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Comprehensive Monitoring of Older Forests in the Northwest Forest Plan

This study focuses on spatial monitoring of older forests in the Northwest Forest Plan through integrative methods of plot and remotely sensed data. The project aims to assess forest change, conservation, and effectiveness monitoring for late-successional and old-growth forest. Utilizing tools like Gradient Nearest Neighbor Imputation and Landsat time series, the study reports on trends every 5 years. Results show a net loss of older forest on federal land, highlighting the need for conservation efforts.

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Comprehensive Monitoring of Older Forests in 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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