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Advances in the use of eCognition for forest research and applications
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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.€
NDVI =r 750 - r 680
r 750 + r 680=
NIR - RED
NIR + RED
€
FDI = r 838 - r 714 + r 446( ) = NIR - (RE + BLUE )
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 VariableSimplify your process with a variable:
WithoutWith
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 CrownsBright point merging Including small objects
Before AfterBefore 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
Eucalyptus populnea
Eucalyptus melanaphloia
Stereo Air-Photo
26
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
27
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
28
Comparison to Landsat
29
CASISpeciesCrown Cover
Identifying thresholds
30
eCognition Process
31
Classification of Communities
• Integration of L-band (HH/HV) SAR and optical Landsat data.
• Rules identified using communities identified from the high resolution datasets.
32
33
Future Work…
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
LiDAR v TLS
