Environmental Impact Assessment (EIA) is a tool to study various impact to be occurred due to new development actions. Transportation Project are the projects which provides ease to the movement of vehicles. This Paper presents a case study for analysis of EIA for a transportation project. This Paper would provide a methodology which will allow transportation planers to make a cost effective coordination of environmental information and data management. The results assess the environmental vulnerability around the road and its impact on environment by integration the merits of GIS.

1. GUJARAT TECHNOLOGICAL
UNIVERSITY
BIRLA VISHWAKARMA
MAHAVIDHYALAYA
PAPER REVIEWED
1. ENVIROMENTAL IMPACT ASSESSMENT FOR TRANSPORTATION
PROJECTSCASE STUDY USING REMOTE-SENSING TECHNOLOGY,
GEOGRAPHIC INFORMATION SYSTEMS, AND SPATIAL MODELING
KUSHALKUMAR G PATEL
140080713009

2. INTRODUCTION
 Environmental Impact Assessment (EIA) is a tool to study various impact to be
occurred due to new development actions.
Transportation Project are the projects which provides ease to the movement of
vehicles.
This Paper presents a case study for analysis of EIA for a transportation project. This
Paper would provide a methodology which will allow transportation planers to make a
cost effective coordination of environmental information and data management.
The results assess the environmental vulnerability around the road and its impact on
environment by integration the merits of GIS.

3. METHODOLOGY

4. SYSTEM STRUCTURE

5. ASSESSMENT FACTORS

6. STEPS FOR USING GIS MODULE
ArcInfo software was used as a GIS platform to prepare the data processing and
preparation:
1. File format conversion: the software was used to convert the image data from the
Geo tiff format to the img format, which is the ERDAS IMAGINE format of the
picture.
2. Reprojection: gathered spatial (GIS) data layers were collected from different
sources and were projected with different projections. All data layers were
reprojected to the same geo- graphic reference to establish an implicit spatial
correspondence relationship between the layers, which facilitates subsequent
data processing and analysis.
3. Area of interest (AOI) clipping: the satellite image, aerial photographs, and spatial
(GIS) data layers covered different area extents. The ArcInfo sub module was used
to clip the area of the internet to be used for further analysis. The AOI was
identified by the boundary limits of Okaloosa County. For the clipping of the
raster data, the Spatial Analyst calculator was used.

7. DATA BANK USED
The Florida Geographic Data Library (FGDL) was selected as a mechanism for
distributing aerial photographs and spatial (GIS) data. The main reason for choosing it is
that this library has been designed carefully and is well managed. Following table shows
the list of GIS maps used from library.
Partial List of Data Layers Required
Description File name Type Source Extent
2000 Census blocks CENBLK Polygon USCB County
Drastic coverage for the surficial
aquifer system
AQDRSU Polygon FDEP County
FEMA Flood Insurance Rate Maps
1996
FEMA96 Polygon FEMA County
Historic bridges SHPO_BRIDGES Line BAR State
Strategic Habitat and Conservation
Areas 2000
SHCA Polygon FFWCC State

8. CRITERIA WEIGHTING MODULE
• For the measurement of criteria-related weights, pairwise comparisons
were utilized for practicality, operational efficiency, and reliability, as
well as acceptance by decision makers.
• The experts were asked to compare all criteria, thus creating a matrix of
values for each comparison pair. The average of all the experts’ matrices
was used to determine the main comparison matrix. Then the weights of
the vector were normalized to apply Yager’s methods (1977) for
calculating the required weights.
• Factors’ weights were calculated by the “BLZPACK” software that is an
implementation of the block Lanczos algorithm (Marques and Vasco
2000).

9. Pairwise Comparisons Matrix
CRITERIA Population
density
Arch LULC DRASTI
C
Foundation Eco.
model
Wetla
nd
Floo
d
Wildlif
e
Spec.
water
Milita
ry
Population
density
1 0.91 0.45 0.9 0.23 0.46 1.1 0.45 0.7 0.68 0.23
Arch 1.10 1 0.46 0.91 0.22 0.46 1.14 0.46 0.68 0.7 0.22
LULC 2.22 2.17 1 1.82 0.45 0.91 2.27 0.91 1.3 1.4 0.45
DRASTIC 1.11 1.10 0.55 1 0.22 0.45 1.1 0.45 0.6 0.7 0.23
Foundation 4.35 4.55 2.22 4.55 1 1.8 4.5 1.8 2.7 2.7 0.9
Eco. model 2.17 2.17 1.10 2.22 0.56 1 2.25 0.9 1.3 1.4 0.44
Wetland 0.91 0.88 0.44 0.91 0.22 0.44 1 0.4 0.5 0.6 0.17
Flood 2.22 2.17 1.10 2.22 0.56 1.11 2.50 1 1.3 1.4 0.44
Wildlife 1.43 1.47 0.77 1.67 0.37 0.77 2.00 0.77 1 0.9 0.29
Spec. water 1.47 1.43 0.71 1.43 0.37 0.71 1.67 0.71 1.11 1 0.29
Military 4.35 4.55 2.22 4.35 1.11 2.27 5.88 2.27 3.45 3.45 1

10. ENVIROMENTAL VULNERABLITY GRADE
MAP
Developing the environmental vulnerability grade map can be rep- resented by the
following:
(1) Reclassifying the GIS layers and the classified maps according to the regional weights
within the same criteria to develop criteria vulnerability maps;
(2) Rasterizing all the maps and GIS layers using the Spatial Analyst module, which is an
extension module within ArcInfo (1 m was chosen as the output cell size for the
layers); and
(3) The ERDAS IMAGINE Model Maker is used to construct a script model to combine the
criteria vulnerability maps and the criteria-related weights to develop the
environmental vulnerability map.
Figure below illustrates the concept of combining the criteria vulnerability maps and
the criteria-related weights to develop the environmental vulnerability map.

11. ENVIROMENTAL VULNERABLITY GRADE
MAP

12. CONCLUSION
• Using this methodology a standard analysis approach is formulated based on
the integration between GIS data and the remote sensing data.
• Using inputs by experts and stakeholders, evaluation criteria and the relative
importance of these criteria is determined and the integrated data & the
relative weights and criteria are used to arrive at a measure for the
environment impact of the project.
• With a focus on sustainable planning, urban and regional planners can better
explore long-term impacts caused by growth and revitalization of urban,
suburban, and rural communities and the regions in which they are located.
• In general, this methodological framework is expected to improve the
quality of the decision- making process and planning for built-environment
projects regarding the concerns for environmental impacts.