Albornoz_seminario

8/9/2019 Albornoz_seminario

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Master in Emergency Early Warning and Response Space Applications

Mario Gulich Institute, CONAE, Argentina

LIDAR, a laser alternative for remote

sensing

Author: Felipe Albornoz M.

October, 2014.


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Table of Contents

Pag

1. Abstract 4

2. Introduction 5

3. Chapter I 7

What is LIDAR? 7

LIDAR Accuracy things to consider 11

Sources of error 11

Acquisition Scan Angle 12

Components of the LIDAR system 13

Some definitions 14

Some important parameters 15

Traditional Photogrammetry v/s LIDAR 16

Types of LIDAR products available 17

LIDAR Derived Products 17

Generals Applications 17

4. Chapter II 19

What is a model 19

DTM 20

DEM 22

Uses of DEM 23

5. Conclusions 25

6. References 26


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List of Figures

Pag

Fig.1. Schematic diagram of airborne LIDAR performing line scanning resulting 7

in parallel lines of measured point (other scan pattern exist, but this one is

fairly common), property of Jamie Young.

Fig.2. LIDAR point and surface products, property of NOAA. 9

Fig.3. Schematic diagram showing data acquisition parameters used for the 12

LIDAR survey for Big Pine Key in Florida, property of Qihao Weng.

Fig.4. Components of the LIDAR, property of Ruben Castro. 13

Fig.5. Basic components of an airborne LIDAR system, GPS = global positioning 13

system; IMU = inertial measurement unit, property Qihao Weng.

Fig.6. Future Earth LIDAR Missions, property of NASA. 18

Fig.7. The DTM stored levels of the earth's surface, no vegetation or 21

artificial structures that may exist on it, property of Digimapas, Chile.

Fig.8. The DSM stored dimensions of surfaces including everything that is on 21

the ground (vegetation, manmade structures, etc), property of Digimapas, Chile.

Fig.9. DEM images, property of gis.nic.in. 22

Fig.10. Orthorectified images, property of Digimapas, Chile. 24


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Abstract

LIDAR stands for Light Detection and Ranging, commonly known as Laser Radar.

Light detection and ranging (LIDAR) mapping is an accepted method of generating precise

and directly georeferenced spatial information about the shape and surface characteristics of the

Earth.

LIDAR is not only replacing conventional sensors, but also creating new methods with

unique properties that could not be achieved before. LIDAR is extremely useful in atmospheric and

environmental research as well as space exploration. It also has wide applications in industry,

defense, and military.

High resolution digital terrain models (DTMs) and digital surface models (DSMs) are critical

for predicting flooding, monitoring erosion, landslide and tectonic movements, modeling

ecosystems, and creating digital city models. Recently emerging airborne light detection andranging (LIDAR) technology allows accurate and inexpensive measurements of topography,

vegetation canopy heights, and buildings over large areas. In order to provide researchers with

high quality data, NSF has created the National Airborne Laser Mapping Center (NCALM) to collect,

archive, and distribute the LIDAR data. However, airborne LIDAR systems collect huge volumes of

irregularly spaced, three-dimensional point measurements of ground and non-ground objects

scanned by the laser beneath the aircraft.

To advance the use of the technology and data, there is a need for basic research in

algorithms for data retrieval and transformation, and ground and non-ground measurement

classification.1

1 K. Zhang y Z. Cui, “National Center for Airborne Laser Mapping”, 2007.


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Introduction

LIDAR is not a new technique. Apparently, the first device was successfully operated soon

after the secrecy on radar has been lifted at the end of the Second World War (Jones, 1949).

A high voltage spark between aluminum electrodes was used as a source, two searchlight

mirrors were the transmitter and receiver optics, and a photoelectrical cell the detector. The

system was successfully used to measure cloud-base heights up to 5.5 km in bright daylight.

The term “LIDAR” was coined several years later (Middleton and Spilhaus, 1953) as a mere

analog to the better-know radar, without expressly telling what it could be the acronym of.

Although photomultiplier tubes were already available at the time, sparks and flash lamps

were not the ideal sources for applications that require, in addition to small divergence and shortpulse duration, a spectrally narrow beam as well. If, as it had jokingly been put, the invention of

the laser in 1960 (Maiman, 1960) was “a solution looking for a problem, LIDAR was clearly a

problem).

Albert Einstein developed the foundation of stimulated emission of radiation and published

his findings in 1916 and 1917.

In essence, Einstein demonstrated that atoms can absorb and emit radiation

spontaneously and that atoms in certain excited states can be induced to emit radiation. For about

40 years after Einstein’s theoretical work on stimulated emission was published, the concept was

used only in theoretical discussions and had little relevance in experimental work (Sorin C. Popescu,

2011).

The Man has been using illumination with visible light from artificial sources for active

optical detection of objects. Distance is inferred stereoscopically, that is, from the slightly different

images obtained at the viewing angles of the two eyes, by mental construction, from two two-

dimensional images, of a three-dimensional geometric relationship between different parts of the

scene, or, for more remote objects, from the decrease of visual contrast.

Except for stereoscopic viewing, which fails at longer distance, these methods yield

relative values only. Distance can be determined in a quantitative way by measuring the transit

time of radiation from the source to the object and back. Systems that rely on this principle

require a pulsed or modulated source and a detection system with adequate time resolution,

approximately a million times better in the optical case than in the acoustical case. Man’s senses

do not nearly meet this requirement.

Therefore, the technique became available to us only after the advent of suitable

microwave, light, and sound sources and time-resolving detection systems.


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Depending on whether sound, radiowaves, or light is used, these systems are called

SONAR (SOund Navigation Ranging) or SODAR (SOund Detection And Ranging), RADAR (RAdiowave

Detection And Ranging), or LIDAR (LIght Detection And Ranging). Sonar works under water, the

remaining techniques in the atmosphere.

LIDAR uses not just visible wavelengths (400 nm


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Chapter I

What is LIDAR?

LIDAR has become and established method for collecting very dense and accurate

elevation data across landscape, shallow-water areas, and project sites. This active remote sensing

technique is similar to radar but uses laser light pulses instead of radio waves. LIDAR is typically

“flown” or collected from planes where it can rapidly collect points over large areas (Figure 1).

LIDAR is also collected from ground-based stationary and mobile platforms. These

collection techniques are popular within the surveying and engineering communities because they

are capable of producing extremely high accuracies and point densities, thus permitting the

development of precise, realistic, three dimensional representations of railroads, roadways,

bridges, buildings, breakwaters, and other shorelines structures.Collection of elevation data using LIDAR has several advantages over most other techniques. Chief

among them are higher resolutions, centimeter accuracies, and ground detection in forested

terrain.

Figure1. Schematic diagram of airborne LIDAR performing line scanning resulting in parallel lines of

measured point (other scan pattern exist, but this one is fairly common), property of Jamie Young.


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LIDAR, which is commonly, spelled LIDAR and also LADAR or laser altimetry, in an acronym

for light detection and ranging. It refers to a remote sensing technology that emits intense,

focused beams of light and measures the time it takes for the reflections to be detected by the

sensor.

This information is used to compute ranges, or distances, to objects, in this manner, LIDAR

is analogous to radar (radio detecting and ranging), except that it is based on discrete pulses of

laser light. The three dimensional coordinates (eg., x,y,z or latitude, longitude, and elevation) of

the target objects are computed from 1) the time difference between the laser pulse being

emitted and returned, 2) the angle at which the pulse was fired and 3) the absolute location of the

sensor on or above the surface of the Earth.

There are two classes of remote sensing technologies that are differentiated by the source

of energy used to detect a target: passive systems and active systems.

LIDAR systems are active systems because they emit pulses of light and detect the reflected light.This characteristic allows LIDAR data to be collected at night when the air is usually clearer and the

sky contains less air traffic than in the daytime. In fact, most LIDAR data are collected at night.

Unlike radar, LIDAR cannot penetrate clouds, rain, or dense haze and must be flown during fair

weather.

LIDAR instruments can rapidly measure the Earth´s surface, at sampling rates greater than

150 kilohertz (i.e., 150000 pulses per second). The resulting product is a densely spaced network

of highly accurate georeferenced elevation points (Figure 2) often called a point cloud, than can

be used to generate three dimensional representations of the Earth´s surface and its features.

Many LIDAR systems operate in the near-infrared region of the electromagnetic spectrum,although some sensors also operate in the green band to penetrate water and detect bottom

features. These bathymetric LIDAR systems can be used in areas with relatively clear water to

measure seafloor elevations. Typically, LIDAR derived elevations have absolute accuracies of about

6 to 12 inches (15 to 30 centimeters) for older data and 4 to 8 inches (10 to 20 centimeters) for

more recent data; relative accuracies (e.g., heights of roofs, hills, banks, and dunes) are even

better. The description of accuracy is an important aspect of LIDAR.3

3 National Oceanic and Atmospheric Administration NOAA,“LIDAR101.pdf” .


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Figure2. LIDAR point and surface products, property of NOAA.

The ability to see under trees is a recurring goal when acquiring elevation data usingremote sensing data collected from above the Earth´s surface (e.g., airplanes or satellites). Most of

the larger scale elevation data sets have been generated using remote sensing technologies that

cannot penetrate vegetation. LIDAR Is not exception; however, there are typically enough

individual “points” that, even if only a small percentage of them reach the ground through the

trees, there usually enough to provide adequate coverage in forested areas. In effect, LIDAR is able

to see through holes in the canopy or vegetation.4

4 National Oceanic and Atmospheric Administration NOAA, “LIDAR101.pdf ”.


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Accuracy: is the most important defining characteristic of the sensor over other existing

technologies.

ACCURACY

VERTICAL HORIZONTAL

+/- 0.10 MTS

HARD SURFACES AND

REGULAR TERRAIN

+/-0.25 MTS

SMOOTH SURFACES WITH

VEGETATION UNDULATED

+/-0.30 TO 0.50 MTS

SMOOTH SURFACES WITH

VEGETATION IN

MOUNTAIN TERRAIN

+/- 0.5 TO 0.75 MTSTERRAIN MONTAIN

EXTREME


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LIDAR Accuracy things to consider

Accuracy is dependent on:

Flying height

Sensor parameters

Rep rate

Scan angle – 40 degree of scan angle

Scan frequency

System accuracy

Terrain

Vegetation

Baseline distance Location of base station to Aircraft

Calibration

Sources of Error

Acquisition

Processing

Strip adjustment

Selecting ground points

Thinning

Interpolation

Analysis/Visualization

90% of problems are result of improper installation.5

5 Jamie Young “Young_airborne_fundamentals_final.pdf”.


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Acquisition Scan Angle

LIDAR data should be acquired within 18° of nadir as above this angle the LIDAR footprint

can become highly distorted.

Complex terrain can exacerbate the problem.

Figure3. Schematic diagram showing data acquisition parameters used for the LIDAR survey for Big Pine Key

in Florida, property of Qihao Weng.


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Components of the LIDAR system

Figure4. Components of the LIDAR, property of Ruben Castro.

Figure5. Basic components of an airborne LIDAR system, GPS = global positioning system; IMU = inertial

measurement unit, property Qihao Weng


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Some definitions

Pulse repetition frequency (PRF) or pulse rate: this is the number of pulses sent per

second.

Return Echo (also called Return Pulses): this is the number of pulses received. Reflections

are recorded for a pulses sent.

Read of speed: is the number of analysis models (eg., scan lines) per second.

Field of View (FOV) or Scan angle: through of flights is he angle of laser beam than can

cover the sweep.

Beam Divergence: is the angle than show the deviation of the laser beam parallelism.

Minimum and maximum flying height: Maximum mainly depends of the transmission

power and at minimum of the national or local regulations.

Working width: depends on the flight altitude and FOV.

Laser Footprint (area illuminated by the laser beam) depends on the beam divergence and

height of flight. In the ideal case a circle, ellipse, or really even a more irregular pattern.

Width point density drive: depends on many parameters such as the scan pattern, PRF,

scan speed, flight altitude, aircraft speed, FOV (Castro et al ., 2011).


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Some important parameters

Wavelength: important for the measurement of certain objects (the object must also

reflect the wavelength).

Number of pulses: is the reason because for which the intensity is recorded.

Frequency and accuracy: measurement specifications GPS / INS accuracy for the INS.

Using additional sensors imaging (digital cameras, video, etc.)

Weight, size, power consumption, environmental operating conditions (T, H etc.)

Range resolution and accuracy.

Software, (flight planning, post-processing etc.)6

6 University of Washington


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Traditional Photogrammetry v/s LIDAR

LIDAR Photogrammetric

Day or night data acquisition Day time collection only

Direct acquisition of 3D collection Complicated and sometime unreliable

procedures

Vertical accuracy is better than planimetric* Planimetric accuracy is better than vertical*

Point cloud difficult to derive semantic

information; however, intensity values can be

used to produce a visually rich image like

product (example of an intensity image)

Rich in semantic information

*Complementary characteristics suggest integration


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Types of LIDAR products available

Digital Ortho-Rectified Imagery

Some LIDAR providers collect digital color or black and white orthorectified imagery

simultaneously with the collection of point data. Imagery is collected either from digital cameras

or digital video cameras and can be mosaicked. Resolution is typically 1m.

Intensity Return Images

Images may be derived from intensity values returned by each laser pulse. The intensity values can

be displayed as a gray scale image.

LIDAR Derived Products

Topographic LIDAR systems produce surface elevation x, y, z coordinate data points. There aremany products that can be derived from raw point data. Most LIDAR providers can derive these

products upon request:

Digital Elevation Models (DEMs).

Digital Terrain Models (DTMs) (bald-earth elevation data).

Triangulated Irregular Networks (TINs).

Breakliness – a line representing a feature that you wish to preserve in a TIN (example:

stream or ridge)

Contours.

Shaded Relief.

Slope & Aspect.

Generals Applications

Urban planning (city models)

Wireless network planning

Noise protection planning

Corridor mapping

Forest inventory

Flood plain mapping

Hydraulic simulations

Coastal monitoring Power line mapping

Monitoring of deposits and mines (open pit)

Environmental protection

Disaster management

Archeology7

7 Monika Moskal “bc_fp_LIDAR_pres_moskal.pdf”.


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Figure6. Future Earth LIDAR Missions, property of NASA.


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CHAPTER II

What is a model?

A model is a representation of an object, system or idea, different from the entity same

way.

The purpose of models is to help explain, understand or improve a system. A model of an

object can be an exact replica of this or an abstraction of the key properties of the object (taking

into account that a model will never be an exact representation of reality).

“A model is an object, concept or set of relationships is used to represent and study

intelligible simple a portion of the empirical reality” (Rios 1995).

A widely used classification is that of Turner (1970), which classifies iconic models, analogand symbolic, based on the relationship of correspondence.

Iconic Model : In these models the relation of correspondence is established through the

morphological properties, usually a change of scale to the conservation of the remaining

topological properties. Example, in a model has been established where size reduction while

retaining the basic dimentional relationship.

Analog Model : it is built by a set of conventions that synthesize and codify the properties of the

real object in order to facilitate the compression on this.

Example, a printed map constructed by a set of conventions that make legible cartographic pro

perties such as dimensions, physical location of geographic objects, etc.

Symbolic Model : represent reality through the identification and coding of a geometric structure of

its basic elements.

Reach a higher level of abstraction, and that the real object is represented by a mathematical

symbolization (geometric and statistical).

Example, the representation of a building by identifying and coding of a geometric structure of its

basic elements.


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DTM

A digital terrain model is a topographic model of the bare earth – terrain relief - that can

be manipulated by computer programs. The data files contain the spatial elevation data of the

terrain in a digital format which usually presented as a rectangular grid. Vegetation, buildings and

other man-made (artificial) features are removed digitally - leaving just the underlying terrain ( on

the other hand, Digital Surface Model (DSM) is usually the main product produced from

photogrammetry, where it does contain all the features mentioned above, while a filtered

DSM results in a DTM).

DTM model is mostly related as raster data type (opposed to vector data type), stored

usually as a rectangular equal-spaced grid, with space (resolution) of between 50 and 500 meters

mostly presented in Cartesian coordinate system – i.e. x, y, z (there are DTM s presented in

Geographic coordinate system – i.e. angular coordinates of latitude and longitude). For severalapplications a higher resolution is required (as high as 1 meter spacing). A DTM can be used to

guide automatic machinery in the construction of a physical model or even in computer games,

where is describes the relief map.

The DTM data set are extremely useful for the generation of 3D renderings of any location

in the area described. The 3D models rendered from DTM data can be extremely useful and

versatile for a variety of applications.

DTMs are used especially in civil engineering, geodesy and surveying, geophysics, and

geography. The main applications are (visualization of the terrain, terrain analyses in cartography

and morphology, rectification of airborne or satellite photos, extraction of terrain parameters,model water flow or mas movement)8

8 Technion.ac.il “Intro-DTM.pdf”.


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Figure7. The DTM stored levels of the earth's surface, no vegetation or artificial structures that may exist on

it, property of Digimapas, Chile.

Figure8. The DSM stored dimensions of surfaces including everything that is on the ground (vegetation,

manmade structures, etc), property of Digimapas, Chile.


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DEM

Surfaces such as the surface of the earth are continuous phenomena rather than discrete

objects. To fully model the surface, would need an infinite amount of points. Digital elevation

models are just a way of representing surfaces.

The term digital elevation model or DEM is frequently used to refer to any digital

representation of a topographic surface, however, most often it is used to refer specifically to a

raster or regular grid of spot heights. In DEMs, a raster file containing elevations at regularly-

spaced surface coordinates over an area is interpreted using specialized computer software which

creates a three-dimensional rendering of the surface.

Figure9. DEM images, property of gis.nic.in.

The DEM is the simplest form of digital representation of topography and one of the most

common. The resolution, or the distance between adjacent grid points, is a critical parameter.

Coverage’s of the entire globe, including the ocean floor, can be obtained at various resolutions;

the best resolution commonly available is 30 m, with a vertical resolution of 1 m.9

9 http://gis.nic.in/gisprimer/dem.html


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Several methods have been used to create DEM’s are:

Direct Methods: measure the distance to the sensor directly.

Indirect Methods: the distance to the sensor is obtained indirectly.

Direct methods:

Surveying Method: Need of the presence in the field, for the acquisition takes time and is

costly.

GPS Method: It is necessary to position in the geographical point to measure, requires

measurement times and conditions of all satellites for accurate reference coordinates,also requires a second station support, etc.

Airborne Methods: Not limited by the accessibility of the work area are sensors which are

mounted on an aerial or satellite platform and its main drawback is the roughness of the

terrain.

Indirect methods:

Photogrammetric Restitution: manual or automatic processing method of stereoscopic

images generated by pairs of aerial photos, satellite images or radar interferometry.

Digitalization: they come pre cartography is economically accessible, scanner or digitizing

tables are used and consists in passing information that comes from printed media to

digital format.

Uses of DEM

1. Determining attributes of terrain, such as elevation at any point, slope and aspect.

2. Finding features on the terrain, such as drainage basins and watersheds, drainage networks

and channels, peaks and and other landforms.

3. Modeling of hydrologic functions, energy flux and forest fires.


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Figure10. Orthorectified images, property of Digimapas, Chile.


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Conclusions

LIDAR is a powerful tool that delivers a high density and detail of information for product

generation at scales between (1: 1000 - 1: 2500).

The LIDAR data are widely used for the processing of digital images. Is this the most

important application in which this technology is concerned (orthorectified images) is where the

images are worked, for obtaining a geometric correction due to displacement caused by tilting the

sensor and the terrain is in this case, the digital elevation model is a database tool used for this

process as it allows in conjunction with other input parameters correct distortions of relief and

perspective.

LIDAR is synonymous of minimization time information capture, has a rapid generation of

products can cover large areas with large geometric and altimetry accuracy and low cost over large

areas. By being an active sensor operates at any time of day or night. It is compact and easy toinstall on various platforms, but the precision with which shows the actual topography of the Earth

(model accuracy) depends largely on the methodology of data collection, the density of points,

post-processing, and quality filtering and editing data.

A suitable post-processing ensures consistency in the data set obtained.

Certain disadvantages may be those in which LIDAR does not penetrate the water bodies,

there is little operating in adverse weather conditions and the cost of its implementation in small

areas is a very expensive.


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References

[1] Q. Weng,” Advances in environmental remote sensing: sensors, algorithms, and

application”s. CRC Press, 2011.

[2] Monika Moskal “bc_fp_LIDAR_pres_moskal.pdf”.

[3] A. Ruiz and W. Kornus, “Experiencias y aplicaciones del LIDAR,” V Semana de Geomática,

Barcelona, vol. 11, no. 03, 2003.

[4] “experiencia_y_aplicaciones_LIDAR.pdf”.

[5] “Ruiz y Kornus - 2003 - Experiencias y aplicaciones del LIDAR.pdf”.

[6] “[James_Young]_LiDAR_For_Dummies(BookZZ.org).pdf

[7] A. S. Diamond, Handbook of imaging materials. CRC Press, 2001.

“Intro-DTM.pdf”.

[8] “J Stoker_lidar101_nj_workshop.pdf”.

[9] T. Fujii y T. Fukuchi, Laser remote sensing. CRC Press, 2005. [10] National Oceanic and Atmospheric Administration NOAA,“LIDAR101.pdf ” .

[11] Prof.Xinzhao Chu “LIDARLecture03_LIDARFundamental.pdf”.

[12] “literature_review_of_selective_filtering_of_lidar_data_processing_techniques.pdf

[13] K. Zhang and Z. Cui, “National Center for Airborne Laser Mapping,” 2007.

[14] National Oceanic and Atmospheric Administration NOAA

“Refinement_of_Topographic_LIDAR_to_Create_a_Bare_Earth_Surface.pdf”.

[15] Jamie Young “Young_airborne_fundamentals_final.pdf”.

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