5  Methodology

The analysis combines temperature and terrain data to understand the environmental conditions around Mount Everest. The methodology consists of three main components:

5.1 Temperature Analysis

• Temperature Unit Conversion:
  • Raw MODIS LST data is in Kelvin (K)
  • Conversion formula:
    \[ Celsius = (Kelvin × 0.02) - 273.15 \]
  • Scale factor (0.02) accounts for MODIS data scaling, more details can be found here
  • 273.15 converts from Kelvin to Celsius
• Time Series Analysis:
  • Utilizes ee.Reducer.mean() for spatial aggregation
  • Reducer computes mean temperature within specified region
  • Handles missing data through reducer’s null handling
  • Preserves temporal information using system:time_start
• Statistical Analysis:
  • Temporal aggregation using imageCollection.mean()
  • Spatial statistics using ee.Reducer statistics
  • Point-based analysis for specific locations

5.2 Terrain Analysis

• Elevation Processing:
  • Digital Elevation Model (DEM) processing using ee.Terrain products
  • Slope calculation using ee.Terrain.slope()
  • Aspect calculation using ee.Terrain.aspect()
• Route Analysis:
  • Feature collection processing using ee.FeatureCollection
  • Route geometry intersection using geometry.intersection()
  • Elevation Profile Generation:
    • 100-point sampling along route geometry
    • Uniform point distribution using numPixels parameter
    • 30-meter resolution sampling (scale parameter)
    • Profile data stored as feature properties
    • Interactive visualization of elevation changes
• Terrain Classification:
  • Slope masking using updateMask() for steep areas (>20 degrees)
  • Aspect classification using 8-direction color coding
  • Terrain product generation using ee.Terrain.products()

5.3 Snow Cover Analysis

• Snow Detection:

  • Normalized Difference Snow Index (NDSI) (Salomonson and Appel, 2004):

  • NDSI is calculated using Sentinel-2 imagery, specifically using the green band (B3) and the shortwave infrared band (B11).
    And the formula:
    \[ \text{NDSI} = \frac{\text{Band 3} - \text{Band 11}}{\text{Band 3} + \text{Band 11}} \] where values greater than 0.45 indicate snow.

  • Snow Masking: Areas with NDSI > 0.45 are classified as snow-covered, and the snow mask is applied to create a binary mask (snow vs. non-snow).

• Snow Cover Classification:
  • Snow Percentage: The percentage of snow cover in each region is calculated using a reduceNeighborhood function, which computes the mean snow coverage in a defined neighborhood (kernel size of 10x10 pixels).
  • Snow Class Categories: The snow cover is classified into four categories:
    • 25% less snow (Class 1)
    • 25–50% snow (Class 2)
    • 50–75% snow (Class 3)
    • 75% more snow (Class 4)

5.4 Danger Index Analysis

• The danger index is computed based on the snow class distribution, where each snow class is assigned a weight:
  • Class 1 (<25% snow): Weight = 1
  • Class 2 (25–50% snow): Weight = 2
  • Class 3 (50–75% snow): Weight = 3
  • Class 4 (>75% snow): Weight = 4
    and the formula:
    \[ \text{Danger Index} = \frac{f_1 \times 1 + f_2 \times 2 + f_3 \times 3 + f_4 \times 4}{f_1 + f_2 + f_3 + f_4} \]
• The danger index is calculated for each route based on the frequency of each snow class within the route’s surrounding area (buffered by 50 meters)
  • The frequency of each snow class is obtained by reducing the snow class image (produced from the NDSI calculation) using ee.Reducer.frequencyHistogram().
  • The weighted sum of snow class frequencies is divided by the total number of pixels to produce an average danger index.

5.5 Interactive Analysis

• User Interface:
  • Custom UI panel implementation using ui.Panel
  • Interactive point selection using Map.onClick()
  • Route selection using ui.Select dropdown
• Dynamic Visualization:
  • Real-time chart updates using ui.Chart
  • Layer management using Map.addLayer() and Map.remove()
  • Legend updates using custom ui.Panel
• Data Interaction:
  • Point-based temperature analysis
  • Route-based elevation profiling
  • Dynamic layer toggling