Automated Feature Extraction from Aerial Imagery for Forestry Projects

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1 Automated Feature Extraction from Aerial Imagery for Forestry Projects Esri UC 2015 UC706 Tuesday July 21 Bart Matthews - Photogrammetrist US Forest Service Southwestern Region Brad Weigle Sr. Program Manager Quantum Spatial

2 Imagery of the Planet Major Advances over 85 years USDA B&W - Xm 1930s-1960s Digital 4-band 5cm > LANDSAT - 8 band 30m 1970s -> NIR Film 1m 1980s 2000s

3 LARGE FORMAT DIGITAL AERIAL CAMERAS ULTRACAM EAGLE ADS 100 DMC ii

4 ABGPS & IMU

5 Roadmap to Automated Vegetation Mapping

6 Mapping Technologies for Terrain Data you need a good DEM Survey Photogrammetry Interferometric Synthetic Aperture Radar (IFSAR) Topographic LiDAR

7 Imagery for Topography The forms of the features of the actual surface of the earth which is typically referred to as the Bare Earth. There are three primary terms for topography: Digital Elevation Model (DEM) Digital Terrain Model (DTM) Triangulated Irregular Network (TIN)

8 Topography Examples DTM DEM TIN

9 Digital Surface Model (DSM) Similar to DEMs or DTMs, except that they may depict the elevations of the top surfaces of buildings, trees, towers, and other features elevated above the bare earth. *Maune, D. F., (2001). Digital Elevation Model Technologies and Applications: The DEM Users Manual. Bethesda, Maryland. The American Society for Photogrammetry and Remote Sensing

10 DIGITAL SURFACE MODEL DIGITAL ELEVATION MODEL

11 R3 Forests with Orthophotography

12

13 ERDAS IMAGINE BLOCK FILES Aerial Triangulation Solution for Stereo Analysis & Orthophoto Production

14 Automated Imagery Analysis Creation of high resolution 3D point clouds from stereo images points tagged with spectral info and height above sea level

15 We have developed analytical models for vegetation type, height, & canopy closure even in areas with poor DEMs Maximum point densities from pixels: 30cm = 9 pts/m 2 10cm = 100 pts/m 2 5cm = 400 pts/m 2

16 AUTOMATED FILTERING ESRI MODEL BUILDER placeholder slide

17 AUTOMATED FILTERING NDVI

18 AUTOMATED FEATURE EXTRACTION PRODUCTS 1. Improved Digital Elevation Model (DEM) 2. Improved Slope & Aspect 3. Improved Orthophotography Accuracy 4. Planimetric & Topographic Maps 5. Improved Flood Plain Maps 6. Improved Riparian/Wetland Maps 7. Engineering Preliminary Planning and Design 8. Mining 9. Forestry 10.National Hydrology Dataset 11.Asset Inventory and.

19 R3 Riparian Inventory Pilot Build on Regional Riparian Mapping Project Report and Pilot Project Protocols - analogous to FIA 1ha riparian plots in Cibola and Prescott NFs Interpret vegetation, bank and stream characteristics using 5-8cm stereo aerial photos in Stereo Analyst & Modelbuilder Automate measurements using derivatives from image point clouds using Modelbuilder

20 The Classification Process for Vegetation Dominance Types

21 Tree Size Classes using FIA data Ponderosa Pine 40 Douglas Fir R² = R² = Lodgepole Pine Douglas Fir - Ponderosa Pine R² = R² =

22 FIA plot TS6 Point cloud TS6

23 FIA plot TS6 Point cloud TS3 Questionable FIA Tree Size Summary

24 New Image Analyses Trees & shrubs identified from local maxima of canopy surface model Initial stem locations act as seed points for crown discrimination Point based classification of vegetation types

25 Vegetation Mapping in OR

26 LiDAR / Image Cloud Comparison

27 LiDAR / Image Cloud Comparison

28 LiDAR / Image Cloud Comparison

29 LiDAR / Image Cloud Comparison

30 Imagery for Forest Inventory Utilization of imagery derivatives for quantitative inventories of vegetation Classification Delineation Correlation Extrapolation

31 Conclusions Point clouds derived from stereo pairs are acceptable alternatives to LiDAR for tree types, heights and canopy closure LiDAR is preferred for an accurate DEM & detailed forest structure Historic aerial imagery can provide valuable data for change detection and vegetation growth patterns a virtual time-machine Scanning of historic imagery to digital format is essential before film degrades to ensure timeseries analysis

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