Lidarvisor - Drone Lidar

The Coastal Erosion Challenge

Coastal erosion is a growing global challenge, threatening infrastructure, ecosystems, and communities worldwide. Traditional survey methods struggle to capture the rapid, complex changes occurring along shorelines. LiDAR technology offers a powerful solution, enabling precise, repeatable measurements of coastal terrain that reveal even subtle changes over time.

This guide explains how to use LiDAR for coastal erosion monitoring, from data acquisition to analysis, and how Lidarvisor simplifies the process of generating the Digital Terrain Models (DTMs) that form the foundation of shoreline change detection.

Why LiDAR for Coastal Monitoring?

Coastal environments present unique challenges for surveyors: soft sand surfaces, breaking waves, dense dune vegetation, and rapidly changing conditions. LiDAR addresses these challenges through several key advantages:

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Vegetation Penetration

LiDAR pulses can penetrate dune grass and coastal scrub to capture the true ground surface beneath, essential for accurate erosion measurements.

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High-Density Data Collection

Modern LiDAR sensors capture hundreds of thousands of points per second, creating detailed 3D models of beaches, cliffs, and dunes.

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Rapid Surveys

Drone-mounted LiDAR can survey kilometers of coastline in hours, enabling frequent monitoring that captures seasonal and storm-driven changes.

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Centimeter-Level Accuracy

With proper ground control, LiDAR DTMs achieve vertical accuracies of 5-10 cm, sufficient to detect meaningful erosion or accretion.

Key Applications in Coastal Erosion Studies

Beach Erosion and Sediment Transport

Sandy beaches are dynamic environments where sediment constantly moves alongshore and cross-shore. LiDAR surveys capture the beach profile from the dune crest to the waterline, enabling:

  • Volumetric change analysis between surveys
  • Identification of erosion hotspots
  • Sediment budget calculations
  • Beach nourishment project monitoring

Cliff Retreat Monitoring

Coastal cliffs erode through a combination of wave undercutting, weathering, and mass movement. LiDAR provides:

  • Precise cliff-top position tracking over time
  • Identification of potential landslide areas
  • Volume calculations of material lost
  • Risk assessment for nearby infrastructure

Dune Migration and Stabilization

Coastal dunes serve as natural barriers against storm surge and erosion. LiDAR enables monitoring of:

  • Dune crest elevation changes
  • Blowout formation and expansion
  • Vegetation recovery after storms
  • Effectiveness of stabilization projects

Saltwater Intrusion Risk Assessment

Low-lying coastal areas are vulnerable to saltwater intrusion into freshwater aquifers. Accurate DTMs help identify areas where erosion may compromise the barrier between the sea and groundwater resources.

The LiDAR Coastal Monitoring Workflow

1. Data Acquisition

Coastal LiDAR surveys are typically conducted using:

Drone-mounted LiDAR: Ideal for detailed surveys of specific beach sections or cliff faces. Flight altitudes of 30-50m provide high point densities.

Airborne LiDAR: Used for regional shoreline mapping, covering tens or hundreds of kilometers in a single mission.

Timing matters: surveys should be conducted at low tide to maximize beach coverage, and ideally during calm conditions to minimize wave interference.

Lidarvisor - Point Cloud Classification
Lidarvisor - Hillshade DTM

2. Point Cloud Classification

Raw LiDAR data includes returns from the ground, vegetation, water surface, and noise. Accurate coastal erosion analysis requires proper classification to separate:

  • Ground points: The actual terrain surface
  • Vegetation: Dune grass, shrubs, and coastal plants
  • Water: Waves, standing water, and tidal pools
  • Noise: Birds, atmospheric interference, and sensor artifacts

With Lidarvisor, this classification happens automatically when you upload your LAS file. The platform uses advanced algorithms to identify ground, vegetation, water, and other features without manual parameter tuning.

3. DTM Generation

The Digital Terrain Model is the foundation of coastal erosion analysis. A DTM represents the bare-earth surface, with vegetation and other features removed. For coastal monitoring:

  • DTMs reveal the true beach profile beneath dune vegetation
  • Hillshade visualizations highlight subtle terrain features
  • Slope maps identify steep, erosion-prone areas

Lidarvisor automatically generates DTMs and hillshades from your classified point cloud, exportable as georeferenced TIFF files ready for GIS analysis.

Lidarvisor - Gridded DTM

4. Change Detection

The real value of LiDAR coastal monitoring comes from comparing surveys over time. By differencing DTMs from different dates, you can:

  • Map areas of erosion (negative change) and accretion (positive change)
  • Calculate volumetric changes in cubic meters
  • Identify seasonal patterns versus long-term trends
  • Assess storm damage immediately after events

Processing Coastal LiDAR with Lidarvisor

Lidarvisor streamlines the coastal erosion monitoring workflow by automating the most time-consuming steps:

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1. Upload

Drag and drop your coastal LiDAR data into the platform.

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2. Classify

The platform classifies ground, vegetation, water, and other features automatically.

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3. Generate

With one click, create DTMs, DSMs, hillshades, and slope maps.

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4. Export

Download georeferenced TIFFs for GIS or DXF contours for CAD.

The cloud-based platform means you can process coastal data from anywhere, without investing in expensive desktop software or high-powered workstations.

Best Practices for Coastal LiDAR Surveys

  • Establish consistent survey timing — Conduct surveys at similar tidal stages for comparable results.
  • Use ground control points — Place GCPs on stable features away from the active beach for accurate georeferencing.
  • Plan for repeatability — Document flight parameters so future surveys can replicate the same coverage.
  • Consider seasonal timing — Pre- and post-storm surveys capture event-driven changes; annual surveys track long-term trends.
  • Archive all data — Keep raw point clouds, not just derived products, for future reprocessing as algorithms improve.

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