This document describes a methodology for forest monitoring using hyperspectral images with sparse regularization. Field surveys were conducted to collect ground truth data on forest stands. Hyperspectral and SAR remote sensing data were then used as inputs for classification and regression models with sparse regularization techniques like sparse discriminant analysis and LASSO. The models accurately predicted tree species composition, canopy cover, timber volume, and tree height at the subcompartment level. Validation showed the predictions were consistent with actual forest conditions observed on the ground. Hyperspectral data provided better results than simulated multispectral data. This sparse regularization approach can provide effective forest monitoring from remote sensing with less data collection needed in the field.

1. IGARSS 2011, Vancouver
TU4.T08.1: Hyperspectral Monitoring of the Environment I
A Methodology of Forest Monitoring from
Hyperspectral Images with Sparse Regularization
Jul. 26, 2011
MITSUBISHI RESEARCH INSTITUTE, INC.
Keigo YOSHIDA, Takashi OHKI, Masahiro TERABE, Hozuma SEKINE (MRI)
Tomomi TAKEDA (ERSDAC)
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2. Introduction：Forest Monitoring by Remote Sensing
 Decision making for Forest Management
 Disaster prevention planning
Accurate Info.
Needs  Finding forest of poor growth
Needs of Present Forest
 GHG Credit estimation
 Resource management
 Forest conditions change dynamically
High Cost of
 Conduct periodical field survey
Problem Forest Survey
Problem  estimate 600-1200 USD / Km2 / year
& Monitoring
※ in case of Japan; 1 USD = 81 yen
Forest Monitoring  No need for field survey all over the area
Solution
Solution by Remote Sensing
 Highly-frequent observation
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3. Introduction：Highly-developed Sensing Tech. & Challenges
Sensing Tech. Challenges
 Hard to bring out potential of big sensor data
Hyperspectral sensor [e.g.] NDVI use just 2 bands, or Red and IR
& have to select optimal band combinations
 provide detailed optical info.
on forest physiognomy  Complexity of prediction model increases,
 growth situation resulting in poor prediction performance
 character of tree species  Dimension is high but sample size is small
etc. due to limitation of field survey
 This causes model overfitting
Sensor fusion  Modeling is not easy for several sensor data
 Reflect diverse property of different physical property
of targets  Statistical or Data-driven approach is needed
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4. Research Outline
Utilize rich data by a machine learning technique (sparse regularization)
and achieve accurate, informative, & less costly forest monitoring
 Remote Sensing Data Fusion
Input Data （CASI-3 hyperspectral images + SAR signals）
Input Data
 Field Survey Results
 Sparse Regularization
Methodology
Methodology （Sparse Discriminant Analysis、LASSO regression）
 Predicted Stand Factors of each subcompartments
for Forest Management
Output Data
Output Data （Species, Canopy cover, Timber volume, Tree height）
 Prediction Models
Subcompartment： a general spatial unit for forest monitoring
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5. Target Site
Town-owned forest in Shimokawa,
Hokkaido, Japan
 Approx. 90 % of town is covered by forest
 Utilize local conifer resources for business
 Environmental model city for low-carbon society
Shimokawa
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6. Remote Sensing Data
Species and Canopy-cover prediction: CASI-3 hyperspectrum
Volume and Height prediction: Data fusion (CASI-3 + PALSAR)
 Remote Sensing
 Hyperspectral sensor (optical property)
 Airborne hyperspectral imager CASI-3
 84 bands from 400 to 1060 nm (wavelenght res. : 8 nm)
 Original spatial res.: 2.0 m
→ Resolution is decreased to 30m to simulate satellite-based operation
 PALSAR (shape or volume property)
 Microwave backscattering org. image resized
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7. Field Survey
During aircraft obs., conduct field survey to collect data for modeling & validation
 Field Survey:
 Place 25-sq-m quadrats
 Inventory study for trees whose DBH > 5cm & and record tree species
 Canopy cover measurement with whole-sky camera
 Height measurement for sampled 10 trees
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8. What is Sparse Regularization ? Why Do I Use it ?
“Sparse” means the model has a low # of nonzero parameters
■ Optimal Band Selection
Ineffective parameters will be removed from prediction model
automatically by solving convex optimization problem
■ Higher Generalization Capability
simple model with smaller # of bands achieves less
overfitting; better prediction performance
■ More Interpretable Model
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9. Sparse Regularization：Theoretical Overview
Add penalty to loss function to obtain model with small num. of variables
 LASSO (R. Tibshirani et al., 96) Loss function (LS)
（penalty） norm
 Optimal Scoring (T. Hastie et al., 94)
 Perform Fisher’s linear discriminant analysis as regression by score
 convert categorical variables for class membership into quantitative
 Optimize and weight vector simultaneously
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10. Intuitive Explanation of Sparse Regularization
 To reduce empirical errors, ＜penalty＞
W moves away from 0,
then penalty increases
 L1-norm: attraction force to 0 is const.
-> Small values in W tend to be 0
<attraction force to 0>
L2-norm: attraction force is small around 0
-> Small values in W remain
 Coefficients
L1-regularization L2-regularization
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11. Experimental Flow
1. Modeling Prediction
Prediction
Performance
Performance
Hyperspectral Reflectance
Hyperspectral Reflectance Classification
Classification
Sparse LDA
(ave. w/in each quadrat)
(ave. w/in each quadrat) Model
Model
Regression
Regression
PALSAR Signals
PALSAR Signals LASSO Regression
Model
Model
2. Prediction for Subcompartments
Forest Pixel Extraction
Hyperspectral Reflectance
Hyperspectral Reflectance
Semisupervised LDA Forest Pixels
Forest Pixels
(30m x 30m pixels)
(30m x 30m pixels)
Subcompartment Prediction
Averaged Reflectance
Averaged Reflectance Predicted
Predicted
w/in each Subcomp. Obtained Model
w/in each Subcomp. Forest Condition
Forest Condition
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12. Variety in a Subcompartment
There is a large variety inside a subcompartment
Non-forest area
• Deforestation area
• Canopy gaps
Invading woods other than planted species
• they’re not recorded on forest register
（Subcompartment）
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13. Experimental Setting (1/2)
Dataset:
 Target category: 4 species
 Larix kaempferi, Abies sachalinensis, Picea glehnii, other Broadleaf
 Source
 Hyperspectral reflectance by CASI-3
 84 bands, 400 – 1060 nm
 9 signals given by PALSAR data
 polarimetries (HH/HV/VV)
 Three scattering components proposed by Freeman
i.e. surface scattering, double bounce scattering, volume scattering
 Averaged alpha angle
 Polarimetric entropy
 Anisotropy
 Quadrats:
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14. Experimental Setting (2/2)
Validation：
 100 times iteration of 5-fold cross valiadtion
Comparison：
 Methodology
 Classification
 Spectral Angular Mapper ; SAM
 Regularized Discriminant Analysis; RDA （L2-norm regularization）
 ν-Support Vector Machines; SVM （w/ Linear and RBF kernel）
 Regression
Partial Least Squares; PLS
 Input data # wavelength range (nm)
 pseudo multi-spectral image Band 1 520 – 600
 ASTER image simulated from CASI-3 data Band 2 630 – 690
 3 bands Band 3 760 - 860
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15. Result: Quadrat Species Classification
SDA achieves highest performance （87％ prediction accuracy）
Results by using hyperspectral images outperform pseudo-images
■ Prediction accuracy (Mean of 100 times 5-fold CV)
Method SDA SAM RDA SVM(Lin.) SVM(RBF)
CV-Accuracy 87.0 % 69.0 % 82.2 % 84.0 83.0 %
■ Confusion Matrix（SDA, Mean of 100 times 5-fold CV)
Upper：Hyperspec Predicted
Lower： Multi-spec Larix Abies Picea Broadleaf
92.0 % 1.8 % 6.2 % 0.0 %
Larix
78.8 % 16.3 % 4.3 % 0.8 %
0.4 % 84.4 % 15.1 % 0.1 %
Abies
23.3 % 76.4 % 0.3 % 0.0 %
Actual
20.2 % 6.2 % 70.8 % 2.8 %
Picea
22.4 % 0.0 % 77.6 % 0.0 %
7.6 % 0.0 % 6.0 % 86.4 %
Braodleaf
32.7 % 0.2 % 0.0 % 67.1 %
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16. Result: Quadrat Stand Factor Regression
LASSO with hyperspectral data provides best performance for all stand factor
RMSE
(10-fold CV)
Prediction
(10-fold CV) Canopy Cover Timber Volume Tree Height
poor results
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17. Result: Forest Extraction by Semi-supervised LDA
Cloud
deforestation
Bare ground
Grass & Bamboo
※ Dots indicate top-left
point of each pixel
Clouds
Sparse forest
Validation with 39 pixels selected manually
 Forest vs. non-forest: 100 %
 Overall Accuracy: 97.4 % （38/39）
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18. Result: Tree Species Composition in Subcompartments
Confirm consistency between actual and estimated species by field survey
Legend
Larix kaempferi
Abies sachalinensis 30 m resized pixels Abies sachalinensis
Picea glehnii
Natural Broadleaf
▲ Field Survey Points
※ Dots indicate top-left
point of each pixel
Broadleaf Original CASI-3
Invading broadleaf trees were found
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19. Predicted Tree Species Distribution Map
registry
Young Picea glehnii
Legend
Invasion of broadleaf
to larch plantation
Predicted
mixed = below 70% dominancy
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20. Predicted Maps
Canopy Cover Timber Volume Tree Height
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21. Validation and Evaluation – Canopy Cover Map
Confirm prediction reflects forest conditions rightly by field survey
non-thinned non-thinned
Line-thinned Line-thinned
Canopy Density
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22. Validation and Evaluation – Tree Height Map
Young Picea glehnii
& Broadleaf forest of low height
Larix trees with relatively higher
stand age were observed
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23. Conclusions
 Present forest monitoring method from hyperspectral and SAR image
 Integrate diverse data source with different property of targets
 To overcome high-dimension-small-sample-size problem resulting in over-fitting,
sparse regularization techs (LASSO & Sparse Discriminant Analysis) are adopted
 3 advantages of sparse regularization
 Generalization, Interpretability, Optimal Band Selection
 Experimental simulations of satellite-based operation prove effectiveness
 Advantage in prediction accuracy to several supervised methods
 Advantage of hyperspectral data to multispectral
 Prediction results reflect existing forest conditions rightly
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24. Many thanks for your kind attention.
Questions ?
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25. Supplementary Slides
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26. How sparse ?
Canopy Cover Model: 8 / 84 params. = 9.5% Tree Height Model: 14/ 93 params. = 15.0%
# Parameter Coef. # Parameter Coef.
1 418.28 nm 6.32 1 473.31 nm 12.51
2 441.81 nm -1.36 2 560.97 nm -22.81
3 520.95 nm -4.20
3 640.38 nm 22.12
4 626.01 nm -1.07
4 671.51 nm -25.78
5 766.86 nm 21.45
6 923.70 nm 6.71 5 706.10 nm 18.61
7 962.66 nm -8.60 6 835.50 nm -65.16
8 1055.39 nm -21.93 7 877.98 nm 27.13
- Intecept 75.98 8 1011.70 nm 40.04
Timber Vol. Model: 5 / 93 params. = 5.4% 9 1038.75 nm 42.92
# Parameter Coef. 10 1055.39 nm -26.74
1 426.17 nm -0.59 11 1061.01 nm -31.73
2 441.81 nm -58.71 12 HH -2.40
3 513.13 nm -18.67
13 HV -1.83
4 Vol. Scattering Coef. 2.87
14 VV 5.14
5 VV 26.10
- Intercept 198.50 - Intercept 11.68
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27. Frequently Selected Parameters for Species Classification
Distinct bands for confier/broadleaf properties & feature of species are selected.
 Around 450nm: absorption peak of G-type lignin richly contained in conifer wood
 Around 520nm: absorption peak of S-type lignin richly contained in broadleaf wood
 Red edge
出現率 Ratio [%]
100
90 ：freq. selected
Frequency used 80
70
bands
Appearance[%]
60
in prediction model 50
40
30
by 100 bootstrapping 20
10
0
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80
Band No.
バンド番号
Picea
Reflectance
Reflectance Larix
Abies
spectrum Broadleaf
Wavelength [nm]
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