The document summarizes a study that used airborne laser scanning (ALS) to estimate tree biomass. It describes the methodology which involved: (1) preprocessing ALS data to classify ground and aboveground points and create a digital terrain model, (2) detecting individual trees, (3) calculating point cloud metrics to describe tree crowns, and (4) developing linear regression models to predict aboveground biomass using the metrics. The results showed the metrics had strong correlations with field-measured biomass, and the models improved biomass estimation accuracy with adjusted R2 values from 0.74-0.86.

1. TREE BIOMASS ESTIMATION
USING AIRBORNE LASER
SCANNING
Student:
Iva Nurwauziyah/娜文蒂
Advisor:
Chi-kuei Wang
Remote Sensing for Environmental Lab

2. Outline
1. Introduction
2. Method
3. Result and Discussion
4. Conclusion
1

3. BIOMASS COMPONENT
Introduction
(Matti, M, et al. 2014)
ABOVEGROUND-BIOMASS
CROWN-BIOMASS
STEM-BIOMASS
FINE ROOT-BIOMASS
BELOW GROUND-BIOMASS
2

4. THE IMPORTANCE OF BIOMASS
 Forest Above-Ground Biomass (AGB) is an important parameter for assessing forest
productivity and carbon sequestration rates
 Therefore, rapid and accurate estimation of forest AGB is critical to quantify carbon
stocks and essential for evaluating global carbon cycle.
Introduction
3

5. AIRBORNE LASER SCANNING
Introduction
ALS is one of remote sensing technology which can
provide effective solutions to estimate forest AGB.
(Hyypä, J et al 2017)
4

6. WORKFLOW
Method
ALS Data
ALS Data Pre-Processing
Tree Level Aboveground Biomass Estimation
5
Individual Tree Detection
Point Cloud Metrics
Calculation
Regression Analysis

7. WORKFLOW
Method
ALS Data Pre-Processing
6
STEP 1
1. Extracted point cloud were stored in LAS 1.3 format
2. The data were classified as ground points and aboveground points
3. Created DTM by calculating the average elevation from the ground points

8. WORKFLOW
 “
Method
Individual Tree Detection
7
STEP 2
1. CHM = DSM - DTM
“Individual trees were detected from
the CHM (Canopy Height Model)”

9. WORKFLOW
Method
Point Cloud Metrics
Calculation
8
STEP 3
The metrics based
on point clouds
were used to
describe the crown
structural
characteristics of
the detected trees

10. WORKFLOW
Method
Regression Analysis
9
STEP 4
𝐶𝑜𝑟𝑟 𝐵, 𝐹 =
𝑖=1
𝑛
(𝐵𝑖 − 𝐵)(𝐹𝑖 − 𝐹)
(𝑛 − 1)𝑆 𝐵 𝑆 𝐹
Pearson Correlation :
Where,
𝐶𝑜𝑟𝑟  is Correlation
𝐵  is Biomass
𝐹  is ALS metrics
𝑆 𝐵 𝑆 𝐹  sample deviations
1. Linear Regression models were fit to predict using point cloud metrics by
statistical software R
Point cloud with low correlation coefficients (<0.60) were then used in regression
analysis

11. WORKFLOW
Method
10
1. Linear Regression models were fit to predict using point cloud metrics by
statistical software R
2. All of the dependent variables (field estimate) and independent variables (ALS
metrics) were transformed using natural logarithm
3. Then, the AGB was back-transformed from natural logarithm to arithmetic units
using a Bias Correction Factor (BCF)
4. Using the “regsunset” function, an “all subset” regression were performed
5. Using the “relweights” function, calculated the average increase in R2
6. Finally, Akaike Information Criterion (AIC) to select the best models to predict
AGB

12. PAPER 1
11
PAPER 2

13. Result and Discussion
The Correlation of the Metrics.
( Cao, Lin, et al, 2016)
12

14. Result and Discussion
Aboveground predictive models based on poin cloud metrics
( Cao, Lin, et al, 2016)
13

15. Result and Discussion
Bar graph of relative importance (weights) of the mostly
selected metrics for all subsets of aboveground biomass
estimation models.
( Cao, Lin, et al, 2016)
14

16. Result and Discussion
Scatterplots Of Aboveground Biomass Between the Field
Estimated And Model Predicted Results
( Cao, Lin, et al, 2016) 15

17. Result and Discussion
The best models from aboveground biomass estimation
based on point cloud metrics
( Cao, Lin, et al, 2016)
16

18. Result and Discussion
The Accuracy the Developed Models
( Kankere, V, et al, 2013)
17

19. Result and Discussion
Comparison of the RMSE% of biomass estimation between models
(BIOMASS DBH_H, BIOMASS DBH_H_ALS AND BIOMASS ALS) for pine
(left) and spruce (right).
( Kankere, V, et al, 2013)
18

20. Conclusion
 The point cloud metrics extracted from ALS data have strong
capabilities for tree level AGB estimation, the adjusted R2 0.74-0.86.
 The developed models improved the accuracy for estimating forest
AGB.
Conclusion
19

21. REFERENCES
 Hyypä, J., Kelle, O., Lehikoinen, M., Inkinen, M., 2001. A Segmentation-Based Method to Retrieve
Stem Volume Estimates from 3-D Tree Height Models Produced by Laser Scanners. IEEE
TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 39, NO. 5
 Kankare, V., Räty, M., Yu, X., Holopainen, M., Vastaranta, M., Kantola, T., Hyyppä, H., Alho, P.,
Viitala, R., 2013. Single Tree Biomass Modelling Using Airbone Laser Scanning. ISPRS Journal of
Photogrammetry and Remote Sensing. 85, 66-73.
 Lin, C., Gao, S., Li, P., Yun, T., Shen, X., Ruan, H., 2016. Aboveground Biomass Estimation of
Individual Trees in a Coastal Planted Forest Using Full-Waveform Airbone Laser Scanning Data.
Remote Sensing. Vol.8 No.729
 Matti Maltamo, Erik Næsset, Jari Vauhkonen. 2014. Forestry Applications of Airborne Laser
Scanning: Concepts and Case Studies. London: Springer
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