International Journal of Research in Social Sciences
Year : 2017, Volume : 7, Issue : 5
First page : ( 17) Last page : ( 28)
Online ISSN : 2249-2496.

Predicting and assessing the biophysical environmental impacts of a proposed dam project

Madzimur Farai

Online published on 20 June, 2019.

Abstract

This paper demonstrates the application of Geographic Information System (GIS) and Remote sensing techniques in quantifying the biophysical impacts such as the actual area to be flooded by the dam, habitat loss and ecosystem productivity loss in terms of average biomass index and average diversity index during Environmental Impact Assessment (EIA). Quantifying biophysical attributes such as ecosystem productivity is critical considering the widespread concern about global warming and climate change which has led to an interest in reducing emissions. The major challenge in dam EIA is identifying and quantifying the ecological impacts of dam construction in cases where the proposed dam is to be situated in a forest which is a habitat for animals. In most cases the people carrying out EIA find it difficult to quantify ecological attributes such as the implications of vegetation clearance to ecosystem productivity and habitat loss. The major difficulty is to quantify and attach a value to each vegetation type that will be flooded by the proposed dam project. This study therefore presents how the spatial capabilities of GIS and remote sensing can be manipulated in order to quantify different ecological attributes such ecosystem productivity so as to determine the extent of the negative implications of the dam project. The paper also demonstrates the use of GIS operations such as the neighbourhood analysis in Integrated Land Water Information Systems (ILWIS) GIS to accurately estimate the area to be covered by water after the dam is constructed. Such knowledge is critical as it reveals the magnitude of the predicted impacts so as to develop appropriate mitigation strategies.

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Keywords

Environmental impact assessment, dam construction, GIS, Remote Sensing.

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