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

1 Pacific Northwest Research Station, US Forest Service, Corvallis, OR USA

2 Dept. of Forest Ecosystems and Society, Oregon State University, Corvallis, OR USA

3 Pacific Northwest Region, US Forest Service, Portland, OR USA

Needs for regional vegetation information• Complexity and scope of current forest issues (sustainability, climate

change, etc.) are pushing technology to provide information that is:

– 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)

– 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

species on federal lands

• Effectiveness Monitoring:

– Develop maps for assessing change in older forest and habitat, 1996 to 2006

Provinces(23 mill.

ha.)

USA

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)*

• 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• Simple definition for this analysis:

– QMD > 50 cm

– > 10% canopy cover

• Compute from tree-level data, associate with GNN pixels

• 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• Slight net loss (33.2% to 32.5%)

• 3% of 1996 LSOG lost, mostly to large wildfires, partially offset by regrowth in other areas

• Over 10 years, net change signal is swamped by noise

Based on LSOG % correct from cross-validation

Spatial change in Klamath province,

1996-2006

• Change is dramatic in some landscapes (2002 Biscuit Fire)

• Spatial change is quite noisy

Spatial change at landscape level

Not LSOGLSOG gainLSOG lossLSOG Nonforest

1996 Landtrendr B-G-W

2006 Landtrendr B-G-W

GNN change

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.)

• GNN cross-validation applies to 2-date ‘hybrid’ model, not spatial change

All plots1991-2008

How reliable is spatial change from two GNN models?• 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

• A different approach to validation is needed...

Oregon Western Cascades

FIA Annual plots

2001-2008

TimeSync validation(Cohen et al. in press, RSE)

• Expert interpretation of Landsat time series and ancillary data

1998 2005

Adapting TimeSync to validation of GNN change (1996-2006)

Plot ID

Canopy cover

Conifersize

LSOG-like 1996

LSOG-like 2006

1 increase

increase 2 4

2 decrease

decrease

7 5

3 stable stable 10 10

4 stable increase 4 6

5 decrease

decrease

5 2

. . . . .

. . . . .

. . . . .

TimeSync interpre-

tation

GNN change

LSOG gain

LSOG loss

LSOG stable

Not-LSOG stable

LSOG increase

LSOG decrease

LSOG stable

Not-LSOG stable

Data recording in TimeSync:

Confusion matrices:

TimeSync interpre-

tation

GNN change

CanCov increase

CanCov stable

CanCov decrease

CanCov increase

CanCov stable

CanCov decrease

Lessons learned: multi-temporal GNN for monitoring• 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...

• Yearly matching of plots to imagery

• Prior disturbance and growth (from LandTrendr) informs model

Imagery years

Disturbance Magnitude (1996 to 2006)

Normalized Landsat mosaics (Remote Sensing Applications Center, USFS)

1996 2006

1996 GNN QMDGNN QMD “change”

(bias associated with aspect)

2006 GNN QMD

1996 B-G-W 2006 B-G-W