Photogrammetry vs. LIDAR: what sensor to choose for a given application

WingtraOne GEN II with LIDAR and photogrammetry

In drone survey missions, the choice between photogrammetry and LIDAR depends heavily on the exact application. You also need to consider operational factors, such as cost and complexity. Knowing what outputs you really need will help you make the right decision.

We get a lot of questions about LIDAR sensors and their application to 3D surveys using drones. What is LIDAR and how does its output compare with results obtained with high-resolution RGB cameras and drone photogrammetry? In this article, we’ll explore the ways photogrammetry and LIDAR are actually quite different from each other, even if some of their outputs look similar. We’ll then dig deeper into specific applications and how photogrammetry can provide exceptional results for most missions at a fraction of the cost and complexity of LIDAR.
Orthomosaic DSM

Photogrammetry and professional, high-resolution cameras can cost-effectively generate 2D and 3D surveys like this one, with absolute accuracies down to 1 cm (0.4 in) RMS horizontal and 3 cm (1.6 in) RMS vertical.

Waldhauesern LIDAR output from Wingtra LIDAR
LIDAR as an active sensor bounces millions of laser points off the surfaces below to read bounce rates and distances that give a detailed idea of vegetation, infrastructure and topography down to 3 cm (1.2 cm) absolute vertical accuracy.
Table of Contents

What is photogrammetry?

In photogrammetry, a drone captures a large number of high-resolution photos over an area. These images overlap such that the same point on the ground is visible in multiple photos and from different vantage points. In a similar way that the human brain uses information from both eyes to provide depth perception, photogrammetry uses these multiple vantage points in images to generate a 3D map. 

The result: a high-resolution 3D reconstruction that contains not only elevation/height information, but also texture, shape, and color for every point on the map, enabling easier interpretation of the resulting 3D point cloud.

Drone systems that use photogrammetry are cost effective and provide outstanding flexibility in terms of where, when, and how you capture 2D and 3D data.

photogrammetry photo overlap illustration
Lidar drone mapping with detail zoom on dump truck
Photogrammetry combines images that contain the same point on the ground from multiple vantage points to yield detailed 2D and 3D maps.

What is LIDAR?

LIDAR, which stands for “light detection and ranging,” sends out pulses of laser light and measures the exact time it takes for these pulses to return as they bounce from the ground. It also measures the intensity of that reflection. LIDAR is a technology that has been around for many decades but has only recently been available in a size and power feasible for carrying on drones. And advances in this lightweight drone LIDAR category are happening quickly.

WingtraOne GEN II with LIDAR
LIDAR uses oscillating mirrors to send out laser pulses in many directions so as to generate a “sheet” of light as the drone moves forward. Through measuring the timing and intensity of the returning pulses, it can provide readings of the terrain and of points on the ground.

The sensor itself is only one part of a LIDAR system. Critically important for capturing usable data, you’ll also need a high-precision satellite positioning system (GNSS) as well as a high-accuracy inertial measurement unit (IMU). All of these high-end subsystems must work in perfect orchestration to enable processing of the raw data into usable information, a process called direct geo-referencing.

As the sensors have evolved, there’s now the option to capture aerial LIDAR data from one of two types of systems: classical manned airborne and lightweight UAV, which can be divided into three classes: entry-range, mid-range and high-end.

Classical airborne LIDAR surveys are conducted from a manned airplane and are less accurate but capable of covering more ground than lightweight UAV LIDAR operations. Specifically, you can cover between 10 and 1000 km2 (4 and 400 mi2) in one flight. The absolute accuracy depends on the flight height and sensor choice. At a typical flight height of 2000 m (6600 ft) above ground level (AGL), you can expect an absolute accuracy limit of about 20 cm (8 in) horizontal and 10 cm (4 in) vertical.

Lightweight drone LIDAR systems cover as much as the drone allows per flight. As we will discuss in detail in below sections, these systems can be more accurate than those carried by manned aircraft. Specifically, fixed-wing drones carrying a LIDAR payload can cover up to 380 ha (930 ac) in a flight, with absolute accuracy limits down to 3 cm (1.2 in) vertical accuracy.

While the accuracy of lightweight LIDAR systems is limited to 3 cm vertical in all tested cases, the ability to capture ground beneath relatively open canopy and vegetation is a critical advantage. Over the past few years, these sensors have gotten easier to use, with the Wingtra mid-range leading the way in easy integration. 

Wingtra LIDAR output Waldhausen
This Wingtra LIDAR 3D output provides elevation information, which can be colorized based on either elevation or intensity to aid interpretation. Depending on the exact needs from the survey, a high-end, mid-range or even low-end sensor may deliver what is needed. It's important to note that if multiple attachment points are necessary, or if the vegetation is extremely dense, high-end is going to be the only way to get readings from a non-nadir perspective.

How does LIDAR differ from photogrammetry?

LIDAR is an active remote sensing method based on laser technology and the measurement of rebounding light points. Photogrammetry is a passive remote sensing method that involves capturing and aligning a series of digital images that overlap, as well as location data associated with pixels.

While both of these methods capture mapping information, the way to process that information and the analytics they avail differ.

It is true that taking pictures strategically and using software and base station data to line them up and geotag them is relatively simple compared to active sensing. We are talking about a camera and PPK unit working in harmony vs. three pieces of sophisticated hardware casting out millions of data points and recording their activity based on precise location information.

Yet in both cases, to capture and process the data is getting easier and more accessible. Especially since Wingtra LIDAR entered the market. So let’s focus a bit more on some differences and then get into what you need, when and why.

The key difference between photogrammetry and LIDAR involve capabilities and results—and when you know what these are, you can see they actually complement each other for complex projects. While LIDAR offers precise outputs that outline canopy and reach through thicker vegetation to provide terrestrial information, photogrammetry results in life-like and accurate perspectives.

What about accuracy: Photogrammetry vs LIDAR

As we have seen, photogrammetry and aerial LIDAR differ in the way points on the ground are registered. This directly affects the final point cloud accuracy and we will see that, especially for horizontal accuracy of areas free from dense forest canopy, photogrammetry clearly outperforms aerial LIDAR.


In the case of photogrammetry, a quality, high-resolution, full-frame sensor camera like WingtraOne’s Sony RX1R II can yield outputs with horizontal (x-y) accuracies in the range of 1 cm (0.4 in) and elevation (z) accuracies in the range of 2 to 3 cm (0.8 to 1.2 in) over hard surfaces, enabling precise volumetric analysis.

Note, however, that in order to achieve such performance the payload used for photogrammetry must be a professional one, with the right image sensor and lens to capture more detail. It’s not just about the number of pixels. In fact, two cameras with the same number of megapixels and different size sensors provide different image quality and accuracy. 

Not all pixels are created equal.
Francois Gervaix
Geospatial expert

Proper mission planning and post-processing are also important for achieving optimal accuracy: good overlap among images increases accuracy and provides better error correction. A high-end drone system with professional mission planning and post-processing workflow helps ensure that you capture quality data that generates accurate results.

Stockpile volume measurement
Stockpile measurement based on photogrammetry. Accurate 3D models with 2 to 3 cm (0.8 to 1.2 in) of vertical accuracy can be used for precise volumetric calculations across a number of industries. Image from 3DR Site Scan platform


As for aerial LIDAR methods, the sensor does not target specific features on ground but instead shoots the beams at a set frequency in a defined pattern. Even if the horizontal accuracy of the single point might be higher, the best horizontal accuracy of a point of interest on the ground is limited by the point density.

In the case of LIDAR, horizontal accuracy is the key analytic, since it is excellent at gathering data to plan based on terrain and vegetation outlines.

Manned aerial LIDAR can provide a point density of up to 50 pts/m2 and offers a typical absolute accuracy of 20 cm horizontal and 10 cm vertical if flown at a standard height of 2000 m (6600 ft) AGL.

By flying lower, lightweight UAV LIDAR provides a higher point density than manned aerial LIDAR and can achieve better accuracy. Mounted on a multi-copter, point density and the resulting point cloud accuracy can be improved by flying low and slow at the expense of reduced efficiency.

Wingtra LIDAR data profile
UAV LIDAR data has come a long way in the last several years, enabling cost-effective capture of terrestrial information and details for site planning and infrastructure assessment.

In the case of LIDAR on fixed-wing drones, a point density between 50 and 250 pts/m2 (10 ft2) is possible. So an absolute vertical accuracy of about 3 cm (1.2 in) can be achieved.

LIDAR-derived point cloud accuracy depends on the precision of the LIDAR itself and the quality of the INS—inertial measurement unit (IMU) and GNSS—system. The IMU is especially important in providing data in strips that are aligned for construction of an accurate and precise 3D rendering of terrain and vegetation. Few lightweight sensors offer data right after the flight that is strip aligned. So Wingtra LIDAR set a benchmark in offering this based on a top-quality combination of components. 

Other key specs comparing photogrammetry and LIDAR


If you are working with classical airborne LIDAR, you can cover up to 1000 km2 (400 mi2). More and more surveyors and businesses are choosing to conduct photogrammetry with UAVs due to better accuracy, lower prices, safety and on-demand capture. In terms of LIDAR, as the lightweight sensors for drone capture improve, we are seeing more and more efficiency, drones to capture the data. To the point where you can now cover up to 380 ha (930 ac) in a sub hour flight.


Photogrammetry offers photorealistic mapping results in the form of orthomosaics, point clouds and textured mesh. A true, life-like digital twin. This can be extremely helpful for identifying and measuring features, especially when the accuracy is so good. LIDAR offers a laser point cloud that offers topographical and 3D details amidst vegetated areas, something that photogrammetry is weaker at. You can also colorize LIDAR data with RGB data for analysis of features and vertical information.

Drone 3d mapping with photogrammetry reconstruction of Regensberg
Wingtra LIDAR output field and forest and road

Photogrammetry (top) and LIDAR (bottom) both allow creation of accurate 3D maps. Photogrammetry outputs also include high-resolution visual data in full color for every point on the map to aid in the interpretation. For ease of analytics, LIDAR outputs are often colorized based on intensity of reflection, such that soft objects (leaves of trees, grass) can be more easily differentiated from hard objects (walls, roofs, cars, etc.). 

Vegetation penetration

In the cases of both LIDAR and photogrammetry, the sensors do not penetrate through the leaves. However the nature of the two methods allows for LIDAR to offer information on the ground in densely wooded areas that feature a tight canopy.

Specifically, the laser beams shower down over a forested area, and some can reach the floor and bounce back up. Photogrammetry, which relies on photographs, will not give as much information on canopy-covered grounds due to the darkness and shadows that dense tree stands create. Note that photogrammetry will offer topographical information in lightly wooded areas or forested areas that do not feature heavy canopy.

It's important to note that LIDAR pulses don't go through solid surfaces; they travel just like light would. So if you can't see the sky or any light penetrating dense canopy, the laser won't make it either. I.e., mapping terrain under very dense vegetation is still not possible, even with LIDAR.

Workflow and support

In the case of photogrammetry, companies like Pix4D, Agisoft, Bentley CC, Propeller and Dronedeploy have optimized workflows over years of experience with much data. In some cases, the workflow is turnkey—one software suite offers clients processing and accuracy verification within 24 hours when they simply upload the data. Cloud/server solutions allow for large-scale data processing with minimal hardware investment. And dedicated support teams are available to help. 

Point clouds from manned aerial LIDAR require trained professionals to oversee post processing. While the processing is well-established, it requires expertise that cannot be picked up along the way and is not an inherent part of the software available.

In the case of lightweight drone LIDAR, the processing is getting easier and easier, as companies like Wingtra offer point clouds soon after landing, and processing from there is developing across a range of drone LIDAR processing and analytics software. 

Processing time

Photogrammetry processing for full resolution takes several hours (or days) depending on the project size. If you only need a sparse set of accurate tie points (like from a LIDAR source), photogrammetry tools offer downsampled processing options.

LIDAR point clouds are directly geo-referenced with real-time kinematic (RTK) positioning during flight, or via post-processed kinematic (PPK) positioning after the flight. Typical entry-level post-processing steps include trajectory adjustments, strip adjustments and noise filtering. 

Depending on the project size and the level of the sensor, the process takes from less than an hour (smaller projects with lightweight drone LIDAR) to several days (large projects with manned aerial LIDAR). 

WingtraOne GEN II with LIDAR payload and pointcloud on screen
Wingtra's mid-range LIDAR system is focused on precision right after the flight, post-process strip alignment and adjustments have been removed as a matter of efficiency.

Photogrammetry and LIDAR in specific applications

Photogrammetry provides photorealistic range of 2 and 3D results. Yet there are some applications—specifically featuring power lines or large areas of dense forest canopy—that you can really only tackle with LIDAR. So having both payloads available would be a benefit across the entire workflow of some projects. Let’s look across a range of actual applications as follows:

Topographical maps featuring light vegetation (sparse tree stands or open canopy) are best captured with high-resolution RGB data capture available through payloads like the RX1R II with PPK. The resolution and photorealistic results are useful in cases like wildfire management in residential areas, and have been used by some of the world’s largest urban fire and rescue services, since the information serves many stakeholders who need a real view of what’s happened. This payload is also highly accurate, offering dependable, survey-grade results to government agencies, as in this Indiana Port Authority survey case. Finally, and not the least important is price and ease of workflow. For businesses like this vineyard, which would benefit greatly from detailed and accurate information without extensive training and overhead.

Topographical maps with medium vegetation can be obtained via a combination of photogrammetry and LIDAR. To capture the additional information below the vegetation, ground survey methods or aerial LIDAR can be used. The combination with ground survey methods keeps the price down while guaranteeing high accuracy plus the resolution and photorealistic results available through photogrammetry. Detailed tutorials offering a reasonable learning curve on this approach are available.

Drone lidar survey results from motion forest compared
While LIDAR can provide more detail underneath denser vegetation, both photogrammetry (lower graph) and LIDAR (top graph) can generate terrain models underneath sparse vegetation where the ground is partially visible from the air. (The data shown in this graphic was captured at 30 m above the ground.*)

Large-scale topographical maps featuring heavy vegetation are best acquired via LIDAR. A digital terrain model (DTM) of the forest ground provides useful information for project planning in construction (e.g., the planning of new roads), forest biomass or detailed information on vegetation and habitats via topography and underlying terrain, Applications falling under these circumstances will always require LIDAR at least in part to normalize topographical data, as is shown in research that examines the strengths and limits of photogrammetry in such cases.

Typically state agencies try to maintain reasonably accurate digital terrain models (DTMs) of the forest grounds. For these kinds of large-scale projects with low resolution requirements, LIDAR is the most cost-effective option available. 

Bare-Earth mining, volumetric and natural resource surveys are best handled by high-end RGB payloads like the RX1R II. Even massive surveys, like those performed by an energy firm in Finland and the US, are ideal with the right drone and RGB camera. Established mining firms like Jellinbah and Westmoreland have offered examples of how they’ve incorporated photogrammetry into their workflows because of the accuracy, resolution and photorealistic results that enable efficient mine management. On top of this, photogrammetry is cost effective and saves time not only to capture and process data related to cut and fill volumes, stockpile assessments and status reports, but also to share this information and reconcile with contractors and stakeholders.

Large-scale RGB drone mapping output from TOGO mapping project
Large-area DSM drone mapping output from TOGO

Professional drones like the WingtraOne can capture up to 400 hectares (988 acres) in a single flight at resolutions of 2.5 cm/pixel (1 in/pixel). In this example above, WingtraOne was used to efficiently create surveys over tens of thousands of hectares (2D RGB map on top, 3D digital surface model at the bottom).

Power line surveys for vegetation control can be done with LIDAR or high-resolution photogrammetry and powerline extraction features on software like Pix4Dsurvey. For the sake of photorealism, price and workflow, we recommend the later option. A good example is this one from Poland, where FlyTech UAV used photogrammetry to revolutionize its powerline vegetation management. Research is ongoing around photogrammetry as a go-to, cost-effective solution that is even incorporated into a management update to the largest European power grid operator.

Power line pole tower inspection benefits from live video inspection with a multicopter carrying an RGB or thermal payload. These are usually relatively small areas that multicopters can maneuver around and take oblique shots of easily and safely, as this overview demonstrates. With this method, you get all information within a very short amount of time. Zoom cameras allow detailed inspection that can not be offered by photogrammetry or LIDAR.


SBB train spraying hot water on vegetation
Drone data and artificial intelligence combined help Swiss Federal Railways (SBB) target and apply hot water precisely to encroaching vegetation.

Rail track inspection is still most often carried out from the ground—by a train equipped with ultrasonic, LIDAR, and visual sensors. Inspection from the air with either photogrammetry or aerial LIDAR is gaining more and more interest but both methods are in early stages. High-resolution photogrammetry offers data that avails outputs with all of the essential details accurately and autonomously while saving time. Plus the photorealism adds an element of easy identification and versatility that can answer to a range of questions. In the end, more and more firms are making the case for this methodology.

City mapping with vertical structures requiring 3D vantage points has been widely demonstrated with photogrammetry based on imagery captured with a payload featuring oblique capabilities. For cityscapes with many high-rises and intense levels of vertical detail, multicopters work well, although their ability to cover wide-spread areas per flight is compromised. VTOL drones carrying oblique payloads can still capture wide areas and achieve impressive vertical accuracy. In fact, more and more cases of city mapping are being reported with VTOL drones.

WingtraOne photogrammetry drone flying over Cancún
WingtraOne gathered photogrammetric data to map the entire city of Cancún down to 5 cm (2 in) absolute accuracy in 19 days flight time.

Operational considerations

The difference between photogrammetry and LIDAR is narrowing when considering operational and logistical factors. The concern has long been that a LIDAR, as an active sensor, requires more components to work perfectly in sync and that small gaps or errors in sensor measurements can lead to significant errors in outputs. Or worse, outputs that “look” right but are not. That’s why when  lightweight LIDAR first hit the market, these concerns came along with it more than ever. 

Wingtra LIDAR has addressed this with its mid-range lightweight system, offering reliable data that is strip aligned upon landing and an intuitive app-driven point cloud generation workflow for those with minimal experience.

Both methods require a good setup on the ground, complete with check points and ground control points (GCPs) in some cases. I.e., it doesn’t matter what quality the system you use is, the data will be determined by what you test it against.

LIDAR projects have become more and more accessible with UAVs over the years, and this has never been more true than now, with the launch of Wingtra LIDAR.

Field operator flying WingtraOne from a gravel terrain site
The ease of use of photogrammetry solutions like the WingtraOne translates into greater operational flexibility, the ability to deploy multiple systems to cover distributed sites, greater frequency of captures, and overall reduced costs.

Final thoughts

We have explored the differences between how photogrammetry and LIDAR work and the similarities in their outputs and learned about situations where each technology can be best applied. We can see that a combination of LIDAR and photogrammetry can meet most of the everyday challenges presented across a range of projects and industries. While photogrammetry provides exceptional accuracy and stunningly detailed maps, LIDAR reaches places it simply cannot, and both are types of data are now, more than ever, available on demand and with minimal expertise overhead.

Learn more about the use of 3D models and photogrammetry in surveying and mining and how the WingtraOne system enables efficient, accurate, and cost-effective mapping missions.

Gabriel Torres (guest expert blogger)*

Gabriel Torres (guest expert blogger)*

Co-founder and former CEO of MicaSense, Inc.
*Contributed in collaboration with Wingtra's media team

Published by


Wingtra develops, produces and commercializes high precision VTOL drones that collect survey-grade aerial data.

Read about our drone

Wingtra LIDAR is here

Experience unmatched efficiency with user-friendliness and reliability.

Watch demo

Watch a recorded session of our online demo webinar and see the WingtraOne GEN II in action.

We use cookies to provide a user-friendly experience. By continuing to browse this site, you give consent for cookies to be used and stored on your device. To find out more please read our Privacy Policy.