Landsat Change Detection of Forests with a Modified Enhancement Classification Methodology

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Landsat Change Detection of Forests with a
Modified Enhancement Classification Methodology

• Jean Beaubien, Nick Walsworth and Don Leckie
• Canadian Forest Service
• Natural Resources Canada
• Presented by Don Leckie

Third International Workshop on the Analysis of
Multi-temporal Remote Sensing Images 16-18 May
2005 Biloxi, Mississippi USA
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Next Generation Forest Measuring and Monitoring
System for Canada
NFIS
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NFI Photo Plot Design

• Photo Plots
• Provide estimates of basic attribute data
• Sample units are 2 x 2 km on a 20 km grid
• Located at network points
• Air photos for Southern
• Satellite data for Northern

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NFI Remeasurement Overview

• NFI report is expected in 2005 or 2006
• Remeasure on 10-year cycle
• Southern Canada measure one photo plot panel
  one ground plot panel/year
• Northern Canada measure all plot panels every 5
  years
• Update all photo plots annually for significant
  disturbances
• Produce annual estimates and publish periodic
  reports

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Earth Observation for Sustainable Development of
Forests (EOSD) National Forest Inventory Product

• Core NFI data in sections (e.g., north)
• Confirm/Verify the Inventory
• Assess Change
• Indicate update priorities
• Accuracy assessment

• 2 x 2 km photo plot based on the centroid of a
  20 x 20 km grid

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Overview, EOSD Land Cover Quebec
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EOSD Land Cover Product

• Land cover tile over Chibougamau

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EOSD Biomass Product (preliminary)
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EOSD Change Product (preliminary)
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Deforestation Overview

• Pilots
• Methods
• EOSD change
• Carbon modeling
• Kyoto Protocol reporting

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Deforestation and EOSD Change Pilot Project and
RD Overview
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Deforestation and EOSD Change Pilot Project and
RD Overview
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Saskatchewan Pilot Project
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Meadow Lake Study Area
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Prince George Pilot Change
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Prince George Pilot
Stand Initialization
1990 Landsat TM
1990 Inventory
1990 Carbon stocks
Growth yield
Modeling parameters
1990-1999 Change Map
CBM
Disturbance list
1999 Carbon stocks
1999 Landsat TM
1999 Inventory
Landsat images T1 August 12, 1990 T2
September 12, 1999
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Prince George, British ColumbiaPilot Project

• CBM-CFS3 used to calculate carbon stock change

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EOSD National Change Product

• Map of major changes in the forests of Canada
• complete coverage two date change
• 5 -10 year time frame (e.g., 2000-2007-2012)
• clearcuts, major burns, deforestation, some
  regeneration or lack thereof

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Annual Change Product

• A sample of annual change (e.g. PS2Ps)
• monitoring or history of change on the sample
• partial cut, regeneration, roads, landings, soil
  disturbance, blowdown, some ID
• composition change, normal growth, succession
• no change

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EOSD Change Approach Overview(Two Date)
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1. Data selection/Compilation
Image Collection and Assembly
Top of Atmosphere Reflectance, Orthorectification,
Radiometric Normalization
2. Pre-processing
Evidence for Change
3. Has a pixel or polygon changed?
Yes! - Aggregate
No! - Assign T2 class
4. Spatial amalgamation
Aggregation

Spatial Change Unit
Pixels not changed and not included in change
units

5. What is the change?
Evidence for Change Type
Manual Vetting
Quality Control
6. Change Output and Validation
T1-T2 Change map
Accuracy Assessment
7. What is the T2 land cover?
Change Type, Evidence, Focused T2 Classification
T1 Classification for Non Change Pixels

T2 Landcover
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Recognition of Change - Two Date Change
Clustering

• A main change evidence element is a two date
  unsupervised methodology using normalized images
  change mask
• Currently a two-date x 6 band (12 bands total)
  clustering under a generous change mask is used
• Tasseled-cap has also shown promise.
• Issues
• differentiating some change types, especially if
  subtle
• eliminating false alarms
• capturing small classes a common problem in
  change mapping
• Enhancement Classification Method (ECM) -
  effective in single date land cover
  classification could potentially solve some of
  these difficulties ability to impose a classes
  useful

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Two Date Change Enhancement for ECM

• Principle is to select band combinations such
  that in a 3 band colour rendition, change classes
  are distinct widely separated colours use this
  for the clustering

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Enhancement Rendition Petawawa, Ontario
R (MIR (T1) MIR (T2)) /2 2(NIR (T2) -
NIR (T1) 128) -256 G MIR (T2) B MIR
(T1) RED (T1)
T11984 and T21988
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Two Date Unsupervised ECM Classification
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Two Date Unsupervised Enhancement and Cluster
Emplacement
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BAND4 BAND5 BAND3
BAND4 BAND5 BAND3
Changes image 123 classes
1988
1984
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1
1
2
2
2
3
3
3
4
4
4
5
5
5
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Clearing Recognition

• Clearing and cuts were well recognized from
  stable areas.
• Some confusion occurs with subtleties of time 1
  forest and time 2 ground compositions, in part
  due to the two date nature of the clustering.

• Labels associated with land usage defaulted to
  the dominant classes associated with forestry
  cutting activities.
• Deforestation associated with urban development
  agriculture were included in clearcuts.
• Small events (roads and landings showed some
  confusion with stable due to mixed pixel edges.

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BAND4 BAND5 BAND3
BAND4 BAND5 BAND3
Changes image 123 classes
1988
1984
1
1
1
2
2
2
3
3
3
4
4
4
5
5
5
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Partial Disturbance / Removal Results

• Partial cuts are characteristically a mixture of
  stable, clearcut and partial cut pixels
• The process causing depletion is not well
  discriminated (e.g., hail typically in the same
  clusters as partial cuts)
• Removal intensity and age
• not well differentiated
• Some confusion existed
• with burns and wetlands

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Partial cut preliminary two date results
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Spatial Aggregation into Change Units

• Individual pixel changes may be
• smaller than a minimum mapping unit
• classes may be contextually linked
  hierarchically i.e. partial cut
• or simply be an aberrant pixel
• Methods
• Sieving small polygons ( e.g., all conifer to
  shrub lt 3 pixels no change)
• Modal filters, can be iterative to build up edges
  in highly fragmented areas
• Use of secondary class probabilities to merge
  neighboring pixels
• Rules based system

MIR Pixel Difference
2 Date Unsupervised
Rule based aggregation
If ((diff gt 30) or (diff gt20 and neighbor diff
gt30) and polygon has 60 partial cut class
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Change Typing

• Change clusters, labeling and classification
  cant identify all types of change
• After spatial aggregation, evidence identifies
  some further change types
• Need manual tools to help change typing
• Will need vetting / proofing

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Evidence and Vetting
Vetting includes all the information used in the
process, and is directed to checking and
upgrading delineations and type calls.
1990 Image
1999 Image
MIR Comp w. Inv.
New cut not updated on inventory
Not forest at T1
Inventory with Change Units
1995 Orthophoto
1998 Roads
2 Date Unsupervised
Quarry Expansion
Residential
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Conclusions

• ECM differentiated some classes better
  (regeneration) than a 6 band two date
  classification
• Still unable to differentiate hail, deforestation
  detailed partial cut intensity
• ECM change enhancement effective at highlighting
  change
• ECM made cluster labeling easier
• ECM captures some subtler changes but still has
  false alarms
• Mechanically the methodology improves clustering
  and improves identification of smaller classes
• Three band enhancement makes identifying the need
  for imposing a new cluster and creating them
  easier
• Evident that clustering techniques will not
  differentiate all change types desired
• Need new tools to help identify change type of
  selected events

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Test Area Recognition
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Earth Observation for Sustainable Development
of Forests (EOSD) Overview, land cover
products

• Paletted GeoTiff file
• FGDC Compliant Metadata
• Accuracy information
• Shape file of mosaic lines
• User Agreement

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Interim Approach
Remote Sensing Mapping
GIS Systems Integration

• change is an event caused by a process no
  change is an important product

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BAND4 BAND5 BAND3
BAND4 BAND5 BAND3
Changes image 33 classes
1988
1984
1
1
1
1
2
2
2
2
3
3
3
3
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Acknowledgements

• The authors would like to thank the staff of the
  Pacific Forestry Center in Victoria, B.C. for
  there inputs and support, as well as the Canadian
  Space Agency and the Canadian Forest Service for
  funding the temporal change research component of
  the Earth Observation for Sustainable Development
  project.

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Landsat Temporal Sequence
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Role of Low Resolution Imagery

• assume if change large enough for detection at
  low resolution, it will be detected at 5 year
  Landsat cycle
• some ephemeral changes not detectable on 5-10
  year Landsat-type cycle will be detectable
• a main role is to add evidence to occurrence of
  change and change type observed on Landsat
• sometimes will help time stamping event for some
  time sensitive changes
• to confirm regional trends representativeness
  (vice versa)
• benefit from a higher repeat cycle increased
  temporal sampling intensity

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Modis Simulation
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Information Needs

• Earth systems science (processes, carbon, climate
  modeling, biophysical parameters, ----)
• Forest inventory and/or management
• national and regional mapping and statistics
• local mapping, inventory update
• application specific change (e.g., damage,
  deforestation, burns)
• carbon accounting/inventory
• confirmation of currency of inventories
• aid in forest classification/mapping

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