The document discusses LiDAR technology and its applications in forestry. It begins by explaining what LiDAR is and the different platforms it can be used on, including satellites, aircraft, and ground-based systems. It then discusses several forestry applications of LiDAR such as measuring tree height, crown width, biomass, and basal area. Two case studies are presented, one on estimating carbon stock in an urban forest using LiDAR data, and another on estimating forest canopy fuels. The document concludes that LiDAR provides highly accurate, high-resolution data that allows forest characteristics to be acquired quickly and is useful for applications like biomass estimation and modeling fire behavior.

1. LIDAR TECHNOLOGY
&
IT’S APPLICATION ON FORESTRY
K.ABHIRAM
131865
1

2. Contents
 Introduction
 Types of Platforms
 Applications
 Literature Review
 Case study
 Summary
 References
2

3. What is LiDAR ? (Light Detection And Ranging)
 Lidar is a remote sensing technology that measures distance by
illuminating a target with a laser and analysing the reflected light.
Why LiDAR ?
 It is used generating precise and directly geo-referenced spatial
information.
 Airborne LiDAR systems use 1064nm
 Lasers produce a coherent light source.
 It is Active sensor , do not require sunlight, they can be used either
during the day or at night.
3
Figure 1

4.  Lidar is popularly used as a technology used to make high
resolution maps, with applications in
 Archaeology
 Geography
 Geology & Geomorphology
 Seismology
 Forestry
 Atmospheric physics
 Flood Mapping , and
 Contour mapping
 Military
 Mining
 Transport
4
Figure 2

5. PLATFORMS of LiDAR
1.Earth-orbiting satellites
2.Fixed-wing aircraft, manned
and unmanned
3.Rotary-wing aircraft
(helicopters)
4.Static ground-based systems
(tripods)
5.Dynamic ground-based
systems (vehicles)
6.Bathymetric mapping system
1
.
2.
3. 4.
5
.
6.
5Figure 3

6. Parameters of a LiDAR sensor
 Repetition rate: LiDAR will pulse at
200,000 times per second.
 Scan frequency: how fast the
scanner is oscillating
 Nominal point spacing (NPS)
 Scan angle
 Flying attitude
 Flight line spacing
 Across track resolution
 Along track resolution
 Swath
 Overlap
6
Figure 4

7. LiDAR System Components
1.Laser Scanner
2. GPS
3. Inertial measurement unit (IMU)
4. Computer Processing Resources
7Figure 4

8. GPS
 LiDAR systems use robust dual-frequency receivers
and differential post-processing, utilizing a fixed
ground reference station.
 Lock on the GPS satellites must be maintained and
the LiDAR system must stay within 50 miles of the
reference base station.
 Typically with not more than 3 to 4 cm of error.
 A laser scanner has three sub-components: the opto-
mechanical scanner, the ranging unit, and the control
processing unit.
 Laser scanners are distinguished by high timing (range)
accuracy, high sampling density, a high degree of automation.
Laser Scanner
8Figure 5

9. Inertial Measurement Unit (IMU):
 Lidar systems contain three inertial gyroscopes. The
angular rotations of the sensor from vertical can be
measured.
 Inertial measurement systems also contain
accelerometers to measure the velocity
Computer Processing Resources:
 Reliable computer systems are required to ensure
that each individual component is performing
correctly.
 These computer systems must ultimately integrate
the component data streams into usable, accurate
elevations on the ground.
Data format: LAS format
.
9
Figure 6
Figure 7

10. Forestry applications require a precise inventory of individual trees
and groups, or "stands" of trees in order to address
 Forest management and planning,
 Study forest ecology and habitats,
 Quantify forest fire fuel, and
 Estimate carbon absorption.
Application on Forestry
10

11. Direct measurements include:
•Stand Density
•Tree Height
•Crown Width
•Crown Length
Measuring Forests with LiDAR
Estimates include:
•Volume
•Biomass
•Basal Area
•DBH (diameter at breast
height)
11
Figure 8
Figure 9

12. Characteristics of LiDAR Data
Discrete Return LiDAR
Visualization of multiple LiDAR returns in a forest
canopy, showing first returns from the top of
canopy, second returns from forest understory, and
third returns near or on the ground
Full Waveform LiDAR
The load increases at about 30 to 60 times.
The opportunities presented by full waveform
technology are mostly in the analysis of
vegetation density, mapping live versus dead
vegetation, forest fuels analysis, and wildlife
habitat mapping.
12
Figure 10
Figure 12
Figure 11

13. Literature Review
Kevin et al.,(2005) studied the horizontal and vertical information of forest from
the LiDAR data.
 It is concluded that direct retrieval of canopy height provides opportunities to
model above-ground biomass and canopy volume.
 The information also offers new opportunities for enhanced forest monitoring,
management and planning.
Monika et al., (2006) conducted a research with aerial and terrestrial LiDAR to
provide detailed forest inventory characteristics such as canopy heights and
volumes as well as diameter at breast height.
 The estimation of Leaf Area Index (LAI) and forest fuel metrics are also
addressed.
13

14. Michael et al.,(2008) studied that the number of forest inventory attributes that may be
directly measured with LiDAR is limited.
 They present the status of LiDAR remote sensing of forests, including issues related
to instrumentation, data collection, data processing, costs, and attribute estimation.
Nicholas et al.,(2008) assess the forest structure with airborne LiDAR and the effects of
platform altitude .
 Including three different platform altitudes (1000, 2000, and 3000 m), two scan
angles at 1000 m (10° and 15° half max. angle off nadir), and three footprint sizes
(0.2, 0.4, and 0.6 m).
 The comparison was undertaken in eucalypt forests at three sites, varying in
vegetation structure and topography.
Birgit Peterson et al.,(2010) demonstrates how LIDAR data were used to predict
canopy bulk density (CBD) and canopy base height (CBH) . The LIDAR data were
used to generate maps of canopy fuels for input into a fire behaviour model.
14

15. CASE STUDY-1: Carbon Accounting of Urban Forest in
ChennaiCity using LiDAR Data
Study Areas:
1. Guindy Reserve Forest (GRF),
2. Indian Institute of Technology Madras (IITM),
3. Central Leather Research Institute (CLRI)
4. Anna University (AU)
Total area of the study site is 6.67km2
Objectives:
Estimation of carbon stock in the center of the city of chennai.
To provide more accurate forest biomass estimation
15

16. Methodology
Lidar data Ortho images
Regression
modelling
nDSM=DSM-DTM Object based supervised
classification
Plot level
Biomass Canopy Height
Canopy cover Extraction
Tree inventory
data
Percentage MAX &
MIN heights
Regression
Equations
TGB Estimation
Carbon Stock
Estimation
16

17. Field Survey:
A Total of 11 square sample plots with the size of 20m
were selected on the basis of stratified random sampling.
 Each tree coordinates were recorded by using handheld
(GPS) unit.
Tree height was measured by using a vertex Hypsometer.
For each tree, stem diameter was measured at 1.3 m
above ground with a diameter tape and the species name
was recorded.
For Borassus flabellifer height only measured. In each
sampling plot of trees, for multi-stemmed trees, bole
circumference was measured separately, and summed.
The digital ortho image which was preloaded in the GPS
instrument was used for verify the location of the trees.
17Figure 13

18. Biomass Estimation in Trees
 The diameter at breast height (DBH) and height of trees were measured, then
both ends of main trunks and length were measured and volume is calculated.
 AGB ( above ground biomass) for trees Y = 1.9724x – 1.0717
 For the unavailability of the existing equation for Borassus flabellifer trees.
Y = 4.5 + 7.7H
Where Y = biomass, kg
H= stem height and
x =DBH
18
Canopy Cover Extraction
Before the LIDAR analysis the canopy cover area should be extracted from the
urban features.
(1) Generating DTM, DEM, and nDSM from ALTM data,
(2) Generating Height thresholding image for masking,
(3) Segmentation of ortho image,
(4) Supervised Classification for extraction of canopy cover

19. LiDAR Predicted Biomass
canopy densities, mean and percentile heights, and second-order height
statistics . For this study height percentile parameters are developed for
biomass estimation.
Regression analysis is the common way to develop the AGB estimation
models
 The biomass value converted to carbon stock using a conversion factor
with the equation Biomass values were multiplied by 0.45 to get carbon
storage values of trees .
C= TGB.CF
19

20. s.no Biomass (Mg) Trees count Location
1 102.6494 41 IIT
2 130.2500 56 IIT
3 126.0892 33 GRF
4 109.2947 34 GRF
5 17.5677 16 CLRI
6 47.9295 35 IIT
7 63.9100 45 IIT
8 95.8963 28 AU
9 63.8459 43 AU
10 1018.9200 22 CLRI
11 85.6047 27 AU
Shows the estimated biomass on the field with site location
20
RESULTS & DISCUSSION
Table 1:

21. The relationship between field-measured height and LIDAR-measured
maximum height (total 438trees)
 The field-measured tree height and LIDAR measured height of 438
trees had an R of 0.957 and RMSE of 0.59 m
2
21
Figure 14

22. S.NO NAME AGB (Mg ha-1) BGB(Mg ha-1) TGB(Mg ha-1) CARBON STOCK(Mg ha-1)
1 Guindy RF 42150.2 10959.05 53109.25 23899.16
2 Anna
University
8220.029 2137.2 10357.23 4660.75
3 CLRI 4872.509 1226.85 6099.359 2744.70
4 IITM 31847.4 8280.22 40127.62 18057.42
TOTAL 87090.13 22603.32 109693.45 49362.03
Carbon Stock Estimation of Chennai Urban Forest at the all 4 segments
 A very good relationship within the LIDAR 75th percentiles and the field
measured AGB.
Simple LIDAR metrics such as height percentiles which was derived from
canopy heights within plots, gives an impressive capacity to estimate
biomass over an urban environment..
22
Table 2:

23. STUDY AREA: Ahtanum State Forest in Washington State
Fusion of LiDAR and imagery for
estimating forest canopy fuels
CASE STUDY-2:
23
Estimation of canopy fuels by using LiDAR data.
 Canopy fuels are defined as all burnable materials, which include live and dead
foliage, and redundant stem and branch wood located in the upper forest canopy.
 Canopy fuels are important inputs for fire behaviour models that predict crown
fire behaviour and spread . Parameters considered as follows
1. Canopy Height (CH),
2. Canopy bulk density (CBD),
3. Canopy base height (CBH),
4. Available Canopy Fuel(ACF)
Objective:

24. METHOLODY
Field Data Raw LiDAR Raw Imagery
Data Processing
(Fuel Calculation)
Data Processing
(FUSION)
Data Processing
(ENVI,ArcGIS)
Independent Variables
LiDAR
metrics
Imagery
metrics
Regression
Analysis
LiDAR
Models
LiDAR/Imagery
fusion models
Imagery
models
CH
CBH
CBD
ACF
Dependent
Variables
24

25. 25
Results
The regression models organized by canopy fuel metrics by considering the four parameter
of Canopy height ,Canopy base height ,Canopy bulk density ,Available canopy fuel, and
calaculated the RMSE for every regression model of Lidar data,Imagery data, and Lidar +
Imgaery data and compare the values for these data and observed better results in
combination of Lidar and imagery data.
Figure 15

26. 26
Figure 16

27. 27
SUMMARY
It gives directly geo-referenced spatial information.
Highly accurate, high-resolution LiDAR data have particular
utility in forest mapping.
By using the LiDAR technology ,the characteristics of forest
can be acquired in a short period .
The first case study shows how to estimate the biomass by using
LiDAR technology.
The second case study shows the accuracy of estimation of
canopy fuels by using LiDAR data.

28. References
Birgit Peterson, Ralph Dubayah, Peter Hyde, Michelle Hofton, J. Bryan Blair, and JoAnn Fites-
Kaufman,(2010) “Use of LIDAR for Forest Inventory and Forest Management Application” Canadian
Journal of Remote Sensing, 29, 650– 657.
Kevin Lima, Paul Treitza, Michael Wulderb, Benoît St-Ongec and Martin Flood,(2005) “LiDAR remote
sensing of forest structure” Progress in Physical Geography 27,1 (2003) pp. 88–106
Michael A. Wulder Christopher W. Bater, Nicholas C. Coops, Thomas Hilker and Joanne C. White
(2008) “The role of LiDAR in sustainable forest management” Remote Sensing of Environment 90:
415–423.
Monika Moskal, Todd Erdody, Akira Kato, Jeffery Richardson, Guang Zheng and David Briggs, (2006)
“Lidar Applications in Precision Forestry”, Journal of Remote Sensing.
28

29. Muneeswaran Mariappan ,Subbaraj Lingava, Ramalingam Murugaiyan,Vani Krishnan, Srinivasa
Raju Kolanuvada ,Rama Subbu Lakshmi Thirumeni,(2012) “Carbon Accounting of Urban Forest in
Chennai City using Lidar Data”, European Journal of Scientific Research ISSN 1450-216X Vol.81
No.3 (2012), pp.314-328
Nicholas R. Goodwin , Nicholas C. Coops , Darius S. Culvenor, (2008) “Assessment of forest
structure with airborne LiDAR and the effects of platform altitude”, .
Todd L. Erdody, L. Monika Moskal (2010) “Fusion of LiDAR and imagery for estimating forest
canopy fuels”, Remote Sensing of Environment 114 (2010) 725–737
29

30. THANK YOU
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