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Predicting Biophysical Properties of Mixed Conifer Stands in Northern Idaho with Small Footprint LiDAR. Jennifer Jensen, Karen Humes, PhD Geography Department,

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Presentation on theme: "Predicting Biophysical Properties of Mixed Conifer Stands in Northern Idaho with Small Footprint LiDAR. Jennifer Jensen, Karen Humes, PhD Geography Department,"— Presentation transcript:

1 Predicting Biophysical Properties of Mixed Conifer Stands in Northern Idaho with Small Footprint LiDAR. Jennifer Jensen, Karen Humes, PhD Geography Department, University of Idaho Laurie Ames, Jeff Cronce, John Degroot, Land Services and Forestry Departments, Nez Perce Tribe

2 Overview Research Background Nez Perce LiDAR Research Objectives Study Area Overview LiDAR and Field Data Acquisition Statistical Analysis Results/Operational Models Conclusions/Considerations Current/On-going Research

3 Research Background LiDAR research funded by NASA BAA-01-OES- 01 Nez Perce Tribe and UI engaged in cooperative research agreement to complete various performance metrics General purpose of grant to aid in operational decision-making on tribal-owned lands on Nez Perce reservation utilizing remote sensing technology

4 LiDAR and Forest Management Forest characteristics such as vegetation height, basal area, volume, and crown properties are common attributes assigned to entire stands based on plot surveys Operational decisions are based on varying biophysical properties and management strategies within a stand(s) Current surveying techniques are relatively costly and time-consuming, yet necessary for quantification of forest attributes

5 LiDAR and Nez Perce Forests The Nez Perce Tribe manages approximately 55,000 forested acres High sample density LiDAR data acquired for ~33,000 acres of forested land across 5 separate geographic regions Specific research objective: –Assess the ability of LiDAR data to predict forest stand characteristics similar accuracy as those measurements conducted in situ for 5 study areas comprising a total of 32,978 acres.


7 Diversity of Study Area Selection Stands selected to represent greatest range of size (DBH) and density (crown closure). Study plots further sub-selected from stands based on mean canopy height, stem density, and species composition

8 LiDAR Data Acquisition Leica Geosystems ALS-40 sensor mounted on a Cessna 310 aircraft Flight Height: 6,000 feet above mean terrain at ~110 knots (127 mph) Footprint: 60 cm Post-spacing: average 2 meters Scan angle: 12.5 degrees each side of nadir (25 degrees total swing) Average swath width: 2,660 feet (810.768 meters) July 13 th mission: RMSE of 0.141 meters vertical accuracy July 26 th mission: RMSE of 0.159 meters

9 Data Deliverables Files delivered post-processed from Spenser B. Gross, Inc. –Each region has separate DEM and SEM shapefiles –TIN was created from DEM point features –TIN and SEM are used with the [TINSPOT] command in ArcInfo to determine the corresponding DEM elevation TINSPOT necessary to calculate vegetation heights

10 Deriving Vegetation Heights SEM points are overlaid on the TIN Function enables a corresponding TIN (DEM) elevation to be determined Additional elevation field is added to SEM attribute table By subtracting the TIN elevation from the SEM elevation, vegetation heights can be calculated SEM POINTS TIN 14 13.3 13.2

11 Extracting Vegetation Heights After heights are derived from [TINSPOT], a noise check was performed to determine if any unreasonable values (spikes) existed in the dataset Plot centerpoints (acquired in field) were buffered 16.1 meters (1/5-acre) and the [CLIP] command used to extract individual plot attributes

12 LiDAR Metrics Predictor variables (x’s)- calculated from vegetation heights MetricParameters (meters)Label Number of Points in PlotAll Points & All Points > 1.37 m LN, LUPPN MeanAll PointsLH Mean VarianceAll PointsLH Variance Coefficient of variationAll PointsLH Coef Canopy PercentilesAll PointsCAN 25ile, CAN 50ile, CAN 75ile, CAN 95ile Understory Cover0.03 – 1.37LUSC Canopy Cover 11.38 – 10.67LCCO1 Canopy Cover 210.68 – 18.29LCCO2 Canopy Cover 318.30 – 28.96LCCO3 Canopy Cover 4> 28.96LCCO4 Canopy Cover TotalAll Points > 0.05LCCO Total Upper-story MeanAll Points > 1.37LUPP Mean Upper-story VarianceAll Points > 1.37LUPP Variance Upper-story PercentilesAll Points > 1.37LUPP 25ile, LUPP 50ile, LUPP 75ile, LUPP 95ile

13 Field Data Acquisition Detailed field data was collected during summer 2002 –Used existing CFI (Continuous Forest Inventory) plots –Generated several new plots for representative sampling of stands based on size/density and management type Ground metrics calculated from inventory data and used as dependent variables in regression analysis

14 Ground Metrics Dependent variables (y’s) – calculated from field data 1. Maximum Tree Height2. Mean tree height (m) 3. Lorey’s Mean Tree Height4. Quadratic Mean DBH (cm) 5. Total Basal Area (m 2 /ha)6. Mean Basal Area (m 2 ) 7. Percent Canopy Closure (%)8. Canopy Structure 9. Ellipsoidal Crown Closure10. Total wood volume (m 3 /ha) 11. Volume Pole (m 3 /ha)12. Volume Small Saw (m 3 /ha) 13. Volume Large Saw (m 3 /ha)14. Total Wood Volume (m 3 /ha) 15 Volume Pole (m 3 /ha)16. Volume Small Saw (m 3 /ha) 17. Volume Large Saw (m 3 /ha)18. Stem Density (trees/ha) 19. Shrubs, Grass, Forbs, Seedling Percent Cover (%) 20. Saplings Percent Cover (%) 21. Saplings count

15 Regression Analysis LiDAR-derived height metrics were used as independent variables for developing multiple regression models to estimate ground metrics Model development utilized 2/3 of plots –Remaining 1/3 used as validation dataset Potential models were selected according to several criteria: –Coefficient of determination (R 2 ) –Overall model and individual variable significance –RMSE –Cp statistic –Tolerance close to 1; VIF < 10

16 Recommended Model Results

17 Variables with “Potential”

18 Discussion Specific geographic study-unit groupings likely not as effective as management- or species- specific analysis Confidence in final recommended models to perform as well on an independent dataset Results are based on only 2 pulse returns (first and last). Inclusion of intermediate return data may provide more information for predicting finer- scale characteristics

19 Additional/Current Research Assess ability of LiDAR to classify forest structure (single vs. multi-story) –PCA and/or factor analysis of LiDAR-derived metrics –Discriminant and/or cluster analysis Fusion/integration of LiDAR and spectral data (HyMap and SPOT) – to improve species and biophysical estimates Incorporate final regression models into mapping software –Final stages of AML scripting.





24 Summary Direct benefits of LiDAR datasets for Nez Perce: –High resolution digital terrain and surface elevation models Varying resolution for project specific goals –Three-dimensional canopy cover visualizations GIS-based maps of LiDAR-derived height distributions (can provide sub-canopy level structural information) GIS-based maps of selected stand-level forest attributes Advantages of cooperative agreement: –Diverse range of professional and academic expertise –Improved access to software and analytic tools –Increased outreach opportunities for tribal agencies to present operational products development in research- dominated forums

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