2017
DOI: 10.1007/s10708-017-9819-2
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Analyzing spatiotemporal land use/cover dynamic using remote sensing imagery and GIS techniques case: Kan basin of Iran

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Cited by 22 publications
(10 citation statements)
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“…In addition, remote sensing techniques were employed to monitor changes in agricultural and urban expansion from 1975 to 2018, and urban and agricultural sprawl in the study area for that time period was mapped. Urban and agricultural areas are typically mapped from digital remotely sensed data [ 48 , 49 , 50 , 51 ]. Lillesand and Kiefer [ 52 ] indicated that the main objective of the image classification procedure is to automatically categorize all pixels in images that relate to urban areas.…”
Section: Data and Methodology Usedmentioning
confidence: 99%
“…In addition, remote sensing techniques were employed to monitor changes in agricultural and urban expansion from 1975 to 2018, and urban and agricultural sprawl in the study area for that time period was mapped. Urban and agricultural areas are typically mapped from digital remotely sensed data [ 48 , 49 , 50 , 51 ]. Lillesand and Kiefer [ 52 ] indicated that the main objective of the image classification procedure is to automatically categorize all pixels in images that relate to urban areas.…”
Section: Data and Methodology Usedmentioning
confidence: 99%
“…Also, significant changes were noticed in the LULC with 3% increase soil crust, and 2.43%, 0.6%, 0.55% and 0.22% decrease in vegetation cover, built-up area, bare land and water body respectively. Likewise, in [28] study the LULC of Kan basin from 2000 to 2016. Supervised classification of (MLA) method and Change Detection Analysis were used.…”
Section: Empirical Literature Reviewmentioning
confidence: 99%
“…This abstract seeks to delve into the intricate fusion of machine learning and GIS, elucidating their collaborative potential in extracting nuanced insights from spatial data, streamlining analytical workflows, and augmenting predictive modeling capabilities. By harnessing a diverse array of machine learning algorithms, including neural networks, random forests, and support vector machines, GIS practitioners can effectively tackle complex spatial challenges across a myriad of domains [1][2][3][4][5].…”
Section: Introductionmentioning
confidence: 99%