Photogrammetry vs. LiDAR accuracy in RTK drone mapping

When precision matters in drone surveying, one question always arises: photogrammetry vs LiDAR, which delivers better accuracy? Understanding how these technologies differ is crucial for making the right choice for your project, whether you’re mapping urban landscapes or forested terrain. This article compares the accuracy of LiDAR and photogrammetry, discusses use-case scenarios, and highlights why using an RTK base station is essential for achieving consistent, high-accuracy results.

How do LiDAR and photogrammetry work?

When comparing photogrammetry vs LiDAR accuracy, it’s essential to understand how each technology captures and processes data.

LiDAR survey

LiDAR (Light Detection and Ranging) technology uses laser pulses to capture precise distance measurements. Beyond its reputation for centimeter-level accuracy—even in dense forests or rugged terrain—LiDAR delivers rich spatial datasets. These include detailed 3D point clouds, elevation models, and orthophotos, making it a powerful tool for generating topographic maps, infrastructure models, and environmental insights across industries.

• Strengths: Direct laser measurements make LiDAR less susceptible to errors caused by lighting conditions, vegetation, or complex terrains.
• Limitations: LiDAR is often more expensive and requires specialized equipment and expertise.

Point cloud collected with LiDAR

Photogrammetry survey

Photogrammetry relies on overlapping 2D images to create 3D models. By using advanced software to stitch these images together, photogrammetry delivers detailed results that are highly effective for many surveying applications.

• Strengths: Cost-effective, accessible, and ideal for capturing visual context.
• Limitations: Accuracy can be affected by shadows, lighting variations, and dense vegetation.

3D model created from a photogrammetry survey

What affects LiDAR and photogrammetry accuracy?

The accuracy of LiDAR and photogrammetry depends on external conditions, such as lighting, weather, and surface type. Photogrammetry requires consistent daylight and struggles with shadows, overexposure, or low-texture surfaces. LiDAR performs well in darkness and can penetrate vegetation, but is affected by rain, snow, or reflective materials. Both methods show reduced accuracy in extreme weather or complex terrains unless carefully planned.

Vegetation

Photogrammetry can struggle in thick vegetation due to occlusions and variations in lighting. While it can still produce detailed models in open areas, results degrade in shadowed or uniform regions with little texture.

LiDAR’s laser pulses can penetrate canopies and map beneath vegetation, but extremely dense vegetation will limit ground detection and precision.

Mountainous terrain

Rugged terrain challenges both LiDAR and photogrammetry. Steep slopes can lead to uneven point distribution in LiDAR data, especially on angled or vertical surfaces that are difficult to capture with downward-facing sensors. Flying at a constant altitude above takeoff often results in varying distances to the ground, reducing point density and increasing errors at higher elevations.

For photogrammetry, these elevation changes affect ground sampling distance (GSD). While flat terrain ensures consistent resolution, mountainous areas create uneven GSD—ridges appear sharper, while valleys suffer from lower detail due to increased distance from the drone.

Reflective surfaces

Reflective surfaces like water, snow, glass, and shiny metals affect both LiDAR and photogrammetry. Water absorbs or reflects LiDAR pulses and lacks texture for photogrammetry, leading to data gaps and matching errors.

Snow and ice return strong LiDAR signals but hide the true ground. In photogrammetry, they often cause overexposure and poor contrast, reducing model accuracy, especially under bright light or glare.

Glass and glossy metals reflect both laser pulses and visible light unpredictably, resulting in ghost points in LiDAR and distorted or inaccurate surfaces in photogrammetry. Careful planning, controlled lighting, and post-processing help reduce these effects.

Weather conditions

Fog and rain interfere with LiDAR by scattering or absorbing laser pulses, reducing point density and accuracy. While some LiDAR sensors can operate in light rain, heavy moisture introduces noise and weakens ground returns, lowering overall data quality.

In photogrammetry, rain and fog reduce visibility and image clarity. Fog blurs features, making it harder for software to align images, while raindrops or motion blur distort photos, leading to inaccurate or incomplete models.

Wind affects both methods by destabilizing the drone. It can cause tilting or drifting, which leads to slight misalignment in LiDAR point clouds and blurred or mispositioned images in photogrammetry. Flying in calm conditions or at slower speeds helps preserve accuracy.

Lighting conditions

Photogrammetry requires consistent daylight. Its accuracy is sensitive to poor lighting, shadows, or overexposure—conditions that can distort the final model. Surveys must be carefully planned around weather and sun position to achieve optimal results. As an active sensing technology, LiDAR works independently of natural light and can operate in complete darkness. 

Electromagnetic environments

Electromagnetic objects—such as power lines, radio towers, and equipment emitting strong signals—can interfere with GNSS reception, which is critical for accurate georeferencing in both LiDAR and photogrammetry. 

In LiDAR surveys, this may result in subtle misalignments or drift in the data if the drone’s position or orientation is incorrectly recorded. In photogrammetry, electromagnetic interference can distort the spatial accuracy of reconstructed models, especially if image positions are off due to poor satellite lock. To minimize these effects, surveyors often use PPK processing, ground control points, and avoid flying too close to high-power sources.

When to choose LiDAR and photogrammetry

Choosing between LiDAR and photogrammetry starts with understanding your project’s specific conditions, deliverables, and budget. Each technology has unique advantages that shine in different environments, and in some cases, using both may be the best approach. Let’s explore how each method performs and when it’s most effective.

When to choose LiDAR

• Forested or vegetated areas: capture the ground beneath the canopy with ease.
• Steep, uneven terrain: ensure consistent elevation models despite visibility challenges.
• Low-light or night operations: operate flawlessly in dark or indoor settings.

When to choose photogrammetry

• Flat, open terrain: survey in areas like beaches, agricultural fields, and construction zones.
• Visual inspections, asset management, and architecture: deliver photo-realistic 3D reconstructions and identify surface detail, including damage, cracks,  and color for documentation.
• Budget-sensitive projects: enjoy lower equipment costs and simpler workflows.
  

Hybrid approaches

In many modern workflows, professionals combine both technologies—leveraging LiDAR’s accurate geometry with photogrammetry’s visual fidelity. This hybrid method is especially useful in BIM modeling, smart city planning, and digital twin projects.

LiDAR vs Photogrammetry comparison

Feature	LiDAR	Photogrammetry
Accuracy	Horizontal: ±1 cm; Vertical: ±1–3 cm (with RTK/PPK support)	Horizontal: ±1–3 cm; Vertical: ±2–4 cm (with RTK/PPK; needs more GCPs)
Data capture	Direct laser scanning	High-res image capture + image stitching
Terrain adaptability	Better in vegetated and uneven terrain	More limited in vegetated and uneven terrain
Lighting dependency	Not affected; works in darkness	Dependent on daylight and clear weather
Deliverables	Dense point clouds, digital elevation models with limited visual fidelity	Rich visual outputs: high-resolution orthophotos, textured 3D models, and point clouds with color information
RTK & PPK compatibility	Fully compatible with both RTK and PPK workflows	Fully compatible with both RTK and PPK workflows
Use case fit	Best for forested, rugged, complex, or poorly lit areas	Best for open, well-lit, and texture-rich environments
Training	Requires specialized training and experience.	Easier to learn with widely available tools and tutorials.
Cost	Higher equipment costs	More affordable

Why is an RTK base needed for both LiDAR and camera drones? 

Regardless of whether you use LiDAR or photogrammetry, accurate georeferencing is critical for high-quality results. This is where an RTK (Real-Time Kinematic) and PPK (Post-Processed Kinematic) come into play.

A GNSS base station, such as the Emlid Reach, provides RTK corrections during data capture, allowing for immediate accuracy in the field. The base sends real-time corrections to the drone’s GNSS receiver, typically through the drone’s controller or a ground station connected via radio or network link. This connection allows the drone to adjust its positioning data live, ensuring centimeter-level precision in both LiDAR and photogrammetry workflows.

With PPK, the base and the drone log raw data during the mission. The GNSS corrections are applied after data collection using post-processing software. PPK offers the same level of precision and can be particularly useful in areas with unreliable radio or cellular coverage.

In RTK and PPK workflows, ground control points are usually used as checkpoints to validate accuracy and align data with the real-world coordinates. 

Both RTK and PPK can deliver outstanding results. The choice depends on your workflow, environment, and connectivity. With Emlid Reach, you’re equipped for both.

The Emlid Reach base station is versatile, supporting both LiDAR and camera RTK drones. It seamlessly integrates into various workflows, making it an excellent choice for surveyors who need flexibility without compromising accuracy.

LiDAR and photogrammetry in real-life survey projects

In addition to its theoretical accuracy, LiDAR’s and photogrammetry’s real-world precision is best illustrated through practical field applications. One such example is a project conducted by The Indiana Drones at a 62,000-seat football stadium covering 20 square acres. The goal was to create a precise 3D model of the entire facility to aid engineers and facility operators in updates and renovations.

Indiana Drones set out to create a detailed as‑built model of a football stadium, leveraging two drone-based approaches: LiDAR and photogrammetry. Both missions were supported by the Emlid Reach RS2 RTK base station to ensure centimeter-level geolocation accuracy.

LiDAR approach

• Equipped with a  DJI M600 drone and a LiDAR system, the team captured hundreds of thousands of points per second, enabling rapid data collection, even over complex structures.
  
• The result: a dense, uniform point cloud accurately penetrating vegetated or overhung surfaces, delivered with ~2 cm precision.
  

Photogrammetry approach

• Using a DJI Inspire 2 drone with a GNSS module, the team collected overlapping aerial imagery processed into high-resolution textured meshes.
  
• The result: visually rich models with surface texture and color details.
  

The project showed that LiDAR and photogrammetry both deliver centimeter-level accuracy when paired with RTK from Emlid Reach RS2, but each excels in different scenarios. LiDAR stands out for fast data capture, structure penetration, and high geometric precision, making it ideal for complex or obstructed sites.

Photogrammetry, meanwhile, offers rich visual detail and cost-efficiency, perfect for open-area mapping. Together, they highlight the importance of matching the right technology to your project needs, and in some cases, combining both for the best results.

LiDAR vs. photogrammetry accuracy: is there a winner?

Both LiDAR and photogrammetry can deliver centimeter-level accuracy, but the best choice depends on your project’s specific conditions and required deliverables. LiDAR stands out in complex environments—like dense vegetation or steep terrain—where it penetrates obstructions to capture detailed elevation data. Photogrammetry, on the other hand, offers highly detailed, visually rich models and is often more cost-effective for open-area surveys.

By combining either technology with an RTK base station like Emlid Reach, surveyors can maximize positioning accuracy and data reliability. Whether you’re mapping construction sites, forests, or farmland, choosing the right approach starts with understanding the environment and your project goals.

Remember: accuracy isn’t about picking a winner—it’s about choosing the right tool for the job.

FAQ

What is more accurate, LiDAR or photogrammetry?

LiDAR and photogrammetry can achieve centimeter-level accuracy. LiDAR is typically more accurate in vertical measurements and complex terrains. Photogrammetry can match it horizontally in ideal conditions, but it has more limits in challenging terrain and weather conditions.

Why is LiDAR better for forests or hilly sites?

LiDAR’s laser pulses penetrate foliage and aren’t affected by lighting, making it reliable for undergrowth, slopes, and shaded zones.

Does photogrammetry accuracy change in poor lighting?

Yes—low light, shadows, and overexposure can distort results. Proper lighting and flight planning are essential for accurate image capture.