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Landform Database Development Study - Final Report
final report of the project, which contains much more detail about the
procedures used to create and assess the datasets.
Service contracted with Pacific Meridian Resources to develop and produce
a comprehensive GIS vegetation land cover and geomorphic landform database
for the four large parks in the Pacific Northwest (Olympic, North
Cascades, Mt. Rainier, and Crater Lake National Parks). The study was
designed to develop a comprehensive, consistent inventory and mapping of
the vegetation and landform characteristics for the park using digital
Landsat Thematic Mapper (TM) satellite imagery and field collected data as
the primary information bases.
Final products resulting from the study are three separate raster GIS data
layers of tree size and forest structure, forest species, and forest crown
cover. Accuracy assessment procedures were implemented to assess the
accuracy of the satellite image classification. In addition, a spatially
related database of vegetation characteristics was developed from the
compilation and analysis of an extensive vegetation inventory completed
for each park. This database contains detailed vegetation information that
can not be measured using remotely sensed data such as satellite imagery
or aerial photography. Also, a digital map of geomorphologic landforms was
produced through the analysis and interpretation of digital elevation
data, digital shaded relief maps, high-altitude aerial photography, and
digital satellite imagery.
This study provides the National Park Service with a powerful set of
baseline spatial and tabular data and information for the four national
parks with which more effective monitoring, evaluation, and management of
the parks' natural and cultural resources. In addition, the maps,
databases, and procedures developed in this study will facilitate an
evaluation of the utilization of integrating digital satellite imagery and
field-based observations and inventories for vegetation mapping,
characterization, and monitoring for the national parks of the Pacific
Northwest.
GIS database that would increase the knowledge base for ecosystems in the
four parks and could be used by NPS managers to more effectively manage
park resources. In addition, the database provides the NPS with a
framework on which to build more extensive and detailed information as
future studies allow.
This layer is one part of one of the primary components of the study: the
characterization of the variation in forested and non-forested vegetative
types across all areas of the parks as defined by tree species, stem
density, stand age, tree diameter size class, crown cover by species,
standing dead trees, woody debris accumulation, dominant understory
species in forested areas, and species cover in non-forested areas.
A second, equally important objective was to design the database so that
it is compatible with databases of neighboring land managers, particularly
the GIS database developed by the USDA Forest Service Pacific Northwest
Region. Common database elements, including forest crown cover, tree size
class, forest structure, and tree species were designed to be as similar
as possible, while not compromising the NPS data needs.
The specific need addressed through this study was that of designing and
developing a comprehensive, detailed and accurate GIS database describing
the diverse vegetation, topography and landforms in the parks in order to
improve management of park ecosystems and wildlife species. The vegetation
information developed through this study, in particular, is of sufficient
detail to describe structural components and biodiversity attributes
necessary to defining wildlife habitat and understanding forest stand
dynamics.
assess the datasets than this metadata provides, please consult the final
report of the project. It is available on-line at the address given above
in "larger work citation".
Office GIS Group
Freet, Roger Hoffman, Ed Schriener, Darin Swinney
the Final Report for this study. The full report is available at the URL
given in Supplemental Information, above.
Accuracy assessment procedures fall into three steps: 1) sample selection
2) labeling of reference and map sites and 3) generation of error matrices
and estimates of accuracy. A population of vegetation polygons 5 acres or
greater from which to sample was generated from the unclassified Landsat
TM imagery using Image Segmentation. The resulting polygons were then
overlaid on the image-based raster classification data. Summaries of the
raster classification data for size/structure, species and crown cover
data layers were generated for each of the segmentation polygons. Labeling
rules were developed to assign a map label to each polygon based on the
summaries of the pixel data falling within the polygon. The relative
proportion of each class labels in the classified pixel maps was used to
determine the number of samples to select in each class from the polygon
coverage. The three pixel layers were overlain and the number of acres of
each crown-cover/size-structure/ species combination was determined. The
relative proportion was then applied to each strata in the polygon
coverage and a random number generator was used to select the proper
number of samples from each strata.
The location of the accuracy assessment sites were displayed over the
imagery on screen and were then manually delineated on aerial photographs.
Each of these reference sites were photo-interpreted and labeled with the
appropriate size/structure, species, and crown class label. To account for
variation in human photo interpretation, fuzzy logic was incorporated in
the reference call. After photo interpreting the site, the interpreter
assigned a "best", "good", "acceptable", "poor" or "unacceptable" call to
all possible labels. Approximately 4000 field based accuracy assessment
sites were established across the four parks in the study. An accuracy
assessment field form was completed for each site. Each of these sites
were re-interpreted based on the field collected data and notes and a
fuzzy-logic matrix was completed. These field-based accuracy assessment
sites were then integrated with the office-based photo-interpreted
accuracy assessment sites to comprise the final reference data set. The
dates of the imagery and the aerial photographs differed, so some land
cover changes had occurred. If the vegetation on the site had changed
(e.g., fire, vegetation regrowth, etc.) between the dates of the imagery
and the photos, the site was eliminated from further analysis.
Difference matrices were employed to analyze the differences between the
reference data and the map data in this study. An important factor
influencing the accuracy of the North Cascades map layers was landcover
change. Many significant changes were detected on the Landsat TM imagery
that were not present on the 1975/76 aerial photography utilized as
ancillary data for the classification project. Forest harvesting on
adjacent lands, regrowth of previously disturbed areas, and fire are
examples of significant change factors influencing the image
classification and map evaluation processes for the park.
crown cover present, according to the crown cover error matrix. The
majority of the map error defined by the matrix occurred when the map
labeled a site as 41-70% crown cover and the photo-interpreted reference
data labeled the site as 71-100% crown cover. This apparent variation
between map tendencies between Olympic and North Cascades parks is due
in large part to the fact that the reference data for the two parks were
developed by two different photo-interpreters with two different
tendencies toward estimating crown cover from photography. While this
variation in human photo-interpretation is not at all uncommon, it does
provide an inappropriate indication that the image classification data
itself is inconsistent.
North Cascades National Park
the Final Report for this study. The full report is available at the URL
given in Supplemental Information, above.
All six of the reflective wavelength bands from the Landsat Thematic
Mapper data (bands 1,2,3,4,5, and 7) were utilized for this mapping
project. In addition, an additional imagery band was created and
utilized by ratioing original wavelength band 3 (red visible) with band
4 (near infra-red). This particular band combination has been shown to
assist in minimizing the impact of shadowing in satellite image
classification. This new ratio band was added to the original six
reflective bands to create a new seven-band imagery data set that was
used for all project landcover classification. Once a complete image
data set was built for each park, the satellite image data was further
subset into "eco-regions" for Olympic and North Cascades parks.
Field data collection for the purpose of supporting the image
classification efforts was completed simultaneously with the vegetation
inventory field data collection. While the primary objective of field
data collection for this study was to complete a comprehensive
vegetation inventory for the park, valuable information was also
gathered to assist project image analysts in the classification of the
Landsat TM satellite image data. This information was primarily in the
form of detailed field notes for specific land areas, field descriptions
of ecological trends, and identification of anomalies and/or ecotones.
Image analysts also made extensive use of the specific plot data and
notes collected as part of the vegetation inventory.
Upon completion of the band ratioing described in the preprocessing
section above, an unsupervised classification was performed on the
imagery set for each park. Seventy-five to one-hundred spectral classes
were identified in the classification. Clustering analysis performed on
these classes identified several classes which were spectrally very
similar and represented very general landcover types within the study
area. For example, water, snow, and obvious bare ground were each
represented by multiple spectral classes. In cases where a spectral
class could reliably be found to represent a single land cover type,
i.e. water, snow, etc., the spectral classes were simply relabeled to
that land cover type.
The remainder of the spectral classes were then given a unique color so
that it could be easily distinguished from the other spectrally
different classes. This newly colored spectral variation map was used to
identify areas that may represent acceptable training sites for
subsequent image supervised classification based on their spectral
homogeneity. An image training site is an area of consistent tree crown
cover, tree species or species mixes, tree size or size mixes, forest
structure, or non-forest type that is utilized by imagery analysts as an
example or representation of an area possessing those landcover
characteristics. Training sites are defined as having evenly distributed
vegetation or other land cover throughout the entire training site
polygon. This is evidenced by uniform texture, color, and tone
throughout the polygon on both aerial photography and satellite imagery.
Individual pixels within a single training site polygon should all have
similar spectral reflectance in a single satellite data band.
Following field data collection, 550 training sites were delineated. All
training site polygons were digitized directly on the digital satellite
imagery. These polygons encompassed the imagery pixels that contain the
spectral reflectance values associated with the vegetation described by
the training site. Various statistical parameters describing the digital
numbers of the pixels from all seven bands of imagery were generated for
each training site. In addition, identical statistical parameters were
generated from each of the initial unsupervised classes produced prior
to field data collection. Utilizing a multivariate cluster analysis
statistical technique, training statistics generated from both
supervised and unsupervised approaches were grouped together. The
results of the cluster analysis was a final set of supervised training
sites that represents all the vegetation types to be mapped in the study
areas as well as representing the range of spectral variation in the
satellite imagery within the study area. A series of supervised
classifications was run on the study area image data set utilizing the
spectral statistics of the digitized training sites. After each
classification, the map was evaluated for accuracy and consistency.
Areas that were consistently classified correctly were subsequently set
aside in an evolving raster GIS data set and removed from any further
classification processing. Another iteration of spectral analysis,
classification, review, and masking was performed. Draft plots of all
three raster map layers were produced at a scale of 1:24,000 and
reviewed for accuracy by the image analysts. These hardcopy maps were
also presented to each specific park headquarters for National Park
Service (NPS) review and comments. In conjunction with the image
analysts, about 30 NPS employees were estimated to have reviewed and
commented on the maps during multiple weeks. Park personnel used aerial
photography, orthophotography, existing ancillary GIS and plot data, and
most importantly personal knowledge to evaluate the maps and comment on
their accuracy and consistency. Image analysts and NPS field personal
field verified the many of the maps for most parks over the course of
several months. After comments were submitted, maps were edited and
reprocessed.
During and following the image classification process, ecological
modeling was utilized for the park as a tool for enhancing the
identification and classification of forest species throughout the park.
Two distinct types of ecological species models were executed: 1)
park-wide model; and 2) site/region specific model. The park-wide model
was developed and executed for the entire park as a single unit. This
model was designed to simply identify and "flag" potential species
misclassifications for various elevation/aspect zones throughout the
park where particular species occurrence could be fairly reliably
predicted.
The North Cascades National Park imagery was classified using three
independent eco-zones: the Chilliwack Zone, the Ross Lake Zone, and the
Stehekin Zone. Three models were developed to identify and flag
potential misclassifications based on ecological conditions and
parameters specific to each zone. For instance, the acceptable upper
limit for Douglas-fir on south slopes in the Chilliwack zone was 4,200'
while the acceptable upper limit for the same species in the Stehekin
Zone was 5,000'. Similar site-specific classification models were also
utilized within each of these zones. Approximately 120 species models
were developed for North Cascades National Park.
Support Office GIS Group
described in this coverage. Crown cover is defined as the percentage of tree
crown closure that is present in a unit of space in a particular land area.
Center and Technical Information Center
PWRO-NOCA-ARCGRID-1
Technical Information Center for a price quote.
http://www.nps.gov/gis/TIC-requests.htm to the NPS Denver Service Center
Reprographic Imaging Center and Technical Information Center. Use the
"Resource_Description" above for the Item No.
and Technical Information Center for a quote.
Office GIS Group