Integrating time series of Landsat-based information into FIA's estimation process RMRS: Gretchen Moisen, Todd Schroeder, Sean Healey, Ray Czaplewski PNW: Warren B. Cohen WO: Ken Brewer UMD: Sam Goward, Karen Schleeweis FIA Nat’l User Group Meeting— 7-8 March2012 1

Status: How much is out there now, ….and where is it? Change: What just happened? Trend: What’s happening? Some Simple Questions 2

Status: Change: Trend: How’s FIA Doing? A- I A …for effort I …for accomplishment

Outline 3. NAFD Phase 3 4. How can we integrate Landsat time series into FIA’s estimation processes? 1. Forest disturbance and monitoring 2. History of the North American Forest Dynamics (NAFD) Project

Impacts ~ 1-3% of a forest area per year Occurs at different spatial scales, temporal scales, and intensities Can impact canopy, understory and forest floor Climate change and growing human population may alter the frequency and severity of future disturbance regimes Monitoring has taken on renewed importance FireClearcut SpatialTemporal Forest Disturbance

6 Disturbance and Time (Brewer, 2009)

7 Disturbance and Space (Brewer, 2009)

Monitoring Through Plots Unbiased estimates at broad scales Sampling error is well understood Measurement error can be assumed to be negligible for many variables Results are not spatially explicit at the local level Revisit frequency may miss disturbance events Post-dating is problematic Difficult to see upper canopy disturbances from the ground

Monitoring Through Landsat Time Series 16-day repeat cycle and 40-year historical archive allows development of dense image times series which can be used to detect changes in forest cover over large areas. Spatial grain (30m) and variety of spectral bands allows detection and causal attribution of most natural and anthropogenic disturbances. Can be used for mapping forest change and for collecting human interpreted reference data (e.g. Timesync). There is no sampling error BUT measurement error is variable and often poorly understood.

Different monitoring methods are appropriate for different purposes Joining traditional forest inventory data with temporally dense satellite data results in new information for monitoring change and trend

Outline 3. NAFD Phase 3 4. How can we integrate Landsat time series into FIA’s estimation processes? 1. Forest disturbance and monitoring 2. History of the North American Forest Dynamics (NAFD) Project

North American Forest Dynamics (NAFD) (UMD, NASA-Goddard, FIA, PNW, NRS, CFS, CONAFOR)

North American Forest Dynamics NASA-funded project designed to characterize disturbance patterns and recovery rates of forests across the continent Goal: Determine the role of forest dynamics in North American carbon balance

Phase I & II Sample Sites Eastern Stratum Western Stratum Phase I Phase II Phase I Phase II Processed time series ( ) of Landsat satellite imagery using FIA inventory data for validation and training

Vegetation Change Tracker Lake Anna, VA, 60 km NW of Richmond, VA (Huang et al. 2006, 2008)

NAFD “Science” (NASA, PNW, UMD, CONAFOR, CFS, and others) Characterizing disturbance and regrowth patterns on US forests by analyzing a biennial time series of Landsat imagery over a sample of Landsat data cubes spread across US forests. Objectives include: 1.Produce nationwide estimates of forest dynamics for NACP 2.Convert data cube reflectance to data cube biomass 3.Develop nationwide maps predictions of forest dynamics 4.Begin trials in Canada and Mexico 5.Quantify forest component of woody encroachment nationally

NAFD “Applications” (NASA, PNW, UMD, all FIA units) Illustrate how FIA data can be combined with temporal disturbance and biomass products to answer management questions relevant to FIA users. Developed FIA monitoring products that take advantage of satellite-derived disturbance and biomass data (storm- related loss, harvest rates across time and ownerships, fragmentation, carbon considerations) Note studies by Sean Healey, Mark Nelson, Randy Morin, Hobie Perry, Andy Lister, John Coulston, and others

Detour: A Model for Collaboration Pre-proposal communication with FIA Engagement of FIA scientists and managers Common problem identification Memorandum of Understanding Sensitivity to logistical and political constraints Patience

Outline 3. NAFD Phase 3 4. How can we integrate Landsat time series into FIA’s estimation processes? 1. Forest disturbance and monitoring 2. History of the North American Forest Dynamics (NAFD) Project

NAFD Phase 3 (Goward, Huang,Cohen, Masek, Moisen, Nemani) 1) Conduct an annual, wall-to-wall analysis of US disturbance history between ) Undertake a detailed validation of the resultant national disturbance map 3) Examine variation in post-disturbance forest recovery trajectories, using repeat measurements from FIA plot data, 4) Determine disturbance causal agents ***

Cause of Disturbance Maps Disturbance YearDisturbance Type

GeoDatabase Forest Change Processes User Community Change AgentForestry Suburbanization/ Urbanization Pests and Pathogens Hurricanes/ Tornadoes FiresConversion Data Source Timber Treatment & Removals Decadal Census – # new housing units Digitized Aerial sketches of insect damage Ground measurements-wind speed Landsat NDVI change Landsat change detection Reference UFSF FIA (Smith et al. 2009) (Theobald 2004) US Forest Health Program s/r3/resources/heal th/fid_surveys.shtm l s/r3/resources/heal th/fid_surveys.shtm l U.S. National Hurricane Center (Jarvinen et al. 1984) MTBS (Eidenshenk et al. 2007) NLCD Retrofit Data Set (Fry et al. 2009) Spatial Grain County polygons or > 100m grid polygon <1 ha to county lines30m grid30m Extent sampled - national nationalsampled - nationalnational National Temporal Grain 5-10 year cyclesdecadalannual decadal Extent varies by region varies by region Web Browser/Distribution Database of Forest Change Processes (Scleeweis et al., In Review)

Incorporating Textural Metrics Patch level spatial metrics Continuous Discrete Homogeneity Edge Contrast Heterogeneity Texture Range/Mean HarvestFireSuburbanization Different disturbance processes result in different patterns of landscape structure and fragmentation that are visible in Landsat Imagery. Shape Direction Fractal dimension Area Compactness

Spectral-Temporal Patterns of Disturbance Green Leaf AreaStructure (Schroeder et. al, 2010)

25 Pilot Phase for Attributing Cause of Disturbance Ten sample scenes were identified as good candidates for testing, representing a range of causal agents and varying forest types and prevalence.

Outline 3. NAFD Phase 3 4. How can we integrate Landsat time series into FIA’s estimation processes? 1. Forest disturbance and monitoring 2. History of the North American Forest Dynamics (NAFD) Project

Post-stratification Disturbance YearDisturbance Type

Alternatives to the Moving Average 28 (Czaplewski, 2008)

Endogenous Post-stratification (Breidt and Opsomer 2008; Dahlke et. al In Press, Tipton et. Al In Prep) 29 Using FIA as training data to make maps Then using those maps to post-stratification that same FIA data

Mapping Plot Attributes Through Time

31 Strategic Timing of Ground Observation BeforeAfter AREBA: Accelerated Remeasurement and Evaluation of Burned Areas (RSAC 2009)

Trend Anomaly LandTrendr (Kennedy et al.) TimeSync (Cohen et al.)

Plot History: Clearcut and recovery Andy Gray 33

Plot History: Defoliation, delayed mortality, recovery, salvage, and recovery Andy Gray 34

Very few disturbances are detected by both Timesync and FIA. FIA records lots of disturbances which are undetectable by Landsat (e.g. animal damage). 82% of disturbances detected only by Timesync fall outside FIA’s observation window (i.e. disturbance date is > 5 yrs before or is after plot measurement date). Disturbance is less common thus overall accuracy is inflated by high proportion of undisturbed plots. A Utah Example: Comparing FIA and Timesync Observations of Disturbance (Schroeder et. al, In Prep.)

36 Integrating Landsat time series into FIA’s estimation processes?

1.Make best use of (endogenous) post-stratification 2.Incorporate Landsat (photo)-based “observations” on field plots 3.Consider alternative sampling frequency for disturbed strata 4.Develop alternatives to the MA and make best use of RS data through model-assisted or model-based methods 5.Ensure compatibility between status maps and status estimates 6.Ensure compatibility in maps through time 7.Explore ways to reduce costs through these processes

FIA Nat’l User Group Meeting— 7-8 March Third phase of NAFD is providing annual maps of forest disturbance along with attribution, validation, and re-growth analyses nationwide We need to keep pushing our statistical tools beyond post-stratification and moving average into more integrated ground and RS approaches