Eco-environmental Quality Evaluation Using Remote Sensing and Artificial Neural Network(Case Study: Tabriz-Rasht)

In this study, to evaluate the eco-environment value of 500 pixels around the city of Tabriz in the East Azarbaijan Province, Iran, as well as 500 pixels around the city of Rasht in Gilan Province, Iran, which have different climates, the Eco-environment Background Value index (EBV) has been investigated using soft computations and remote sensing tools to determine the eco-environment value of the areas. For modeling, indicators including vegetation index, soil wetness index, Land Surface Temperature (LST), and Digital Elevation Model (DEM) data collected using remote sensing tools as well as data on precipitation and temperature obtained using ground-based weather stations were exploited as input into the three-layer back propagation based artificial neural network (BPANN) model. The average of the data for the past 8 years for these indicators, once seasonally for four seasons and once annually for the regions under study around Tabriz and Rasht, entered the network. The results indicated a better performance of the network for Tabriz region in the spring with root mean square error (RMSE) = 0.0219 and R = 0.9961. It seems that the better network performance for Tabriz compared to Rasht could be due to the weakness of the remote sensing tool in examining areas like Gilan, which has a dense vegetation and high atmospheric humidity. It seems that the high vegetation density and high humidity impede proper reflection without deviation from the land surface and disrupts the reception of the required data.

Article Type:
Case Study
Iran Water Resources Research, Volume:15 Issue:3, 2019
218 - 233  
روش‌های دسترسی به متن این مطلب
اشتراک شخصی
در سایت عضو شوید و هزینه اشتراک یک‌ساله سایت به مبلغ 300,000ريال را پرداخت کنید. همزمان با برقراری دوره اشتراک بسته دانلود 100 مطلب نیز برای شما فعال خواهد شد!
اشتراک سازمانی
به کتابخانه دانشگاه یا محل کار خود پیشنهاد کنید تا اشتراک سازمانی این پایگاه را برای دسترسی همه کاربران به متن مطالب خریداری نمایند!