RESULTS AND DISCUSSION

Land use analysis for the period 1973 to 2010 has been done using Gaussian maximum likelihood classifier and the temporal land use details are given in table 2. figure 3.1 provides the land use in the region during the study period. Overall accuracy of the classification was 72% (1973), 75% (1992), 71% (1999), 80% (2002), 73% (2006) and 86% (2010) respectively. Land use analysis was done using the open source programs (i.gensig, i.class and i.maxlik) of Geographic Resources Analysis Support System (http://wgbis.ces.iisc.ernet.in/grass). From the classified raster maps, urban class was extracted and converted to vector representation for computation of precise area in hectares. There has been a 632% increase in built up area from 1973 to 2009 leading to a sharp decline of 79% area in water bodies in Greater Bangalore mostly attributing to intense urbanisation process. Analyses of the temporal data reveals an increase in urban built up area of 342.83% (during 1973 to 1992), 129.56% (during 1992 to 1999), 106.7% (1999 to 2002), 114.51% (2002 to 2006) and 126.19% (2006 to 2010). figure 3.3 illustrates the zone-wise temporal land use changes at local levels. This aids in the identification the agents responsible for land use changes. figure 4 shows Greater Bangalore with 207 water bodies (in 1973), which declined to 93 (in 2010). The rapid development of urban sprawl has many potentially detrimental effects including the loss of valuable agricultural and eco-sensitive (e.g. wetlands, forests) lands, enhanced energy consumption and greenhouse gas emissions from increasing private vehicle use (Ramachandra and Shwetmala, 2009). Vegetation has decreased by 32% (during 1973 to 1992), 38% (1992 to 2002) and 63% (2002 to 2010).

Disappearance of water bodies or sharp decline in the number of water bodies in Bangalore is mainly due to intense urbanisation and urban sprawl. Many lakes (54%) were encroached for illegal buildings. Field survey of all lakes (in 2007) shows that nearly 66% of lakes are sewage fed, 14% surrounded by slums and 72% showed loss of catchment area. Also, lake catchments were used as dumping yards for either municipal solid waste or building debris (Ramachandra, 2009a). The surrounding of these lakes have illegal constructions of buildings and most of the times, slum dwellers occupy the adjoining areas. At many sites, water is used for washing and household activities and even fishing was observed at one of these sites. Multi-storied buildings have come up on some lake beds that have totally intervene the natural catchment flow leading to sharp decline and deteriorating quality of water bodies. This is correlated with the increase in built up area from the concentrated growth model focusing on Bangalore, adopted by the state machinery, affecting severely open spaces and in particular water bodies. Some of the lakes have been restored by the city corporation and the concerned authorities in recent times.

Study area was divided into concentric incrementing circles of 1 km radius (with respect to centroid or central business district) in each zone as shown in figure 3.2.  This illustrates radial pattern of urbanization for the period 1973 to 2010. In 1973 the growth was concentrated closer to the central business district and was very minimal. In 1992 Bangalore grew intensely in the NW and SW zones. This growth can be attributed to the policy of industrialization consequent to the globalization during early 90’s. Consequent to this, the industrial layouts came up in these areas specially in the NW and SW intensified the urban growth and as a result land was also acquired for housing and urban sprawl was noticed in others parts of the Bangalore. These phenomena intensified during post 2000 as the SE and NE Bangalore saw intense growth for development of IT and  BT sectors. Subsequent to this, relaxation of FAR (Floor area ratio) in mid 2005, lead to the  spurt in residential sectors, paved way for large scale conversion of land leading to intense urbanization in these localities. This also led to the compact growth at central core areas of Bangalore and sprawl at outskirts which are deprived of basic amenities. The gradient analysis showed that Bangalore grew radially from 1973 to 2010 indicating that the urbanization is intensifying from the city centre and has reached the periphery of the Greater Bangalore.

Figure 3.1: Greater Bangalore in 1973, 1992, 1999, 2002, 2006 and 2010

Figure 3.2: Gradient analysis of Greater Bangalore-  Built-up density circlewise & zonewise from 1973 to 2010

Figure 3.3: Zone-wise and Gradient-wise temporal land use

Figure 4: Greater Bangalore with 207 water bodies (1973), 93 water bodies (2010)
Erstwhile Bangalore city 58 water bodies (1973), 10 water bodies (2010)
Note - BMP: Bangalore Mahanagara Palike, BBMP: Bruhat Bangalore (Greater Bangalore) Mahanagara Palike

Table 2: Greater Bangalore LC statistics

LST were computed from Landsat TM and ETM thermal bands. The minimum and maximum temperature from Landsat TM data of 1992 was 12 and 21 with a mean of 16.5±2.5 while for ETM+ data was 13.49 and 26.32 with a mean of 21.75±2.3. MODIS Land Surface Temperature/Emissivity (LST/E) data with 1 km spatial resolution with a data type of 16-bit unsigned integer were multiplied by a scale factor of 0.02 (http://lpdaac.usgs.gov/modis/dataproducts.asp#mod11). The corresponding temperatures for all data were converted to degree Celsius. Figure 5 shows the LST map and NDVI of Greater Bangalore in 1992, 2000 and 2007. The minimum (min) and maximum (max) temperatures were computed as 20.23, 28.29 and 23.79, 34.29 with a mean of 23.71±1.26, 28.86±1.60 for 2000 and 2007 respectively. Data were calibrated with in-situ measurements. NDVI was computed to study its relationship with LST. The Landsat TM NDVI had a mean of 0.04±0.4543, ETM+ data had a mean of 0.0252±0.5369 and MODIS had a mean of -0.0917±0.5131.

The correlation between NDVI and temperature of 1992 TM data was 0.88, 0.72 for MODIS 2000 and 0.65 for MODIS 2007 data respectively, suggesting that the extent of LC with vegetation plays a significant role in the regional LST. Respective NDVI and LST for different land uses is given in table 3 and further analysis was carried out to understand the role of respective land uses in the regional LST’s.

Table 3: LST (°C) and NDVI for various land uses

Figure 5: LST and NDVI from Landsat TM (1992)

It is clear that urban areas that include commercial, industrial and residential land exhibited the highest temperature followed by open ground. The lowest temperature was observed in water bodies across all years and vegetation. Spatial variation of NDVI is not only subject to the influence of vegetation amount, but also to topography, slope, solar radiation availability, and other factors (Walsh et al., 1997). The relationship between LST and NDVI was investigated for each LC type through the Pearson’s correlation coefficient at a pixel level and are listed in table 4. The significance of each correlation coefficient was determined using a one-tail Student’s t-test. It is apparent that values tend to negatively correlate with NDVI for all LC types. NDVI values for built up ranges from -0.05 to -0.6. Temporal increase in temperature with the increase in the number of urban pixels during 1992 to 2009 (113%) is confirmed with the increase in ‘r’ values for the respective years. The NDVI for vegetation ranges from 0.15 to 0.6. Temporal analyses of the vegetation show a decline of 65%, with a consequent increase in the temperature.

Table 4: Correlation coefficients between LST and NDVI by LC type (p=0.05)

A closer look at the values of NDVI by LULC category (table 3) indicates that the relationship between LST and NDVI may not be linear. Clearly, it is necessary to further examine the existing LST and vegetation abundance relationship using fraction (proportion of land use in a pixel) as an indicator. The fraction of land use in a pixel is estimated by linear unmixing technique.  Abundance is computed using linear unmixing from ETM+ bands were further analysed to see their contribution to the UHI by separating the pixels that contains 0-20%, 20-40%, 40-60%, 60-80% and 80-100% of urban pixels. Table 5 gives the average LST for various land use classes.

Table 5: Mean LST for various land use classes for different abundances

8 transacts were laid across the city in different directions (north [N], north-east [NE], east [E], south-east [SE], south [S], south-west [SW], west [W] and north-west [NW]) and LST was analysed as shown in figure 6, to understand the temperature dynamics.

Figure 6: Transect lines superimposed on Greater Bangalore boundary along with LST in various directions.

The temperature profile was analysed by overlaying the LST map on the land use classified map to visualise the effect of vegetation, built-up, water bodies and open ground. The temperature profile plot fell below the mean when a vegetation patch or water body was encountered on the transact beginning from the centre of the city and moving outwards along the transact. The corresponding graphs are shown in figure 7. The major natural green area and water bodies responsible for temperature decline are marked with circle. The spatial location of these green areas and water bodies are shown in figure 8.

Figure 7: Temperature profile in various directions.  X axis – Movement along the transacts from the city centre, Y axis - Temperature (°C).

Figure 8: Google Earth image showing the low temperature areas (refer figure 7
[Source: http://earth.google.com/]