Emilie Henderson, Janet Ohmann , Matthew Gregory, Heather Roberts and Harold Zald
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![Page 1: 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.](https://reader036.fdocuments.us/reader036/viewer/2022062300/56649cba5503460f949815e8/html5/thumbnails/1.jpg)
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 SocietyOregon 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)
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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
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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
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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.
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Overview of LSOG monitoring for 15-year report:
integration of map- and plot-based analysesMap-based analyses
Plot-based analyses• Successive
inventories (where available)
• FIA Annual inventory, all ownerships (no remeasurement)
Habitat and Watershed Condition
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Gradient Nearest Neighbor Imputation (GNN)
k=1
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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
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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.
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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)
Forest class
Conifer canopy cover
Avg. DBH of dom/codom
conifers
Open <10% --
Young >10%0.0 to 19.9
in
LSOG >10% >20 in
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Mapping LSOG change
Not LSOGLSOG gainLSOG lossLSOG Nonforest
1996 B-G-W 2006 B-G-W Disturbance
1996 LSOG 2006 LSOG LSOG change
Land-Trendr
GNN
- - 10 miles - -
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Accuracy assessment (‘obsessive transparency’)• 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
1
2 3 4
5 6 7* 8 9
10 11 12
13
local(1-ha
plot) scale
regionalscale
landscape- or watershed-
scale
Oregon
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Results
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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 hexagonsLSOGchange(% of forest)
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Spatial change in Klamath province,
1996-2006
• Change is dramatic in some landscapes (2002 Biscuit Fire)
• Spatial change is quite noisy
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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
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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
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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.
Federal Nonfederal All owners
Acknowledgment: Olaf Kuegler and Karen Waddell for FIA Annual estimates
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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?
??
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Change in habitat suitabilityNWFP Effectiveness
Monitoring
• Maxent (machine learning) models based on forest structure and composition attributes from GNN, trained with nest location data
• Subtract models to get change
Marbled murrelet
Northern spotted owl
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Error and Uncertainty in the Monitoring Data
“Those pixels are wrong!”
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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.
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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
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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
Federal lands
Baseline LSOG map estimates
10-year report* (IVMP,
CalVeg)
15-year report (GNN)
Difference
Thousand acres
Washington 2,131 2,131 +0.4
Oregon 3,379 3,400 +20.5
California 2,358 1,754 -603.4
Range-wide 7,868 7,286 -582.4
* 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.
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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
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Thanks for your attention!