## 3 Results and Discussion
In order to generate meaningful comparisons across time between image-based planform maps, many image sources must be utilised. Commonly, satellite sensor data are used to examine Earth surface processes such as volcanic glacier flow, surface and internal mechanisms, coastal and estuarine activities, and fluvial processes. Because \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Stream Length (Km)** & **Valley Length (Km)** & **Sinuosity Ratio** \\ \hline
1973 & 275.81 & 209.18 & 1.31 \\
1984 & 279.90 & 209.72 & 1.33 \\
1996 & 287.52 & 208.63 & 1.37 \\
2006 & 298.11 & 208.98 & 1.43 \\
2016 & 308.42 & 209.10 & 1.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Measurement of Sinuosity Ratio of the Ganges-Padma River during 1973–2016. of the potentially vast coverage area, synoptic perspective, and the capability to capture data regularly [18, 19, 20, 21, 22, 23, 24, 25, 26, 27], satellite sensor data are particularly useful for monitoring changes in river planform, particularly for large and dynamic rivers. Since rivers are continuous systems, accurate documentation of their structure and attributes is crucial and necessitates the collection of a continuous time series of data across a variety of temporal and spatial scales. The record of attempts to simulate the long-term behaviour of meandering rivers shows the problems posed by the dearth of continuous data sets. Consequently, satellite sensor data are able to give uniform river planform mapping at regular intervals). The year 1972 marked the beginning of the era of space-based Earth observation with sufficient spatial resolution for the charting and monitoring of mega-rivers. The resolution of the spatial multispectral scanning system (MSS) sensor on the Landsat satellite is 80 m. The Thematic Mapper (TM) sensor was added to the Landsat platform in 1982, and Landsat 7 carried the Enhanced Thematic Mapper in 1999 [28]. Remote sensing is an excellent method for identifying and monitoring the environment, including channel migration patterns over human timescales. It can assist in identifying similar geomorphological and geological characteristics that define the morphogenetic process of channel migration. Multitemporal satellite sensor data enable river planform evolution over time. Monitoring channel migration is crucial for mitigating the risks caused by bank deterioration. Specifically, time series remote sensing data spanning decadal time spans are vital to study planform change. In the current context, free access of Landsat sceneries (beginning in the mid-1990s) inaugurated a new period in time series satellite remote sensing research. The river system is crucial, as is the mechanism that determines meander dynamics. On the one hand, the means of bank erosion, means of cut-offs, the consequences along a river system's forks, and natural hard banks due to antecedent geology are present, which is crucial to comprehending the river system's dynamics. Moreover, the presence of a reservoir also affects the river system downstream. River is a contentious topic. A study of the river's avulsion frequency would also enable exploration of any system periodicity or phase of meander migration along the river network [28]. In order to calculate the braiding index using [PERSON]'s formula, the total and stream length of bars are displayed in Table 3. (1964). In 1973, the braiding index of 1.3 for the Ganges-Padma River suggested that it was not braided. The index was 1.43 in 1984, which also indicated non-braided, but it has since climbed, passing the barrier of 1.5, indicating that braiding has occurred. A look at the data reveals that the braiding index was 1.62 in 1996 and 2006, 1.92 in 2007, and 2.15 in 2016. When the braiding index for a river is more than 1.5, the river will be braided. Starting in 1996, we saw that the river began to take on a braided appearance. There is a greater rate of change in the braiding index compared to the sinuosity ratio.