Oshnavieh, Maragheh and Saghez, respectively. The results of statistical analysis obtained from satellite images and climatic data showed that changes in phonological parameters are location dependent and also decreased and increased in cold nights and hot days at the beginning of the growing season, respectively. But at the end of the growing season, the warm days have increased. These changes increased the slope of the plant growth phenology curve at the time of plant aging and ultimately reduced the length of the growing season.
Elham Khodabandehloo; Mohsen Azadbakht; Soheil Radiom; Davood Ashourloo; Abas Alimohammadi
Abstract
Rapid increase of the world population growth and the demand for food security makes increasing yield as an essential strategy for solving the food supply problem. What is more, because of the restrictions in increasing crop cultivation areas and the decrease in some crops such as wheat in Iran, increasing ...
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Rapid increase of the world population growth and the demand for food security makes increasing yield as an essential strategy for solving the food supply problem. What is more, because of the restrictions in increasing crop cultivation areas and the decrease in some crops such as wheat in Iran, increasing the yield potential can be an effective way to respond to this requirement. Fusarium Head Blight (FHB) is one of the most important wheat diseases and for prediction FHB some methods have already been developed in the USA, Canada, Argentina and Brazil. As there is no model for predicting FHB in Iran, in this study, a method for predicting severity of FHB based on spatial analysis and using environmental parameters and meteorological data was developed for the Moghan, which is in the northwest of Iran. An Internet of Things (IoT) network was established in the study area for measurement of environmental data, including relative humidity, rainfall and air temperature for evaluating the developed model. Random Forests (RF) and extracted indices were used for predicting FHB severity and calculating the relative importance of the indices. We evaluated FHB for the period of 1389 to 1396 and the results show the effectiveness of the developed model and the capability of IoT and spatial analysis for predicting FHB.
