Spatial monitoring of late-successional forest habitat over large regions with nearest-neighbor imputationJanet 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 USA2 Dept. of Forest Ecosystems and Society, Oregon State University, Corvallis, OR USA3 Pacific Northwest Region, US Forest Service, Portland, OR USA

Needs for regional vegetation informationComplexity 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 1994Conservation plan for older forests and species on federal landsEffectiveness Monitoring:Develop maps for assessing change in older forest and habitat, 1996 to 2006

Gradient Nearest Neighbor Imputation (GNN)

Regional inventories: unbalanced in space and timeChoose one plot per locationMatch to closest (96 or 06) imagery dateDevelop single gradient model with all plotsApply model to each imagery yearImagery is only source of change (gradient model, plot sample, and other GIS layers held constant)

Landsat Detection of Trends in Disturbance and Recovery (LandTrendr)*Normalizes across time-series at pixel levelChange trajectories describe sequences of disturbance, regrowthFrequent time-stepsDetect gradual and subtle changesTemporally normalized imagery for multi-year GNN*Kennedy et al. (in press), Rem. Sens. Env.

Defining late-successional and old growth (LSOG) forestSimple definition for this analysis:QMD > 50 cm> 10% canopy coverCompute from tree-level data, associate with GNN pixelsIdeally, ecological definition (index based on multiple components): Large, old live treesLarge snagsLarge down woodMulti-layered canopy

Preliminary Results

Aggregate change in older forest (LSOG) at regional levelSlight net loss (33.2% to 32.5%)3% of 1996 LSOG lost, mostly to large wildfires, partially offset by regrowth in other areasOver 10 years, net change signal is swamped by noise

Based on LSOG % correct from cross-validation

Spatial change in Klamath province, 1996-2006Change is dramatic in some landscapes (2002 Biscuit Fire)Spatial change is quite noisy

Spatial change at landscape level1996 Landtrendr B-G-W2006 Landtrendr B-G-WGNN change

Pixel-level noise in GNN modelsGNN with k=1 is inherently noisy: sensitive to slight spectral shiftsMinor 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

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 inventoryWithin 1% of previous 1996 estimate (different methods)Slight net loss corroborated by remeasured plotsA different approach to validation is needed...

Oregon Western Cascades

TimeSync validation(Cohen et al. in press, RSE)Expert interpretation of Landsat time series and ancillary data19982005

Adapting TimeSync to validation of GNN change (1996-2006)Data recording in TimeSync:Confusion matrices:

Plot IDCanopy coverConifersizeLSOG-like 1996LSOG-like 20061increaseincrease242decreasedecrease753stablestable10104stableincrease465decreasedecrease52...............

TimeSync interpre-tationGNN changeLSOG gainLSOG lossLSOG stableNot-LSOG stableLSOG increaseLSOG decreaseLSOG stableNot-LSOG stable

TimeSync interpre-tationGNN changeCanCov increaseCanCov stableCanCov decreaseCanCov increaseCanCov stableCanCov decrease

Lessons learned: multi-temporal GNN for monitoringOnly feasible with temporally normalized imageryNet change over large spatial extents is reasonableMore work to quantify our ability to map pixel-level change10 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 imageryPrior disturbance and growth (from LandTrendr) informs modelDisturbance Magnitude (1996 to 2006)

Normalized Landsat mosaics (Remote Sensing Applications Center, USFS)1996 GNN QMDGNN QMD change(bias associated with aspect)2006 GNN QMD1996 B-G-W2006 B-G-W

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