Automated Feature Extraction from Aerial Imagery for Forestry Projects

Transcription

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 The Power of 4

3 Imagery of the Planet Major Advances over 85 years USDA B&W 50cm 1930s-1960s Digital 4-band 5cm s - Present LANDSAT 30m 1970s - Present RGB, NIR Film 1m 1960s 2000s

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

5 GPS & IMU

6 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)

7 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.

8 DIGITAL SURFACE MODEL DIGITAL ELEVATION MODEL DSM DEM = Height of features (ndsm or PHASE)

9 Region 3 Forests with Digital Orthophotography Recent Imagery: 4 band, 30cm pixels, 60% overlap, 30% sidelap

10 ERDAS IMAGINE Block Files - an Aerial Triangulation Solution for Orthophoto Production & Stereo Analysis - a starting point to produce point clouds

11 Point Clouds for Automated Imagery Analysis Software to Create: Image Station, ERDAS, SocetSet, Inpho, Product: High resolution 3D point clouds from stereo images points tagged with spectral info and height above sea level

12 We have developed analytical models to derive vegetation type, height, & canopy closure even in areas with poor DEMs Maximum point densities from pixels: 50cm = 4 pts/m 2 30cm = 9 pts/m 2 10cm = 100 pts/m 2 5cm = 400 pts/m 2

13 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

14 Modelbuilder: densify riparian centerlines & select sample locations Centerlines of riparian polygons densified to 1m vertex spacing Randomized starting location Systematic site selection at 25600m intervals Additional selection until all riparian types have 10+ sites

15 Modelbuilder Toolbox for Automated Calculations Manually digitize R&L banks in 3D using Stereo Analyst Models: Divide R&L banks into 50 sample sites Join R&L banks to create transects Divide transects into 5 sample points Measure 3D depth: transect point to bed Determine flood plain along streambed Calculate sinuosity & stream type

16 Other Projects: Determining Tree Size (DBH) from Point Clouds Determine Species Composition of homogenous polygons Create Point Clouds from Stereo Imagery Use FIA data to correlate Spp. Height to DBH Use Modelbuilder to: Determine Pixel Heights above Surface (PHASE = DSM DEM) Characterize distribution of PHASE for Individual Polygons Classify polygons with Tree Size and Canopy Cover

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

18 FIA plot TS6 Point cloud TS6 FIA plot TS6 Point cloud TS3

19 Vegetation Mapping in Oregon

20 LiDAR / Image Cloud Comparison

21 LiDAR / Image Cloud Comparison

22 LiDAR / Image Cloud Comparison Up to 60 cm offset & 10 cm or less near ground control

23 In Conclusion Modelbuilder works extremely well to automate geospatial processing of point cloud rasters and production of derivative data layers Point clouds derived from stereo pairs are acceptable alternatives to LiDAR for determining vegetation types, tree heights and canopy closure LiDAR is preferred, but a more costly option, for an accurate DEM & detailed forest structure Historic aerial imagery can provide valuable data for change detection and vegetation growth patterns a virtual timemachine Scanning of historic imagery to digital format is ESSENTIAL before film degrades to ensure time-series analysis.

24 Our Contact Information Bart Matthews - Photogrammetrist US Forest Service Southwestern Region bartmatthews@fs.fed.us (505) Brad Weigle Sr. Program Manager Quantum Spatial bweigle@quantumspatial.com (727)