The Development of Geospatial Datasets The Development of Geospatial Datasets for Estimating Forest Inventory Variables for Estimating Forest Inventory Variables

Using Small Footprint Laser Altimetry.Using Small Footprint Laser Altimetry.

Eric RowellEric Rowell11, Lee Vierling, Lee Vierling22, Wayne Sheppard, Wayne Sheppard33, Carl Seielstad, Carl Seielstad11, and Lloyd Queen, and Lloyd Queen11

11National Center for Landscape Fire Analysis, University of Montana, Missoula, Mt 59801National Center for Landscape Fire Analysis, University of Montana, Missoula, Mt 5980122College of Natural Resources, University of Idaho, Moscow, ID 83844College of Natural Resources, University of Idaho, Moscow, ID 8384433U.S. Forest Service, Rocky Mountain Research Station, 240 West Prospect, Ft. Collins, CO 80526U.S. Forest Service, Rocky Mountain Research Station, 240 West Prospect, Ft. Collins, CO 80526

BackgroundBackground

• Pilot study: – Research conducted to quantify forest biophysical

variables in the Black Hills of South Dakota

• Development of geospatial data products from these variables for use by managers and scientists– Individual tree scale– Broader stand/patch scale– Fire behavior modeling

I n t r o d u c t i o nI n t r o d u c t i o n

• Near continuous sampling of large areas: – Generating large volumes of height data– Representative of information regarding

the canopy, understory, and ground.

• The ability to distinguish unique biophysical variables – (e.g. Stem ID, DBH, and Crown Width)

• These biophysical variables can be used to drive the derivation of stand level attributes and indices – (e.g. SDI)

What’s the advantage of laser altimetry?What’s the advantage of laser altimetry?I n t r o d u c t i o nI n t r o d u c t i o n

Rationale I: Biophysical VariablesRationale I: Biophysical VariablesTree HeightTree Height

• Primary variable derived from laser altimetry data (drives the whole system).

• Usually underestimates maximum tree height in coniferous forests

Crown WidthCrown Width • Tertiary variable derived from DBH

Crown BaseCrown Base • Derived from the Canopy Height Model (CHM)

Diameter Diameter (DBH)(DBH)

• Secondary variable that is derived from tree height

I n t r o d u c t i o nI n t r o d u c t i o n

Rationale II: Geospatial Data ProductsRationale II: Geospatial Data Products

• What scale do these data products need to be?– Individual Trees– Stands (e.g. structure, age class, condition)– Patches (areas of equal ecological quality)– Landscape (a sum of patches or stands)

• Which biophysical variables are important to quantify?– For silvics (e.g. tree height, DBH, stem count)– For canopy fuels (e.g. tree height, crown width, HTCB, crown shape, PCC)– For forest ecology (e.g. structure (encompasses silvics and fuels), understory)

• From the biophysical variables, what additional variables or indices can be generated?

– For silvics: SDI, stem volume, sapwood area– For canopy fuels: CBD, crown weight

I n t r o d u c t i o nI n t r o d u c t i o n

Research Objectives IResearch Objectives I

Forest Biophysical Variable Inventory:

• Estimation of biophysical variables from laser altimetry at an individual tree scale.

– Height, DBH, crown width, and height to crown base– Stem count

• Discussion of data segmentation techniques to refine biophysical variable estimation

• Validation of estimated biophysical variables from laser altimetry by comparison with coincident plot scale field data.

I n t r o d u c t i o nI n t r o d u c t i o n

Research Objectives IIResearch Objectives II

Generation of geospatial mapping products:

• Aggregate plot scale biophysical variables to a stand/patch scale.

• Additional data products and indices for silviculture and fire behavior modeling.

I n t r o d u c t i o nI n t r o d u c t i o n

Laser Altimetry Data AcquisitionLaser Altimetry Data Acquisition

System: Leica Geosystems ALS40 Airborne Laser Scanner Acquired: August 16, 2002Nominal Post Spacing: 1.5 m

System Specifications:– GPS/IMU correction– High accuracy 15cm vertical, 20-25 cm horizontal– Up to 3 returns per pulse– A return at a minimum every 5 meters or 2 nanoseconds – Variable pulse rate of 15,000- 25,000 pulses per second– Variable scan rate of ±15hz to 20hz– ± 30 centimeter foot print dependent on altitude– Reflectance Intensity

M e t h o d sM e t h o d s

Data SegmentationData Segmentation

• Separate ground returns from vegetation returns (two steps)– Virtual Deforestation Algorithm; open source (Haugerud, 2001)– Terrascan software; closed source (TerraSolid, Helsinki, Finland)

• Adapted Variable Sized Window Algorithm – Determines individual tree stems from Canopy Height Model (CHM)– Uses a local maximum filter window using a window size determined

from an allometric model for crown width from height– Data is smoothed to create a convex hull shape for easier ID of

individual trees– End product is a feature class that has stem locations and height

associated with each stem

M e t h o d sM e t h o d s

Data Interpolation IssuesData Interpolation Issues

• Using a smoothing array to better estimate stem locations leads to further underestimation of height

• Heights from the unaltered canopy height model are tagged to the stem locations

Adapted Variable Widow Analysis ProcessAdapted Variable Widow Analysis Process

Field measured DBH (cm)

Lase

r estim

ate

d D

BH

(cm

)

706050403020

90

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60

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S 7.49586R-Sq 84.5%R-Sq(adj) 83.0%

Field measured versus laser estimated plot level DBH (SDSMT Plots)LaserDBH= - 49.65 + 2.906 *DBH

- 0.01748 *DBH**2

M e t h o d sM e t h o d s

Study Area: Black Hills Experimental ForestStudy Area: Black Hills Experimental Forest

Plots:

SDSMT n = 25

RMRS n = 41

Total Number of Trees Sampled:

SDSMT n = 2111

RMRS n = 2885

Data Collected for each tree:

Height

DBH

Crown Width

HTCB

Ikonos false color Ikonos false color infrared image infrared image August 2001 August 2001 overlayed on lidar overlayed on lidar baldearth DEMbaldearth DEM

M e t h o d sM e t h o d s

Data Product Flow ChartData Product Flow ChartM e t h o d sM e t h o d s

Formulas for Forest Biophysical VariablesFormulas for Forest Biophysical VariablesM e t h o d sM e t h o d s

HeightHeight Z = ZZ = Zcc – Z – Zbebe

Height Correction (Height Correction (BH P. PineBH P. Pine)) HC = 0.9892(HHC = 0.9892(Hlala) - 1.7076 ) - 1.7076

Crown WidthCrown Width CW = a*(DBH)2 + b*(DBH) + c CW = a*(DBH)2 + b*(DBH) + c

Height to Crown BaseHeight to Crown Base HTCB = CHPHTCB = CHPmeanmean - CHP - CHPstdvstdv

DBHDBH DBH = a*(HDBH = a*(Hlala) + b) + b

• Height is the native data product– Tends to underestimate the maximum height– Needs to be corrected for this underestimation

• Height to Crown Base is derived from the native height data – Canopy Height Model (CHM)

• DBH and Crown width are allometrically derived

Predicted Canopy Cover (laser)

BAC 5mSD = 156.7mDBH = 16.4cmmBA = 43.1 m2

BAC 4mSD = 96.7mDBH = 21.9cmmBA = 29.3 m2

BAC 3mSD = 43.5mDBH = 30.4cmmBA = 20.5 m2

BAC 2mSD = 29.0mDBH = 29.9cmmBA = 12.1 m2

BAC 1mSD = 7.0 mDBH = 36.9cmmBA = 5.3 m2

Observed Basal Area (observed)

Basal Area Class

Refinement of DBH EstimationRefinement of DBH Estimation

• Because different thinning treatments and stocking density a single model proves inadequate to estimate DBH

• To correct for these conditions we apply a classification to stratify the height data.

– The data is stratified as a function of field observed basal area (BA) and laser derived percent crown cover (PCC)

M e t h o d sM e t h o d s

DBH Models by Basal Area ClassificationDBH Models by Basal Area ClassificationM e t h o d sM e t h o d s

0.612y = 0.3937x + 8.04365

0.595y = 1.5921x - 3.61924

0.755y = 1.6768x - 0.57043

0.791y = 1.7796x + 1.06482

0.786y = 1.9763x - 0.60471

RR22DBH from HeightDBH from HeightLinear ModelLinear ModelBasal Area Class Basal Area Class

• Specific models are applied to each BA class to better estimate DBH from laser estimated tree height.

Biophysical Variable EstimationBiophysical Variable EstimationR e s u l t s R e s e a r c h O b j e c t i v e IR e s u l t s R e s e a r c h O b j e c t i v e I

• Comparison of observed and predicted:

VariableVariable RR2 2 (maximum)(maximum) RR2 2 (mean)(mean)

HeightHeight 0.940.94 --

With Height CorrectionWith Height Correction 0.900.90 0.730.73

Crown WidthCrown Width 0.700.70 0.860.86

Height to Crown BaseHeight to Crown Base 0.790.79 0.760.76

DBHDBH 0.600.60 0.760.76

Comparison of Stem CountsComparison of Stem Counts

• Stem locations were most accurately predicted in low density stands.

• Sensitivity analysis of stands where stem density is greater than 200 stems/900m2 show the variable sized window underestimates stems by a factor of two.

• The key to accurate stem identification is smoothing the data to create a convex hull.

R e s u l t sR e s u l t sR e s u l t s R e s e a r c h O b j e c t i v e IR e s u l t s R e s e a r c h O b j e c t i v e I

Stem Counts

y = 0.927x + 1.4082R2 = 0.91

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Observed

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Forest Stand ModelForest Stand Model

Stand Density IndexStand Density Index

• SDI is a summation index used to assess levels of growing stock

• The index is a relative density measure that is a function of tree DBH and stem density per hectare

• For Even Aged ponderosa Pine– DBH*(TPH/25)1.6

R e s u l t sR e s u l t sR e s u l t s R e s e a r c h O b j e c t i v e I R e s u l t s R e s e a r c h O b j e c t i v e I II

Stand Density Index

y = 0.9495x + 72.67R2 = 0.84

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Patch Analysis ResultsPatch Analysis ResultsR e s u l t s R e s e a r c h O b j e c t i v e I R e s u l t s R e s e a r c h O b j e c t i v e I II

Aggregated mean percent canopy closure and mean basal area

R2 = 0.97

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Observed Basal Area (m2)

Pre

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anop

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BAC 1

BAC 2

BAC 3

BAC 4

BAC 5

• Using the basal area classification we can separate patches of trees from laser estimated PCC

• We can then populate the PCC feature class with mean height, DBH,HTCB, and crown width values

Mapping ProductMapping ProductR e s u l t s R e s e a r c h O b j e c t i v e I R e s u l t s R e s e a r c h O b j e c t i v e I II

• Patch scale mapping products are of better scale for forest managers making decisions for thinning prescriptions

• The values associated with each class can be used in fire behavior modeling (FARSITE) and forest growth models (FVS)

ConclusionsConclusions

• We successfully predicted individual tree biophysical variables:– Height, DBH, crown width, and HTCB – laser altimetry data were segmented to refine DBH

estimates• Laser altimetry data were aggregated to stand

scale:– By development of biophysically based indices (SDI)– And data stratification through comparisons of field

observed (BA) and laser predicted variables (PCC)

Phase II: Fuels MappingPhase II: Fuels Mapping

• Our next phase of research– Surface fuels mapping

• Transpose research conducted in the Tenderfoot Experimental Forest by Seielstad and Queen to the Black Hills Experimental Forest

– Canopy fuels mapping• Transpose research conducted in the Black Hills

Experimental Forest by Rowell to the Tenderfoot Experimental Forest

Surface FuelsSurface Fuels

• Used ground/near surface roughness to explain presence or absence of coarse woody debris– Obstacle density: # of near-ground returns

normalized by all points (ground + fuel bed)

– Standard deviation and Kurtosis of the ground and near-ground height distributions.

Results of Surface Fuel EstimatedResults of Surface Fuel Estimated

• Woody Debris Estimates:– Obstacle density, STD, and Kurtosis of the ground

height distribution can be used to estimate total dead fuel volume and 1000 hr fuels

– Roughness metrics did not prove effective for identifying fine fuels

– Roughness metrics predicted fuel volumes within ± 25 percent of field estimated fuel volumes

– Variability within the laser estimated fuel volumes was significantly less than field estimated suggesting that the volume of laser estimates may be better characterizing the fuel bed

Canopy FuelsCanopy Fuels

• Crown bulk density: – Predicted from laser estimated live crown

weight (Brown, 1978)

EXP( 0.2680 + 2.0740(LN DBH))

– Crown volume estimated from tree crown width, crown base height, and modeled tree crown shape

Initial findingsInitial findings

• Live crown weight correlated well between observed and predicted (RR2 2 == .78).78)

• Crown width and CBH relationships areCrown width and CBH relationships are equally well correlated

• Crown shape is the unknown:– Do we model a cone, ellipsoid, or paraboloid

• We will then apply the model on an individual tree scale to produce canopy bulk density estimates across the landscape

AcknowledgementsAcknowledgements

• My field crew Meghan Calhoon, Shane Hansen, and Beth Hansen.

• We would like to thank Horizons, Inc. for the acquisition of the laser altimetry data and the support of ER during this study.

• Thanks also to the USDA Rocky Mountain Research Laboratory for the contribution of field data collection and logistical support.

• This work was partially supported by NASA EPSCoR grant NCC5-588 and the Upper Midwest Aerospace Consortium.