*2.1. Study Area*

The study area English Bazar block is an important political, cultural, and administrative centre in Malda District, West Bengal, and covers an estimated area of 251.8 km2. It is located between 24◦50–25◦05N latitude and 88◦–88◦10E longitude (Figure 1). The study area is located at a height of 15–17 m above sea level. It consists of one municipality and 108 inhabited villages. This region is mainly made up of the younger alluvial plain of the Ganga River and the Mahananda River. The English Bazar, like most other parts of West Bengal, experiences highly humid and tropical weather. In May and June, temperatures can reach 42–45 ◦C during the day, but in December and January, they can drop to 8 ◦C overnight. According to the 2011 Census, the English Bazar block has a total population of 274,627, and the region is recognised as having an urban agglomeration. In the last decade (2001–2011), this region has witnessed a rapid rise in urbanisation, with the highest urbanisation rate in West Bengal at 124.81%. Various social and economic factors influenced this significant built-up expansion. The intersection of NH-34 (national highway), the state highway (SH-10), the north-eastern frontier railway, and the eastern railway all lead to the enhancement of the built-up expansion in this region. As increased urbanisation in a developing country, this region is challenged with uncontrolled built-up growth, insufficient critical infrastructure and utilities, air, noise, and water pollution, as well as bad governance.

### *2.2. Data Base and Its Preprocess*

The LULC maps for the years 2001, 2011, and 2021 were created using the satellite images provided by different sensors of Landsat, such as Landsat 5 thematic mapper (TM) and the Landsat 8 operational land imager (OLI). Before proceeding for further research, we considered the technical issues of both sensors. However, Landsat TM started providing multispectral data in 1984. Landsat 8 launched recently (2013) with the OLI and TIRS sensors, resulting in a new orthorectified dataset (L1T) [50]. Despite the fact that Landsat 5 is no longer in operation and Landsat 7 is hampered by the failure of the Scan Line Corrector (SLC-off), the successful launch of Landsat 8 has supplied a steady stream of intermediate spatial resolution data for long-term LULC mapping and trend analysis [51]. Therefore, most research on long-term vegetation changes and LULC mapping uses different Landsat sensors together, such as TM, ETM+, and the OLI sensor. In comparison to TM and ETM+, OLI has two new spectral bands: an ultra-blue band (0.43–0.45 μm) and a cirrus band (1.36–1.39 μm). In the near-infrared (NIR), the OLI bands are generally narrower than the equivalent TM and ETM+ bands. Given these factors, it is necessary to compare Landsat 8 data to the preceding Landsat sensors before integrating them with other sensors for the trend analysis. Several researchers have examined clear sky observations taken 8 days apart for the same site to compare Landsat 5, 7, and 8 data [51,52]. These studies assumed that the phenology and land cover did not change between acquisitions and found that surface reflectance variances of about 2% and NDVI differences of about 5% exist between the two sensors [21]. However, Vinayak et al. [44] concluded that the TM, ETM+, and OLI images are comparable enough to be used as complementary data.

Therefore, based on the previous literature, we used TM and OLI sensors for our research, with two considerations. First, we did not use all bands of the TM and OLI sensors for our LULC mapping. We used only those bands that are common in both sensors and relevant for LULC research, such as red, blue, and green, while the thermal bands of both sensors, coastal and circus bands of the OLI sensors, were excluded for the analysis. Additionally, these bands did not have much influence on the LULC research. Second, before using the selected bands for further research, we did radiometric correction, like topof-atmosphere (TOA) reflectance and/or radiance using radiometric rescaling coefficients provided in the metadata file that is delivered with the Level-1 product.

**Figure 1.** Location map of the study area, with the major built-up node and distribution of the training and testing datasets.

The Landsat datasets were downloaded from the Earth Explorer website (http:// earthexplorer.usgs.gov/, accessed on 16 December 2021) of the United States Geological Survey. In a subtropical country like India, satellite data are generally used for the months

of March and April to ge<sup>t</sup> cloud-free scenes [28]. However, this part of India receives significant rainfall during the pre-monsoon months (March to April) due to the nor'wester activity (locally known as Kaalbaisakhi) and, thus, experiences frequent cloud cover [12]. Therefore, in this study, the satellite data were used for the months of December to avoid any kind of errors due to local climatic effects, and we go<sup>t</sup> the cloud-free images [21]. The Landsat images offer a medium spatial resolution of 30 m, which is suitable for the mapping of urban landscape pattern at different levels (Table 1). Further, for the validation of prepared LULC maps, the samples were collected through a field survey, as well as the Google Earth Pro domain.


**Table 1.** Details of multitemporal satellite images and their characteristics.
