1. Integrating Airborne LiDAR and Terrestrial Laser Scanner for Accurate Tropical Forest Biomass/Carbon Monitoring Accuracy Matters
Muluken Nega Bazezew (Dilla University, P.O. Box 419, Dilla, Ethiopia)
National Conference on Geo-Information and Earth Observation: Dec , 2019
Mekelle University, Mekelle, Ethiopia
Dilla University
Ethiopia
Mekelle University, Ethiopia
ITC, The Netherlands
University Putra Malaysia

2. OUTLINES ___________________________________________________________________________________________________________________________________________________________
INTRODUCTION
OBJECTIVES
RESEARCH METHODS
RESULTS AND DISCUSSION
CONCLUSION AND RECOMMENDATIONS
ACKNOWLEDGMENTS
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3. INTRODUCTION ___________________________________________________________________________________________________________________________________________________________
GHGs emissions; Impact on climate
UNFCCC, Kyoto Protocol (192 countries)
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4. INTRODUCTION ___________________________________________________________________________________________________________________________________________________________
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5. The growing need of REDD+ MRV!!
INTRODUCTION ___________________________________________________________________________________________________________________________________________________________
The growing need of REDD+ MRV!!
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6. INTRODUCTION ___________________________________________________________________________________________________________________________________________________________
To meet the requirements of REDD+ MRV for
Accurate Forest Inventory!!
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7. To meet the requirements of REDD+ MRV for
INTRODUCTION ___________________________________________________________________________________________________________________________________________________________
To meet the requirements of REDD+ MRV for
Accurate Inventory !!
ALS
Integration of ALS and TLS for accurate topical forest monitoring
ALS- Upper canopy trees characterization
TLS- Lower canopy trees characterization
TLS
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8. OBJECTIVES ___________________________________________________________________________________________________________________________________________________________
The aim of this research is to develop an approach for estimating accurate AGB/Carbon of the tropical rainforests with integrating Airborne LiDAR Scanning (ALS) and Terrestrial Laser Scanning (TLS).
Specifically:
Assess LiDAR-CHM in tree crown delineation of the tropical forests
Assess the accuracy of forest parameters (DBH, height) measurement with ALS and TLS
Compare the estimated AGB/Carbon between RS (ALS + TLS) and traditional field-based methods
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9. STUDY SITE ___________________________________________________________________________________________________________________________________________________________
Located 3º0’0” to 3º2’0” lat. and 101º38’0” to 101º40’0” lon.
Elevation (15 to 233 m a.s.l).
Encompasses 430 plant species, and >60% of its emergent and middle canopy trees, and the rest understory trees and shrubs.
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10. METHODS ___________________________________________________________________________________________________________________________________________________________
ALS data acquiring, processing and analyzing
TLS data acquiring, processing and analyzing
Data Integration
Various Image processing, and analysis software were used:
ERDAS Imagine and ENVI for image processing,
LAStool for ALS point cloud data processing,
RiSCAN PRO for TLS point cloud data processing,
eCognition for ALS-CHM segmentation,
ArcGIS,
R-studio.
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11. RIEGL VZ-400 Terrestrial Laser Scanning sensor features
TLS and Dataset Processing ___________________________________________________________________________________________________________________________________________________________
RIEGL VZ-400 Terrestrial Laser Scanning sensor features
Measurement range m
Precision
3 mm
Accuracy
5 mm
Beam divergence
0.35 mrad
Footprint size at 100m
30 mm
Measurement (pulse) rate
kHz
Scan angle range (degree)
100o (+60o / -40o)
Laser wavelength
Near-infrared (1550 nm)
GPS receiver
Integrated, L1, with antenna
Scanning mechanism
Rotating multi-facet mirror
Scan speed
lines/sec
Weight
9.6 kg
Operating temperature
0 to +40 oC; standard operation
Humidity
Max. 80%, non-condensing at +30 oC
Riegl, 2017:
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12. TLS and Dataset Processing ___________________________________________________________________________________________________________________________________________________________
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13. TLS and Dataset Processing ___________________________________________________________________________________________________________________________________________________________
Individual tree extracted in RiSCAN PRO software
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14. TLS and Dataset Processing ___________________________________________________________________________________________________________________________________________________________
Measuring with RiSCAN PRO
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15. ALS and Dataset Processing ___________________________________________________________________________________________________________________________________________________________
Sensor Feature
Description
Pulse rate
Range between 70 kHz and 240 kHz
Scan angle
60°
Scan pattern
Regular
Beam divergence
0.5 mrad
Line/sec
Max. 160
A/c ground speed
90 kts
Target reflectivity
% (vegetation 30%, cliff 60%)
Flying height m
Laser points/m2
5 to 6 points with 808 m to 1155 m swath width
Spot diameter (laser) m
Max (above ground level)
1040
Ilustration of Airborne LiDAR:
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16. ALS and Dataset Processing ___________________________________________________________________________________________________________________________________________________________
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17. ALS-TLS Integration ___________________________________________________________________________________________________________________________________________________________
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18. Results ___________________________________________________________________________________________________________________________________________________________
TLS-based DBH Accuracy
Tree parameter
No. of Observations R2 r
RMSE
Bias (cm)
(cm)
(%)
DBH
735
0.98
0.99
1.30
6.52
– 0.52
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19. Results ___________________________________________________________________________________________________________________________________________________________
Segmentation Accuracy and canopy cross-sectioning
Reference polygons
1:1 matched polygons
Over-segmentation
Under-segmentation
Goodness of fit (D)
132
103
0.24
0.30
0.27
Accuracy (%) 78 73
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20. Results __________________________________________________________________________________________________________________________________________________________
The average success rate of treetops detection by LiDAR
Success rate displays the number of trees (%) that their treetops identified from ALS or TLS.
Overall- ALS treetop detection potential is 57% of field identified trees, while TLS is 37%.
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21. Results ___________________________________________________________________________________________________________________________________________________________
LiDAR (ALS and TLS)-based trees height accuracy
Tree Parameter
No. of Observations R2 r
RMSE
Bias (m)
(m)
(%)
Upper canopy trees height
451
0.61
0.78
3.24
20.18
– 1.20
Lower canopy trees height
290
0.69
0.83
1.45
14.77
0.42
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22. Results ___________________________________________________________________________________________________________________________________________________________
LiDAR- Vs Field-based AGB/C
Method
No. of sampled plots r
Intercept
Slope
P value
RMSE
RMSE (%)
TLS 27
0.93
0.4955
1.0399
<
1.088
13.31
ALS
0.95
1.1477
0.8418
0.964
11.79
TLS-ALS Integration
0.98
0.909
0.624
7.64
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23. Results ___________________________________________________________________________________________________________________________________________________________
There was a significant difference between field and various RS techniques-based AGB.
Traditional field-based methods underestimated the AGB.
Complementary use of TLS with ALS improved the accuracy of estimating AGB/C.
If the goal is to get highly accurate AGB/C, integration of TLS and ALS should be chosen.
If the simple parsimonious model is desired, then ALS-based AGB model could be chosen.
Method
No. of sampled plots r
Intercept
Slope
P value
RMSE
RMSE (%)
TLS 27
0.93
0.4955
1.0399
<
1.088
13.31
ALS
0.95
1.1477
0.8418
0.964
11.79
TLS-ALS Integration
0.98
0.909
0.624
7.64
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24. Results ___________________________________________________________________________________________________________________________________________________________
Statistics
RS-based AGB (Mg)
Field-based AGB (Mg)
Combination of upper and lower canopies AGB (Mg)
Upper canopy trees (ALS)
Lower canopy trees (TLS)
Upper canopy trees
Lower canopy trees
RS (ALS + TLS) methods
Field-methods
Mean/Plot
9.1536
8.1762
Std. Dev.
3.3146
3.0655
Min.
3.9807
3.9390
Max.
Sum
Of a total AGB calculated from RS, 92% is the upper canopy trees AGB, and 8% is lower canopy trees AGB (168.8 Mg.ha-1, 14.2 Mg.ha-1, respectively).
It implies that it was able to capture an average of 14.2 Mg.ha-1 (0.71 Mg.plot-1) with the compliment of TLS.
Field methods underestimated AGB by Mg.ha-1, accordingly for about 10.70%.
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25. CONCLUSION AND RECOMMENDATIONS ___________________________________________________________________________________________________________________________________________________________
Using ALS for detecting single tree in the complex biophysical structure of tropical rainforest have a potential to recognize not more than two-thirds of the trees.
The dense canopy and interlocking crown structure of the tropical forest results in a low accuracy of crown segments.
The TLS-based DBH measurements method used in this paper through distance function algorithm in the RiSCAN PRO offers more accurate result than the automatic detection of trees and determination of DBH.
Results from model evaluations based on ALS and TLS dataset proof that this approach can enhance the accuracy of predicted AGB or carbon stock than traditional field-based.
Integration enables to detect a comparable number of trees identified in the field.
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26. CONCLUSION AND RECOMMENDATIONS ___________________________________________________________________________________________________________________________________________________________
Main sources of errors
Field-based tree height measurements with handheld instruments.
Height measurement errors contribute to a substantial uncertainty in estimated biomass.
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27. CONCLUSION AND RECOMMENDATIONS ___________________________________________________________________________________________________________________________________________________________
Although the number of plots acquired in this study was small, the range of canopy structures and the approach used for individual tree parameters measurement provide a clear indication of the potential of integrating ALS and TLS system.
However, the TLS data acquisition through observations of plots from multiple scanning viewpoints to reduce the occlusion effect requires more investigation.
The proposed approach can also be used to accurately predict forest structural variables other than AGB, such as stand density, basal area, and stand volume.
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28. CONCLUSION AND RECOMMENDATIONS ___________________________________________________________________________________________________________________________________________________________
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29. Acknowledgment ___________________________________________________________________________________________________________________________________________________________
Institute of Geo-information and Earth Observation Sciences (I-GEOS) Organizers of THE NATIONAL CONFERENCE ON GEO-INFORMATION AND EARTH OBSERVATION 27

30. THANK YOU FOR LISTENING!!
Integrating ALS and TLS for Accurate Tropical Forest / Vegetation Monitoring
END
THANK YOU FOR LISTENING!!
National Conference on Geo-Information and Earth Observation: Dec , 2019
Mekelle University, Mekelle, Ethiopia
Dilla University
Ethiopia
Mekelle University, Ethiopia 28
ITC, The Netherlands
University Putra Malaysia