With the latest developments in ölçme measuring with unmanned aerial vehicles Li, the legend about LiDAR and Photogrammetry methods has been circulating more than ever. So it is inevitable to compare these two methods. In fact, these two technologies have similarities as well as differences. It is therefore important to understand at the outset that they offer significantly different products, produce different outputs and require different conditions. Compared to traditional land surveying methods, there is no doubt that both technologies; it delivers results faster and with a much higher data density (both methods measure all objects without interpolation). However, the choice of the best technology for your project depends, of course, on your usage, environmental conditions, delivery conditions and other factors as well as your budget.
How Do These Technologies Work?
LiDAR is a technology based on laser beams. It expels the laser and measures the time it takes for the light to return. On the other hand, photogrammetry is a passive detection system based on the conversion of photographs from 2D to 3D cartometric models. It uses the working principle of human eyes or 3D videos to create a depth perception that allows users to view and measure three-dimensional objects. The limitation of photogrammetry is that it can only produce dots based on what the camera sensor illuminates with ambient light. To summarize, LiDAR uses lasers to measure, while photogrammetry makes measurements using processable and combinable images.
Outputs of Lidar and Photogrammetry Measurements
The main product of LiDAR measurements is a 3D point cloud. Point cloud density depends on sensor parameters (scan frequency and repeat rate) as well as flight parameters. Assuming that the scanner vibrates and oscillates at a constant rate, the point cloud density depends on the flight altitude and speed of the aircraft. Various examples of usage may require different point cloud parameters, for example, you might want to create a dense cloud cloud with more than 100 points per square meter for power line modeling, while the 10 point / m2 cloud is good enough to create a Digital Terrain Model of the rural area. On the other hand, it produces full color 2D and 3D models of the earth, which are easier to visualize and interpret than photogrammetry. The main output of photogrammetry is unprocessed images, orthophoto maps, Digital Surface Model and 3D point clouds created by processing and stitching hundreds or thousands of images. Outputs can be displayed at pixel sizes below 1 cm or within the sampling range. Given this information, photogrammetry is a preferable technology for use cases where visual assessment is required (eg construction inspections, asset management, agriculture). Since LIDAR can identify narrow and poor-looking objects, modeling of narrow objects such as power lines or telecom towers may be preferred for samples where light is weak or night measurements are required. Accuracy Accuracy in measurement always has two dimensions: relative and absolute. Relative accuracy is a measure of how objects are positioned relative to each other. Absolute accuracy refers to the difference between the locations of objects and their actual position on Earth (therefore the relative accuracy of any measurement may be high, but the absolute accuracy may be low).
LİDAR is one of the most accurate measurement technologies. By using geodetic methods with terrestrial platform lasers, accuracy of centimeters can be obtained. However, in airborne LIDARs, it is much more difficult to obtain high accuracy since the sensor is in motion. For this reason, the sensor is always connected to the IMU and the GNSS receiver, which provides information on position, rotation and movement on the air platforms. All of this data is instantly combined and ensures high relative accuracy (1-3 cm). To achieve high absolute accuracy, 1-2 Ground Control Points (YKN) and several control points should be added for verification. In some cases, advanced RTK positioning systems can be used when additional GNSS positioning accuracy is required.
Photogrammetry can also achieve an accuracy of 1-3 cm, but this requires significant experience in selecting the appropriate equipment, flight parameters and processing data appropriately. RTK and additional Ground Control Points (YKN) should be used to achieve high absolute accuracy. However, it is possible to achieve an absolute accuracy of 5-10 cm with a medium-cost drone and several YKNs.
Data Collection, Processing and Productivity
There are significant differences in the speed of data collection between the two technologies. One of the critical parameters required to accurately process data in photogrammetry is the 60-90% front and side overlap of photographs depending on the structure of the field and the equipment applied. In typical LiDAR measurement, it requires only 20-30% overlap between flight lines, which makes data collection much faster. In addition, more YKN should be used in photogrammetry to achieve accuracy at the LIDAR level. This means more time and cost to measure YKN. Moreover, it is quite quick to process LIDAR data. To obtain the final product from the raw data, a calibration process that lasts only 5-30 minutes is sufficient. In photogrammetry, data processing is the most time consuming part of the overall process. In addition, high-data images require powerful computers capable of processing. The process takes an average of 5 to 10 times longer than the field data acquisition. On the other hand, for many use cases, such as power line inspections, LiDAR point clouds often require additional classification procedures that require expensive software such as TerraScan.
When we look at the overall cost of LiDAR and photogrammetry research, there are multiple cost elements to consider. The first is hardware. UAV LiDAR sensor sets (scanner, IMU and GNSS) cost between $ 50,000-300,000 and need to spend an additional $ 25,000-50,000 for the appropriate UAV platform. This means that the max. It costs $ 350,000. For photogrammetry, all you need is a camera-equipped drone, which is much less costly ($ 2,000-5,000). Another important factor affecting cost is labor and time. Here, LiDAR has a significant advantage over photogrammetry because it takes less time to process the data. There is no need to place and mark YKN at the same time. In general, photogrammetry services are cheaper than LiDAR due to the amortization of hardware investment, depending on the case and business model. In some cases, however, it can compensate for sensor costs through efficiency gains from LiDAR.
LiDAR is more useful in researching narrow structures such as power lines or telecommunications towers and mapping under tree shade areas, while photogrammetry will be the best option for projects that require visual data such as construction inspections, asset management, agriculture. both are powerful technologies. With the fall in hardware and software prices, it will become increasingly available. Both technologies are still counted in the early days when UAV applications are concerned, and in the following years they will undoubtedly be used much more, especially due to hardware prices and machine learning software automation.