US Army Corps

of Engineers

Construction Engineering

Research Laboratory

Vegetation Map Accuracy Assessment:

Fort Benning, Georgia

J. Watts, W.R. Whitworth, A. Hill, G.I. Wakefield, T. Davo, and L.J. O’Neil

In 1993 the U.S. Army Construction Engineering

Research Laboratory (CERL) became involved

in the United States Geological Survey, Division

of Biological Resources/National Park Service

(USGS/NPS) National Vegetation Mapping Program by serving as an interagency peer

reviewer. This initial involvement led to discussions on vegetation mapping alternatives for

military lands and a completed vegetation map

for Fort Benning, GA. To validate the vegetation

map and help managers realize its potential use

as a natural resources and military training

management tool, an accuracy assessment was

conducted.

Armed with a field key to the plant communities

on the Fort Benning map and a project sampling

design, intensive fieldwork was conducted to

collect the data required to assess the vegetation map’s accuracy. Field data was analyzed

and an error analysis of the map was completed. This report includes lessons learned

and recommendations for future vegetation

mapping and accuracy assessment projects.

CERL Technical Report 99/76

August 1999

Approved for public release; distribution is unlimited. WWW.CECER.ARMY.MIL/PUBLICATIONS/TECHREPORTS
CERL TR 99/76 2

Foreword

This study was conducted for the Environmental Management Division, Directorate of Public Works, Fort Benning, Georgia, under Military Interdepartmental

Purchase Request (MIPR) No. MIPR8CDPW10029; Work Unit NN8, “Accuracy

Assessment for Fort Benning Georgia.” The technical monitor was Theresa

Davo, (ATSH-OTR) LCTA Coordinator at Fort Benning.

The work was performed by the Ecological Processes Branch (CN-N) of the Installations Division (CN), Construction Engineering Research Laboratory

(CERL). The initial CERL Principal Investigator was Dr. Alison Hill, now with

the U.S. Forest Service. Under her leadership, the project was developed, tasks

defined, the multidisciplinary team assembled, and fieldwork completed. She

assisted in the preliminary fieldwork and provided valuable comments on the final report. The current Principal Investigator is William Whitworth. Joe Watts

and Gerald Wakefield are Principle Investigators at the U.S. Army Topographic

Engineering Center (TEC), and L. Jean O’Neil is a Principal Investigator at the

U.S. Army Waterways Experiment Station (WES).

Fieldwork was critical to the success of the project, and was largely provided by

Verl Emrick and Eric Schreiber of CERL. Additionally, Dennis Gravatt of WES

and William Whitworth provided significant assistance collecting the plot data in

the field. Finally, thanks to the Range Control Branch at Fort Benning for their

professionalism, helpfulness, and accommodation of our presence on the training

areas.

Dr. Harold E. Balbach is Branch Chief, CECER-CN-N, and Dr. John T. Bandy is

Division Chief, CECER-CN. The technical editor was Gloria J. Wienke, Information Technology Laboratory.

The Director of CERL is Dr. Michael J. O’Connor.
CERL TR 99/76 3

Contents

Foreword .........................................................................................................................................2

1 Introduction..............................................................................................................................7

Background.................................................................................................................... 7

Objective........................................................................................................................ 8

Approach....................................................................................................................... 8

Scope............................................................................................................................ 9

Mode of Technology Transfer.......................................................................................... 9

2 Methods..................................................................................................................................10

Sampling Design.......................................................................................................... 10

Field Data Collection.................................................................................................... 12

Error Analysis .............................................................................................................. 13

Overall Accuracy.................................................................................................................... 13

User’s Accuracy..................................................................................................................... 13

Producer’s Accuracy .............................................................................................................. 13

Kappa Coefficient .................................................................................................................. 14

3 Results....................................................................................................................................16

Field Data Collection.................................................................................................... 16

General Vegetation Map Characteristics....................................................................... 17

Sampling Design.......................................................................................................... 17

Error Analysis .............................................................................................................. 20

4 Discussion .............................................................................................................................25

5 Recommendations................................................................................................................28

Literature Cited.............................................................................................................................31

Appendix A: Common Names for the Woody Plant Species Found in the Field Key.........33

Appendix B: Alliance ID and Name..........................................................................................35

Appendix C: ARC/INFO Macro Language Programs.............................................................37

Appendix D: Field Notes...........................................................................................................57
CERL TR 99/76 4

CERL DISTRIBUTION...................................................................................................................60

REPORT DOCUMENTATION PAGE.............................................................................................61
CERL TR 99/76 5

List of Figures and Tables

Figures

1 Comparison of theoretical versus actual sampling................................................. 19

Tables

1 Sampling by map unit. .......................................................................................... 18

2 The primary error matrix. ...................................................................................... 21

3 Major Alliance errors............................................................................................. 22

4 Formation error matrix. ......................................................................................... 23

5 Subgroup/Group/Subclass error matrix. ................................................................ 23

6 Class error matrix. ................................................................................................ 24

7 NIMA error matrix. ................................................................................................ 24

8 Summary accuracy table....................................................................................... 24

CERL TR 99/76 7

1 Introduction

Background

In 1993 the U.S. Army Construction Engineering Research Laboratory (CERL)

became involved in the National Biological Survey/National Park Service

(NBS/NPS) National Vegetation Mapping Program by serving as an interagency

peer reviewer. (Due to reorganization, the program is now sponsored by the U.S.

Geological Survey/National Park Service [USGS/NPS].) This initial involvement

led to discussions on vegetation mapping alternatives for military lands and the

identification of partnering opportunities. One such opportunity was initiated at

Fort Benning, GA, in 1994 and enabled CERL to test the USGS/NPS vegetation

mapping procedures in the context of a military setting. The resulting map,

completed in 1996, was the first known Army effort to map vegetation in strict

accordance to the Federal Geographic Data Committee (FGDC) standards and

methodology, and was at the floristic alliance level. Funding to perform an accuracy assessment of the map was not available until 1998. This research project

involves conducting the accuracy assessment.

Fort Benning is the home of the U.S. Army Infantry Training Center. Its 73,653

ha (182,000 acres) of land contain important habitat for over 90 threatened, endangered, and special concern plant and animal species. To validate the Fort

Benning vegetation map and realize its potential use as a natural resources and

military training management tool, an accuracy assessment (AA) is critical (The

Nature Conservancy [TNC] and Environmental Systems Research Institute

[ESRI], Draft, 1994b). This AA should help installation personnel determine

how much confidence they should assign to the data and the resulting products,

and will allow them to better understand its appropriateness for various applications. These determinations are especially important for compliance with

various environmental regulations, such as Army Regulation (AR) 200-3 (Headquarters, Department of the Army 1995), Department of Defense Instruction

4715.3 (3 May 1996), the Sikes Act (16USC), the Deputy Chief of Staff for Operations’ (DCSOPS) Integrated Training Area Management (ITAM) program

strategy (AR 350-4, 8 May 1998 and HQDA 17 August 1995), the Assistant Chief

of Staff for Installation Management’s (ACSIM) Army Goals and Implementing

Guidance for Natural Resources Planning Level Surveys (PLS), and installation

Integrated Natural Resource Management Plan (INRMP)(DA Memo 21 March
CERL TR 99/76 8

1977). These regulations and guidance, among other items, require Army installations to map and monitor vegetation.

Objective

The objective of this effort is to assess the accuracy of an alliance-level vegetation map previously developed for Fort Benning, GA.

Approach

This project consisted of four relatively discrete phases. First, under a separate

work unit, a field key to the plant communities comprising the Fort Benning

map was developed by The Nature Conservancy (TNC). Appendix A contains a

list of the woody plant species included in the key. Funding for this effort was

obtained through the Army’s Conservation Assistance Program (CAP). The TNC

general classification scheme was based on published information (TNC and Environmental Systems Research Institute [ESRI] Draft, November 1994a), and,

other than LCTA data, little was known about the diversity of communities

found on Fort Benning. Second, the U.S. Army Topographic Engineering Center

(TEC) generated a project sampling design in accordance with the sampling criteria used by the USGS/NPS in the National Vegetation Mapping Program.

After discussions between CERL and TEC, the Fort Benning alliances were reduced from 83 to 51 (crosswalking) in an effort to be consistent with revisions in

TNC’s vegetation classification system for the southeastern United States. GeoMarine, Inc., Plano, TX, created the original vegetation map and was tasked to

do the crosswalk. The alliance names and their associated map numbers are

found in Appendix B. Third, intensive fieldwork, using the TNC-developed field

key, was conducted at Fort Benning in order to collect the data required to assess

the vegetation map’s accuracy (TNC and ESRI, Draft 1994b). The fieldwork was

conducted by CERL and the U.S. Army Waterways Experiment Station (WES).

Fourth, TEC also performed and interpreted an error analysis of the Fort Benning vegetation map based on the collected field data. Finally, the data, results,

and other input from CERL, TEC, and WES were integrated into this report.

The overall objective of assessing the alliance-level accuracy of the Fort Benning

vegetation map can be divided into the following questions:

1. What are the general characteristics of the vegetation map in terms of minimum

mapping unit, number of polygons per class, and thematic accuracy?
CERL TR 99/76 9

2. How do the collected samples relate to the theoretical sample design, and do the

differences between the actual and theoretical samples affect the error analysis?

3. What are the overall accuracy statistics of the alliance vegetation map?

4. Which alliances are mapped well and which are mapped poorly?

5. How does thematic map accuracy vary as map units are collapsed in accordance

with The Nature Conservancy Vegetation Classification System for the Southeastern United States hierarchy and recoded in the National Imagery and Mapping

Agency’s vegetation classification system?

6. What is the approximate spatial accuracy of the alliance map?

Answers to these questions can be found in Chapter 3, Results. Chapter 4, Discussion, focuses on the question: What are some error sources and recommendations for future mapping projects?

Scope

Findings contained within this report are most useful to the Fort Benning natural resource managers, although other installations considering a similar

accuracy assessment project may find this document equally informative. Information on the international classification of ecological communities and the

terrestrial vegetation of the southeastern United States can be obtained by

writing The Nature Conservancy, Southeast Regional Office, P.O. Box 2267,

Chapel Hill, North Carolina, 27515-2267.

Mode of Technology Transfer

This report, and the supporting data will be provided to Fort Benning staff.
CERL TR 99/76 10

2 Methods

The following section details the project sampling design, including the techniques used to automate the sample allocation process. It explains the field

sampling protocols related to navigation, sample elimination, field key use, and

data recording procedures, and describes the error analysis terms and procedures used (TNC and ESRI, Draft 1994a,b).

Sampling Design

The two main steps in generating a sample design are determining the per class

sample sizes and spatial allocation of samples. The primary determinants of per

class sample size for this project were the criteria used in the National Vegetation Mapping Program. Those criteria are based on the establishment of

maximum (30) and minimum (5) per class sample sizes. Five sample size categories were used:

1. Abundant classes:

• total acreage > 50 hectares

• number of polygons ≥ 30

• sample size = 30

2. Relatively abundant classes:

• total acreage > 50 hectares

• number of polygons < 30

• sample size = 20

3. Relatively rare classes:

• total acreage < 50 hectares

• number of polygons ≥ 30

• sample size = 20

4. Rare classes:

• total acreage < 50 hectares

• number of polygons > 5 but < 30

• sample size = 5
CERL TR 99/76 11

5. Very rare classes:

• total acreage < 50 hectares

• number of polygons < 5

• sample size = 1 per polygon

The next step in the sample design was sample placement. Our approach was a

stratified, random sampling design. In an attempt to ensure representation of

all vegetation alliances in the error assessment, alliances portrayed on the subject vegetation map (Geo-Marine 1997a and b) were used as strata. The word

“strata” in this context simply means independent sampling units (i.e., placement of samples within one vegetation type [strata] does not affect sample

placement in other vegetation types). Recognizing that scientific rigor and crew

safety are both important, vegetation ecotones, reforested areas, dud areas, and

exclusion areas were avoided as potential sample locations. The avoidance areas

(environmental constraints) came from geographic information system (GIS) layers provided by Fort Benning personnel.

Given the large number of polygons for some vegetation types, it was neither

feasible nor necessary to sample every polygon. Within each alliance type, polygons outside of avoidance areas were randomly selected. In the selected

polygons, point locations in the Universal Transverse Mercator (UTM) coordinate

system on the World Geodetic System-1984 datum were randomly selected. Any

random point falling within 30 meters of a vegetation boundary portrayed on the

subject vegetation map was eliminated. This decision recognizes: (1) the interpretive nature of vegetation mapping and the realization that sharp demarcation

lines between vegetation types are rare in nature, (2) the difficulty in assigning

vegetation alliances in ecotonal areas, and (3) the confusion of spatial error for

thematic error when polygon boundaries are strictly adhered to. We also realize

that field time could be optimized by establishing buffers of 500 meters from the

road network portrayed on the installation GIS road layer. All random points

outside of the road buffers were eliminated. These sample placement criteria are

embodied in several ARC/INFO∗ Macro Language programs, which were used to

develop the project sample list (Appendix C).

In addition to thematic accuracy assessment, the spatial accuracy of the Fort

Benning vegetation maps was also evaluated in this project. Efforts in this area

 

∗ ARC/INFO is a product of Environmental Systems Research Institute, Inc., 380 New York Street, Redlands, CA

92373-8100. Citing product names does not constitute endorsement by the U.S. Army or the Federal government.
CERL TR 99/76 12

were limited since the subject vegetation map does not contain a large number of

well-defined (topographic) features required to perform a robust spatial accuracy

assessment. All features assessed were road intersections shown on both the

vegetation map and the Fort Benning road coverage that served as the reference

data source. Note that road intersections are depicted on the vegetation map as

intersections of “maintained grass” and “bare ground” polygons rather than as

road line features.

Field Data Collection

The fieldwork portion of this study required researchers to navigate, via a global

positioning system (GPS), to a number of random survey points. Field data collection occurred between 22 June 1998 and 12 July 1998. Crews normally

consisted of two people, but occasionally only one person surveyed. The survey

plot allocation procedure followed a stratified random design weighted by the

area that each vegetation class covered on the installation. A total of 1255 locations were identified as potential survey points, with the actual number of

visited sites being determined by manpower availability and time. Survey points

represented each of the 51 alliance-level vegetation classes thought to occur

(TNC 1998) on the installation.

Prior to the start of installation-wide independent data collection, individual

field crew members practiced using the plant community field key on the same

plot locations. This was an effort to promote quality control through consistency

in key interpretation and species identification.

Each day, adequately trained field crews were given an assignment list of GPS

point coordinates with plot locations. The crews were not provided the vegetation class associated with each point (coordinates) to prevent bias. Using handheld Rockwell Precision Lightweight GPS Receiver (PLGR) units and other land

navigation aids (e.g., compass and topography maps), observers navigated to the

plot locations. In navigating to a point, a PLGR accuracy reading of plus or minus 20 meters or less was deemed acceptable for the purposes of this study. The

actual accuracy reading was recorded on each plot form. It was anticipated that

in some instances observers would not be able to reach a point due to a barrier

(swamp/stream, impassible road, or persistent military activity). If the original

point was within 50 meters of the field crew’s forward-most position, a new point

would be randomly selected and the GPS location recorded. If the barrier was

greater than 50 meters to the original point, the point was omitted from the

day’s schedule and the observers moved on to the next point.
CERL TR 99/76 13

Once at a point, the crew used the key to the plant community alliances provided

by The Nature Conservancy (1998). The extent of the area used to identify the

community is equal to the original minimum mapping unit (MMU) of 0.8 ha (2

acres). That is, this MMU indicates that all plant community key-related observations are made within 50 meters surrounding the GPS point. Based on woody

species present and relative dominance, the crew recorded a primary alliance for

the point. A secondary alliance was recorded when there were two possible designations based on the criteria and/or alliance characteristics given within the

field key. On those occasions where a secondary alliance was recorded, the degree of confidence was recorded for each designation (high, medium, or low). In

addition, field notes were recorded about the site, including tree and shrub stratum species present, percent vegetative cover, and other incidental notes useful

in determining the appropriate class (Appendix D).

Error Analysis

The thematic error analysis methods used for this project were standard techniques widely used in map accuracy assessment. The primary data

representation form used was an error matrix, which is a cross tabulation of

field-derived (rows) and map-derived (columns) vegetation type assignments at

individual field sample locations. Four key metrics derived from error matrices

are defined below:

Overall Accuracy

• Definition: The percentage of correctly mapped samples.

• Computation: The sum of the main diagonal elements of the error matrix divided by the total number of samples.

User’s Accuracy

• Definition: The percentage of map-derived samples that are correctly

mapped.

• Computation:

- Individual map units: dividend of the main diagonal element and the corresponding column total.

- Overall: Average of individual user’s accuracies.

Producer’s Accuracy

• Definition: The percentage of field-derived samples that are correctly

mapped.
CERL TR 99/76 14

• Computation:

- Individual vegetation types: dividend of the main diagonal element and

the corresponding row total.

- Overall: Average of individual producer’s accuracies.

Kappa Coefficient

• Definition: A measure of the difference between the observed agreement between the reference data and the subject map and the chance agreement

between those two items.

• Computation: Kappa = (Pcorrect - Pchance) / (1 - Pchance)

where:

Pcorrect = proportion of correctly mapped entries

Pchance = proportion of samples that could be expected to be correctly mapped

by chance

Using the hierarchical nature of the TNC Vegetation Classification System used

in this project, the above metrics were computed at the alliance, formation, subgroup/group/subclass, and class levels. The subgroup, group, and subclass levels

were combined because the Fort Benning vegetation map has the same number

of map units (nine) for all three levels. The above metrics were computed after

recoding the alliance types into the National Imagery and Mapping Agency’s

vegetation classification system (brushland, grassland, deciduous forest, evergreen forest, and mixed forest). Translation of vegetation type labels from the

TNC scheme was performed to assess the impacts that choice of classification

system has on map accuracy assessment.

The spatial accuracy of the subject vegetation map was assessed using the

widely accepted root mean square error (RMSE) formula as implemented in the

ERDAS Imagine∗ software package. The RMSE is calculated as follows:

RMSE = √[(Emap

-Eroad)

2

 + (Nmap-Nroad)

2

]

where:

Emap

 and Nmap are sample UTM coordinates derived from the subject vegetation map

Eroad and Nroad are sample UTM coordinates derived from the Fort road coverage

 

∗

Imagine is a product of ERDAS, Inc., 2801 Buford Highway, NE, Atlanta, GA 30329-2137. Citing product names

does not constitute endorsement by the U.S. Army or the Federal government.
CERL TR 99/76 15

The following chapter addresses the research questions posed in the Approach

section. Numeric results and associated interpretations of the error analysis are

presented.
CERL TR 99/76 16

3 Results

This chapter briefly describes the field data collection effort and the theoretical

and actual sampling designs of the project. It addresses the overall characteristics of the Fort Benning vegetation map, and provides relevant tables and graphs

quantifying the map’s accuracy. Answers to the project questions posed in the

Approach section of this document are provided.

Field Data Collection

Each day crews visited plots with the intent of maximizing productivity. Specifically, areas of military activity were avoided and plots were grouped when

possible to allow visits to multiple plots from a single departure point. A total of

1255 potential points were identified prior to the start of field work. Field crews

were only able to visit and make alliance-level determinations on 750 of these

points (60 percent). Often, crews were not able to visit a site because of the difficulty in finding a “passable” road/trail; fallen trees made many documented

roads/trails impassible. Additionally, some of the passible roads/trails could not

be accessed due to training activity in an adjacent training area. More problematic, however, was the consistently high temperature (95 to 105°F [35 to 40 °C])

and relative humidity experienced through the field effort. The weather conditions, and the time to safely navigate (by vehicle then on foot) to the more

isolated points, increased the survey time. The researchers collecting data during the field period averaged 11.5 days per researcher, with an estimated total of

500 man-hours of labor. Or, in other terms, we estimate the field effort at 0.67

man-hours expended per site visited. Daily field time was calculated as the time

field crews left the Fort Benning quarters in the morning to just after the last

plot was visited in the evening. Also included were incidental events such as

trips to pick up equipment, eat lunch, and check in with Fort Benning range control and/or natural resource personnel. While not data collection per se, they

represent essential tasks associated with field work that need to be recognized

and allowed for.
CERL TR 99/76 17

General Vegetation Map Characteristics

What are the general characteristics of the vegetation map in terms of minimum

mapping unit, number of polygons per class, and thematic accuracy?

The Fort Benning vegetation map is a single ARC/INFO polygon coverage with

seven attributes: area, perimeter, software-assigned identification number, user

assigned identification number, vegetation alliance name, vegetation alliance

numeric code, and a symbol code. A total of 51 vegetation alliances delineated on

the map represented a total of 6,490 polygons. At the other National Vegetation

Classification System (NVCS) levels, there are 15 formations, 9 subgroups, 9

groups, 9 subclasses, and 4 classes. The median number of polygons per alliance

is 65 (mean = 127) ranging from a minimum of 3 for the Pinus serotina Saturated Woodland Alliance and the Fraxinus pennsylvanica - (Ulmus americana) -

Celtis (occidentalis, laevigata) Temporarily Flooded Forest Alliance to a maximum of 582 for the Pinus taeda Forest Alliance. The distribution of polygons per

class is skewed toward low polygon totals with 23 classes (45 percent) mapped

using 50 or fewer polygons. The overall MMU per alliance is 0.54 ha ranging

from a minimum of 0.0017 ha for the Pinus palustris - Pinus elliotii Temperate

Forest Alliance to a maximum of 4.67 ha for the Pinus serotina Saturated

Woodland Alliance.

Sampling Design

In this section the following questions are addressed:

How do the collected samples relate to the theoretical sample design, do the differences between the actual and theoretical samples affect the error analysis?

What are the overall accuracy statistics of the alliance vegetation map?

As previously discussed, project sample sizes per map unit were based on guidance from the National Vegetation Mapping Program. Of the 750 samples

collected, 83 were not categorized into the vegetation system used on the Fort

Benning vegetation map and were instead assigned to the following classes:

Forestry/Recently Cut, Heavily Disturbed/Unclassifiable, Not Disturbed/ Unclassifiable, Nyssa biflora - Acer rubrum - (Liriodendron tulipifera) Saturated Forest

Alliance, Quercus alba - Quercus (falcata, stellata) Forest Alliance, Successional

Shrubland, Pine Plantation < 5 meters in height, Ecotonal Community, and

Wetland Community. That left 667 samples, or 53 percent of the target sample

size, for use in the accuracy assessment.
CERL TR 99/76 18

Table 1 shows the required per class sampling sizes, and the number and percentage of samples actually collected; Figure 1 graphically depicts

undersampling by percentage class (alliance). For example, 13 alliances were

undersampled by 51-75 percent of what they should have been sampled. Note

that the sum effects of the sampling placement criteria chosen for this project

Table 1. Sampling by map unit.

Class/Alliance Target Actual Percent

1 30 2 0.07

2 30 1 0.03

3 31 28 0.90

4 30 0 0.00

5 20 0 0.00

6 30 6 0.20

7 20 3 0.15

8 30 8 0.27

9 30 19 0.63

10 27 3 0.11

11 30 1 0.03

12 20 0 0.00

13 30 8 0.27

14 30 2 0.07

15 3 7 2.33

16 30 29 0.97

17 30 10 0.33

18 20 2 0.10

19 30 20 0.67

20 20 18 0.90

21 30 0 0.00

22 20 0 0.00

23 30 8 0.27

24 30 1 0.03

25 30 17 0.57

26 30 24 0.80

27 30 18 0.60

28 30 14 0.47

29 20 13 0.65

30 30 19 0.63

31 20 15 0.75

32 5 9 1.80

33 30 19 0.63

34 30 56 1.87

35 20 35 1.75

36 30 86 2.87

37 3 0 0.00

38 20 12 0.60

39 30 25 0.83

40 20 5 0.25

41 29 1 0.03

42 30 0 0.00

43 5 0 0.00

44 20 16 0.80

45 5 1 0.20

46 30 14 0.47

47 30 55 1.83

48 7 1 0.14

49 30 5 0.17

50 30 7 0.23

51 30 24 0.80
CERL TR 99/76 19

Figure 1. Comparison of theoretical versus actual sampling.

(road buffers, ecotone avoidance, dud areas, etc.) eliminated the possibility of total compliance with USGS/NPS guidelines. As Table 1 and Figure 1 show, many

vegetation classes were undersampled. The two primary reasons for this undersampling are: (1) discrepancies between the classification system used in the

mapping and field observations and (2) map error. The required field time to

survey all potential points was underestimated. This underestimation likely

contributed to undersampling as well, the degree to which is difficult to assess.

Field notes by the CERL field crew leader question the prevalence of several alliances, all of which were severely undersampled despite being used extensively

on the vegetation map. Also, since the vegetation map polygons were used to

stratify the field sampling, map units mapped with low accuracy tend to be undersampled, especially for rare vegetation types.

The impacts of undersampling on the map error analysis are moderate. For all

unsampled map units (n = 3), no producer’s accuracy values can be computed.

Likewise, no user’s accuracy values can be computed for unsampled vegetation

types (n = 8). Only one category (Arundinaria gigantea Saturated Shrubland

Alliance) fell in the overlap category. This category was neither visited in the

field nor portrayed on the map at sample locations, and was therefore not included in the computation of the Kappa Coefficient. Caution should be used,

however, in interpreting per class user’s and producer’s accuracies for classes

sampled at low frequencies. As a general rule, results of classes falling into the

abundant, relatively abundant, and relatively rare classes in which less than five

field plots were taken represent minimal information.

Oversampling of certain alliances also occurred. The actual sample sizes for six

classes were substantially above that required by the sampling design, leading to

Numbers represent number of alliances in each undersampling group.

13+23+6+9=51 alliances

undersampling group.

13 + 23 + 6 + 9 = 51 alliances

MAP UNIT UNDERSAMPLING

23

6

9

13 0-25%

26-50%

51-75%

76-100%

Numbers represent number of alliances in each undersampling group.

13 + 23 + 6 + 9 = 51 alliances
CERL TR 99/76 20

a total of 130 extraneous plots. Much of that oversampling (56 plots) occurred in

the Pinus taeda Woodland Alliance, reflecting, in part, map confusion between

different pine-dominated alliances. Other than deflecting field resources away

from undersampled vegetation types, oversampling does not affect the map error

analysis.

Error Analysis

In this section, the remaining project questions are addressed:

Which alliances are mapped well and which are mapped poorly?

How does thematic map accuracy vary as map units are collapsed in accordance

with the Nature Conservancy Vegetation Classification System for the Southeastern United States hierarchy and recoded in the National Imagery and Mapping

Agency's vegetation classification system?

What is the approximate spatial accuracy of the alliance map?

What are some error sources and recommendations for future mapping projects?

Table 2 contains the primary error matrix for this project, which represents the

accuracy of vegetation alliances as mapped on the Fort Benning vegetation map.

In general, the map thematic quality is poor as quantified by:

Overall Accuracy 22%

User’s Accuracy 17%

Overall Producer’s Accuracy 26%

Kappa Coefficient 20%

Again, User’s Accuracy indicates the percentage of map-derived samples that are

correctly mapped while Producer’s Accuracy indicates the percentage of fieldderived samples correctly mapped. The Kappa Coefficient is a measure of the

difference between the observed agreement between the reference data and the

map, and the chance agreement between those two items. A Kappa value between 0.4 and 0.75 suggests a moderately accurate map.
C

E

R

L

T

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Table 2. The primary error matrix.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 Tot Prod

1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0.50

2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0.00

3 3 2 1 0 0 0 0 0 0 2 0 1 0 0 0 1 0 0 1 1 1 0 0 0 1 1 1 0 6 0 0 0 0 1 0 0 0 0 0 4 0 0 0 0 0 0 0 1 0 0 0 28 0.04

4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00

5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00

6 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0.00

7 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0.00

8 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0.13

9 0 0 1 4 0 0 0 3 5 4 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19 0.26

10 0 0 1 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0.67

11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0.00

12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00

13 0 0 0 0 0 2 0 0 0 0 1 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0.63

14 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0.50

15 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0.00

16 0 0 0 0 10 0 0 1 0 1 0 0 0 0 0 4 4 0 0 1 0 4 0 0 0 0 0 0 0 2 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 29 0.14

17 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0.30

18 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0.00

19 0 0 0 4 2 0 0 0 1 0 0 0 0 0 0 2 0 1 2 3 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 0 0 0 0 0 20 0.10

20 0 0 1 1 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 12 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 18 0.67

21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00

22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00

23 1 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 1 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0.00

24 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0.00

25 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 5 2 2 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 17 0.29

26 1 0 0 0 0 0 0 0 2 2 2 0 0 0 0 0 0 0 1 1 0 0 1 0 1 3 1 0 0 3 0 0 0 0 0 0 0 2 0 1 0 0 0 1 0 0 0 0 0 0 2 24 0.13

27 2 0 0 0 1 0 0 3 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 1 0 1 0 0 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 18 0.00

28 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0 0 0 1 0 0 0 0 2 1 3 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 14 0.21

29 0 0 3 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 3 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 13 0.15

30 2 0 1 0 0 0 0 0 0 0 0 0 0 2 0 1 0 3 0 1 1 2 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 19 0.00

31 1 0 0 0 0 5 0 0 0 0 0 0 0 0 0 1 0 2 0 0 0 1 1 0 0 1 0 2 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 15 0.07

32 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 3 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0.00

33 2 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 1 1 2 2 0 0 0 0 0 2 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 19 0.00

34 2 2 1 0 0 0 0 0 0 0 2 0 1 0 0 0 0 0 1 0 0 0 2 6 5 3 5 0 0 1 0 0 2 5 4 5 0 0 1 1 5 0 0 0 0 0 1 0 1 0 0 56 0.09

35 0 2 0 0 0 0 0 0 0 0 1 0 2 0 0 0 0 0 0 0 0 0 0 3 1 0 1 2 0 0 1 0 5 1 0 1 0 2 1 8 3 0 0 0 0 0 1 0 0 0 0 35 0.00

36 6 6 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 6 0 0 4 2 2 0 0 7 2 4 4 6 3 1 13 0 0 4 2 4 1 0 3 0 0 0 0 2 0 0 86 0.15

37 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00

38 0 0 0 0 0 0 0 2 0 0 1 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 12 0.00

39 0 0 0 0 0 0 0 0 0 1 0 0 17 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 2 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 0 25 0.04

40 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 5 0.40

41 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1.00

42 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00

43 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00

44 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 8 0 0 0 0 0 0 0 16 0.50

45 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0.00

46 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 14 0.50

47 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 1 0 0 0 0 27 0 10 14 0 55 0.49

48 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1.00

49 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 0 5 0.40

50 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 5 0 7 0.71

51 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 22 24 0.92

Tot 27 17 12 10 13 9 0 11 13 15 8 14 29 16 0 17 9 8 7 19 10 10 9 18 19 18 16 12 16 10 14 5 18 16 9 23 1 13 9 19 16 7 0 20 1 9 29 6 16 20 24 667

User 0.04 0.00 0.08 0.00 0.00 0.00 0.00 0.09 0.39 0.13 0.00 0.00 0.17 0.06 0.00 0.24 0.33 0.00 0.29 0.63 0.00 0.00 0.00 0.00 0.26 0.17 0.00 0.25 0.13 0.00 0.07 0.00 0.00 0.31 0.00 0.57 0.00 0.00 0.11 0.11 0.06 0.00 0.00 0.40 0.00 0.78 0.93 0.17 0.13 0.25 0,92 0.22

The Overall Accuracy = 22%

The Kappa Index of Agreement = 0.198.

The standard deviation of kappa = 0.006.

The Z score of kappa = 32.07.
CERL TR 99/76 22

Table 3 lists the major errors, defined as a user’s or producer’s error rate of

greater than or equal to 50 percent for all plots with a sample size of 10 or

greater. These errors include confusions between (1) deciduous dominants (expected with leaf-off photography), woodlands, and forests of the same

community; (2) natural herbaceous communities and wildlife clearings; (3) natural herbaceous communities and bare ground (probably due to scale

discrepancies between photointerpretation and field work); (4) different palustrine communities (identified in field notes as difficult to discriminate); and (5)

short trees and tall shrubs. However, there were serious problems in mapping

evergreen-dominated forest alliances as well. For example, Pinus taeda (loblolly

pine)-dominated forest alliances comprised 7 of 51 alliances (13.7 percent), and

189 of the 667 field samples (28.3 percent). Accuracy of these alliances ranged

from 0 to just 21 percent (Table 2).

Tables 4, 5, and 6 present error matrices at the Formation, Subgroup/Group/

Subclass, and Class levels.

Table 7 represents the error matrix for the National Imegry and Mapping

Agency (NIMA) classification system. The trend that emerges from these four

tables is that the accuracy of the Fort Benning vegetation map increases slightly

with decreasing information content (increasing classification level). Table 8 is a

summary table of Kappa values and overall accuracies. For example, the overall

Table 3. Major Alliance errors.

USER’S

FIELD CODE* MAP

CODE**

% OF INCORRECT SAMPLES

16 5 77

15 12 50

39 13 59

36 21 60

44 38 62

47 49 62

47 50 70

PRODUCER’S

FIELD CODE* MAP

CODE**

% OF INCORRECT SAMPLES

17 12 60

38 44 50

39 13 68

44 38 50

46 14 50

* The field codes/alliance IDs are listed in Appendix B. The code/ID is

assigned to a plot based on the vegetation present.
CERL TR 99/76 23

** The map codes/alliance IDs are assigned based on the vegetation

map and reflect the vegetation expected to be on the plot.

Accuracy approached 60 percent when discriminating forests versus woodlands,

but rapidly degraded at smaller scales such as the three P. palustris-dominated

alliances (alliance numbers 33, 34, and 35). While overall accuracy at the class

level may or may not be adequate when determining a training area’s suitability

for a particular military exercise, it clearly can present problems for natural resource personnel managing wildlife species associated with specific alliances.

The endangered red-cockaded woodpecker (RCW), for example, is strongly associated with longleaf pine communities and occurs on Fort Benning. The low

overall accuracy at the alliance level clearly suggests the current map has limited value as a management tool in the management of longleaf pine

communities and the RCW on Fort Benning.

Table 4. Formation error matrix.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Tot Prod

1 9 2 2 1 0 3 6 0 1 0 4 0 0 0 0 3 31 0.29

2 2 28 7 1 0 2 1 0 2 0 0 0 0 0 0 5 48 0.58

3 0 4 19 4 0 2 2 0 1 0 0 0 0 2 0 36 70 0.27

4 2 2 2 12 0 3 0 2 0 0 0 0 0 0 0 3 26 0.46

5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00

6 9 10 5 4 0 24 4 2 4 2 2 1 0 0 0 7 74 0.32

7 6 0 8 1 0 2 3 4 0 2 0 1 0 0 0 5 32 0.09

8 1 5 3 1 0 3 0 1 0 0 0 0 0 0 0 1 15 0.07

9 25 8 4 2 0 42 10 5 59 8 25 3 0 0 2 12 205 0.29

10 0 24 0 0 0 1 0 0 2 1 1 6 1 0 0 1 37 0.03

11 1 1 0 0 0 1 0 0 0 0 3 0 0 0 0 0 6 0.50

12 0 0 0 0 0 0 0 0 0 8 0 8 0 0 0 0 16 0.50

13 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0.00

14 0 0 7 0 0 0 0 0 0 0 0 0 0 7 0 0 14 0.50

15 0 1 0 0 0 0 0 0 2 0 0 0 0 0 27 25 55 0.49

16 1 0 0 2 0 0 0 0 0 1 0 0 0 0 0 33 37 0.89

Tot 56 85 57 28 0 83 26 14 71 22 35 20 1 9 29 131 667

Users 0.16 0.33 0.33 0.43 0.00 0.29 0.12 0.07 0.83 0.05 0.09 0.40 0.00 0.78 0.93 0.25 0.35

Table 5. Subgroup/Group/Subclass error matrix.

1 2 3 4 5 6 7 8 9 10 Tot Prod

1 9 5 9 1 0 4 0 0 0 3 31 0.29

2 4 79 12 3 0 0 0 2 0 44 144 0.55

3 16 37 43 4 4 2 2 0 0 13 121 0.36

4 25 14 57 59 8 25 3 0 2 12 205 0.29

5 0 24 1 2 1 1 6 1 0 1 37 0.03

6 1 1 1 0 0 3 0 0 0 0 6 0.50

7 0 0 0 0 8 0 8 0 0 0 16 0.50

8 0 7 0 0 0 0 1 7 0 0 15 0.47

9 0 1 0 2 0 0 0 0 27 25 55 0.49
CERL TR 99/76 24

10 1 2 0 0 1 0 0 0 0 33 37 0.89

Tot 56 170 123 71 22 35 20 10 29 131 667

Users 0.16 0.47 0.35 0.83 0.05 0.09 0.40 0.70 0.93 0.25 0.40

Table 6. Class error matrix.

1 2 3 4 5 Tot Prod

1 214 18 4 0 60 296 0.72

2 124 99 10 2 13 248 0.40

3 7 8 16 0 0 31 0.52

4 1 2 0 27 25 55 0.49

5 3 1 0 0 33 37 0.89

Tot 349 128 30 29 131 667

Users 0.61 0.77 0.53 0.93 0.25 0.58

Table 7. NIMA error matrix.

1 2 3 4 5 6 Tot Prod

1 16 0 15 0 0 0 31 0.52

2 6 71 20 72 4 0 173 0.41

3 9 9 133 31 0 0 182 0.73

4 5 27 68 86 1 3 190 0.45

5 1 0 2 4 39 14 60 0.65

6 0 0 3 0 1 27 31 0.87

Tot 37 107 241 193 45 44 667

User 0.43 0.66 0.55 0.45 0.87 0.61 0.56

Table 8. Summary accuracy table.

Kappa Overall

Alliance 0.20 0.22

Formation 0.29 0.35

Subgroup/Group/Subclass 0.30 0.40

Class 0.39 0.58

NIMA 0.42 0.56

With respect to spatial accuracy, the root mean square error for the 40 control

points evaluated was 69.4 meters. This number should be considered as an overstatement of the actual spatial error given that the reference data (the Fort

Benning road coverage) most likely is not error-free and that the subject vegetation map does not explicitly show road intersections. The actual spatial error is

probably less than 60 meters.
CERL TR 99/76 25

4 Discussion

The alliance-level accuracy of the Fort Benning vegetation map is estimated to

be 22 percent. From a practical standpoint, an alliance level accuracy of 70 percent should be considered a minimum level, with a map accuracy of 90 to 95

percent recommended. A Kappa value between 0.4 and 0.75 suggests a moderately accurate map; however, an overall NIMA Kappa of 42 percent indicates the

map is only marginally acceptable at even this relatively broad scale. The discrepancy between the current map’s overall accuracy and the minimum accuracy

indicates the possibility of numerous error sources. Those error sources are related to the vegetation classification system, registration of aerial photographs

and other mapping procedures, and error analysis procedures. The vegetation

classification system used in the original compilation of the Fort Benning vegetation map was a draft version of a TNC document (Weakley et al. 1996) describing

a classification of the terrestrial vegetation of the southeastern United States.

Since that classification system was superceded by a 1998 TNC document, the

Fort Benning vegetation map was reclassified into the newer system for accuracy

assessment purposes.

Given the floristic complexity of Fort Benning and the incomplete alliance descriptions, thematic error could have been introduced during the classification

system crosswalking procedures. Classification system uncertainty was evident

during field sampling using the key developed by TNC in 1998 as indicated by

the large number of plots that were not classified. The TNC system tends to focus on natural vegetation communities and gives lesser emphasis to

anthropogenically-modified vegetation types common on Fort Benning. In general, the functionality of the field key used to identify the sample plots was good.

The distinction between forest and woodland was sometimes difficult, particularly in Pinus taeda-dominated alliances. Some of this difficulty could be

expected due to timber thinning and other disturbances occurring during the gap

of more than 5 years between initial map development and fieldwork. The primary difficulty encountered in the field was the lack of choices at the alliance

level. This perception could be the result of encountering alliances not previously identified as occurring on Fort Benning, and alliances thought to occur but

which may not. Two such alliances in the field key not found on Fort Benning

are: Fagus grandifolia – Quercus alba Forest Alliance (#4) and Quercus shumardii – Q. pagoda Forest Alliance (#12). A more detailed vegetation analysis
CERL TR 99/76 26

would be required to identify and describe additional alliances and/or verify the

nonexistence of others.

The Fort Benning vegetation map was compiled by standard photo analysis procedures using 1:12,000 color infrared (CIR) aerial photography taken in January

of 1993 during leaf-off conditions (Geo-Marine, Inc., 1996). The base maps for

the project were 7.5-minute USGS topographic quandrangles covering Fort Benning. Both the aerial photography and base maps were suboptimal and likely

error sources. A recommended set of source data would have been CIR, leaf-on,

stereoscopic photography at scales of 1:12,000 and greater for alliance interpretation and digital orthophoto mosaics for geodetic base purposes. The addition of

near-infrared reflectance information, deciduous leaf morphology, and superior

geometric accuracy provided by these alternative source materials would have

very likely resulted in higher map accuracy. Additional field time for the photo

interpretation analysts in collaboration with vegetation ecologists would also

have promoted higher map accuracy. An important lesson to learn from this

project is the value of quantitative field data collected on military lands in the

development of regional classification systems. Field data collected during this

project and, even more substantially, through Fort Benning’s Land Condition

Trend Analysis (LCTA) program have added much needed information to the

southeastern vegetation database. Moreover, photo analysis must be based on a

sound understanding of the complexity and diversity of actual field vegetation

conditions so that image analysts can reliably correlate photo signatures with

alliance types.

A source for the error analysis procedures used in this project is the USGS/NPS

National Vegetation Mapping Program. Using these guidelines, a sampling

design was created that proved difficult to fully implement. Due to map error,

classification system ambiguities, logistic complications, and insufficient

resources, the majority of alliance types were substantially undersampled. Class

accuracy values associated with substantially undersampled alliances should be

used with much discretion. In addition to undersampling, a clear source of bias

on active military training and testing lands such as Fort Benning is the time

delay between source imagery collection and field assessment. In this project,

approximately 51⁄2 years passed between image acquisition and fieldwork.

Within this extended period, both natural and anthropogenic events occurred on

Fort Benning. Adding to the temporal error is spatial error resulting from the

interacting influences of 20-meter average GPS position errors and the inherent

error in locating image patterns observed in the aerial photography on

topographic maps. A final error source could be discrepancies between field and

air photo estimation of canopy cover, a critical split variable in the TNC
CERL TR 99/76 27

classification system. Past experience indicates that field cover estimates are

generally lower than aerial estimates.
CERL TR 99/76 28

5 Recommendations

The results of this study, while not especially supportive of the current vegetation map, do provide some important lessons learned. The following

recommendations reflect those lessons and are made for the benefit of future

mapping projects on military reservations:

1. Use a stable, site-specific classification system that fully represents the vegetation communities found on the mapping project area.

This often is not possible on military lands for areas that have been previously unmapped, and is a challenge for an alliance-level mapping project due

to emerging state-of-the-art of vegetation classification systems. Rather,

military installations often provide data augmenting recognized vegetation

databases such as the Natural Heritage database. Adoption and support of

the NVCS, of which the TNC southeastern classification system is an important precursor, is the recommended long-term approach to solving this

problem. The process for field data collection, data analysis, alliance description, and problem adjudication currently in work by the Ecological

Society of America in support of the NVCS will eventually provide a framework for resolving this issue. It is recommended that LCTA activities on

military training and testing lands incorporate and support the NVCS system and associated procedures.

2. Acquire and use large-scale, leaf-on photography.

Vegetation mapping at the alliance level mandates the discrimination of

dominant plant species from imagery sources. This extremely difficult task

requires the use of large-scale photography (ideally) both in leaf-on and leafoff condition. Leaf-on photography provides clues about tree crown shape

and leaf morphology, facilitating the separation of closely related alliances.

The combined use of leaf-on and leaf-off photography ensures accurate separation of evergreen and deciduous types and their associated alliances.

3. Acquire and use digital orthophotographs as the geodetic base map.

Standard aerial photography exhibits geometric displacement due to variations in distance from the photo center point (tilt displacement) and

topography (relief displacement). These error sources must be reconciled

when producing vegetation maps to ensure adequate spatial accuracy. The
CERL TR 99/76 29

preferred solution from a technical perspective is to use orthorectified image

base maps in which tilt and relief displacement have been removed through

standard photogrammetric practices. Use of imagery-based mosaics is especially helpful in the accurate transfer of patterns found in the unrectified,

stereoscopic, aerial photography. The disadvantage to digital orthophoto

bases is cost: standard, easily affordable orthophotos available from the

USGS may not have sufficient spatial resolution for alliance-level vegetation

mapping while custom orthophoto mosaics tend to be expensive.

4. Allow time for substantial field reconnaissance and polygon label validation

by a joint team of vegetation ecologists and photointerpretation analysts.

In the original compilation of the Fort Benning vegetation map, limited resources greatly constrained the amount of local floral expertise available

during the photo interpretation procedures. Ideally, a vegetation mapping

project has three phases involving extended field time: initial reconnaissance, intermediate validation, and post-mapping accuracy assessment. The

validation phase is particularly important to map accuracy so that preliminary alliance assignments can be field checked to ensure that photo patterns

are being reliably correlated with vegetation types. The validation phase can

also serve as a time to address perceived deficiencies in the classification

system and to deal with alliance types that cannot accurately be identified

from aerial photography. Close tracking of field progress is essential to

minimize or avoid oversampling and undersampling.

5. Improve the cost estimation for reference data collection.

An accurate estimate of the amount of funding necessary to perform a rigorous accuracy assessment was not accomplished for this project. While it is

true that some of the undersampling of alliance types was due to poor map

quality and underestimation of required field resources, it also became evident that considerably greater funding was necessary to fully satisfy the

USGS/NPS sampling protocols. Based on 60 percent sampling of the target

pool achieved in this project, it is reasonable to conclude that approximately

twice as much funding would be required on a similar project.

6. Integrate map compilation and accuracy assessment.

This coupling of the two elements is highly recommended. If this is not possible, conduct the accuracy assessment within a reasonable amount of time

following creation of the vegetation map, especially on areas where environmental impacts are significant.

The vegetation on military reservations can be highly dynamic due to military training and natural resource management activities. These dynamics
CERL TR 99/76 30

not only include natural (and generally predictable) plant community succession, but also human-induced environmental perturbations such as

ordinance firing and off-road driving impacts, accidental fires, TES habitat

improvements, and commercial forestry activities. Some environmental impacts can merely delay natural community succession, while others can

transform areas to earlier successional stages. Thus, to minimize temporal

error and maximize the accuracy of future installation vegetation maps, proponents of vegetation mapping programs should consider accuracy

assessments to be a core requirement in the vegetation mapping process.
CERL TR 99/76 31

Literature Cited

Army Regulation (AR) 200-3, Natural Resources—Land, Forest, and Wildlife Management (Headquarters, Department of the Army [HQDA], Washington, DC, February 1995).

AR 350-4, Integrated Training Area Management (ITAM), HQDA, Washington, DC, 8 May 1998.

Department of the Army Memo, Assistant Chief of Staff for Installation Management, “Army Goals

and Implementing Guidance for Natural Resources Management Planning Level Surveys and

Integrated Natural Resource Management Plans,” 21 March 1997.

Department of Defense (DoD) Instruction 4715.3, Environmental Conservation Program (Department of Defense, 3 May 1996).

Geo-Marine, Inc. 1996. “Vegetation Community Mapping from Color Infrared Arial Photography of

Fort Benning, Georgia.” Final Report Prepared for U.S. Army Construction Engineering Research Laboratory (CERL), Champaign, IL. Purchase order No. DACW88-92-R-0010. GeoMarine, Inc., Plano, TX.

Geo-Marine, Inc. 1997a. “Fort Benning Vegetation Classification and Mapping: ARC/INFO Coverages and AMLs.” Prepared for U.S. Army Construction Engineering Research Laboratory

(CERL), Champaign, IL. Purchase order No. DACW88-98-M-0088. Geo-Marine, Inc., Plano,

TX.

Geo-Marine, Inc. 1997b. “Fort Benning Vegetation Mapping and Classification: Cross-walk Tables

and Attribute Assignments.” Prepared for U.S. Army Construction Engineering Research

Laboratory (CERL), Champaign, IL. Purchase order No. DACW88-98-M-0088. Geo-Marine,

Inc., Plano, TX.

Headquarters, Department of the Army, Office of the Deputy Chief of Staff for Operations and

Plans, “Integrated Training Area Management (ITAM) Program Strategy,” 17 August 1995.

Tazik, D.J., S.D. Warren, V.E. Diersing, R.B. Shaw, R.J. Brozka, C.F. Bagley, and W.R. Whitworth.

1992. US Army Land Condition-Trend Analysis (LCTA) Plot Inventory Field Methods. CERL

Technical Report N-92/03, Champaign, IL.

The Nature Conservancy (TNC) and Environmental Systems Research Institute (ESRI) for U.S.

Department of the Interior, “Standardized National Vegetation Classification System.

NBS/NPS Vegetation Mapping Program,” Final draft, November 1994a.

TNC and ESRI for U.S. Department of the Interior, “Accuracy Assessment Procedures. NBS/NPS

Vegetation Mapping Program,” Final draft, November 1994b.
CERL TR 99/76 32

TNC. 1998. International Classification of Ecological Communities: Terrestrial Vegetation of the

Southeastern United States, Fort Benning Subset. The Nature Conservancy, Community Ecology Group, Southeastern Conservation Science Department, Chapel Hill, NC.

Weakley, A.S., K.D. Patterson, S. Landaal, M. Pyne, and others (compilers). 1996. “International

Classification of Ecological Communities: Terrestrial Vegetation of the Southeastern United

States.” Working draft of April 1996. The Nature Conservancy, Southeast Regional Office,

Southern Conservation Science Department, Community Ecology Group, Chapel Hill, NC.

16 U.S. Code, Chapter 56, Conservation Programs on Government Lands, Sub-Chapter 1, Conservation Programs on Military Reservations, paragraphs 670a-670o, commonly know as The

Sikes Act.
CERL TR 99/76 33

Appendix A: Common Names for the

Woody Plant Species Found

in the Field Key

SCIENTIFIC NAME COMMON NAME

Acer rubrum Red maple

Acer saccharinum Silver maple

Alnus serrulata Hazel alder

Andropogon virginicus Broomsedge bluestem

Arundinaria gigantea Giant cane

Betula nigra River birch

Carya glabra Pignut hickory

Carya illinoensis Pecan

Carya ovata Shagbark hickory

Carya pallida Sand hickory

Carya tomentosa Mockernut hickory

Celtis laevigata Soapberry

Celtis occidentalis Hackberry

Cornus florida Flowering dogwood

Crataegus crus-galli Cockspur hawthorn

Crataegus marshallii Parsley hawthorn

Crataegus spathulata Littlehip hawthorn

Diosporus virginiana Common persimmon

Fagus grandifolia American beech

Fraxinus pennsylvanica Green ash

Gordonia lasianthus Loblolly bay

Juniperus virginiana Eastern red cedar

Liquidambar styraciflua Sweetgum

Liriodendron tulipifera Yellow polar; Tuliptree

Magnolia grandiflora Southern magnolia

Magnolia virginiana Sweetbay

Myrica cerifera Southern bayberry

Nyssa aquatica Water tupelo

Nyssa biflora Swamp tupelo

Nyssa ogeche Ogeechee tupelo

Nyssa sylvatica Black tupelo

Persea palustris Swamp bay

Pinus echinata Shortleaf pine

Pinus elliottii Slash pine

Pinus palustris Longleaf pine
CERL TR 99/76 34

SCIENTIFIC NAME COMMON NAME

Pinus serotina Pond pine

Pinus taeda Loblolly pine

Platanus occidentalis Sycamore

Prunus angustifolia Chickasaw plum

Quercus alba White oak

Quercus falcata Southern red oak

Quercus incana Bluejack oak

Quercus laevisTurkey oak

Quercus laurifolia Laurel oak

Quercus margarettiae Shrubby post oak

Quercus marilandica Blackjack oak

Quercus michauxii Swamp chestnut oak

Quercus nigra Water oak

Quercus pagoda Cherrybark oak

Quercus phellos Willow oak

Quercus rubra Northern red oak

Quercus shumardii Shumard oak

Quercus stellata Post oak

Quercus velutina Black oak Taxodium distichum Baldcypress

Ulmus americana American elm
CERL TR 99/76 35

Appendix B: Alliance ID and Name

Alliance ID Alliance Name

1 Pinus echinata Forest Alliance

2 Pinus palusris - P. elliotii Temperate Forest Alliance

3 Pinus taeda Forest Alliance

4 Fagus grandifolia - Quercus alba Forest Alliance

5 Fagus grandifolia - Quercus rubra - Quercus alba Forest Alliance

6 Liquidambar styraciflua Forest Alliance

7 Liriodendron tulipifera Forest Alliance

8 Quercus alba - (Q. nigra) Forest Alliance

9 Quercus alba - (Q. rubra, Carya spp.) Forest Alliance

10 Quercus falcata Forest Alliance

11 Quercus laevis Forest Alliance

12 Quercus shumardii - Quercus pagoda Forest Alliance

13 Quercus stellata - Q. marilandica Forest Alliance

14 Betula nigra - (Plantanus occidentalis) Temp. Flooded Forest Alliance

15 Fraxinus pennsylvanica - (Ulmus americana) - Celtis (occidentalis, laevigata)

Temp. Flooded Forest Alliance

16 Liquidambar styraciflua (Liriodendron tulipifera, Acer rubrum) Temp. Flooded

Forest Alliance

17 Plantanus occidentalis- (Fraxinus pennsylvanica, Celtis laevigata,

Acer saccharinum) Temp. Flooded Forest Alliance

18 Quercus (michuaxii, pagoda, shumardii) - Liquidambar styraciflua Temp. Flooded

Forest Alliance

19 Quercus (phellos, nigra, laurifolia) - Temp. Flooded Forest Alliance

20 Nyssa (aquatica, biflora,ogeche) Floodplain Seasonally Flooded Forest Alliance

21 Nyssa aquatica - (Taxodium distichum) Semi-permanently Flooded Forest Alliance

22 Fagus grandifolia - Magnolia grandiflora Forest Alliance

23 Pinus echinata - Quercus (alba, falcata,stellata,velutina) Forest Alliance

24 Pinus echinata - Quercus stellata - Q. marilandica Forest Alliance

25 Pinus palustris - P. (echinata, taeda) - Quercus (incana, margarettiae,falcata,

laevis) Forest Alliance

26 Pinus taeda - (Liquidambar styraciflua, Liriodendron tulipifera) Forest Alliance

27 Pinus taeda – Quercus (alba, falcata, stellata) Forest Alliance

28 Pinus taeda – Quercus nigra Forest Alliance

29 Pinus taeda – Liriodendron tulipifera Temp. Flooded Forest Alliance

30 Pinus taeda – Quercus (phellos, nigra, laurifoli) Temp.Flooded Forest Alliance

31 Pinus taeda – Liquidambar styraciflua - Acer rubrum Saturated Forest Alliance

32 Pinus echinata Woodland Alliance

33 Pinus palustris Woodland Alliance

34 Pinus palustris – Pinus (echinata, taeda) Woodland Alliance

35 Pinus palustris / Quercus spp. Woodland Alliance
CERL TR 99/76 36

Alliance ID Alliance Name

36 Pinus taeda Woodland Alliance

37 Pinus serotina Saturated Woodland Alliance

38 Quercus laevis Woodland Alliance

39 Quercus stellata - Q.marilandica Woodland Alliance

40 Pinus echinata - Quercus (alba, falcata, stellata, velutina) Woodland Alliance

41 Pinus echinata - Quercus stellata - Q. marilandica Woodland Alliance

42 Myrica cerifera Seasonally Flooded Shrubland Alliance

43 Arundinaria gigantea Saturated Shrubland Alliance

44 Crataegus spathulata Shrubland Alliance

45 Prunus angustifolia Shubland Alliance

46 Alnus serrulata Temp. Flooded Shrubland Alliance

47 Andropogon virginicus Herbaceous Alliance

48 Carya illinoensis Plantation

49 Wildlife Clearing (maintained, Non-maintained) grasses

50 Bare ground

51 Water
CERL TR 99/76 37

Appendix C: ARC/INFO Macro Language

Programs

/* srpstart.aml

/* Gery Wakefield

/* Science and Technology Corp.

/* March 26, 1998

/*

/* This AML simply hardcodes the coverages to

/* be used. It can be put in the working

/* directory, to simplify the use of the

/* aml.

&args out_pts

&sv in_poly_cov = /u11/benning/veg_maps/veg_final

&sv numeric_item = subclass

&sv road_cov = /u11/benning/coverages/roads

&sv .amlpath = /u11/benning/aml

&r %.amlpath%/preextract.aml %in_poly_cov% %numeric_item% %road_cov%

%out_pts%

&return

/* preextract.aml

/*

/* Gery Wakefield

/* Science and Technology Corporation

/* 23 March, 1998

/*

/* Arguments:

/* in_%poly_cov%---The polygonal thematic coverage used to derive the

sampling points. This coverage must contain a binary item defining the classes to be sampled.

/* numeric_item--The item in <in_%poly_cov%) defining the classes to be

sampled.

/* road_cov------The line coverage representing the roads to be buffered.

/* A 500 meter buffer is set as the default distance. No point

will be placed further than 500 meters from the road, unless a

class has no polygons with 500 meters of a road.
CERL TR 99/76 38

/* out_pts The output point coverage to be created.

/*

/* This aml calls extract.aml.

&args in_poly_cov numeric_item road_cov out_pts

&if not [iteminfo %in_poly_cov%.pat -info %numeric_item% -exists] &then

 &do

 &type Item %numeric_item% was not found in %in_poly_cov%.pat. Exiting...

 &return

 &end

&if [null %in_poly_cov%] or [null %road_cov%] or [null %out_pts%] &then

 &do

 &type Usage &run <aml> <input poly coverage> <item> {road coverage}

<output>

 &return

 &end

&if not [exists %in_poly_cov% -cover] &then

 &do

 &type %in_poly_cov% was not found. Exiting...

 &return

 &end

&if not [exists %road_cov% -cover] &then

 &do

 &type The line coverage %road_cov% was not found.

 &type No buffering will be done.

 &end

/* Copy the in_poly_cov coverage to a scratch file.

&sv poly_cov [scratchname -directory]

copy %in_poly_cov% %poly_cov%

&if not [iteminfo %in_poly_cov%.pat -info %numeric_item% -exists] &then

 &do

 additem %poly_cov%.pat %poly_cov%.pat subclass 4 5 b

 &end

tables

sel %poly_cov%.pat

calc subclass = %numeric_item%

q
CERL TR 99/76 39

&sv temp_grid [scratchname -directory]

&type ***********************************************************

&type Creating a temporary grid with a cell size of 25 coverage units.

&type The cell size of this grid sets the minimum distance between

&type two sampling points. In this case no two points will be closer

&type than 25 coverage units.

&type ***********************************************************

polygrid %poly_cov% %temp_grid% subclass

25

y

&sv points [scratchname -directory]

&type ***********************************************************

&type Converting the temporary grid into a point coverage to be used

&type in the random point selection routines.

&type ***********************************************************

gridpoint %temp_grid% %points% subclass

kill %temp_grid% all

&type ***********************************************************

&type Buffering input polygon coverage. The buffering distance

&type is set at 30 coverage units meaning that no point will fall

&type within 30 units of the polygon boundary.

&type ***********************************************************

&sv poly_buf = [scratchname -directory]

buffer %poly_cov% %poly_buf% # # 30

&sv point_buf1 = [scratchname -prefix pt -directory]

&if [exists %road_cov% -cover] &then

 &do

 &type ***********************************************************

 &type Buffering input line coverage. The buffer distance is

 &type set at 500 coverage units. No point will be located outside

 &type of 500 coverage units from the line (road), unless the only

 &type polygons for a thematic type are found outside the buffer

 &type distance.

 &type ***********************************************************
CERL TR 99/76 40

 &sv road_buf = [scratchname -directory]

 buffer %road_cov% %road_buf% # # 500

 intersect %points% %road_buf% %point_buf1% point

 ae

 edit %point_buf1%

 editf points

 sel inside ne 100

 &sv road_sel = [show number selected]

 &if %road_sel% > 0 &then

 &do

 delete

 &end

 save

 q

 &end

&if not [exists %road_cov% -cover] &then

 &do

 copy %points% %point_buf1%

 &end

kill %points% all

kill %road_buf% all

&sv point_buf2 = [scratchname -directory]

dropitem %point_buf1%.pat %point_buf1%.pat inside

intersect %point_buf1% %poly_buf% %point_buf2% point

kill %point_buf1% all

kill %poly_buf% all

ae

edit %point_buf2%

editf points

sel inside = 100

delete
CERL TR 99/76 41

save

q

&sv point_cov = [scratchname -directory]

intersect %point_buf2% %poly_cov% %point_cov% point

kill %point_buf2% all

tables

indexitem %point_cov%.pat subclass

indexitem %point_cov%.pat %poly_cov%-id

q

&r %.amlpath%/extract %poly_cov% %point_cov% %out_pts% 1

&return

/* extract.aml

/*

/* Gery Wakefield

/* Science and Technology Corporation

/* 23 March, 1998

/*

/* This aml can be called by the preextract.aml or can be run on its own.

/* This aml creates a stratified random coverage of points using an input

/* thematic polygon coverage. The three arguments are; poly_cov,

/* the input thematic polygonal coverage; point_cov, the input point coverage;

/* and out_cov, the output point coverage. The input poly_cov must have a

/* binary code for the thematic classes in an called item 'subclass'. The

/* coded numbers do not need to be sequential, but it does make processing

/* faster if they are.

/*

/* The argument source is assigned by the preextract.aml. Otherwise, the

/* variable 'source' is left empty.

/*

/* A seperate point file is created for each thematic class. The point
CERL TR 99/76 42

/* file prefix is 'subclass' and the suffix is the id number associated

/* with the item subclass for that theme. The user is prompted at the end

/* of the processing if these point files should be deleted.

&args poly_cov point_cov out_cov source

&if [null %poly_cov%] or [null %point_cov%] or [null %out_cov%] &then

 &do

 &type Usage &run <aml> <input poly coverage> <point coverage> <output>

 &return

 &end

&if not [exists %poly_cov% -cover] &then

 &do

 &type %poly_cov% was not found. Exiting...

 &return

 &end

&if not [exists %point_cov% -cover] &then

 &do

 &type The line coverage %point_cov% was not found. Exiting...

 &return

 &end

&sv id = 1

/* Determine maximum class code.

tables

sel %poly_cov%.pat

sort subclass

&sv norec = [show number select]

&sv max = [show record %norec% item subclass]

&type Re-sorting table on %poly_cov%#.

sort %poly_cov%#

q

/* Begins the reselect process

ap
CERL TR 99/76 43

&sv totclass = %max%

&do &until %id% = %max% + 1

&if [exists subclass%id% -cover] &then

 &do

 arc kill subclass%id% all

 &end

resel %poly_cov% poly subclass = %id%

&sv norec = [extract 1 [show select %poly_cov% poly]]

/* Reselect Section. There are five unique category assignments that can

/* be made. The first determinant of how many points get assigned to

/* a particular type is polygon size. If the type has a minimum of one

/* polygon that is larger than 50 hectares the type will require a

/* minimum of 20 sampling points. If there are more than thirty polygons

/* that exceed a size of 50 hectares then that type receives 30 points.

/* The second major reselect is types that have no polygons over 50

hectares.

/* If the type has more than 30 polygons then 20 points are required. If

/* the type has less than 30 but more than 5 polygons then 5 points are

/* required. Less than five polygons and one point per polygon is assigned.

/*

/* The points are chosen from a point file derived from the coverage

/* being converted into a grid, and the grid being converted into points.

/* This results in a uniform network of points over the entire coverage

/* area. The points are weeded out through a series of buffers. The

/* points are then randomly selected by virtue of thematic type.

/*

/* It is possible, if the acceptable polygon area is small enough, that

/* no points are available for random selection for a particular polygon.

/* This usually only occurs for the final selection class of a type having

/* less than five polygons, none larger than 50 hectares. It can also

/* happen if the type falls completely outside of the buffered areas.

/* In these cases, the aml is set up to select the center point of the

/* polygon of interest as the random point.

&if %norec% ^= 0 &then

 &do
CERL TR 99/76 44

 resel %poly_cov% poly area > 500000

 &sv norec = [extract 1 [show select %poly_cov% poly]]

 &if %norec% ^= 0 &then

 &do

 &if %norec% > 30 &then

 &do

 &type Class %id% has %norec% polygons > 50 ha.

 &type 30 random points will be created.

 &sv class = 30

 &call point_create

 &end

 &if %norec% < 30 &then

 &do

 &type Class %id% has %norec% polygons > 50 ha.

 &type 20 Random points will be created.

 &sv class = 20

 &call point_create

 &end

 &end

 &if %norec% = 0 &then

 &do

 asel %poly_cov% poly

 resel %poly_cov% poly subclass = %id%

 resel %poly_cov% poly area < 500000

 &sv norec = [extract 1 [show select %poly_cov% poly]]

 &if %norec% > 30 &then

 &do

 &type Class %id% has %norec% polygons < 50 ha.

 &type 20 random points will be created.

 &sv class = 20

 &call point_create

 &end

 &if %norec% < 30 and %norec% >= 5 &then

 &do

 &type Class %id% has %norec% polygons < 50 ha.
CERL TR 99/76 45

 &type 5 random points will be created.

 &sv class = 5

 &call point_create

 &end

 &if %norec% < 5 &then

 &do

 &type Class %id% has %norec% polygons < 50 ha.

 &type %norec% random points will be created.

 &sv class = %norec%

 &call point_per_poly_create

 &end

 &end

 &end

&if %norec% = 0 &then

 &do

 &type Class %id% has %norec% polygons.

 &end

asel %poly_cov% poly

&sv id = %id% + 1

&end

q

&call append_cover

&if %source% = 1 &then

 &do

 kill %poly_cov% all

 kill %point_cov% all

 &end

dropitem %out_cov%.pat %out_cov%.pat %poly_cov%#

dropitem %out_cov%.pat %out_cov%.pat %poly_cov%-id

dropitem %out_cov%.pat %out_cov%.pat %temp_out%#

dropitem %out_cov%.pat %out_cov%.pat %temp_out%-id
CERL TR 99/76 46

&if [query 'Do you wish to delete individual type point coverages?' .FALSE]

~

 &then

 &do

 &r %.amlpath%/kill.aml

 &end

&return

/*************************point_create routine****************

/*

/*

/* This routine creates the individual point coverages from the points

/* reselected according to type.

&routine point_create

asel %point_cov% points

resel %point_cov% points subclass = %id%

&sv nopts = [extract 1 [show select %point_cov% point]]

&if %nopts% < %class% &then

 &do

 &type The number of points available for selection, %nopts% is

 &type less than the number of points required for sampling, %norec%.

 &sv class = %norec%

 &call point_per_poly_create

 &return

 &end

&if %nopts% > %class% &then

 &do

 resel %point_cov% points random %class%

 &end

create subclass%id% select %point_cov% points

arc build subclass%id% points
CERL TR 99/76 47

&return

/***********************point_per_poly_create routine************

/*

/*

/* This routine takes care of the problem noted above, that is, if the

/* type has no points, or not enough points available, it creates the

/* points by using the center point of the polygon(s) of interest as

/* the sample point.

&routine point_per_poly_create

&sv p = 1

&do &until %p% = %class% + 1

 &if [exists subclass%id%%p% -cover] &then

 &do

 arc kill subclass%id%%p% all

 &end

 &sv poly = [show select %poly_cov% poly %p% item %poly_cov%-id]

 &type %poly%

 asel %point_cov% points

 resel %point_cov% points %poly_cov%-id = %poly%

 &sv nopts = [extract 1 [show select %point_cov% point]]

 &if %nopts% > 1 &then

 &do

 resel %point_cov% points random 1

 create subclass%id%%p% select %point_cov% points

 arc build subclass%id%%p% points

 &end

 &if %nopts% = 1 &then

 &do

 create subclass%id%%p% select %point_cov% points

 arc build subclass%id%%p% points

 &end
CERL TR 99/76 48

 &if %nopts% = 0 &then

 &do

 asel %poly_cov% poly

 resel %poly_cov% poly %poly_cov%-id = %poly%

 &type Using polygon label location as the random point.

 create subclass%id%%p% select %poly_cov% poly

 arc build subclass%id%%p% points

 asel %poly_cov% poly

 resel %poly_cov% poly subclass = %id%

 &end

&sv p = %p% + 1

&end

arc append subclass%id% point

&sv totclas = %class%

&sv n = 1

&do &until %n% = %totclas% + 1

 &if [exists subclass%id%%n% -cover] &then

 &do

 subclass%id%%n%

 &end

&sv n = %n% + 1

&end

~

y

y

&sv totclas = %class%

&sv n = 1

&do &until %n% = %totclas% + 1

 &if [exists subclass%id%%n% -cover] &then
CERL TR 99/76 49

 &do

 arc kill subclass%id%%n%

 &end

&sv n = %n% + 1

&end

&return

/****************************append_cover routine**************

/*

/*

/* This routine appends all of the type point coverages into one

/* coverage containing all of the attributed points. The attribute

/* is the same as the input attribute specified to run the aml.

&routine append_cover

&sv temp_out = [scratchname -directory]

append %temp_out% point

&sv n = 1

&do &until %n% = %max% + 1

 &if [exists subclass%n% -cover] &then

 &do

 subclass%n%

 &end

&sv n = %n% + 1

&end

~

y

y
CERL TR 99/76 50

intersect %temp_out% %poly_cov% %out_cov% point

kill %temp_out% all

&return

/* kill.aml

/*

/* Gery Wakefield

/* Science and Technology Corp.

/* March 26, 1998

/*

/* This AML is called by the extract.aml

/* It’s purpose is to remove subclass files

/* created following user prompt.

&sv x = 1

&do &until %x% = 1000

 &if [exists subclass%x% -cover] &then

 &do

 kill subclass%x% all

 &end

&sv x = %x% + 1

&end

&return

/* AML: confusion_matrix.aml

/* AUTHOR: Gery Wakefield

/* ORGANIZATION: Topographic Engineering Center

/* DATE: 29 Apr 1998

/*

/* This aml takes a point coverage and creates an error matrix. The

/* arguments are as follows:
CERL TR 99/76 51

&args point_cov item1 item2

/* point_cov must contain an attribute representing the test data (item2)

/* and an attribute representing the thematic data (item1). The thematic

/* data can come from intersecting the points into a polygonal coverage,

/* or they can be created using pre_confusionmatrix.aml if the thematic

/* data are in a grid.

&if [null %point_cov%] or [null %item1%] or [null %item2%] &then

 &do

 &type Usage: &run <aml> <point cover.pat> <groundtruth item> <thematic

item>

 &return

 &end

&if ^ [exists %point_cov% -info] &then

 &do

 &type [upcase %point_cov%] not found. Exiting...

 &return

 &end

&sv covitems = [show columns info %point_cov%]

&sv itemfound = [token %covitems% -find %item1%]

&if %itemfound% = 0 &then

 &do

 &type Item [upcase %item1%] not found in [upcase %point_cov%]. Exiting...

 &return

 &end

&sv itemfound = [token %covitems% -find %item2%]

&if %itemfound% = 0 &then

 &do

 &type Item [upcase %item2%] not found in [upcase %point_cov%]. Exiting...

 &return

 &end

&sv covname = [entryname %point_cov% -noext]
CERL TR 99/76 52

tables

sel %point_cov%

resel %item1% = 0

nsel

&sv total = [show number select]

sort %item1% %item2%

&sv item1_max = [show record %total% %item1%]

sort %item2% %item1%

&sv item2_max = [show record %total% %item2%]

&sv itemfound = [token %covitems% -find %covname%#]

&if ^ %itemfound% = 0 &then

 &do

 sort %covname%#

 &type FAT file re-sorted on [upcase %covname%#]

 &end

&sv max = [max %item1_max% %item2_max%]

sel %point_cov%

&sv row0 = [format '%1,-3%' 0]

&sv col_totals = Tot

&sv x = 1

&sv y = 1

&do x = 1 &to %max%

&messages &off

 &sv all = [format '%1,-3%' %x%]

 &do &until %y% = %max% + 1

 resel %item1% = %x%

 &sv r%x%_total = [format '%1, -6%' [show number select]]

 resel %item2% = %y%

 &sv cello = [show number select]

 &sv r%x%c%y% = [format '%1,-5%' %cello%]

 &sv all = %all% [value r%x%c%y%]

 &sv y = %y% + 1
CERL TR 99/76 53

 asel

 &end

 &sv row0 = %row0% [format '%1,-5%' %x%]

 &sv row%x% = %all% [value r%x%_total]

 &sv x = %x% + 1

 &sv y = 1

&end

&messages &on

q

&sv x = 0

&sv y = 0

&do y = 1 &to %max%

&sv col%y% = 0

 &do x = 1 &to %max%

 &sv col%y% = [calc [value col%y%] + [unquote [value r%x%c%y%]]]

 &sv x = %x% + 1

 &end

&sv c%y%_total = [format '%1,-5%' [value col%y%]]

&sv col_totals = %col_totals% [value c%y%_total]

&sv y = %y% + 1

&sv x = 0

&end

&sv khat2 = 0

&sv diag_tot = 0

&sv row_moa_col = [format '%1, -3%' MoA]

&do n = 1 &to %max%

 &sv khat1 = [calc [unquote [value r%n%_total]] * [unquote [value

c%n%_total]]]

 &sv khat2 = %khat2% + %khat1%

 &sv diag_tot = [calc %diag_tot% + [unquote [value r%n%c%n%]]]
CERL TR 99/76 54

 &if [unquote [value r%n%_total]] ne 0 &then

 &sv moa_row = ~

 [calc [unquote [value r%n%c%n%]] / [unquote [value r%n%_total]]]

 &else

 &sv moa_row = 0

 &if [unquote [value c%n%_total]] ne 0 &then

 &sv moa_col = ~

 [calc [unquote [value r%n%c%n%]] / [unquote [value c%n%_total]]]

 &else

 &sv moa_col = 0

 &sv moa_row%n% = [round [calc %moa_row% * 1000]] / 1000

 &sv moa_col%n% = [round [calc %moa_col% * 1000]] / 1000

 &sv row_moa_col = %row_moa_col% [format '%1,-5%' [value moa_col%n%]]

&end

&sv kappa = [calc [calc %total% * %diag_tot%] - %khat2%] ~

 / [calc [calc %total% * %total%] - %khat2%]

&sv diag_total = %diag_tot% / %total%

&sv fc = [calc %khat2% / %total%]

&sv sk = [sqrt [calc [calc %diag_tot% * [calc %total% - %diag_tot%]] / ~

 [calc %total% * [calc [calc %total% - %fc%] * 2]]]]

&sv sko = [sqrt [calc %fc% / [calc %total% * [calc %total% - %fc%]]]]

&sv z = [calc %kappa% / %sko%]

&sv confintmin = [calc %kappa% - [calc %sk% * 1.96]]

&sv confintmax = [calc %kappa% + [calc %sk% * 1.96]]

&sv n = 0

&sv file = errmat.txt

&sv unit = [open %file% status -write]

&if %status% ne 0 &then

 &do

 &type Unable to open file due to status %status%.

 &return
CERL TR 99/76 55

 &end

&type [format '%1,-30%' 'Truth Data']

&sv status = [write %unit% [format '%1,-30%' 'Truth Data']]

&type %row0% [format '%1,-4%' Tot] [format '%1,-5%' MoA]

&sv toprow = %row0% [format '%1,-4%' Tot] [format '%1,-5%' MoA]

&sv status = [write %unit% [quote [unquote %toprow%]]]

&do &until %n% = %max%

 &sv n = %n% + 1

 &type [value row%n%] [format '%1,-5%' [value moa_row%n%]]

 &sv rowwrite = [value row%n%] [format '%1,-5%' [value moa_row%n%]]

 &sv status = [write %unit% [quote [unquote %rowwrite%]]]

&end

&type %col_totals% [format '%1,-4%' %total%]

&sv col_tot_row = %col_totals% [format '%1,-4%' %total%]

&sv status = [write %unit% [quote [unquote %col_tot_row%]]]

&type %row_moa_col% [format '%1,-10%' %diag_total%]

&sv col_moa_row = %row_moa_col% [format '%1,-10%' %diag_total%]

&sv status = [write %unit% [quote [unquote %col_moa_row%]]]

&type

&sv status = [write %unit% '']

&type The Kappa Index of Agreement is %kappa%.

&sv kappa_out = The Kappa Index of Agreement is %kappa%.

&sv status = [write %unit% [quote [unquote %kappa_out%]]]

&type The sigma k = %sk%.

&sv sigmak = The sigma k = %sk%.

&sv status = [write %unit% [quote [unquote %sigmak%]]]

&type The sigma ko = %sko%.

&sv sigmako = The sigma ko = %sko%.

&sv status = [write %unit% [quote [unquote %sigmako%]]]

&type The z score for %kappa% = %z%

&sv zscore = The z score for %kappa% = %z%

&sv status = [write %unit% [quote [unquote %zscore%]]]

&type The 95 percent confidence interval is %confintmin% and %confintmax%.

&sv confint = The 95 percent confidence interval is %confintmin% and %confintmax%.

&sv status = [write %unit% [quote [unquote %confint%]]]

&sv status = [close %unit%]
CERL TR 99/76 56

&sv outfile = %item1%X%item2%.txt

&sys cp errmat.txt %outfile%

&type

**************************************************************************

&type Error matrix displayed has been saved to a file called %file% & %outfile%.

&type

**************************************************************************

&return
CERL TR 99/76 57

Appendix D: Field Notes

This summary consists of miscellaneous observations made by field observers on

the alliances forming the current vegetation map. Notes are based primarily on

the 750 plots visited during June 1998 through early July 1998, and were compiled largely by Verl Emrick. In general, the functionality of the key in the field

was good. Difficulties encountered in the field were not with the mechanics of

the key, but the lack of choices. There was a similar problem with the original

map. In our opinion there are more alliances at Fort Benning than were identified in the map, but a more detailed vegetation analysis would be needed to

identify and describe additional alliances. The following discussion consists of

notes on most of the 51 classes identified in the vegetation map and TNC field

key. The number in parentheses below indicates the numerical Class Number

associated with the vegetation map:

(1) Pinus echinata Forest Alliance: Present on the installation, although

pure stands were rarely encountered.

(2) Pinus palustris - P. elliotii Temperate Forest Alliance: This Alliance

was not encountered at Fort Benning.

(3) Pinus taeda Forest Alliance: Very common, with the distinction between

woodland and forest sometimes difficult.

(4) Fagus grandifolia - Quercus alba Forest Alliance: Not encountered at

Fort Benning.

(6) Liquidambar styraciflua Forest Alliance: Uncommon in pure stands.

Occurred most often in combination with Q. nigra and/or P. taeda. Stump

sprouts of L. styraciflua dominated many of the heavily disturbed successional

shrublands.

(7) Liriodendron tulipifera Forest Alliance: Rarely occurred in pure stands.

(8) Quercus alba (Q. nigra) Forest Alliance: Rarely observed. Q. nigra was

an associate in early successional forests.
CERL TR 99/76 58

(11) Quercus laevis Forest Alliance: Identified primarily as a woodland.

Xeric conditions led to many mature trees being less than 5 meters tall. Pinus

palustris was an occasional associate. There were many instances where all of

the Q. laevis trees had been cleared from the immediate area surrounding emergent P. palustris.

(12) Quercus shumardii - Quercus pagoda Forest Alliance: Never encountered. Occasional lone Q. pagoda in mesic bottomlands.

(13) Quercus stellata - Q. marilandica Forest Alliance: Identified primarily as a woodland rather than forest. Common in buffer zones surrounding

live fire ranges in areas that burn yearly.

(16) Liquidambar styraciflua (Liriodendron tulipifera, Acer rubrum)

Temporarily Flooded Forest Alliance: A common palustrine community.

Overall many palustrine communities were combinations of two, sometimes three alliances and proved difficult to differentiate at the scale we

were observing them.

(21) Nyssa aquatica - (Taxodium distichum) Semi-permanently

Flooded Forest Alliance: Nyssa aquatica almost always occurred in combination with other Nyssa species. Communities with N. aquatica were often

identified as Nyssa (aquatica, biflora, ogeche) Floodplain Seasonally

Flooded Forest Alliance. No T. distichum was observed.

(22) Fagus grandifolia - Magnolia grandiflora Forest Alliance: Occurrence questionable. Possible occurrence on Shell Creek approximately 1 km

upstream from where Sunshine Road crosses the creek. The area was less than

2 acres and located approximately 5 - 10 meters up the bluff in a cove.

The Shell creek region in general was very diverse. The terrain was very

dissected and bluffs very steep. The steep biophysical gradients made the

identification of alliances, at the scales necessary for mapping, problematic.

(26) Pinus taeda - (Liquidambar styraciflua, Liriodendron tulipifera)

Forest Alliance: Common, although the P. taeda - L. styraciflua combination

appeared to occur with greater frequency.
CERL TR 99/76 59

(29) Pinus taeda -Liriodendron tulipifera Temporarily Flooded Forest

Alliance, and

(31) Pinus taeda - Liquidambar styraciflua - Acer rubrum Saturated

Forest Alliance: The two alliances were often intermixed and difficult to differentiate at the mapping scale used.

(43) Arundinaria gigantea Saturated Shrubland Alliance: None of the

points we observed were placed within this alliance. However, this alliance was

observed in a buffer zone for a live fire range in an area that received significant

impacts from ordnance-caused wildfires.

(44) Crataegus spathulata Shrubland Alliance: The Crataegus spp. in

this Alliance was misidentified in the vegetation map. The key indicated that

further information was needed on the Crataegus spp. Shrubland Alliance.

Field notes indicated that this alliance occurred in exposed topographic

settings where fire was a common disturbance (Pyric Disclimax ?). The

Crataegus spp Shrubland Alliance would sometimes be intermixed with

the (39) Quercus stellata - Q. marilandica Woodland Alliance in areas where fire appeared to be less frequent.

Woody Associates: P. taeda, P. palustris, Q. incana, Q. laevis, Q. marilandica, Q. stellata, Nyssa sylvatica.

Herbaceous and Shrub Associates: Andropogon virginicus, Danthonia spicata (?), Rhus radicans, R. copallina, Eupatorium spp., Silphium

spp., Rubus flagellaris (?), Pteridium aquilinum.

(47) Andropogon virginicus Herbaceous Alliance: This alliance was

problematic in the sense that it was the only herbaceous class we had to choose

from. The vast majority of the areas identified as the A. virginicus Herbaceous Alliance were in-fact drop zones and had a mix of herbaceous species. In

many of these instances Paspalum laeve appeared to be the most common species.
CERL TR 99/76 60

CERL DISTRIBUTION

Chief of Engineers

ATTN: CEHEC-IM-LH (2)

ATTN: CEHEC-IM-LP (2)

ATTN: CECC-R

ATTN: CERD-L

ATTN: CERD-M

Defense Tech Info Center 22304

ATTN: DTIC-O (2)

Fort Benning 31905 (5)

 14

 11/96
REPORT DOCUMENTATION PAGE Form Approved

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existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding

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1. AGENCY USE ONLY (Leave Blank) 2. REPORT DATE

August 1999

3. REPORT TYPE AND DATES COVERED

Final

4. TITLE AND SUBTITLE

Vegetation Map Accuracy Assessment: Fort Benning, Georgia

6. AUTHOR(S)

J. Watts, W.R. Whitworth, A. Hill, G.I. Wakefield, T. Davo, and L.J. O’Neil

5. FUNDING NUMBERS

MIPR

8CDPW10029

NN8

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

U.S. Army Construction Engineering Research Laboratory (CERL)

P.O. Box 9005

Champaign, IL 61826-9005

8. PEFORMING ORGANIZATION

REPORT NUMBER

TR 99/76

9. SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES)

U.S. Army Infantry School

ATTN: ATSH-OTR

Bldg 2905

Fort Benning, GA 31905

10. SPONSORING / MONITORING

AGENCY REPORT NUMBER

9. SUPPLEMENTARY NOTES

Copies are available from the National Technical Information Service, 5385 Port Royal Road, Springfield, VA 22161

12a.DISTRIBUTION / AVAILABILITY STATEMENT

Approved for public release; distribution is unlimited.

12b.DISTRIBUTION CODE

13. ABSTRACT (Maximum 200 words)

In 1993 the U.S. Army Construction Engineering Research Laboratory (CERL) became involved in the United States

Geological Survey, Division of Biological Resources/National Park Service (USGS/NPS) National Vegetation Mapping Program by serving as an interagency peer reviewer. This initial involvement led to discus-sions on vegetation mapping

alternatives for military lands and a completed vegetation map for Fort Benning, GA. To validate the vegetation map and

help managers realize its potential use as a natural resources and military training management tool, an accuracy assessment

was conducted.

Armed with a field key to the plant communities on the Fort Benning map and a project sampling design, intensive fieldwork

was conducted to collect the data required to assess the vegeta-tion map’s accuracy. Field data was analyzed and an error

analysis of the map was com-pleted. This report includes lessons learned and recommendations for future vegetation

mapping and accuracy assessment projects.

15. NUMBER OF PAGES

64

14. SUBJECT TERMS

Ft. Benning, GA land management

mapping military training

natural resource management vegetation

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