Table 1. Parameters of the LiDAR data sets.
Scanner Riegl LMS-Q680i Leica ALS60
Wavelength, nm 1550 1064
Date May 28, 2013 June 16, 2013
Pulse repetition frequency, Hz 240 106
FWHM, transmitted pulse, ns 4.5 7.8
Pulse density p×m–2 12−20 10
Flying height, m 750 700
Flying speed, m×s–1 41 62
Strip overlap, % 75 55
Scan angle, ±° 30 15
Footprint diameter (86%), cm 36 16
WF samples per pulse n × 80 1 × 256
WF sampling rate, GHz 1 1
Discrete returns per pulse 1−10 1−4
1

Fig. 1. Sample waveforms (WFs) of three LMS680i and three ALS60 pulses intersecting a football goal’s crossbar (height 2.5 m) and grass. The scan zenith angles (SZAs) are marked in degrees. WF sequences start with a buffer of amplitude values that display the signal prior to the storage-triggering echo. Their length is approximately 12 and 20 nanoseconds in the two sensors. The WF peaks (crossbar–grass) are separated more in the oblique pulse with an SZA of 27° compared to the vertical pulse (SZA = 1°). Return pulses of LMS-Q680i are symmetric, whereas ALS60 return pulses display ‘a tail’ even in hard targets. The asymmetry is due to the shutter in the laser transmitter that closes ‘slowly’.

2

Fig. 2. Illustration of echo-widening in the LMS-Q680i. The four return waveforms are from wooden benches with a 42-cm vertical spacing. Echo widths (defined as full-width- half-maximum, FWHM) range from 4.5 ns to 6.9 ns. The width was 4.5 ns for pulses that illuminated a single planar surface. The pulse that displayed a 6.9-ns echo width intersected two bench layers, and the 5.3-ns pulse likely also illuminated the vertical part of the structure (illustrated by the drawing with three pulses).

3

Fig. 3. Map of reference trees in Hyytiälä (61°49´N, 24°18´E), Finland. Field plots OG and IM are marked separately on the map, as well as the larch (L), and alder (A) stands. The map shows an area of 3×5 km.

4

Fig. 4. Stem diameter(dbh)×height distributions of trees in field plots IM and OG.

5

Fig. 5. Height distributions of trees measured in aerial images. Dead refers to dead spruce.

6

Fig. 6. Example of visual multi-image interpretation with 9 aspen crowns detected in leaf-off and leaf-on images captured in 11/2011 (left), 5/2013 (middle) and 6/2012 (right). The line segments show the ‘stem’ of a 24-m-high birch, which is used as the center point. The other ‘greyish’ trees in the leaf-off image are birches, and the green crowns are spruces.

7

Fig. 7. Illustration of crown modeling of an urban Norway spruce. Aerial image on the left has the target tree in the middle with the current solution of the crown model superimposed as a colored wireframe graph. Terrestrial image on the right was taken from the roof of a building and was included here for illustration only. The graph in the left part of the aerial image shows the stem (yellow line) connecting the treetop and the base. The colored points are the LiDAR points near the tree. Their x-coordinate is the horizontal distance to the stem. Returns from the neighboring spruce are visible in the colored point cloud.

8

Fig. 8. Illustration of the capture of WF segments of a pulse that intersects the crown of a reference tree. Points Penter, Pexit and PGround are 3D intersection points used for splitting the entire WF between the segments. Because of convolution, the segments include amplitude values ‘before and after’ the ‘exact’ points and the segments have some overlap following Pexit and 1−2 m above the ground. WF attributes derived for the amplitude sequences are listed in Table 2.

9

Fig. 9. An ALS60 WF of a 21.5-m-high pine. Graph illustrates the three WF segments and their WF attributes. WFCrown extends from 21 to 12 m and has four peaks (nCROWN = 4). eCROWN (crown energy) is the sum of amplitude values in WFCrown. pDist is the average distance between the peaks in WFCrown. The first-return NEAS amplitude sequence has three peaks (nNEAS = 3) and the length was 27 nanoseconds (lNEAS). The value of EQ50 is <0.5 as the NEAS energy is concentrated in the initial part. Understory energy (eUNDER) is due to the pulse intersecting the trunk or a dead branch 3−4 m above the ground. The pulse gave rise to a weak ground signal, and eGND (ground energy) is the sum of amplitude values belonging to WFGround. Echo width (FWHM) is computed using pA, which is the maximum amplitude in the NEAS.

Table 2. WF attributes of each pulse intersecting a tree crown. Attributes marked with * were directly adopted from Hovi et al. (2016). See Fig. 8 for the definition of WF segments representing the crown, understory and ground.
Attribute Definition
eCROWN Crown energy. Sum of amplitude values assigned to WFCrown
eNEAS* Energy of the (first-return) noise-exceeding amplitude sequence, NEAS
eUNDER Understory energy. Sum of amplitude values assigned to WFUnderstory
eGND Ground energy. Sum of amplitude values assigned to WFGround
nCROWN Number of local maxima in WFCrown
nNEAS* Number of local maxima in the (first) NEAS
pA* Maximum amplitude in the NEAS, ‘peak amplitude’
FWHM* Width of the echo giving pA, nanoseconds
lNEAS* Total length of the NEAS, nanoseconds
pDist Mean distance between local peaks in WFCrown, meters
MinRelDist Minimum relative horizontal pulse-trunk distance inside the crown, 0−1
pADist Horizontal distance to the trunk of point pA, meters
pARelDist Horizontal distance to the trunk of point pA, relative, 0−1
EQ50 Relative distance of the energy median from the start of the NEAS, 0−1
SZA Scan zenith angle, degrees
Table 3. Comparison of mean features by species relative to pine. Features are described in Table 2 and Fig. 9. The colors highlight the differences. Dead S refers to dead spruce.
Mean feature Pine Spruce Birch Aspen Alder Larch Dead S
m_eCROWN_1064 100 120 136 140 165 135 86
m_eUNDER_1064 100 151 138 135 105 127 135
m_eGND_1064 100 60 64 40 44 66 136
m_nCROWN_1064 100 103 108 109 104 106 117
m_eNEAS_1064 100 118 135 139 166 135 79
m_pA_1064 100 124 128 133 164 127 84
m_nNEAS_1064 100 100 106 105 101 105 103
m_lNEAS_1064 100 100 114 115 118 114 88
m_FWHM_1064  100 93 104 101 98 104 94
m_pDist_1064  100 107 109 114 121 102 108
m_EQ50_1064  100 103 100 97 93 101 106
m_MinRelDist_1064  100 101 104 105 103 102 95
m_pARelDist_1064  100 93 103 106 112 104 90
m_eCROWN_1550  100 95 105 82 111 115 146
m_eUNDER_1550  100 125 106 102 95 119 198
m_eGND_1550  100 86 88 68 80 86 114
m_nCROWN_1550 100 97 105 110 114 103 100
m_eNEAS_1550 100 92 105 77 109 115 142
m_pA_1550 100 102 99 79 105 112 140
m_nNEAS_1550 100 93 103 96 107 104 100
m_lNEAS_1550 100 88 106 90 107 108 103
m_FWHM_1550 100 96 104 106 106 105 98
m_pDist_1550 100 99 105 109 110 99 101
m_EQ50_1550 100 106 100 103 97 100 107
m_MinRelDist_1550 100 98 103 106 105 102 85
m_pARelDist_1550 100 92 103 107 111 105 78
10

Fig. 10. Comparison of 1064-nm and 1550-nm features m_eCROWN, m_lNEAS and m_FWHM by species. Features are described in Table 2 and Fig. 9. View larger in new window/tab.

Table 4. Correlation of mean waveform features with tree height by wavelength (1064, 1550) and species. Features are described in Table 2 and Fig. 9. The colors highlight the differences. Dead S refers to dead spruce.
  1064 1550 1064 1550 1064 1550 1064 1550 1064 1550 1064 1550 1064 1550 1064 1550
All Pine Spruce Birch Aspen Alder Larch Dead S
m_eCROWN 0.01 0.30 0.40 0.66 0.36 0.53 –0.22 –0.23 0.01 0.17 0.10 0.06 –0.44 0.21 0.30 0.42
s_eCROWN –0.01 0.23 0.34 0.63 –0.04 0.33 –0.15 –0.20 0.19 0.17 0.15 0.16 –0.14 0.42 –0.21 0.12
m_eUNDER –0.07 0.07 0.14 0.22 0.04 0.13 –0.29 –0.18 –0.23 –0.36 –0.14 0.14 –0.29 –0.19 0.17 0.24
s_eUNDER 0.02 0.14 0.16 0.23 0.27 0.29 –0.23 –0.06 –0.09 –0.12 –0.18 –0.13 –0.25 –0.16 0.25 0.28
m_eGND –0.22 –0.06 –0.36 –0.20 –0.41 –0.11 –0.28 –0.13 0.00 0.04 –0.57 –0.51 0.13 0.30 –0.59 –0.33
s_eGND –0.10 –0.05 –0.27 –0.05 –0.16 –0.18 –0.11 0.04 0.18 0.02 –0.50 –0.30 0.18 0.23 –0.48 –0.24
m_nCROWN 0.39 0.50 0.18 0.31 0.51 0.70 0.40 0.44 0.23 0.62 0.26 0.40 0.45 0.65 0.52 0.56
s_nCROWN 0.44 0.60 0.21 0.51 0.57 0.72 0.48 0.56 0.39 0.70 0.31 0.50 0.41 0.66 0.48 0.63
m_eNEAS –0.05 0.17 0.31 0.55 0.17 0.31 –0.27 –0.36 –0.03 0.04 0.04 –0.09 –0.47 –0.11 0.20 0.23
s_eNEAS 0.09 0.25 0.43 0.63 0.27 0.41 –0.08 –0.25 0.24 0.09 0.21 0.08 –0.07 0.47 0.05 0.22
m_pA 0.00 0.26 0.48 0.63 0.29 0.44 –0.24 –0.26 0.00 0.04 0.03 –0.11 –0.30 0.17 0.15 0.19
s_pA 0.11 0.29 0.44 0.54 0.45 0.55 –0.03 –0.06 0.18 0.09 0.07 –0.12 –0.16 0.30 0.05 0.15
m_nNEAS 0.05 0.11 –0.22 –0.25 0.07 0.26 0.11 –0.09 –0.08 0.14 0.04 0.10 0.01 0.02 0.30 0.42
s_nNEAS 0.11 0.23 –0.19 –0.12 0.16 0.33 0.18 0.07 0.06 0.23 0.05 0.23 0.07 0.28 0.32 0.49
m_lNEAS –0.05 0.02 –0.24 –0.27 0.01 0.08 –0.07 –0.24 –0.02 0.08 0.08 –0.04 –0.44 –0.29 0.30 0.34
s_lNEAS 0.15 0.26 –0.10 –0.09 0.16 0.37 0.20 0.01 0.23 0.28 0.24 0.29 0.24 0.54 0.29 0.43
m_FWHM –0.24 –0.23 –0.50 –0.44 –0.43 –0.40 –0.18 –0.10 –0.20 –0.06 –0.06 –0.30 –0.44 –0.51 0.08 –0.25
s_FWHM –0.01 –0.16 –0.11 –0.34 –0.07 –0.21 0.03 –0.05 0.08 –0.13 –0.12 –0.15 0.00 –0.45 0.02 –0.09
m_pDist 0.50 0.55 0.61 0.71 0.63 0.63 0.55 0.63 0.46 0.62 0.42 0.41 0.48 0.69 0.28 0.31
s_pDist 0.52 0.50 0.58 0.63 0.61 0.58 0.51 0.51 0.41 0.51 0.32 0.32 0.46 0.49 0.35 0.24
m_EQ50 –0.24 –0.21 –0.43 –0.42 –0.32 –0.31 –0.35 –0.12 –0.29 –0.20 –0.15 0.06 –0.35 –0.06 0.03 –0.17
s_EQ50 –0.09 0.00 –0.08 0.05 –0.10 0.00 –0.05 –0.01 –0.23 –0.03 –0.12 –0.09 –0.18 0.11 0.25 0.11
m_MinRelDist –0.07 –0.14 –0.25 –0.33 –0.02 –0.15 –0.07 –0.05 0.02 –0.13 –0.09 –0.24 –0.23 –0.31 0.22 0.09
s_MinRelDist 0.07 0.12 0.08 0.16 0.11 0.21 0.01 0.23 –0.11 –0.21 –0.50 –0.07 –0.09 –0.05 0.14 0.12
m_pARelDist 0.30 0.23 0.36 0.33 0.37 0.31 0.48 0.45 0.26 0.27 0.01 0.16 0.25 0.34 0.32 0.23
s_pARelDist –0.14 –0.08 –0.27 –0.13 –0.30 –0.15 0.06 0.11 –0.16 –0.25 –0.12 –0.06 –0.03 –0.10 –0.29 –0.07
11

Fig. 11. 1064 and 1550 nm mean crown energy and mean relative distance of the strongest return from the trunk in 2532 trees. Dead spruce separates well in eCROWN_1064 and eCROWN_1550. Some of the outliers may be due to species errors in the field data or in image interpretation. The asymmetry of the m_pARelDist point pattern shows how the strongest echoes of 1550-nm pulses (36 cm footprint diameter) have reflected from farther off the stem compared to 1064-nm pulses (16 cm). Features are described in Table 2 and Fig. 9.

12

Fig. 12. Correlation between 1064 and 1550 nm features. The diagonal elements show the between-wavelength correlation of the same feature. Features are described in Table 2 and Fig. 9.

13

Fig. 13. Correlation of 1064 and 1550 nm features in all trees. Features are described in Table 2 and Fig. 9. View larger in new window/tab.

Table 5. Average overall accuracy (OA,%) and kappa in QDA classifications using repeated (n = 1000) random sub-sampling validation with random splits between training and validation data using ratios 100:100, 90:10, 80:20, 50:50 and 20:80. Case 100:100 was computed only once and represents an optimistic scenario. Classification was done using 7, 6, 4 and 3 species classes using ten WF features ranked by F-test or Gini-importance. Only the range is given for kappa, not the values of all five validation scenarios.
Classes Wavelength OA % with F-test variables OA (%) with Gini-variables F-test Gini-vars
100 90 80 50 20% 100 90 80 50 20% kappa kappa
All seven 1064 76.7 73.5 73.3 72.5 69.9 76.7 73.6 73.3 72.5 69.8 0.711→0.624 0.711→0.624
1550 78.1 76.0 75.9 75.6 73.3 80.3 78.1 77.9 77.5 75.1 0.729→0.669 0.756→0.691
1064+1550 84.1 83.0 82.7 82.3 79.9 86.5 84.7 84.6 84.0 81.5 0.802→0.750 0.833→0.801
6 classes, without larch 1064 83.5 80.9 80.6 80.1 77.6 83.5 80.8 80.7 80.1 77.6 0.785→0.706 0.785→0.706
1550 83.2 81.6 81.6 81.0 79.1 84.0 82.3 82.4 81.7 79.5 0.783→0.727 0.793→0.732
1064+1550 88.7 87.9 87.8 87.4 85.5 91.3 90.0 90.1 89.7 87.6 0.855→0.810 0.889→0.838
4 classes, pine, spruce, birch, dead spruce 1064 88.7 87.6 87.6 87.3 86.1 88.7 87.7 87.7 87.4 86.1 0.838→0.800 0.838→0.800
1550 88.2 87.3 87.2 86.8 85.7 89.0 88.1 88.1 87.8 86.7 0.831→0.795 0.842→0.809
1064+1550 91.1 90.6 90.6 90.4 89.6 93.5 92.7 92.7 92.6 91.8 0.871→0.850 0.906→0.881
3 classes,
pine, spruce, birch
1064 90.8 89.8 89.7 89.5 88.4 90.8 89.9 89.8 89.5 88.4 0.855→0.819 0.855→0.817
1550 88.7 87.9 87.6 87.1 85.9 89.2 88.4 88.4 88.0 86.8 0.824→0.780 0.830→0.793
1064+1550 91.6 91.2 91.1 90.8 89.9 93.9 93.5 93.3 93.0 92.2 0.868→0.841 0.905→0.878
Table 6. Average confusion matrix in 1000 randomized validations using 80% of the trees for training and 20% for validation. The predictors were the five best 1064 and 1550 nm features by Gini-importance (Kappa = 0.808, OA = 84.6%). Dead refers to dead spruce.
QDA class  True class
Pine Spruce Birch Aspen Alder Larch Dead
Pine 436 19 9 10 4
Spruce 38 741 15 2 47 6
Birch 4 13 328 23 13 60 1
Aspen 1 12 129 9 3
Alder 10 10 125 1
Larch 7 13 44 3 241
Dead 7 11 2 175
Table 7. Average Producer’s accuracy (%) by species in classifications of seven species classes using predictors selected by Gini-importance (90:10 ratio of training and validation). Dead refers to dead spruce.
Wavelenght Pine Spruce Birch Aspen Alder Larch Dead
1064 nm 87 85 51 46 78 61 79
1550 nm 78 88 68 67 58 76 90
1064+1550 nm 91 87 75 84 86 79 89
Table 8. Feature ranking by F-test and Gini-importance in data combining all seven species. 1064 and 1550 refer to the wavelength. Features are described in Table 2 and Fig. 9.
F-test 1064 F-test 1550 Gini 1064 Gini 1550
m_eCROWN s_eNEAS m_lNEAS m_lNEAS
m_eNEAS s_eCROWN m_eCROWN s_pA
m_pA m_eCROWN s_pA s_eNEAS
s_pA m_eNEAS m_eNEAS m_pDist*
s_eNEAS s_eUNDER* m_pA m_FWHM
m_lNEAS m_pARelDist m_FWHM s_eCROWN
s_eCROWN s_pA s_eNEAS m_eNEAS
m_pARelDist m_eUNDER* s_eCROWN m_EQ50*
m_EQ50 m_lNEAS m_EQ50 m_pARelDist
m_FWHM m_pA* m_pARelDist m_eCROWN