Spatially explicit information regarding a wide range of forest attributes is often required for land

management decision-support, inventory and monitoring, and policy formulation. Nearest neighbor (NN)

imputation maps are widely used in the Pacific Northwest region of the US, when spatially explicit

information regarding a wide range of forest attributes are required at regional and watershed scales.

However, regional NN imputation maps have generally not been suitable for fine-scale (i.e. stand level)

decision-making for activities such as fuel reduction and forest restoration treatments, which would benefit

greatly from NN imputation maps developed for both regional and stand-level applications.

NN imputation maps are predicated on relating forest attributes from field plots to spatial predictors such as

climate, topography, satellite imagery, and LiDAR data. Potential sources of error in NN imputation maps

include the incorrect extraction of remotely sensed spatial predictors for a given field plot, which can result

from poor positional accuracy of field plots or poor geo-referencing of spatial predictors such as satellite

imagery. The extent to which positional accuracy of these two data sources (plots and satellite imagery)

influences NN imputation map accuracy is poorly understood, but is increasingly important if NN

imputation maps are developed for stand level applications. To address these questions we wanted to:

1. Quantify the positional error of federal forest inventory plots and Landsat TM imagery within a 500,000

ha area of Eastern Oregon Cascades, USA.

2. Quantify individual and combined influences of positional error from plot locations and Landsat TM

imagery on NN imputation map accuracy for a variety of forest composition and structural attributes.

3. Determine if the influence of field plot and Landsat TM positional error on NN imputation accuracy is

greater when NN imputation maps are developed using field data of smaller spatial grain.

Influence of inventory plot and Landsat imagery positional accuracies on nearest-neighbor (NN) imputation maps

of vegetation composition and structure. Harold Zald, College of Forestry, Oregon State University, Corvallis, OR USA

Janet Ohmann, Pacific Northwest Research Station, USDA Forest Service, Corvallis, OR USA

Heather Roberts, College of Forestry, Oregon State University, Corvallis, OR USA

Robert McGaughey, Pacific Northwest Research Station, USDA Forest Service, Seattle, WA USA

Matt Gregory, College of Forestry, Oregon State University, Corvallis, OR USA

Robert Kennedy, Department of Earth and Environment, Boston University, Boston, MA USA

Introduction

Objectives

Study Area

Plot & Imagery Accuracy Methods

Plot & Imagery Positional Accuracy

Results: Species Composition

Conclusions

Acknowledgements

References

Data Sources

The study area encompasses 539,269 ha (1,332,561 ac) of the

Eastern Oregon Cascades, covering most of the Deschutes

National Forest (gray area in map).

Large-scale gradients of topography, climate, and parent material

influence vegetation composition and productivity in the study

area. Western proportions of the study area are characterized by

high elevations, high precipitation, and moderated temperature

ranges; resulting in montane and subalpine conifer forests. The

eastern portion of the study area is lower in elevation, and has a

more arid and continental climate; resulting in lower productivity

drier conifer forests.

In contrast to large-scale species composition gradients, variability

in forest structure is more strongly influenced by smaller-scale

disturbances such as wildfire, insect outbreaks, and timber

harvests.

232 plots from the USDA Forest Service Forest

Inventory and Analysis Program (FIA) and The

USDA Forest Service Region 6 Current Vegetation

Survey (R6). Plots measured between 2004-2009.

Vegetation attributes (% cover, DBH, TPH, Snags,

CWD, etc.) calculated at plot-level and subplot1.

Official coordinates collected using recreational

grade GPS at time of field measurement. Sub-meter

coordinates collected using a Trimble WAAS

enabled and post-processed DGPS during 2009-

2010.

Spatial predictors are grids with 30m cell.

Climate data comes from PRISM (Daly et al. 2008)

30 year normals (1971-2001).

Topography and LiDAR vegetation structure from

LiDAR data collected in 2009 and 2010.

Topographic spatial predictors calculated from

LiDAR 30m bare earth model. Vegetation structure

spatial predictors calculated from LiDAR point

cloud using the grid metrics function in FUSION.

Choice of LiDAR-derived vegetation predictors

based on prior work relating LiDAR metrics to

forest structure in the Pacific Northwest region

(Hudak et al. 2008, Falkowski et al. 2010, Kane et

al. 2010)

Landsat TM images developed using the

LandTrendr algorithms (Kennedy et al. 2007,

2010), a trajectory-based change detection method

using a time-series of yearly Landsat TM images

that are cloud-free, geometrically corrected, and

radiometrically normalized.

Spatial predictors from LandTrendr include: fitted

tasseled cap brightness, greenness, and wetness

from 2004-2009 matching plot measurement years;

and 1984-2008 magnitude, duration, and years

since disturbance.

Subplot 1

FIA and R6

R6 Subplots

(7.32m radius)

FIA Subplots

(7.32m radius)

30m Landsat cell

Daly C., Halbleib M., Smith JI, Gibson WP, Doggett MK, Taylor GH, Curtis J, and PA Pasteris. 2008. Physiographically-sensitive mapping of temperature and

precipitation across the conterminous United States. International Journal of Climatology, 28: 2031-2064.

Falkowski MJ, Hudak AT, Crookston NL, Gessler PE, Uebler EH, and AMS Smith. 2010. Landscape-scale parameterization of a tree-level forest growth model: a k-

nearest neighbor imputation approach incorporating LiDAR data. Canadian Journal of Forest Research, 40: 184-199.

Hudak AT, Crookston NL, Evans JS, Hall DE, and MJ Falkowski. 2008. Nearest neighbor imputation of species-level, plot-scale structure attributes from LiDAR

data. Remote Sensing of Environment, 112: 2232-2245.

Kane VR, McGaughey RJ, Bakker JD, Gersonde RF, Lutz JA, and JF Franklin. 2010. Comparisons between field- and LiDAR-based measures of stand structural

complexity. Canadian Journal of Forest Research, 40: 761-773.

Kennedy RE, and WB Cohen. 2003. Automated designation of tie-points for image-to-image coregistration. International Journal of Remote Sensing, 24: 3467-

3490.

Kennedy RE, Cohen WB, and TA Schroeder. 2007. Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote

Sensing of Environment, 110: 370-386.

Kennedy RE, Yang Z, and WB Cohen. 2010. Detecting trends in forest disturbance and recover using yearly Landsat times series: 1. LandTrendr – Temporal

segmentation algorithms. Remote Sensing of Environment, 114: 2897-2810.

Ohmann, JL, MJ Gregory. 2002. Predictive mapping of forest composition and structure with direct gradient analysis and nearest-neighbor imputation in coastal

Oregon, USA. Canadian Journal of Forest Research, 32:725-741.

Pierce KB, Lookingbill T, and D Urban. 2005. A simple method for estimating potential relative radiation (PRR) for landscape-vegetation analysis. Landscape

Ecology, 20: 137-147.

Riemann R, Wilson BT, Lister A, and S Parks. 2010. An effective assessment protocol for continuous geospatial datasets of forest characteristics using USFS Forest

Inventory and Analysis (FIA) data. Remote Sensing of Environment, 114: 2337-2352

NN Imputation Methods

Official plot coordinates compared to sub-meter plot coordinates collected by

sub-meter GPS RMSE and distribution positional errors calculated.

2010 Landsat TM tasseled cap brightness image was referenced to a LiDAR-

derived canopy cover above 2m tall using ITPFind (Kennedy and Cohen

2003), an automated area-based method for identifying image tie-points.

Target and referenced image tie-point locations were compared to quantify

target image positional accuracy, Landsat TM geo-rectified using a 1st order

polynomial and nearest neighbor resampling in ERDAS IMAGINE.

Gradient Nearest Neighbor (GNN) method

of imputation mapping (Ohmann and

Gregory 2002), in which relationships

between forest attributes and spatial

predictors is quantified via Canonical

Correspondence Analysis

8 models comparing spatial grain of field

data, positional accuracy of plots and

imagery:

Observed versus predicted compared for a 3 x 3 pixel block

(plot-level), and single pixel (subplot 1 – level).

Accuracy of species predictions assessed with Kappa

statistic. Accuracy of structure predictions assessed using

selected protocols outlined by Riemann et al. (2010) at plot

and subplot scales:

1. Kolmogorov-Smirnov (KS) statistic calculated to quantify

maximum distance between observed and predicted

empirical cumulative distribution functions (ecdf)

2. Systematic difference (ACSYS) and unsystematic

difference (ACUNS) calculated to compare estimated bias

and random error of predictions between models.

KS, ACSYS, and ACUNS values presented are averages for the

following 7 selected structural attributes:

• Density shade intolerant species (TPHINTOL_ GE_3)

• Density shade tolerant species (TPHTOL_ GE_3)

• Density small trees 3-25 cm dbh (TPH_3_25)

• Density large conifers ≥ 75 cm dbh (TPHC_GE_75)

• Density of snags ≥ 25 cm dbh (STPH_GE_25)

• Density large snags ≥ 75 cm dbh (STPH_GE_75)

• Down wood volume ≥ 12 cm dia (DVPH_GE_12)

1:1 line

(ACSYS)

Systematic difference

measure of

“estimated bias”

(ACUNS)

Unsystematic difference

measure of random error

Geometric mean

functional relationship

(GMFR)

Observed values

Pre

dic

ted v

alues

Sub-meter DGPS accuracy for the 232 plots was 0.91m (95% CI

0.81 – 1.01m).

On average, accuracy of old official plot coordinates was poor

(RMSE_total = 24.69m)

39%, 12%, and 4% of official plot coordinates had positional error

greater than 15, 30, and 60 m respectively

Based on over 27,000 RTK survey points, LiDAR positional

accuracy was very high (RSME total = 0.04m)

On average, positional accuracy of 358 tie points was very good

(RSME = 7.6m).

9% of image tie-points had positional error greater than ½ width of

a Landsat TM pixel (> 15m), No image tie-points had positional

error greater 30m.

From plot and image positional accuracy results above, we would

expect plot positional errors to be more important for NN map

accuracy than positional errors in the Landsat TM imagery.

Model Field Data Plot Positional Accuracy Imagery Positional Accuracy

1 Plt-OldGPS-OldGeoRef Plot-level Old Official GPS Coordinates Old Original Geo-referenced

2 Plt-SubMeterGPS-OldGeoRef Plot-level New Sub-meter DGPS New Geo-rectified against LiDAR

3 Plt-OldGPS-NewGeoRef Plot-level Old Official GPS Coordinates Old Original Geo-referenced

4 Plt-SubMeterGPS-NewGeoRef Plot-level New Sub-meter DGPS New Geo-rectified against LiDAR

5 Subplt1-OldGPS-OldGeoRef Subplot 1 Old Official GPS Coordinates Old Original Geo-referenced

6 Subplt1-SubMeterGPS-OldGeoRef Subplot 1 New Sub-meter DGPS New Geo-rectified against LiDAR

7 Subplt1-OldGPS-NewGeoRef Subplot 1 Old Official GPS Coordinates Old Original Geo-referenced

8 Subplt1-SubMeterGPS-NewGeoRef Subplot 1 New Sub-meter DGPS New Geo-rectified against LiDAR

Important to note only a limited number and type of accuracy diagnostics used. For example, Kappa

statistic does not provide components of map error needed to assess potential limitations.

For this data set, finer spatial grain field data and increased positional accuracy of spatial

predictors did not improve accuracy of NN maps of species composition and structure.

Despite plot (and to a lesser extent Landsat TM imagery) positional errors that often were ≥ ½

pixel width of spatial predictors, positional accuracy consistently did not influence NN prediction

accuracy

Potential explanations for these findings include:

Poor sample size (232 plots)

Hybrid species-structure response matrix used in gradient analysis may confound relationships

between field data and spatial predictors operating separately for composition and structure at

large (climate, topography) and small (LiDAR, forest disturbance history) spatial scales.

Tuning separate models for composition and structure may be needed to maximize predictive

accuracy.

Higher positional accuracy may not matter if within context of forest spatial heterogeneity

operating at larger scales (i.e. larger gradients and patch sizes of species and structure).

Subplot may be too small of a sample unit, resulting in high measurement variability and

inadequate species/structural representation for relating to spatial predictors

Accuracy of species predictions varied by species, spatial grain of field plots, positional accuracy of

plots, and positional accuracy of Landsat TM imagery. Accuracy was especially poor for PIMO, which

had the lowest prevalence of the selected species.

In general, accuracy of species predictions was greater at larger spatial grain (i.e. full plots vs.

subplots). Co-mission errors were greater for predictions based on full plots.

For many species, prediction accuracy either declines or is not significantly different with increased

positional accuracy of plots or Landsat TM imagery. likely the result of fine-scale spatial predictors

being over-weighted in gradient analysis when species composition is more strongly controlled by

large-scale climatic and topography gradients.

Funding provided by the USDA Forest Service Pacific Northwest Research Station, and the Western Wildland Environmental Threat Assessment Center. Special

thanks to Mike Simpson and staff at the Deschutes National for providing sub-meter DGPS coordinates for FIA and R6 plots, Justin Braaten and Zhiqiang Yang

for assistance with LandTrendr fitted tassel cap imagery and disturbance maps, and Emilie Henderson with R code for GNN and GNN map accuracy assessments.

Observed

data ecdf F

Attribute values

Per

cent

of

dat

aset

0 1

00

Predicted

data ecdf G

Results: Vegetation Structure

Accuracy of spatial predictions calculated by comparing observed and predicted forest attributes using

a modified leave-one-out cross validation approach.

Mod1

Mod2

Mod3

Mod4

Mod5

Mod6

Mod7

Mod8

Predicted distribution of Ponderosa pine (PIPO)

Plt-OldGPS-OldGeoRef Subplt1-OldGPS-OldGeoRef

PIPO present PIPO absent

Open water Ice fields

Predicted density of small trees (TPH_3_25)

Plt-NewGPS-NewGeoRef Subplt1-NewGPS-NewGeoRef

Open water Ice fields

Models with larger spatial grain (i.e. field plots) have smaller maximum distances between modeled

and reference ecdfs (lower KS values), and have higher unsystematic agreement (ACUNS colored

triangles). There was little difference in systematic (ACUNS colored squares) between models.

Variable

subset

Code Description

Climate ANNPRE Mean annual precipiation (natural logarithm, mm)

ANNTMP Mean annual temperature (°C)

AUGMAXT Mean maximum temperature of hottest month (August) (°C)

CONTPRE Precentage of annual precipitation falling during the growing

season (June - August)

DECMINT Mean minimum temperature of coldest month (December)

(°C)

SMRTP Growing season moisture stress, the ratio of mean temperature

(°C) to precipiation (natural logarithm, mm), May -

September

Topography ELEV Elevation (m)

ASP Cosine transformation of aspects (degrees)

PRR Cumulative potention relative radiation during the growing

season (Pierce et al. 2005)

SLOPE Slope (%)

TPI150 Topographic position index, calculated as the difference

between a cell's elevation and the mean elevation of cells

within a 150-m radius window

TPI450 Topographic position index, calculated as above but with a

450-m radius window

Parent Material ASH Total ash deposition (feet), primarily from Mt. Mazama in the

eastern Cascades (unpubl. data from Mike Simpson)

GLACIAL Ice transported deposits (categorical)

PYROCLST Pyroclastic deposits (categorical)

PUMICE Pumice desposits (categorical)

Landsat TM TC1 Brightness axis from tasseled cap transformation

TC2 Greenness axis from tasseled cap transformation

TC3 Wetness axis from tasseled cap transformation

YSD Years since disturbance, from multitemporal Landat TM

analysis (Kennedy and Cohen 2011)

MAG Magnitude of disturbance (change in canopy cover) (%), from

multitemporal Landsat TM analysis (Kennedy and Cohen

2011)

DUR Duration of disturbance (years), from multitemporal Landsat

TM analysis (Kennedy and Cohen)

LiDAR VEGP95 Height (m) of 95th percentile of vegetation height returns

VEGMEAN Height (m) of mean of vegetation height returns

COVER Canopy cover above 2-m (percent), calculated as the

proportion of first returns greater than a lower height limit of

2-m above ground in the digital terrian model

VEGSD Standard deviation of vegetation height (m)

Location EAST Universal Transverse Mercator easting (m)

NORTH Universal Transverse Mercator northing (m)

LandTrendr method

D

DKS = max|F(x) – G(x)|

Image tie-points

Plot and Subplot Footprints

List of Spatial Predictors