In Finland, the possibility of utilizing two or more ALS datasets from a large-scale inventory 628
area is unlikely, although the potential could be realized in the future. The first complete ALS 629
data coverage of Finland will be completed by 2020. In addition, several forest operators have 630
acquired their own ALS datasets. Therefore, operational applications of the fusion of leaf-off 631
and leaf-on ALS data are possible. At the very least, the finding in this study that metrics de-632
rived from leaf-off ALS data could substitute for aerial image metrics could be applied in cases 633
where the acquisition of aerial images has failed, for example, due to unexpected weather con-634
ditions. The determination of a satisfactory time interval between bi-temporal ALS data acqui-635
sitions for forest inventories requires further study. The acquisition of both leaf-off and leaf-on 636
ALS data for an inventory area over a short time interval is not reasonable from an economical 637
viewpoint. Therefore, single sensor solutions, such as multispectral ALS, may be more attrac-638
tive for future species-specific forest inventories. Unfortunately, the results of this study did not 639
indicate that multispectral ALS data could offer a single sensor solution for species-specific 640
ALS-based forest inventories. In general, the findings of this study reveal new possibilities to 641
combine multiple RS data sources and encourage repeat national ALS data acquisitions in for-642
ested countries.
643 644
5 Conclusions 645
The results indicate that the combination of leaf-on and leaf-off ALS metrics provide lower 646
error rates than the traditional combination of ALS and aerial image metrics for the prediction 647
of species-specific logwood volumes. Our findings also show that recent leaf-off ALS data can 648
be replaced by older leaf-off ALS data without a significant deterioration in logwood prediction 649
error rates. We also explored metrics derived from multispectral ALS data. The results indicate 650
that the multispectral metrics lead to a slight improvement in the error rates associated with 651
logwood volume predictions. Overall, the outcome of this study encourages a deeper investiga-652
tion of the possibilities of combining different multi-temporal ALS datasets in area-based forest 653
inventories. In the future, multi-temporal ALS datasets will be increasingly available from in-654
ventory areas. As such, methods and applications that incorporate all available ALS data from 655
an inventory site are welcome.
656 657
Funding details 658
This study is a contribution to the project Comparative test to predict species-specific diameter 659
distributions in forest information systems financed by the Finnish Forest Centre. This study 660
was also supported by the project Sustainable, climate-neutral, and resource-efficient forest-661
based bioeconomy (FORBIO, decision number 314224), funded by the Strategic Research 662
Council at the Academy of Finland. The Finnish Society of Forest Sciences supported this work 663
with a scholarship granted to the corresponding author.
664 665 666
Disclosure statement 667
No potential conflict of interest was reported by the authors.
668 669
670
Availability of data and material 671
The raw leaf-off ALS datasets supporting the conclusions of this article are available in the 672
repository of National Land Survey Finland, https://tiedostopalvelu.maanmittauslai-673
tos.fi/tp/kartta. The multispectral ALS data, and the field data will not be shared due to the 674
ownership of the data.
675 676
Data deposition 677
No data deposition.
678
Acknowledgements 679
We acknowledge the support provided by the Strategic Research Council of the Academy of 680
Finland for the FORBIO project (decision number 314224), led by Prof. Heli Peltola at the 681
School of Forest Sciences, UEF. We would like to express our gratitude to Prof. Heli Peltola 682
and Prof. Jyrki Kangas for the acquisition of the financial support for the fieldwork needed to 683
conduct this study. We also would like to thank the Finnish Society of Forest Science for the 684
scholarship awarded to the corresponding author.
685 686
Captions for figures 687
Figure 1. Location of the study area and sample plots in Finland.
688
689 690
Figure 2. This figure demonstrates the multispectral ALS data (M-ALS). The leftmost figure 691
describes the data around a validation plot in a coniferous dominated forest, and the rightmost 692
in a deciduous dominated forest. In the color ramp, black describes the lowest heights and yel-693
low describes the highest heights.
694
695 696
Figure 3. This figure demonstrates leaf-off ALS datasets used in this study (the uppermost 697
figures: S16-ALS; the bottom figures: S11-ALS). The leftmost figures describe the data around 698
a validation plot in a coniferous dominated forest, and the rightmost in a deciduous dominated 699
forest. In the color ramp, black describes the lowest heights, and yellow describes the highest 700
heights.
701
702 703
Figure 4. Species-specific root mean squared error (RMSE; %) and mean difference (BIAS;
704
%) error values for logwood volume predictions in terms of remote sensing data and response 705
configurations. For the abbreviations of remote sensing data combinations, please refer to sec-706
tion 2.3.
707
708
709 710
Figure 5. Predicted vs. observed dominant and minor tree species logwood volumes presented 711
in 30 x 30 m validation data. Remote sensing data combinations of M-CH2-ALS + S16-ALS 712
(leftmost) and M-CH2-ALS + AI (rightmost) were used. The SimLog response configuration 713
was employed in all combinations. The median iteration (25th of 50) with respect to the root 714
mean squared error (RMSE) value associated with the dominant logwood volume is presented.
715
For the abbreviations of remote sensing data, please refer to section 2.3.
716 717
718
Figure 6. Predicted vs. observed dominant and minor tree species logwood volumes presented 719
with 30 x 30 validation data. Remote sensing data combination of M-CH2-ALS + S11-ALS 720
was used. The SimLog response configuration was employed in all combinations. The median 721
iteration (25th of 50) with respect to the root mean squared error (RMSE) value associated with 722
dominant logwood volume is presented. For the abbreviations of remote sensing data, please 723
refer to section 2.3.
724 725
726
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918 919 920 921 922 923 924 925 926 927 928 929 930 931 932
Appendix 933
Table 7. This table shows the minimum, maximum, and standard deviation of root mean 934
squared error (RMSE) values (%) among the 50 prediction iterations when the SimLog response 935
configuration was used.
936
Dominant logwood Minor logwood Total logwood
Min Max Sd Min Max Sd Min Max Sd
M-ALS 37.8 50.0 2.5 219.2 399.9 43.1 26.3 40.3 3.3
M-ALS + AI 32.4 43.7 2.5 128.6 332.5 42.0 21.1 37.7 3.3
M-ALS + S16-ALS 28.4 42.3 2.8 96.3 230.5 26.4 19.7 27.5 1.7 M-ALS + S11-ALS 29.6 41.8 2.3 89.8 249.5 31.2 22.1 30.2 1.9
M-CH2-ALS 41.1 50.2 2.2 300.3 393.2 23.6 33.3 41.0 2.0
M-CH2-ALS + AI 32.6 44.3 2.4 116.7 306.8 40.5 23.5 35.4 2.6 M-CH2-ALS + S16-ALS 29.4 39.5 2.3 105.3 211.6 22.3 20.0 28.3 1.6 M-CH2-ALS + S11-ALS 29.4 40.7 2.4 82.6 202.1 23.1 22.0 32.3 1.9
S16-ALS 34.8 47.4 2.8 129.5 223.3 20.1 20.7 32.1 1.8
S16-ALS + AI 30.3 45.9 3.2 94.1 242.0 22.7 21.1 29.1 1.8
S16-ALS + S11-ALS 32.2 44.9 3.0 119.2 199.9 17.6 20.8 28.5 1.9 937
Table 8. This table shows the minimum, maximum, and standard deviation of root mean 938
squared error (RMSE) values (%) among the 50 prediction iterations when the SepSM response 939
configuration was used.
940
Dominant logwood Minor logwood Total logwood
Min Max Sd Min Max Sd Min Max Sd
M-ALS 39.2 47.7 2.1 158.3 348.4 41.9 26.0 42.1 3.2
M-ALS + AI 32.9 45.5 2.5 118.0 308.8 45.0 25.7 41.8 3.5
M-ALS + S16-ALS 32.7 43.2 2.1 117.1 233.0 30.1 24.5 36.8 2.5 M-ALS + S11-ALS 31.9 40.6 1.9 84.1 210.0 27.1 24.4 38.6 2.6
M-CH2-ALS 38.2 46.1 1.8 206.3 380.2 39.3 29.0 40.6 2.9
M-CH2-ALS + AI 30.2 42.9 2.6 86.2 261.0 38.8 24.1 37.3 3.1 M-CH2-ALS + S16-ALS 33.2 44.6 2.4 89.2 285.3 37.6 24.8 35.9 2.5 M-CH2-ALS + S11-ALS 30.7 42.0 2.6 93.9 230.5 29.0 22.2 32.8 2.5
S16-ALS 33.8 47.5 3.4 147.5 274.8 35.9 25.5 37.8 3.2
S16-ALS + AI 31.7 44.5 2.9 100.2 252.9 32.2 25.4 38.9 3.1
S16-ALS + S11-ALS 32.7 45.8 3.3 113.3 259.9 33.1 25.7 36.1 2.7 941
942 943 944
Tables and captions for the tables 945
946
Table 1. Means and standard deviations of the main forest attributes in the training and valida-947
tion data. DGM = diameter of basal area median tree.
948
Training Validation
Forest attribute Tree species Mean Sd Mean Sd
Volume (m3∙ha-1) Pine 76.0 83.4 77.1 90.7
Spruce 87.7 109.1 87.5 109.9
Deciduous 22.8 36.1 41.0 62.2
Total 186.4 100.4 205.6 94.6
Logwood volume (m3∙ha-1) Pine 46.3 61.3 50.3 67.9
Spruce 55.4 90.6 57.7 94.2
Deciduous 5.8 16.4 17.7 40.6
Total 107.5 98.5 125.6 101.1
Basal area (m2∙ha-1) Pine 8.8 9.1 8.6 9.7
Spruce 9.9 10.5 9.8 10.3
Deciduous 2.9 4.2 4.4 5.9
Total 21.6 8.2 22.8 7.7
DGM (cm) Pine 21.0 6.5 22.2 6.4
Spruce 17.1 8.4 17.8 9.8
Deciduous 14.8 7.5 16.8 8.5
949 950 951 952 953 954 955 956 957 958
Table 2. Bucking parameters used for the logwood calculations.
959
Tree group Logwood minimum Diameter (cm)
Logwood minimum length (m)
Logwood maximum length (m)
Pine 15 3.7 6.1
Spruce 16 3.7 6.1
Deciduous 18 3.7 6.1
960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979
Table 3. Summary of the parameters used in airborne laser scanning (ALS) acquisitions for 980
multispectral leaf-on (M-ALS), unispectral leaf-off (S16-ALS), and older unispectral leaf-off 981
(S11-ALS) ALS data.
982
Airborne laser scanning data
M-ALS S16-ALS S11-ALS
Year 2016 2016 2011
Type Multispectral leaf-on Unispectral leaf-off Unispectral leaf-off
Device Optech Titan Leica ALS60 Leica ALS60
Flying altitude (m) 850 2400 2200
Scanning half angle (deg) 20 20 20
Lateral overlap 55 % 20 % 20 %
Pulse density (m-2) 13.5 0.9 0.8
983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000
Table 4. Summary of the metrics used as predictor variables in nearest neighbor (NN) imputa-1001
tions.
1002
Metric ALS data Aerial Images Description
Mean x x Mean of echo heights or spectral values
computed from aerial images.
Standard deviation x x Standard deviation of echo heights or spectral values computed from aerial images.
Maximum x x Maximum of echo heights or spectral
values computed from aerial images.
Minimum x x Minimum of echo heights or spectral values computed from aerial images
Kurtosis x Kurtosis of echo heights or intensities.
Skewness x Skewness of echo heights or intensities.
Height percentiles x Height percentiles for 5, 10, …, 95 %.
Densities x Densities for 0.5, 2, 5, 10, 15, and 20 m
Echo proportion x Proportions for echo categories: first, last, and intermediate.
Intensity percentiles x Intensity percentiles for 5, 10, …, 95 %.
1Ratio of height
per-centiles x
Ratio metrics between the height percentiles of separate ALS channels.
Computed for height percentiles of 60, 65, …, 95 %.
1Ratio of intensity percentiles
x Ratio metrics between the intensity percentiles of separate ALS channels.
Computed for intensity percentiles of 55, 60, …, 95 %.
1 When using a combination in which multispectral ALS data is included, a metric from the second channel of
1003
multispectral ALS is as a numerator.
1004
Note: All ALS metrics are computed separately for the channels of multispectral ALS and for unispectral ALS
1005
datasets. All ALS metrics are computed separately for echo categories: first, last, and intermediate. Aerial image
1006
metrics are computed for spectral bands of red, green, blue and near-infrared.
1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017
Table 5. Relative root mean squared error (RMSE; %) and mean difference (BIAS; %) values 1018
(in parenthesis) for the prediction of dominant, minor, and total logwood volumes using Sim-1019
Log with different remote sensing data combinations. Please see Appendix for detailed statistics 1020
related to the RMSE values among the iterations.
1021
Data combination Dominant logwood Minor logwood Total logwood
M-ALS 45.6 (-21.7) 337.2 (141.2) 34.8 (1.7)
M-ALS + AI 38.6 (-17.7) 219.8 (87.2) 27.2 (-2.7)
M-ALS + S16-ALS 34.3 (-15.6) 136.1 (39.0) 23.2 (-7.7)
M-ALS + S11-ALS 34.7 (-15.1) 126.2 (35.6) 25.3 (-7.9)
M-CH2-ALS 46.6 (-22.1) 359.2 (146.6) 36.9 (2.1)
M-CH2-ALS + AI 37.9 (-17.6) 219.8 (86.2) 27.4 (-2.7)
M-CH2-ALS + S16-ALS 34.0 (-15.9) 134.1 (39.7) 23.1 (-8.0) M-CH2-ALS + S11-ALS 34.5 (-15.5) 122.1 (32.3) 25.7 (-8.7)
S16-ALS 39.9 (-19.6) 165.6 (47.6) 25.0 (-10.0)
S16-ALS + AI 38.7 (-19.1) 152.5 (41.0) 25.1 (-10.5)
S16-ALS + S11-ALS 38.7 (-19.0) 148.6 (41.6) 24.5 (-10.4) Note: 30 x 30 m plots were used for the validation. For the abbreviations of remote sensing 1022
data combinations, please refer to section 2.3.
1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037
Table 6. Relative root mean squared error (RMSE; %) and mean difference (BIAS; %) values 1038
(in parenthesis) for the prediction of dominant, minor, and total logwood volumes using SepSM 1039
with different remote sensing data combinations. The presented values are as a mean of 50 1040
iterations. Please see Appendix for detailed statistics related to the RMSE values among the 1041
iterations.
1042
Data combination Dominant logwood Minor logwood Total logwood
M-ALS 43.3 (-19.2) 247.1 (101.5) 33.7 (-0.8)
M-ALS + AI 37.6 (-15.1) 207.0 (77.0) 33.3 (0.1)
M-ALS + S16-ALS 37.9 (-17.4) 157.7 (55.9) 29.3 (-3.8)
M-ALS + S11-ALS 36.3 (-15.3) 141.9 (45.2) 28.7 (-2.3)
M-CH2-ALS 42.6 (-19.2) 278.3 (114.4) 35.3 (-0.1)
M-CH2-ALS + AI 35.9 (-13.9) 150.8 (51.7) 29.8 (-1.6)
M-CH2-ALS + S16-ALS 38.1 (-17.8) 150.7 (49.9) 29.9 (-4.8) M-CH2-ALS + S11-ALS 35.8 (-15.3) 137.6 (45.3) 27.3 (-2.8)
S16-ALS 40.4 (-18.8) 209.7 (73.4) 30.8 (-1.8)
S16-ALS + AI 38.1 (-17.5) 159.2 (44.2) 31.1 (-4.3)
S16-ALS + S11-ALS 39.8 (-18.6) 183.6 (62.8) 30.9 (-2.8) Note: 30 x 30 m plots were used for the validation. For the abbreviations of remote sensing 1043
data combinations, please refer to section 2.3.
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