Semi-Automated Imagery Analysis and LiDAR …€¦ · 6 What is LiDAR? Light Detection And Ranging...
Transcript of Semi-Automated Imagery Analysis and LiDAR …€¦ · 6 What is LiDAR? Light Detection And Ranging...
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Ministry of Natural Resources
Murray WoodsMurray WoodsOntario Ministry of Natural ResourcesOntario Ministry of Natural Resources
Southern Science and InformationSouthern Science and InformationNorth BayNorth Bay
[email protected]@Ontario.ca
SemiSemi--Automated Imagery AnalysisAutomated Imagery Analysisand LiDAR Enhancements toand LiDAR Enhancements toNatural Resource InventoriesNatural Resource Inventories
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AAdvanceddvanced FForestorest RResourceesource IInventorynventory TTechnologiesechnologies
Doug Pitt – Don Leckie – François Gougeon – Paul Treitz - Al Stinson – Murray Woods - Dave Nesbitt
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Project Objective• push “beyond” traditional forestinventory descriptors• leverage existing projects currentlyunderway• advance existing methods andapproaches• link multiple remote-sensing techniquesto derive products at multiple scales
(Tree → Stand → Landscape)
• move toward linking solid inventoryattributes and wood fibre characteristics• develop tools to transfer science &research efforts to government and theprivate sector• ‘operationalize’ the methods developed
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Project Focus
• Use of LiDAR technologies to enhance thescope and resolution of natural resourceinventories
• Improvement of semi-automated image analysistechniques to classify tree crowns and developtree/stand polygon inventories
• Use independently or fuse these technologies toimprove natural resource inventories.
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Explore the potential of:
1. Using LiDAR formeasurement andprediction of stand levelinventory metrics
2. Determine of appropriateacquisition intensities for arange of forest types
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What is LiDAR?What is LiDAR?LLiightght DDetectionetection AAndnd RRanginganging
• Active remote sensing technology
• Involves transmitting and receiving~150,000 pulses of laser light persecond
• Pulses strike the surface of theearth and with each pulse get ameasurement of the time and angleof each return
• If a laser pulse hits an objectthrough which it can penetrate, it willproduce range and intensitymeasurements for each surface it hitsresulting in multiple returns
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LiDAR Intro 1LiDAR Intro 1x,y,z
x,y,z+range
range
)/(__*2
)(_)( smlightofspeed
stimeelapsedmrange
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LiDAR Intro 2LiDAR Intro 2
x+x1,y,z+z1
z1
x1
θ
range
x,y,z
x,y,z+range
range
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Example of flight line RAW LiDAR stripExample of flight line RAW LiDAR strip
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Ground Hits OnlyGround Hits Only
NonNon--Ground Hits OnlyGround Hits Only
Classification of ReturnsClassification of Returns
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RGB
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DSM
RGB
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DSM
DTM
RGB
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DSM
DTM
RGB
Profile
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Digital Surface Model (DSM)Digital Surface Model (DSM)
Digital Terrain Model (DTM)Digital Terrain Model (DTM)
Canopy Height Model (CHM)Canopy Height Model (CHM)
From St-Onge, B., Treitz, P., Wulder, M., Kurtz, W., Gillis,M. 2004. Retrospective mapping of structural andbiomass changes in forest ecosystems usingphotogrammetryand laser altimetry,AmercianGeophysical Union/Canadian Geophysical Union JointAssembly, Montreal, May 17-21.
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Quick and Easyproduction of raster-based data productsfrom lidar data..
• Digital surface models• Digital terrain models• Canopy height model• Point density grids• Canopy closure grids• Height structure index grids
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• Detailed Surface Models– Digital Surface Models (DSM)– Digital Terrain Models (DTM)– Canopy Height Models (CHM)
LiDAR’s Contribution to Precision Forest Inventory
DTMDTM
DSMDSM
CHMCHM
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LiDAR’s Contribution to Precision Forest Inventory• Detailed Surface Models
– Digital Surface Models– Digital Terrain Models– Canopy Height Models
• Detailed Digital Terrain Model– Supporting
• Identifying surficial geologyValue A
ddedV
alue Added
Eskers Dunes
Used with permission from Al Thorne, Tembec.Used with permission from Al Thorne, Tembec.
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Northern Ontario 20m DTMNorthern Ontario 20m DTM
LiDAR provides Improved Digital Terrain Models
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LiDAR derived 5m DTMLiDAR derived 5m DTM
LiDAR provides Improved Digital Terrain Models
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OBM 10m DEM 2.0
LiDAR 1m DEM
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LiDAR’s Contribution to Precision Forest Inventory• Detailed Surface Models
– Digital Surface Models– Digital Terrain Models– Canopy Height Models
• Detailed Digital Terrain Model– Supporting
• Identifying surficial Geology• Hydrological modellingV
alue Added
Value A
dded
Predictive Hydrology Models
Predictive Streams
Predicted Drainage requiring Culvert installationPredicted Drainage requiring Culvert installation
•• 86% reliable86% reliable predictions either right on or withinpredictions either right on or within 15 meters15 meters ofofpredicted streams.predicted streams.
•• 95% reliable95% reliable predictions either right on or withinpredictions either right on or within 35 meters35 meters ofofpredicted streams.predicted streams.
OBM Mapped Stream Locations areOBM Mapped Stream Locations are……““completely unreliable for block layout and water crossingscompletely unreliable for block layout and water crossings””
Mark Joran, Millson Forestry ServiceMark Joran, Millson Forestry Service
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LiDAR’s Contribution to Precision Forest Inventory• Detailed Surface Models
– Digital Surface Models– Digital Terrain Models– Canopy Height Models
• Detailed Digital Terrain Model– Supporting
• Identifying surficial Geology• Hydrological modelling• Wetland classification
Value A
ddedV
alue Added
Improved Predictive Wetland Classification in N.Improved Predictive Wetland Classification in N.Ontario when compared to current 20m DEMOntario when compared to current 20m DEM
•• ““LiDAR had problemsLiDAR had problemswith obtaining barewith obtaining bareearth returns in alderearth returns in alderthickets and cedarthickets and cedarwetlandswetlands””
Adam HoggAdam Hogg -- IMAIMA
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LiDAR’s Contribution to Precision Forest Inventory• Detailed Surface Models
– Digital Surface Models– Digital Terrain Models– Canopy Height Models
• Detailed Digital Terrain Model– Supporting
• Identifying surficial Geology• Hydrological modelling• Wetland identification• Predictive ELC
Value
Value
Added
Added
Output from LandMapR
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• Detailed Surface Models– Digital Surface Models– Digital Terrain Models– Canopy Height Models
• Detailed Digital Terrain Model– Supporting
• Identifying surficial Geology• Hydrological modelling• Wetland identification• Predictive ELC
Value
Value
Added
Added
LiDAR’s Contribution to Natural Resource Inventories
Predicted Ecosite MappingProviding additional information:• vegetation communities• Soil depth,• Soil texture• Soil moisture regime,• Soil nutrients• Landform•Geomorphology descriptors (sand,gravel, rock, fluvial, alluvial fans,lacustrine, glaciofluvial, etc…).
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LiDAR’s Contribution to Precision Forest Inventory• Detailed Surface Models
– Digital Surface Models– Digital Terrain Models– Canopy Height Models
• Detailed Digital Terrain Model– Supporting
• Identifying surficial Geology• Hydrological modelling• Wetland identification• Predictive Ecosystem mapping• Operational considerations
– road construction– skid trail layout– water crossings
Value A
ddedV
alue Added
Virtual Road layoutVirtual Road layout
Origin
Destination
DEM 2m
Profile from DEM
Wetness index and trail layoutWetness index and trail layout
FPInovations Ferric
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LiDAR’s Contribution to Precision Forest Inventory• Detailed Surface Models
– Digital Surface Models– Digital Terrain Models– Canopy Height Models
• Detailed Digital Terrain Model– Supporting
• Identifying surficial Geology• Hydrological modelling• Wetland identification• Predictive Ecosystem mapping• Operational considerations
– road construction– skid trail layout– water crossings
• Direct measurement of:• Stand/Tree Heights• Crown Closure
Value A
dded
1m Canopy Height Model1m Canopy Height Model
Canopy Closure Comparison
0
10
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100
1 2 3 4 5 6
Plot
0
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Calculated Stand Top H
eight (m)
Lidar Maximum Vegetation Return vs. Calculated Top Height
0
5
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0 5 10 15 20 25 30 35 40
Lidar Max Vegetation Return (m)
Mean - -0.79m
Min - -4.85m
Max - 4.07m
Std - 1.50m
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LiDAR’s Contribution to Precision Forest Inventory• Detailed Digital Surface Models
– Digital Surface Models– Digital Terrain Models– Canopy Height Models
• Detailed Digital Terrain Model– Supporting
• Identifying surficial Geology• Hydrological modelling• Wetland identification• Predictive Ecosystem mapping• Operational considerations
– road construction– skid trail layout– water crossings
• Direct measurement of:• Stand/Tree Heights• Crown Closure
• Individual Tree Information– Tree crown centre– Tree crown radii– Tree height
Value A
ddedV
alue Added
ITC Suite – François GougeonTreeVaw - Sorin Popescu
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LiDAR’s Contribution to Precision Forest Inventory• Detailed Digital Surface Models
– Digital Surface Models– Digital Terrain Models– Canopy Height Models
• Detailed Digital Terrain Model– Supporting
• Identifying surficial Geology• Hydrological modelling• Wetland identification• Predictive Ecosystem mapping• Operational considerations
– road construction– skid trail layout– water crossings
• Direct measurement of:• Stand/Tree Heights• Crown Closure
• Individual Tree Information
• Statistically based predictions ofstand attributes
Value A
ddedV
alue Added
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Lidar-basedmodels of
forestparameters
• Height• Volume (GTV, GMV)• Basal area• Density• Quadratic mean DBH• Biomass• Diameter and Basal
Area Distributions
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Canopy Height Model (CHM)
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LiDAR Predictors
• Statistical– Mean, Standard Deviation,
Absolute Deviation, Skew, Kurtosis
• Percentiles– Deciles (p10 … p90) and Maximum Height
• Density– d1 … d9
– Da : Number of first returns divided by all returns.
– Db : Number of first returns classified as non-ground divided by all returns.
• derived from First, or All returns; with or without a z threshold
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P0
heig
ht
1
q(ht)
q(ht)
Concept of Canopy Height Metrics
0
5
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4.2966e+54.2967e+5
4.2968e+54.2969e+5
4.2970e+54.2971e+5
4.681400e+64.681405e+64.681410e+64.681415e+64.681420e+64.681425e+64.681430e+64.681435e+6
Z Data
X Data
Y Data
ACFL
9 values that divide sorted data into 10 equal parts with each part representing1/10th of the sample or population
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Concept of Canopy Density Metrics
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4.2966e+54.2967e+5
4.2968e+54.2969e+5
4.2970e+54.2971e+5
4.681400e+64.681405e+64.681410e+64.681415e+64.681420e+64.681425e+64.681430e+64.681435e+6
Z Data
X DataY Data
ACFL
P0
heig
ht
1
D(%)
• Range of heights divided into 10 equal intervals.
• Cumulative proportion of returns starting from the lowest interval.
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Statistical Analyses
• Linear / Non-Linear Regression
• Model Diagnosis– Test for Normality: Shapiro-Wilks Test– Test for Homoschedasticity: Modified Levene’s Test– Multicollinearity: Variance Inflation Factors (VIF) < 10– Natural Logarithm Transformation
• Validation– Independent data sets or PRESS Procedure
nn xxy ...110
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Workflow – Model Building
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Decimation 0 ~3 p/mDecimation 0 ~3 p/m22 Decimation 1 ~1.6 p/mDecimation 1 ~1.6 p/m22 Decimation 2 ~0.5 p/mDecimation 2 ~0.5 p/m22
What is the appropriate density to acquireWhat is the appropriate density to acquireLiDAR at for stand level prediction?LiDAR at for stand level prediction?
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Decimation 0 ~3 p/mDecimation 0 ~3 p/m22 Decimation 1 ~1.6 p/mDecimation 1 ~1.6 p/m22 Decimation 2 ~0.5 p/mDecimation 2 ~0.5 p/m22
Cumulative Proportion of LiDAR Returnsfor Level of LiDAR Decimation
0
0.1
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2 4 6 8 10 12 14 16 18 20 22 24 26
Height Class
%D0D1D2
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Study Sites
• Three (3) main Ontario study sites:– Swan Lake (SL) Reserve (n=32)– Petawawa Research Forest (PRF) (n=32)– Romeo Mallette (RM) Forest (n=130)
Jack PineJack Pine Black SpruceBlack SpruceGrtGrt LksLks PinePine
TolerantTolerant HwdsHwds IntolerantIntolerant HwdsHwds MixedwoodsMixedwoods
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LiDAR DataPeriod: Summer 2007Sensor: Optech ALTM 3100Altitude: 1000 mOverlap: N/ASpeed: 120 knotsSystem PRF: 100 kHzScan Freq: 54 HzScan Half Angle: 13°Cross Track Resolution: 0.499 mDown Track Resolution: 0.572 m
~ 3 point/m2
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WorkflowDecimate LiDAR Data~3 pulses/m2
~1.6 pulses/m2
~.5 pulses/m2
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Petawawa Research Forest – Grt. Lakes Pine(Pw/Pr/Pj Natural + Planted + Thinned)
Variable Decimation Level 0 Decimation Level 1 Decimation Level 2
R2 RMSE(%)
R2 RMSE(%)
R2 RMSE(%)
SUMBA .89 4.8(13.3)
.89 4.7(13.2)
.88 4.9(13.7)
SUMGTV .93 56.1(13.4)
.94 51.2(12.3)
.92 60.0(14.4)
DENSITY .74 207.8(34.7)
.72 214.9(35.8)
.68 226.9(37.8)
QMDBH .87 3.9(12.7)
.86 4.0(12.9)
.87 3.8(12.3)
AVGHT .94 1.3(5.7)
.94 1.3(6.0)
.94 1.3(5.7)
TOPHT .95 1.2(4.4)
.95 1.2(4.5)
.94 1.3(4.7)
SUMBIO .74 29,818(21.0)
.76 28,743(20.3)
,78 27,572(19.4)
Current StatusPreliminary Results
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Variable Decimation Level 0 Decimation Level 1 Decimation Level 2
R2 RMSE(%)
R2 RMSE(%)
R2 RMSE(%)
SUMBA .49 2.7(10.6)
.49 2.7(10.7)
.58 2.4(9.7)
SUMGTV .59 25.4(11.2)
.60 24.9(11.0)
.61 24.8(11.0)
DENSITY .84 42.8(10.5)
.86 39.8(9.8)
.83 43.7(10.7)
QMDBH .69 2.0(7.3)
.72 1.9(7.0)
.70 2.0(7.1)
AVGHT .84 0.6(3.4)
.84 0.6(3.4)
.85 0.6(3.2)
TOPHT .82 0.7(3.0)
.85 0.7(2.8)
.86 0.7(2.7)
SUMBIO .46 24,795(12.6)
.50 23,811(12.1)
.58 23,811(12.1)
Tolerant Hardwoods
Current StatusPreliminary Results~ 3 pts/m2 ~ 1.6 pts/m2 ~ 0.4 pts/m2
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Romeo Malette Forest – Mixedwoods
Preliminary ResultsDensity Basal Area
Dbhq GTV Biomass
Mixedwoods - Decimation Level 0
0
10
20
30
40
50
60
70
0 10 20 30 40 50 60 70
Measued BA m2/ha
Pred
icte
d BA
m2/
ha
Mixedwoods - Devimation Level 0
-100
0
100
200
300
400
500
600
700
800
0 100 200 300 400 500 600 700 800
Measured GTV m3/ha
Pred
icte
d G
TV m
3/ha
Mixedwoods - Decimation Level 0
0
5
10
15
20
25
30
35
0 5 10 15 20 25 30 35
Measured Dbhq cm
Pred
icte
d D
bhq
cm
Mixedwoods - Decimation Level 0
0
50000
100000
150000
200000
250000
300000
350000
0 50000 100000 150000 200000 250000 300000 350000
Measured Biomass Kg/ha
Pred
icte
d Bi
omas
s Kg
/ha
Top Height
Mixedwoods - Decimation Level 0
0
5
10
15
20
25
30
35
40
0 5 10 15 20 25 30 35 40
Measured Top Height m
Pred
icte
d To
p H
eigh
t m
Mixedwoods - Decimation Level 0
0
500
1000
1500
2000
2500
3000
0 500 1000 1500 2000 2500 3000
Measured Density Stems/ha
Pred
icte
d D
ensi
ty S
tem
s/ha
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Romeo Malette Forest – MixedwoodsVariable Decimation Level 0 Decimation Level 1 Decimation Level 2
R2 RMSE(%)
R2 RMSE(%)
R2 RMSE(%)
SUMBA .72 5.5(16.8)
.73 5.3(16.4)
.72 5.5(16.8)
SUMGTV .82 48.2(18.2)
.83 47.1(17.8)
.82 48.2(18.2)
DENSITY .12 309.4(28.1)
.11 310.9(28.2)
.12 309.4(28.1)
QMDBH .61 1.6(8.5)
.62 1.6(8.4)
.61 1.6(8.5)
AVGHT .78 0.9(6.0)
.82 0.8(5.3)
.78 0.9(6.0)
TOPHT .97 0.6(2.8)
.97 0.6(2.8)
.97 0.6(2.8)
SUMBIO .75 22,510(16.2)
.81 19,469(14.1)
.75 22,510(16.25)
Current Status
This analysis: All Returns – No Z Threshold imposed
Preliminary Results
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Applying Models to the LandscapeLiDAR Predictor Surfaces
•Each surfacecorresponds to a LIDARpredictor
•Example is 12,000 ha ofPetawawa ResearchForest
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Image based Interpretation
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Image based Interpretation – Polygon Mask
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LiDAR derived Canopy Height Model
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Operational LiDAR Enhancements
Stratify by:
•Forest type
•Forest unit
•etc…
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Operational LiDAR Enhancements
Not to scale – illustrative purposes only
400m400m22 Predictive Grid CellsPredictive Grid Cells
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Operational LiDAR EnhancementsAverage Height (m) 400mAverage Height (m) 400m22 RasterRaster
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Operational LiDAR EnhancementsAverage Height (m) 400mAverage Height (m) 400m22 RasterRaster
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Operational LiDAR EnhancementsAverage Height (m) 400mAverage Height (m) 400m22 RasterRaster
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Operational LiDAR EnhancementsBasal Area (mBasal Area (m22/ha) 400m/ha) 400m22 RasterRaster
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Operational LiDAR EnhancementsGTV (mGTV (m33/ha) 400m/ha) 400m22 RasterRaster
Dbhq (cm) 400mDbhq (cm) 400m22 RasterRaster
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Diameter Distribution Modelling
From: Quantifying diameter distributions of uneven-aged toleranthardwood stands using LiDAR
Jones, T, Woods, M. and Lim K 2009 Inpress Silvilaser 2009
Sawtimber size class
pole small medium large
Basal area (m2 ha -1)
0
2
4
6
8
10
Observed basal areaLiDAR predicted basal area
a
b
aa
aa
aa
SLFRR validation plots
0.00
0.05
0.10
0.15
0.20
0.25
0.00
0.05
0.10
0.15
0.20
0.25
0 20 40 60 800.00
0.05
0.10
0.15
0.20
0.25
0.00
0.05
0.10
0.15
0.20
0.25Observed diameter distributionLiDAR estimated
0.00
0.05
0.10
0.15
0.20
0.25
0 20 40 60 800.00
0.05
0.10
0.15
0.20
0.25
Relative Frequency
Relative Frequency
Diameter class (cm)
Validation Data
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All Rondeau Vegegation PlotsComparing LiDAR against Ground Measured Heights (Bird Monitoring Classes)
0
5
10
15
20
25
30
0-50 .51-1.30 1.31-3.0 3.1-6.0 6.1-10.0 10.1-20.0 20+
Height Class
Heig
ht m First Returns
All_ReturnsMeasured HT
LiDAR unlocking Vertical Structure for Habitat ModelingLiDAR unlocking Vertical Structure for Habitat Modeling
Percent Vegetation Returns
0 5 10 15 20 25 30 35 40 45
.51-1.3
1.31-3.0
3.0-6.0
6-10
10.1-20
20+
101C
Percent Vegetation Returns
0 5 10 15 20 25 30 35 40 45
.51-1.3
1.31-3.0
3.0-6.0
6-10
10.1-20
20+
105A
% Vegetation Cover by Height Class
Canopy Height Model Sliced Canopy Height Model
Foliage Height Diversity
19
0
5
10
15
20
25
430015 430020 430025 430030 430035 430040 430045 430050 430055 430060
Z
102C
0
5
10
15
20
25
30
430285 430290 430295 430300 430305 430310 430315 430320 430325 430330
Z
Vertical Distribution Ratio = 0.64Vertical Distribution Ratio = 0.64Coefficient of Variation = 56.19Coefficient of Variation = 56.19
Vertical Distribution Ratio = 0.51Vertical Distribution Ratio = 0.51Coefficient of Variation = 39.95Coefficient of Variation = 39.95
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The Role of MultibandThe Role of Multiband Orthophotography in theOrthophotography in theproduction of Semiproduction of Semi--Automated Enhanced ForestAutomated Enhanced Forest
Inventories in the Great Lakes St. Lawrence ForestInventories in the Great Lakes St. Lawrence Forest
Enhanced Forest Productivity FundEnhanced Forest Productivity Fund
Murray Woods, Dave NesbittMurray Woods, Dave NesbittSouthern Science & InformationSouthern Science & Information
FranFranççoisois Gougeon, Don LeckieGougeon, Don LeckieCanadian Forest ServiceCanadian Forest Service
Paul Courville, Sue PickeringPaul Courville, Sue PickeringForest Research PartnershipForest Research Partnership
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Project Focus
Explore the potential of:• Using high-resolution multi-band
digital imagery for automatedinventory production– Stand Level– Tree Level
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Working with Drs GougeonWorking with Drs Gougeonand Leckie at CFSand Leckie at CFS-- PFCPFC ––application evolutionapplication evolution
Working with SilvatechWorking with SilvatechConsulting Ltd.Consulting Ltd. ––operational applicationoperational application
RESEARCH
OPERATIONS
SemiSemi--automatedautomated -- Individual Tree Crown ClassificationIndividual Tree Crown Classification
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SemiSemi--automatedautomated -- Individual Species ClassificationIndividual Species Classification
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SemiSemi--automatedautomated -- Individual Species ClassificationIndividual Species Classification
RGB
Spectral-Based Masking
Lidar-Based Masking
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SemiSemi--automatedautomated -- Individual Species ClassificationIndividual Species Classification
RGB
Spectral-Based Masking
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SemiSemi--automatedautomated -- Individual Species ClassificationIndividual Species Classification
RGB
Spectral-Based Masking
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SemiSemi--automatedautomated -- Individual Species ClassificationIndividual Species Classification
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SemiSemi--automatedautomated -- Individual Species ClassificationIndividual Species Classification
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SemiSemi--automatedautomated -- Individual Species ClassificationIndividual Species Classification
RGB
Spectral-Based Masking
Ground trainingGround training
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SemiSemi--automatedautomated -- Individual Species ClassificationIndividual Species Classification
RGB
Spectral-Based Masking
Soft copy trainingSoft copy training
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SemiSemi--automatedautomated -- Individual Species ClassificationIndividual Species Classification
RGB
Spectral-Based Masking
Green and Blue by species and 0.7 SD of Green
Pw im
Pw o
Pw br
Mh
Ms
Bwd
Bw
AtAtpq
Alt
By
La
Pr
Sw
Sb
Pj BD
Bf
110
120
130
140
150
160
170
130 140 150 160 170 180
Intensity of Green
Inte
nsity
of B
lue
Pw imPw oldPw brMhMsBwdBwAtAtpqAltByLaPrSwSbPjBDBf
nIR and Red by species and 0.7 SD of nIR
Pw im
Pw old
Pw br
Mh
Ms
Bwd
Bw
At
At p Alt
By
La
Pr
Sw
Sb
Pj
BDBf
120
130
140
150
160
170
160 170 180 190 200 210 220
Intensity of near Infrared
Inte
nsity
of R
ed
Pw imPw oldPw brMhMsBwdBwAtAtpqAltByLaPrSwSbPjBDBf
Species Separability
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nIR and Red by species and 0.7 SD of nIR
Pw im
Pw old
Pw br
Mh
Ms
Bwd
Bw
At
At p Alt
By
La
Pr
Sw
Sb
Pj
BDBf
120
130
140
150
160
170
160 170 180 190 200 210 220
Intensity of near Infrared
Inte
nsity
of R
ed
Pw imPw oldPw brMhMsBwdBwAtAtpqAltByLaPrSwSbPjBDBf
ITC Species Signatures in Multispectral Space for PRF
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SemiSemi--automatedautomated -- Individual Species ClassificationIndividual Species Classification
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SemiSemi--automatedautomated -- Individual Species ClassificationIndividual Species Classification
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SemiSemi--automatedautomated -- Individual Species ClassificationIndividual Species Classification
Pw Mh Ms Bw As La Pr Sw Sb PjPw 54.19 26.23 0.00 0.00 0.00 12.44 8.87 0.78 3.83 2.17Mh 2.86 66.12 0.00 0.00 0.00 0.46 0.00 0.52 0.18 0.00Ms 2.66 0.00 66.67 0.00 9.24 0.00 9.61 0.00 0.00 0.00Bw 0.00 0.00 0.00 96.77 9.24 0.00 0.18 0.00 0.00 0.00As 3.27 0.55 33.33 3.23 75.63 5.53 6.47 0.00 0.00 0.00La 14.72 3.83 0.00 0.00 0.00 73.27 0.55 0.00 1.28 0.00Pr 11.25 0.55 0.00 0.00 4.20 4.15 68.21 2.08 0.00 4.35Sw 0.41 0.00 0.00 0.00 0.00 0.00 0.92 67.79 5.28 2.17Sb 6.75 0.00 0.00 0.00 0.00 0.46 0.00 10.91 83.97 1.09Pj 2.45 0.00 0.00 0.00 0.00 0.92 3.51 15.06 2.19 86.96Un 1.43 2.73 0.00 0.00 1.68 2.76 1.66 2.86 3.28 3.26
# ITCs 489 183 18 31 119 217 541 385 549 92
Confusion Matrix Results for PRF
Overall accuracy (precision) of 70.4%
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SemiSemi--automatedautomated -- Individual Species ClassificationIndividual Species Classification
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SemiSemi--automatedautomated -- Individual Species ClassificationIndividual Species Classification
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SemiSemi--automatedautomated -- Individual Species ClassificationIndividual Species Classification
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SemiSemi--automatedautomated -- Individual Species ClassificationIndividual Species Classification
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SemiSemi--automatedautomated -- Individual Species ClassificationIndividual Species Classification
Automated Polygon DelineationAutomated Polygon Delineationwith Species Classificationwith Species Classification
49 Pw 21 Sw 9 Bf 8 Sb 7 Bd 3 Pr 3 Ce31m
42% Crown Closure238 Stems
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Semi-Automated Imagery Classificationand LiDAR for Inventory Production
• Semi-automated imagery classification is a maturing area –focus of ongoing national research efforts with CWFC
• Boreal forest species currently best opportunity to benefitfrom imagery analysis techniques– Another “tool” for an interpreter– Objective vs. Subjective approach– Able to process large areas very quickly– Ability to improve and recompile– Driving towards tree level inventories– Hardwood aggregation or over splitting
receiving attention
Romeo Malette Forest Example
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Semi-Automated Imagery Classificationand LiDAR for Inventory Production
• LiDAR methods well understood and ready to operationallyimplement– Become an affordable technology– Demonstrating that higher sampling point densities are not required
for better estimates of forest inventory variables.– Current inventory efforts on a 650K ha NE Ontario Forest– Planned inventory project for 1.3 Million ha forest in 2010– Need to continue to expand the areas in which LiDAR has the
potential to help add value to inventories – eg. Stream prediction,forest habitat modeling, renewable energy modeling
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In Conclusion, I believe that…These technologies offergreat potential to improvecurrent methods ofinventory production.Sound sustainablemanagement andbusiness decisionsrequire detailed, highquality & spatially explicitresource inventoryinformation…we can’tkeep putting it off.
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Ministry of Natural Resources
Author: Mike Baldwin
Thank You
Questions
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Murray Woods at: