فصلنامه سنجش از دور و GIS ایران
سال سیزدهم شماره 4 (پیاپی 52، زمستان 1400)

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.

From the beginning of the Earth until now, humans have affected their environment more than anyother creature. With increasing population, water and soil constraints, and climate change, foodsupply has faced serious challenges. Among agricultural products, wheat is one of the most widelyused strategic crops in Iran and many countries in the world, which can be grown in hot and dryclimates with good yields. In Iran, about 10% of the demand for agricultural products is suppliedfrom Fars province. Fars province also has the second rank of wheat production among the provincesof Iran. This study intends to evaluate the suitability of lands in this province for dryland wheatcultivation. In the first step, climatic, soil and topographic data in the area of Fars province arereviewed and analyzed. In the next step, the information layers are entered into the GIS software andthe suitability map of wheat cultivation lands is determined using the VIKOR multi-criteria decisionanalysis method. The results of this study have shown that about 32% of the Fars province lands is inthe first and second rank of land suitability for wheat cultivation. Also, the distribution of suitableareas for wheat cultivation is higher in the western and northwestern regions of Fars province.

The expansion of urbanization and the increase of population in metropolises and the growth ofindustrial activities of cities, It has caused changes in urban area climate. One result of these changesis the city's heat islands. The city of Mashhad has also grown rapidly in recent years. This studyinvestigates the heat/cold island of Mashhad metropolis based on the background climate in order toidentify its spatiotemporal behavior. For this purpose The MODIS Terra and Aqua land surfacetemperature (LST) data were obtained and the heat island was examined accordingly. A new wasused to measure the heat island. In this method, Modis land use data was used to determine the urbanand suburban boundaries as well as to determine the land use type of the study area. The backgroundclimate was determined based on Far-side temperature and the representative non urban area wasselected based on the most frequent temperature and the heat island was calculated. Survey ofheat/cold island in the daily period showed that during the day the average temperature of city islower than non urbun temperature and at night is higher. Also the seasonal survey of heat island/couldisland of Mashhad metropolitan shows that daily cold island is the highest during the warm seasonsand lowest in the cold seasons and the seasonal variability of nightly heat island is less than the dailycold island. The core of the daily cold island is located between the Haram and the Shahid FehmidahSquare towards the western area of Mashhad. The day time cold island matches the areas of the citywith high vegetation coverage. The core of the nightly heat island is consistent with the old textureand dense area around the Haram towards the northwest of the city. The heat/cold island intensity isalso directly related to the wind speed. The role of land use in intensifying or reducing the intensity ofthe heat island of Mashhad is well seen. In the development of the city, more attention can be paid tothe use of urban land use in order to moderate the temperature of the city.

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.

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.

About 80% of world transportation happens at sea. Therefore the safety of vessels, in particularduring vessels’ movement, is crucially important. As different contextual parameters affect vessels’movement, selecting optimal contextual parameters is one of the main changes in vessels’ Context-Aware movement analysis. Toward this end, a Long Short-Term Memory (LSTM) network is usedfor wrapper feature selection to identify optimal contextual parameters for vessels’ movementprediction. To do this, the Automatic Identification System (AIS) dataset from the eastern coast of theUnited States of America collected from December 2017 is used. All possible combinations of threecontextual parameters, including speed, course and vessels’ presence probability in different positionsat sea, were evaluated using the wrapper method in the LSTM network. In all evaluations, 70% ofdata was used for training and the remaining for cross-validation. The results selected speed andpresence probability as optimal contextual parameters for vessel movement prediction. The modeltrained with optimal contextual parameters is 26.98% more accurate than a model trained with allavailable contextual parameters and 16.14% better than a model without contextual parameters.Therefore, selecting optimal parameters from available contextual parameters can help improve theaccuracy of vessels’ predictions. Keywords: Context-Aware, Long Short-Term Memory, AutomaticIdentification System, wrapper, Movement prediction, Context.

Due to the scarcity and crisis of water resources, the issue of optimal use and management of it is ofparticular importance. Improper pattern of water consumption in different areas of a city can be one ofthe cases that cause water crisis in a city. Therefore, there is necessary to apply methods in order toidentify consumption patterns in different areas of the city. The purpose of this study is to investigatethe spatial pattern of water consumption in Qom city using spatial autocorrelation techniques. For thisreason, the consumption of 117 neighborhoods of Qom city during 2017 was collected and theaverage household water consumption for each neighborhood was calculated. Moran index was usedto identify the type of consumption pattern and local Moran index and hot spot technique were usedfor spatial distribution of the consumption pattern. The results of spatial autocorrelation showed thatthe largest cluster pattern of water consumption in Qom city occurred in summer with the value ofMoran index (I = 0.24). Also, the highest significance of the index (z = 7.02) was observed in thisseason. In both local and hot spot analysis, it was observed that high consumption has a high clusterpattern compared to low consumption. Spatially, high consumption clusters were observed in thecentral and western neighborhoods of the city and low consumption clusters were observed in thesouthern, eastern and northern neighborhoods of the city. Temporally, high consumption clusters wereobserved in central and western neighborhoods in summer and winter, respectively and lowconsumption clusters were observed in cold seasons.