A Comparison of Multiple Algorithms for Deriving Regional-Scale Biomass Maps with Airborne Lidar Metrics and Multispectral Datasets
Nian-Wei Ku and Sorin C. Popescu
Lidar Applications for the Study of Ecosystems with Remote Sensing Laboratory (LASERS), Department of Ecosystem Science and Management, Texas A&M University
Abstract
Aboveground biomass maps spatially show the distribution of
aboveground biomass of rangelands and forests. Thus, this research
investigated possible approaches to generate an aboveground biomass
map of rangelands and forests. We used three approaches to generate
regional-scale aboveground biomass maps with different combinations of
airborne lidar metrics and multispectral dataset. The first and second
approaches were the stepwise regression and least absolute shrinkage and
selection operator (LASSO) to establish the regression relationship of the
in-situ aboveground biomass samples with lidar metrics and a
multispectral dataset to build a biomass map. The third approach
imported the airborne lidar metrics and multispectral dataset into the
random forest algorithm to generate an aboveground biomass map. The
goal of this map is to investigate the most suitable approach for deriving
the aboveground biomass with airborne lidar and multispectral remote
sensing technologies. The results showed that the stepwise regression and
LASSO approach showed the similar results that the lidar metrics and
multispectral dataset have limitations in explaining the variance
associated with the aboveground biomass maps. However, the random
forests approach estimate the feasible amount of aboveground biomass at
a regional scale map. In summary, results proved that the random forest
approach is the most reliable and reasonable approach to generate an
aboveground biomass map. Moreover, the point density of airborne lidar
data constrains the accuracy of the map and the NAIP image dataset was
useful to create an acceptable aboveground biomass map when the lidar
data is difficult or expensive to acquire.
Methods and Materials
Airborne lidar and multispectral data preprocessing
The list of lidar metrics
The aboveground biomass map made by Stepwise Regression
Results
Discussions and Conclusion
Contact
Nian-Wei Ku (Tony)
Lidar Applications for the Study of Ecosystems with Remote Sensing Laboratory (LASERS)
Department of Ecosystem Science and Management
Texas A&M University
Selected References
Methods and Materials
Results
Study area
The figures (above) are (A) the false color NAIP image (near-infrared, red, and
green) of Smith Walker research unit and the study transects location, and (B) the
classified image of the study area.
(A) (B)
Image Layers
Segmentation
Algorithms -
Multiresolution
Segmentation
Criteria:
o Layer weight
o Scale parameter
o Color vs. Shape
o Compactness vs.
Smoothness
Supervised Classification
Classes:
o Grass
o Mesquite
o Non-Vegetation
o Water
Final Classification Map
Post Classification
Accuracy Assessment:
o Overall accuracy
o Kappa coefficient
The figure (above) is a concept flowchart of the objected-oriented classification
with multiresolution segmentation algorithm.
Woody plant biomass field measurements and data processing
The woody plant aboveground biomass field data was collected during the
leaf-off season in December 2008, March 2009, and December 2009 from 18
study transects in the Smith Walker research unit.
The woody plant aboveground biomass was estimated using the following
allometric equation which measured the basal stem diameter at 5 cm to 15 cm
height above ground related to the total woody plant mass (Ansley et al.,
2010).
𝑌 = 0.34𝑥1.73
Where Y represents the total woody plant mass (kg) and the x is basal stem
diameter (cm).
2010
Lidar
point
cloud
data
2010
NAIP
imagery
Final 27
lidar
metric
layers
Generate
layers
from each
metric
Calculate
lidar metrics
at each cell
as a CSV
table
Clip the
polygon
to one by
one cell
Covert
NAIP to
Polygon
Create
1 m
NDVI
1 m Blue,
Green, Red,
and Near
Infrared
bands Create
1 m
CHM
Generate
point
cloud data
without
elevation
Create
1 m
DEM
Extract
point
cloud data
between 0
to 7 m
Index Variables Index Variables
1 Canopy Height Model 15 20th percentile value for cell
2 Minimum height\Minimum value for cell 16 25th percentile value for cell
3 Maximum height\Maximum value for cell 17 30th percentile value for cell
4 Mean height\Mean value for cell 18 40th percentile value for cell
5 Mode height (The most count of
returns)\mode value for cell 19 50th percentile value for cell
6 Standard deviation of cell values 20 60th percentile value for cell
7 Variance of cell values 21 70th percentile value for cell
8 Coefficient of variation for cell 22 75th percentile value for cell
9 Interquartile range (IQR) 23 80th percentile value for cell
10 Skewness computed for cell 24 90th percentile value for cell
11 Kurtosis computed for cell 25 95th percentile value for cell
12 1st percentile value for cell 26 99th percentile value for cell
13 5th percentile value for cell 27 Generalized means for the 2nd (Height
quadratic mean) power p=2
14 10th percentile value for cell 28 Generalized means for the 3rd (Height
cubic mean) power p=3
The aboveground biomass map made by LASSO
The aboveground biomass map made by Random Forests
Stepwise Regrssion
Data Coefficient MSE Adj. R2 R2
NAIP Intercept:
NDVI:
1.14
18.46 13.23 0.34 0.34
Lidar Intercept:
CHM:
Skewness:
Kurtosis:
Percentile 50th:
0.97
1.69
0.39
-0.40
0.25
9.58 0.52 0.52
NAIP & Lidar Intercept:
CHM:
Kurtosis:
Percentile 50th:
0.83
1.74
-0.24
0.14
9.66 0.52 0.52
LASSO
Data Coefficient MSE Adj. R2 R2
NAIP Intercept:
NDVI:
Red:
6.13
5.43
-0.03
12.87 0.36 0.36
Lidar Intercept:
CHM:
Variance:
1.12
1.36
0.04
10.08 0.50 0.50
NAIP & Lidar Intercept:
Red:
CHM:
Variance:
2.4
-0.01
1.2
0.16
9.66 0.52 0.52
Random Forests
Data MSE Pseudo R2
NAIP 12.03 0.41
Lidar 9.39 0.54
NAIP & Lidar 7.68 0.62
The figures (above) are the aboveground biomass maps made by (A) NAIP
imagery, (B) Lidar data, and (C) the combination of NAIP and Lidar.
(C) (B) (A)
(C) (B) (A)
(C) (B) (A)
The figures (above) are the aboveground biomass maps made by (A) NAIP
imagery, (B) Lidar data, and (C) the combination of NAIP and Lidar.
The figures (above) are the aboveground biomass maps made by (A) NAIP
imagery, (B) Lidar data, and (C) the combination of NAIP and Lidar.
The airborne lidar data provides enough information to all approaches for
separating woody plants from grass in aboveground biomass maps.
The combination of NAIP and lidar shows the lowest MSE and highest R-
squared in random forests approach.
The random forests approach has better performance than the other two with
different remote sensing variables.
Though the NAIP-based aboveground biomass estimations has higher MSE
and lower R-squared in all three approaches, the biomass estimations separate
the bare ground from the grass and woody accurately.
In contract, the lidar-based aboveground biomass estimation does not find the
bare ground better.
The point density of airborne lidar data constrains the accuracy of the map and
the NAIP image dataset was useful to create an acceptable aboveground
biomass map when the lidar data is difficult or expensive to acquire.
• Ansley, R. James, Mustafa Mirik, and Michael J. Castellano. "Structural biomass
partitioning in regrowth and undisturbed mesquite (Prosopis glandulosa):
implications for bioenergy uses." GCB Bioenergy 2.1 (2010): 26-36.
• Breiman, Leo. "Random forests." Machine learning 45.1 (2001): 5-32.
• Tibshirani, Robert. "Regression shrinkage and selection via the lasso." Journal of
the Royal Statistical Society. Series B (Methodological) (1996): 267-288.