Photogrammetry vs. LIDAR: what sensor to choose for a given application | Wingtra (2024)

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.

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.

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.

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.

Before you can make an informed decision about what LIDAR drone system is best for your projects, you need to understand how it works. This way, you can assess specs and performance for yourself.

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.

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.

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.

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.

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.

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.

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).

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.

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 circ*mstances 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.

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.

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.

Both methods require a good setup on the ground, complete with check points andground 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.

In some cases, LIDAR will outperform photogrammetry, just like in some cases photogrammetry will outperform LIDAR. The difference is what information you need and why. If you need terrestrial information from a greenfield with moderate vegetation, LIDAR drone capture will definitely work better. If you need a detailed orthom*osaic with photorealistic, high-resolution looks at the ground and features in a certain area that is not very vegetated, photogrammetry will deliver what you need.

For more insights between the two, you can read our in-depth comparison on lidar vs. photogrammetry.

If it isn’t crystal clear, LIDAR is a way to get precise and accurate ground information in the midst of vegetation. While photogrammetry is disrupting many industries with its ability to provide precise and efficient aerial data capture, it simply cannot offer this needed terrain information.

It wasn’t five years ago that you had to hire an airplane and spend tens of thousands to conduct a broad-ranging LIDAR survey. This cost was prohibitive to doing it as often as truly needed in many cases, such as vegetation encroachment on powerlines or forest monitoring surveys.

Other exploration projects could also be delayed by this prohibitive cost when planning a business budget. With drone LIDAR, the cost is coming down, and the doors of progress are opening for those stepping forward to use it.

The world of drone photogrammetry is not confined to one single player. DJI’s Matrice 300 and Mavic 3E, as well as Sensefly’s eBee X, are alternatives that cater to varying needs and budgets.

Each of these drones brings its own strengths to the table, and choosing the right one often depends on specific project requirements and preferences. Below are a few key factors to consider when choosing.

RTK works with more signals and gathers location correction data in real time. This system mandates working connections during the entire flight, so if one fails, the data will be compromised.

In the end, while RTK is instant data correction and may seem more efficient, in real conditions, obstacles can block or interrupt the signals, lowering the reliability of this method.

You can also read more about the pros and cons of RTK and PPK in this detailed article.

From multirotors to fixed-wing to vertical take-off and landing (VTOL), the drone type significantly impacts its functionality.

WingtraOne, with its VTOL capabilities, merges the best of both worlds, offering the efficiency of a fixed-wing drone and stability of a multirotor.

A LIDAR drone involves a LIDAR scanner that shoots millions of laser light pulses to the ground below its flight path. It receives the pulse information that bounces off the surfaces below—hard ground, leaves, branches, infrastructure.

The sensor calculates the timing of all the bounces to read the distance the pulse travelled. This data offers precise horizontal and vertical insights about the surface below the flight area.

LIDAR is useful in cases of lightly to densely vegetated survey areas, because the pulses can reach the ground just like light would from the sky.

This is in contrast to photogrammetry, where a photograph cannot capture ground-level detail in the case of vegetation, shadows or intensely hom*ogeneous environments, like large areas of unmarked asphalt or large areas of sheer white snow with no features.

As you can imagine, LIDAR drones rely on a lot of hardware and software orchestration to deliver high quality results. Beyond this, processing data from a drone-mounted LIDAR sensor has until recently required extra know-how.

1. A suitable drone: Ideally, a drone equipped with a high-resolution camera and stable flight capabilities. Some drones are specially designed for photogrammetry purposes.
2. Cameras and sensors: While many drones come with built-in cameras, for professional-grade photogrammetry, you might consider drones that allow for sensor swaps. Ensure the sensor has a good enough resolution, and appropriate lenses for your project’s needs.
3. Ground control points (GCPs): For more absolute accuracy, and when you’re not using a PPK receiver, you should place GCPs on the ground. These are visible markers that help verify whether all geo locations on the map tightly align with the actual points on the Earth’s surface that they represent. The WingtraOne Gen II uses reliable PPK software, ensuring you don’t have to spend extra time setting up GCPs for every flight. Learn more about the cases when just checkpoints are enough.
4. Flight planning software: It’s good to have a drone that comes with its own software so that it works seamlessly. It also helps to have an intuitive interface, safety checklists and prompts that are easy to follow and understand. This software helps you plot the drone’s course and visualize where the drone will fly. This gives you a good understanding. With a good level of automation in the technology itself, this combination helps you know how to avoid running into any complications.
5. Sufficient battery power: Depending on the size of the area you need to cover, you might require additional batteries. That way you can switch out the batteries easily and continue flying, rather than waiting for them to recharge and losing time.

1. Data collection: Ensuring all captured images are of high quality and covering the entire area of interest, with sufficient overlap among photos.For RGB cameras, the overlap should be 60% to 70%, except for the RGB oblique camera, which should have a side overlap of 80%. Multispectral cameras on the other hand have a side overlap of 70%.
2. Geotagging: Combining the data collected with the PPK receiver, and the GNSS data from the CORS base station, correcting positional errors and adding accurate geospatial data to each image.
3. Post-processing: Importing the geotagged images to post-processing photogrammetry software such as Pix4D, Agisoft Metashape, Propeller, Bentley ContextCapture or ESRI SiteScan, you can then generate outputs including orthom*osaics, point clouds, 3D textured maps, and DSMs.
4. Analysis: Transferring the generated outputs into analysis software such as ArcGIS, Strayos or GlobalMapper, you can vamp up your reporting with accurate, up-to-date data and retrieve actionable insights.