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# Reeves: Modelling & Estimating Forest Structure Attributes Using LiDAR

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### Reeves: Modelling & Estimating Forest Structure Attributes Using LiDAR

1. 1. 1 By: Brittany Reeves Major Research Project for Remote Sensing
2. 2. 1. Data Processing 2. Extracting LiDAR data 3. Modelling in R 4. Applying the Models 5. Generating the Results 6. Assessing the Quality of the Model 7. Limitations 8. Conclusions 2
3. 3.  Mathematically model and estimate various Permanent Sample Plot (PSP) attributes (Average Height, Basal Area, Biomass Bole, and Biomass Whole) using various LiDAR Metrics (Height Percentiles, Height Ratios, etc.).  Apply models to a larger LiDAR dataset within testing area.  Explore quantitative results 3
4. 4. 2-DIMENSIONAL VIEWER 3-DIMENSIONAL LDV 4 COMMAND-LINE PROCESSING
5. 5. 5
6. 6.  Data was acquired by the Applied Geomatics Research Group (AGRG) using an Optech ALTM 3100.  The Keji dataset was acquired on August 18th, 2010 over Kejimkujik National Park.  The Mersey dataset was acquired on August 4th and August 12th of 2009 over a series of watersheds North of Kejimkujik. 6
7. 7.  Average Height: top of tree to stump; metres (m)  Basal Area: sum of cross-sectional areas of all trees within a plot (density) [16]; centimeters2 (cm2)  Biomass (whole): trunk + canopy; kilograms (kg)  Biomass (bole): trunk of tree; kilograms (kg) 7 Source [cite7]Figure: measuring tree height with a hypsometer
8. 8.  Training: Keji; measured 2006 to 2009  Testing: Mersey; measured 2005 to 2008  All trees within a 20 metre diameter circular region.  Height was derived from the manual measurement using a hypsometer.  Attributes such as Basal Area and Biomass were derived using equations described by Lambert (2005). [5]  PSP’s are randomly distributed to represent a wide range of forests within the study area.  Low grade GPS Easting/Northing for each plots centre; not post-processed; accuracy of ~10 metres. [8] 8
9. 9. 9
10. 10.  Performed by AGRG.  The LiDAR points were processed with TerraMatch and TerraScan (remove boresight errors, classify into Ground and Non-ground, etc.).  LiDAR point data has been converted into relative heights (height above ground). 10
11. 11. HEIGHT ABOVE DATUM HEIGHT ABOVE GROUND 11
12. 12.  FUSION: ClipData – “creates sub-samples of LIDAR data… The sub-sample can be round or rectangular…often used to create sample of LIDAR returns around a specific point of interest such as a GPS measurement point”. [15]  Creates a “cookie cutter” to extract Mersey LiDAR points over the known PSP.  Output = .las file; view in ArcMap or FUSION 12
13. 13.  FUSION: CloudMetrics – “computes a variety of statistical parameters describing a LIDAR data set.” [15]  Considers LAS files (those corresponding with PSP’s)  Output = .csv file containing all returns of all LiDAR Metrics for the PSP’s 13
14. 14. Y = C0 + C1X1 + C2X2 + … CnXn Where: Y = Forest Attribute C0 = Constant/Intercept C1, C2, Cn = Coefficients X1, X2, Xn= LiDAR Metrics Forest Attribute = Intercept + (Coeff. 1 * LiDAR Metrics #1) + (Coeff. 2 * LiDAR Metric #2) 14
15. 15.  LiDAR Metrics with lower inter-correlation among other variables preform better.  Ex: Biomass Bole LiDAR Metrics = 48%  LiDAR Metrics with high (positive or negative) correlation with attribute are more favorable.  Ex: Average Height LiDAR Metrics = 86% 15
16. 16.  F-Statistic (p-value): All models pass this criteria (0.05)  T-test (p-value): most studies often use a 0.05 significance level or 95% Confidence as determining a reliable model.  Lowest significance levels in the 4 models created: of 0.079 [Avg_Height - Percentage.first.returns.above.1.50] and 0.066 [Basal_Area - Elev.maximum].  Multiple R-Squared, Adjusted R-Squared, and Residuals also of great importance. For Example:  By removing these LiDAR Metrics from the equation, the T-test (p-value) would then become significant.  However for Avg_Height the Multiple R-Squared value increased [0.75 to 0.79], the Adjusted R-Squared value increased [0.74 to 0.77] with the addition of the second LiDAR Metric, as well as the Residual Error decreased [1.18m to 1.07m]. 16
17. 17. - Values derived using the “lm(formula)” and “summary” function in R; print of comprehensive summary of the results of the linear regression analysis. Avg_Height = 8.20 + (0.67 * Elev.P70) + (-0.04 * Percentage.first.returns.above.1.50) Basal_Area = -6047.70 + (373.20 * Elev.maximum) + (124.10 * All.returns.above.mean…Total.first.returns…100) Biomass_Bole = -4043.10 + (795.15 * Elev.stddev) + (60.29 * Percentage.all.returns.above.mean) Biomass_Whole = -2763.92 + (819.21 * Elev.stddev) + (36.97 * All.returns.above.mean…Total.first.returns…100) 17
18. 18. 18 LiDAR Metric Description Elev.P70 70th Height Percentile Elev.stddev Standard Deviation of Height Elev.maximum Maximum Height Percentile Percentage.all.returns.above.mean Percentage of all returns above Mean Height Percentage.all.returns.above.1.50 Percentage of all returns above a Height of 1.5m All.returns.above.mean…Total.first.returns…100 All returns above Mean Height, divided by the total number of 1st returns, times 100
19. 19. 19 Within FUSION:  Generate batch files that pull out the specific LiDAR Metric info. (Elev.P70 [column 33] for Avg_Height) from a MS Excel file containing LiDAR info.  Apply to a particular LiDAR block (MRS218).  Result = 2 ASCII files (one for Elev.P70, one for Percentage.first.returns.above.1.50). Within ArcMap:  Convert ASCII to .img (Export Data as…).  Apply the Linear Regression equation in Raster Calculator.
20. 20. Plot in ArcMap: Avg_Height derived from model PSP (known value) versus model (estimated value) 20
21. 21. Identify – PSP (known values) compared to single cell (20x20m grid cell) in the LiDAR Block (estimated values) to examine how closely they resemble one another. 21
22. 22. 22 Forest Structure Attribute LiDAR Block Forest Type Known Value Estimated Value Error Diff. Average Height 455 Hardwood 12.0806 m 12.92 m 6.5% 010 Softwood 8.5107 m 10.14 m 16.1% 218 Mixedwood 11.456 m 12.105 m 5.4% Basal Area 455 Hardwood 10861.38291 cm2 14509.46 cm2 25.2% 010 Softwood 9154.08046 cm2 8732.09 cm2 4.7% 218 Mixedwood 11594.67 cm2 13676.758 cm2 15.3% Biomass Bole 455 Hardwood 3851.94 kg 5164.39 kg 25.5% 010 Softwood 1887.6582 kg 3368.93 kg 44.0% 218 Mixedwood 3411.9453 kg 4720.95 kg 27.8% Biomass Whole 455 Hardwood 5127.1459 kg 4441.455 kg 13.4% 010 Softwood 2878.8341 kg 2991.6125 kg 3.8% 218 Mixedwood 4792.3178 kg 4350.37 kg 9.3%
23. 23. Error based on LiDAR: - Geometric representation of trees within the LiDAR waveform used [18] - Using a small footprint (low density) – laser beam may miss tree tops; Using a large footprint (high density) – may “count” more than one tree top per tree. [19] 23
24. 24. A. 1 and 3 are representative of “good” or accurate heights, while the top of the tree has been missed in 2. B. Counting these two hits as two separate trees [6] 24 Figure: Describing how LiDAR density effects correct measurements
25. 25. Error based on Field Measurements: - More often for deciduous species because of closed canopy/homogenous environment – difficult to separate crowns with complete certainty. [20] - The taller the forest, the more it tends to be overestimated in field measurements; greater opportunity for a small difference in angle to have a large difference in scale. [21] 25
26. 26. 26  RMSE: a measure of the magnitude of error with no indication of direction (positive or negative). [21]  Residual Standard Error: summary of the models residuals but accounts for DOF – unbiased result.  Error Margin: averaging all estimated values for an attribute (HW + SW + MW) and dividing the standard error by that value.  Multiple and Adjusted R-squared: represent the overall “fitness” of the model; Adjusted R-squared considers DOF.
27. 27. 27 Residual Standard Error Error Margin (%) RMSE Multiple R-squared Adjusted R-squared Avg_Height 1.07 10.02 1.078742 0.7959 0.7719 Basal_Area 2408 22.85 2220.007 0.7284 0.6964 Biomass_Bole 859.8 28.19 792.6957 0.7665 0.7391 Biomass_Whole 1158 27.14 1067.817 0.7293 0.6975
28. 28.  The GPS accuracy for PSP’s.  Precision and accuracy would be improved so long as ALS data and PSP data are temporal similar (1 to 4 years difference). - Possible storm damage that knocked down trees in that period  Biomass and Basal Area estimates – limited accuracy because they are interpreted values [dependent upon their confidence level].  PSP data for Forest type: if subdivided into as many unique tree species as possible would increase accuracy.  Scan Angle and Scan Pattern affecting what is being sensed; shadows can block out other smaller trees. 28
29. 29.  Models are a fair representation for demonstration purposes.  Quantitative results in models accuracy could be increased given fewer limitations.  Given the results, the presentation is more geared towards showing how models are created and what to look for (statistically speaking). 29
30. 30. Lambert, M.C., Ung, C.H., Raulier, F. 2005. Canadian National Tree Aboveground Biomass Equations. Can. J. For. Res. (35), pp. 1996-2018 [5] Zimble, D.A., et al., 2003. Characterizing Vertical Forest Structure using Small-Footprint Airborne LiDAR. Remote Sensing of Environment 87, pp. 171-82. [6] USDA Forest Service. Remote Sensing Applications Centre. FUSION Tutorial. Retrieved on May 5th, 2014 from http://www.fs.fed.us/eng/rsac/fusions/ [7] Hopkinson et al., 2011. A Biomass Estimation Procedure Utilizing LiDAR Derived Forest Metrics and Forest Resource Inventory Data [8] Applied Geomatics Research Group: LiDAR Inventory – LiMeRiC. Google Earth [9] Wikipedia: R (Programming Language). Retrieved on May 9th, 2014 from http://en.wikipedia.org/wiki/R_(programming_language) [13] Milne, T, and Monette, S., 2014. Forestry Applications of LiDAR. LiDAR Operations & Applications, Centre of Geographic Sciences, NSCC [14] FUSION Manual [15] Koetz, B. et al, 2006. Inversion of a Lidar Waveform Model for Forest Biophysical Parameter Estimation. IEEE Geoscience and Remote Sensing Letters, (3) 1 [18] Weinacker, B.H., Koch, B., and Weinacker, R. Developement of Filtering, Segmentation and Modelling Modules for LiDAR and Multispectral Data as a Fundament of an Automatic Forest Inventory System. Institute for Remote Sensing and Landscape Information Systems, Germany [19] Hajnsek, I., et al., 2009. Tropical-Forest-Parameter Estimation by Means of Pol-InSAR: The INDREX-II Campaign. IEEE Transsaction on Geoscience and Remote Sesning (47) 2 [20] O’Beirne, D., 2012. Measuring the Urban Forest: Comparing LiDAR Derived Tree Heights to Field Measurements. San Francisco State University. [21] 30
31. 31. 31
32. 32.  LiDAR Data: Applied Geomatics Research Group (AGRG)  PSP Data: Nova Scotia Dept. of Natural Resources  GeoNova: Nova Scotia Shapefile (NAD83 CRSR UTM 20) 32
33. 33. KEJI DATASET MERSEY DATASET  Resolution: 1.43m  Pulse Repetition Frequency (PRF): 50 kHz (50,000 Hz) cycles per second  Point Spacing: 0.9 pts/m2  Flight Overlap: 20%  Flying Height: 1800m  Scan angle: 23 degrees  Resolution: 1.34m  PRF: 50 kHz (50,000 Hz)  Point Spacing: 1.4 pts/m2  Flight Overlap: 50%  Flying Height: 2000m  Scan Angle: 20 degrees Sources: [8], [9] 33
34. 34. 34
35. 35. Average Height: Elev.P70 + Percentage.all.returns.above.1.5 Basal Area: Elev.maximum + All.returns.above.mean…Total.first.returns…100 Biomass (Whole): Elev.stddev + All.returns.above.mean…Total.first.returns…100 Biomass(Bole): Elev.stddev + Percentage.all.returns.above.mean 35
36. 36. The flatter (horizontally) the line is, and the closer the line is to 0 represents less residual error and less deviation from that residual error. Analyst must put residual error in context of Error Margin (considers units) for a particular attribute. For example (Avg_Height): the Standard Residual Error is 1.07m; however for the scale of the attribute this is only a 10.95% Error Margin. 36
37. 37. 37
38. 38. Luther, J.E., et al., 2013. Predicting Wood Quantity and Quality Attributes of Balsam Fire and Black Spruce using Airborne Laser Scanner Data. Forestry 2013: An International Journal of Forest Research0, pp. 1-14 [1] Woods, M. et al., 2011. Operational Implementation of a LiDAR Inventory in Boreal Ontario. The Forestry Chronicle: July/August 2011, (87) 4, pp. 512-28 [2] Thomas, V., et al., 2006. Mapping Stand-Level Forest Biophysical Variables For a Mixedwood Boreal Forest Using LiDAR: an Examination of Scanning Density. NRC Canada, Can. J. For. Res. (36), pp. 34-47 [3] Means, J.E., 2000. Predicting Forest Stand Characteristics with Airborne Scanning Lidar. Photogrammetric Engineering & Remote Sensing (66) 11, pp. 1367-71 [4] Lambert, M.C., Ung, C.H., Raulier, F. 2005. Canadian National Tree Aboveground Biomass Equations. Can. J. For. Res. (35), pp. 1996-2018 [5] Zimble, D.A., et al., 2003. Characterizing Vertical Forest Structure using Small-Footprint Airborne LiDAR. Remote Sensing of Environment 87, pp. 171-82. [6] USDA Forest Service. Remote Sensing Applications Centre. FUSION Tutorial. Retrieved on May 5th, 2014 from http://www.fs.fed.us/eng/rsac/fusions/ [7] Hopkinson et al., 2011. A Biomass Estimation Procedure Utilizing LiDAR Derived Forest Metrics and Forest Resource Inventory Data [8] Applied Geomatics Research Group: LiDAR Inventory – LiMeRiC. Google Earth [9] McGaughey, R.J. 2010. Fusion/LDV: Software for LiDAR Data Analysis and Visualization. USDA Forest Service. FUSION Manual. March 2010 – FUSION Version 2.80. [10] West, P.W., 2009. Tree and Forest Measurement. - 2nd Edition. School of Environmental Science and Management. Southern Cross University Lismore, Australia [11] The R Project for Statistical Computing. Institute for Statistics and Mathematics of WU (Wirtschaftsuniversität Wien). Retrieved on May 8th, 2014 from: http://www.r-project.org/ [12] Wikipedia: R (Programming Language). Retrieved on May 9th, 2014 from http://en.wikipedia.org/wiki/R_(programming_language) [13] Milne, T, and Monette, S., 2014. Forestry Applications of LiDAR. LiDAR Operations & Applications, Centre of Geographic Sciences, NSCC [14] FUSION Manual [15] University of Minnesota, 2011. Woodland Stewardship. WordPress. Retrieved on May 2nd, 2014 from http://woodlandstewardship.org/?page_id=1118 [16] Schuckman, K., and Renslow, M., 2014. LiDAR Technology and Applications: LiDAR Applications – Forestry. The Pennsylvania State University. Retrieved on May 2nd, 2014 from https://www.e-education.psu.edu/lidar/book/export/html/1808 [17] Koetz, B. et al, 2006. Inversion of a Lidar Waveform Model for Forest Biophysical Parameter Estimation. IEEE Geoscience and Remote Sensing Letters, (3) 1 [18] Weinacker, B.H., Koch, B., and Weinacker, R. Developement of Filtering, Segmentation and Modelling Modules for LiDAR and Multispectral Data as a Fundament of an Automatic Forest Inventory System. Institute for Remote Sensing and Landscape Information Systems, Germany [19] Hajnsek, I., et al., 2009. Tropical-Forest-Parameter Estimation by Means of Pol-InSAR: The INDREX-II Campaign. IEEE Transsaction on Geoscience and Remote Sesning (47) 2 [20] O’Beirne, D., 2012. Measuring the Urban Forest: Comparing LiDAR Derived Tree Heights to Field Measurements. San Francisco State University. [21] 38