Light Detection And Ranging (LiDAR), also known as laser altimetry, is an active remote sensing technology that determines ranges (i.e., distances) by measuring the roundtrip time of a laser pulse that travels between a sensor and a target object [1]. The round trip time can be measured using either pulsed ranging (recording time directly) or continuous wave ranging (time is estimated from phase change in a transmitted sinusoidal signal) [2]. Lidars use a narrow bandwidth, around 1-2 nm, with high intensity and minimal divergence (less than 1 mm per meter). High intensity of laser beam enables lidars to penetrate through leaves and even canopies to reach branches or soil and have multiple return signals.  Each pulse illuminates a near-circular point (or area) on the ground, referred to as footprint. The small divergence feature of the beams allows lidars to focus on as small footprints as possible. Depending on altitude, footprint ranges from blow 10 mm for sUAS mounting lidars (example: Phoenix LiDAR Systems Ranger-HA[3]) to tens of meters for satellite based lidars (example: ~30 m - Global Ecosystem Dynamics Investigation (GEDI)[4] and 70 m for Geoscience Laser Altimeter System (GLAS)[5]). The returned signals will contain height information of all objects within the footprint in the order of their height. For instance, if a footprint contains a tree, the first returned signal will be from the top of the canopy, then the lower branches, and at the end from the trunk or the ground producing a vertical profile (discrete or continuous). Since most of the last returned signals would be from the soil, Digital Terrain Model (DTM, bare-earth model) is one of the lidars' useful outcomes.

Lidars need an accurate positioning system (usually RTK-GPS) to calculate their own location first and then that of the points in footprints. In terms of scanning, lidars work similarly to the line scanner and point scanner sensors explained in the Hyperspectral section. As a result, the distance of footprints on the ground in the x and y direction depends on the platform's forward speed and angular step between consecutive laser shots. In low altitude lidar applications, i.e. sUAS platforms, footprints might overlap generating millions of accurately georeferenced points from the scanned area called point cloud or mass points. The point cloud can be used to extract accurate physical characteristics of the objects in the area, such as plants' volume and height. At higher altitudes, however, footprints neither overlap nor form a point cloud, but they form a grid of footprints with cross-track and along-track distances.

Although, in theory, lidars can work in any wavelength, depending on the application, one or several bands will be selected to be used as a single spectral or, newly emerging, multispectral lidar [6]. Multispectral lidars take advantage of differences in penetration for different wavelengths to extract spectral and vertical profile information simultaneously. However, multispectral lidars are not fully operational yet, and single band lidars are typical of most agricultural remote sensing applications. Two of the most common wavelengths used in lidars, especially low altitude lidars, are 905 and 1550 nm; each has its pros and cons. The primary advantage of 905 nm is lower cost; due to the lower price of silicon-based photodetectors used at this wavelength than infrared photodetectors needed to detect 1550 nm [7]. Moreover, the laser signal's attenuation at 905nm wavelength is usually lower and less vulnerable to weather conditions and the target surface compared to 1550 nm. However, 1550 nm is safer for human-vision, allowing lasers with a considerable radiant energy per pulse [2]. As mentioned earlier, other wavelengths might be used as well. For instance, GLAS and GEDI use 1064 nm and 532 nm simultaneously while Phoenix LiDAR Systems use 1550 nm. Due to higher operational and computational cost of lidar data collection and processing, these systems are not as common as optical remote sensing sensors. Nevertheless, lidar data can be used for various applications such as high-precision terrain mapping, vegetation profile, and canopy foliar biomass estimations.

Although UAS- based lidars offer data in astonishing spatial resolutions (millimeter scale), they suffer from several disadvantages: 1) Processing millions of data points is computationally expensive and needs substantial computational resources, 2) Most of the time lidar data need accompanying images or videos for interpretation. 3) Prices for the software processing lidar data could go as high as the sensor itself or even higher. Nonetheless, with advances in computer hardware and software more vendors are offering lidar related products such as software, hardware, and consulting packages and more affordable prices are anticipated.


[1]       R. O. Dubayah and J. B. Drake, “Lidar remote sensing for forestry,” Journal of Forestry, vol. 98, no. 6, pp. 44–46, 2000.

[2]       K. Lim, P. Treitz, M. Wulder, B. St-Onge, and M. Flood, “LiDAR remote sensing of forest structure,” Progress in physical geography, vol. 27, no. 1, pp. 88–106, 2003.

[3]       Phoenix LiDAR Systems, “RANGER SERIES,” Phoenix LiDAR Systems, 2020. (accessed Mar. 11, 2021).

[4]       LP DAAC, “LP DAAC - GEDI Overview,” 2019. (accessed Feb. 23, 2021).

[5]       GLAS, “The Geoscience Laser Altimeter System (GLAS) Website,” 2002. (accessed Mar. 11, 2021).

[6]       S. Morsy, A. Shaker, and A. El-Rabbany, “Multispectral LiDAR data for land cover classification of urban areas,” Sensors, vol. 17, no. 5, p. 958, 2017.

[7]       J. Wojtanowski, M. Zygmunt, M. Kaszczuk, Z. Mierczyk, and M. Muzal, “Comparison of 905 nm and 1550 nm semiconductor laser rangefinders’ performance deterioration due to adverse environmental conditions,” Opto-Electronics Review, vol. 22, no. 3, pp. 183–190, 2014.