Mirhassan Miryaghoubzadeh; Seyed-Amin Khosravi
Abstract
Nowadays remote sensing is known as practical method for studying Land Use (LU)/Land Cover (LC) changes. Due to the vast area of agricultural lands, Barandouzchay Basin is one of the important watersheds among all of watersheds in Lake Urmia River Basin. In this study, in order to evaluate LU/LC change, ...
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Nowadays remote sensing is known as practical method for studying Land Use (LU)/Land Cover (LC) changes. Due to the vast area of agricultural lands, Barandouzchay Basin is one of the important watersheds among all of watersheds in Lake Urmia River Basin. In this study, in order to evaluate LU/LC change, Landsat-5 TM and Sentinel-2A satellite images were used from 2005 to 2016. The maximum likelihood classification method was used to prepare LU/LC maps. The results of overall accuracy and Kappa coefficient showed high accuracy of maximum likelihood classification method. In order to extract the change detection maps, image difference method was used. Results showed that orchard and nonproductive trees have been increased during 2010-2016 years in Barandouzchay Basin. In the years before 2010, trees were relocated by young trees in Barandouzchay Basin. Drylands and bare lands are classified in the 2005-2010 years which has been increased. The most land use change was related to urban and lowest change was related to rainfed area from 2005 to 2010 and the most land use change is related to bare lands and lowest rate is related to nonproductive tree area from 2010 to 2016.
Narges Ghasemiamin; Nasim Arman; Hossein Zeinivand
Abstract
Land use involves exploitation type of land for resolving human different needs. Land use changes is the result of interaction between human and affective factors on environment which is considered in spatial and temporal scale. Awareness of land use rates and its change in time is one of the most important ...
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Land use involves exploitation type of land for resolving human different needs. Land use changes is the result of interaction between human and affective factors on environment which is considered in spatial and temporal scale. Awareness of land use rates and its change in time is one of the most important factors in planning and management. By knowing rate of land use changes time scale, forecasting feature changes will be possible and do appropriate act. In this research, 2014 land use map was prepared by RS with Kappa coefficient of 0.88 and overall accuracy of 0.86 which has high accuracy. For investigating each effective factor on land use in CLUE-S model logistic regression was used and for assessment of logistic regression, ROC curve was used. After determination of demand ratio according to past changes, land use map of 2025 was prepared. Assessment of CLUE-S model showed its high accuracy (Kappa coefficient is 0.88). Also, the results demonstrated that the most land use change are related to forests and ranges to farmlands, as range and forest lands decreases 28.12 and 82.20 present respectively and farmlands increases 10.33 percent until 2025.
