1. Mapping change in live and dead forest biomass with Landsat time-series, remeasured plots, and nearest-neighbor imputation Janet Ohmann 1, Matt Gregory 2, Heather Roberts 2, Robert Kennedy 3, Zhiqiang Yang 2, Justin Braaten 2, Scott Powell 4, Warren Cohen 1, Van Kane 5, and Jim Lutz 5 1 Vegetation Monitoring and Remote Sensing Team (VMaRS) Resource Monitoring and Assessment Program (RMA) PNW Research Station, USFS, Corvallis, OR 2 Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR 3 Department of Geography and Environment, Boston University, Boston, MA 4 Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT 5 College of the Environment, University of Washington, Seattle, WA Funded by: USDA-NIFA, USDA-FS Region 6 (Northwest Forest Plan)

2. Needs for regional vegetation information Complexity and scope of forest policy issues (climate change, sustainability, etc.) pushing technology to provide vegetation information that... –Supports integrated landscape analyses of multiple forest values –Consistent over large, multi-ownership regions (“all lands”) –Spatially explicit (mapped) –Detailed attributes of forest composition and structure –Latest challenge: trend information (monitoring)

3. Goal: develop a system for monitoring forest change over large regions Can we marry two current technologies to better meet needs? –LandTrendr to map change from Landsat time-series (trends) –Nearest-neighbor imputation (detailed forest attributes) Illustrate methods with example for forest biomass

4. Objective and strategies (nearest-neighbor component) Develop methods for integrating field plot and satellite data to monitor yearly changes in biomass –and other forest attributes (e.g., older forest) Why nearest-neighbor imputation? –Multiple attributes in one map, serves many applications –Difficult-to-predict attributes swept along (e.g. species, CWD) –When k=1, maintain covariation among multiple attributes in map units, range of variability over study region

5. Gradient Nearest Neighbor Imputation (GNN)

6. The imputation component of GNN (2) calculate axis scores of pixel from mapped data layers (3) find nearest- neighbor plot(s) in gradient space (k=1, Euclidean distance in n-dimensional gradient space, with n axes weighted by explanatory power (eigenvalues)) (4) impute nearest neighbor’s value to pixel gradient spacegeographic space CCA Axis 2 (e.g., climate) CCA Axis 1 (e.g., elevation, wetness) (1) conduct gradient analysis of plot data

7. 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 Normalized imagery for multiple years for GNN modeling *Kennedy et al. (2010), Rem. Sens. Env.

8. Matching plots to LandTrendr imagery for GNN modeling Unbalanced in time and space, 1 to 3 per location Match plots to imagery of same year Single gradient model from all plots, applied to each imagery year Imagery is only change – assumes normalization Response vars.: basal area by species + size-class Yearly matching of plot and spectral data 1 2 4 3 FIA Annual plot 30-m Landsat pixels

9. Preliminary results: Oregon and California Cascades region 4,812,550 ha. (48,126 sq.km.) n = 6,207 plots

10. Dominant gradients in forest composition and structure (axis scores from CCA) % variation explained Climate8.5 Landsat4.5 Topography4.2 Location3.7 Axis 1 (red): temperature, elevation, TC greenness, TC wetness Axis 3 (blue): summer moisture stress, TC brightness Axis 2 (green): latitude, seasonality of precipitation

11. Biomass estimation: component ratio method (CRM) National FIA approach (Heath et al. 2009) Component ratios from Jenkins et al. (2003) Bole volume equations from FIA-PNW: –Live trees (≥3 cm): 47 equations (species, DBH, height, location) –Snags (≥12 cm): Kozak’s taper equations –Coarse woody debris (CWD) (≥25 cm): Smallian’s formula (small- and large-end diam.) or Huber’s (intercept diam. only). Live (83%) > CWD (11%) > snag (6%) Jenkins components

12. Local- (plot-) level GNN map accuracy Cross-validation applies to all map dates, not spatial change Accuracy varies among attributes: –Best for live trees, density measures; poor for snags and CWD GNN maps (k=1, Landsat) are inherently noisy, sensitive to slight spectral shifts, exacerbated by map differencing Live ≥ 3 cmSnags ≥ 12.5 cmCWD ≥ 25 cm nRMSE: 0.54 r: 0.78 r 2 : 0.60 nRMSE: 1.59 r: 0.43 r 2 : 0.21 nRMSE: 1.61 r: 0.30 r 2 : -0.08 Predicted (kg/ha)

13. Regional accuracy (area distributions) -- 2005 GNN single year (2005) vs. FIA Annual sample (2001-2010) GNN (k=1) maintains distributions in plot sample, even for attributes poorly predicted at local scale Map has no “unsampled” area Live ≥ 3 cm (kg/ha) Snags ≥ 12 cm (kg/ha) CWD ≥ 25 cm (kg/ha)

14. Agreement Coefficient (Ji and Gallo 2006) Compares GNN to FIA Annual plots (n=1,560) over a range of scales AC UNS poor at plot scale, improves with scale AC SYS is good at all scales AC UNS : -0.39 AC SYS : 0.92 AC UNS : 0.66 AC SYS : 0.96 AC UNS : 0.92 AC SYS : 0.97 Plots 10-km hexagons (8,660 ha) 30-km hexagons (78,100 ha) Live above-ground biomass (trees ≥ 3) (Mg/ha)

15. Mapping live biomass change, 1990-2010 1990 B-G-W2010 B-G-WDisturbance 1990 (mg/ha)2010 (mg/ha)Change (mg/ha) Land- Trendr GNN - - 10 miles - -  10 km 

16. Land ownership and allocation in the Oregon and California Cascades movie location 90 km

17. Total live above-ground biomass (trees ≥ 3 cm) (1990 – 2010)  90 km  0 – 54.6 884.1 – 1,374.4 54.6 – 125.7 125.7 – 202.4 202.4 – 281.8 281.8 – 373.0 373.0 – 475.8 475.8 – 581.0 581.0 – 724.0 724.0 – 884.1 Mg/ha (metric tons) Nonforest

18. Biomass of snags ≥ 12.5 cm (1990 – 2010)  90 km  0 – 6.2 253.3 – 378.1 6.2 – 15.6 15.6 – 25.9 25.9 – 37.7 37.7 – 54.2 54.2 – 76.0 76.0 – 99.6 99.6 – 171.1 171.1 – 253.3 Mg/ha (metric tons) Nonforest

19. Disturbance and live biomass change (%), 1990-2010 Net loss in harvested landscapes (nonfederal) Net loss in areas of natural disturbance (fire, I&D) Net gain in areas of regrowth (federal reserves) GNN live % change Land- Trendr % disturbed

20. Disturbance and snag biomass change (%), 1990-2010 Net loss in harvested landscapes (nonfederal) Net gain in areas of natural disturbance (fire, I&D) Land- Trendr % disturbed GNN snag % change

21. Disturbance and CWD biomass change (%), 1990-2010 Disturbance effects are more subtle, at least in the short term Land- Trendr % disturbed GNN CWD % change

22. Disturbance and biomass change (live, snag, down) All disturbances are not created equal: –Harvest results in net biomass loss (live and snag) –Natural disturbances result in flux between live and dead pools Components of structural diversity don’t vary in tandem –Disturbance ≠ biomass (or habitat) loss –Live biomass loss may coincide with gain in diverse early seral forest Patterns of change are complex in both time and space –Stay tuned! LiveSnagCWD

23. Lessons learned: multi-temporal GNN for monitoring Only feasible with “temporally normalized” imagery –Reliability rests on assumption of normalization, particularly in older, undisturbed, stable forest –Technical challenges are substantial, especially in “production” Large error in mapped change at pixel scale, but reasonable trends emerge at coarser grains