Combining Lidar and Satellite Data for Flood-Risk Assessment

Category: Computer Vision in Finance • Article #18 • Reading time: 5 minutes

Introduction

Flood risk assessment requires precise topography and land cover. LiDAR provides 3D elevation models; satellite provides land use classification. Combining both enables accurate flood risk mapping, informing insurance pricing and infrastructure investment.

LiDAR Data for Elevation

LiDAR (Light Detection and Ranging) measures ground elevation with 1-2 meter accuracy, 1-2 meter spacing. Creates digital elevation model (DEM). Expensive ($1-5 per square km) but available for many developed regions.

Alternative: SRTM (30m resolution, global, free) or ASTER DEM (30m resolution, global, free). Lower resolution but adequate for regional flood modeling.

Satellite Imagery for Land Use

Classify land use: vegetation, impervious (roads, buildings), water. Sentinel-2 or Landsat provides spectral bands for classification. CNN classifies pixels: vegetation (permeable), impervious (runoff-prone), water (already flooded). Accuracy 85-90%.

Hydrologic Modeling with Fusion Data

Feed DEM (from LiDAR) + land use (from satellite) into hydrologic model (e.g., HEC-RAS). Model simulates water flow during rainfall:

DEM determines flow direction and accumulation
Land use determines infiltration rates (vegetation: high, impervious: low)
Combine to predict flood extent and depth

Run model under various rainfall scenarios (10-year, 100-year, 500-year storms).

Flood Risk Mapping

Output: flood risk maps showing inundation probability and depth. Overlay on properties: identify at-risk structures. Quantify risk: X% probability of 1-meter inundation in 100-year storm.

Case Study: Coastal Flood Risk (US East Coast)

Assess flood risk for 50,000 coastal properties combining LiDAR + satellite data:

LiDAR elevation data: 1m accuracy
Satellite land use: vegetation, impervious surface, water
Hydrologic model: simulate 100-year storm
Results: identified 8,000 properties with >50% inundation risk
Insurance companies adjust premiums based on predicted risk

Sea-Level Rise Integration

Combine static flood risk with sea-level rise projections. LiDAR provides baseline elevation; add sea-level rise (1-4 feet by 2100). Rerun flood models: much larger inundation areas with elevated sea levels.

This enables long-term risk assessment for 30-year mortgages, long-term property investments.

Infrastructure Optimization

Identify critical infrastructure in flood risk areas: power substations, water treatment plants, hospitals. Prioritize hardening projects: elevate, build levees, relocate. Quantify ROI: flood damage avoided vs. mitigation cost.

Climate Adaptation Strategies

Use flood risk maps to guide adaptation: green infrastructure (permeable pavement, wetlands), managed retreat (relocate away from floodplain), flood insurance availability. Communities can make informed decisions about growth and development.

Data Limitations

LiDAR expensive, not available globally. SRTM/ASTER free but lower resolution. Satellite land use classification has errors (30-40% misclassification in some regions). Combine multiple data sources for robustness.