photogrammetric and gis techniques for the development … · photogrammetric and gis techniques...

Photogrammetric and GIS techniques for the development

of vegetation databases of mountainous areas:

Great Smoky Mountains National Park

Roy Welch*, Marguerite Madden, Thomas Jordan

Center for Remote Sensing and Mapping Science (CRMS), Department of Geography, The University of Georgia, Athens, GA 30602 USA

Received 15 January 2002; accepted 23 August 2002

Abstract

Detailed vegetation databases and associated maps of the Great Smoky Mountains National Park, a rugged, forested area of

more than 2000 km2, were constructed to support resource management activities of the U.S. National Park Service (NPS).

These detailed vegetation databases and associated maps have a terrain relief exceeding 1700 m and a continuous forest cover

over 95% of the Park. The requirement to use 1:12,000- and 1:40,000-scale color infrared aerial photographs as the primary data

source for mapping overstory and understory vegetation, respectively, necessitated the integration of analog photointerpretation

with both digital softcopy photogrammetry and geographic information system (GIS) procedures to overcome problems

associated with excessive terrain relief and a lack of ground control. Applications of the vegetation database and associated

large-scale maps include assessments of vegetation patterns related to management activities and quantification of forest fire

fuels.

D 2002 Elsevier Science B.V. All rights reserved.

Keywords: vegetation mapping; softcopy photogrammetry; GIS; mountainous terrain; fuel modeling

1. Introduction

The Appalachian Mountain chain in eastern United

States reaches elevations of over 2000 m and extends

for more than 2000 km from Maine in the northeast to

Alabama in the southeast (Fig. 1a). Much of this scenic

region is under the jurisdiction of the Federal Govern-

ment and the 14 states through which the chain passes.

One very rugged area, the Great Smoky Mountains

National Park, which encompasses approximately

2070 km2 of continuous forest cover with few roads,

but over 1290 km of hiking trails, is located along the

North Carolina–Tennessee border in southeastern

United States (Fig. 1b). This national park receives

as many as 10 million visitors each year, contains one

of the most diverse collection of plants and animals in

the world and has been designated as an International

Biosphere Reserve and a World Heritage Site.

Although the Great Smoky Mountains National

Park was mapped at 1:24,000 scale by the U.S. Geo-

logical Survey (USGS) in the 1960s and 1970s, these

topographic maps, while essential, do not provide the

detailed information and flexibility required to manage

0924-2716/02/$ - see front matter D 2002 Elsevier Science B.V. All rights reserved.

PII: S0924 -2716 (02 )00118 -1

* Corresponding author.

E-mail address: [email protected] (R. Welch).

www.elsevier.com/locate/isprsjprs

ISPRS Journal of Photogrammetry & Remote Sensing 57 (2002) 53–68

Fig. 1. (a) Location of Appalachian Mountains chain and Great Smoky Mountains National Park in eastern United States. (b) The major road

through Great Smoky Mountains National Park connects the towns of Gatlinburg, TN and Cherokee, NC. Clingmans Dome, with an elevation

of 2025 m, is the highest point in the Park.

R. Welch et al. / ISPRS Journal of Photogrammetry & Remote Sensing 57 (2002) 53–6854

the Park, protect it from threats due to fire and pop-

ulation pressure or to monitor changes caused by air

pollution and invasive exotic plants and animals. These

problems at Great SmokyMountains National Park and

other parks have led the USGS and the U.S. National

Park Service (NPS) to sponsor the development of

detailed vegetation databases in digital format from

remotely sensed data that can be used in a geographic

information system (GIS) environment to create large-

scale map products and conduct analyses of change

(Welch et al., 1995, 1999, 2000; USGS, 2002).

The objectives of this paper are to demonstrate: (1)

how digital photogrammetry, photointerpretation and

GIS techniques were refined, adapted and integrated

to permit the construction of a geocoded vegetation

database from more than 1000 large-scale aerial

photographs of the rugged, high relief Great Smoky

Mountains National Park; and (2) the possibilities for

undertaking GIS analyses of vegetation and the devel-

opment of fuel models for the management and

control of forest fires. Because Great Smoky Moun-

tains National Park is considered one of the most

difficult terrain areas to map in the United States due

to high relief and nearly continuous forest cover, it is

envisioned that the techniques discussed below can be

modified as necessary and applied to rugged and

remote forested lands in other areas of the world.

2. Study area

Great Smoky Mountains National Park was estab-

lished in 1934 in an attempt to halt the damage to

forests caused by erosion and fires associated with

logging activities of the 1800s and early 1900s (Houk,

2000). By the 1920s, nearly two-thirds of the lands

that would become Great Smoky Mountains National

Park had been logged or burned. The Park now

protects a large tract of forestland within the southern

Appalachian Mountains—among the oldest mountain

ranges on earth. Elevations in Great Smoky Moun-

tains National Park range from approximately 250 m

along the outside boundary of the Park up to 2025 m

at Clingmans Dome (Figs. 1b and 2). Rock formations

in the region are sedimentary, the result of silt, sand

and gravel deposits into a shallow sea that covered the

area approximately 600 million years ago (Moore,

1988). More than 900 km of streams and rivers that

flow within the Park are replenished by over 200 cm

of rainfall per year. High rates of evaporation and

transpiration of moisture through the leaves of the

Park’s vast forest produce a blue haze from which the

Smoky Mountains gets its name.

Environmental conditions within Great Smoky

Mountains National Park result in a variety of habitats

that support a high diversity of mountain flora and

fauna. More than 1570 species of flowering plants,

10% of which are considered rare, and over 4000

species of nonflowering plants are found in the Park

(Walker, 1991). The forestlands include over 100

different species of trees and contain the most exten-

sive virgin hardwood forest in the eastern United

States (Kemp, 1993).

Scientists estimate that the flora and fauna cur-

rently identified in the Park represent only 10% of the

species documented to date (Kaiser, 1999). In order to

discover the full range of life in Great Smoky Moun-

tains National Park, an ambitious project led by the

National Park Service and a nonprofit organization,

Discover Life, is underway. Known as the All Taxa

Biodiversity Inventory (ATBI), the project aims to

identify every life form in the Park (possibly over

100,000 species) over the next 10–15 years (White

and Morse, 2000). The efforts of ATBI participants

rely heavily on map information in order to locate

various habitats, conduct fieldwork and establish

sample plots.

Natural resource managers at Great Smoky Moun-

tains National Park realized early on the value of

producing a detailed vegetation database that could

be used to map habitats and aid researchers in their

quest for identifying new mountain species. They

also required a vegetation database for providing

baseline information for future monitoring and man-

agement tasks. Facing threats by air pollution, inva-

sive exotic plants and animals, large numbers of Park

visitors and forest fires, Park managers needed

analysis tools to assist them in the preservation of

valuable resources. Consequently, in 1999, the Cen-

ter for Remote Sensing and Mapping Science at

the University of Georgia entered into a cooperative

agreement with the NPS to create a detailed digital

database for Great Smoky Mountains National Park

that includes both overstory and understory vegeta-

tion and an analysis of fuels that can cause forest

fires in the Park.

R. Welch et al. / ISPRS Journal of Photogrammetry & Remote Sensing 57 (2002) 53–68 55

Fig. 2. Perspective view of Great Smoky Mountains National Park looking east across a mosaic of SPOT multispectral images draped over a

digital elevation model. Elevations range from about 250 to over 2000 m above sea level.

Fig. 3. A representative large-scale color infrared aerial photograph recorded in October 1997 reveals the diversity of vegetation and the

transition of species from low to high elevation. The terrain relief in this photo is approximately 570 m.


3. Methodology

The main requirement for the project was to

produce a vegetation database and associated maps

in vector format that contained polygons for over 100

overstory and 70 understory plant communities plot-

ted to within approximately F 5 to F 10 m of their

true ground locations. Overstory vegetation was to be

mapped using more than 1000 color infrared aerial

photographs of 1:12,000 scale in film transparency

format recorded from a flying height of approximately

1800 m for the NPS by a commercial contractor

(using a Wild RC20 photogrammetric camera, f = 15

cm) in late October of 1997 and 1998 when the leaves

were still on the trees (leaf-on) and displayed a color

diversity that allowed the vegetation communities/

species to be identified (Table 1, Fig. 3). Displace-

ments due to terrain relief were a major problem, in

some cases reaching more than 40 mm on the

23� 23-cm format photographs. Unfortunately, the

photographs were acquired before the Center for

Remote Sensing and Mapping Science became

involved in the project and little, if any, consideration

appeared to have been given to terrain relief in

relation to mapping and/or GIS database construction.

The understory vegetation was less complex than

the overstory vegetation and could be mapped sepa-

rately from available 1:40,000-scale color infrared pho-

tographs recorded (with a Wild RC30 camera, f= 15

cm) in the early spring of 1998 as part of the USGS

National Aerial Photography Program (NAPP). At that

time of year, deciduous trees have lost their leaves

(leaf-off) and it is possible to delineate the understory

evergreen shrubs and trees that are both combustible

and sufficiently dense to restrict crews combating fires

or conducting search and rescue missions.

With the dense forest cover, steep slopes, absence

of ground control and relief often exceeding 30% of

the flying height for the 1:12,000-scale photographic

coverage, the construction of a vegetation database

accurate in both the spatial and thematic context

necessitated a combination of softcopy photogramme-

try, photointerpretation and GIS procedures organized

in parallel as shown in Fig. 4. These are discussed

below.

3.1. Photogrammetric operations

The main objective of the photogrammetric proce-

dure was to densify the sparse ground control in the

Park by means of aerotriangulation. At the outset, the

1:12,000-scale color infrared film transparencies were

scanned using an Epson Expression 836xl desktop

scanner to create black and white digital photos of 42-

Table 1

Specifications of data sources available for map/database development of Great Smoky Mountains National Park

Data source Format and

type of data

Flying height

(FH) and/or scale

Resolution No. required to

cover the park

Comments and/or

problems

CIR air photos

(October 1997–1998)

23� 23 cm (analog

film transparencies)

FH= 1800 m

(1:12,000)

f 0.4 m f 1000 Terrain relief in excess of 30%

of flying height.

Fall leaf-on conditions are

ideal for mapping overstory

forest communities.

USGS NAPP air photos

(March/April 1997–1998)

23� 23 cm (analog

film transparencies)

FHc 6000 m

(1:40,000)

f1 m f130 Scale is too small for mapping

overstory vegetation. Leaf-off

conditions are ideal for mapping

understory vegetation.

USGS topographic maps Paper maps 1:24,000 – 25 Last updated 1960–1970s.

USGS DOQQs

(Pan and CIR)

Digital – 1 m 80 USGS DOQQs have a

planimetric accuracy of

approximately F 3 m RMS.

USGS Level 2 DEMs Digital 1:24,000 30 m

postspacing

25 USGS Level 2 DEMs have a

vertical accuracy of approximately

F 3–5 m RMS.

USGS DRGs Digital 1:24,000 2.4 m 25 USGS topographic maps

scanned at 100 Am.


Am pixel resolution, providing a file of 35 MB for

each photo. Color, which would have tripled the per

photo file size, was not required for the aerotriangu-

lation task. These digital photos were then displayed

on the computer monitor and with the aid of the R-

WEL Desktop Mapping System (DMS) software

package, the image (x,y) coordinates of passpoints

and ground control points (GCPs) were measured in

the softcopy environment. In the absence of cultural

features and the near continuous tree canopy cover,

Fig. 4. Diagram showing the relationships between photogrammetric, photointerpretation and GIS operations.


the passpoints, in the majority of instances, were

individual treetops that had to be identified uniquely

on the scanned stereopairs—not an easy job in terrain

of high relief recorded on large-scale photographs

(Fig. 5).

Ground control points (GCPs) were, for the most

part, natural features (e.g., rock outcrops and forks in

stream channels) identified on both the 1:12,000-scale

color infrared transparencies and USGS Digital Ortho-

photo Quarter Quads (DOQQs) produced from

1:40,000-scale panchromatic aerial photographs re-

corded in 1993. The Universal Transverse Mercator

(UTM) grid coordinates (X,Y) of these GCPs were

measured directly from the DOQQs (accurate to

within F 3 m). Elevations for the GCPs were derived

by interpolating the Z-coordinates to within F 3 to

F 5 m from USGS Level 2 Digital Elevation Models

(DEMs) with 30-m postspacing (Fig. 6). Thus, in this

project, no ground survey work was required to obtain

the GCPs needed as a framework for the aerotriangu-

lation process.

Analytical aerotriangulation using the scanned

1:12,000-scale photos was undertaken for blocks of

up to 90 photos, where each block corresponded to the

area covered by one of the 25 USGS 1:24,000-scale

map sheets covering the Park. The PC Giant software

package, in conjunction with the DMS software, was

employed for the aerotriangulation process. Output

from the aerotriangulation was a set of X, Y and Z-

coordinates in the UTM coordinate system for the

nine or more passpoints on each photo. Typical root–

mean–square error (RMSE) values for these coordi-

nates averaged F 7 m for the X,Y vectors and F 10 m

for elevation (Z).

The planimetric (X,Y) errors were determined by

point comparisons with the USGS DOQQs (which,

typically, have planimetric errors of approximately 2–

3 m), whereas the Z-error reflects the difference

between the ‘‘true’’ ground elevation as derived from

Fig. 5. Location and measurement of treetop pass points in overlapping images is a difficult task in the heavily forested Great Smoky Mountains.

Fig. 6. Determination of GCP elevations from the DEM using a

bilinear interpolation algorithm. The DEM postspacing is 30 m.


the USGS Level 2 DEMs and the treetops which

correspond to the passpoints. Consequently, it must

be noted that: (1) the elevation errors computed for the

passpoints are much less than 10 m; and (2) Z-

coordinate errors of a few metres at the passpoints

have no noticeable influence on planimetric accuracy

or the production of orthophotos—which were the

only issues of significant concern.

The passpoints with their X-, Y- and Z-coordinates

derived from the aerotriangulation process provided

the ground control required to compute orientation

parameters for the photos. These orientation parame-

ters, in conjunction with the DEM, were used to

differentially rectify the scanned air photos and gen-

erate orthophotos and mosaics employed in the editing

and attributing operations required to build the vector

database (Fig. 7). Most importantly, however, the

orientation parameters allowed the vector overlays

generated as part of the photointerpretation procedure

described below to be differentially rectified as well

and assigned correct UTM coordinates referenced to

the North American Datum of 1927 (NAD 27) as

specified by the NPS.

3.2. Photointerpretation operations

The steps of the photointerpretation process listed

in Fig. 4, proceeded in parallel with the photogram-

metric operations. Overstory vegetation was inter-

preted from the 1:12,000-scale leaf-on color infrared

aerial photographs. On the other hand, the more

general understory vegetation was interpreted from

the 1:40,000-scale (leaf-off) transparencies.

Although it might appear desirable to scan the

color infrared transparencies at high resolution and

undertake the vegetation classification as an onscreen

interpretation and digitizing procedure, this has

proved to be exceedingly time consuming, cumber-

some and expensive compared to more traditional

approaches (Welch et al., 1995, 1999, 2002; Rutchey

and Vilchek, 1999). Moreover, photointerpreters must

view the vegetation in stereo and color within the

context of a relatively large area of terrain in order to

identify the vegetation communities. This is most

easily done using a stereoscope to view the analog

air photos so that the vegetation patterns can be

assessed in relation to slope, aspect and elevation.

Fig. 7. A mosaic of orthorectified 1:12,000-scale photographs is employed for quality assurance and checking. In this mosaic, the terrain

features are well aligned between individual photographs, indicating a good solution overall.


Recognizing the need to augment manual procedures

with automated techniques, the steps described below

integrate conventional photointerpretation procedures

with digital processing technology in an attempt to

streamline the database and map compilation process.

At the beginning of the Great Smoky Mountains

National Park mapping project, the photointerpreters,

in conjunction with NPS plant specialists, conducted

field investigations to collect data on the forest com-

munities and correlate signatures evident on the color

infrared aerial photographs with ground observations.

Consequently, UTM coordinates and field data were

collected at over 2000 locations with the aid of a

Garmin III Plus hand held Global Positioning System

(GPS) receiver and a Kodak Digital Field Imaging

System (FIS) 265 digital camera system. The hand

held Kodak digital camera was connected to the

Garmin GPS that ‘‘stamped’’ the location, date and

time on each image (Fig. 8). These images were input

to ESRI ArcView software to provide a pictorial

record of field observations accurate to within F 5

to F 10 m, which is adequate for locating points

within vegetation polygons of 0.5 ha (the minimum

mapping unit) or larger.

A compilation of all field information was used by

Center for Remote Sensing and Mapping Science

ecologists to organize the Great Smoky Mountains

National Park overstory and understory vegetation into

a classification system with over 100 overstory and 70

understory association-level classes suitable for use

with the large- and medium-scale (1:12,000 and

1:40,000, respectively) color infrared aerial photo-

graphs (Jackson and Madden, 2002, Table 2). These

classification systems were based on the newly

released USGS Biological Resources Division/Natio-

nal Park Service National Vegetation Classification

System developed by The Nature Conservancy

(TNC) as part of a national vegetation mapping pro-

gram (The Nature Conservancy, 1999).

In order to accommodate the complex vegetation

patterns found in Great Smoky Mountains National

Park and generally maintain a minimum mapping unit

of 0.5 ha, a three-tiered scheme was developed for

attributing vegetation polygons, similar to that devel-

oped for an earlier project in the Everglades of south

Florida (Madden et al., 1999). The three-tiered

scheme allowed photointerpreters to annotate each

polygon delineated on clear plastic overlays registered

Fig. 8. Ground digital image of overstory and understory vegetation recorded with a Kodak FIS 265 digital camera. The text stamp across the

top of the image indicating the time and location was recorded by the GPS unit interfaced to the camera.


to the photos with a primary or dominant vegetation

class accounting for more than 50% of the vegetation

in the polygon. Where appropriate, secondary and

tertiary vegetation classes are added to describe

mixed-plant communities within the polygon.

A separate classification system containing over 70

classes was developed to map the understory vegeta-

tion from the 1:40,000-scale photographs. The term

‘‘understory’’ denotes woody vegetation of medium

height (3–5 m) that does not reach the forest canopy

level of roughly 20 m. Understory classes were also

assigned density values of light (l), medium (m) or

heavy (h). These understory densities are important

considerations for conducting search and rescue mis-

sions in the Park, as well as for describing fuel classes,

as explained in more detail below.

Once the overstory and understory vegetation clas-

sification systems were established, the photointerpre-

tation proceeded by taping transparent plastic overlays

to the film transparencies and transferring the photo

numbers and fiducial marks to the overlays by means

of a Rapidograph technical pen. The film transparen-

cies, with plastic overlays, were then placed on a high

Table 2

Sample hierarchy of subalpine forest classes within the overstory

vegetation classification system for Great Smoky Mountains

National Park

Subalpine forest

(1) Fraser Fir F

(a) Formerly Fraser Fir (F)

(2) Red Spruce–Fraser Fir S–Fa, S/F, F/S

(a) Red Spruce–Fraser Fir/

Rhododendron

S–F/R

(b) Red Spruce–Fraser Fir/

Low Shrub–Herb

S–F/Sb

(3) Red Spruce S

(a) Red Spruce/Rhododendron S/R

(b) Red Spruce/Birch S/NHxB

(c) Red Spruce/Northern

Hardwoods/Rhododendron

S/NHx

(4) Exposed Northern Hardwoods NHxE

(a) Exposed Northern

Hardwoods/Red Spruce

NHxE/S

(5) Beech Forest Be

(a) Beech Gap Forest NHxBe

(b) North Slope Tall Herb Type NHxBe/Hb

(c) South Slope Sedge Type NHxBe/G

a Symbols: (– ) designates an equal mix and (/) designates the

first class listed is dominant (>50 percent) over the second class that

is listed.

Fig. 9. (a) Original photo overlay depicting vegetation polygons and

a 1-cm grid prior to correction for relief displacement. (b) Overlay

and grid after orthorectification showing the extreme corrections

required to accommodate the large range of relief in the area.


intensity light table and the polygons corresponding to

the vegetation classes outlined on the overlay using

the Rapidograph pen while viewing the photographs

through a stereoscope and correlating the photo sig-

nature, slope, aspect and elevation with ground truth

vegetation information. This is a simple, fast, inex-

pensive and flexible method of creating a vegetation

overlay that can be scanned to create a raster file.

The scanning process for delineated overlays again

involved the use of the desktop Epson 836xl scanner,

at a resolution set to 42 Am. All annotated point, line

and polygon information on the overlay was con-

verted to raster format. The parameters derived from

the differential rectification of the scanned 1:12,000-

scale photos were applied to the scanned overlay files

via registration with the transferred fiducial marks.

Fig. 9 illustrates the magnitude of polygon displace-

ment, as well as distortion in polygon shape and size,

due to variable relief displacements across the photo-

graph.

After differential rectification of the scanned raster

overlay files, these files were converted to vector

format with the software package R2V by Able

Software and saved in ArcInfo line format. Vector

files from approximately 45 photographs needed to be

Fig. 10. Individual vector files from four adjacent photos that have

been edited and edge matched.

Fig. 11. Drape of large-scale color infrared orthophoto on a portion of the Great Smoky Mountains National Park DEM.


edited, edge matched and incorporated into a single

ArcInfo coverage in order to produce a vegetation

map corresponding to a single USGS topographic

quadrangle (Fig. 10). A typical coverage for the area

corresponding to a USGS 1:24,000-scale map can

contain over 4500 polygons that must be attributed

with a dominant vegetation class, and possibly sec-

ondary and tertiary vegetation classes. More than 700

man hours were required to produce an overstory

vegetation map from the 1:12,000-scale photos,

including quality control checks of labels/line work

within and between adjacent maps.

Understory maps corresponding to a USGS topo-

graphic quadrangle were much less complex and

typically required the use of only four 1:40,000-scale

aerial photographs, resulting in the delineation of

approximately 2000 polygons. Approximately 100

man hours were needed to generate an understory

vegetation map.

Although the funds available for the project pre-

cluded an exhaustive check of thematic accuracy, draft

maps of both overstory and understory vegetation

were taken into the field when completed and eval-

uated for classification accuracy by resource manag-

ers. Any discrepancies in vegetation identification

were noted in the field and corrected in the vegetation

databases. Overall, the thematic classification accu-

racy of all map products has been found to be better

than 85% correct—a figure considered acceptable by

the NPS for large area thematic maps and databases.

4. Map and database products and applications

Final products included separate seamless GIS

databases of both overstory and understory vegetation

communities for the entire park, along with corre-

sponding hardcopy maps plotted at 1:15,000 scale.

Each map sheet contains a color-coded legend and

brief description of all vegetation classes found in

Great Smoky Mountains National Park. Additional

digital/hardcopy products that can be created for

particular areas of interest as a result of the vegetation

database development are color orthophoto mosaics

and drapes of maps/images on the USGS DEM to

enhance visualization of vegetation patterns with

respect to the terrain (Madden and Jordan, 2001,

Fig. 11).

The overstory and understory maps are used in

Great Smoky Mountains National Park for daily

assessment of vegetation patterns in relation to man-

agement activities. For example, the maps target areas

that require exotic vegetation eradication in order to

maintain healthy, native vegetation communities. The

vegetation database also can be queried to identify

areas of interest, such as, Fraser fir (Abies fraseri) die-

off and damage caused by the non-native balsam

woolly adelgid (Adelges piceae), or pure stands of

high elevation table mountain pine (Pinus pungens)

requiring controlled burning to eliminate hardwood

invasion.

Fire managers in Great Smoky Mountains National

Park are especially interested in assessing the over-

story and understory vegetation in terms of fuels for

potential forest fires. Until recently, all fires in the Park

were historically suppressed, resulting in the accumu-

lation of flammable woody debris and the potential for

intense wild fires. To this end, the vegetation databases

and maps are being employed with GIS modeling

techniques to assess forest fire fuels within Great

Smoky Mountains National Park (Dukes, 2001). A

well-tested and popular fuel classification system,

known as the Anderson Fuel Classification System,

contains 13 fuel models originally defined for fire

behavior prediction as applied to the more xeric

vegetation of the western United States (Anderson,

1982). These fuel models were re-evaluated by NPS

fire managers to relate fire behavior to vegetation of

eastern deciduous forests.

The vegetation database with 100 overstory classes

was generalized to facilitate fire fuel assessment by

using a lookup table to reclassify polygons and create

Fig. 12. (A) Color infrared orthophoto mosaic for a portion of Great Smoky Mountains National Park created from leaf-off 1:40,000-scale air

photos. (B) Map of understory vegetation prepared from 1:40,000-scale color infrared air photos depicting generalized classes of deciduous

shrubs (white), pine and Kalmia (light tans and yellows) and Rhododendron with hemlock (dark greens). (C) Map of overstory vegetation

prepared from large-scale 1:12,000 color infrared air photos depicting generalized xeric oak/pine forest (oranges), xeric oak/pine woodland (tans

and yellows), Kalmia shrubland (light beige) communities. (D) Composite map of fuel classes based on overstory vegetation (numbered 1–13)

with added decimal values denoting the type and density of understory vegetation.


new coverages having 25 general vegetation classes.

These new coverages were intersected with the under-

story vegetation database to create a composite over-

story and understory dataset. A customized ArcInfo

script was then used to apply a set of rules for the

selection of particular combinations of overstory and

understory vegetation and the assignment of polygons

to fuel classes numbered 1–13 (Fig. 12, Table 3).

Next, a decimal value, indicative of the understory

type and density, was added to the fuel class to

provide fire managers with additional information

on understory fuel conditions (Table 4).

In the final analysis step, the reclassified fuel class

coverages were dissolved to aggregate adjacent poly-

gons having the same fuel class and understory

decimal value. The resulting fuel class data layers

and associated 1:15,000-scale maps provide fire man-

agers with information that can be quickly assessed to

determine general patterns of potential fire ignition

and spread, optimize the deployment of control meas-

ures and estimate the impact of fires on Park facilities.

Further, refinement of the GIS modeling procedure

is currently underway to allow rule-based decisions to

account for particularly wet or dry weather conditions.

For example, under normal conditions an area of

white pine (Pinus strobus) and mixed oak overstory

with an understory of light density mountain laurel

(Kalmia latifolia) would be assigned a fuel class 8.1

(typical of slow-burning ground fires). During partic-

ularly dry conditions, a forest manager can toggle a

‘‘dry conditions’’ button to reassign these same poly-

gons to fuel class 9.1, indicative of fires that run

through the surface litter faster than fuel class 8.

5. Conclusion

The construction of vegetation databases over

extensive areas of mountainous terrain should focus

on the acquisition and use of aerial images recorded by

a standard 23� 23 cm format photogrammetric film

camera system (or the newer digital photogrammetric

cameras) equipped with a lens of not less than 15-cm

focal length and, preferably, 30 cm. While this neces-

sitates an aircraft operating at higher altitudes for a

given scale or pixel resolution, the greater flying height

significantly reduces displacements due to terrain re-

lief—amost serious problemwhen attempting to create

detailed GIS databases from large numbers of aerial

photographs of rugged terrain. Furthermore, consider-

ation should be given to interfacing the camera system

to an inertial/GPS-based orientation and position sys-

tem at the time of photo acquisition so that exterior

Table 3

Fuel classes assigned to generalized overstory vegetation classes

Generalized overstory vegetation class Fuel class

(I) Forest

Fraser Fir (F) 10

Red Spruce (S) 10

Northern Hardwoods (NHx) 9

Montane Red Oak/White Oak (MO) 9

Montane Cove Hardwoods (CHx) 9

Hemlock (T) 9

Montane Alluvial Wetland (MAL) 0

Mixed Mesophytic Hardwoods (Hx) 9

Sub-Mesic to Mesic Oak Hardwoods (OmH) 9

Sub-Xeric to Xeric Oak Hardwoods (OzHf) 8

(II) Woodland (Wd) 6

Sub-Xeric to Xeric Oak Hardwoods (OzH) 9

Pine (PI) 8

(III) Shrubland (Sb) 5

Rhododendron (R) 4

Kalmia (K) 4

Heath Bald (Hth) 4

(IV) Graminoid and Herbaceous (G) 1

Pasture (P) 1

(V) Herbaceous Wetland (Wt) 0

(VI) Additional Categories

Dead Vegetation (Dd) 12

Sparse Vegetation (SV) 2

Water (W) 0

Road (Rd) 0

Successional Vegetation (SU) 5

Table 4

Decimal fuel classes assigned to generalized understory vegetation

classes

Generalized understory

vegetation class

Decimal

fuel class

Kalmia, light density (Kl) 0.1

Rhododendron, light density (Rl) 0.2

Kalmia, medium density (Km) 0.3

Rhododendron, medium density (Rm) 0.4

Kalmia, heavy density (Kh) 0.5

Rhododendron, heavy density (Rh) 0.6

Mixed Rhododendron and Kalmia (R–K) 0.7

Mixed Heath (Hth) 0.8

Other Understory (Ou) 0.9


orientation parameters are collected automatically and

available for input to softcopy photogrammetric soft-

ware. This will provide a potential for minimizing

ground control and aerotriangulation over rugged,

forested terrain, and reduce the time required to com-

plete the project.

The spatial accuracy requirements for constructing

GIS databases and mapping vegetation polygons are

appropriately based on the reliability to which the

photointerpreters can delineate individual vegetation

community boundaries and the smallest polygons to

be mapped in rough terrain—that is, the minimum

mapping unit, which in this study was 0.5 ha. In

general, the coordinate accuracy requirements for GIS

database and/or thematic maps of vegetation in rugged

terrain will not be as stringent as those for low relief

areas with a good distribution of readily identifiable

features and where it is possible to premark control

points. Thus, when planning a vegetation mapping

project, it is appropriate to note that photogrammet-

rists can use color infrared photographs of relatively

small-scale and/or coarse pixel resolution for control

generation tasks, whereas the photointerpreters may

insist on photographs of larger scale and/or higher

resolution more suitable for the extraction of thematic

detail. It pays to use the smallest scale possible that is

acceptable for both control generation and photointer-

pretation. This recommendation will help to reduce

the burden of aerotriangulation, interpretation, editing,

edge matching and attributing polygons associated

with large numbers of photographs having extraordi-

nary displacements due to terrain relief.

The construction of vegetation databases in moun-

tainous areas such as Great SmokyMountains National

Park can be facilitated by the integration of traditional

analog and newer digital data processing techniques.

For example, in this instance softcopy photogrammet-

ric techniques offered significant advantages for con-

trol extension, generation of orientation parameters and

the production of digital orthophoto mosaics employed

in the editing process to finalize vegetation polygons

delineated by the photointerpreters. Traditional analog

photointerpretations techniques permit the rapid delin-

eation of vegetation polygons on transparent overlays

registered to the color infrared film transparencies.

These overlays can then be scanned, and in raster

digital format, rectified based on known camera ori-

entation parameters and an available DEM, to place the

polygons in the map or ground coordinate system. The

rectified polygons may then be converted to digital

vector format for input to GIS software, where editing,

edge matching and attributing operations are con-

ducted to form a vegetation database.

Once a vegetation database is in place, it provides

baseline information on vegetation community distri-

butions and heterogeneity that can be employed with

GIS software for a variety of inventory and analysis

tasks, including the production of large-scale thematic

maps, assessment of growth patterns and changes over

time, and the quantification of fuels and fire risk. The

ability to drape maps and images over DEMs is useful

in planning search and rescue missions and for depict-

ing vegetation patterns as a function of elevation.

Terrain visualization is also an attractive mechanism

for displaying the beauty of the natural environment to

visitors and tourists.

In summary, this study integrated traditional fea-

ture extraction with new digital data processing tech-

niques to produce vegetation databases and associated

large-scale map products of high spatial and thematic

accuracy for a rugged, forested national park in east-

ern United States. It is anticipated that the method-

ologies established for this project can be adapted to

meet the requirements of vegetation mapping efforts

in other mountainous areas of the world.

Acknowledgements

This study was sponsored by the U.S. Department

of Interior, National Park Service, Great Smoky

Mountains National Park (Cooperative Agreement

no. 1443-CA-5460-98-019). The authors wish to

express their appreciation for the devoted efforts of

the staff at the Center for Remote Sensing and

Mapping Science, The University of Georgia, and

Great Smoky Mountains National Park.

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