E Cognition User Summit2009 Pbunting University Wales Forestry

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Advances in the use of eCognition for forest research and applications

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  • Species, cover, shannon, Simpson
  • 8% decrease in rainfall..
  • E Cognition User Summit2009 Pbunting University Wales Forestry

    1. 1. Advances in the use of eCognition for forest research and applications Dr. Pete Bunting
    2. 2. Contents <ul><li>Individual tree analysis </li></ul><ul><ul><li>High resolution forest mask </li></ul></ul><ul><ul><li>Delineation Approach </li></ul></ul><ul><li>Fusing with other high resolution data </li></ul><ul><li>Scaling to the landscape </li></ul><ul><li>Future work… </li></ul>
    3. 3. Individual Tree Analysis
    4. 4. Individual Tree Analysis
    5. 5. Forest Mask <ul><li>To delineate crowns the non-tree areas need to be removed. </li></ul><ul><ul><li>Otherwise, bright areas (e.g., bare soil) would be delineated as if they were crowns. </li></ul></ul><ul><li>Unfortunately, there is no single solution to the classification of forest/non-forest from high resolution imagery. </li></ul><ul><ul><li>But, there are methodologies which can help. </li></ul></ul>
    6. 6. Indexes and Indices for Forest Discrimination <ul><li>Normalised Difference Vegetation Index (NDVI). </li></ul><ul><li>Forest Discrimination Index (FDI) </li></ul><ul><ul><li>Requires hyper-spectral data over the red edge. </li></ul></ul>
    7. 7. Forest Discrimination Methodology <ul><li>A common problem is the variability in image brightness across the scene. </li></ul><ul><ul><li>North/South facing slops </li></ul></ul><ul><ul><li>Sensor noise </li></ul></ul><ul><ul><li>Contrast with other ground cover types. </li></ul></ul><ul><li>Using two levels where the discrimination threshold(s) is varied with respect to the brightness of the upper level. </li></ul>
    8. 8. Forest Discrimination Methodology <ul><li>Image processed in sections (large segments). </li></ul><ul><ul><li>Do not need to be squares any segmentation will do. </li></ul></ul>
    9. 9. Individual Tree Analysis
    10. 10. Hill and Valley Model <ul><li>It is helpful to view the data with this model. </li></ul><ul><li>Works with either brightness or height. </li></ul><ul><li>High points the crown tops. </li></ul><ul><li>Valleys crown edges. </li></ul>
    11. 11. Individual Tree Analysis
    12. 12. Splitting the Forest into Crowns We locate the bright areas of the crown and grow to the crown edge.
    13. 13. Using a Global Variable <ul><li>Simplify your process with a variable: </li></ul>Without With Setup variable Loop until reach the required value Increment the variable
    14. 14. Individual Tree Analysis
    15. 15. Merging Small Objects <ul><li>During the splitting process small bits of crowns can ‘knocked off’. </li></ul><ul><li>Following splitting a process which merges small objects (a few pixels in size) with their largest neighbor is executed. </li></ul>
    16. 16. Individual Tree Analysis
    17. 17. Classifying Tree Crowns <ul><li>Objects representing whole crowns were classified to prevent further splitting. </li></ul><ul><li>Rules to identify crowns are mostly based on their shape properties, including </li></ul><ul><ul><li>Elliptical fit, </li></ul></ul><ul><ul><li>Roundness, </li></ul></ul><ul><ul><li>Length/width ratio. </li></ul></ul><ul><li>Additionally, some spectral properties can be useful </li></ul><ul><ul><li>For example, standard deviation. </li></ul></ul>
    18. 18. Individual Tree Analysis
    19. 19. Examples of Merging Crowns Bright point merging Including small objects Before After Before After Relative Border Relative size Before After Before After
    20. 22. Parco Nazionale d’Abruzzo, Lazio e Molise, Italy www.definiens.com
    21. 23. Object Variables: Mean-lit Spectra <ul><li>To associate delineated crowns with a species type, we extract and use the reflectance spectra from the ‘brightest’ part of the crown. </li></ul><ul><li>These ‘mean-lit’ spectra allow better discrimination between tree species. </li></ul><ul><li>eCognition allows the extraction of values on a per object basis and their assignment as local variables (e.g., tree reflectance spectra). </li></ul><ul><li>These can be used as object features in the subsequent classification of species. </li></ul>Level 2 Level 1
    22. 24. Object Variables for Tree Species Classification Object Mean Object Variables
    23. 25. An example of tree species classification in Australia Stereo Air-Photo Eucalyptus populnea Eucalyptus melanaphloia
    24. 26. LiDAR Height CASI reflectance LiDAR HSCOI CASI band ratio CASI Tree Crowns <ul><li>LiDAR Tree Crowns </li></ul><ul><ul><li>Before auto-registration of CASI data </li></ul></ul><ul><li>LiDAR Tree Crowns </li></ul><ul><ul><li>After auto-registration of CASI data </li></ul></ul>Species Map of crowns from CASI data Biomass Map Stem Locations Integration of CASI/LIDAR Data Branch Locations
    25. 27. Automated delineation of forest communities
    26. 28. Landsat / AIRSAR Classification <ul><li>Using grids (at 25 m resolution) and the dominate and co-dominate species </li></ul><ul><li>Landsat spectral data </li></ul><ul><li>Landsat FPC </li></ul><ul><li>AIRSAR LHH and LHV (Available on ALOS-PALSAR) </li></ul><ul><li>Produce a rulebase object-oriented classification </li></ul>
    27. 29. Comparison to Landsat CASI Species Crown Cover
    28. 30. Identifying thresholds
    29. 31. eCognition Process
    30. 32. Classification of Communities <ul><li>Integration of L-band (HH/HV) SAR and optical Landsat data. </li></ul><ul><li>Rules identified using communities identified from the high resolution datasets. </li></ul>
    31. 33. Future Work…
    32. 34. Long-term change observed from LiDAR, Injune August 2000 – Optech ALTM1020 April 2009 – Riegl LMS-Q560 0m 30m Height Jorg Hacker, Ariborne Research Australia, Alex Lee/John Armston
    33. 35. LiDAR v TLS
    34. 36. Thank you for listening [email_address]

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