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Mapping Current Vegetation in the Pacific Coast States with Gradient Nearest Neighbor Imputation Landscape Ecology, Modeling, Mapping, and Analysis (LEMMA)

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Presentation on theme: "Mapping Current Vegetation in the Pacific Coast States with Gradient Nearest Neighbor Imputation Landscape Ecology, Modeling, Mapping, and Analysis (LEMMA)"— Presentation transcript:

1 Mapping Current Vegetation in the Pacific Coast States with Gradient Nearest Neighbor Imputation Landscape Ecology, Modeling, Mapping, and Analysis (LEMMA) team ( : Janet Ohmann 1, Matt Gregory 2, Ken Pierce 1, Tim Holt 2, Heather May 2, Emilie Grossmann 2 Collaborators: Jeremy Fried 3, Jimmy Kagan 4, Ken Brewer 5, Miles Hemstrom 6, Melinda Moeur 7, Tom DeMeo 7, Gary Lettman 8, Mike Wimberly 9 1 USDA FS, PNW, Ecosystem Processes; 2 Oregon State University, Forest Science Department; 3 USDA FS, PNW, Forest Inventory and Analysis; 4 Oregon State University, Institute of Natural Resources; 5 USDA FS, Remote Sensing and Applications Center; 6 USDA FS, PNW, Focused Science Delivery; 7 USDA FS, Region 6; 8 Oregon Department of Forestry; 9 South Dakota State University

2 Outline... Motivations for GNN Key attributes of GNN –Canonical correspondence analysis Overview of GNN applications (or focus on GAP Ecological Systems mapping???) Areas of research interest

3 Primary motivation (GNN niche): supply missing data for analysis and modeling of forest ecosystems at the regional level (not estimation of sub-population totals) - Gradient Nearest Neighbor Method Satellite imagery GIS data Landscape vegetation map Fuel models, wildlife models, etc. Fuel maps Field plots Predicted future landscapes Stand and landscape simulators (FVS-FFE, VDDT, TELSA, etc.) Fire behavior models (FARSITE, FLAMMAP) Fire effects models (FOFEM, CONSUME) Habitat maps Etc.

4 Criteria for maps of current vegetation in the Pacific Northwest Spatially complete (spatial pattern, small geographic areas) Consistent across large, multi-ownership regions Rich in detail on species composition and forest structure –Reasonable covariance structure among attributes Suitable for input to stand and landscape simulation models Flexibility to meet a variety of analytical needs –Too expensive to develop multiple vegetation maps at the regional scale –Advantages in using consistent information for multiple analyses, by multiple agencies

5 COLA CLAMS, GNNfire GNNFire GNN projects GNN mapping in the Pacific Coast States Analysis of forest policy effects via landscape modeling and scenario analysis (CLAMS, COLA, IMAP) Regional risk assessments (WWETAC, RSAC 250-m study) Assessing fire hazard, planning fuel management, modeling fire behavior and effects (GNNFire) Regional inventory and monitoring (FIA, NWFP Effectiveness Monitoring) National Forest planning, BLM cumulative effects analysis Conservation planning (GAP) 3-D visualization using computer gaming technology (JFSP) GAP

6 Overview of Gradient Nearest Neighbor Imputation (GNN)

7 Components of GNN Imputation (Current Incarnation) Statistical model: –direct gradient analysis (canonical correspondence analysis) –explanatory variables: satellite imagery, other GIS layers –response variables = abundance (basal area, importance value, cover, etc.) of ‘species’ on plots (FIA, CVS, etc.) –other vegetation variables retained with plot-map link Similarity measure: –Euclidean distance in n-dimensional gradient space, where n = first n axes (usually 8) from CCA –Axes weighted by their explanatory power (eigenvalues) Imputation method: –Single nearest neighbor (k=1), to maintain covariance structure –Summary statistics of multiple neighbors

8 Why Canonical Correspondence Analysis??? (ter Braak 1986) Multivariate predictors and multivariate response –Results in a weight for each of many spatial variables, based on its relationship with the multiple response variables Grounded in ecological theory, widely used in practice –Heuristic mathematical approximation to a Gaussian response curve of species along environment gradients, but robust to violations (Palmer 1993) –Used primarily for exploratory and descriptive studies, not prediction or hypothesis testing Robust to sparse data matrices (species on plots), common across regions with long gradients and high species turnover (Palmer 1993) Robust to multicollinearity among explanatory variables (Palmer 1993) Alternative models can be specified, depending on objectives

9 Canonical Correspondence Analysis (CCA) Algorithms LC = linear combination site scores (used in GNN) WA scores = weighted averaging site scores End result: ordering of plots (LC scores) along n orthogonal axes such that most similar plots are nearest one another Assign species scores as weighted average of LC scores Arbitrarily assign LC scores Assign WA scores as weighted average of species scores Create LC scores as predicted values from multiple regression Start Stop Any change in scores? Yes No (adapted from Palmer 1993)

10 Guru #1: yes Guru #2: no Guru #3: both Guru #4: dumb question Guru #5: why do you want to know? Ask the ordination guru... “Is CCA non-parametric?”

11 Gradient Nearest Neighbor Method Plot data Climate Geology Topography Ownership Remote sensing PredictionSpatial data Plot locations Direct gradient analysis Plot assigned to each pixel Statistical model Imputation Pixel PSME (m 2 /ha) CanCov (%) Snags >50 cm (trees/ha) Old-growth index Etc

12 Regional Plot Data Sourcen (OR)n (WA) FIA (nonfederal) BLM (BLM)99-- CVS (Natl. Forest)2791,596 Ecology (Natl. Park)--52 Total7632,093 Coastal Oregon Eastern Washington * Plot layout (~1 ha) Vegetation data: Live trees Snags Down wood Understory vegetation

13 Landsat TMBands, transformations, texture ClimateMeans, seasonal variability TopographyElevation, slope, aspect, solar DisturbancePast fires, harvest, I&D LocationX, Y OwnershipPublic, private Eastern Washington Coastal Oregon Explanatory Variables

14 (2) calculate axis scores of pixel from mapped data layers (3) find nearest- neighbor plot in gradient space Axis 2 (climate) gradient spacegeographic space Axis 1 (Landsat) (1) conduct gradient analysis of plot data field plots study area (4) impute nearest neighbor’s ground data to mapped pixel The imputation component of GNN

15 GNN model specification Species Species + structure Structur e Image segments (polygons), watersheds (imagery not used) Median-filtered√√ Unfiltered√√ Coarse grain Fine grain Model response variables Spatial grain of Landsat variables Emphasis on species composition Emphasis on forest structure ‘Tuning’ of GNN models

16 Accuracy assessment (‘obsessive transparency’) Local-scale accuracy (at plot locations) via cross-validation: –Confusion matrices –Kappa statistics –Correlation statistics Regional-scale accuracy: –distribution of forest conditions in map vs. plot sample –range of variation in map vs. plot sample Spatial depictions: –Variation among k nearest neighbors –Distance to nearest neighbor(s) (sampling sufficiency) General findings re. GNN map accuracy: –Excellent for regional patterns and amounts, imperfect for local sites (mid-scales???) –Appropriate for regional planning and policy analysis

17 Results: What factors are associated with regional gradients in forest composition and structure?

18 What factors are associated with vegetation gradients? (variation explained in GNN models, % of total inertia) Subset of explanatory variables Species composition (‘species’ model) Stand structure (‘species-size’ model) Coastal Oregon Central Oregon Calif. Sierra Coastal Oregon Central Oregon Calif. Sierra Topography* Climate Location Disturbance: Landsat Ownership ? Full model * Includes elevation.

19 Dominant Gradients in Forest Structure (coastal Oregon) Explanatory Variables: yellow=climate, pink = topography, white = location, blue = Landsat TM, red = disturbance

20 Dominant Gradients in Forest Structure (California) Explanatory Variables: yellow=climate, pink = topography, white = location, blue = Landsat TM, red = disturbance ANNPRE ANNSW ANNVP AUGMAXT CONTPRE DECMINT SMRTP X Y TC1 TC2 TC3 TC4 TC5 ADTC1 ADTC3 ADTC4 DEM PRR SLP CHG FIRE Axis 1 Axis 2

21 QUCH2 Tree Species Positions Along Dominant Gradients (California) red = hardwoods blue = high-elevation Axis 1 Axis 2 low precip., southeast high precip., northwest low elevation, warm, high moisture stress high elevation, cold, low moisture stress ABCO ABMA ACMA3 AECA ALRH2 ARME CADE27 CONU4 FRLA JUGLA JUOC JUOS LIDE3 PIAL PIAT PIBA PICO PICO3 PIJE PILA PIMO PIMO3 PIPO PISA2 PLRA POBAT POFR2 POTR5 PSME QUDO QUGA4 QUKE QULO QUWI2 SALIX SEGI2 TABR2 TOCA TSME UMCA

22 Structure Axis 1 Coastal Oregon: Dominant Gradients in Vegetation and Environment low elevation Young forests, open canopies, hardwoods, private lands Old forests, closed canopies, public lands Species Axis 1 Species Axis 2 interior climate high elevationmaritime climate

23 Dominant Gradients in California (Scores on CCA Axes) Axis 1, species and structure Axis 2, species high elevation, cold, low moisture stress low elevation, warm, high moisture stress high precip., northwest low precip., southeast Axis 2, structure high greenness, high wetness, high precip. low greenness, low wetness, low precip.

24 Species Gradients (Linked to Environment) CCA axis 1 (climate) CCA axis 2 (elevation) Maritime Interior (Valley) Forest Types Picea sitchensis Tsuga heterophylla Quercus woodlands Abies amabilis/ procera Dry T. heterophylla/ mixed evergreen High Low Pacific Ocean (Ohmann and Gregory, in press)

25 Forest Structural Conditions Young Old Coast: linked to disturbance history and ownership Cascades: confounding of environment, disturbance, ownership

26 GNN Kappa Statistics for Species Presence/Absence Tree species Coasta l OR Central OR Casc. East. WA CA Sierra OR Blue Mtns. Abies amabilis Abies concolor/grandis Abies procera Juniperus occidentalis Pinus contorta Pinus ponderosa Pseudotsuga menzieseii Thuja plicata Tsuga heterophylla Tsuga menzieseii Acer macrophyllum Alnus rubra

27 Example applications of GNN maps - Gradient Nearest Neighbor Method Satellite imagery GIS data Landscape vegetation map Fuel models, wildlife models, etc. Fuel maps Field plots Predicted future landscapes Stand and landscape simulators (FVS-FFE, VDDT, TELSA, etc.) Fire behavior models (FARSITE, FLAMMAP) Fire effects models (FOFEM, CONSUME) Habitat maps Etc.

28 Coastal Landscape Analysis and Modeling Study (CLAMS): a simulation approach Current policy Alt A Alt B Alt C Natural Processes Landowner Behavior t =1 Biophysical Response t =n Landscape/ Watershed Condition t =1 t =n Socio-economic Response t =1 Coast Range Ecosystem t =n Conceptual model Map of current vegetation needed for: Stand and landscape simulation models Response models for wildlife, aquatic, timber Requires: – tree list for each pixel – species and structure – reasonable covariance structure – fine-scale pattern

29 100-Year Change in Vegetation Classes 1996 (GNN) 2096 projected (base policy) 1996 (GNN) 2096 projected (base policy) (Spies et al., in press)

30 Northern Spotted Owl Habitat Capability Index Nesting capability (patch level) –Trees/ha >100 cm dbh –Diameter Diversity Index Foraging capability (patch/landscape level) –Canopy height –Diameter Diversity Index –Habitat availability within 2.2 km 1996 (GNN) 2096 projected (base policy) (McComb et al. 2002)

31 * Wimberly and Ohmann (2004) Coastal Oregon: Change in Large Conifer Forest, * % change

32 Shrub/Tree Regeneration 0 – 25 years Open Mid-height Shrub Grass/Forb 1 – 15 years Closed Herbland Interior Ponderosa Pine 150 years or more Late-Seral, Single-Layer Forest Interior ponderosa pine 150 years or more Late-Seral, Multi-layer Forest Interior Ponderosa Pine 40 – 85 years Stem Exclusion Forest Interior Ponderosa Pine 20 – 60 years Forest Stand Initiation Interior Ponderosa Pine 75 – 175 years Forest Understory Reinitiation Crown Fire Mixed Severity Fire Insects or Disease Ground Fire Ground Fire Ground Fire Insects or Disease Growth and Development Growth and Development Growth and Development Growth and Development Growth and Development Growth and Development VDDT state-and-transition model for warm, dry ponderosa pine forest

33 100-year change in ‘Giant Tree’ forest, Central Oregon Cascades Pixel data aggregated to strata defined by 5 th -field HUC, owner class, potential vegetation type, cover type, structure class Aggregated data input to VDDT state-and-transition models for scenario analysis Approach adopted by IMAP across Pacific Northwest (Hemstrom et al.)

34 Fuel Models in Yosemite (GNN species-size model)

35 FLAMMAP Inputs Canopy bulk density Fuel model Moderate Fuel Moisture, 10 mph Wind Very Low Fuel Moisture 25 mph Wind FLAMMAP Outputs

36 Mapping Ecological Systems (ESs) for Gap Analysis Program Relatively new national classification of existing vegetation, based on floristics (Comer et al. 2003) Use GNN ‘species model’ to map plant communities Classify plots into ESs –Guidance from qualitative descriptions and LANDFIRE ‘sequence table’ rules based on species relative abundance –Species often are site indicators (mesic vs. dry) and/or understory species, inconsistently measured on inventory plots –Sensitive to minor shifts in relative abundance, often caused by disturbance and/or localized microsites (within-plot variability) ‘Mask’ GNN (forest) model with nonforest maps from other sources

37 Forested Ecological Systems of the Blue Mountains Ecoregion, Eastern Oregon

38 Kappa statistics for 18 forest Ecological Systems % correct: 52%, % ‘fuzzy’’ correct: 79% Many ESs are rare (only 4 are >5% of forest area) Many ESs are similar (mixed-conifer species) Confusion with nonforest ESs not shown Ecological SystemKappa Fuzzy kappa NRM W. larch RM aspen CP w. juniper EC mesic montane mixed-conifer NP mountain hemlock0.00 NRM dry-mesic montane mixed-conifer NRM subalpine NRM mesic montane mixed conifer RM lodgepole pine NRM ponderosa pine RM subalpine dry-mesic spruce-fir RM subalpine mesic spruce-fir RM subalpine-montane limber- bristlecone pine MRM montane Douglas-fir RM poor site lodgepole pine EC oak-ponderosa pine IMB mountain mahogany0.00 CP = Columbia Plateau EC = East Cascades IMB = Inter-mountain basins NP = North Pacific NRM = northern Rocky Mountain RM = Rocky Mountain

39 Area of Ecological Systems from plot-based estimates and GNN prediction (Blue Mtns.)

40 GNN-predicted occurrence of Juniperus occidentalis in the Central Oregon Cascades Species model (tree species) (n=1415, kappa=0.72)

41 Ongoing areas of research for gradient modeling... More and better data (e.g., LIDAR, nonforest vegetation) Map evaluation tools (spatial error depictions, assessing spatial pattern) Comparison of alternative statistical models (imputation, other) Isses of spatial scale and pattern –Fine-scale heterogeneity and pattern: What is real and what is useful? Optimal spatial resolution? Pixels vs. polygons? –Multi-scale and hierarchical modeling approaches, e.g. partial bivariate scaling (Thompson and McGarigal 2002), hierarchical variance partitioning (Cushman and McGarigal 2003, 2004) Ecological research –Ecological characterization –Linkages to stand and landscape models for ecological analysis Technology transfer: –Database and software systems to support imputation mapping –Tools for serving and interacting with maps (web server, visualization using computing gaming software)


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