A brief introduction to statistical aspects of

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A brief introduction to statistical aspects of
the Forest Inventory and Analysis Program of the
USDA Forest Service Ronald E.
McRoberts Patrick D. Miles Forest Inventory
and Analysis North Central Research
Station USDA Forest Service
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Forest Inventory and Analysis (FIA) Mission
To conduct forest inventories of the
United States to estimate ? the extent
(area) of forest land ? the volume, growth,
and removal of forest resources ? the
health and condition of the forest
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Forest Inventory and Analysis Regions
Rocky Mountain Research Station
North Central Research Station
Pacific Northwest Research Station
Northeastern Research Station
Southern Research Station
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Strategic features A standard set of
variables with consistent meanings and
measurements Field inventories of all
forested lands A national sampling design and
plot configuration A systematic, annual
sample of each state A national database with
user friendly access
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FIA A 3-phase program Phase 1 Entails use
of remotely sensed data to obtain initial plot
land cover observations and to stratify land
areas with the objective of increasing
precision Phase 2 Entails field crew visits
to locations of plots with accessible forest to
measure traditional suite of mensurational
variables Phase 3 Entails field crew
measurements of an additional suite of
variables related to the health of the forest on
a 116 proportion of Phase 2 plots
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Genesis of the FIA sampling design
With thanks to Tony Olsen US EPA
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Phase 3 (Forest Health Monitoring) hexagons
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FIA plot configuration
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FIA Phase 2 observed variables
Plot/subplot identification and
location Observed condition (within
subplots) - land cover, ownership, forest
type, stand age, size class, productivity
class - origin, slope, aspect, physiographic
class, disturbance Observed tree
attributes - location - species, status,
lean, diameter, height, crown ratio, crown
class, damage, decay
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FIA Phase 2 calculated variables Tree
attributes - volume Subplot attributes per
unit area - number of trees, volume,
biomass By category - species/species
groups - status live, mortality, etc
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Classification
Stratification
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Using a forest/non-forest map as a means of
stratification
Number Relative efficiency of strata IN
IA MN MO 1 1.00 1.00 1.00 1.00
2 2.00 1.68 2.82 2.33 4 3.94 1.89 3.22 2.89

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Forest Health Monitoring (FHM) - Detection
monitoring through aerial and ground surveys
- Evaluation monitoring for particular
situations - Research on monitoring
techniques - Intensive site ecosystem
monitoring.
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FHM Ground Detection Monitoring Fully
integrated as Phase 3 of the FIA
program Indicators monitored - tree crown
condition - tree damage - ozone injury to
vegetation - lichen diversity - vegetation
diversity - soil chemistry and erosion - coarse
woody debris
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DWM Sample Design
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Fuel loadings (tons/acre)
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Output Products
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Spatial output products ? National attribute
maps ? Ownership maps ? Map-based
estimation - confidential plot
location - proprietary information
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National forest biomass map
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Small area mapping and map-based
estimation Users want estimates at spatial
scales for which FIA does not report
estimates Users want to use FIA data to train
satellite image classifiers or as accuracy
assessment data Requires access to plot data
and locations Plot locations are
confidential - protect integrity of
sample - deter owner access denials - protect
proprietary information
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Small area mapping and map-based estimation
30 km
30 km
Volume
Proportion forest
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Radius Plots Design-based
Model-based (km) Mean SE Mean SE
t Volume 3 1 1032.2 ------- 916.7 162.2 -----
6 6 1026.6 405.0 886.1 80.5 0.35 9 10 871.6 281.7
841.6 54.1 0.01 12 16 899.4 286.5 833.2 39.7 0.2
3 15 25 807.6 210.4 843.6 30.5 -0.17 Proportion
forest area 3 1 1.000 ------- 0.775 0.055 -----
6 6 0.833 0.164 0.730 0.025 0.63 9 10 0.700 0.153
0.649 0.015 0.33 12 16 0.625 0.125 0.632 0.011 -
0.04 15 25 0.600 0.100 0.648 0.009 -0.48
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SpaRRS Spatial Resource Support System
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Forest Inventory Mapmaker
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Geographic options
County retrievals ?
Circular retrievals ?
Polygon retrievals ?
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Generate tables, maps and data
Figure 1. Private timberland as a proportion of
all land.
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Percent of timberland with ash
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Fuel Treatment Evaluator
Applies thinning prescription to each plot
Estimates torching and crowning index for
each plot before and after treatment Estimates
revenues for each plot by tree component Estim
ates harvest costs for each plot
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Map results of silvicultural prescription
Initial biomass
Removed biomass
Remaining biomass
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Graph results of silvicultural prescription
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SpaRSSSpatial Resource Support System
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Focus of support assembly of relevant
digital data layers analyses based on the
integration of spatial data comparison of
results from different integration
approaches comparison of results from
different decision alternatives.
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Objective Identify forested areas of the
USA that satisfy three criteria high wildfire
risk close to rural communities in need of
economic assistance
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Removable biomass from FIA data
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Removable biomass - upper 50th percentile
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Upper 50 removable biomass
Condition classes 2 and 3
lt 25 miles to rural community
Lower two economic classes
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2002 SPF EAP-NFP funding allocation
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Outstanding statistical issues ? Sampling
frame and variance estimation ? Modeling
issues - model-based estimation - propagation
of error - regional variations ? Combining
data from multiple panels ? Plots, subplots,
and microplots ? Non-sampled areas - Western
Texas and Oklahoma - Piñon-Juniper area (what
is a tree?) - Interior Alaska
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Technical statistical issues ? Stratification
schemes - plots that sample multiple
strata - squeezing more precision from
stratifications The effects of measurement
error ? Map accuracy assessment ? Modeling
issues - design-based versus model-based
estimation - propagation of error - regional
variants
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Summary Forest Inventory and Analysis
U. S. Forest Service Nationally
consistent inventory Emphasis on spatial
products Resource support tools - Mapmaker
data access - Fuel Treatment
Evaluator - Spatial Resource Support
System Statistical issues
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8 FIA sessions at MSTS Wed am Forest inventory
and monitoring policy am Remote sensing
applications pm Statistical applications pm Ass
essing forest sustainability Thu am Diversity,
change, and stability am Approaches to forest
health monitoring pm Forest health criteria and
indicators pm Carbon accounting applications
FIA scientists will also be speaking in non-FIA
sessions
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Remember ..
.. only YOU can prevent forest fires!