Lidar and photogrammetry are two technologies that have been long used in remote sensing. In the past decade, the development of UAVs has made both these technologies more accessible. Both Lidar and photogrammetry have their own advantages and disadvantages. Despite Lidar being more accurate than photogrammetry, there are several instances where the latter has an edge. In this article, we will look at the comparison between both the remote sensing technologies:

Photogrammetry

Photogrammetry is the science of making measurements from photographs. In remote sensing, a drone captures several images of a site with a certain degree of overlap between two images. The images are then stitched together to form a larger, highly detailed image, also known as an orthophoto. You can then make accurate measurements on this orthophoto, create elevation models and textured 3D models. Through multiple overlapping photographs, photogrammetry can deduce the depth of individual features on the images.

Lidar vs Photogrammetry
Photogrammetry produces high-resolution digital Orthophotos.

There are two basic equipment requirements for photogrammetry. A drone with a high-resolution camera, and aerial image processing software like WebODM to stitch together all the captured images. This setup is relatively cheap (check out our list of the best photogrammetry drones here) and can be done by anyone.

Lidar

Lidar stands for Light Detection and Ranging. The way Lidar works is similar to SONAR or RADAR. It sends out laser beams at an object and measures the time it takes for the reflected light to return to the receiver. This in turn gives the distance (range), size, and location of the object. Using this data, a specialist can create point clouds and elevation models. Lidar beams can penetrate thick canopies and vegetations, thus, it makes Lidar highly accurate. This method is widely used in forest surveys, topographical maps, archaeological surveys, and more.

Lidar vs Photogrammetry
Lidar-derived Digital Elevation Model over Zion National Park, Utah.  Lidar provides more depth and accuracy.

Employing Lidar technology is much more expensive compared to photogrammetry. Lidar scanners have become less bulky and can be mounted on larger UAVs. Despite that, Lidar is still reserved for large-scale industrial applications.

Lidar vs Photogrammetry

Accuracy

Lidar, as mentioned, is far more accurate than photogrammetry. Since Lidar uses pulses of light to physically measure the distance from the receiver to the actual object, it produces an extremely dense point cloud with much more information than a photogrammetry point cloud. Additionally, the global accuracy of the Lidar models can be further increased up to 1cm using GCPs. Another factor that makes Lidar more accurate is that it does not rely on visual light. Since the measurement is through the reflected pulses of light, Lidar can even work in low light or unfavorable weather conditions.

Lidar scanners work extremely well in dense environments like forests and urban environments with tall buildings. Under good conditions, Lidar has a 90% penetration rate in thick vegetation.  So if a survey was done in a canopy-covered area using both the technologies, a photogrammetry-produced elevation model would only display the top of the canopies, while the Lidar model would provide more depth.

However, there are areas where Lidar, despite its better accuracy, isn’t a good candidate. Photogrammetry is better for any operation which relies on visual data. Construction site inspections, stockpiles measurement, bridge inspections, roads, and urban planning, rely on high resolution colored orthophotos which is possible through photogrammetry.

Lidar vs Photogrammetry
A colored 3D model produced in photogrammetry. Textured 3D models look highly realistic and visually accurate compared to Lidar models.

Another area where photogrammetry fares better is in producing highly realistic textured 3D models. Since RGB imagery is used in photogrammetry, the 3D models produced of a site are visually accurate to real-time changes. Additionally, photogrammetry can also achieve high global accuracy through the use of GCPs, RTK, and GNSS systems, or PPK. RTK drones like the DJI Phantom 4 RTK demonstrate global accuracy of up to 1cm.

Agriculture

Agriculture is another sector that utilizes remote sensing for vegetation management. Both Lidar and photogrammetry are used in vegetation management.

Lidar: Lidar can help predict yield rates in an agricultural field. It creates a topographical map and highlights the slopes and sun exposure on the field. Researchers have used this topographical data with the farmland yield results from previous years, to categorize land into zones of high, medium, or low yield. Therefore, this result can assist farmers to spray fertilizers more accurately.

Secondly, Lidar is also used to monitor insects in the field. It can detect the movement and behavior of individual flying insects along with their species. Lastly, this technology is most useful in orchards or vineyards, where thick canopies block GNSS satellite signals.

Photogrammetry: Photogrammetry presents a more comprehensive use in agriculture compared to Lidar. Since agricultural photogrammetry relies on images of different spectrums, it becomes much easier to identify plant health, soil health, pests, and several other factors. Plants tend to absorb visible red light while reflecting near-infrared wavelengths. Thus, this makes using advanced indices like ENDVI, NDVI, and SAVI a great tool to analyze an agricultural field.

ENDVI | Lidar vs Photogrammetry
An ENDVI output using a photogrammetry software called WebODM. This output directly measures and displays plant health.

idar can map out the topography of the agricultural field and yield rates can be predicted using that data. However, through photogrammetry, one can monitor and assess the real-time health of the crops. Read more about vegetation management using photogrammetry software, like WebODM.

Cost

While Lidar dominates relative accuracy in complex environments and the creation of elevation models, it does come with a steep price tag. Integrating a Lidar scanner into a survey drone, hiring a Lidar specialist, and processing the data, can all add up to about $350,000. This makes Lidar greatly inaccessible.

On the contrary, photogrammetry can be employed under a few thousand dollars by almost anyone. A DJI Phantom 4 RTK drone with cloud-based photogrammetry software like WebODM is far more accessible and affordable by most professionals.

Conclusion

In conclusion, both Lidar and photogrammetry have their own applications. Selecting either one depends on the type of operation. If you want to capture data of sites which have dense vegetation (over 60%) it is better to invest in a UAV Lidar system. However, for most small scale projects, plant health management, and visual data, photogrammetry can produce great results at a far lesser price.

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