LiDAR Technology & IT’s Application on Forestry

1. LiDAR Technology & IT’S
Application On Forestry
Muhammad Irsyadi Firdaus
P66067055
Laser Remote Sensing

2. Outline
2
Introduction
Methods
Results
Conclussion
1
2
3
4
5 Reference

3. Introduction
3
Forest Structural Diversity
Forests are
complex spatial
structures
Most forest stand structure
descriptors are traditionally
based on measures easily
obtainable from the ground level
Characterizing forest
structure is an important
part of any comprehensive
biodiversity assessment.
Measuring structural
complexity require a laborious
process that involves many
logistically expensive point
based measurements

4. Introduction
4
BIOMASS COMPONENT
ABOVEGROUND-BIOMASS
CROWN-BIOMASS
STEM-BIOMASSBELOWGROUND-BIOMASS

5. Introduction
5
Airborne Laser Scanning is one
of remote sensing technology
which can provide effective
solutions to Forestry

6. 6
Estimating and mapping forest structural
diversity using airborne laser scanning
data [2015]
Single Tree Biomass Modelling using
Airborne Laser Scanning [2013]
Characterizing Forest Ecological Structure
Using Pulse Types and Heights of Airbone
Laser Scanning [2010]
1
2
3
Papers

7. 7
The study had two primary objectives: (i) to estimate
indices of structural diversity for the entire study area,
and (ii) to construct maps depicting the spatial pattern
of the structural diversity indices.
Papers 1

8. 8
Paper 1
METHODS
Study Area
The study area is located in the southwestern part of Molise
Region in central Italy and includes 36,360 ha.
( Mura, M, et al, 2015)

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Paper 1
METHODS
ALS Data Field Data
Workflow
ALS Data Pre-Processing Structural Diversity Indices
Model Development
and Spatial Predictions
Inference
Horizontal structural
diversity was assessed
using the standard
deviation of DBH
vertical structural
diversity was assessed
using the standard
deviation of H
Removal of outliers,
ground/non-ground
classification, and
computation of
normalized height
Using a branch-and-bound
algorithm addressing the
optimization of R2
The model-assisted,
generalized regression
(GREG) estimators of
means and variances
were used

10. 10
Paper 1
RESULTS
( Mura, M, et al, 2015)
Scatterplot of observed against
predicted values for σ DBH.
Scatterplot of observed against
predicted values for σ H.

11. 11
Paper 1
RESULTS
( Mura, M, et al, 2015)
Spatial predictions for σ DBH for the
forested portion of the study area
(hillshade background).

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Paper 1
RESULTS
( Mura, M, et al, 2015)
Spatial predictions for σ H for the forested
portion of the study area (hillshade
background).

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Paper 1
CONCLUSION
LiDAR data are a valid source of information for estimating
and spatially predicting forest structural diversity and gains
relevance for its potential role in characterizing forest
ecosystems.
The maps of structural diversity can be used not only for
planning management strategies addressing biodiversity but
also for preliminary hypotheses regarding silvicultural
management systems because tree diameter and height are
basic information for assessing the commercial the value of
tree logs.

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Papers 2
The objective of this study: to present protocol for
characterizing the ecological structure of a (dry
Eucalypt) forest landscape using LiDAR data alone

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Paper 2
METHODS
Study Area
Lidar Data
The study area covers the Rubicon catchment in the
Cradle Coast Region of Tasmania, Australia, and is
approximately 20,000 ha.
LiDAR data was acquired over the study area using a
RIEGL LMS-Q560 sensor in February 2007
The overall survey was coordinated using static and
rapid static GPS methods.

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Paper 2
METHODS
Workflow
LiDAR Data Field Data
LiDAR data
and field plots
Derived field
variables
Characterization
scheme
To quantify the amount
of CWD, total volume was
calculated for each plot.
To compensate for this GPS
error and to obtain a better
registration between LiDAR
data and the field plot area,
the LiDAR point cloud
data was first classified
into four vertical layers;
Ground, low vegetation,
medium vegetation and
high vegetation.
Subsequently, LiDAR
returns from each
of these layers were
sorted into “Types”

17. 17
Paper 2
RESULTS
( Miura, N, et al, 2010)
LiDAR point cloud classification.
(a) LiDAR point cloud data was first
classified into four layers;
Ground, low vegetation, medium
vegetation and high vegetation.
(b) Four types of LiDAR returns; Type
1 (singular returns), Type 2 (first
of many returns), Type 3
(intermediate returns) and Type 4
(last of many returns).

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Paper 2
RESULTS
( Miura, N, et al, 2010)
Illustration of forest characterisation scheme for different forest structures a (sparse
foliage or low optical canopy depth) and b (dense foliage or high optical canopy
depth). LiDAR returns are symbolised circles as Type 1, triangles as Type 2, crosses as
Type 3 and squares as Type 4. V H is the same between a and b, however D H is
greater in b and CC is greater in a.

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Paper 2
CONCLUSION
In conclusion, the proposed FCS method has the
ability to characterize some elements of the
ecological structure of a dry Eucalypt forest
landscape
The proposed scheme demonstrated the potential
of different laser pulse return properties from a full
waveform LiDAR to provide information on the
complexity of habitat structure in an efficient and
cost-effective manner

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The objective of this study: To develop the new
methods for tree-level biomass estimation using
metrics derived from ALS point clouds.
Papers 3

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Paper 3 METHODS
Study Area
Sample trees were collected in Evo, southern during the summer
of 2010 and is approximately 2,000 ha. Scots pine and Norway
spruce are dominant tree species in the study area
BiomassDBH_H_ALS
ALS Data
Point Cloud Metrics
Calculation
BiomassALS
(Biomass based ALS Data)
BiomassDBH_H
(Existing Model)
DBH (Diameter at Breast
Height) and (H) Height
Derived from Point
Cloud Metrics
Workflow

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Paper 3
RESULTS
( Kankere, V, et al, 2013)
Example of ALS points inside one tree canopy
segment and of CHM with 0.5 m grid size.

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Paper 3
RESULTS
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)

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Paper 3
CONCLUSION
The point cloud metrics extracted from ALS data
have strong capabilities for tree level AGB
estimation.
The developed models improved the accuracy for
estimating forest AGB.

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References
Matteo Mura, Ronald E. McRoberts, Gherardo Chirici, Marco
Marchetti. (2015). Estimating and mapping forest structural
diversity using airborne laser scanning data. Remote Sensing
of Environment, 170,133–142.
Naoko Miura, Simon D. Jones. (2010). Characterizing Forest
Ecological Structure Using Pulse Types and Heights of
Airbone Laser Scanning. Remote Sensing of Environment,
114, 1069–1076.
Ville Kankare, Minna Räty, Xiaowei Yu. (2013). Single Tree Biomass
Modelling using Airborne Laser Scanning. ISPRS Journal of
Photogrammetry and Remote Sensing, 85, 66-73

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Thank you for your attention