**3. Material and method**

The present study uses a collection 2 level 1 images from LANDSAT series of satellites available from USGS Earth Explorer website from 1975 to 2022. To get cloud-free clear images of the study area, images are quired in the month of December each year where possible, or in adjacent months. For the years 1975 and 1980, images were selected from the Landsat Multi-Spectral Scanner (MSS), and the rest images were from Landsat Thematic Mapper (TM, 4 and 5) and Enhanced TM (ETM+) (**Figure 1**). Two adjacent Landsat images of path 148 rows 45 and 147 rows 45 for Landsat MSS; path 138 rows 45 and 137 rows 45 for TM and ETM+ are needed for the Sundarbans coastline of Bangladesh and India (**Table 1**).

#### **Figure 1.**

*Location of the study area: Sundarban coastline along bay of Bengal.*


#### **Table 1.** *Landsat images used in the study.*

### *A Study of Morphological Changes in the Coastal Areas and Offshore Islands of Sudarban… DOI: http://dx.doi.org/10.5772/intechopen.112243*

Images downloaded from USGS Earth Explorer are first georeferenced with a final georeferenced image having <±0.5 pixel root mean squared error (RMSE). As the adjacent images fall in two different countries with different UTM Zones of 46 N and 45 N The images are reprojected to Lambert Azimuthal Equal area projection to preserve the area of individual polygon and a true sense of direction from the center. This projection is also preferred for statistical analysis of land change. To maintain the spectral integrity of the image nearest neighbor resampling was used. All images from Landsat MSS1 are resampled to 30 m resolution. Resampling the 60 m MSS pixels to 30 m does not impact the spatial resolution of the images, whereas resampling the 30 m TM pixels to 60 m MSS pixels would degrade spatial resolution of the images.

To enable accurate classification and change detection from multi-temporal satellite imagery, it is crucial to perform radiometric calibration. This process involves correcting for gain and bias variations in the satellite data. In the case of Landsat data, the scattering effect is particularly prominent [13]. Additionally, for vegetation cover identification, atmospheric correction is necessary to mitigate the impact of scattering.

In our study, we conducted atmospheric correction on the Landsat visible and near-infrared (VNIR) bands. This correction involved radiometric calibration, which transformed the digital number (DN) values of the bands into the top of the atmosphere radiance (LTOA) using a sensor calibration function (Eq. 1) proposed by Chander et al. [14]. Subsequently, we converted the radiance of the VNIR bands into accurate surface reflectance using an image-based atmospheric correction model developed by Chavez [15]. This model was chosen for its simplicity and because radiosounding data was not readily available (Eq. 2).

$$L\_{\rm TO} = \left(\frac{L\,\mathrm{max}\_{\lambda} - L\,\mathrm{min}\_{\lambda}}{QCAL\,\mathrm{max} - QCAL\,\mathrm{min}}\right) \times \left(DN - QCAL\,\mathrm{min}\right) + L\,\mathrm{min}\_{\lambda} \tag{1}$$

Where Lmaxλ and Lminλ represent the maximum and minimum radiance (in W/ m−2 sr−1 μm−1), QCALmax and QCALmin represent the maximum and minimum DN value possible (255/1).

$$\rho = \frac{\left(L\_{\text{TOA}} - L\_p\right) \pi d^2}{ESUN\_\lambda \cos \theta\_\varepsilon T\_\varepsilon} \tag{2}$$

Where ρ represents the surface reflectance. d denotes the Earth-sun distance, which is measured in Astronomical Units (AU). ESUNλ refers to the band-pass solar irradiance at the top of the atmosphere (TOA) for a specific wavelength (λ); Z represents the solar zenith angle, measured in degrees; TZ represents the atmospheric transmission between the ground and the TOA. For band 4, the value of TZ is assumed as 0.85, while for band 5, it is taken as 0.95 (**Figure 2**) [15]. Lp represents the radiance that results from the interaction of aerosols and atmospheric particles. Its estimation is based on the studies conducted by Song et al. [13], Chavez [15], and Sobrino et al. [16].

FCC (false-color composite) images are commonly used in remote sensing and satellite imaging to enhance the interpretation of land cover and vegetation.

These images are created by combining different bands of electromagnetic radiation, typically in the green, red, and near-infrared regions of the spectrum. The interpretation of FCC images relies on the fact that different materials reflect and absorb different wavelengths of light. In the case of distinguishing between land and water, the choice of bands is important. By using the green, red, and nearinfrared bands, it becomes possible to differentiate between various land cover types. In an FCC image, vegetated areas appear in shades of red. This is because

## *A Study of Morphological Changes in the Coastal Areas and Offshore Islands of Sudarban… DOI: http://dx.doi.org/10.5772/intechopen.112243*

healthy vegetation strongly reflects near-infrared light while absorbing more of the red light. As a result, when the near-infrared band is assigned to the red channel in the composite image, it gives vegetation a distinct red color. Bare soils, on the other hand, appear in tones of brown. Since bare soils have little vegetation cover, they reflect both green and red light, giving them a brownish appearance in the composite image. Mudflats or sandy beaches generally appear as shades of white in an FCC image. These areas have a high reflectance in both the green and red bands, resulting in a bright appearance. Water bodies, such as lakes or oceans, appear blue or black in the FCC image. This is because water absorbs near-infrared light, and in the absence of strong vegetation reflectance, it reflects more of the blue and green light. Therefore, water bodies tend to appear darker compared to other land cover types. In addition to FCC images, the normalized difference vegetation index (NDVI) can be calculated using the red and near-infrared bands. The NDVI is a quantitative measure of vegetation health and density. It is computed by taking the difference between the near-infrared and red reflectance values and dividing it by their sum. The resulting NDVI values can help confirm the land-water boundary. Water bodies typically have a negative NDVI value, indicating the absence of vegetation, while dry land usually has a positive NDVI value, reflecting the presence of vegetation.

Overall, FCC images and the NDVI provide valuable information for land cover classification, vegetation monitoring, and understanding the distribution of different land and water features in remote sensing applications.
