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Published on: December 12, 2013
Geospatial passives for dynamic vegetation monitoring around thermal power plants
Fiza Faizi1, Khalid Mahmood2,3, Iqra Basit1
1Remote Sensing, GIS and Climate Research Lab (National Center of GIS and Space Applications), Center for Remote Sensing, University of the Punjab, Lahore, 54590, Pakistan.
Abstract:
As point sources of pollution, thermal power plants (TPPs) emanate hazardous gaseous and particulate matter that are of significant detriment to surrounding biological landscapes. To provide support to ecological conservation and resource management in developing countries, this study aims to establish a cost effective and robust geospatial methodology for dynamic vegetation monitoring of local pollution zones around TPPs using passive satellite-based indicators. The extent and severity of hazardous bio-influence around four TPPs is identified and monitored for a period of 5 years, using vegetation indices (VIs). High correlations of vegetation health with distance from TPPs have also been identified, signifying the hazardous impact of TPP emissions to surrounding vegetation. Variations in behavior of zones of high pollutant concentration are observed both in space and time, as a response to local seasonal weather, nature of fuel used in TPP, and type and areal coverage of vegetation around the power plants. Winter and Monsoon seasons have been identified to create favorable conditions for sustaining high pollution concentration around TPPs, and hence, the extent of hazardous bio-influence zones in these seasons is maximum. Moreover, oil-based power plant is revealed to be associated with large radial zones of degraded vegetation around it and, therefore, poses greater ecological hazard than gas-powered TPPs. The average bio influence zone measured for the test sites has been found to be 1660 m that ranges from 1600 to 1730 m for different power plants, explaining variable behavior of the used fuel and surrounding vegetation conditions. In this way, the study stresses upon the importance of geospatial data and analytical frameworks in reliable and economical monitoring of environmental pollution associated with anthropogenic sources, using passive environmental indices derived from remote data.
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