Quantifying Land System Changes
Using Supervised image classification on Landsat images from 1992, 2001, 2010, and 2020, 13 different LULC types were classified for the study area. These results of land system changes in Srinagar city for 1992, 2001, 2010, and 2020 are shown in Fig. 2, respectively. Table 2 represents the statistics of LULC and its change detection. It was observed that in 1992 agriculture was the most dominant class in the study area covering 35% of the total area followed by plantation at 26%, aquatic vegetation with 13%, and built-up 12%. Pastures, exposed rocks, river beds, and scrubs were the least dominant classes at 0.84%, 0.28%, 0.22%, and 0.16% respectively. However, the statistics (Table 2) of the 2020 LULC classes revealed that Built-up was the dominant class covering an area of 40% followed by agriculture at 18%, and plantation at 17%.
Table 2
Land use Land cover area changes in Srinagar between 1992 and 2020.
LULC
|
2020
|
2010
|
2001
|
1992
|
change
|
Km2
|
Agriculture
|
43.21
|
57.61
|
75.42
|
81.49
|
-38.28
|
Plantation
|
39.62
|
42.63
|
54.34
|
60.09
|
-20.47
|
Aquatic Vegetation
|
16.27
|
18.24
|
26.39
|
30.59
|
-14.32
|
Forests
|
5.13
|
6.38
|
7.72
|
8.83
|
-3.71
|
Barren Land
|
0.59
|
1.53
|
2.97
|
3.41
|
-2.82
|
Water
|
11.11
|
12.76
|
13.02
|
13.93
|
-2.82
|
Pastures
|
0.54
|
2.69
|
2.12
|
1.97
|
-1.43
|
River bed
|
0.40
|
0.41
|
0.47
|
0.51
|
-0.11
|
Exposed rocks
|
1.08
|
0.78
|
0.71
|
0.66
|
0.42
|
Scrubs
|
0.92
|
0.49
|
0.41
|
0.38
|
0.53
|
Horticulture
|
21.89
|
18.20
|
11.19
|
3.59
|
18.29
|
Builtup
|
93.25
|
72.30
|
39.33
|
28.54
|
64.71
|
Overall Accuracy (%)
|
85
|
89
|
87
|
91
|
|
In 1992 built-up was spread over an area of 28 km2, which increased to 41km2 in 2001, 72 km2 in 2010, and 93 km2 in 2020. According to India's censuses between 2001 and 2011, the population of Srinagar increased from 0.9 million to 1.2 million. During the three-decade observation period, a commensurate growth has been seen in the regions covered by settlements, which has increased by 325%. The city's increased built-up area has also likely had an impact on its hydrological connection, as many smaller streams have dried up as a result of landfilling and the subsequent extension of built-up regions. Agriculture comprising the cumulative land under different crops decreased by 38.28 km2 from 1992 to 2020, and plantation cover also decreased by 20.47 km2. The forest cover of the study area showed a declining trend from 8.83 km2,7.72 km2, 6.38 km2, and 5.13 km2 in 1992, 2001, 2010, and 2020 respectively. Other land classes which also showed a decreasing area include Barren Land (2.82 km2), Water bodies (2.81 km2), Pasture lands (1.43 km2), and River beds (0.11 km2). While as horticulture, which included fruit crops increased by 18.29 km2 during the study period. Similarly, scrubs and exposed rocks also showed an increasing area of 0.53 km2 and 0.42 km2 respectively from 1992 to 2020.
Figure 2 shows that land use and cover have changed significantly in Srinagar City. Significant changes have occurred in the city's built-up, agricultural, plantation, and horticultural classes. LULC transformations in Srinagar are caused by a variety of events, including changes in climatic conditions as well as a result of numerous anthropogenic activities, such as population expansion and better social and economic opportunities (Pawe 2019). However, anthropogenic-induced changes in land use/cover are currently the most substantial and rapidest of all changes (Lal et al. 2021; Kusiima et al. 2022). Since Srinagar is a major commercial centre, local masses choose to reside close to where they work, and as such have shifted from non-urban areas of the Kashmir valley to Srinagar city, which ultimately results in the land system transformations.
Table 3 shows the accuracy assessment matrix of the 2005 classified map. It was observed that the 1992 land cover map had an accuracy of 85%, whereas the accuracies for 2001, 2010, and 2020 maps were 89%, 87%, and 90%, respectively.
Table 3
Statistical analysis of LST, NDVI, and NDWI from 1992 to 2020.
Value
|
Average
|
Standard deviation
|
1992
|
2001
|
2010
|
2020
|
1992
|
2001
|
2010
|
2020
|
LST
|
12.84
|
16.94
|
22.76
|
24.53
|
1.28
|
2.74
|
1.86
|
2.10
|
NDVI
|
0.27
|
0.32
|
0.31
|
0.36
|
0.15
|
0.18
|
0.16
|
0.14
|
NDWI
|
-0.29
|
-0.25
|
-0.21
|
-0.27
|
0.16
|
0.19
|
0.11
|
0.10
|
Land Surface Temperature (LST) Assessment
LST represents the temperature of an object within a pixel, which may include several land cover types. LST maps are prepared to show the spatial distribution of LST within the study area (Fig. 3). The study revealed that the maximum LST for the area went up by 11°C from 1992 to 2020, during the same period of time, the minimum LST increased by 4°C. It was further observed that the higher LST values were recorded from the city centre, particularly from the built-up classes and the lower LST values were observed over the water bodies. Trend analysis of LST data from 1992 to 2020 (summer season) reveals that there has been a significant increase in the LST of Srinagar city with inter-annual variations ranging from a minimum of 4.81°C to a maximum of 33.81°C (Fig. 4). LST of LULC dated images from the years 1992, 2001, 2010, and 2020, range between 5–25°C, 6–30°C, 8–34°C, and 9–36°C, respectively. The land surface temperature data in the city showed a significant increasing trend (p < 0.01). Table 3 represents the variability in average and standard deviation of LST from 1992 to 2020.
According to the analysis of the observed LULC classes, the city has significant built-up areas, which directly absorb the incident solar radiation and resulting in higher land surface temperatures compared to water bodies, open spaces and greener areas. This clearly indicates the impact of urbanization on the land surface temperatures of Srinagar because the key process working in the background for raising the temperature of the urban areas is concretization and infrastructure development. Each LULC class's LST is consequently determined by its unique feature. Weng 2010 demonstrated that the most effective method for comprehending how LST is impacted by LULC changes is investigating the relationship between land cover types and thermal signatures. To comprehend the connection between LST and land cover, it is crucial to investigate each LULC type's thermal signature (Fu and Weng 2016; Ibrahim 2017). In order to compare LULC and LST, sampling points for each LULC category in the research area were chosen, and the LST values were then compared. The average of all consistent pixels within a specific LULC category was used to determine the mean temperature for each land use/cover class. The results showed that the built-up and exposed rock classes had the highest LST, whilst water bodies and natural vegetation classes had the lowest.
Comparison between LST and Air Temperature
Observed air temperature from 1992 to 2019 of Srinagar city was obtained from the IMD station Srinagar. LST of the city was compared with the observed air temperature (summer season). As the extreme temperature of summer occurs between June to Oct and the LSTs are extracted during this period, therefore, the comparison between LST and air temperature is made during this time. Trend analysis of both LST and Air temperature depicts that there has been a significant increase in the temperatures (Fig. 5). However, LST increased drastically almost by more than 5°C during the study period. The higher increase of LST indicated the effect of urban expansion as urban areas exposes to higher LST during summer. The reason being that the constructed impervious surfaces retain more heat as compared to the atmosphere (Sharma et al. 2016; Mathew et al. 2016). The results of the linear regression model indicate that there was a good consistency between LST and observed air temperature with the observed air temperature being higher than the LST. A significant correlation was found between the observed summer temperature and LST (r = 0.87, p < 0.01; RMSE = 2.39°C).
Assessment of NDVI and NDWI
NDVI is a very sensitive indicator to detect changes in vegetation (Bhardwaj et al. 2016; Varade and Dikshit 2019). Figure 6 shows the distribution of NDVI for three years in Srinagar city. The range of NDVI is -0.48 to 0.76 in 1992, -0.38 to 0.71 in 2001, -0.31 to 0.64 in 2010, and − 0.27 to 0.61 in 2020. The cities' settlements with the least vegetation are indicated by the lowest NDVI value, while plantation and forest areas are indicated by the highest value. We tried to find out how the land system transformations in Srinagar city affect NDVI. The land system transformations revealed that agricultural lands, plantations, and forests have been mostly converted into built-up. Analysis of the NDVI values for these converted locations showed a steady decline. The data indicated a decline ranging from 0.45 to 0.32 at several locations where natural vegetation was transformed into built-up areas. Figure 4 shows the relationship between NDVI and LST. It has been demonstrated that the NDVI and surface temperature have a negative correlation. The correlation coefficient is thus calculated to be r=-0.84. This is a blatant indication that the LST and NDVI have a high and negative correlation. The least vegetated areas thus have greater land surface temperatures and vice versa. Table 3 represents the variability in average and standard deviation of NDVI from 1992 to 2020. NDWI was calculated in the present study to extract the water bodies of the current study area (Jain et al. 2015; Guha et al. 2020). Figure 7 shows the distribution of NDVI for three years in Srinagar city. Major water bodies of the study area include River Jhelum and Dal Lake. There are minor changes in NDWI value during the study period. The resultant minimum and maximum NDWI values ranged from − 0.67 to 0.54 in 1992, -0.42 to 0.49 in 2001, -0.53 to 0.38 in 2010, and − 0.54 to 0.29 in 2020. In the current study, NDWI and LST were found to be negatively correlated with the correlation coefficients of r = -0.76. Table 3 represents the variability in average and standard deviation of NDWI from 1992 to 2020.