e cognition user summit2009 pbunting university wales forestry
DESCRIPTION
Advances in the use of eCognition for forest research and applicationsTRANSCRIPT
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Advances in the use of eCognition for forest research and applications
Dr. Pete Bunting
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Contents
• Individual tree analysis– High resolution forest mask
– Delineation Approach
• Fusing with other high resolution data
• Scaling to the landscape
• Future work…
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Individual Tree Analysis
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Individual Tree Analysis
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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.
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Indexes and Indices for Forest Discrimination
• Normalised Difference Vegetation Index (NDVI).
• Forest Discrimination Index (FDI)– Requires hyper-spectral data over the red
edge.€
NDVI =r 750 - r 680
r 750 + r 680=
NIR - RED
NIR + RED
€
FDI = r 838 - r 714 + r 446( ) = NIR - (RE + BLUE )
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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.
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Forest Discrimination Methodology
• Image processed in sections (large segments).– Do not need to be squares any segmentation
will do.
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Individual Tree Analysis
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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.
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Individual Tree Analysis
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Splitting the Forest into Crowns
We locate the bright areas of the crown and grow to
the crown edge.
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Using a Global VariableSimplify your process with a variable:
WithoutWith
Setup variable
Loop until reach the required value
Increment the variable
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Individual Tree Analysis
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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.
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Individual Tree Analysis
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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.
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Individual Tree Analysis
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Examples of Merging CrownsBright point merging Including small objects
Before AfterBefore After
Relative Border Relative size
Before After Before After
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Parco Nazionale d’Abruzzo, Lazio e Molise, Italy
www.definiens.com
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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
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Object Variables for Tree Species Classification
Object Mean Object Variables
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An example of tree species classification in Australia
Eucalyptus populnea
Eucalyptus melanaphloia
Stereo Air-Photo
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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
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Automated delineation of forest communities
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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
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Comparison to Landsat
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CASISpeciesCrown Cover
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Identifying thresholds
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eCognition Process
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Classification of Communities
• Integration of L-band (HH/HV) SAR and optical Landsat data.
• Rules identified using communities identified from the high resolution datasets.
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Future Work…
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Long-term change observed from LiDAR, Injune
August 2000 – Optech ALTM1020
April 2009 – Riegl LMS-Q560
0m 30m
HeightJorg Hacker, Ariborne Research Australia, Alex Lee/John Armston
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LiDAR v TLS