7.         RESULTS AND DISCUSSION

 7.1       STUDY AREA 1: THE BANGALORE - MYSORE HIGHWAY

 7.1.1.   Image Analyses

The built-up area for 1972 was extracted from the digitised toposheets and is shown in Figure 5. The villagewise built-up area was computed by overlaying the layer with village boundaries (taluk map with village boundaries) and added to the attribute database for further analyses. 

LISS III sensor data obtained from the NRSA in three bands, viz., Band 2 (green), Band 3 (red) and Band 4 (near infrared), were used to create a False Colour Composite (FCC). Training polygons (with their co-ordinates) were chosen from the composite image and corresponding attribute data was obtained in the field for these polygons (using GPS). Based on these signatures, corresponding to various land features, image classification was done using Guassian Maximum Likelihood Classifier. From the original classification of land-use of 16 categories the image was reclassified to four broader categories as vegetation, water bodies, open land, and built-up. The built-up theme identified from the image is shown in Figure 6.  

From the classified image the area under the built-up theme was computed. Area under villagewise built-up theme in the study area was also computed by overlaying a vector layer with village boundaries and tabulated accordingly for further analyses. 

The built-up area computed for temporal data indicated that there was 194% increase in the built-up area from the seventies to late nineties. A more detailed investigation of the distribution of the built-up (Table 2) revealed that the change is higher as the proximity to Bangalore increases. The Bangalore North - South segment had the highest increase in built-up area while it was least in Srirangapatna - Mysore segment with 128%. It can also be observed that there is a declining trend in the change in built-up area as one moves from Bangalore towards Mysore.  

Table 2: Built-up Area and Shannon's Entropy for the Study Area 

Figure 5: Built-up area in the Bangalore - Mysore segment in 1972

Figure 6: Built-up area in the Bangalore - Mysore segment in 1998

 i. Shannon's Entropy

Shannon's entropy was computed for villagewise built-up area, wherein each village was considered as an individual zone (n = total number of villages). This revealed that the distribution of built-up in the region in 1972 was slightly dispersed than in 1999. However, the degree of dispersion has come down marginally and that distribution is less dispersed or there is the less presence of sprawl when the entire stretch is considered. The values obtained ranges from 2.658 (in 1972) to 2.556 (in 1998) and log n for this region is 4.477. These are higher than the half way mark of log n (that is 2.238) and show some degree of dispersion of built-up in the region. This non-uniform dispersed growth along the road connecting Bangalore and Mysore necessitated further detailed investigations for identifying the pockets of higher growth. 

Detailed investigation on the phenomenon was done in the next phase by dividing the study area in to segments / zones. The entire segment of Bangalore - Mysore was split in to two as Bangalore - Ramanagaram and Channapatna - Mysore segments. The percentage built-up change and entropy was calculated for these two segments. The Bangalore - Ramanagaram segment had a higher value of percentage built-up change (330%) than the Channapatna - Mysore segment (181%). The entropy values for the Bangalore - Ramanagaram segment ranges from 2.690 (for 1972) to 2.699 (for 1998) while log n for this region is 3.583 suggesting a similar trend. In the Channapatna - Mysore segment the entropy value ranged from 2.320 (for 1972) to 2.083 (for 1998) and log n is 3.951. This analysis reveals that the distribution is more dispersed in Bangalore - Ramanagaram segment compared to the later. 

On further division of the two segments into four to identify where the actual sprawl is occurring, segments such as, Bangalore North - South, Ramanagaram - Channapatna, Maddur - Mandya and Srirangapatna - Mysore were obtained. The results of these (Table 2) clearly indicated that there was more dispersed distribution of built-up in the region closer to Bangalore and this decreased as the proximity to the city increased. The Bangalore North – South taluks had the highest increase in terms of the percentage built-up change and the entropy values also showed that there was more dispersion in this taluk, thus indicating higher sprawl in this region. Higher sprawl due to the proximity of Bangalore is observed till Channapatna - Ramanagaram segment and declines towards Mandya. It also infers that in the Mandya - Maddur segment the distribution was slightly compact with radial sprawl. But in the Srirangapatna - Mysore segment the value of entropy showed marginal change in entropy. Here, the degree of sprawl is not as severe as that in case of other segments, but the marginal increase in entropy value certainly indicates the possibility of increasing sprawl due to enhanced economic activities at Mysore. 

With the results of the Shannon's entropy indicating that regions nearer to Bangalore city had more degree of sprawl, it was decided to work out and understand the patterns of growth in the regions surrounding Bangalore city, in all directions. It was seen that Bangalore was sprawling in radial direction from the city centre and linear growth was noticed along all five major roads connecting the city - spreading as five arms stretched outwards (Figure 7). The space between the arms or the major roads acts as the sinks for city development. Further, it is seen that the development occurred around the ring roads that connected these major roads.

Figure 7: FCC of Bangalore city showing radial pattern of growth from the city centre and linear pattern along the highways

Map density values are computed by dividing number of built up pixels to the total number of pixels in a kernel. This when applied to a classified satellite image converts land cover classes to density classes, which is given in Figure 8. Depending on the density levels, it could be further grouped as low, medium and high density (Figure 9). The relative share of each category was computed (area and percentage).  

Table 3: Different Densities of Built-up and their Area 

High density of built-up would refer to clustered or more compact nature of the built-up theme, while medium density would refer to relatively lesser compact built-up and low density referred to loosely or sparsely found built-up. The percentage of high-density built-up area was only 13.08 % as against 22.81 % of medium density and as much as 64.11 % of low density built-up (Table 3). This revealed that more compact or highly dense built-up was a smaller amount and more dispersed or least dense built-up was larger in the study area. An important inference that could be drawn out of this was that high and medium density was found all along the highways along with the city centres. Most of the high density was found to be within and closer to the towns and cities. The medium density was also found mostly along the city periphery and on the highways. The distribution of the low density was the maximum in the study area and this could be inferred as the higher dispersion of the built-up in the study area. This further substantiates the results of Shannon's entropy, which revealed a dispersion of the built-up theme in the study area.

7.2       STUDY AREA 2 - TIRUCHIRAPALLI - TANJAVORE – KUMBAKONAM – Thiruvarur HIGHWAY

 7.2.1.   Image Analyses

The built-up area for 1972 was extracted from the digitised toposheets and is shown in Figure 10. The standard image processing techniques such as, image extraction, rectification, restoration, and classification were applied in the current study. The image obtained from the NRSA in three bands, viz., Band 2 (green), Band 3 (red) and Band 4 (near infrared), were used to create a False Colour Composite (FCC) as shown in Figure 11. Based on the histogram, corresponding to various peaks, the number of clusters for image classification was done using Guassian Maximum Likelihood Classifier and the built-up theme identified in the classified image is given in Figure 12. The built-up area computed for temporal data indicated that there was 133.93 % increase in the built-up area from the seventies to late nineties (Table 4).

Table 4: Details of Built-up Area for 1972 and 1999

Figure 10: Built-up Area of the Region along with the Road Network for 1972

Figure 11: False Colour Composite of the Region

Figure 12: Built-up Area of the Region for 1999

7.3       STUDY AREA 3: UDUPI - MANGALORE HIGHWAY

 7.3.1.   Image Analyses for LANDSAT TM Image of 1987

The standard image processing techniques such as, image extraction, rectification, restoration, and classification were applied to the current image. The Landsat TM 5 data supplied by NRSA were extracted into the three bands, viz., Green, Red and Near-infrared. These were used to create a False Colour Composite (FCC) as shown in Figure 13. Training polygons were created along with corresponding attribute data obtained in the field using GPS and verified with the composite image. Based on these signatures, corresponding to various land features, image classification was done using Guassian Maximum Likelihood Classifier. The classified image showing the built-up of 1987 and 1999 are given in Figure 14 and 15.

 7.3.2.   Image Analyses for IRS LISS III Image of 1999

The standard image processing techniques such as, image extraction, rectification, restoration, and classification were applied in the current study. The image obtained from the NRSA in three bands, viz., Band 2 (green), Band 3 (red) and Band 4 (near infrared), were used to create a False Colour Composite (FCC) as shown in Figure 16. Training polygons were chosen from the composite image and corresponding attribute data was obtained in the field using GPS. Based on these signatures, corresponding to various land features, image classification was done using Guassian Maximum Likelihood Classifier and the classified image is given in Figure 17.

 7.3.3.   Population Growth and Built-up Area

The rate of development of land in Udupi - Mangalore region, is far outstripping the rate of population growth. This implies that the land is consumed at excessive rates and probably in unnecessary amounts as well. The built-up area computed for temporal data indicated that there was 107.52 % increase in the built-up area from 1972 to 1987 (Table 5). While the percentage increase from 1987 to 1999 was only 18 %. However the cumulative increase for nearly three decades was 145.68 %. Between 1972 and 1999, population in the region grew by about 54% (Census of India, 1971; 1981; and 1991) while the amount of developed land grew by about 146%, or nearly three times the rate of population growth (Figure 18). This means that the per capita consumption of land has increased markedly over three decades.

 Table 5: Details of Built-up Area for 1972, 1987 and 1999 

 The per capita land consumption refers to utilisation of all lands for development initiatives like the commercial, industrial, educational, and recreational establishments along with the residential establishments per person. Since most of the initiatives pave way for creation of jobs and subsequently help in earning livelihoods, the development of land is seen as a direct consequence of region’s economic development and hence one can conclude that the per capita land consumption is inclusive of all the associated land development.

Figure 18: Rates of Growth in Population and Built-up from 1971 - 1999

 7.3.4.   Metrics of Urban Sprawl

Characterising pattern involves its detection, quantifying with appropriate scales and summarising it statistically. There are scores of metrics now available to describe landscape pattern, but there are still only two major components viz., composition and structure, and only a few aspects of each of these. The landscape pattern metrics are used in studying forest patches (Trani and Giles, 1999; Civco, et al., 2002). Most of the indices are correlated among themselves, because there are only a few primary measurements that can be made from patches (patch type, area, edge, and neighbour type), and all metrics are then derived from these primary measures. The landscape metrics applied to analyse the built-up theme for the current study is discussed next.  

The Shannon's entropy (Yeh and Li, 2001) was computed to detect the urban sprawl phenomenon. This value ranges from 0 to log n, indicating very compact distribution for values closer to 0. The values closer to log n indicates that the distribution is very dispersed. Larger value of entropy reveals the occurrence of urban sprawl. Table 5 shows the built-up area, population and Shannon's entropy for 1972 and 1999. 

Shannon's entropy was calculated from the built-up area for each village wherein each village was considered as an individual zone (n = total number of villages). From the Shannon's entropy calculation, it revealed that the distribution of built-up in the region in 1972 was more dispersed than in 1999. However the degree of dispersion has come down marginally and that distribution is predominantly dispersed or there is the presence of sprawl. The values obtained here being 1.7673 in 1972 to 1.673 in 1999, are closer to the upper limit of log n, i.e. 1.914, thus showing the degree of dispersion of built-up in the region.

Table 6: Built-up Area, Population and Shannon's Entropy for the Study Area 

 ii. Patchiness

Patchiness or NDC (Number of Different Classes) is the measurement of the density of patches of all types or number of clusters within the n x n window. In other words, it is a measure of the number of heterogeneous polygons over a particular area. Greater the patchiness more heterogeneous is the landscape (Murphy, 1985). In this case the density of patches among different categories was computed for a 3x3 window.

 Table 7: Percentage Distributions of Patchiness 

Map density values are computed by dividing number of built up pixels to the total number of pixels in a kernel. This when applied to a classified satellite image converts land cover classes to density classes, which is given in Figure 20. Depending on the density levels, it could be further grouped as low, medium and high density (Figure 21). Based on this, relative share of each categories were computed (area and percentage). This enabled in identifying different urban growth centres and subsequently correlating the results with Shannon's entropy for identifying the regions of high dispersion. 

The computation of built-up density gave the distribution of the high, medium and low-density built-up clusters in the study area. High density of built-up would refer to clustered or more compact nature of the built-up theme, while medium density would refer to relatively lesser compact built-up and low density referred to loosely or sparsely found built-up. The percentage of high-density built-up area was only 12.67 % as against 25.67 % of medium density and as much as 61.66 % of low density built-up (Table 8). This revealed that more compact or highly dense built-up was a smaller amount and more dispersed or least dense built-up was larger in the study area. An important inference that could be drawn out of this was that high and medium density was found all along the highways along with the city centres. Most of the high density was found to be within and closer to the cities. However, the medium density was also found mostly along the city periphery and on the highways. The distribution of the low density was the maximum in the study area and this could be inferred as the higher dispersion of the built-up in the study area. This further substantiates the results of Shannon's entropy, which revealed a high dispersion of the built-up theme in the study area. 

Table 8: Different densities of built-up and their area