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E Cognition User Summit2009 Pbunting University Wales Forestry

E Cognition User Summit2009 Pbunting University Wales Forestry



Advances in the use of eCognition for forest research and applications

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 E Cognition User Summit2009 Pbunting University Wales Forestry Presentation Transcript

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