Application of LIDAR data to characterize structure types of multifunctional Pinus
sylvestris, L. stands
C. Pascual1, A. García-Abril2, S. Martín-Fernández2 and L.G. García-Montero2
1
Universidad Rey Juan Carlos, Department of Chemical and Environmental
Technology, c/ Tulipán s/n 28933, Móstoles (Madrid), SPAIN e-mail:
cristina.pascual@urjc.es
2
Universidad Politécnica de Madrid, Forestry Engineering Technical School, Ciudad
Universitaria s/n, 28040 Madrid, SPAIN
_______________________________________________________________________
Abstract
This paper describes the suitability of LIDAR data to identify forest structure
types of Pinus sylvestris, L. The variable considered was the spatial distribution
of total height of the pines by means of a LIDAR Digital Canopy Model (DCM).
The main objective is to develop a methodology for mapping the forest structure
to support the decision-making process in sustainable forest management. Five
structure types were established using the LIDAR DCM, and they were
characterized by means of field measurements. Also, an ANOVA test was
applied to several indices to validate how different the structure types were.
Results indicate that LIDAR height is an important variable for forest structure
characterization.
_______________________________________________________________________
Keywords: LIDAR, forest structure types, forest structure cartography
Introduction
Deep knowledge of forest structure is essential for sustainable forest management (Spies
1998; del Río, 2003). Forest structure may be defined by the size, age, and species
distributions of living and dead vegetation, often with a focus on the tree component (Spies &
Franklin, 1991; Poage & Tappeiner, 2005). Understanding forest structure (and therefore
sustainable forest management) requires cartography of forest structure.
Forest structure can be characterized by different indices and attributes that quantify tree
distribution, species composition (species diversity and distribution in the stand), and
diameter, height, crown size and vertical strata differentiation (Pommerening, 2002; del Río
et al., 2003). More recently, LIDAR data have turned out to be a strong tool for forest
attributes estimation (Naesset, 1997; Magnussen & Boudewyn, 1998; Lefsky et al., 1999;
Means et al., 1999; Naesset & Bjerknes, 2001; Drake et al., 2002; Naesset, 2002; Riaño et
al., 2004; Andersen et al., 2005). However, Zimble et al. (2003) point that forest structure
mapping using laser altimeter data is not yet properly developed, and they have begun to
study the vertical distribution component of forest structure by mapping the vertical strata
complexity of conifers.
These references show an increasing interest in characterizing forest structure using LIDAR
data, but they also point out that works related to mapping forest structure and stand
delineation are still limited. The objective of the present work is to propose an approach for
cartography of forest structure stands of Pinus sylvestris in the Madrid region to support
decision making in sustainable forest management.
Materials and Methods
Study area
A forest area in the Fuenfría Valley (40º45´N 4º5´W, 1310–1790 m a.s.l.), in the mountain
range of the North-Western part of the Madrid region was selected. Fuenfría Valley is a
Mediterranean forest dominated by Scots pine (Pinus sylvestris). Other species that usually
go with pine are Quercus pyrenaica, Cytisus scoparius and Pteridium aquilinum. In the
highest part of the mountains, Juniperus nana and Adenocarpus hispanica are also present.
LIDAR data
A small-footprint LIDAR mission flown by TopoSys covered the test area in August 2002.
The TopoSys II LIDAR system recorded first and last returns with a footprint diameter of 0.95
m. The flight was conducted with a nominal height over ground of 950–1000 m. The laser
pulse density was between 2 m across track and about 0.11 m along track. This
configuration provided a density of about 5 points/m2.
The raw data delivered by the sensor (x,y,z-triples) was processed into two digital elevation
models by TopoSys using the company’s own processing software. The Digital Surface
Model (DSM) was processed using the first pulse reflections, and the Digital Elevation Model
(DEM) was constructed using the last returns and filtering algorithms. In both cases, the pixel
resolution was 1 m and the height resolution was 0.1 m. According to TopoSys's
calculations, in both LIDAR models (DSM and DEM), the horizontal positional accuracy was
0.5 m, and the vertical accuracy was 0.15 m. Finally, to get the canopy height estimates of
the study site — the Digital Canopy Model (DCM) — the DEM values were subtracted from
DSM values.
Methodology for mapping structure types
The methodology to identify the structure types of the pines in the study area comprises the
following steps:
1. Definition of 8 height intervals to reclassify the DCM LIDAR data into a categorized
variable based on field diameter – height relationships.
2. Segmentation of the reclassified LIDAR image into units or polygons with a
homogeneous spatial pattern distribution of the height intervals.
3. Application of statistics algorithms (cluster analysis) to combine similar units or
polygons into 5 forest structure types.
4. Characterization of the 5 forest structure types with field data in 10 field plots (240 m2
each)
5. Validation of the structure types, applying the following indices:
• Indices based on the topography of the LIDAR DCM:
1. Elevation–relief ratio (E)
2. Crown area–volume ratio (Ac/V)
3. Crown area–ground area ratio. (Ac/S)
• Hypsograph of canopy surface (H10%, H25%, H50%, H75%, H90%)
• Indices of the distribution of the gaps in the forest
1. The gap surface area (Sh)
2. Percentage of specific voxels in the vertical axis (EST1, EST2,
EST3, EST4)
•
Landscape ecology indices
1. Shannon Index (SHDI)
2. Fractal dimension (Fr. Dim)
3. Mean texture and standard deviation of texture (M_5x5, SD_5x5).
For all these indices, ANOVA tests using LSD´s method of post hoc analysis were performed
to test whether indices describing forest canopy structure varied significantly among the five
structure units defined.
Results and Discussion
Five structure types from LIDAR data
The regression model obtained to transform the diametric classes into height classes has
been h (m) =2.2132 +0.3752 dbh (cm), where h is the total height of the tree in m and dbh is
the diameter breast height in cm (r2=0.891, p<0.01, n=1129).
The result of the reclassified LIDAR DCM into 8 height intervals is presented in figure 1 (first
step in the methodology for mapping structure types). Figure 1 also presents the units or
polygons digitalized attending the spatial distribution of height intervals (second step in the
methodology for mapping structure types). The 40 polygons are grouped in 5 types of
structure considering the mean height and standard deviation of the height of each polygon
as entries in the cluster analysis (K-means algorithm) (third step). Numbers 1 to 5 of figure 1,
represent the 5 structure types obtained from the cluster analysis (k-means) applied to the
40 polygons covered by pines.
Height
Classes
(m)
Nº
interval
0 – 0.99
1
1 – 2.99
2
3 – 5.99
3
6 – 9.99
4
10 - 14.99
5
15 – 19.99
6
20 – 24.99
7
25 - 32
8
Fig. 1. This figure shows the LIDAR DCM reclassified into 8 height interval. Segmentation of the LIDAR
reclassified image in 40 polygons attending to the spatial pattern distribution of height classes is also
presented. Numbers 1 to 5 represent the 5 structure types obtained from the cluster analysis (k-means)
applied to the 40 polygons covered by pines.
Field data characterization of the 5 forest structure types
Field data measurements (dbh and height) in 10 plots allow characterization of the 5 forest
structure types as follows:
a. Type 1: Dense uneven aged structure (800 tree/ha; G=39.9 m2/ha) (Fig 2a)
b. Type 2: Multidiametric dense structure (640 tree/ha; G=640 m2/ha) (Fig 2b)
c. Type 3: Low density multidiametric in small woods (378 tree/ha; G=35.3 m2/ha) (Fig
2c)
d. Type 4: Low density even aged structure (175 tree/ha; G=26.2 m2/ha) (Fig 2d)
e. Type 5: Shrubs and areas with low pine cover (75.6 tree/ha; G=6.6 m2/ha) (Fig 2e)
Number trees / ha TYPE 1
Number trees / ha
180
TYPE 2
Number trees / ha
TYPE 3
180
180
160
160
160
140
140
140
120
120
120
100
100
100
80
80
80
60
60
60
40
40
40
20
20
20
0
0
5
10
15
20
25
30
35
40
45
50
55
60
a)
0
65
Diameter classes (cm)
0
5
10
15
b)
Number t rees / ha
20
25
30
35
40
45
50
55
60
0
c)
Diameter classes (cm)
TY P E 4
0
65
5
10
15
20
25
30
35
40
45
50
Diameter classes (cm)
Number trees / ha TYPE 5
180
180
160
160
140
140
120
120
100
100
80
80
60
60
40
40
20
20
0
0
5
10
d)
15
20
25
30
35
40
45
Di ameter classes (cm)
50
55
60
65
0
e)
0
5
10
15
20
25
30
35
40
45
50
55
60
65
Diameter classes (cm)
Fig 2. Diameter distribution of the 5 forest structure types based on field data measurements
Indices validation-discrimination (ANOVA test)
The results of the ANOVA test (Table 1) show that only two indices (Ac/S) and the Fractal
dimension (Fr. Dim) are unable to differentiate among the five structure types.
Table 1
ANOVA test results for all the indices
E
Ac/V
Ac/S
H 10%
H 25%
H 50%
H 75%
EST3
EST2
EST1
SHDI
Fr dim
M_5x5
SD_5x5
F
38.4604
49.3547
2.3005
25.3917
39.4290
80.5062
98.9548
25.9525
50.9377
107.8608
14.6277
1.6290
8.4456
4.7988
p
0.00000
0.00000
0.07811
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.18882
0.00007
0.00342
55
60
65
On the other hand, the LSD´s method of post hoc analysis (Table 2) show that the indices
that identify the five structure types more clearly are strongly correlated with the variable
mean LIDAR height.
Table 2
LSD´s method of post hoc analysis
STRUCTURE
E
Ac/V
H 50%
H 75%
H 25%
EST 1
CLASS
5
***
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4
***
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***
3
***
***
***
***
***
***
2
***
***
***
***
***
***
1
***
***
***
***
*** ***
The cluster and LSD post-hoc test results support the importance of mean and variability of
LIDAR height as the most efficient LIDAR variables to synthesise the spatial distribution of
the size of the trees (forest structure). These hypotheses have also been pointed out by
Zimble et al. (2003). They indicate that height variances generated from LIDAR data can be
used to differentiate two structure classes (monostratified and plural stratification). On the
other hand, Lefsky et al. (2005) have concluded that mean height, cover or leaf index area,
and height variability represent the same kind of enhancement of LIDAR data that the
Tasselled Cap indices represent for optical remote sensing.
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