Transcript Spatial monitoring of late-successional forest habitat over large regions with nearest-neighbor imputation Janet Ohmann1, Matt Gregory2, Heather Roberts2, Robert Kennedy2, Warren Cohen1, Zhiqiang Yang2,

Spatial monitoring of late-successional forest
habitat over large regions with
nearest-neighbor imputation
Janet Ohmann1, Matt Gregory2, Heather Roberts2, Robert Kennedy2, Warren Cohen1,
Zhiqiang Yang2, Eric Pfaff2, and Melinda Moeur3
Pacific Northwest Research Station, US Forest Service, Corvallis, OR USA
Dept. of Forest Ecosystems and Society, Oregon State University, Corvallis, OR USA
3 Pacific Northwest Region, US Forest Service, Portland, OR USA
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Needs for regional vegetation information
• Complexity and scope of current forest issues (sustainability, climate
change, etc.) are pushing technology to provide information that is:
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– Consistent over large regions, detailed forest attributes, spatially explicit
(mapped)... with trend information (monitoring)
Can we marry two current technologies to better meet needs?
– Nearest-neighbor imputation (detailed attributes)
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– Change detection from Landsat time series (trends)
Approach: minimize sources of error in two model dates, map real change
Northwest Forest Plan of 1994
• Conservation plan for older forests and
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Provinces
(23 mill. ha.)
USA
species on federal lands
Effectiveness Monitoring:
– Develop maps for assessing change in
older forest and habitat, 1996 to 2006
Gradient Nearest Neighbor Imputation (GNN)
k=1
Regional inventories: unbalanced in space and time
• Choose one plot per location
• Match to closest (96 or 06) imagery date
• Develop single gradient model with all plots
• Apply model to each imagery year
• Imagery is only source of change (gradient model, plot sample, and other
GIS layers held constant)
Imagery years
Landsat Detection of Trends in Disturbance and
Recovery (LandTrendr)*
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Normalizes across
time-series at pixel
level
Change ‘trajectories’
describe sequences of
disturbance, regrowth
Frequent time-steps
Detect gradual and
subtle changes
‘Temporally
normalized’ imagery
for multi-year GNN
*Kennedy et al. (in press), Rem. Sens. Env.
Defining ‘late-successional and old growth’ (LSOG) forest
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Simple definition for this analysis:
– QMD > 50 cm
– > 10% canopy cover
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Compute from tree-level data,
associate with GNN pixels
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Ideally, ecological definition (index
based on multiple components):
– Large, old live trees
– Large snags
– Large down wood
– Multi-layered canopy
Preliminary Results
Aggregate change in older forest (LSOG) at regional level
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Slight net loss (33.2% to 32.5%)
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Over 10 years, net change signal is swamped by noise
3% of 1996 LSOG lost, mostly to large wildfires, partially offset by
regrowth in other areas
Based on LSOG % correct
from cross-validation
Spatial change in Klamath province,
1996-2006
Not LSOG
LSOG gain
LSOG loss
LSOG
Nonforest
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Change is
dramatic in some
landscapes (2002
Biscuit Fire)
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Spatial change is
quite noisy
Spatial change at landscape level
1996 Landtrendr
B-G-W
2006 Landtrendr
B-G-W
GNN change
Not LSOG
LSOG gain
LSOG loss
LSOG
Nonforest
Pixel-level noise in GNN models
• GNN with k=1 is inherently noisy: sensitive to slight spectral shifts
• Minor changes cause plots to cross definition threshold (QMD)
• Problems magnified by model ‘subtraction’ (spatial predictors, plot
sampling and location errors, model specification, etc.)
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GNN cross-validation applies to 2-date ‘hybrid’ model, not spatial change
All plots
1991-2008
How reliable is spatial change from two GNN models?
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What is truth? No data available for validating spatial change.
Corroborates other estimates:
– Plot-based estimates from FIA Annual inventory
– Within 1% of previous 1996 estimate (different methods)
– Slight net loss corroborated by remeasured plots
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A different approach
to validation is needed...
Oregon Western
Cascades
FIA Annual
plots
2001-2008
TimeSync validation
(Cohen et al. in press, RSE)
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Expert interpretation of
Landsat time series and
ancillary data
1998
2005
Adapting TimeSync to validation of GNN change (1996-2006)
Confusion matrices:
Data recording in TimeSync:
Plot
ID
Canopy
cover
Conifer
size
LSOGlike
1996
LSOGlike
2006
TimeSync
interpre-tation
1
increase
increase
2
4
CanCov
increase
2
decrease
decrease
7
5
CanCov stable
3
stable
stable
10
10
4
stable
increase
4
6
CanCov
decrease
5
decrease
decrease
5
2
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TimeSync
interpretation
LSOG
increase
LSOG
decrease
LSOG
stable
Not-LSOG
stable
GNN change
CanCov
increase
CanCov
stable
CanCov
decrease
GNN change
LSOG
gain
LSOG
loss
LSOG
stable
Not-LSOG
stable
Lessons learned: multi-temporal GNN for monitoring
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Only feasible with “temporally normalized” imagery
Net change over large spatial extents is reasonable
More work to quantify our ability to map pixel-level change
10 years is insufficient to reliably map ‘ingrowth’ of older forest,
but loss from disturbance is feasible
Thank you
Improvements coming soon...
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Yearly matching of plots to imagery
Prior disturbance and growth (from LandTrendr) informs model
Disturbance Magnitude
(1996 to 2006)
Imagery years
Normalized Landsat mosaics
(Remote Sensing Applications Center, USFS)
1996
GNN QMD “change”
(bias associated with aspect)
2006
1996 B-G-W
2006 B-G-W
1996 GNN QMD
2006 GNN QMD