6668

6219

كارشناسي ارشد

مرتعداري

اصفهان: دانشگاه صنعتي اصفهان، دانشكده منابع طبيعي

1390

چهارده، 123ص.: مصور، جدول، نمودار

ص.ع. به فارسي و انگليسي

جمال الدين خواجه الدين

رضا جعفري

13/2/91

حسين خادمي، مجيد ايرواني

1396/10/06

كتابنامه

منابع طبيعي

مهندسي منابع طبيعي

ID6219

به فارسي و انگليسي: قابل رويت در نسخه ديجيتالي

124 Evaluation of vegetation function in carbon sequestration using remote sensing Hossein Purghaumi Email Hpurghaumi@gmail com 2 1 2012 Department of Natural Resources Isfahan University of Technology Degree M Sc Language Farsi Dr Khajedin Email Khaajedin@Gmail comAbstract There are concerns about the level of carbon dioxide entering the atmosphere and its impacts on climate is growing day by day Carbon sequestration CS in vegetation biomass and soils is the simplest and most practical economical solution to reduce carbon emission Rangelands are served as one of the most important arid ecosystems for CS So far no study has been done about using remote sensing technology to assess the rangeland carbon sequestration in our country Iran This technology can be used to mapp soil and vegetation organic carbon OC Hence IRS P6 AWiFS sensor data was selected and geometric correction was applied to the images with 49 ground control points with RMSe less than 0 3 pixel The study area was located in Dehaghan region in the southern part of Isfahan province Field data was collected using stratified random sampling method Soil samples were taken from two depths 0 15 and 15 30 cm at each point Bulk density of soil samples and soil organic carbon SOC were determined by Hunk method and wlaky Black method respectively Direct method was used for aboveground biomass estimation and Allometric equation was selected for assessing biomass tree species To estimate the underground biomass cylinder method was applied The combustion method was applied to determine the conversion ratio of plant biomass to OC In order to map land use cover and create false color composite FCC images the supervised classification with a maximum likelihood algorithm and optimum index factor OIF were carried out recpectively Land use map in five categories including agricultural lands orchards rangelands outcrops and urban area were extracted This overall accuracy of the classification was 81 78 and overall Kappa value was 0 75 Multivariate regression model forward was established between field and image data for mapping vegetation using vegetation indices NDVI SAVI RVI in SPSS 18 software According to the results of regression analysis among the indices NDVI had the highest relationship thus was selected for vegetation mapping in the study area In order to provide continuous mapping of OC in different soil depths and below and above ground biomass the multiple regression model was applied In all produced maps of SOC in the study area using regression models the second band red band had the highest negative correlation and was selected for further analysis The findings of this study indicate that the CS has a negative relationship with increasing soil depth the southwestern part of the study area is in a better rangeland condition therefore all the maps had higher CS values than other parts of the region

جمال الدين خواجه الدين

رضا جعفري

حسين خادمي، مجيد ايرواني