4587

4315

كارشناسي ارشد

محيط زيست

اصفهان: دانشگاه صنعتي اصفهان ، دانشكده منابع طبيعي

1387

سيزده ،121 ، [II] ،ص: مصور ، جدول ، نمودار

ص. ع. به: فارسي وانگليسي

عليرضا سفيانيان ، جمال الدين خواجه الدين

حميد رضا ضيائي

88/5/13

سعيد سلطاني ، رضا جعفري

مهندسي منابع طبيعي

ID4315

به فارسي و انگليسي : قابل رويت در نسخه ديجيتال

Land Cover Change Detection of Isfahan Using Remote Sensing and GIS Samereh Falahatkar Email address S7falahatkar@na iut ac ir 14 2 2009 Department of natural resource Isfahan University of Technology Isfahan 84156 83111 Iran Degree M Sc Language Farsi Ali Reza Soffianian Email address Soffianian@cc iut ac ir Sayed Jamaledin Khajedin Email address Khajedin@cc iut ac irAbstractIn recent years remote sensing data and geographic information system GIS have been widely applied inidentifying and analyzing land use and land cover change Accurate change detection of earth s surfacesprovides better understanding of relationships and interactions between human and natural phenomena forprefect resource management There are a lot of methods for land cover change detection such as imagerationing image difference change vector analysis image regression composite analysis and post classification Post classification is one of the most effective change detection methods The objective ofstudy is change detection of Isfahan land cover from 1951 to 2006 Aerial photos at a scale of 1 50000 in1955 and MSS TM and ETM images of LandSat satellite in 1972 1990 and 2001 respectively were used inthe current study to produce land cover maps and study trend of changes in various years The predictive landcover map by CA Markov for 2006 was used for completing the study period For studying this research allof the aerial photos and satellite images were rectified by using first degree polynomial equation and nearestneighbor sampling Aerial photos were interpreted visually by using color tone pattern shape location andvarious phenomena of area Each Landsat image was enhanced using linear contrast stretching and histogramequalization to improve the image To make the best false color composite we used Optimal Index Factor OIF to identify best bands with minimum correlation and maximum variance RMSE of aerial photos wasin a range of 0 1 to 0 45 pixels The resultant root mean squared error of MSS TM and ETM images werefound 0 73 0 68 and 0 6 pixel respectively To classify images hybrid method which is a combination ofunsupervised and supervised classification was used For separating of some of land cover layers we usedNDVI index and principle component analysis by appropriate threshold Finally land cover maps wereprovided whit 5 classes that including mountain barren land urban green cover and river Error matrix wascalculated for accuracy assessment of all maps Kappa coefficient of produced land cover maps from aerialphotos and MSS TM and ETM images was 0 98 0 90 0 93 and 0 92 respectively Therefore changes

عليرضا سفيانيان ، جمال الدين خواجه الدين

حميد رضا ضيائي

سعيد سلطاني ، رضا جعفري