Using Lidar and ArcGIS to Predict Forest Inventory Variables
Using Lidar and ArcGIS to Predict Forest Inventory Variables Dr. Kevin S. Lim [email protected]
P.O. Box 45089, 680 Eagleson Road Ottawa, Ontario, K2M 2G0, Canada Tel.: 613-686-5735 Fax: 613-822-5145
Presentation Outline
•
Background
•
Methodology
•
Results
•
Conclusions
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Background
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Purpose of Research
•
To determine the accuracy and precision that forest inventory variables can be predicted using airborne lidar remotely sensed data. -
Gross total volume Gross merchantable volume Basal area Density Quadratic mean DBH Average height Top height Aboveground biomass Diameter distributions (in progress) © 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Forest Inventory Variables Variable
Abbrev.
Definition
Top Height (m)
TOPHT
Calculated as the average of the largest 100 stems per hectare.
Average Height (m)
AVGHT
Calculated as the average height of all trees
Density (stems/ha)
Density
Number of trees per hectare
Quadratic Mean Diameter (cm)
QMDBH
Basal Area (m2/ha)
BA
é( êë
å DBH 2
n )ù úû
DBH2 * 0.00007854
Gross Total Volume (m3/ha)
GTV
Honer et al. (1983) equations
Gross Merchantable Volume (m3/ha)
GMV
Honer et al. (1983) equations
Total Above Ground Biomass (Kg/ha)
SUMBIO
Ter-Mikaelian and Korzukhin (1997) equations
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Plot Data
•
Fixed circular plots -
11.28 m radius | 0.04 ha
•
Plots were geo-referenced to sub-meter accuracy
•
Trees with DBH ≥ 9.1 cm assessed -
DBH Species Crown class Height to base of live crown Total tree height
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Location of Study Site
•
Located in the northeast portion of Ontario’s Boreal Forest near Timmins, Ontario.
•
Active forest management unit with approximately 532,000 productive forest hectares.
•
Dominant species are: -
•
black spruce, white birch, trembling aspen, jack pine, eastern white cedar, white spruce, eastern larch, and balsam fir
Other minor species include: -
black ash, yellow birch, soft maple and red and white pine © 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Stratification – Four Forest Model Units
Intolerant Hardwood
Mixedwood
white birch and poplar ≥ 70%
conifer (Sb, Pj, Sw, Ce, Bf ≥ 40% and Po + Wb = 60%)
Jack Pine
Black Spruce
Pj ≥ 70%
Sb ≥ 70%
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Coverage
•
630,000 ha (2,400 square miles) in Boreal forest
•
136 model calibration plots
•
138 model validation plots
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Airborne Lidar Data
•
Data acquired in summer of 2004 and 2005
•
Leica ALS sensor
•
Point (pulse) density approximately 0.5 points/m2
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Methodology
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Methodology Acquire LIDAR Data
Define Stratification
LIDAR Data
Classify Point Cloud Acquire Field Data For Sample Plots Ground Points
Vegetation Points
Intersect With Sample Plots
Create TIN
Field Data Normalize Points To Terrain
Vegetation Points Per Plot Calculate Forest Variables
Forest Variable Statistics
Perform Statistical Analyses
Regression Models
TIN
Calculate LIDAR Predictors
Normalized Vegetation Points Per Plot
Normalized Vegetation Points
LIDAR Predictors
LIDAR Predictor Surfaces
Calculate LIDAR Predictors
Apply Models to Landscape
Forest Inventory Surfaces
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Data Processing Approach
•
Divide and Conquer Strategy
•
Custom code leveraging ArcObjects
•
ArcGIS Desktop and Catalog
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
zveg
Normalize Points to Terrain
∆ zgrd
∆ = Znorm = Zveg - Zgrd TILE TIN © 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Canopy Height Models
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Lidar Predictors
•
Statistical -
•
Percentiles of height -
•
Mean Standard deviation Deciles (p10 … p90) Maximum height
Canopy density -
d1 … d9 Da: Number of first returns divided by all returns. Db: Number of first and only returns divided by all returns.
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Lidar Predictor Surfaces
Apply a mask (optional).
Each surface corresponds to a lidar predictor. Cell resolution of 20m (or 400m2 in area).
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Regression Modelling Jack Pine Dependent variable BA (m2 ha-1) GTV (m3 ha-1) GMV (m3 ha-1) QMDBH (cm) AvgHT (m) TopHT (m) Biomass (Kg ha-1)
RMSE 5.92 44.85 36.67 1.54 1.05 0.76 24478
RMSE % 19.0 18.0 18.8 9.0 6.5 3.8 19.2
RMSE 4.83 39.57 26.25 1.37 1.13 1.24 19996
RMSE % 16.1 17.0 20.9 9.0 6.1 3.9 23.2
Mixedwoods
Black Spruce Dependent variable BA (m2 ha-1) GTV (m3 ha-1) GMV (m3 ha-1) QMDBH (cm) AvgHT (m) TopHT (m) Biomass (Kg ha-1)
Intolerant Hardwood Dependent RMSE variable BA (m2 ha-1) 5.05 3 -1 GTV (m ha ) 45.18 GMV (m3 ha-1) 45.17 QMDBH (cm) 1.64 AvgHT (m) 1.02 TopHT (m) 0.88 -1 Biomass (Kg ha ) 29880
RMSE % 18.7 24.3 24.1 9.7 8.8 7.4 19.8
Dependent variable BA (m2 ha-1) GTV (m3 ha-1) GMV (m3 ha-1) QMDBH (cm) AvgHT (m) TopHT (m) Biomass (Kg ha-1)
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
RMSE 5.58 48.66 42.72 2.01 1.20 0.94 25998
RMSE % 17.1 18.4 19.1 10.3 7.7 4.2 18.8
Apply Models to Landscape
Methodology Acquire LIDAR Data
Define Stratification
LIDAR Data
Classify Point Cloud Acquire Field Data For Sample Plots Ground Points
Vegetation Points
Intersect With Sample Plots
Create TIN
Field Data Normalize Points To Terrain
Vegetation Points Per Plot Calculate Forest Variables
Forest Variable Statistics
Perform Statistical Analyses
Regression Models
TIN
Calculate LIDAR Predictors
Normalized Vegetation Points Per Plot
Normalized Vegetation Points
LIDAR Predictors
LIDAR Predictor Surfaces
Calculate LIDAR Predictors
Apply Models to Landscape
Forest Inventory Surfaces
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Output
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Spatially Explicit Predictions
A prediction for every 20 m cell! © 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
What’s Next?
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Advanced Forest Resource Inventory Decision Support System (AFRIDS)
Lightning Talk: Advanced Forest Resource Inventory Decision Support System - LIDAR in Action
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Conclusions
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Concluding Remarks
•
The science behind using airborne lidar to predict forest inventory variables has been published on extensively.
•
Lidar data is affordable.
•
GIS technology is well suited to handling the large lidar data volumes.
•
A lidar enhanced forest inventory supports both tactical and strategic needs.
•
Consider lidar as a complementary technology to traditional approaches instead of as a replacement.
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Why settle for the traditional…
When you can have predictions for every cell!
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Acknowledgements
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
P.O. Box 45089, 680 Eagleson Road Ottawa, Ontario, K2M 2G0, Canada Tel.: 613-686-5735 Fax: 613-822-5145
Presentation Outline
•
Background
•
Methodology
•
Results
•
Conclusions
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Background
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Purpose of Research
•
To determine the accuracy and precision that forest inventory variables can be predicted using airborne lidar remotely sensed data. -
Gross total volume Gross merchantable volume Basal area Density Quadratic mean DBH Average height Top height Aboveground biomass Diameter distributions (in progress) © 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Forest Inventory Variables Variable
Abbrev.
Definition
Top Height (m)
TOPHT
Calculated as the average of the largest 100 stems per hectare.
Average Height (m)
AVGHT
Calculated as the average height of all trees
Density (stems/ha)
Density
Number of trees per hectare
Quadratic Mean Diameter (cm)
QMDBH
Basal Area (m2/ha)
BA
é( êë
å DBH 2
n )ù úû
DBH2 * 0.00007854
Gross Total Volume (m3/ha)
GTV
Honer et al. (1983) equations
Gross Merchantable Volume (m3/ha)
GMV
Honer et al. (1983) equations
Total Above Ground Biomass (Kg/ha)
SUMBIO
Ter-Mikaelian and Korzukhin (1997) equations
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Plot Data
•
Fixed circular plots -
11.28 m radius | 0.04 ha
•
Plots were geo-referenced to sub-meter accuracy
•
Trees with DBH ≥ 9.1 cm assessed -
DBH Species Crown class Height to base of live crown Total tree height
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Location of Study Site
•
Located in the northeast portion of Ontario’s Boreal Forest near Timmins, Ontario.
•
Active forest management unit with approximately 532,000 productive forest hectares.
•
Dominant species are: -
•
black spruce, white birch, trembling aspen, jack pine, eastern white cedar, white spruce, eastern larch, and balsam fir
Other minor species include: -
black ash, yellow birch, soft maple and red and white pine © 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Stratification – Four Forest Model Units
Intolerant Hardwood
Mixedwood
white birch and poplar ≥ 70%
conifer (Sb, Pj, Sw, Ce, Bf ≥ 40% and Po + Wb = 60%)
Jack Pine
Black Spruce
Pj ≥ 70%
Sb ≥ 70%
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Coverage
•
630,000 ha (2,400 square miles) in Boreal forest
•
136 model calibration plots
•
138 model validation plots
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Airborne Lidar Data
•
Data acquired in summer of 2004 and 2005
•
Leica ALS sensor
•
Point (pulse) density approximately 0.5 points/m2
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Methodology
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Methodology Acquire LIDAR Data
Define Stratification
LIDAR Data
Classify Point Cloud Acquire Field Data For Sample Plots Ground Points
Vegetation Points
Intersect With Sample Plots
Create TIN
Field Data Normalize Points To Terrain
Vegetation Points Per Plot Calculate Forest Variables
Forest Variable Statistics
Perform Statistical Analyses
Regression Models
TIN
Calculate LIDAR Predictors
Normalized Vegetation Points Per Plot
Normalized Vegetation Points
LIDAR Predictors
LIDAR Predictor Surfaces
Calculate LIDAR Predictors
Apply Models to Landscape
Forest Inventory Surfaces
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Data Processing Approach
•
Divide and Conquer Strategy
•
Custom code leveraging ArcObjects
•
ArcGIS Desktop and Catalog
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
zveg
Normalize Points to Terrain
∆ zgrd
∆ = Znorm = Zveg - Zgrd TILE TIN © 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Canopy Height Models
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Lidar Predictors
•
Statistical -
•
Percentiles of height -
•
Mean Standard deviation Deciles (p10 … p90) Maximum height
Canopy density -
d1 … d9 Da: Number of first returns divided by all returns. Db: Number of first and only returns divided by all returns.
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Lidar Predictor Surfaces
Apply a mask (optional).
Each surface corresponds to a lidar predictor. Cell resolution of 20m (or 400m2 in area).
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Regression Modelling Jack Pine Dependent variable BA (m2 ha-1) GTV (m3 ha-1) GMV (m3 ha-1) QMDBH (cm) AvgHT (m) TopHT (m) Biomass (Kg ha-1)
RMSE 5.92 44.85 36.67 1.54 1.05 0.76 24478
RMSE % 19.0 18.0 18.8 9.0 6.5 3.8 19.2
RMSE 4.83 39.57 26.25 1.37 1.13 1.24 19996
RMSE % 16.1 17.0 20.9 9.0 6.1 3.9 23.2
Mixedwoods
Black Spruce Dependent variable BA (m2 ha-1) GTV (m3 ha-1) GMV (m3 ha-1) QMDBH (cm) AvgHT (m) TopHT (m) Biomass (Kg ha-1)
Intolerant Hardwood Dependent RMSE variable BA (m2 ha-1) 5.05 3 -1 GTV (m ha ) 45.18 GMV (m3 ha-1) 45.17 QMDBH (cm) 1.64 AvgHT (m) 1.02 TopHT (m) 0.88 -1 Biomass (Kg ha ) 29880
RMSE % 18.7 24.3 24.1 9.7 8.8 7.4 19.8
Dependent variable BA (m2 ha-1) GTV (m3 ha-1) GMV (m3 ha-1) QMDBH (cm) AvgHT (m) TopHT (m) Biomass (Kg ha-1)
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
RMSE 5.58 48.66 42.72 2.01 1.20 0.94 25998
RMSE % 17.1 18.4 19.1 10.3 7.7 4.2 18.8
Apply Models to Landscape
Methodology Acquire LIDAR Data
Define Stratification
LIDAR Data
Classify Point Cloud Acquire Field Data For Sample Plots Ground Points
Vegetation Points
Intersect With Sample Plots
Create TIN
Field Data Normalize Points To Terrain
Vegetation Points Per Plot Calculate Forest Variables
Forest Variable Statistics
Perform Statistical Analyses
Regression Models
TIN
Calculate LIDAR Predictors
Normalized Vegetation Points Per Plot
Normalized Vegetation Points
LIDAR Predictors
LIDAR Predictor Surfaces
Calculate LIDAR Predictors
Apply Models to Landscape
Forest Inventory Surfaces
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Output
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Spatially Explicit Predictions
A prediction for every 20 m cell! © 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
What’s Next?
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Advanced Forest Resource Inventory Decision Support System (AFRIDS)
Lightning Talk: Advanced Forest Resource Inventory Decision Support System - LIDAR in Action
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Conclusions
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Concluding Remarks
•
The science behind using airborne lidar to predict forest inventory variables has been published on extensively.
•
Lidar data is affordable.
•
GIS technology is well suited to handling the large lidar data volumes.
•
A lidar enhanced forest inventory supports both tactical and strategic needs.
•
Consider lidar as a complementary technology to traditional approaches instead of as a replacement.
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Why settle for the traditional…
When you can have predictions for every cell!
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
Acknowledgements
© 2011 Lim Geomatics Inc. All rights reserved. Do not reproduce or distribute without permission from Lim Geomatics Inc.
