LiDAR Remote Sensing for
Natural Resource Management
Paul Treitz
Department of Geography, Queen’s University
Murray Woods
Ontario Ministry of Natural Resources
Kevin Lim
Lim Geomatics Inc.
Valerie Thomas
Department of Forestry, Virginia Tech
Harry McCaughey
Department of Geography, Queen’s University
Light Detection and Ranging (LiDAR)
Active remote sensing
system
 Up to 200,000 pulses of
laser light per second
 Pulses strike the
objects/surface of the
earth and with each pulse
the sensor receives a
measurement of the time
and angle of each return.
 As the laser pulse strikes a
surface, it will produce
range and intensity
measurements (multiple).
Canopy Height Models
Digital Surface
Model (DSM)
Digital Terrain
Model (DTM)
Canopy Height
Model (CHM)
LiDAR’s Contribution to Forest Inventory
Detailed Surface Models
– Digital Surface Models
– Digital Terrain Models
– Canopy Height Models
Road layout using least cost path analysis
techniques.
Destination
Detailed Digital Terrain Model
– Supporting
Value Added
Identifying surficial geology
Hydrological modelling
Wetland identification
Predictive ecosystem mapping
Operational considerations
– road construction
– skid trail layout
– water crossings
Profile from DEM
Origin
DEM 2m
Slope
Predicting Forest Inventory Variables
Swan Lake Research Forest
 tolerant hardwood forest no–
harvest/single tree selection.
Petawawa Research Forest
 plantation, natural unharvested, and
silviculturally treated conifer stands.
Nippising Forest Sites
 young yellow birch
 red oak conditions; and
 natural white pine conditions
Woods, M., K. Lim, and P. Treitz. 2008. Predicting forest stand variables from LiDAR data in
the Great Lakes St. Lawrence Forest of Ontario, Forestry Chronicle, 84(6): 827-839.
55
Forest Stand Characteristics
6
Forest Variables
 Top Height (m)(TOPHT)
 Calculated as the average of the largest 100 stems per hectare.
 Average Height (m)(AVGHT)
 Calculated as the average height of all trees.
 Density (stems ha-1)(DENSITY)
 Number of live trees.
 Quadratic Mean Diameter (cm)(QMDBH)



 DBH 2
n 

 Basal Area (m2 ha-1)(SUMBA)
 DBH2 * 0.00007854
 Gross Total Volume (m3 ha-1)(SUMGTV)
 Honer et al. (1983) equations.
77
LiDAR Predictors
Derived from all returns
 Statistical
 Mean, Standard Deviation
 Percentiles
 Deciles (p10 … p90)
 Maximum Height
 Density
 d1 … d9
• Range of heights divided into 10 equal intervals.
• Cumulative proportion of returns starting from the lowest interval.
 Da : Number of first returns divided by all returns.
Concept of Canopy Height Metrics
ACFL
Data sorted into 10 equal parts with each part
representing 1/10th of the sample or population
30
q(ht)
q(ht)
q(ht)
q(ht)
q(ht)
q(ht)
25
height
q(ht)
15
10
5
4.2971e+5
4.2970e+5
4.2969e+5
4.2968e+5
4.2967e+5
4.2966e+5
4.681400e+6
q(ht)
q(ht)
ata
XD
Z Data
20
0
4.681435e+6
4.681430e+6 4.681425e+6 4.681420e+6 4.681415e+6 4.681410e+6 4.681405e+6
Y Data
0
P
1
LiDAR Vertical Structure in Forest
Conditions
Plot 94a
30
29
28
27
26
25
24
23
22
21
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0
429960
20.00+
10.01-20.00
Height Class
Height m
94a
6.01-10.00
3.01-6.00
1.31-3.00
0.51-1.30
429965
429970
429975
429980
429985
X position
429990
429995
430000
430005
0
5
10
15
20
25
30
35
% Vegetation Returns
Vertical profile indicates vegetation between 0.5 and 1.3m with most of the
vegetation present in the 6-10 & 10-20m class.
40
Statistical Analysis
 Best Subsets Regression
 A model-building technique that identifies subsets of
variables that best predict responses on a dependent
variable by linear or non-linear regression.
 Model Diagnosis




Test for Normality: Shapiro-Wilks Test
Test for Homoschedasticity: Modified Levene’s Test
Multicollinearity: Variance Inflation Factors (VIF) < 10
Natural Logarithm Transformation
 Validation
 PRESS Procedure
11
11
Regression Models: Natural Hardwoods
R2
p
RMSE
(%)
PRESS RMSE
(%)
SUMBA (m2/ha)
0.82
< 0.001
3.46
(17.2)
3.99
(19.9)
SUMGTV (m3/ha)
0.90
< 0.001
39.35
(21.9)
52.03
(29.0)
DENSITY
(stems/ha)
0.77
< 0.001
196.03
(43.7)
214.98
(47.9)
QMDBH (cm)
0.82
< 0.001
3.07
(12.4)
4.17
(16.8)
AVGHT (m)
0.87
< 0.001
1.10
(5.7)
1.25
(6.4)
TOPHT (m)
0.96
< 0.001
0.80
(3.5)
0.89
(3.8)
Variable
Canopy Height Model (CHM)
Petawawa Research Forest
14
14
LiDAR Data Acquisition Standards
Goal:
 To develop standards for LiDAR data
acquisition in support of modelling forest
inventory variables.
Objective(s):
 Examine the impact of changes in pulse
densities on modelling forest inventory
variables.
LiDAR Data Acquisition Standards
Natural Tolerant Hardwood
RGB Image
0.5
pulses/m2
3 pulses/m2
Natural Conifer Shelterwood
Conifer Plantation
Preliminary
Results
Current Status
Swan Lake – Tolerant Hardwoods
Variable
Decimation Level 0
Decimation Level 1
Decimation Level 2
R2
RMSE
(%)
R2
RMSE
(%)
R2
RMSE
(%)
SUMBA
(m2/ha)
.49
2.7
(10.6)
.49
2.7
(10.7)
.58
2.4
(9.7)
SUMGTV
(m3/ha)
.59
25.4
(11.2)
.60
24.9
(11.0)
.61
24.8
(11.0)
DENSITY
(stems/ha)
.84
42.8
(10.5)
.86
39.8
(9.8)
.83
43.7
(10.7)
QMDBH
(cm)
.69
2.0
(7.3)
.72
1.9
(7.0)
.70
2.0
(7.1)
AVGHT
(m)
.84
0.6
(3.4)
.84
0.6
(3.4)
.85
0.6
(3.2)
TOPHT
(m)
.82
0.7
(3.0)
.85
0.7
(2.8)
.86
0.7
(2.7)
SUMBIO
(kg/ha)
.46
24,795
(12.6)
.50
23,811
(12.1)
.58
23,811
(12.1)
Preliminary
Results
Current Status
Petawawa Research Forest – Great Lakes
Pine
Variable
Decimation Level 0
Decimation Level 1
Decimation Level 2
R2
RMSE
(%)
R2
RMSE
(%)
R2
RMSE
(%)
SUMBA
(m2/ha)
.89
4.8
(13.3)
.89
4.7
(13.2)
.88
4.9
(13.7)
SUMGTV
(m3/ha)
.93
56.1
(13.4)
.94
51.2
(12.3)
.92
60.0
(14.4)
DENSITY
(stems/ha)
.74
207.8
(34.7)
.72
214.9
(35.8)
.68
226.9
(37.8)
QMDBH
(cm)
.87
3.9
(12.7)
.86
4.0
(12.9)
.87
3.8
(12.3)
AVGHT
(m)
.94
1.3
(5.7)
.94
1.3
(6.0)
.94
1.3
(5.7)
TOPHT
(m)
.95
1.2
(4.4)
.95
1.2
(4.5)
.94
1.3
(4.7)
SUMBIO
(kg/ha)
.74
29,818
(21.0)
.76
28,743
(20.3)
,78
27,572
(19.4)
Estimating forest canopy chlorophyll
concentration using remote sensing
technologies.
Goal: To investigate the potential of combining lidar and hyperspectral
data to improve estimates of canopy chlorophyll concentrations.
Objectives:
1.
2.
3.
To test hyperspectral indices at the canopy scale for estimating
chlorophyll concentrations.
To identify lidar structural metrics that are related to chlorophyll
concentration.
To combine lidar and hyperspectral indices to improve estimates of
chlorophyll concentration.
Thomas, V., P. Treitz, J.H. McCaughey, T. Noland and L. Rich, 2008. Canopy chlorophyll concentration estimation
using hyperspectral and lidar data for a boreal mixedwood forest in northern Ontario, Canada. International
Journal of Remote Sensing, 29(4): 1029-1052.
Study Area
Groundhog River Fluxnet Site (GRFS)
Timmins, Ontario, Canada
Lat/Long: 48 °N, 82 °W
Boreal Mixedwood Site
 Trembling Aspen (TA)
 White Birch (WB)
 White Spruce (WS)
 Black Spruce (BS)
 Balsam Fir (BF)
 White Cedar (C)
Plots
11.3 m radius; 400 m2
Height, dbh, crown width
Calibration Plots (24)
Validation Plots (9)
Results – Lidar Data
Lidar first return point clouds for: open black spruce canopy;
and trembling aspen canopy with balsam fir understory.
Results – Lidar Data
Relationships between average total leaf
chlorophyll concentration (a+b) and mean
lidar height above ground during August
2003 (lidar data) and August 2004 (leaf
chlorophyll concentration).
Groundhog River Flux Site,
August 2004, maps of mean of
25th percentile of lidar heights
above ground (m).
Results – Hyperspectral / Lidar
Map of total chlorophyll (a+b)
(μg/cm2) derived from the integrated
lidar-hyperspectral model.
Integrated lidar-hyperspectral model
(lidar25/DCI) for average leaf total
chlorophyll (a+b).
Acknowledgements