Sustainable Development Practices Using Geoinformatics. Группа авторов

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Figure 1.7 Land Surface Temperature (LST) map for the year 2001, 2011, and 2017.

      1.4.5 Relationship Between NDVI and LST

The vegetation cover of a locality significantly influences LST. LST can reflect the scenario of land surface water and heat exchange process broadly. Therefore, in this study, we retrieved and tried to explore relationship between LST, NDVI, and LULC using temporal Landsat 5 and Landsat 8 remote sensing images. The results obtained shows that the spatial distribution of LST and NDVI is having negative relationship, analyzed after removing water body data (Figure 1.8A, 1.8B & 1.8C). Moreover, it is clear from the figures given below that in all directions from center of the city the NDVI is showing inverse relationship in 2001, 2011, and 2017. However, it is also deduced from the analysis of LST against land use (LULC) that built-up area shows highest LST as against green space and built-up area with green space (Figure 1.9). Moreover, the present analysis also shows that there is average rise in temperature from 2001 to 2017 through 2011 for each land use class. The LST observed for the green space is also comparable to the water body. The coefficient of regression is found to be negative as observed from regression analysis between NDVI and LST for the Barasat Municipality (Figure 1.10).

      1.4.6 Urban Heat Island

We have analyzed the Barasat municipality for Urban Heat Island (UHI) formation and found that, although it is a small city in comparison to Mega city like Delhi, still, it is showing a very clear UHI formation over the study area (Figure 1.11). Temporal MODIS night time LST product (MOD11A1) downloaded from USGS were used to assess the UHI formation in Barasat. The plotted graph as shown in Figure 1.11 is clearly showing the formation of UHI in the year 2001 with lesser flattening in the center of the city, which gradually increased in 2011 and 2017. Moreover, for the same time period, Landsat data was also analyzed for daytime UHI formation, and it was found that, although UHI formation is subdued in comparison to the night time UHI, still, it is significant enough to be reported.

Figure 1.8A The relationship pattern between NDVI and LST from center to outward (north, south, east, and west) of Barasat municipality for the year 2001.

Figure 1.8B The relationship pattern between NDVI and LST from center to outward (north, south, east, and west) of Barasat municipality for the year 2011.

Figure 1.8C The relationship pattern between NDVI and LST from center to outward (north, south, east, and west) of Barasat municipality for the year 2017.

Figure 1.9 Relationship of LST with LULC classes.

1.5 Conclusion

      In summary, the satellite-based remote sensing images are very impressive dataset for urban sprawl study. Moreover, GIS platform provides toolset for analyzing temporal LST and NDVI retrieval over a period, giving chance for comparison and trend delineation. Barasat municipality, although a small city is showing UHI formation, may be due to poor planning and indiscriminate urbanization. However, Barasat municipality being a sub-urban part of Kolkata is facing problem of rapid urbanization and degradation of natural vegetation. Due to poor planning and giving no space for surface air ventilation and greenery in the city, the city is under environmental stress leading to rise in LST and UHI formation. Availability of legacy remote sensing data provides good chance for comparison of changes in environmental conditions in the study area in wholesome perspective.

Figure 1.10 Regression analysis of LST vs. NDVI.

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