Operational Regional Carbon Assessment Andrew Hudak1, Patrick Fekety2, Michael Falkowski3, Robert Kennedy4, Nick Crookston5, Grant Domke6, Tristan O’Mara7, Van Kane7, Bob McGaughey8, Peter Gould9, George McFadden10, Alistair Smith11, Nancy Glenn12, Jinwei Dong13 1US Forest Service, Rocky Mountain Research Station 2University of Minnesota 3Colorado State University 4Oregon State University 5Private Consultant 6US Forest Service, Northern Research Station 7University of Washington 8US Forest Service, Pacific Northwest Research Station 9Washington Department of Natural Resources 10Bureau of Land Management, Oregon/Washington State Office 11University of Idaho 12Boise State University 13Oklahoma State University NASA Carbon Monitoring Systems Program (Project: 14-CMS14-0026; Award: NNH15AZ06I)

Bottom-Up Approach Carbon Monitoring System (CMS) Light Detection and Ranging (LiDAR) point cloud at the scale of a 400 m2 forest inventory plot Tree Plot Landscape Region SCALE Forest Inventory Airborne LiDAR DATA Landsat Time Series (LandTrendr) and Climate MODEL Forest Vegetation Simulator (Current & Future AGB) Random Forests

Reference Plot Database (This “living” database is growing as more reference data are collected.) Number of field plots by state ID: 942 (17 data sets) WA: 1469 (3 data sets) OR: 1261 (4 data sets)  

“Living” Database of Project-Level Reference Plots (This “living” database is comprised of field AND lidar reference data.) Build a database of reference plots. This database includes the lidar metrics along with the field collected measurements. Tree diameters, heights, etc. Stratify the landscape so you sample the different structural conditions In this example, there are 4 reference plots. The graphs to the left of the figures represent the distribution of LiDAR returns from the plot. The images are examples of what the stand might look like. The reference plots are used to build a prediction model. P. A. Fekety

Highlighting the AGB distribution from field plots I want to highlight the heteroscadastic nature of AGB. This common in natural systems, and RF does not require data to be normally distributed.

Model Specification – LiDAR Model Response variable – AGB Candidate predictor variables: Lidar (canopy height and density) metrics Topographic metrics Climate metrics Remove multicollinear variables (cor ≥ 0.85) Select predictors using rfUtilities package in R Model selection tool was run 100 times Predict AGB using Random Forests Pct 1st returns > 1.37 m is essentially a measure of canopy cover. The last 2 are measures of canopy density.

Plot-level LiDAR AGB vs Field Plot AGB Lidar Model training data [1] "Canopy.relief.ratio" [2] "Elev.mean" [3] "Elev.P05" [4] "Elev.stddev" [5] "Elev.strata..16.00.to.32.00..return.proportion" [6] "Elev.strata..48.00.to.64.00..return.proportion" [7] "Elev.strata..above.64.00..return.proportion" [8] "Percentage.first.returns.above.2.00" Observed vs predicted for the lidar – based model Correlation is the Spearman Rank coefficient b/c the data are not normally distributed. More scatter at the higher biomass values

Northern and central Idaho 10-15 counties 944 field plots Sites in Idaho that have lidar data that are Included in this study. Northern and central Idaho 10-15 counties 944 field plots ~55 disjunct lidar collections (24 with plot data) 858,000 ha (420,000 ha)

Map AGB and AGB uncertainty (e.g., Clearwater National Forest) Lidar- derived AGB for a region along the Clearwater River. Clearwater River / Hwy 12 is the low biomass region through the center. Highlights the management activity on the Nez Perce – Clearwater NF. Standard deviation of the terminal nodes for Lidar- derived AGB for a region along the Clearwater River. Clearwater River / Hwy 12 is the low biomass region through the center. Highlights that std dev of the terminal nodes is positively correlated with AGB.

LandTrendr (LT) data LandTrendr - Landsat based detection of trends in disturbance and recovery algorithm (Kennedy et al., 2010) Input: Annual Landsat images stacked from 1984-2016 Output: Trajectories describing trends for each 30-m pixel from multiple spectral variables Primary predictors we are using for annual AGB prediction are the tasselled cap indices: Brightness Greenness Wetness Other important LT metrics: Magnitude of greatest disturbance Time since disturbance http://landtrendr.forestry.oregonstate.edu/content/how-landtrendr-works

Model Specification – Landsat Model Drew a stratified random sample of pixels from the 30m AGB maps 20 levels of AGB 20,000 pixels randomly selected for training data, using Landsat data matching the year of the lidar collection. rfUtilities package in R Same predictor variable selection procedure as the lidar model Validation Drew an additional 20,000 pixels to test the model Training pixels were chosen within 5 strata of AGB based of percentiles (strata represent equal proportion of landscape) 4 LT variables, 2 Climate Variables I am doing model predictions on a lat / long tile basis. That is why I am able to pick up a few counties in Washington.

Pixel-level Landsat AGB vs LiDAR AGB Landsat Model training data ~20,000 pixels [1] "aspect" [2] "delta_tcw" [3] "ftv_tcb" [4] "ftv_tcg" [5] "ftv_tcw" [6] "mtcm_tenths" [7] "SCosAsp" [8] "SimardCHM" [9] "sprp" [10] "SSinAsp" This is for the 20,000 pixels that were selected to be validation data. These pixels were selected using the same stratification as the AGB training pixels (5 strata of AGB, 4000 pixels per strata) The scale (0 – 800) represents the range of field plot AGB estimates (i.e. lidar model training data) Over predicts at the low AGB, Underpredicts the high biomass … this is expected from ensemble methods -> predictions are pulled toward the mean. Transformed aspect- I use the transformation that Nick used.  (1 - cos((TopoMetrics$aspect - 30) * pi/180))/2

Monitoring, Reporting, Verification (MRV) 2010 Summarize annual AGB maps at county level Apply Forest / Nonforest Mask Compare to independent, annual, county-level AGB summaries prepared nationally by the USFS Forest Inventory and Analysis (FIA) Program FIA provides an unbiased estimate of AGB Report county-level AGB biases Summarize biases spatially and temporally Regional AGB map (landsat model) for year=2000

FIA (Blackard et al. 2008) 250 m Forest / nonforest mask – Blackard (published resolution = 250 m) Latah County, Idaho

Inland West FIA (Blackard 2009) FIA (Blackard et al. 2008) Inland West FIA (Blackard 2009) 250 m 250 m Forest / nonforest mask – IW FIA (published resolution = 250 m) Latah County, Idaho

Inland West FIA (Blackard 2009) FIA (Blackard et al. 2008) Inland West FIA (Blackard 2009) 250 m 250 m Gap Analysis Program (2011) 30 m Forest / nonforest mask - GAP (published resolution = 30 m) Latah County, Idaho

Inland West FIA (Blackard 2009) PALSAR JAXA (2014) FIA (Blackard et al. 2008) Inland West FIA (Blackard 2009) PALSAR JAXA (2014) 250 m Gap Analysis Program (2011) 30 m Forest / nonforest mask – PALSAR (published resolution ~ 30 m; using a Lat / lon mask) Latah County, Idaho !!Notice the presence of Moscow, and the harvest units being classified as non-forest

(n = 20 counties in northern Idaho and western Montana) Mean county-level AGB map biases using different forest/nonforest masks compared to FIA AGB (Tg) (n = 20 counties in northern Idaho and western Montana) Total AGB and %Bias using different masks. This only includes 18 counties in Idaho and 2 counties in MT. WA and OR were not included because IW FIA mask does not cover those states. Mapping Goals: Temporal: Annual 2000-2016 Spatial: 30m resolution across Northwest USA

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FIA AGB On ID Forested Land: ID Map AGB = 873.5 Tg ID FIA AGB = 840.4 Tg On ID Forested Land: ID Bias = 33.1 Tg Bias = Map AGB – FIA AGB % Bias = (Map AGB – FIA AGB) * 100 FIA AGB ID % Bias = 3.9 %

ID: 942 (17 data sets) WA: 1469 (3 data sets) OR: 1261 (4 data sets) Training Plots: (non-FIA) Figure is out of date…there is much more lidar coverage that we’re using, especially in WA and OR.

Questions? Citations [1] Kennedy, R. E., Z. Yang, and W. B. Cohen. 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms. Remote Sensing of Environment. 114:2897-2910. doi:10.1016/j.rse.2010.07.008   [2] Crookston, N. 2016. Climate Estimates and Plant-Climate Relationships. Climate-FVS [Available: http://charcoal.cnre.vt.edu/climate/customData/fvs_data.php] [3] Blackard, J.A. and 18 others. 2008. Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information. Remote Sensing of Environment. 112:1658-1677. [Available: http://data.fs.usda.gov/geodata/rastergateway/biomass/] [4] Blackard, Jock A. 2009. IW-FIA Predicted Forest Attribute Maps - 2005. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. http://dx.doi.org/10.2737/RDS-2009-0010 [5] PALSAR JAXA. 2014. New global 25m-resolution PALSAR mosaic and forest/non-forest map (2007-2010) - version 1. Japan Aerospace Exploration Agency Earth Observation Research Center. [Available: http://www.eorc.jaxa.jp/ALOS/en/palsar_fnf/fnf_index.htm] [6] Miles, P.D. Forest Inventory EVALIDator web-application Version 1.6.0.03. St. Paul, MN: U.S. Department of Agriculture, Forest Service, Northern Research Station. [Available only on internet: http://apps.fs.fed.us/Evalidator/evalidator.jsp] [7] US Geological Survey, Gap Analysis Program (GAP). May 2011. National Land Cover, Version 2 [Available: https://gapanalysis.usgs.gov/gaplandcover/data] Acknowledgements This research was funded by the NASA Carbon Monitoring Systems Program (Grant: 14-CMS14-0026, Award: NNH15AZ06I) and through Joint Venture Agreements with the University of Minnesota (15-JV-044), Colorado State University (16-JV-061), Oregon State University (15-JV-041), and the University of Idaho (15-JV-040).