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Spatial monitoring of older forest for the Northwest Forest Plan

Spatial monitoring of older forest for the Northwest Forest Plan. Janet Ohmann 1 , Matt Gregory 2 , Heather Roberts 2 , Robert Kennedy 2 , Warren Cohen 1 , Zhiqiang Yang 2 , Melinda Moeur 3 , and Maria Fiorella 4 1 Vegetation Monitoring and Remote Sensing Team (VMaRS)

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Spatial monitoring of older forest for the Northwest Forest Plan

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  1. Spatial monitoring of older forest for the Northwest Forest Plan Janet Ohmann1, Matt Gregory2, Heather Roberts2, Robert Kennedy2, Warren Cohen1, Zhiqiang Yang2, Melinda Moeur3, and Maria Fiorella4 1 Vegetation Monitoring and Remote Sensing Team (VMaRS) Resource Monioring and Assessment Program (RMA) PNW Research Station, USFS, Corvallis, OR 2 Department of Forest Ecosystems and Society Oregon State University, Corvallis, OR 3 Region 6, USFS, Portland, OR; 4 BLM, Portland, OR Funding contributed by: Region 6, USFS PNW Research Station (WWETAC and ECOP)

  2. Needs for regional vegetation information • Methods that integrate plot and remotely sensed data to provide info.: • Consistent over large, multi-ownership regions (“all lands”) • Spatially explicit (mapped) • Detailed attributes of forest composition and structure • Support integrated landscape analyses of multiple forest values • Latest challenge: provide trend information that is spatial • Monitoring older forest for Northwest Forest Plan

  3. Northwest Forest Plan of 1994 • Conservation plan for older forests and species on 57 mill. ac. of federal land • Effectiveness Monitoring modules for older forest, n. spotted owl, marbled murrelet, watershed condition • Key questions for monitoring older forest: • How much, how is it changing, how might it change in the future? • Is the Plan providing for its conservation and management? Physiographic provinces (57 mill. ac., 46 mill. ac forest) USA

  4. Effectiveness Monitoring for Late-Successional and Old-Growth Forest (LSOG) • Objective: develop tools and data to assess change in older forest • Gradient nearest neighbor (GNN) imputation (maps of detailed forest attributes) • Change detection from Landsat time series (LandTrendr) (trends) • Approach: minimize sources of error in models, map real change • Corroborate with sample-based estimates • Monitoring report every 5 years • 10-year report (Moeur et al. 2005) • In progress: 15-year report • 1996 to 2006 (Wash. and Oreg.), 1994 to 2007 (Calif.) * Moeur, M., et al. 2005. Northwest Forest Plan–The first 10 years (1994-2003): status and trend of late-successional and old-growth forest. Gen. Tech. Rep. PNW-GTR-646.

  5. Overview of LSOG monitoring for 15-year report:integration of map- and plot-based analyses Map-based analyses Habitat and Watershed Condition Plot-based analyses • Successive inventories (where available) • FIA Annual inventory, all ownerships (no remeasurement)

  6. Gradient Nearest Neighbor Imputation (GNN) k=1

  7. Regional inventory plots for GNN modeling • Multiple data sources, unbalanced in time and space • One plot per location, matched to 94/96 or 06/07 imagery • Develop single gradient model with all plots • Apply model to each imagery year • Imagery is only source of change – assumes normalization Imagery years

  8. Landsat Detection of Trends in Disturbance and Recovery (LandTrendr)* • Temporal normalization and segmentation at pixel level • Minimizes noise from sun angle, phenology • Segments describe sequences of disturbance, regrowth • Yearly time-step • Detects gradual and subtle changes • Normalized imagery for multiple years for GNN modeling *Kennedy et al. (2010), Rem. Sens. Env.

  9. Defining‘late-successional and old growth’ (LSOG) forest • Single, simple definition, applied to tree-level data associated with GNN pixels • LSOG ≠ habitat! • More ecologically-based definition => different answers (but not necessarily more accurate)

  10. Not LSOG LSOG gain LSOG loss LSOG Nonforest Mapping LSOG change - - 10 miles - - Land- Trendr 1996 B-G-W 2006 B-G-W Disturbance GNN 1996 LSOG 2006 LSOG LSOG change

  11. 1 2 3 4 7* 5 6 8 9 10 11 12 13 Oregon Accuracy assessment (‘obsessive transparency’) local (1-ha plot) scale • Local- (plot-) scale accuracy via cross-validation: • Confusion matrices, kappa statistics, root mean square errors, scatterplots, etc. • Landscape- to regional-scale accuracy: • Area distributions in map vs. plot sample • Range of variation in map vs. plot sample • Riemann et al. (2010) diagnostics • Bootstrap variance estimators for kNN (Magnussen et al. 2010) • Spatial depictions of uncertainty: • Variation among k nearest neighbors • Distance to nearest neighbor(s) (sampling sufficiency) • ‘Look-and-feel’ issues landscape- or watershed- scale regional scale

  12. Results

  13. LSOG change from GNN ‘bookend’ maps, 1994/6 to 2006/7 • GNN models and change at 30-m pixel scale • Recommend summarizing to coarser scales • Example: 10-km hexagons LSOG change (% of forest)

  14. Not LSOG LSOG gain LSOG loss LSOG Nonforest Spatial change in Klamath province, 1996-2006 • Change is dramatic in some landscapes (2002 Biscuit Fire) • Spatial change is quite noisy

  15. Change in older forest on federal land • ~ 2/3 of total LSOG • Net loss of 1.9%, 7.3 to 7.1 mill. ac, from 33.2% to 32.5% of forest • >200,000 acres lost in large fires (LandTrendr disturbance), 90% in reserves • Losses roughly offset by recruitment, but difficult to reliably map • Small amount of change relative to level of uncertainty

  16. Change in older forest on nonfederal lands • ~ 1/3 of total LSOG • Net loss of 9.9%, from 3.9 to 3.5 mill. ac. • >500,000 acres lost, mostly timber harvest (LandTrendr disturbance maps) • Losses not offset by recruitment • Small amount of change relative to level of uncertainty

  17. Comparison of GNN and FIA Annual estimates • GNN shows less LSOG on federal, more LSOG on nonfederal, very similar for all ownerships • Many reasons for differences: different plots, different dates, sample- vs. model-based, unsampled area, nonforest area, etc. etc. etc. Nonfederal All owners Federal Acknowledgment: Olaf Kuegler and Karen Waddell for FIA Annual estimates

  18. Change in older forest from successive inventories • National Forest and Oregon BLM lands only • Differences between estimates were not significant (all provinces, states) • GNN estimates are within the sampling error (90% C.I.) • Except Calif. (Region 5 FIA vs. FIA Annual) – data problem? ? ?

  19. Change in habitat suitabilityNWFP Effectiveness Monitoring Northern spotted owl Marbled murrelet • Maxent (machine learning) models based on forest structure and composition attributes from GNN, trained with nest location data • Subtract models to get change

  20. “Those pixels are wrong!” Error and Uncertainty in the Monitoring Data

  21. How good are the GNN ‘bookend’ maps? • Local-scale accuracy (cross-validation) • LSOG is 80% correct, kappa 0.49 • Normalized RMSE for CANCOV = 0.33, QMDCDOM = 0.53 • Best in closed-canopy, conifer-dominated, even-aged forest (challenges in patchy stands of mixed ages and species) • Regional LSOG area estimates are comparable to FIA Annual • Need kNN bootstrapped variance estimators for kNN to statistically compare two models (Magnussen et al. 2010) • How reliably can we map LSOG change? • TimeSync validation tool (Cohen et al. 2010) to assess change spatially Cohen, W.B.; Zhiqiang, Y.; Kennedy, R.E. 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync—tools for calibration and validation. RSE 114: 2911-2924. Magnussen, S.; McRoberts, R.E.; Tomppo, E.O. 2010. A resampling variance estimator for the k nearest neighbours technique. CJFR 40:648-658.

  22. Sources of uncertainty in overall monitoring results • Multiple estimates, lots of moving parts with different limitations • Map- and plot-based estimates can’t be compared statistically • Look for corroboration • Complexity and uncertainty pose challenges for users • Error in model-based estimates • Error in plots, spatial predictors; model specification; etc. • Limitation of Landsat for mapping LSOG recruitment • Time period is short (10-13 years), and data will improve • Uncertainty associated with LSOG definition: • Simple QMD threshold, can be affected by one or a few trees • Disturbance can => LSOG gain, LSOG loss, or no change

  23. Monitoring: improving methods, rewriting history? • Capability to re-run models for previous years (can users stomach it?) • 10-year* and 15-year monitoring data: • Map analyses: similar estimates for WA/OR, very different for CA • Plot analyses: large amount of projected LSOG recruitment not supported * Moeur, M., et al. 2005. Northwest Forest Plan–The first 10 years (1994-2003): status and trend of late-successional and old-growth forest. Gen. Tech. Rep. PNW-GTR-646.

  24. Products from NWFP monitoring study • GNN models and diagnostics available for download • 2006/7 now available, 1994/96 pending peer review and publication • 15-year reports (PNW GTRs) in review: • LSOG, northern spotted owl, marbled murrelet, watershed condition • Article (in prep.) for Forest Ecology and Management http://www.fsl.orst.edu/lemma/nwfp

  25. Thanks for your attention!

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