Spatial monitoring of late-successional forest habitat over large regions with nearest-neighbor imputation Janet Ohmann 1, Matt Gregory 2, Heather Roberts 2, Robert Kennedy 2, Warren Cohen 1, Zhiqiang Yang 2, Eric Pfaff 2, and Melinda Moeur 3 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, Not LSOG LSOG gain LSOG loss LSOG Nonforest Change is dramatic in some landscapes (2002 Biscuit Fire) Spatial change is quite noisy

Spatial change at landscape level Not LSOG LSOG gain LSOG loss LSOG 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 plots

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

TimeSync validation (Cohen et al. in press, RSE) Expert interpretation of Landsat time series and ancillary data

Adapting TimeSync to validation of GNN change ( ) Plot ID Canopy cover Conifer size LSOG- like 1996 LSOG- like increase 24 2decrease 75 3stable 10 4stableincrease46 5decrease 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) GNN QMD GNN QMD “change” (bias associated with aspect) 2006 GNN QMD 1996 B-G-W2006 B-G-W