15.3 Digital elevation models and terrain analysis

Digital elevation models (DEMs) are powerful tools for analyzing Earth's surface. They create 3D terrain representations, enabling geomorphologists to study landforms, quantify processes, and identify hazards across large areas. DEMs extract crucial topographic attributes like slope and curvature.

DEM applications in geomorphology include watershed delineation, flood modeling, and tectonic studies. While incredibly useful, DEMs have limitations such as potential errors from data collection and interpolation. Understanding these constraints is key to effectively using DEMs in landscape analysis.

Digital Elevation Models for Geomorphology

Fundamentals and Applications of DEMs

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• Digital Elevation Models (DEMs) create 3D terrain surface representations stored as raster grids with elevation values for each cell
• DEMs provide continuous topography coverage enabling quantitative analysis of landscape features and processes across large areas
• DEM resolution affects terrain feature representation (higher resolutions capture finer details but require more data storage and processing power)
• Geomorphologists utilize DEMs to study landform evolution, quantify erosion and deposition rates, and identify geohazard-susceptible areas
• DEMs extract crucial topographic attributes for understanding surface processes and landform development
  • Slope
  • Aspect
  • Curvature

DEM Applications in Geomorphology

• Watershed delineation determines drainage basin boundaries and stream networks
• Slope stability analysis identifies areas prone to landslides or mass wasting
• Flood modeling simulates inundation extents and depths for various flood scenarios
• Landform classification automatically categorizes terrain into distinct geomorphic units (ridges, valleys, plains)
• Sediment transport modeling estimates erosion rates and sediment flux across landscapes
• Tectonic geomorphology studies landscape response to active tectonics and uplift

Limitations and Considerations

• Potential errors arise from data collection methods (instrument accuracy, point density)
• Interpolation techniques can introduce artifacts or smooth out important features
• DEMs cannot represent subsurface features or fully capture vegetation canopy structure
• Temporal resolution limits ability to capture rapid landscape changes (landslides, volcanic eruptions)
• Vertical accuracy varies depending on terrain type and data collection method
• Edge effects can occur at the boundaries of DEM datasets, requiring careful merging techniques

DEM Generation and Processing

Data Acquisition Methods

• LiDAR (Light Detection and Ranging) provides high-resolution elevation data
  • Laser pulses penetrate vegetation to create bare-earth models
  • Capable of sub-meter vertical accuracy in ideal conditions
• Photogrammetry techniques construct DEMs through image correlation and triangulation
  • Structure from Motion (SfM) uses overlapping aerial or drone imagery
  • Satellite stereo imagery enables global-scale DEM generation (ASTER GDEM, SRTM)
• Interferometric Synthetic Aperture Radar (InSAR) generates DEMs by measuring radar signal phase differences
  • Useful for large-scale mapping and detecting surface deformation
  • Can operate through cloud cover and at night
• Traditional field surveying methods provide accurate point data for smaller study areas
  • Total station measurements
  • Differential GPS surveys
  • Terrestrial laser scanning for high-resolution local DEMs

DEM Creation Process

• Data preprocessing prepares raw elevation data for interpolation
  • Noise removal filters out erroneous points or outliers
  • Georeferencing aligns data to a common coordinate system
  • Point classification separates ground returns from vegetation or buildings (for LiDAR)
• Interpolation creates a continuous surface from discrete elevation points
  • Inverse Distance Weighting (IDW) uses nearby points weighted by distance
  • Kriging applies geostatistical methods to estimate optimal interpolation
  • Triangulated Irregular Network (TIN) creates a mesh of triangles from input points
• Resampling adjusts DEM resolution to match project requirements or computational constraints
  • Bilinear interpolation for smoother transitions between cells
  • Nearest neighbor resampling preserves original cell values

Quality Assessment and Error Correction

• Identify and mitigate artifacts, voids, and systematic errors in DEMs
  • Visual inspection using hillshade and contour maps
  • Statistical analysis of elevation distributions and derivatives
  • Comparison with reference data or higher-accuracy DEMs
• Common DEM errors require specific correction techniques
  • Stripe removal for sensor-related artifacts
  • Void filling using interpolation or auxiliary data sources
  • Hydrological correction ensures proper flow routing across the landscape
• Uncertainty assessment quantifies DEM quality and limitations
  • Error propagation analysis for derived products
  • Monte Carlo simulations to model impact of DEM uncertainty on analyses

Terrain Analysis with DEMs

Topographic Attribute Extraction

• Slope analysis quantifies terrain steepness and orientation
  • Calculated using finite difference or polynomial fitting methods
  • Critical for understanding erosion, mass wasting, and hydrological flow
  • Typically expressed in degrees or percent rise
• Aspect calculation determines compass direction of slope faces
  • Influences factors like solar radiation receipt and vegetation distribution
  • Often represented using cardinal or intercardinal directions
• Curvature analysis identifies convex and concave landforms
  • Profile curvature measures curvature parallel to the slope
  • Plan curvature measures curvature perpendicular to the slope
  • Helps delineate ridges, valleys, and areas of potential erosion or deposition

Hydrological Modeling

• Flow direction algorithms determine paths of water movement across the landscape
  • D8 algorithm assigns flow to one of eight neighboring cells
  • Multiple flow direction algorithms allow for flow divergence
• Flow accumulation calculates upstream contributing area for each cell
  • Essential for stream network delineation and watershed analysis
  • Often used to define channel initiation thresholds
• Topographic wetness index (TWI) predicts areas of soil moisture accumulation
  • Combines slope and flow accumulation: $TWI = ln(a / tan β)$
  • Where a is the upslope contributing area and β is the local slope angle
  • Higher values indicate greater potential for saturation

Advanced Terrain Analysis

• Roughness indices quantify terrain complexity
  • Vector Ruggedness Measure (VRM) uses 3D vector dispersion
  • Terrain Ruggedness Index (TRI) calculates elevation differences between cells
  • Useful for identifying geomorphic features and assessing landscape heterogeneity
• Geomorphon classification automatically identifies and maps landforms
  • Uses pattern recognition based on local geometry and landscape context
  • Typically classifies terrain into 10 common landform types (peak, ridge, slope, etc.)
• Topographic Position Index (TPI) compares elevation of each cell to mean elevation of neighborhood
  • Positive values indicate local ridges or peaks
  • Negative values indicate local valleys or depressions
  • Used for landform classification and habitat modeling

Interpreting DEM-Derived Products

Visualization Techniques

• Hillshade rendering creates 3D-like terrain representation
  • Simulates illumination to enhance visual interpretation of landforms
  • Adjustable light source angle and elevation for optimal feature highlighting
• Contour maps provide traditional 2D elevation representation
  • Useful for identifying gradients and landform boundaries
  • Contour interval selection balances detail and map readability
• 3D visualization techniques enhance landscape feature interpretation
  • Draping satellite imagery or thematic maps over DEMs
  • Virtual fly-throughs for immersive landscape exploration
  • Augmented reality applications for field-based visualization

Quantitative Landscape Analysis

• Hypsometric analysis uses elevation distribution to infer geomorphic development
  • Hypsometric curve plots cumulative area against relative elevation
  • Hypsometric integral quantifies overall landscape convexity or concavity
  • Indicates stages of landscape evolution and tectonic influence
• Cross-sectional profiles reveal vertical relationships between landforms
  • Useful for identifying erosional or depositional signatures
  • Can be stacked to create swath profiles for broader landscape characterization
• Slope-area analysis examines relationship between drainage area and local slope
  • Helps identify process domains in fluvial landscapes (hillslopes vs. channels)
  • Used to estimate channel concavity and steepness indices

Multi-temporal and Integrated Analysis

• Change detection analysis quantifies surface changes over time
  • Requires multi-temporal DEMs of consistent resolution and accuracy
  • Applications include landslide volume estimation, glacier mass balance, and coastal erosion monitoring
• DEM differencing calculates elevation changes between two time periods
  • Produces maps of erosion and deposition
  • Requires careful error propagation and significance testing
• Integration of DEM-derived products with other spatial data in GIS
  • Combining slope and geology maps to assess landslide susceptibility
  • Merging land cover data with TWI to model habitat suitability
  • Incorporating climate data with terrain attributes for soil erosion modeling