## 4 Methodology
### Preparation of the Landslide Causative Factors (LCFs)
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**LCFS** & **Data Used** & **Scale** & **Sources** & **Method and Formula** & **References** \\ \hline \multirow{3}{*}{Seismic zones} & Last 200 years point data & \multirow{3}{*}{30 m \(\times\) 30 m} & National Centre for \multirow{3}{*}{30 m \(\times\) 30 m} & Gridding and interpolation (inverse distance weight \(\quad\) method) & [50] \\  & of earthquake & & India & & \\ \hline \multirow{2}{*}{Road Density} & \multirow{2}{*}{Open series toposheets} & \multirow{2}{*}{1:50,000} & SOI & \(RD\ =\ \frac{\sum_{i}L_{i}}{\sum_{i}}\) & [49] \\  & & & where “\(L_{i}\)” is road length and “\(A_{i}\)” is the study area. & & \\ \hline NDVI & \multirow{2}{*}{Sentinel-2} & \multirow{2}{*}{10 m \(\times\) 10 m} & https: \multirow{2}{*}{\(\quad\)/eartheexplorer.usgs.gov.} & \multirow{2}{*}{\(\quad\)} & \(NDVI\ =\ \frac{NDVI-md}{NDVI-md}\) where, NIR is near infrared band & [51] \\  & & & and IR is the infrared band. & & \\ \cline{1-1}  & & & & and IR is the infrared band. & \\ \cline{1-1}  & & & & Supervised classification (Maximum likelihood) & [10] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Topographic LCFs were extracted from a PALSAR DEM with the resolution of \(12.5\times 12.5\) m using'surface' tool in ArcGIS 10.3.1 software. These LCFs are altitude, slope gradient, slope aspect, curvature and stream power index. Higher altitude areas, especially downhill segments of hilly regions, are favourable for landslides (LS) and the study area has a range of 500 ft. to 2936 ft. elevation (Figure 4a). Slope gradient (Figure 4b) is the result of a combination of factors, i.e., drainage intensity, vegetation, run-off, and geologic. Thus, it was selected as one of the key LCFs in studying landslide susceptibility. The slope angle in the Rudraprayag district varies from 0 to 87\({}^{\circ}\) indicating gentle to very-steep gradient. A very steep slope intensifies the potentiality of a landslide (LS) event [40; 41]. Duration and magnitude of sunray over the surface is not similar in all facets of slope, which in terms endures varying rate of physical weathering, vegetation growth and soil moisture thereby producing different potential segments for LS hazard. Therefore, slope aspect (Figure 4c) is a vital LCF, particularly in the regions of higher elevation. In the case of curvature (Figure 4d), a rise of curvature value denounces the landslide probability. SPI (Figure 4e) in the process of landmass movement indicates the power of erosion by water which stimulates energy to disintegrate and transport the surface elements. Figure 4: _Cont._  
Hydroological LCFs selected for this study are rainfall, drainage density and topographical wetness index (TWI). For spatial mapping of the rainfall distribution, the precipitation data from 1901 to 2019 (119 years) was considered. The average rainfall map of the study area was prepared using the Kriging method based on the data collected from different stations. Rainfall (Figure 4f) is a widely used LCF in LSS mapping as it triggers the processes like soil slip, debris fall by declining the stability, integration and compaction of the surface forming materials. Towards the north and north-eastern part of the district, amount of average annual rainfall gradually increases from 150 cm to 300 cm. The arrangement of the drainage is the outcome of long-term interactions among geology, topography, climate and lithology. Generally, high intensity of drainages epitomizes low infiltration and prompt run-off and vice versa [52; 53]. A large share of the inventory landslides was found along the high drainage density regions (Figure 4g). TWI (Figure 4h) is controlled by topographic factors and the index values represent the tendency of flow accumulation at a specific point and ability of the gravitational forces to flow the water downward in a slope segment [54]. Soil related LCFs in this research are soil texture and soil depth.