1. Why LiDAR makes hyperspectral imagery more valuable for forest species mapping OLI 2018
Andrew Brenner, Scott Nowicki & Zack Raymer

2. Presentation Outline
Why hasn’t hyper-spectral met the hype?
Why has LiDAR become foundational to forestry?
Potential of data fusion
Species classification
Health classification
Summary
Questions

3. QSI Introduction
Quantum Spatial (QSI)
Largest Purely Geospatial Company in the US
Full Service Geospatial Firm

4. QSI Overview Acquire Analyze Answer Airborne LiDAR (NIR & Bathy)
Ground Mobile LiDAR
Large Format Cameras
Oblique Cameras
Thermal Infrared Imaging
Hyperspectral Imaging
HD Videography
Analyze
Ortho Imagery
Classified LiDAR Point Cloud
Full Feature Classification
Topographic /Planimetric Mapping
3D and 4D Models
Structure from Motion
Geospatial Cloud Solutions
Enterprise GIS
Answer
Emergency Response
Disaster Preparedness
Hazard Detection
Law Enforcement/Interdiction
Regulatory Compliance
Vegetation Analysis
Environmental Monitoring
Infrastructure Management

5. Hyperspectral data has been around for decades in forestry
Why Hasn’t Hyperspectral Data Met the Hype
Hyperspectral data has been around for decades in forestry
Was anticipated to be the next great technology
Rarely used operationally
Why
Expensive to collect
Vegetation spectra change seasonally
Need field training
Need perfect acquisition conditions
Otherwise …..

6. Lots of Noise Within class variation > between class variation
Low accuracy and high cost means not operational

7. Sub-optimal conditions and hyperspectral only (no Lidar)
SVM PC
Classification
SAM results over RGB image
Sub-optimal conditions and
hyperspectral only (no Lidar)

8. LiDAR is now considered vital to forest operations
LiDAR is Foundational
LiDAR is now considered vital to forest operations
Being collected extensively by both private and public sector
With increase of acquisition
More players
More competition
Better sensors
Lower cost
Better delivery
Better datasets

9. LiDAR is used in Forestry for
Operations and infrastructure
Hydrology
Tree canopy
Wildlife management
Fisheries management
Silviculture
Archeology
Hazard Assessment
Fire risk
…………

10. Automated Point Classification (APC)
Point cloud classification –Any source
Airborne/mobile/terrestrial scanning LiDAR
Single photon/Geiger mode LiDAR
Photo derived (PhoDAR)
Advanced machine learning algorithms
Feature identification and delineation: structures, infrastructure, streetscape and landscape

11. Lidar Point-Based Vegetation Delineation: Tree Top Points

12. Lidar Point-Based Vegetation Delineation: Tree Polygons

13. Lidar Point-Based Vegetation Delineation: Tree Polygons

14. Data Fusion
High density Lidar provides physical characteristics at landscape, stand and tree-level, but only coarse tree type differentiation
Many difference between genus/species types are physically observable only at the <cm scale
Reflectance spectroscopy can be quantitatively used to measure many of the characteristics used to determine species in the field
Airborne hyperspectral data can be collected at resolutions that capture those characteristics and map them at the individual crown level
Applications: Forest inventory, utility veg management, invasive species and disease detection

15. ---VSWIR---------------
465 Channels ( )
VNIR
Reflectance
---VSWIR
Wavelength (nm)
Visible wavelengths: color, chlorophyll absorption and other pigments
Short-Wave Infrared (SWIR) wavelengths: canopy water content, carbon content, drought stress
Near-Infrared (NIR) wavelengths: leaf IR-scatter, leaf structure, general health

16. VSWIR push-broom hyperspectral imaging system
Acquisition System
VSWIR push-broom hyperspectral imaging system nm
Max 465 bands
co-acquired with high-density LiDAR
± 1.5 hours of solar noon
Leaf on
Clear – low cloud .
VNIR
SWIR
Max cross-track pixels
1600
320
Max Spectral Resolution
335
130
Spectral Range nm nm

17. Hyperspectral Classification
Acquisition: Hyperspectral imagery (VNIR or VSWIR) and Lidar
Lidar analytics: Vegetation segmentation, tree polygons, DEM for image orthoprojection
Field collected ground truth training database, validation database
Hyperspectral image cube data reduction and optimization
Classification: Support Vector Machine (SVM)
Raster classification results to tree polygon
Verification and Validation

18. True-color image derived from hyperspectral cube

19. Vegetation height model

20. Principal Component Analysis

21. Tree Species Assigned to Polygons

23. 2016 Urban tree assessment in Louisville, KY.
A hyperspectral imagery based species and health assessment aided with municipal tree inventory data.
Collection date:
August, 2016
Area: 2500 acres

25. Individual tree species can be differentiated based upon spectral variation due to leaf chlorophyll content, leaf structure, flowers and canopy structure that changes throughout the growing season.

29. Verification Results
Total Accuracy
84%

30. Tree polygons allow the reduction of noise in the HSI classification
Summary
Combining hyperspectral imagery and LiDAR provide the data needed for accurate tree level classification
Tree polygons allow the reduction of noise in the HSI classification
HSI can also support health analyses at a tree level
The combination provides the level of data that will support the cost of acquisition and processing

31. Questions