| Environmental & Energy Engineering |
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| Environmental impact prediction using remote sensing images |
| Pezhman ROUDGARMI, Masoud MONAVARI, Jahangir FEGHHI, Jafar NOURI, Nematollah KHORASANI |
| Tehran Agricultural and Natural Resources Research Center, Agricultural Research and Education Organization (AREO), Tehran, Iran; Department of Environmental Management, Graduate School of the Environment and Energy, Science and Research Campus, IAU, Tehran, Iran; Department of Environmental Science, Graduate School of the Environment and Energy, Science and Research Campus, IAU, Tehran, Iran; Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, Iran; Department of Environmental Health Engineering, School of Public Health and Center for Environmental Research, Medical Science, University of Tehran, Tehran, Iran; Department of Environmental Science, Faculty of Natural Resources, University of Tehran, Karaj, Iran |
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Abstract Environmental impact prediction is an important step in many environmental studies. A wide variety of methods have been developed in this concern. During this study, remote sensing images were used for environmental impact prediction in Robatkarim area, Iran, during the years of 2005~2007. It was assumed that environmental impact could be predicted using time series satellite imageries. Natural vegetation cover was chosen as a main environmental element and a case study. Environmental impacts of the regional development on natural vegetation of the area were investigated considering the changes occurred on the extent of natural vegetation cover and the amount of biomass. Vegetation data, land use and land cover classes (as activity factors) within several years were prepared using satellite images. The amount of biomass was measured by Soil-adjusted Vegetation Index (SAVI) and Normalized Difference Vegetation Index (NDVI) based on satellite images. The resulted biomass estimates were tested by the paired samples t-test method. No significant difference was observed between the average biomass of estimated and control samples at the 5% significance level. Finally, regression models were used for the environmental impacts prediction. All obtained regression models for prediction of impacts on natural vegetation cover show values over 0.9 for both correlation coefficient and R-squared. According to the resulted methodology, the prediction models of projects and plans impacts can also be developed for other environmental elements which may be derived using time series remote sensing images.
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Received: 20 October 2007
Published: 27 January 2008
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