Editing Lidar (section)

==Classification==

Lidar can be oriented to [[nadir]], [[zenith]], or laterally. For example, lidar altimeters look down, an atmospheric lidar looks up, and lidar-based [[collision avoidance system]]s are side-looking.

Laser projections of lidars can be manipulated using various methods and mechanisms to produce a scanning effect: the standard spindle-type, which spins to give a 360-degree view; solid-state lidar, which has a fixed field of view, but no moving parts, and can use either MEMS or optical phased arrays to steer the beams; and flash lidar, which spreads a flash of light over a large field of view before the signal bounces back to a detector.<ref name=":14">{{Cite web|title=The Wild West of Automotive Lidar|url=https://spie.org/news/photonics-focus/marapr-2020/wild-west-of-automotive-lidar|access-date=2020-12-26|website=spie.org}}</ref>

Lidar applications can be divided into airborne and terrestrial types.<ref name=":0">{{Cite book|title = Airborne and terrestrial laser scanning|last1 = Vosselman|first1 = George|publisher = Whittles Publishing|year = 2012|isbn = 978-1-904445-87-6|last2 = Maas|first2 = Hans-Gerd}}</ref> The two types require scanners with varying specifications based on the data's purpose, the size of the area to be captured, the range of measurement desired, the cost of equipment, and more. Spaceborne platforms are also possible, see [[satellite laser altimetry]].

Airborne lidar (also ''airborne laser scanning'') is when a laser scanner, while attached to an aircraft during flight, creates a [[Point cloud|3-D point cloud]] model of the landscape. This is currently the most detailed and accurate method of creating [[digital elevation model]]s, replacing [[photogrammetry]]. One major advantage in comparison with photogrammetry is the ability to filter out reflections from vegetation from the point cloud model to create a [[Digital Terrain Model|digital terrain model]] which represents ground surfaces such as rivers, paths, cultural heritage sites, etc., which are concealed by trees. Within the category of airborne lidar, there is sometimes a distinction made between high-altitude and low-altitude applications, but the main difference is a reduction in both accuracy and point density of data acquired at higher altitudes. Airborne lidar can also be used to create bathymetric models in shallow water.<ref>{{Cite journal|title = Airborne laser bathymetry for documentation of submerged archaeological sites in shallow water|journal = ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences|pages = 99–107|volume = XL-5/W5|doi = 10.5194/isprsarchives-xl-5-w5-99-2015|first1 = M.|last1 = Doneus|first2 = I.|last2 = Miholjek|first3 = G.|last3 = Mandlburger|first4 = N.|last4 = Doneus|first5 = G.|last5 = Verhoeven|first6 = Ch.|last6 = Briese|first7 = M.|last7 = Pregesbauer|bibcode = 2015ISPArXL55...99D |year = 2015|doi-access = free|hdl = 1854/LU-5933247|hdl-access = free}}</ref>

The main constituents of airborne lidar include [[digital elevation model]]s (DEM) and digital surface models (DSM). The points and ground points are the vectors of discrete points while DEM and DSM are interpolated raster grids of discrete points. The process also involves capturing of digital aerial photographs. To interpret deep-seated landslides for example, under the cover of vegetation, scarps, tension cracks or tipped trees airborne lidar is used. Airborne lidar digital elevation models can see through the canopy of forest cover, perform detailed measurements of scarps, erosion and tilting of electric poles.<ref>{{Cite journal|last1=Chiu|first1=Cheng-Lung|last2=Fei|first2=Li-Yuan|last3=Liu|first3=Jin-King|last4=Wu|first4=Ming-Chee|title=National Airborne Lidar Mapping and Examples for applications in deep-seated landslides in Taiwan|journal=Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International|issn= 2153-7003}}</ref>

Airborne lidar data is processed using a toolbox called Toolbox for Lidar Data Filtering and Forest Studies (TIFFS)<ref name=":10" /> for lidar data filtering and terrain study software. The data is interpolated to digital terrain models using the software. The laser is directed at the region to be mapped and each point's height above the ground is calculated by subtracting the original z-coordinate from the corresponding digital terrain model elevation. Based on this height above the ground the non-vegetation data is obtained which may include objects such as buildings, electric power lines, flying birds, insects, etc. The rest of the points are treated as vegetation and used for modeling and mapping. Within each of these plots, lidar metrics are calculated by calculating statistics such as mean, standard deviation, skewness, percentiles, quadratic mean, etc.<ref name=":10">{{Cite journal|last1=Yuan|first1=Zeng|last2=Yujin|first2=Zhao|last3=Dan|first3=Zhao|last4=Bingfang|first4=Wu|title=Forest Biodiversity mapping using airborne and hyper-spectral data|journal=Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International |issn=2153-7003}}</ref>

[[File:Yellowscan LIDAR on OnyxStar FOX-C8 HD.jpg|thumb|Lidar scanning performed with a multicopter [[Unmanned aerial vehicle|UAV]]]]

Multiple commercial lidar systems for [[unmanned aerial vehicle]]s are currently on the market. These platforms can systematically scan large areas, or provide a cheaper alternative to manned aircraft for smaller scanning operations.<ref>{{Cite journal|title = Drone remote sensing for forestry research and practices|journal = Journal of Forestry Research|date = 2015-06-21|issn = 1007-662X|pages = 791–797|volume = 26|issue = 4|doi = 10.1007/s11676-015-0088-y |first1 = Lina|last1 = Tang|first2 = Guofan|last2 = Shao| bibcode=2015JFoR...26..791T |s2cid = 15695164}}</ref>

[[File:Airborne Lidar Bathymetric Technology.jpg|thumb|Airborne Lidar Bathymetric Technology-High-resolution multibeam lidar map showing spectacularly faulted and deformed seafloor geology, in shaded relief and coloured by depth]]

The airborne lidar [[bathymetric]] technological system involves the measurement of [[time of flight]] of a signal from a source to its return to the sensor. The data acquisition technique involves a sea floor mapping component and a ground truth component that includes video transects and sampling. It works using a green spectrum (532&nbsp;nm) laser beam.<ref name="Nayegandhi">{{Cite web|url=https://www.ngs.noaa.gov/corbin/class_description/Nayegandhi_green_lidar.pdf |archive-url=https://ghostarchive.org/archive/20221009/https://www.ngs.noaa.gov/corbin/class_description/Nayegandhi_green_lidar.pdf |archive-date=2022-10-09 |url-status=live|title=Nayegandhi Green Lidar}}</ref> Two beams are projected onto a fast rotating mirror, which creates an array of points. One of the beams penetrates the water and also detects the bottom surface of the water under favorable conditions.

Water depth measurable by lidar depends on the clarity of the water and the absorption of the wavelength used. Water is most transparent to green and blue light, so these will penetrate deepest in clean water.<ref name="Bathymetric"/> Blue-green light of 532&nbsp;nm produced by [[frequency doubled]] solid-state IR laser output is the standard for airborne bathymetry. This light can penetrate water but pulse strength attenuates exponentially with distance traveled through the water.<ref name="Nayegandhi" /> Lidar can measure depths from about {{cvt|0.9|to|40|m|ft|0}}, with vertical accuracy in the order of {{cvt|15|cm|in|0}}. The surface reflection makes water shallower than about {{cvt|0.9|m|ft|0}} difficult to resolve, and absorption limits the maximum depth. Turbidity causes scattering and has a significant role in determining the maximum depth that can be resolved in most situations, and dissolved pigments can increase absorption depending on wavelength.<ref name="Bathymetric">{{cite web |url=http://home.iitk.ac.in/~blohani/LiDAR_Tutorial/Bathymetric%20LiDAR.htm |title=1.2.2 Bathymetric LiDAR |website=home.iitk.ac.in |access-date=15 January 2023 }}</ref> Other reports indicate that water penetration tends to be between two and three times Secchi depth. Bathymetric lidar is most useful in the {{cvt|0|-|10|m|ft|0}} depth range in coastal mapping.<ref name="Nayegandhi" />

On average in fairly clear coastal seawater lidar can penetrate to about {{cvt|7|m|ft|0}}, and in turbid water up to about {{cvt|3|m|ft|0}}. An average value found by Saputra et al, 2021, is for the green laser light to penetrate water about one and a half to two times Secchi depth in Indonesian waters. Water temperature and salinity have an effect on the refractive index which has a small effect on the depth calculation.<ref name="Saputra et al 2021" >{{cite journal |last1=Saputra |first1=Romi |last2=Radjawane |first2=Ivonne |last3=Park |first3=H |last4=Gularso |first4=Herjuno |date=2021 |title=Effect of Turbidity, Temperature and Salinity of Waters on Depth Data from Airborne LiDAR Bathymetry |journal=IOP Conference Series: Earth and Environmental Science |volume=925 |issue=1 |page=012056 |doi=10.1088/1755-1315/925/1/012056 |bibcode=2021E&ES..925a2056S |s2cid=244918525 |doi-access=free }}</ref>

The data obtained shows the full extent of the land surface exposed above the sea floor. This technique is extremely useful as it will play an important role in the major sea floor mapping program. The mapping yields onshore topography as well as underwater elevations. Sea floor reflectance imaging is another solution product from this system which can benefit mapping of underwater habitats. This technique has been used for three-dimensional image mapping of California's waters using a hydrographic lidar.<ref name="Wilson 2008">{{Cite book |isbn=978-1-4244-2126-8 |doi=10.1109/OCEANSKOBE.2008.4530980|chapter=Using Airborne Hydrographic LiDAR to Support Mapping of California's Waters|title=OCEANS 2008 - MTS/IEEE Kobe Techno-Ocean|pages=1–8|year=2008|last1=Wilson|first1=Jerry C.|s2cid=28911362}}</ref>

Airborne lidar systems were traditionally able to acquire only a few peak returns, while more recent systems acquire and digitize the entire reflected signal.<ref name=":15" /> Scientists analysed the waveform signal for extracting peak returns using [[Gaussian decomposition]].<ref>{{Cite journal|last1=Wagner|first1=Wolfgang|last2=Ullrich|first2=Andreas|last3=Ducic|first3=Vesna|last4=Melzer|first4=Thomas|last5=Studnicka|first5=Nick|date=2006-04-01|title=Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner|url=https://www.sciencedirect.com/science/article/pii/S0924271605001024|journal=ISPRS Journal of Photogrammetry and Remote Sensing|volume=60|issue=2|pages=100–112|doi=10.1016/j.isprsjprs.2005.12.001|bibcode=2006JPRS...60..100W|issn=0924-2716}}</ref> Zhuang et al, 2017 used this approach for estimating aboveground biomass.<ref>{{Cite journal|last1=Zhuang|first1=Wei|last2=Mountrakis|first2=Giorgos|last3=Wiley|first3=John J. Jr.|last4=Beier|first4=Colin M.|date=2015-04-03|title=Estimation of above-ground forest biomass using metrics based on Gaussian decomposition of waveform lidar data|journal=International Journal of Remote Sensing|volume=36|issue=7|pages=1871–1889|doi=10.1080/01431161.2015.1029095|bibcode=2015IJRS...36.1871Z|s2cid=55987035|issn=0143-1161}}</ref> Handling the huge amounts of full-waveform data is difficult. Therefore, Gaussian decomposition of the waveforms is effective, since it reduces the data and is supported by existing workflows that support interpretation of 3-D [[point cloud]]s. Recent studies investigated [[voxel]]isation. The intensities of the waveform samples are inserted into a voxelised space (3-D grayscale image) building up a 3-D representation of the scanned area.<ref name=":15">{{Cite book|last1=Miltiadou|first1=M.|last2=Grant|first2=Michael G.|last3=Campbell|first3=N. D. F.|last4=Warren|first4=M.|last5=Clewley|first5=D.|last6=Hadjimitsis|first6=Diofantos G.|title=Seventh International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2019) |chapter=Open source software DASOS: Efficient accumulation, analysis, and visualisation of full-waveform lidar |editor1-first=Giorgos|editor1-last=Papadavid|editor2-first=Kyriacos|editor2-last=Themistocleous|editor3-first=Silas|editor3-last=Michaelides|editor4-first=Vincent|editor4-last=Ambrosia|editor5-first=Diofantos G|editor5-last=Hadjimitsis|date=2019-06-27|chapter-url=https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11174/111741M/Open-source-software-DASOS--efficient-accumulation-analysis-and-visualisation/10.1117/12.2537915.short|publisher=International Society for Optics and Photonics|volume=11174|pages=111741M|doi=10.1117/12.2537915|bibcode=2019SPIE11174E..1MM|isbn=978-1-5106-3061-1|s2cid=197660590}}</ref> Related metrics and information can then be extracted from that voxelised space. Structural information can be extracted using 3-D metrics from local areas and there is a case study that used the voxelisation approach for detecting dead standing [[Eucalypt]] trees in Australia.<ref>{{Cite journal|date=2018-05-01|title=Detection of dead standing Eucalyptus camaldulensis without tree delineation for managing biodiversity in native Australian forest|journal=International Journal of Applied Earth Observation and Geoinformation|volume=67|pages=135–147|doi=10.1016/j.jag.2018.01.008|issn=0303-2434|doi-access=free|last1=Miltiadou|first1=Milto|last2=Campbell|first2=Neil D.F.|last3=Gonzalez Aracil|first3=Susana|last4=Brown|first4=Tony|last5=Grant|first5=Michael G.|bibcode=2018IJAEO..67..135M|hdl=20.500.14279/19541|hdl-access=free}}</ref>

Terrestrial applications of lidar (also ''terrestrial laser scanning'') happen on the Earth's surface and can be either stationary or mobile. Stationary terrestrial scanning is most common as a survey method, for example in conventional topography, monitoring, cultural heritage documentation and forensics.<ref name=":0" /> The [[Point cloud|3-D point clouds]] acquired from these types of scanners can be matched with [[digital image]]s taken of the scanned area from the scanner's location to create realistic looking 3-D models in a relatively short time when compared to other technologies. Each point in the [[point cloud]] is given the colour of the pixel from the image taken at the same location and direction as the laser beam that created the point.

Terrestrial lidar mapping involves a process of [[Occupancy grid mapping|occupancy grid map generation]]. The process involves an array of cells divided into grids which employ a process to store the height values when lidar data falls into the respective grid cell. A binary map is then created by applying a particular threshold to the cell values for further processing. The next step is to process the radial distance and z-coordinates from each scan to identify which 3-D points correspond to each of the specified grid cell leading to the process of data formation.<ref name=":7">{{cite book | chapter-url=https://doi.org/10.1109/ICIP.2010.5651197 | doi=10.1109/ICIP.2010.5651197 | chapter=A real-time grid map generation and object classification for ground-based 3D LIDAR data using image analysis techniques | title=2010 IEEE International Conference on Image Processing | date=2010 | last1=Lee | first1=Sang-Mook | last2=Im | first2=Jeong Joon | last3=Lee | first3=Bo-Hee | last4=Leonessa | first4=Alexander | last5=Kurdila | first5=Andrew | pages=2253–2256 | isbn=978-1-4244-7992-4 }}</ref>

Mobile lidar (also ''mobile laser scanning'') is when two or more scanners are attached to a moving vehicle to collect data along a path. These scanners are almost always paired with other kinds of equipment, including [[Satellite navigation|GNSS]] receivers and [[Inertial measurement unit|IMUs]]. One example application is surveying streets, where power lines, exact bridge heights, bordering trees, etc. all need to be taken into account. Instead of collecting each of these measurements individually in the field with a [[Tachymeter (survey)|tachymeter]], a 3-D model from a point cloud can be created where all of the measurements needed can be made, depending on the quality of the data collected. This eliminates the problem of forgetting to take a measurement, so long as the model is available, reliable and has an appropriate level of accuracy.