Creating a Consistent and
Standardized Vegetation Database
for Northwest Forest Plan
Monitoring in California
1999
Brian Schwind, Remote Sensing Specialist
Ralph Warbington, Section Head - Plans and Inventory
USDA Forest Service
Pacific Southwest Region Remote Sensing Lab
1920 20th Street
Sacramento, CA 95814
bschwind@fs.fed.us
rjwarbington@fs.fed.us
Chris Curlis, Image Analyst
Pacific Meridian Resources
In Residence at:
Pacific Southwest Region Remote Sensing Lab
1920 20th Street
Sacramento, CA 95814
ccurlis@mp.usbr.gov
Sabrina Daniel, Mapping Ecologist
North State Resources
In Residence at:
Pacific Southwest Region Remote Sensing Lab
1920 20th Street
Sacramento, CA 95814
sstadler@fs.fed.us
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Table of Contents
Abstract…………………………………………………………….……… 4
Introduction………………………………………………………………. 5
Data Standards…………………………………………………………... 6
Data Sources……………………………………………………………... 7
Methods……………………………………………………………………. 9
Spatial Layer Development…………………………………………….. 10
Thematic Layer development………………………………………….. 10
Conclusions………………………………………………………………. 14
Acknowledgments……………………………………………………….. 14
References………………………………………………………………… 15
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Abstract
Since 1988, the Pacific Southwest Region of the Forest Service has had an active and scheduled
program for creating and maintaining existing vegetation data layers of National Forest lands.
These layers have been developed by the classification and modeling of a variety of remotely
sensed and ancillary data. This program has resulted in a library of vegetation layers developed
in a consistent manner that are highly suited for Forest and Regional planning, assessments, and
monitoring. Additionally, other agencies and private entities have developed vegetation
databases to meet a variety of analysis needs outside Forest Service administrative boundaries.
With the advent of Federally mandated monitoring of forest conditions in the Northwest, a need
arose to combine Forest Service data with comparable data sets on adjacent lands into a spatially
uniform layer that met standards recommended by the Regional Ecosystem Office (REO) and
Interorganizational Resource Information Coordinating Council (IRICC). These groups have
released data standards and implementation recommendations for vegetation information in areas
of the Presidents Forest Plan (Interagency Vegetation Information, Data Standards and
Implementation Recommendations 1995). In California, the strategy selected for developing this
comprehensive existing vegetation database involved the aggregation of the component data sets
to a common spatial and thematic level. Subsequently, classification of Landsat TM imagery and
GIS modeling techniques were applied to add the remaining floristic and structural detail
necessary to meet the defined standards. Ground-based field observations, existing vegetation
samples, aerial photography, digital ortho photography, SPOT imagery and field review of draft
maps were all used in validating and correcting classification and modeling errors where
observed.
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Introduction
The Pacific Northwest has been a major focal point of natural resource issues over the past
decade, driving the development and implementation of planning and monitoring policy on
Federal Lands. At the forefront of current Federal policy for forestland resources is the
Northwest Forest Plan (NFP), mandated by the Clinton Administration in 1993. Critical to the
success of the NFP is the development of an effectiveness monitoring program that evaluates the
condition and trends of key resources, including Late Successional Old Growth Habitat,
Northern Spotted Owl, Marbled Murrelet, and Riparian and Aquatic Resources (Effectiveness
Monitoring Team 1997). Direction for monitoring was originally outlined by the Federal
Ecosystem Management Assessment Monitoring Team (FEMAT) , defined in an interagency
report titled "Interagency Framework for Monitoring the President's Forest Ecosystem Plan"
(March 1994). The Regional Interagency Executive Committee (RIEC), part of an
Intergovernmental Advisory Council that oversees the management of the NFP, assigned
responsibility of monitoring plan development to an interagency Effectiveness Monitoring Team
(EMT). The EMT has produced and series of reports laying the scientific foundation of a
monitoring program and outlining the resource needs of such a program (EMT 1997). Included
in those resource needs are the data necessary to answer questions about important forest
resources.
The mandate of an effectiveness monitoring plan has resulted in the need to develop consistent,
regional vegetation databases depicting forest landscape conditions across multiple ownerships.
In order to meet time frames imposed by the NFP and associated committees, agencies
responsible for database development have relied on the classification of digital imagery in
conjunction with existing ancillary and ground-based data sets to describe forestland conditions.
Many of the significant vegetative characteristics identified by the monitoring plans can be
efficiently and cost effectively derived from digital remote sensor data, while vegetative
characteristics not effectively derived from remotely sensed data are better captured through
plot-based sampling procedures. Statistical relationships between mapped vegetation attributes
and sample-based measurements can effectively be used to describe florisitics and the structural
condition of vegetated landscapes at regional scales (Miller et al. 1994, Riemann and Alerich
1998).
Development of the Northwest Forest Plan vegetation database occurred in two zones, based on
the physiographic provinces of western Washington and Oregon and northwestern California.
The Pacific Southwest Region (Region 5) of the USDA Forest Service (USFS) was given lead
responsibility for providing the required vegetation data for the nearly 18 million acres of the
Northwestern California zone. This paper will focus on the methodologies used by Region 5 to
integrate its vegetation mapping efforts with other map products developed within the project
area. Over half of this area existed outside the administrated boundaries of the Forest Service and
had not previously been mapped under the Region 5 vegetation mapping program or to the NFP
interagency standards. Utilizing Region 5 mapping strategies in conjunction with vegetation
layers previously developed for lands adjacent to the National Forests, the components for a
comprehensive, consistent, and standardized vegetation database were produced in a cost
effective and timely manner.
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Data Standards
The vegetation data standards for the monitoring database were the result of work accomplished
by a Vegetation Strike Team and a Data Coordination Team under the oversight of the Regional
Ecosystem Office (REO) and the Interorganizational Resource Information Coordinating
Council (IRICC). A Vegetation Strike Team of interagency specialists in remote sensing and
vegetation inventory was organized by the IRICC to develop standards and implementation
recommendations. The standards were developed from a needs assessment of requirements for
watershed analysis as well as bioregional monitoring. Care was taken to make sure vegetation
maps developed under these standards could later be crosswalked into the FGDC (Federal
Geographic Data Committee) vegetation standards that were simultaneously being developed at
the National level. These standards and recommendations were adopted by the REO for
vegetation information in areas of the Presidents Plan (Interagency Vegetation Information, Data
Standards and Implementation Recommendations, 1995).
Table 1 illustrates the existing vegetation map attributes and data standards defined by the
Vegetation Strike Team and Data Standards Team. Standards focus on the tree lifeform type.
Table 1 - IRICC Recommended Data Standards for Existing Vegetation Data
Attribute
Standard
Method
Coverage
Lifeform
Tree (conifer, hardwood, mixed),
Image
shrub, herb, barren, water, non-forest Classification
Project Extent
Cover Types
SAF/SRM Cover Type (Dominant
Tree Type)
Image
Classification
Project Extent
Cover Types
(alliance level)
species lists/plant associations
Agency records
Agency lands
Total Tree Crown
Closure
10% classes
Image
Classification
Project Extent
Tree Overstory Size 0-5, 5-10, 10-20, 20-30, 30-50, 50+"
Image
Classification
Project Extent
Forest Canopy
Structure
single/multi
Image
Classification
Project Extent
Stand Year of
Origin
even aged conditions only - initiation
year & event for known events or
Agency records
estimated initiation decade for
unknown events
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Agency lands
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Data Sources
Development of the Northwest Forest Plan database for northwestern California has been largely
facilitated by an existing cooperative mapping effort between the USFS and numerous partners.
These partners include the U.S. Fish and Wildlife Service, California Department of Fish and
Game, California Department of Forestry and Fire Protection, Bureau of Land Management,
National Park Service, California State Parks, and Humboldt State University (HSU). This
cooperative effort has resulted in the shared acquisition and terrain correction of NASA donated
1994 Landsat TM imagery, the exchange of existing and newly collected field data, and the
digital capture of USFS ecology plot data. These data were used by the Region 5 Remote
Sensing Lab (RSL) and HSU for separate vegetation mapping projects (USFS Region 5
Corporate Vegetation Data, Klamath Bioregional Assessment Project). Collectively, these data
sets formed the most extensive current vegetation data available for the California portion of the
NFP area. While differing needs and classification systems drove the direction of each project, it
was recognized that integrating both data sets was necessary to cost effectively achieve regional
data consistency. Rectification of the differences in spatial and thematic resolution is discussed
under the methods section of this paper.
A 1990 Landsat TM classification of hardwood rangelands was used for areas not covered by the
RSL and HSU image classifications. Use of this data set was restricted to the hardwood
rangelands along the western foothills of the Sacramento Valley. Additionally, a variety of other
existing vegetation GIS layers were also evaluated for potential integration or use as ancillary
data sources. These include older National Forest vegetation type maps (1975-79), California
GAP Analysis (1990), Jackson State Forest vegetation map (1996), and the Hoopa Reservation
vegetation type map. Evaluation criteria for each data set were based on data source, source date,
map attributes, spatial resolution, and user confidence. Recognizing these existing data sets was
critical to maximizing efficiency and cost effectiveness under an environment of limited
resources. Relying on multiple databases from a variety of sources did not occur without risk to
consistency, however, and understanding the origin, classification system, and mapping method
was necessary under this approach of database development. See Table 2 for a list of the
component map sources used in this project.
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Table 2 - Existing Vegetation Map Products
Project
*USFS Vegetation
Mapping; Mendocino,
Klamath, ShastaTrinity, and Six Rivers
N.F.s
*Klamath Bioregional
Assessment Project
Type
Data Source
MMU
Classification
CALVEG (USFS Regional
Ecology Group 1981)
map
1994 Landsat TM 1 hectare
map
California Wildlife Habitat
Relationships (WHR)
1994 Landsat TM 30 meters
(California Dept. of Fish and
Game 1988)
*California Hardwoods
map
Map
1990 Landsat TM 25 meters CWHR
*Jackson State Forest
Vegetation Map
map
1996 Landsat TM 2 acres
CWHR
*Bureau of Land
Management
Vegetation Maps
map
1:16000 Color
Photography,
DOQQ
not
specified
Region 5 Ecology
Classification System
(Jimerson 1996, Allen and
Diaz 1986)
**California GAP
Analysis
map
various
100
hectares
UNESCO/TNC system
(UNESCO,1993)
**Hoopa Reservation
Vegetation Map
map
1:12000 Color
Photography
not
specified
not specified
**National Park
Service Vegetation
Maps
map
1947-present
Color
PhotographyVarious Scales,
1993 DOQQ
0.5
hectare
not specified
**Redwoods Data Set
map
1986 Color
Infrared Photos at 40 acres
1:130,000
not specified
* Integrated Data
**Ancillary Data
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Two categories of ground-based plot data were assembled for this project. The first included data
used in the development of the component data sets, existing plot data collected for unrelated
purposes, and plot data collected specifically for this project. These data were used to evaluate
the consistency of differing map products in areas of overlap and to provide the ecological basis
for spatial modeling of vegetation types.
The second category of plot data was obtained from the USFS Region 5 Forest Inventory
Analysis (FIA) program (USFS lands) and from the USFS Pacific Northwest Research Station
FIA program (non-USFS lands). Collectively, these programs establish a systematic random set
of permanent sample points across all ownerships in California. These data inventory detailed
vegetation characteristics which are then statistically linked to map labels to aid in the
description of mapped attributes. Significant vegetative characteristics, not feasibly captured
from remotely sensed data, can be statistically described on a broad continuum. They also serve
to inform the map user of floristic and/or structural variance associated with a map label. As map
labels impose a narrow definition on what is often a highly varied condition, plot data are
necessary to explain variation inherent within the landscape.
The FIA data also served as a valuable and convenient independent reference source for the
assessment of map accuracy. All FIA plot data were kept independent of the mapping process in
order to avoid autocorrelation between the map-based and sample-based data sets. Map accuracy
results generated from methods currently used by the RSL (Milliken et al. 1998) will be
published at future date.
Methods
The methods used to integrate multiple classifications and map products into a consistent data
base were based on the mapping approaches employed at the USFS Region 5 RSL. These
methods have been used to develop standardized vegetation databases for all the National Forests
in California. Furthermore, Region 5 vegetation data standards closely match those defined by
the REO, IRICC, and EMT. Central to this approach was the derivation of stand-based polygons
from a single data source and independent of map attributes. These polygons were used to
provide both spatial consistency and a means of efficiently fitting multiple data sources to the
spatial map standard . Stand delineations or regions, meeting the specified minimum mapping
unit (mmu), were systematically derived from Landsat TM imagery. The result was a layer of
uniquely identified stands or regions that corresponded to intuitively recognizable landscape
patterns. Classification and modeling of thematic attributes were performed separately and
hierarchically for each attribute. Landcover features were classified first and subsequently used
as a stratification for classification and modeling of more detailed floristic and structural
vegetative characteristics. Using the image derived stand layer, each pixel-based thematic layer
was then regionalized, creating a stand-based thematic layer. Regionalization was accomplished
by evaluating the thematic variance of map class pixels within each unique stand. With a user
specified set of parameters, the image analysts controlled the sensitivity of each class in the
labeling process. This was an important control measure for evaluating a varied set of vegetation
classifications that were developed under differing classification systems.
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Spatial Layer Development
Independent stand polygons were generated using an image segmentation module developed for
the software Image Processing Workbench (Frew, 1990). This module uses a centroid-linkage
region growing algorithm for multiple-pass, slow growth region formation within a user
specified spectral threshold (Woodcock and Harward 1992). An additional set of auxiliary passes
forces polygons to meet the user specified minimum polygon size. Formation of stand polygons
was spatially constrained within processing regions derived from Miles and Goudey (1997)
ecological section and subsection boundaries. The purpose of this was to 1) establish an
ecologically based tiling system within the project area, and 2) limit the number of uniquely
identified polygons formed to less than 65,535 (16 bit). Without constraining the number of
polygons, an unreasonably large number of unique polygons would have been generated,
resulting in exceedingly poor hardware and software performance.
Inputs to the first stage of the image segmentation module were Landsat TM bands 3, 4 and a
texture derivative of band 4. Inclusion of the texture band as an input to the segmentation
algorithm has been shown to be an important spatial additive (Ryherd and Woodcock 1996).
Differences in vegetative characteristics, structure, canopy closure, stand fragmentation, and land
use patterns, are often reflected in changes of image texture. These are some of the same
characteristics important to the analysis of key forestland resources highlighted by the
effectiveness monitoring plans.
Thematic Layer development
In addition to developing a spatially consistent basis for integrating multiple map products, a
level of thematic commonality was established. Of the component data sets evaluated for
potential integration, all were based on classification systems containing enough floristic detail
to achieve a minimum of Anderson level II classes (Anderson et al. 1976) for non-developed
landcover types. The Hardwood and Klamath Bioregional Assessment data sets were aggregated
to a lifeform level (table 3) to achieve the lowest common denominator of map classes between
all data sets. Additional floristic and structural detail contained within these data sets was not
directly integrated, used instead as ancillary information to subsequent tree size and density
classifications. This was done due to significant differences in classification methods of
vegetation type, tree size, and crown density between the three principal data sets. The
aggregated classifications were then regionalized against the independently derived stand
polygons to create a stand-based lifeform layer. Decision rules for assigning labels to polygons
were based on two factors, 1) the lifeform classification logic, and 2) the idiosyncratic
(subjective) nature of a given classification. This process required detailed evaluation of the
source layers in order to minimize the risk of thematic inconsistency between sources and
ultimately in the final database. Lifeform classes crosswalked from the source data were
reviewed against both aerial photography and field data to ensure that labeling parameters
resulted in attributed polygons consistent with classification logic. A final review of labeled
polygons was performed on-screen and anomalous errors were manually edited to the
appropriate label. This last step, while labor intensive and costly, was considered necessary for
minimizing errors resulting from spectral confusion between map classes. This step also served
to reduce the differences between input data sources that remained following the aggregation to
the lifeform level .
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Table 3 - Classification Logic for Lifeform Classes
Lifeform Type
Classification Logic
Conifer
> 10% total tree crown closure and <20% relative hardwood
crown closure
Hardwood
>10% total tree crown closure and <10% relative conifer
crown closure
Mixed
>10% total tree crown closure, >10% relative conifer crown
closure, and >20% relative hardwood crown closure
Shrub
<10% total tree crown closure and > 10% shrub cover
Dry Herbaceous
<10% total tree crown closure, <10% shrub cover, and
plurality of dry herbaceous
Wet Herbaceous
10% total tree crown closure, <10% shrub cover, and
plurality of wet herbaceous
Agriculture
<10% totall tree crown closure, <10% shrub cover, and
plurality of agricultural condition
Barren
<10% vegetation cover
Water
totality
Labeled and edited lifeform polygons were used as a stratification for the classification of more
detailed vegetation attributes. This hierarchical approach was used to minimize potential error
caused by simultaneously mapping forest characteristics that are not mutually exclusive. Tree
size, tree canopy closure, and stand structure occur in numerous combinations under varying
terrain conditions. Correlating a statistically unique spectral footprint with each of these
combinations is unrealistic in the view and experience of the authors. Furthermore, specific
techniques have been utilized by the USFS Region 5 RSL to capture tree size and tree density
individually (Miller et al. 1994). These techniques were used to ensure consistency of size and
density mapping across the project area.
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Vegetation Type - Prior to classifying the imagery for tree size and density, vegetation types
based on the CALVEG (Classification and Assessment with Landsat of Visible Ecological
Groupings, USDA 1981) classification system were spatially modeled within each tree and shrub
lifeform. The CALVEG classification system describes dominant vegetation types similar to an
alliance level classification. While this level of floristic detail was not required for the regional
scale of analysis proposed by the effectiveness monitoring plan, it was captured to 1) meet the
level of thematic resolution within the existing USFS vegetation layers, and 2) add an additional
level of stratification for tree size and density mapping. Models were developed within
ecologically similar units based on observed correlations of dominant vegetation type to terrain
variables, principally elevation, slope, and aspect. These terrain variables have been shown to be
useful indicators of vegetation associations (Macomber et al. 1991). Matrices depicting the
distribution trends of vegetation types across elevation and slope/aspect classes were built.
Concurrently, other digital data layers were evaluated for potential to further discriminate among
vegetation types. Soils, geology, precipitation, watershed, older vegetation maps, and land use
layers were all found to have utility specific to certain vegetation types and geographic modeling
zones. Predictive decision rules were then written and subsequent outputs reviewed in the field
by mapping technicians and project cooperators. Anomalous map error was manually corrected,
on-screen, based on field observations and plot data.
The remaining structural attributes, specifically canopy closure, tree size, and stand structure,
were mapped by vegetation type or group of physiologically similar types. The purpose of this
was to minimize confusion between spectrally similar CALVEG classes that exhibit differing
crown geometries or size profiles.
Tree Crown Closure - Canopy closure was modeled using the Li-Strahler canopy model
(Strahler and Li 1981, Li and Strahler 1985). This is a geometric optical model that employs the
varied geometry of tree crown structure and four spectral components: sunlit crown, shaded
crown, sunlit background and shaded background to estimate canopy characteristics. The model
is typically calibrated from field data collected to build regression equations relating crown
geometry to stand density. The regressions derive crown geometry ratios for the forest types in
the project which are used as inputs into the canopy model. Training stand characteristics were
obtained from fieldwork or, where insufficient stands existed for a particular type, photo
interpretation. Each type group was evaluated spectrally and plotted in brightness/greenness
feature space. Signatures for each type group were estimated by iteratively comparing the fit of
model derived components to the relationship of crown and spectral background components of
the field measurements. Once the best fit signatures are estimated for each type they are used in
conjunction with the remaining model inputs; stand based slope aspect, brightness and greenness
images to label each stand by its mean spectral statistics. The Li-Strahler model then inverts this
data using the type signatures and imagery characteristics (solar zenith and azimuth) and derives
a variety of canopy characteristics. Among the outputs the inversion generates is a continuum of
crown closure values for each stand ranging from 0 to 100 percent. Canopy closure outputs were
evaluated against plot data and aerial photography to determine if a positive relationship existed
between observed and modeled crown closure. Difficulties with the model output were
encountered when there was minimal differentiation between the spectral response of tree
crowns and background vegetation. This was most evident in low tree density stands with dense
montane shrub understories. The tendency has been for the model to over-predict canopy in these
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conditions. In these cases it was necessary to manipulate the histograms of canopy closure
values. Histogram stretches were very effective in improving overall classification accuracy.
Overstory Tree Size - Tree size was classified as a measure of crown width using iterative
unsupervised classification of Landsat TM bands 1-5, 7, and a spatial texture image derived from
TM band 4. Relationships of crown width to stem diameter have been established for major
forest types in California. These relationships were used to infer stem diameter classes from
mapped crown width classes for each tree type. Subsequent size classifications were compared to
ancillary GIS layers of land use and ownership. Land ownership and corresponding land use
were found to be good indicators of tree size for certain vegetation types. Most notably, the
National and State Park lands in the coastal redwood belt contained extensive stands of trees
greater than 50" dbh. Particular attention was paid to classification results where these unique
and significant stand conditions were known to exist. Tree size classification was prepared using
the Northwest Forest Plan size scheme of 10" dbh classes and then crosswalked to the Region 5
size classification standards for maximum utility.
Canopy Structure - Canopy structure mapping was based on a predictive modeling approach
using floristic and structural characteristics. While Landsat TM data is considered a viable data
source for the classification of single and multi-structured stands (Lannom et al. 1998), only
mapped features were used for canopy structure mapping. A two class structure attribute was
spatially modeled using vegetation type, tree size, and canopy closure as inputs. Existing plot
data were used to evaluate trends in forest canopy structure as it relates to tree type, size, and
density. Decision rules were written based on these observed trends and applied as a raster-based
model. Future structure modeling work will consider Landsat derived textural and/or structural
classes as an additional input.
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Conclusions
The advent of federally mandated monitoring policy for the forests of the Pacific Northwest has
created the need to develop consistent regional scale vegetation databases. Concurrently,
numerous vegetation map products have been derived from remotely sensed data to meet a
variety of natural resource management information needs. Most often these data sets have been
limited in extent or were designed to meet differing map standards. Logically, these products
should be evaluated for their potential to be integrated into more extensive planning and
monitoring databases. To date, however, little has been done to take advantage of the cost
savings and processing efficiency that these data sets potentially provide. Perhaps the risk of
thematic and spatial inconsistency that occurs when assembling unrelated map products has
prevented data producers from considering such an approach.
This project has sought to take advantage of current data availability while avoiding the
incorporation of inconsistencies inherent between independently produced map products. The
use of image segmentation to derive a stand-based spatial layer was found to be effective for
establishing a uniform foundation for map integration. Map components based on hierarchical
classification systems were easily aggregated to a common landcover classification level,
preserving core map integrity while enabling the assembly of varied map products. Fitting
thematically aggregated map components to a uniform spatial base resulted in a quick and
efficient assembly of a project extent landcover classification. The relatively quick production of
a uniform and common base layer allowed the authors to allocate more resources towards the
classification of required vegetation attributes not consistently available in the component layers.
Acknowledgments
A note of thanks to all the cooperators, data stewards, and data producers who contributed data,
time, and expertise to this project. Particular thanks go to Dr. Larry Fox and Humboldt State
University for providing much of the data analyzed in this project. Also, to Mark Rosenberg of
the California Dept. of Forestry and Fire Protection for coordinating product reviews and to
Hazel Gordon for her modeling and reviewing skills.
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