Historical Perspective on Historical Perspective on Forest Area Estimation (SRS)Forest Area Estimation (SRS)

Raymond M. SheffieldRaymond M. Sheffield

In the Beginning….In the Beginning….

SoutheastSoutheast

South CentralSouth Central

SRSSRS

~ 1960’s to 2005~ 1960’s to 2005

Southeast—historical overviewSoutheast—historical overview

1933-19401933-1940– Forest/nonforest determined from classification of Forest/nonforest determined from classification of

land use at ground sample plots (strips 10 mi apart-land use at ground sample plots (strips 10 mi apart-plots at 660 ft. intervals)plots at 660 ft. intervals)

1946-19571946-1957– Photography basedPhotography based– Dot grid overlay (1 acre circles classified)Dot grid overlay (1 acre circles classified)– Forest estimates based on proportion of total points Forest estimates based on proportion of total points

classed as forestclassed as forest– Adjusted based on ground samplesAdjusted based on ground samples– Forest / nonforest samples not proportional to land Forest / nonforest samples not proportional to land

useuse

Southeast—historical overviewSoutheast—historical overview

1957-19681957-1968– Basically same as 1946-1957 except for Basically same as 1946-1957 except for

numerous changes in land use classes numerous changes in land use classes identifiedidentified

1968 1968 – 16-point cluster double sample design16-point cluster double sample design

16-point Cluster Double Sample 16-point Cluster Double Sample DesignDesign

Photo basedPhoto basedEstimation unit: CountyEstimation unit: CountyCounty land area (or total area) obtained from County land area (or total area) obtained from Bureau of CensusBureau of CensusCluster of points rather than single pointsCluster of points rather than single points– Forest/nonforest proportion of each cluster treated as Forest/nonforest proportion of each cluster treated as

a continuous variable a continuous variable

Unadjusted forest proportion based on forest Unadjusted forest proportion based on forest points / total pointspoints / total pointsDouble sample designDouble sample design– Subsample of the clusters are ground checkedSubsample of the clusters are ground checked

16-point Cluster Double Sample 16-point Cluster Double Sample DesignDesign

Linear regression fitted to develop Linear regression fitted to develop photo/ground relationshipphoto/ground relationship

Adjusted proportion of forest developed Adjusted proportion of forest developed from regressionfrom regression

16-point cluster--logistics16-point cluster--logistics

Photos acquired for approx. 100% Photos acquired for approx. 100% coverage of countycoverage of county

25 16-point clusters per photograph25 16-point clusters per photograph

16 point clusters stamped on each ground 16 point clusters stamped on each ground plot (subsample)plot (subsample)

Average area/point classified ranged from Average area/point classified ranged from 12 to 20 acres12 to 20 acres

An ExampleAn Example

Mitchell County, GAMitchell County, GA

Total land area: 327,699 acresTotal land area: 327,699 acres

16-point clusters: 1,47316-point clusters: 1,473

Total points: 23,568Total points: 23,568

Forest points: 8,350Forest points: 8,350

Ground plots: 113Ground plots: 113

An ExampleAn Example

Unadjusted forest proportion (P)Unadjusted forest proportion (P)

P = # Forest points/Total pointsP = # Forest points/Total points

P = 8,350/23,568P = 8,350/23,568

P = 0.354294P = 0.354294

Ground plots: Photo vs GroundGround plots: Photo vs Ground

ForestPi ForestGrd Pi_prop Grd_prop

0 0 0 0

4 4 0.25 0.25

13 14 0.8125 0.875

2 2 0.125 0.125

12 12 0.75 0.75

9 9 0.5625 0.5625

12 13 0.75 0.8125

0 0 0 0

1 1 0.0625 0.0625

2 2 0.125 0.125

2 4 0.125 0.25

Adjusted Forest Proportion (AP)Adjusted Forest Proportion (AP)

AP = a + b (P)AP = a + b (P)

AP = 0.018479 + .994552 (.354294)AP = 0.018479 + .994552 (.354294)

AP = 0.370843AP = 0.370843

NotesNotes

Intensive sample of photo points and Intensive sample of photo points and ground plotsground plots

Labor intensiveLabor intensive

Ground plots randomly distributed….not Ground plots randomly distributed….not on a systematic gridon a systematic grid

South Central—historical overviewSouth Central—historical overview

Not much detail available describing forest Not much detail available describing forest area procedures in first inventoriesarea procedures in first inventories

In 1960’s, a grid of points overlaid on In 1960’s, a grid of points overlaid on photos was standard with a subsample of photos was standard with a subsample of ground plots—Point Based Double ground plots—Point Based Double Sample DesignSample Design

Point-based Double Sample DesignPoint-based Double Sample Design

Photo basedPhoto based

Estimation unit: CountyEstimation unit: County

County land area (or total area) obtained from County land area (or total area) obtained from Bureau of CensusBureau of Census

Single points classed as forest or nonforest. Grid Single points classed as forest or nonforest. Grid of 25 points placed over photos—one photo per of 25 points placed over photos—one photo per ground plot (plots on a 3 x 3 mi. grid)ground plot (plots on a 3 x 3 mi. grid)

Unadjusted forest proportion based on forest Unadjusted forest proportion based on forest points / total pointspoints / total points

Point-based Double Sample DesignPoint-based Double Sample Design

Forest / nonforest classifications made for Forest / nonforest classifications made for each ground ploteach ground plotThe photo vs ground classification is used The photo vs ground classification is used to “correct” the initial forest proportionto “correct” the initial forest proportionGround correction strengthened by using Ground correction strengthened by using intensification plots (only photo and intensification plots (only photo and ground check of land use)ground check of land use)Average area/point classified approx. 228 Average area/point classified approx. 228 acresacres

An ExampleAn Example

See handoutSee handout

AP = ((# forest dots)(CF1) + (# nonforest dots)AP = ((# forest dots)(CF1) + (# nonforest dots)(CF2)) / Total dot count(CF2)) / Total dot count

CF1 = (# plots correctly PI’d forest) / (Total CF1 = (# plots correctly PI’d forest) / (Total number plots PI’d forest)number plots PI’d forest)

CF2 = (# plots PI’d nonforest but actually CF2 = (# plots PI’d nonforest but actually forest) / (total plots PI’d nonforest)forest) / (total plots PI’d nonforest)

AP = ((1962 x .973) + (1288 x .0241)) / 3250AP = ((1962 x .973) + (1288 x .0241)) / 3250

AP = .5969AP = .5969

Current Method of Area Correction (# Forest PI’s x CF1) + (# Nonforest PI’s x CF2) Total PI’s

(1962 x .973) + (1288 x .024) 3250

.5969

Forest area = .5969 x census land

% Forest =

=

=

Photo’sF NF

F

NF

108

3

2

81111 83

110

84194

Plots

Merged SRS-FIAMerged SRS-FIA

Continued use of 25 point double sampling Continued use of 25 point double sampling designdesign

Used from 1998-2004Used from 1998-2004

Converted to a 27x intensification of 6000 Converted to a 27x intensification of 6000 acre hexacre hex

The nested P1 gridThe nested P1 grid

Ground plot sampled by field crews

Ray’s ObservationsRay’s Observations25 point double sampling design would probably 25 point double sampling design would probably have performed better using the survey unit as have performed better using the survey unit as the estimation unitthe estimation unitCorrection factors were often quite large for Correction factors were often quite large for single countiessingle counties– Small countiesSmall counties– Different classifiers at each phase of the double Different classifiers at each phase of the double

samplesample

Implementing in annual inventory mode had Implementing in annual inventory mode had many rough spotsmany rough spots– Changing plot listChanging plot list– Out dated photographyOut dated photography– 25 point grid often overlapped with adjoining plot and 25 point grid often overlapped with adjoining plot and

photophoto

Ray’s ObservationsRay’s ObservationsPhoto based systems consume peoplePhoto based systems consume people

Often utilize inexperienced observers for Often utilize inexperienced observers for photo classificationphoto classification