L. Khodakarami , A. Soffianian,
Volume 16, Issue 59 (4-2012)
Abstract
Precision farming aims to optimize field-level management by providing information on production rate, crop needs, nutrients, pest/disease control, environmental contamination, timing of field practices, soil organic matter and irrigation. Remote sensing and GIS have made huge impacts on agricultural industry by monitoring and managing agricultural lands. Using vegetation indices have been widely used for quantifying net annual production on different scales. The aim of this study was to find a rapid method with acceptable precision for the identification and classification of agricultural lands under cultivation (wheat and barley, alfalfa and potatoes). We used multi-temporal AWiFS data and applied Boolean logic and unsupervised classification.
Results indicated that Boolean logic approach had a higher accuracy and precision in comparison to unsupervised classification, although it is more complicated and time consuming.
S. Youneszadeh Jalili, M. Kamali, P. Daneshkar Arasteh,
Volume 20, Issue 78 (1-2017)
Abstract
Integrated management of watershed basins depends on deep knowledge of basic concepts such as the arrangement of lands and their uses. Location and distribution of agricultural land use help to balance water resources in the watershed basins. In this research with the help of satellite images of Landsat 5 and 8, and the method of maximum likelihood classification algorithm, land use types of water, barren areas and salt lands, and irrigated agriculture were studied in the Urmia watershed in the years 2010 and 2013.Then applications of modis images and product Urmia watershed land cover for years 2010 and 2012 were compared and finally modis and Landsat land covers in 2010 were compared. Results showed that the area of irrigated farmlands of Urmia basin has increased in the years between 2010 and 2013; while, the water zone has declined. Comparison between modis and landsat in 2010 showed that modis can estimate irrigated lands and water zone better than barren areas. The kappa coefficient for years 2010 and 2013 in Landsat images are 0/77 and 0/87, respectively.
