Semi-Automated Natural Resource Inventory Production through Fusing New Technologies Evolving from Research to Operations

Transcription

1 Semi-Automated Natural Resource Inventory Production through Fusing New Technologies Evolving from Research to Operations Murray Woods Southern Science and Information

2 The Role of Integrated LiDAR and Multiband Orthophotography in the production of Enhanced Forest Inventories in the Great Lakes St. Lawrence Forest Murray Woods, Dave Nesbitt Southern Science & Information Paul Courville, Al Stinson, Sue Pickering Forest Research Partnership Enhanced Forest Productivity Fund

3 Project Focus Explore the potential of: Using high-resolution multiband digital imagery for automated inventory production Stand Level Tree Level Using LiDAR for measurement of and prediction of inventory metrics Stand Level Tree Level Fusing these two technologies to maximize inventory resolution

4 Objectives project overview present interim results stimulate your concepts of possibilities & timing in the adoption of semi-automated efri methods encourage discussion Source:

5 Semi-automated - Individual Species Classification R E S E A R C H Working with Drs Gougeon and Leckie at CFS- PFC application evolution Working with Silvatech Consulting Ltd. operational application O P E R A T I O N S

6 Semi-automated - Individual Species Classification

7 Semi-automated - Individual Species Classification Training Methods Source: Murray Radford Soft copy training Ground training

8 Semi-automated - Individual Species Classification ADS40-SH52 SH52 FRI imagery supports 31m an enhanced 42% Crown Closure automated inventory product 49 Pw 21 Sw 9 Bf 8 Sb 7 Bd 3 Pr 3 Ce 238 Stems Colour Infrared Imagery

9 Semi-automated - Individual Species Classification State of this Technology shows large potential to provide another tool for the photo interpreter quick to process large areas enhanced software from single satellite frame to analyze multiple tiles BRDF correction application for mosaiced flight lines and between tiles initial testing shows promising results with larger validation testing currently underway Field Plots Silviculture Records Experience LiDAR Semi-Automated Species Id Soft Copy Image Interpretation

10 Imagery Lessons Learned Image Needs for Soft Copy Interpretation and Computer Image Analysis Soft Copy Interpretation Color-balanced stereo pairs (with a good base) Color-balanced orthoimage mosaic as reference Pan-sharpened (RGB,CIR, RGBI) are preferred 8 bit images are sufficient and preferred Images are preferred compressed, with a pyramid of resolutions for faster loading and fast scale changes Sizeable ground area covered (for context) with large image overlap for stereo viewing Computer Image Analysis Separate sizeable Ortho images or long georeferenced strips (No artificial stretches, color balancing, ) Can benefit from separate panchromatic and prefers as many bands as possible Images reflecting the capabilities of the sensor (8, 12, 14, or 16 bits of radiometry, in 8 or 16 bit image files) Only lossless compression tolerated Some additional pyramids for visualization as a convenience Long strips will improve BRDF corrections and make ITC analysis more efficient

11 Role of ITC in Silvicultural Effectiveness Monitoring (SEM) Using tree-top top approach or local maxima to identify trees Used with permission of Francois Gougeon CFS MEIS image, Nov. 1982, 30 cm/pixel

12 Active remote sensing technology Involves transmitting and receiving ~200,000 pulses of laser light per second Pulses strike the surface of the earth and with each pulse get a measurement of the time and angle of each return If a laser pulse hits an object through which it can penetrate, it will produce range and intensity measurements for each surface it hits What is LiDAR? Light Detection And Ranging

13 Digital Surface Model (DSM) Digital Terrain Model (DTM) Canopy Height Model (CHM) From St-Onge, B., Treitz, P., Wulder, M., Kurtz, W., Gillis, M Retrospective mapping of structural and biomass changes in forest ecosystems using photogrammetryand laser altimetry, Amercian Geophysical Union/Canadian Geophysical Union Joint Assembly, Montreal, May

14 LiDAR provides additional information beyond a DSM and a DTM DSM DTM 3D representation of trees and stands permitting measurements and modeling

15 LiDAR provides Improved Digital Terrain Models Northern Ontario 20m DTM

16 LiDAR provides Improved Digital Terrain Models LiDAR derived 5m DTM

17 LiDAR s Contribution to Precision Forest Inventory Detailed Surface Models Digital Surface Models (DSM) Digital Terrain Models (DTM) DSM DTM

18 LiDAR s Contribution to Precision Forest Inventory Detailed Surface Models Digital Surface Models (DSM) Digital Terrain Models (DTM) Canopy Height Models (CHM)

19 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 Value Added Eskers Dunes Used with permission from Al Thorne, Tembec.

20 LiDAR s Contribution to Precision Forest Inventory Detailed Surface Models Digital Surface Models Digital Terrain Models Canopy Height Models Predictive Hydrology Models Value Added Detailed Digital Terrain Model Supporting Identifying surficial Geology Hydrological modelling Predictive Streams Predicted Drainage requiring Culvert installation 86% reliable predictions either right on or within 15 meters of predicted streams. 95% reliable predictions either right on or within 35 meters of predicted streams. OBM Mapped Stream Locations are completely unreliable for block layout and water crossings Mark Joran, Millson Forestry Service

21 LiDAR s Contribution to Precision Forest Inventory Detailed Surface Models Digital Surface Models Digital Terrain Models Canopy Height Models Value Added Detailed Digital Terrain Model Supporting Identifying surficial Geology Hydrological modelling Wetland classification Improved Predictive Wetland Classification in N. Ontario when compared to current 20m DEM LiDAR had problems with obtaining bare earth returns in alder thickets and cedar wetlands Adam Hogg - IMA

22 LiDAR s Contribution to Precision Forest Inventory Detailed Surface Models Digital Surface Models Digital Terrain Models Canopy Height Models Value Added Detailed Digital Terrain Model Supporting Identifying surficial Geology Hydrological modelling Wetland identification Predictive ELC Predicted Ecosite Mapping Providing additional information: Soil depth, texture, moisture regime, nutrients Landform (cliffs, ridges, eskers, hills, fluvial benches, etc ) Geomorphology descriptors (sand, gravel, rock, fluvial, alluvial fans, lacustrine, glaciofluvial, etc ). Output from LandMapR Used with permission from Tembec

23 LiDAR s Contribution to Precision Forest Inventory Detailed Surface Models Digital Surface Models Digital Terrain Models Canopy Height Models Virtual Road layout using least cost pathway analysis techniques Destination Value Added Detailed Digital Terrain Model Supporting Identifying surficial Geology Hydrological modelling Wetland identification Predictive Ecosystem mapping Operational considerations road construction skid trail layout water crossings Pre-planning skid trail layout Origin

24 Value Added 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 Calculated Stand Top Height (m) Lidar Maximum Vegetation Return vs. Calculated Top Height Lidar Max Vegetation Return (m) Mean m Min m Max m Std m 1m Canopy Height Model Canopy Closure Comparison Plot 0

25 Value Added 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 Statistically based predictions of stand attributes Volume Basal Area DBHq Density Size Class estimates

26 LiDAR s Contribution to Precision Forestry

27 LiDAR s Contribution to Precision Forestry Example of Natural Hardwood Models* Variable R 2 R 2 (Adj.) p RMSE SUMBA < SUMGTV < DENSITY < QMDBH < AVGHT < TOPHT < *Models built from plot data collected from three geographical locations and includes Mh,, By, Bw,, Po, Or Woods, M., Lim, K., and Treitz, P. Predicting forest stand variables from LiDAR data in the Great Lakes St. Lawrence Forest of Ontario Submitted to Forestry Chronicle

28 Lidar s Contribution to Precision Forestry

29 Future of Enhancing Resource Inventories is in Fusing Imagery with other Remote Sensing Information + RGB Imagery LiDAR surfaces or Point Clouds

30 Image based Interpretation

31 Image based Interpretation

32 LiDAR derived Canopy Height Model

33 Methodology and Workflow Normalized Vegetation Points Basal Area Surface Derive LIDAR Predictor Surfaces Canopy Height Predictors Canopy Density Predictors Gross Total Volume Surface Density Surface Other Predictors Calculate Forest Variable Surfaces Quadratic Mean Diameter Surface Stand Polygon Mask (Optional) Regression Models y =... β + β x + + Top Height Surface β n x n Average Height Surface Strata Masks (Optional) Attributes Stand Polygons

34 Methodology and Workflow Normalized Vegetation Points Basal Area Surface Derive LIDAR Predictor Surfaces Canopy Height Predictors Canopy Density Predictors Gross Total Volume Surface Density Surface Other Predictors Calculate Forest Variable Surfaces Quadratic Mean Diameter Surface Stand Polygon Mask (Optional) Regression Models Top Height Surface Average Height Surface Strata Masks (Optional) Attributes Stand Polygons

35 Operational LiDAR Enhancements 400m 2 Predictive Grid Cells

36 Operational LiDAR Enhancements Average Height (m) 400m 2 Raster

37 Operational LiDAR Enhancements Average Height (m) 400m 2 Raster

38 Operational LiDAR Enhancements Average Height (m) 400m 2 Raster %

39 Operational LiDAR Enhancements Basal Area (m 2 /ha) 400m 2 Raster

40 Operational LiDAR Enhancements GTV (m 3 /ha) 400m 2 Raster Dbhq (cm) 400m 2 Raster

41 Closing Messages Semi-automated imagery classification has matured and can be an aid to interpreters - Requires interpreter's skill to make it function - Provincial ADS40 imagery could provide the best test to date - Imagery is still a requirement for species identification LiDAR files are enormous - However, only LAS files are large (Imagery files are larger) - Software exists to process this information divide and conquer strategy - 99% of user s s only work with LiDAR derived products LiDAR data is too expensive to be considered operational - It Depends acquisition requirements area being acquired largest cost is in mobilizing the aircraft what is the cost of basing sustainability, social and industrial l decisions on poor information

42 Next Steps Complete validation efforts from soft copy, ITC, and LiDAR estimation for polygon estimates Continue to develop tools for the LiDAR and automated imagery toolbox to support adoption of these enhancements Undertake a larger operational pilot study with the methods developed Integrate these enhanced information fabrics with Ontario s planning tools. Basal area

43 Questions - Discussion LiDAR density of 0.5 hits/m 2 LiDAR density of 3 hits/m 2