Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/287900
Title: A Geospatial Analysis of the Environmental Impacts Due to Broadening of Highway in Sikkim Himalaya
Researcher: Banerjee Polash
Guide(s): Pradhan Ratika and Ghose Mrinal Kanti
University: Sikkim Manipal University
Completed Date: 2020
Abstract: Since ancient times, the human civilization witnessed the growth and development of roads along with its own progress, prosperity and security. The pathway of primitive age is considered as the first road mark laid on the surface of the earth, which was later widened into a roadway and over the time the concept of highways came into existence with the increasing need for faster and longer transportation, etc.Construction, extension and broadening of highway in Sikkim Himalaya is an obvious outcome of the rise in tourism, industrialization and better connectivity to the main land India. Being a bordering state of China, the need for the same is magnified from the security point of view. However, its rich biodiversity, unique culture and mountainous landscape can be vulnerable to the externalities associated with such developments. Accordingly, the viability of such development projects need to be assessed to commensurate with the tolerance of environmental degradation. The broadening of National highway NH 10 in Sikkim since 2007 is a mixed bag of environmental gains and losses. The conventional Environmental Impact Assessment (EIA) of NH 10 broadening seems to be expensive and difficult due to the steep mountainous terrain, dense forests and lack of human resources. Moreover, such studies may undermine the spatial aspects of the impacts.In this Ph.D. dissertation, an attempt has been made to propose and develop a GIS-based Geospatial Environmental Impact Assessment Model (GEIAM) to complement the conventional EIA and overcome its imitations. GEIAM uses a wide set of spatial analysis techniques, experts opinion based multi-criteria decision making method and spatially explicit sensitivity analysis to predict spatiotemporal distribution of impacts. Several spatial sub-models are constructed for different environmental criteria viz. air quality, biodiversity value, landslide susceptibility, noise level, socioeconomic conditions and water quality.
Pagination: 
URI: http://hdl.handle.net/10603/287900
Appears in Departments:Department of Computer Science and Engineering

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