GIS Development, Nov 2008
Inference Network in Environmental Mapping
Anshu Gupta
Centre for Remote Sensing & GIS,
NIT, Bhopal, India
anshugupta20002001@gmail.com
Vivek Dey
Civil Engineering Department,
Indian Institute of Technology,
Kanpur, India
vivekde@gmail.com
Alok Choudhary
Head of Image Processing, M.P.
Council of Science & Technology,
Bhopal, India
alok@yahoo.com
Fig. 1: Location of wards under the study
Environmental quality assessment is essential for urban development. Rapid urbanisation
has made it all the more essential now than before. But there is dearth of appropriate
techniques to assess urban environment quality (UEQA). Here is a technique that is
feasible, flexible and valid.
UEQA requires environmental information in the form of air quality, noise quality, topography as
slope and aspect, vegetation quantity and quality of greenness, water quality, soil quality etc. This
work deals only with the first four qualities. Environmental information has the obvious spatial
character that can be addressed by geographical information system (GIS). For example, air
quality may vary for different land-use classes. Population density, as a socio-economic factor
involved in urban environmental quality evaluation (UEQE), also changes in the different spatial
unit.
Other environmental factors such as noise and green coverage also have spatial character. So,
while evaluating the UEQ, GIS provides a powerful tool to represent environmental information
in support of environmental evaluation (Dai, Lee et al. 2001). An important feature of GIS is its
ability to generate new information by integrating diverse datasets. The purpose of environmental
evaluation in this study is to represent environmental quality in the form of maps which combines
all information of each of the environmental factors.
STUDY AREA
Bhopal, the capital of Madhya Pradesh, is one of the fastest growing cities of India (Bhopal City
Development Plan under JNNURM). The city, also known as "the city of lakes", is losing its
beauty under the increasing pressures of urbanisation.
DATA AND MATERIAL
Spatial Data: Remote sensing satellite data used are - CARTOSAT- 1 PAN (2.5 m resolution) of
Bhopal municipal corporation area, dated January 2007 and IRS P-6 LISS IV MX (5.8 m
resolution) of Bhopal municipal corporation area, dated January 2007. High resolution images
(2.5 m) provide more details about the spatial features. However, multispectral images provide
more land cover information than panchromatic images, as each spectral waveband provides
specific information about land cover features. Fusion of multispectral and high spatial resolution
panchromatic data enhances the understanding of both spatial and spectral resolution of the
feature and also enhances the accuracy and visual interpretation (Jensen J. R.). Annual mean
concentration levels of air and noise pollution (ward wise and along four major traffic corridors)
for the year 2006 have been collected from Madhya Pradesh State Pollution Control Board, LEA
Associates South Asian Pvt. Ltd. and Egis BCEOM India Pvt. Ltd. Data obtained pertain to
spatially well distributed locations. These locations are considered as sample locations and data of
the complete area was obtained by spatial interpolation IDW using GIS.
METHODOLOGY
Environmental information for UEQE assessment is broken into smaller components or
indicators. Air quality, noise quality, topography, slope and aspects, vegetation as NDVI,
demography of the study area and land use have been evaluated as per their contribution towards
urban environmental pollution (UEP). The contribution of traffic has been given a special
attention in evaluating UEP. Road buffer (Kwang Hoon Chi, No-Wook Park, 2002) has been
created along major traffic corridors to consider the enhanced effect of traffic towards air and
noise pollution. Each of the smaller components has been shown at the topmost level in Figure 2.
The combination of indicators was carried out using the analytical hierarchical process and fuzzy
weights which involves the opinion of experts in urban pollution board, urban development,
meteorology and urban road and traffic development (John M., Martin Hale (2001)). Fuzzy
inference network has been established as shown in Figure 2 to incorporate all the environmental
information in a logical manner. Boolean inference network is similar but the difference lies in
the combination strategy.
Instead of fuzzy weights, Boolean weights of 0 and 1 are considered and Boolean algebraic sum
and Boolean OR are used to evaluate the final quality map (Figure 4). Threshold for the four
evaluation classes is determined by trial and error. Exact classes of few of the locations, used to
evaluate interpolated data, are evaluated by experts. Threshold, which classifies the training
location correctly with maximum accuracy, is chosen as threshold for both the Boolean and fuzzy
approach.
Fig. 2: Fuzzy Inference Network
Each component's effect in enhancing urban environmental pollution is evaluated in a spatial
raster layer format through GIS. Considering the raster data structure, every indicator is
considered as the individual layer in the fuzzy overlay operation. The value of each cell is the
score of the indicator considered (Chi, Park and Chung, 2002).
The implementation process of fuzzy multi-criteria evaluation in GIS through fuzzy inference
network includes three phases. Firstly, every bottom indicator of each component is overlaid
based on fuzzy operation, also called intermediate hypothesis. For example, in air pollution
criteria, the criterion consists of four indicators (SO2, NO2, SPM and CO) in wards as well as
along the four major traffic corridors. The first phase of evaluation is to overlay these four
indicators based on fuzzy operation (fuzzy algebraic sum). That is to say, it is a bottom to top
approach. Secondly, the fuzzy operation is carried out to overlay air pollution in wards and air
pollution along road (Fuzzy OR). Finally, the final
Table 1: Area Distribution
hypothesis performing the fuzzy overlay operation of environment pollution and physical
environment component to get the final quality map (fuzzy GAMMA). 'GAMMA' operator has
been used while applying FUZZY LOGIC technique to obtain the final output map. FUZZY
ALGEBRAIC SUM and FUZZY OR are used as intermediate hypothesis. The final criteria map
by BOOLEAN theory has been processed by using "ARITHMETIC SUM". The results so
obtained have been classified under four categories of pollution. These evaluation classes are
Low, Moderate, High and Critical.
(Fig. 3: Classified Final Quality Map by Fuzzy Approach)
Conventional Approach)
(Fig. 4: Final Quality Map by
DISCUSSION AND ANALYSIS
The major difference between the two maps is on the roadside pollution. From the map using
fuzzy approach, it is clearly seen that the areas along the major traffic corridors are in 'cyan',
indicating that these areas are highly polluted. This is not at all seen in the map using
conventional approach. With Fuzzy approach, the information on pollution is retained and clearly
reflected in the evaluation result for environmental pollution, whereas this information is lost with
the conventional (Boolean) approach during the process of evaluation. This is because the fuzzy
approach employs a set of logically evaluated weights to determine in what degree the component
belongs to one evaluation class.
Table 2: Study Area Covered in Each Class
Ambiguity resolution is more in fuzzy approach because of the continuous range of values
whereas in Boolean approach, it is a discrete integer value. This clearly suggests that fuzzy
approach will give more information about the pollution level than the Boolean approach which is
also evident from the higher percentage of area obtained by fuzzy approach in critical class.
VALIDATION
Validation of the obtained results is carried out by a field visit to a sample location other than the
sample locations used for map evaluation. Urban environmental quality at this sample location is
evaluated by the panel of
experts from urban pollution board, urban development, meteorology and urban road and traffic
development. Some of the major anomalies in Boolean and fuzzy results are illustrated along with
the reasons behind the results.
Fig. 5: Pollution along major roads
As encircled in Figure 6, the area labelled as A (Karariya Sajidabad), shows different behaviour
in fuzzy and Boolean approach. The anomaly in the observed area is that Boolean approach is
showing the area in the High zone while Fuzzy approach categorises it in Low and Moderate
class. The reason for the above difference is the land use. Upon a field visit to the area,
agricultural farms (vegetation) are found. The difference is also obtained around the area shown
in the rectangular box B (Nakkar Khana). Now observe the area covered under rectangle 'B'. The
area has well structured road network (VVIP road) due to which the traffic runs smoothly. But
after just around 0.5 - 1.0 km, the area, Peer Gate, comes in Critical class.
Fig. 6: Critical Condition in Old City of Bhopal
The Boolean approach is highly influenced by this area and shows the area around VVIP road in
critical class where as in fuzzy approach, the quality of environment decreases gradually from
high to critical class which reveals the continuous behaviour of fuzzy approach.
CONCLUSION
Inference network provides a flexible format in which at every stage, any new component can be
added to the network while continuing to maintain the logical format.
Evaluated final quality map with the available data has established the feasibility of the inference
network in urban environmental quality assessment. Further, the integration of fuzzy logic and
GIS through inference network in evaluating urban environmental quality gives better results than
the Boolean approach. The ability of fuzzy approach to quantify the ambiguity of complexity of
urban environmental quality has been established on comparison with the Boolean approach by
using the error matrix.
Error matrix shows the overall accuracy of 55 % of Boolean approach with respect to the fuzzy
approach. This concludes that conventional Boolean approach is insufficient in explaining the
urban environmental quality.