Forestry and Urban Vegetation Mapping Floris Groesz eCognition User Summit München 2009
Blom offices in Europe Countries with Blom Pictometry imagery Blom offices (22+) 1200+ employees
Rotor wing laserscanning - Topeye
Bathymetric laserscanning - Hawkeye
Fixed wing laserscanning
Aerial Photography
Pictometry – Oblique Photography
3D Models - London
3D Models – Stavanger Norway
And many other activities <ul><li>Traditional mapping (manual) </li></ul><ul><li>Magnetic survey </li></ul><ul><li>Hypersp...
Forestry in Blom
Forestry in Blom <ul><li>Finland, Norway, Sweden, </li></ul><ul><li>Spain, Canada </li></ul><ul><li>Focus on production fo...
Forestry – Area based methods <ul><li>Area based methods </li></ul><ul><ul><li>kNN method </li></ul></ul><ul><ul><li>Regre...
Relation between sample plots and LiDAR
Relation between sample plots and LiDAR h max 2m d 0 d 1 d 2 d 3 h 50 h 10 h 90
Example of Results
Forestry – Single tree methods <ul><li>Single tree method </li></ul><ul><li>LiDAR point density  ≈ 5 to 10 points / m 2 </...
Use of eCognition in Blom <ul><li>Innovative mapping projects </li></ul><ul><li>Support for forestry </li></ul><ul><ul><li...
Automatic stand delineation <ul><li>Goal </li></ul><ul><ul><li>Automatic delineation forest stands using LiDAR data </li><...
LiDAR point cloud - Profile
Transformation – heights   above ground   Height
nDSM and basemap <ul><li>Example of a nDSM raster and a basemap (cadastral map, road buffers, water) </li></ul>
Automatic stand delineation Segmentation based on: nDSM and base map (vector) 3 levels Level 1 -  base map
Automatic stand delineation Level 2 – building blocks for forest stands
Automatic stand delineation Identification of clear-cuts
Automatic stand delineation Keeping only the clear-cuts which are large enough Small clear-cuts are ”put back into the for...
Automatic stand delineation Level 3 is used for height calculations
Automatic stand delineation Merging of the forest stands. Image object fusion with several conditions Cleaning up of small...
Automatic stand delineation Smoothing and more cleaning up Export of results
Automatic stand delineation <ul><li>We produce several versions so that the client can choose the best one. </li></ul><ul>...
Smoothing and snapping
Result automatic delineation
Manual made stand lines
Result automatic delineation
Manual made stand lines
Automatic stand delineation <ul><li>Mixed conclusions </li></ul><ul><ul><li>For some Norwegian customers the result requir...
Vegetation around powerlines <ul><li>Goal </li></ul><ul><ul><li>Mapping the trees and shrubs near powerlines </li></ul></u...
Vegetation around powerlines
Powerlines
Powerlines + buffer
Powerlines + buffer
nDSM
Vegetation > 2.5 meter
Vegetation > 2.5 meter
Vegetation > 2.5 meter
Vegetation around powerlines <ul><li>eCognition </li></ul><ul><ul><li>Simple rulesets </li></ul></ul><ul><ul><li>Contrast ...
Trees in urban areas <ul><li>Goal </li></ul><ul><ul><li>Delineation of individual trees from laser data </li></ul></ul><ul...
Trees in urban areas
Trees in urban areas <ul><li>eCognition </li></ul><ul><ul><li>Single tree segmentation (under constant development) </li><...
Conclusions on eCognition <ul><li>   Very versatile </li></ul><ul><li>   Powerful analysis tool </li></ul><ul><li>   Ea...
Some considerations <ul><li>eCognition has competition in Blom. </li></ul><ul><li>Blom uses many different software packag...
Thank you for your attention! Floris Groesz eCognition User Summit München 2009
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E Cognition User Summit2009 F Groesz Blom Forestry And Urban Vegetation Mapping

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Forestry and Urban Vegetation Mapping

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E Cognition User Summit2009 F Groesz Blom Forestry And Urban Vegetation Mapping

  1. 1. Forestry and Urban Vegetation Mapping Floris Groesz eCognition User Summit München 2009
  2. 2. Blom offices in Europe Countries with Blom Pictometry imagery Blom offices (22+) 1200+ employees
  3. 3. Rotor wing laserscanning - Topeye
  4. 4. Bathymetric laserscanning - Hawkeye
  5. 5. Fixed wing laserscanning
  6. 6. Aerial Photography
  7. 7. Pictometry – Oblique Photography
  8. 8. 3D Models - London
  9. 9. 3D Models – Stavanger Norway
  10. 10. And many other activities <ul><li>Traditional mapping (manual) </li></ul><ul><li>Magnetic survey </li></ul><ul><li>Hyperspectral image acquisition </li></ul><ul><li>Location based services / navigation </li></ul><ul><li>Blom Urbex </li></ul>
  11. 11. Forestry in Blom
  12. 12. Forestry in Blom <ul><li>Finland, Norway, Sweden, </li></ul><ul><li>Spain, Canada </li></ul><ul><li>Focus on production forestry </li></ul><ul><li>Different customs and </li></ul><ul><li>traditions = different </li></ul><ul><li>methods and products </li></ul><ul><li>In-house software </li></ul><ul><li>for processing </li></ul>
  13. 13. Forestry – Area based methods <ul><li>Area based methods </li></ul><ul><ul><li>kNN method </li></ul></ul><ul><ul><li>Regression functions </li></ul></ul><ul><li>LiDAR point density ≈ 0.5 points / m 2 </li></ul><ul><li>Map with forest types (strata) </li></ul><ul><li>Estimation of forest parameters </li></ul><ul><ul><li>Volume </li></ul></ul><ul><ul><li>Mean height, Dominant height </li></ul></ul><ul><ul><li>Diameter (diameter distribution – tree list) </li></ul></ul><ul><ul><li>Basal area </li></ul></ul><ul><ul><li>Number of trees </li></ul></ul><ul><li>Sample plots from the field </li></ul>
  14. 14. Relation between sample plots and LiDAR
  15. 15. Relation between sample plots and LiDAR h max 2m d 0 d 1 d 2 d 3 h 50 h 10 h 90
  16. 16. Example of Results
  17. 17. Forestry – Single tree methods <ul><li>Single tree method </li></ul><ul><li>LiDAR point density ≈ 5 to 10 points / m 2 </li></ul><ul><li>Parameters per tree </li></ul><ul><ul><li>Tree species </li></ul></ul><ul><ul><li>Height </li></ul></ul><ul><ul><li>Volume </li></ul></ul><ul><ul><li>Diameter </li></ul></ul><ul><li>Sample trees from the field </li></ul>
  18. 18. Use of eCognition in Blom <ul><li>Innovative mapping projects </li></ul><ul><li>Support for forestry </li></ul><ul><ul><li>Automatic stand delineation </li></ul></ul><ul><li>New markets </li></ul><ul><ul><li>Vegetation around powerlines </li></ul></ul><ul><ul><li>Urban vegetation </li></ul></ul>
  19. 19. Automatic stand delineation <ul><li>Goal </li></ul><ul><ul><li>Automatic delineation forest stands using LiDAR data </li></ul></ul><ul><li>Data </li></ul><ul><ul><li>Laser data (> 0.5 point / m2) </li></ul></ul><ul><ul><li>Base map (e.g. cadastral maps) </li></ul></ul><ul><ul><li>Existing (old) forest maps </li></ul></ul><ul><li>Results </li></ul><ul><ul><li>Forest stands, based on height and density </li></ul></ul><ul><ul><li>No tree species information </li></ul></ul>
  20. 20. LiDAR point cloud - Profile
  21. 21. Transformation – heights above ground Height
  22. 22. nDSM and basemap <ul><li>Example of a nDSM raster and a basemap (cadastral map, road buffers, water) </li></ul>
  23. 23. Automatic stand delineation Segmentation based on: nDSM and base map (vector) 3 levels Level 1 - base map
  24. 24. Automatic stand delineation Level 2 – building blocks for forest stands
  25. 25. Automatic stand delineation Identification of clear-cuts
  26. 26. Automatic stand delineation Keeping only the clear-cuts which are large enough Small clear-cuts are ”put back into the forest”
  27. 27. Automatic stand delineation Level 3 is used for height calculations
  28. 28. Automatic stand delineation Merging of the forest stands. Image object fusion with several conditions Cleaning up of small areas
  29. 29. Automatic stand delineation Smoothing and more cleaning up Export of results
  30. 30. Automatic stand delineation <ul><li>We produce several versions so that the client can choose the best one. </li></ul><ul><li>Workspace processing for large areas </li></ul><ul><ul><li>Making regions based on cadastral boundaries </li></ul></ul><ul><ul><li>Exporting the results seperately to shapefiles </li></ul></ul><ul><li>GIS post processing </li></ul><ul><ul><li>Convert from polygons to polylines </li></ul></ul><ul><ul><li>More GIS smoothing </li></ul></ul><ul><ul><li>Snapping to basemap and removing overlap with basemap </li></ul></ul>
  31. 31. Smoothing and snapping
  32. 32. Result automatic delineation
  33. 33. Manual made stand lines
  34. 34. Result automatic delineation
  35. 35. Manual made stand lines
  36. 36. Automatic stand delineation <ul><li>Mixed conclusions </li></ul><ul><ul><li>For some Norwegian customers the result required too much manual editing to get it 100% correct. </li></ul></ul><ul><ul><li>For a Swedish customer the (same!) result was good enough </li></ul></ul><ul><ul><li>In Finland more simple “micro stands” are used together with larger “administrative stands”. </li></ul></ul><ul><li>Conclusions on eCognition </li></ul><ul><ul><li>Rulesets and parameters are really easy to adapt to new projects and wishes. Easy to make several versions per project so the customer can choose. </li></ul></ul><ul><ul><li>Batch processing runs well. </li></ul></ul><ul><ul><li>Texture based segmentation algorithms would be nice to have. </li></ul></ul>
  37. 37. Vegetation around powerlines <ul><li>Goal </li></ul><ul><ul><li>Mapping the trees and shrubs near powerlines </li></ul></ul><ul><li>Data </li></ul><ul><ul><li>Laser data for vegetation heights (> 1 point / m 2 ) </li></ul></ul><ul><ul><li>Vector map with powerlines </li></ul></ul><ul><ul><li>Buffer around powerlines </li></ul></ul><ul><ul><li>Vector map with buildings </li></ul></ul><ul><li>Results </li></ul><ul><ul><li>Vegetation in the height classes (e.g. ≥ 2.5 m) </li></ul></ul>
  38. 38. Vegetation around powerlines
  39. 39. Powerlines
  40. 40. Powerlines + buffer
  41. 41. Powerlines + buffer
  42. 42. nDSM
  43. 43. Vegetation > 2.5 meter
  44. 44. Vegetation > 2.5 meter
  45. 45. Vegetation > 2.5 meter
  46. 46. Vegetation around powerlines <ul><li>eCognition </li></ul><ul><ul><li>Simple rulesets </li></ul></ul><ul><ul><li>Contrast split segmentation </li></ul></ul><ul><ul><li>Removal of hits on wires and other non-vegetation objects </li></ul></ul><ul><li>Low voltage powerlines (isolated ) </li></ul><ul><ul><li>Several projects on low voltage powerlines </li></ul></ul><ul><ul><li>500 to 1000 km powerline per municipality </li></ul></ul><ul><ul><li>Projects are based on existing LiDAR data </li></ul></ul><ul><li>Pilot on high voltage powerlines </li></ul><ul><ul><li>Mapping of </li></ul></ul><ul><ul><ul><li>Distance of the wire to the ground </li></ul></ul></ul><ul><ul><ul><li>Distance of the vegetation to the wire </li></ul></ul></ul><ul><ul><ul><li>Vegetation height maps </li></ul></ul></ul><ul><ul><li>Preprocessing in Terrasolid software / some manual work </li></ul></ul>
  47. 47. Trees in urban areas <ul><li>Goal </li></ul><ul><ul><li>Delineation of individual trees from laser data </li></ul></ul><ul><li>Data </li></ul><ul><ul><li>Laser data for tree crown delineation (> 2 point / m2) </li></ul></ul><ul><ul><li>Images for tree species classification </li></ul></ul><ul><li>Results </li></ul><ul><ul><li>Information per tree </li></ul></ul><ul><ul><ul><li>Position (x,y) </li></ul></ul></ul><ul><ul><ul><li>Height </li></ul></ul></ul><ul><ul><ul><li>Crown diameter </li></ul></ul></ul><ul><ul><ul><li>Tree species (from images) </li></ul></ul></ul>
  48. 48. Trees in urban areas
  49. 49. Trees in urban areas <ul><li>eCognition </li></ul><ul><ul><li>Single tree segmentation (under constant development) </li></ul></ul><ul><ul><li>Using existing maps as much as possible for masking </li></ul></ul><ul><ul><li>Tree species is a challenge </li></ul></ul><ul><li>Trees in urban areas </li></ul><ul><ul><li>Projects are based on existing LiDAR data and on existing images (not always the best) </li></ul></ul><ul><ul><li>2 pilot projects at the moment </li></ul></ul><ul><ul><li>Results can be used for </li></ul></ul><ul><ul><ul><li>3D visualization </li></ul></ul></ul><ul><ul><ul><li>Urban planning and management </li></ul></ul></ul>
  50. 50. Conclusions on eCognition <ul><li> Very versatile </li></ul><ul><li> Powerful analysis tool </li></ul><ul><li> Easy to adapt to new tasks </li></ul><ul><li> Easy to use (almost dangerously easy…) </li></ul><ul><li> Possible to analyze large datasets </li></ul><ul><li> Doesn’t work directly on the LiDAR point cloud </li></ul><ul><li> Batch license is not so cheap (and doesn’t use dual core) </li></ul><ul><li> Only 1 classification algorithm (Nearest neighbor) </li></ul><ul><li>eCognition is a valuable tool for Blom, </li></ul><ul><li>now and in the future! </li></ul>
  51. 51. Some considerations <ul><li>eCognition has competition in Blom. </li></ul><ul><li>Blom uses many different software packages, both commercial and ”self-made”. </li></ul><ul><li>Blom’s engineers like to write programs! (And besides them we also have our professional software developers). </li></ul><ul><li>Most programs area really good at their tasks (and fast), but not so easy to change (and sometimes hard to understand). </li></ul>
  52. 52. Thank you for your attention! Floris Groesz eCognition User Summit München 2009

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