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Open Access Article
1 - Study of Land Use Change Using Geographic Information Systems and Remote Sensing Techniques
Mehrdad Khanmohammadi Maryam Haghighi khomami Mohammad panahandeh Mahsa Abdoli LaktasaraeiIndeed, protected areas, national parks and biosphere reserves in general, are the natural heritage of each country. Therefore, knowledge of their changes plays an essential role in management of these areas. Remote sensing is one of the most advanced and effective tech MoreIndeed, protected areas, national parks and biosphere reserves in general, are the natural heritage of each country. Therefore, knowledge of their changes plays an essential role in management of these areas. Remote sensing is one of the most advanced and effective technology for monitoring environmental changes and resource management. The purpose of this research is to detect the land use /cover changes in Bojagh National Park in Guilan province during 2000-2017. For this purpose, the images of ETM+ sensor from the landsat 7 were taken in the year 2000 and the images of OLI sensor from the landsat 8 were taken in the year 2017. After applying the necessary preprocessing on the images, the training points were selected for each user class in sufficient number and with appropriate processing then, the land use / cover map was produced using the supervised classification method with maximum likelihood algorithm. Using the Overall accuracy test and Kappa coefficients, accuracy of the produced maps was determined. The results of the study indicated that the areas of the sea, grassland and the areas of the waterbody parts has decreased and the areas of the agricultural, marshland, man-made, woody and bare lands users show an increase during the study period. Manuscript profile -
Open Access Article
2 - Estimation of wheat area cultivation using Sentinel 2 satellite images (Case study: Sojasroud region, Khodabandeh city, Zanjan province)
Seyed Ahmad Seyed Ahmad Nadia Abbaszadeh Tehrani Milad JanalipourWheat is one of the strategic agricultural products which provides one of the most basic nutritional needs of human societies for Iran and the whole world. Having the right statistics and information of the lands under wheat cultivation and estimating the amount of thei MoreWheat is one of the strategic agricultural products which provides one of the most basic nutritional needs of human societies for Iran and the whole world. Having the right statistics and information of the lands under wheat cultivation and estimating the amount of their production in one crop year can help the planners of agriculture and industry to manage the production and consumption of the mentioned product as effectively as possible. One of the tools that can calculate the level of wheat cultivation in the shortest time and with low cost and appropriate accuracy is the science and technology of remote sensing. In the present study, using a supervised classification of images from several time of Sentinel 2, the area under wheat cultivation and its production rate for the 96-97 crop year has been estimated. Supervised classification with the overall accuracy of 80% and a kappa coefficient of 0.8 has acceptable and suitable results for the identification and separation of wheat from other agricultural crops. Manuscript profile -
Open Access Article
3 - Comparison of support vector machine and artificial neural network classification methods to produce landuse maps (Case study: Bojagh National Park)
Mahsa Abdoli Laktasaraei Maryam Haghighi khomamiNational parks and wildlife shelter are the most important natural heritages; therefore, knowing of quantitative and qualitative changes in their land use plays an essential role in the quality of these areas' management. various algorithms have been developed to classi MoreNational parks and wildlife shelter are the most important natural heritages; therefore, knowing of quantitative and qualitative changes in their land use plays an essential role in the quality of these areas' management. various algorithms have been developed to classify satellite imagery in remote sensing, selecting an appropriate classification algorithm is very important in achieving the accurate results. In this research, a more accurate algorithm was determined by comparing the classification accuracy of two artificial neural network and support vector machine algorithms, and it was used to examine the process of the land use changes. The present study was performed in Boujagh National Park, in the Guilan Province, during the years 2000 to 2017, using satellite imagery ETM and OLI of Landsat 7 and 8. The results of the research revealed that the support vector machine algorithm with overall accuracy and Kappa coefficient of 86.42 and 0.83 respectively for the year 2000 and, 90.65 and 0.88 for the year 2017, classified the satellite images more precisely, in comparison with the artificial neural network algorithm with overall accuracy and Kappa coefficient of 83.71 and 0.80 respectively for the year 2000 and overall accuracy and Kappa coefficient of 89.25 and 0.87 for the year 2017. Therefore, the land use maps of the support vector machine algorithm were used to determine the land use changes. The study of land use change by this method concluded that the areas of the waterbody, sea, grassland and agriculture have decreased and marshland, woody and bare lands classes showed an increase during the study period. Manuscript profile -
Open Access Article
4 - Determination of Potato Crop Cultivation in Hamedan Province, Using time series Satellite Images IRSP6
Ali shahbazi Loghman khodakrami kamran nasirahmadiThe aim of this study is to detect and quantify the cultivated area of potato fields in Hamadan Province using remote sensing methods and a time series of satellite photos. As a result, Awifs time-series imaging was used to determine the potato cropping area. For this p MoreThe aim of this study is to detect and quantify the cultivated area of potato fields in Hamadan Province using remote sensing methods and a time series of satellite photos. As a result, Awifs time-series imaging was used to determine the potato cropping area. For this purpose, pictures were taken at three different times when the potato plant turned green and yellow. Processing such as preparation, atmospheric and geometric correction, vegetation index, and unsupervised classification were performed on the images using appropriate training sites for supervised classification. Following the integration of these two layers, the studied area under the cropping map was prepared using the phase classification method. Additionally, by using the vegetation indices NDVI and SAVI, the area under cropping for the three main crop yields is determined first using the threshold level technique and in three temporal intervals. The kapa coefficient for potato under cropping area determined by phase classification, NDVI, and SAVI was 90, 87, and 85%, respectively. In 1998, the potato cropping area was determined to be 38740, 36728, and 36614 acres, respectively. This study clearly shows that the phase classification method and Awif data time series can be used to recognize and estimate potato under cropping area with acceptable precision and that vegetation indices distinguish potato under cropping area faster. Manuscript profile
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