A Statistically Valid Method for Using FIA Plots to Guide Spectral Class Rejection in Producing Stratification Maps

Mike Hoppus & Andrew Lister

USDA-Forest Service

Northeastern Research Station

Newtown Square, Pennsylvania

Objectives

• Use the large number of high quality – expensive FIA ground plots to classify satellite imagery into a forest/non-forest map.

• Use the forest/non-forest map to stratify the ground plots in order to reduce the variance of estimates of “timberland” area and volume.

The Challenge

• Develop a sampling method that both uses the valuable plots AND doesn’t allow the plots to stratify themselves.

CIR Bands of Landsat TM: West Virginia Unit 3 (Southern)

Stratification TechniqueSelect FIA Plots for “Training” sites

• Identify all “forestland” single condition plots;

• Randomly split these plots into 2 groups;

• Divide each group into 4 sets – based on basal area;

• Select “training” plots from each set.

Condition B = Seedling-Sapling Stand

Tree VariablesSpecies Yellow PoplarDBH 12.9 inchesTree history LiveTree Class Growing Stock

Sample-Location VariablesLocation ID St, Un, Co, #Date Mo, Day, YrSample Kind Remeas. 1/5th

GPS Lat-Long. Condition Variables

Slope & Aspect Pct & DegreesLand Use ForestStand Origin NaturalOwner Class Individual

Condition A = Mature Stand

Old 1/5-acre plot

West Virginia FIA Unit 3 – Southern: Counties and Plots

Stratification TechniqueMake 2 maps based on the 2 groups of plots

• Use FIA plot group “A” as ground truth in the IGSCR classification method to produce a Forest/Non-Forest Map(A)of WV Unit #3;

• Use FIA plot group “B” to produce a Forest/Non-Forest Map(B) of WV Unit #3;

• Use “A” plots to assess the accuracy of map B, and visa versa.

Unsupervised Classification “Reject” pure classes

2nd Iteration Unsup Class.

3rd Iteration Unsup Class.of unrejected classes

Maximum Likelihood Class.using spectral training classesfrom 3 unsup classifications

Iterative Guided Spectral Class Rejection Method

Unsupervised Classification: 100 Classes

Unsupervised Classification-Second Iteration Output: Black Areas Were Rejected as Pure Classes in First Iteration

CIR Satellite Image with Corresponding IGSCR Forest Map

Stratification TechniqueStratify the Plots

• Use Map A to label and group “B” plots into map class strata;

• Use Map B to label and group “A” plots into map class strata;

• Use the most accurate of the two maps to “weight” each stratum estimate by the % area of each map class strata;

• Produce an estimate of “Timberland” area using a stratified random sampling procedure.

Two maps made from different random samples of fia plots

Two maps made from different random samples of fia plots

MRLCGAP

IGSCRCIR Image

IGSCR (2000), GAP (1993) and MRLC (1991)

FIA PlotsForest Non-Forest

MaxL Map Forest 204 4Non-Forest 24 84

Accuracy = 0.91Kappa = 0.79

FIA PlotsForest Non-Forest

MRLC Map Forest 308 36Non-Forest 8 52

Accuracy = 0.89Kappa = 0.64

FIA PlotsForest Non-Forest

GAP Map Forest 303 27Non-Forest 12 61

Accuracy = 0.90Kappa = 0.70

Accuracy Assessments

FIA Ground Plot Geometry vs 30m TM PixelsFIA plot design: a cluster of four 0.017 ha plots. Dark gray circles = area of locationaluncertainty due to GPS errors; Larger circles = area of locational uncertainty due to image registration errors.

CIR ImageF/NF, 2 Strata

F/F-edge/NF-edge/NF, 4 Strata F pixel count filter, 4 strata

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 0 - 6 7 -17 18 - 22 23 - 25

MRLC5 Level

% T

imb

erla

nd

Mean % Timberland per MRLC5 Class

Maximum Likelyhood 5X5 Forest Pixel Count Map

Landsat TM Map Forest Area (Acres) Sampling Error (%)

IGSCR (Plot-Pixel) 4038696 1.23IGSCR (2-Pixel Edge) 4038155 1.16IGSCR (5X5 Pixel Sum) 4008660 1.10

MRLC (Plot-Pixel) 3999646 1.40MRLC (2-Pixel Edge) 4024290 1.26MRLC (5X5 Pixel Sum) 4066013 1.18

GAP (Plot-Pixel) 3773196 1.47GAP (2-Pixel Edge) 3996625 1.23GAP (5X5 Pixel Sum) 4005653 1.23

Results of FIA Phase 1 InventoryWest Virginia – Southern Unit

SAMPLE TYPE Timberland Area Sampling Error

(Acres) (%)SIMPLE RANDOM SAMPLE 4,005,380 1.60IGSCR (5X5 Pixel Sum) 4,008,660 1.10

•The FIA Sampling Error Objective is 3% per Million Acresof Timberland.

•The Sampling Error Required for the Southern Unit of WVis: 1.5%.

-1989 Timberland Estimate: 4,139,200 ; SE = 0.7 : 3% more

--Ground Plot Equivalent of Standard Error Differences

One comparison of standard errors is the number of additional ground plots required to bring the less precise estimate to the same level as the estimate provided by the more precise technique.

By evaluating: Sampling Error 1- Sampling Error 2 = 1 - __N1_

Sampling Error 1 N2

1722 FIA Plots required to reduce the variance as much as IGSCR Stratification:An increase of 908 plots @ over $700,000

Questions ?