LiDAR vs Photogrammetry: Complete Comparison of Accuracy, Cost, and Applications

Both LiDAR and photogrammetry create 3D representations of the physical world, but they use fundamentally different approaches. Understanding when to use each technology, or both together—is essential for choosing the right solution for your surveying, forestry, construction, or mapping project.

Understanding the Fundamental Difference

LiDAR (Light Detection and Ranging) actively emits laser pulses and measures the time for each pulse to return, directly calculating distances to surfaces. It is an active sensing technology that works independently of ambient light.

Photogrammetry derives 3D information from overlapping photographs by identifying common points across multiple images and triangulating their positions. It is a passive technology that relies on visible light and texture.

This fundamental difference drives all the practical distinctions in accuracy, cost, capabilities, and ideal applications.

How Each Technology Works

LiDAR Operation

The LiDAR workflow follows a direct measurement approach:

1. Laser scanner emits rapid pulses (millions per second)
2. Pulses travel to target surface and reflect back
3. Sensor measures time-of-flight for each return
4. Distance calculated: Distance = (Speed of Light × Time) / 2
5. Combined with GPS/IMU data for georeferenced 3D coordinates

Key capability: Multiple returns per pulse allow penetration through vegetation to ground level.

Photogrammetry Operation

Photogrammetry reconstructs 3D geometry from 2D images:

1. Camera captures overlapping images (typically 60–80% overlap)
2. Software identifies matching features across images
3. Bundle adjustment calculates camera positions
4. Dense matching generates 3D point cloud
5. Structure from Motion (SfM) algorithms reconstruct geometry

Key capability: Captures rich color and texture information alongside geometry.

Accuracy Comparison

Accuracy depends heavily on platform, conditions, and processing quality. Here are typical ranges:

Vertical (Elevation) Accuracy

Technology	Typical Accuracy	Best Case
Airborne LiDAR	5–15 cm RMSE	3 cm
Drone LiDAR	3–5 cm RMSE	1–2 cm
Terrestrial LiDAR	1–5 mm	Sub-mm
Drone Photogrammetry	5–15 cm RMSE	2–3 cm
Aerial Photogrammetry	10–30 cm RMSE	5 cm

In open, flat terrain with good ground control, both technologies can achieve similar accuracy. LiDAR’s advantage emerges in challenging conditions.

Where LiDAR Excels

• Vegetated terrain: Laser pulses penetrate canopy; cameras see only the surface
• Low-contrast surfaces: LiDAR does not need texture; photogrammetry struggles on uniform surfaces
• Shadows and lighting: LiDAR is independent of illumination
• Steep terrain: Direct measurement vs. multi-image geometry

Where Photogrammetry Excels

• Horizontal accuracy: High-resolution imagery can exceed LiDAR in X-Y precision
• Color and texture capture: RGB information included automatically
• Feature identification: Visual context aids interpretation
• Fine edge detail: Sharp boundaries visible in imagery

Cost Comparison

Equipment Costs (2025)

System Type	LiDAR	Photogrammetry
Entry drone	$15,000–30,000	$2,000–5,000
Professional drone	$50,000–150,000	$10,000–30,000
Survey-grade drone	$150,000–350,000	$30,000–80,000
Manned aircraft	$200,000–600,000+	$50,000–150,000

Project Costs

For a typical 200-hectare aerial mapping project:

Cost Component	LiDAR	Photogrammetry
Data acquisition	$4,000–6,000	$2,500–4,000
Processing	$2,000–3,000	$1,500–2,500
Deliverables	$1,500–2,000	$500–1,000
Total	$7,500–11,000	$4,500–7,500

Bottom line: Photogrammetry costs 30–50% less than LiDAR for equivalent coverage, but the cost difference narrows for large projects.

Data Collection Comparison

Flight Requirements

Factor	LiDAR	Photogrammetry
Altitude	Higher (300–1000m AGL)	Lower (80–150m AGL)
Speed	Faster (100+ knots)	Slower for overlap
Coverage rate	Higher	Lower
Weather dependency	Lower (works in overcast)	Higher (needs good light)
Time window	Anytime (even night)	Daylight, low shadows

Data Volume

Technology	Typical Point Density	File Size (100 ha)
Airborne LiDAR	2–25 pts/m²	2–20 GB
Drone LiDAR	50–200 pts/m²	10–50 GB
Drone Photogrammetry	100–500 pts/m²	5–25 GB (dense cloud)

Photogrammetry can produce denser point clouds, but density does not equal accuracy in vegetated areas.

Processing Differences

LiDAR Processing Pipeline

1. Trajectory computation (GNSS/IMU integration)
2. Point cloud generation
3. Strip adjustment
4. Classification (ground, vegetation, buildings)
5. Product generation (DTM, DSM, contours)

Photogrammetry Processing Pipeline

1. Image alignment and bundle adjustment
2. Dense point cloud generation
3. Mesh/surface reconstruction
4. Orthomosaic generation
5. DSM extraction

The Key Difference

LiDAR directly produces classified ground points for accurate DTM generation. Photogrammetry creates a surface model (DSM) that requires additional processing to derive bare-earth terrain in vegetated areas, often with limited success under canopy.

Application Comparison

Forestry and Vegetation

LiDAR is the clear winner for forestry applications due to its vegetation penetration capability:

• Ground elevation under canopy: LiDAR excels; photogrammetry cannot see through vegetation
• Canopy height measurement: Both technologies work, LiDAR provides more detail
• Understory mapping: Only possible with LiDAR
• Species identification: Photogrammetry offers better visual context

Urban Mapping

Both technologies have roles in urban environments:

• Building heights: LiDAR provides superior accuracy
• Facade detail and textures: Photogrammetry captures visual quality
• Shadow areas: LiDAR measures regardless of lighting
• 3D city modeling: Often requires both for complete results

Construction Monitoring

Photogrammetry is often preferred for construction:

• Progress documentation: Visual records are essential
• Stockpile volumes: Both work; LiDAR slightly more accurate
• Cost-effectiveness: Photogrammetry has significant advantage

Flood Modeling and Hydrology

LiDAR is essential for hydraulic applications:

• Bare-earth DTM accuracy: Critical for flood modeling
• Floodplain mapping: Requires accurate terrain under vegetation
• Channel geometry: Direct measurement needed

Agriculture

Photogrammetry often makes more sense:

• Crop health monitoring: Multispectral imagery provides insights
• Drainage mapping: LiDAR more accurate but often overkill
• Cost-effectiveness: Photogrammetry significantly cheaper

Archaeology

LiDAR has revolutionized archaeological survey:

• Features under vegetation: Reveals hidden structures
• Landscape analysis: Sub-canopy terrain mapping
• Site documentation: Photogrammetry adds visual detail

The Hybrid Approach

Modern projects increasingly combine both technologies for comprehensive results:

Complementary Workflow

1. LiDAR for accurate terrain modeling (DTM)
2. Photogrammetry for visual context and texture
3. Integration for a complete dataset

Benefits of the Hybrid Approach

• Accurate ground surface from LiDAR
• Rich visual detail from imagery
• Color-coded point clouds
• Orthophotos draped on accurate terrain
• Best of both worlds

When to Use Hybrid

• Urban environments requiring both geometry and appearance
• Infrastructure projects needing accurate terrain plus visual documentation
• Forestry combining structure analysis with species identification
• Archaeological sites requiring sub-canopy terrain plus surface documentation

Decision Framework

Choose LiDAR When:

• Vegetation covers the terrain
• Accurate bare-earth elevation is critical
• Working in challenging lighting conditions
• Night operations required
• Power line or corridor inspection
• Flood modeling or hydrology applications

Choose Photogrammetry When:

• Visual documentation is the primary goal
• Budget is constrained
• Terrain is open and accessible
• Color and texture information needed
• Construction monitoring
• Agricultural applications

Consider Hybrid When:

• Budget allows both technologies
• Maximum accuracy and visual quality needed
• Complex environments (urban + vegetation)
• High-value infrastructure projects

Frequently Asked Questions

Is LiDAR more accurate than photogrammetry?

In vegetated terrain, yes. LiDAR’s ability to penetrate canopy and measure ground directly provides superior elevation accuracy. In open terrain with good lighting and texture, photogrammetry can match or exceed LiDAR horizontal accuracy.

Why is LiDAR more expensive?

LiDAR hardware is more complex, requiring laser scanners, precise timing electronics, and tight GPS/IMU integration. Development costs and smaller production volumes keep prices higher than cameras.

Can photogrammetry see through trees?

No. Photogrammetry requires visible texture and light, so it captures the canopy surface, not the ground beneath. LiDAR’s laser pulses can penetrate gaps in vegetation to reach the ground.

Which is better for drone surveys?

It depends on the application. Photogrammetry is more accessible and cost-effective for construction, agriculture, and open terrain. LiDAR is essential for vegetation mapping, corridor surveys, and accurate terrain modeling.

Can I use both together?

Yes, and it is increasingly common. LiDAR provides accurate geometry while photogrammetry adds color and texture. Many sensors now combine cameras with LiDAR for simultaneous collection.

Which technology is easier to learn?

Photogrammetry has a gentler learning curve; modern software automates most processing. LiDAR requires understanding of point cloud classification and additional processing steps.

Conclusion

LiDAR and photogrammetry serve different strengths. LiDAR excels where accurate terrain data matters, especially under vegetation. Photogrammetry delivers visual richness at lower cost in open environments.

The “versus” framing is increasingly outdated. Modern survey practice treats them as complementary tools, selecting based on project requirements or combining both for comprehensive results.

Whether your data comes from LiDAR or photogrammetry, LidarVisor can help you process and analyze your point clouds with automated classification, terrain modeling, and vectorization capabilities.