2.1 Study area
The Kolkata city is located by the side of east and west bank of River Hooghly (Ganges Delta). The business, commercial, and financial activities in eastern and north-eastern India were mainly controlled by the city Kolkata. But, the word ‘city’, in relation to Kolkata, is not an officially recognised term; however, it is popular and used when referred to describe Kolkata Urban Agglomeration (KUA). The KUA includes both rural and urban areas of approximately 1886.67 km2, extends between 22°0′19″ N to 23°0′01″ N and 88°0′04″ E to 88°0′33″ E (Fig. 1). The KUA has five local Governments; however, the overall management, development and plan formulation of the area was maintained by Kolkata Metropolitan Development Authority (KMDA). It is also the third most populous city in India and 8th largest urban agglomeration in the world, with total population of approximately 14.11 million (Census of India, 2011). The population density has been increased by nearly 90.24% from 1901 to 2011. The KUA has been formed with 3 municipal corporations (Howrah, Kolkata and Chandan Nagar), 38 municipalities, 77 non-municipal urban towns, 16 outgrowths and 445 rural villages. The region is experiencing rapid LULC changes and facing population pressure on natural resources, socio-economic challenges and deteriorating ecosystem services (Mukherjee, 2012).
2.2 LULC classification and change detection
Historical LULC data were used to map the dynamics of LULC in KUA. The LULC data includes eight satellite images (Landsat 5+TM, Landsat 7+ETM, Landsat 8+OLI) (path-row/ 138-44 & 138-45) for the years 1990, 2000, 2010 and 2020, which were downloaded from the website https://earthexplorer.usgs.gov. We have classified the LULC with the help of support vector machine (SVM) tool. The radial basis function kernel-based learning model, which is fixed in ENVI 5.3 software, was used for SVM classification to tackle the issues of binary classification (Wijaya et al., 2008). It utilizes hyper planes that augment the edges between the data points, which is called support vector (Vapnik, 1998). A proper kernel function was provided in SVM arrangement to make precise hyperplanes, which lessens the error (Wijaya et al., 2008).
The SVM algorithm is depending on four parameters - the kernel width gamma (γ), the penalty parameter (C), the quantity of pyramids levels to utilize, and threshold value of the classification probability. The classification threshold probability is the most important component among these (Keshtkar et al., 2017). To sort-list the pixels into a single class, zero value has been allotted as the threshold. From that point onward, for the pyramid factor, once more, a value of zero has been allocated, which measure the picture at its full resolution. The reverse of the quantity of bands has been set automatically for the value of gamma. The following formula can express the radial basis kernel technique based SVM algorithm.
![](data:image/png;base64,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)
Where xi and xj denotes vector features in the input space, and γ is the width of the kernel function.
In the present study, supervised image classification and visual interpretation were combined for the preparation of LULC map where the classification scheme of Roy et al. (2015) was used. The prepared seventeen LULC classes were reclassified into six categories: river, vegetation, wetland, cropland, built-up land and bare land. The accuracy assessment is an essential part of the LULC change mapping, because it validates LULC map with ground (Rwanga and Ndambuki, 2017). For this purpose, 140 training points of each LULC types (total 840 points) were obtained from field survey to validate the LULC types (Elkhrachy, 2015). The value of kappa coefficient determined the overall classification accuracy.
Using the LULC maps for the years 1990, 2000, 2010, 2020, 2030 and 2040, the spatio-temporal dynamics and change of LULC were assessed for five decadal periods i.e. 1990-2000, 2000-2010, 2010-2020, 2020-2030 and 2030-2040 with the help of following equation:
![](data:image/png;base64,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)
Where, CLULC is the decadal change of a particular LULC in per cent, LULCf is the LULC at the end of the decade and LULCi is the LULC at the start of the decade.
2.3 LULC prediction using CA-Markov model
LULC maps for the years 1990, 2000, 2010 and 2020 were prepared with the help of GIS and remote sensing techniques. Then, the LULC for the years 2030 and 2040 was predicted using CA-Markov model. The Markov model is a very popular technique used to calculate transitional probability of LULC types within observed time period (Guan et al., 2011; Lou et al., 2014; Shafizadeh and Helbich, 2013). The transitional probability means the probability of change of land cover type (pixels) in future. It expresses as transitional probability matrix if time t0 changes to another land cover type in the time t1. Thus, the technique is helpful to predict LULC scenario but cannot gives spatial information. The combination of Markov model and Cellular Automata (CA-Markov) simulate LULC scenario and gives spatial information with projected map (Sang et al., 2011). The LULC change predicting model was constructed using IDRISI software to explore the spatial patterns of projected LULC scenario. We have used LULC 1990 map as a base map, further used to determine LULC probability and project LULC patterns. This involves three steps. (1) Computation of transitional probability matrix in Markov model based on LULC map 1990 and 2020. (2) Computation of CA filter in CA-Markov model, where we have used 5 × 5 pixels contiguity as filter standard factor and kept the number of cellular automata interactions as 10. It measured the state change of the cells by considering the distance between neighbour and cell. (3) Finally, LULC change prediction map of 2030 has been projected and the same procedure was applied to project the LULC change prediction map of 2040.
2.4 Assessment of ecosystem service value
2.5.1 Estimation and prediction of ESVs
LULC change affects the structure and function of ecosystem, which play a key role in maintaining ecosystem services (Zhange and Gao, 2016). The principals and methods about the value assessment of ESs was analysed by Costanza et al. (1997). They computed global coefficient value for 17 ecosystem services of different LULC types. Recently, Costanza (2014) has modified these values and for Asian countries Xie et al. (2008) and Ye et al. (2011) have given coefficient values to calculate ESV. In the present study we adopted these coefficient values for six LULC types (river, vegetation, wetland, cropland, built up land and bare land) and modified for our need. For the purpose of estimation of ESV, study area has been sub-divided into 1 km × 1 km grids and then the ESVs of different LULC types within each grid were measured. The detail description of adopted coefficient value is given in Table 1.
Table 1
ESV coefficients (US$ ha−1 yr−1) used in KMA for calculation of ESVs
Ecosystem service
|
River
|
Vegetation
|
Wetland
|
Cropland
|
Fallow land
|
Built-up land
|
Gas regulation
|
0
|
0
|
140
|
41
|
28.6
|
0
|
Climate regulation
|
0
|
150.8
|
4.55
|
55.2
|
28.6
|
0
|
Water regulation
|
7.97
|
5.12
|
19.91
|
43.8
|
15.4
|
0
|
Soil formation
|
0
|
492.2
|
0
|
83.6
|
37.4
|
4.4
|
Waste treatment
|
700.39
|
91.6
|
4.55
|
79.1
|
57.2
|
0
|
Biodiversity Maintenance
|
0
|
18.78
|
320.3
|
50.6
|
87.9
|
74.7
|
Food production
|
43.24
|
45.52
|
269.7
|
56.9
|
4.4
|
2.2
|
Raw materials Provision
|
0
|
145.7
|
111.5
|
22.2
|
8.79
|
0
|
Recreation and cultural, and aesthetics
|
242.4
|
71.69
|
1533
|
9.67
|
52.8
|
2.2
|
Total
|
994
|
1021
|
2403
|
442
|
321
|
83.5
|
We estimated the ESV from the year 1990 to 2020 based on the LULC map and predicted ESV for 2030 and 2040 based on the projected LULC map. The following equations were applied to calculate ESV for LULC types, LULC functions and total ESV, which were used in the adopted model for the study area.
![](data:image/png;base64,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)
Where ESV f , ESV k and ESV t represents ecosystem service value of LULC function f, ecosystem service value of LULC type k, and total ecosystem service value, respectively; A k represents the area (km2) for land use type (k); VC kf is the coefficient value (US $/km2/year) for land use type (k) and function (f).
2.5.2 Change rate of ESV
We have analysed grid-wise change rate of ESV to know the spatial locations where ESV has declined rapidly. It was calculated using the following equation:
![](data:image/png;base64,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)
Where, CESV means change rate of ESV; ESVc and ESVp represents ESV for current year and previous year and T represent time period.