Statistical Aspects of the
Forest Inventory and Analysis Program,
US Forest Service
Tracey S Frescino
Paul L Patterson
Statistical Aspects of the
Forest Inventory and Analysis Program
• Overview of FIA
• Sample Design
• Estimation
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
Forest Inventory and Analysis Regions
Pacific
Northwest
(PNW)
Interior
West
(IW)
Pacific
Hawaii
American Samoa
Commonwealth
of the Northern
Mariana Islands
Federated States
of Micronesia
Guam
Palau
Republic of the
Marshall Islands
North
South
National Headquarters
Field Headquarters
Caribbean
Commonwealth
of Puerto Rico
Virgin Islands
Strategic features
•
A standard set of variables with
consistent meanings and
measurements
•
Field inventories of all forested lands
(In some National Forest System regions FIA inventories all
NFS lands)
•
•
•
A national sampling design and plot configuration
A systematic, annual sample of each state
A national database with user friendly access
FIA Program – Structure
Data collection
Prefield – determine if plot is forested
Field visit – visit plot in field and collect addition data
Information Management
Check and compile data and organize in database
Analysis & Reporting
Generate analytical reports for 5 years, 10 years, etc.
Examine trends in forest condition and status
Techniques
Improve quality and efficiency
Develop spatial tools and products
Inventory and monitor disturbance effects and impacts
Support emerging applications
FIA Program – Information Management
• Program/Maintain Personal Data Recorder
• Transfer data from PDR to Oracle database
• Data editing
• Produce calculated variables
• Maintain database…
FIA Program – Database and Tools
http://www.fia.fs.fed.us/
FIA Program – Analysis & Reporting
Analysis & Reporting
Generate analytical reports for 5 years, 10 years, etc.
Examine trends in forest condition and status
• Tables
• Charts
• Maps
FIA Program – Techniques
Techniques
Improve quality and efficiency
Develop spatial tools and products
Inventory and monitor disturbance effects and impacts
Support emerging applications
• Design-based estimators vs. Model-based estimators
• Investigating use of different remotely-sensed data as ancillary
information to estimation procedure (ex. large scale photography, satellite
imagery, etc.)
• Handling plots with nonresponse (i.e. Access denied)
• Automating mapping procedures (ModelMap)
• Automating estimation procedures (FIESTA)
Statistical Aspects of the
Forest Inventory and Analysis Program
• Overview of FIA
• Sample Design
• Estimation
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 1:16
proportion of Phase 2 plots
Genesis of the FIA sampling design
With thanks to:
Tony Olsen
US EPA
Phase 3 (Forest Health Monitoring) hexagons
Each hexagon is approximately 96,000 acres
Each FHM hexagon is divided into 16
hexagons, approximately 6,000 acres per
hexagon
Hexagons are divided into spatially
balanced panels. In the east one panel is
measured each year for a 5 year cycle
In the west
each panel has
two subpanels
for 10 years
per cycle
Sample is quasi-systematic: within each
hexagon a plot location is randomly selected;
• The plot locations are permanent
• Only potentially forested locations are ground visited.
Remotely sensed data collected on office called nonforest
plots
• In IW approximately 40% of plots are ground visited;
percentage varies by state.
• Discussion on going to a
All-Veg inventory
• Urban Inventory
FIA Plot Design
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
FIA Program – Conditions
Observed condition
(within subplots)
land cover,
ownership,
forest type,
stand age,
size class,
productivity class
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
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
Three types of variables
•
Core – measured by all regions using prescribed protocols
•
Core Optional – measured at discretion of each region;
But if measured, must use prescribed protocols
•
Regional – measured using locally defined protocols
Statistical Aspects of the
Forest Inventory and Analysis Program
• Overview of FIA
• Sample Design
• Estimation
FIA Estimation
FIA uses post-stratified estimation; typically a Forest Service database, such
as the NLCD, is used as the stratification tool.
Equations:
with
where
is plot level value of the attribute of interest
FIA data is collected at the condition level, which can be a subset
of the plot
is the number of plots in stratum h excluding the nonresponse plots
FIA Estimation
Equations:
with
where
is the number of plots in stratum h excluding the nonresponse plots
is an adjustment factor used to compensate for
partial nonresponse and partially out of population
plots.
Ignoring the nonresponse plots
assumes that within each stratum the
nonresponses are missing at random,
i.e., the expected value of the
nonresponse plots is the same as the
expected value of the observed plots.
“Or the nonresponse plots on average
are similar to the plots that were
observed”
Techniques – Estimation R Tools
FIESTA
An R Package for automating FIA’s estimation
procedure
Functions for:
Forest inventory data manipulation
Spatial data manipulation
Estimation
Analysis
Onto
More
R
and
FIESTA