NOAA Technical Report NMFS 123

NOAA Coastal Change

Analysis Program (C-CAP):

Guidance for

Regional Implementation

J. E. Dobson

E. A. Bright

R L. Ferguson

D. W. Field

L. L. Wood

K. D. Haddad

H. Iredale III

J. RJensen

V. V. Klemas

RJ.Orth

J. P. Thomas

U.S. Department of Commerce

April 1995
u.s. DepartInent

of COllllIlerce

Ronald H. Brown

Secretary

National Oceanic and

Atmospheric AdnUnistration

D. James Baker

Under Secretary for Oceans and

Atmosphere

National Marine

Fisheries Service

Rolland A. Schmitten

Assistant Administrator for

Fisheries

The NOAA Technical Report NMFS

(ISSN 0892-8908) series is published by

the Scientific Publications Office, National

Marine Fisheries Service, NOAA, 7600

Sand Point Way NE, Seattle, WA 98115-

0070.

Although the contents have not been

copyrighted and may be reprinted entirely, reference to the source is appreciated.

The Secretary of Commerce has determined that the publication of this series is

necessary in the transaction of the public

business required by law of this Department. Use of funds for printing of this

series has been approved by the Director

of the Office of Management and Budget.

For sale by the U.S. Department of

Commerce, National Technical Information Service, 5285 Port Royal Road,

Springfield, VA 22161.

NOAA

Technical

Reports NMFS

Technical Reports of the Fishery Bulletin

Scientific Editor

Dr. Ronald W. Hardy

Northwest Fisheries Science Center

National Marine Fisheries Service, NOAA

2725 Montlake Boulevard East

Seattle, Washington 98112-2097

Editorial Conunittee

Dr. Andrew E. Dizon National Marine Fisheries Service

Dr. Linda L. Jones National Marine Fisheries Service

Dr. Richard D. Methot National Marine Fisheries Service

Dr. Theodore W. Pietsch University of Washington

Dr. Joseph E. Powers National Marine Fisheries Service

Dr. Tim D. Smith National Marine Fisheries Service

Managing Editor

James W. Orr

National Marine Fisheries Service

Scientific Publications Office

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Seattle, Washington 98115-0070

The NOAA Technical Report NMFS series of the Fishery Bulletin carries peerreviewed, lengthy original research reports, taxonomic keys, species synopses, flora

and fauna studies, and data intensive reports on investigations in fishery science,

engineering, and economics. The series was established in 1983 to replace two

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They are also available in exchange for other scientific and technical publications

in the marine sciences. Individual copies may be obtained from the U.S. Department of Commerce, National Technical Information Service, 5285 Port Royal

Road, Springfield, VA 22161.
NOAA Technical Report NMFS 123

A Technical Report of the Fishery Bulletin

NOAA Coastal Change

Analysis Program (C-CAP):

Guidance for

Regional Implementation

J. E. Dobson and E. A. Bright

Oak Ridge National Laboratory

Oak Ridge, TN 37831

R. L. Ferguson, D. W. Field, and L. L. Wood

NOAA/NMFS Beaufort Laboratory

Beaufort, NC 28516

K. D. Haddad

Florida Dept. ofEnvironmental Protection, Marine Research Institute

St. Petersburg, FL 33701

H. Iredale III

NOAA/NESDIS National Oceanographic Data Center

Washington, DC 20235

J. R. Jensen

Dept. of Geography

University of South Carolina

Columbia, SC 29208

V. V. Klemas

College of Marine Studies, University ofDelaware

Newark, DE 19716

R.J. Orth

Virginia Institute of Marine Science

College of William and Mary

Gloucester Point, VA 23062

J. P. Thomas

NOAA/NMFS Office ofProtected Resources

Silver Spring, MD 20910

April 1995

u.s. Department of Commerce

Seattle, Washington

Contents

Pre.face. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. VB

Executive Summary ix

Chapter 1. Introduction

The Coastal Region Management Problem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 1

The NOAA Coastal Change Analysis Program (C-CAP) Solution. . . . . . . . . . . . . . . . . . . . . . .. 1

C-CAP's National Scope and Regional Implementation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 3

Change Detection Every One to Five Years . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 4

The Need for Standardization and Guidelines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 4

General Steps Required to Conduct Regional C-CAP Projects. . . . . . . . . . . . . . . . . . . . . . . . . .. 4

Chapter 2. The C-CAP Coastal Land-Cover Classification System

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 7

Superclasses of the C-CAP System 7

Uplands. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 7

Wetlands. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 9

Water and Submerged Land. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 10

Chapter 3. Monitoring Uplands and Wetlands Using Satellite Remote Sensor Data

Remote Sensing System Considerations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 11

Temporal Resolution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 11

Spatial Resolution and Look Angle 11

Spectral Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 11

Radiometric Resolution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 12

The Preferred C-CAP Satellite Sensor System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 12

Important Environmental Characteristics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 13

Atmospheric Conditions 13

Soil Moisture Conditions 13

Vegetation Phenological Cycle Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 14

Effects of Tidal Stage on Image Classification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 14

Image Processing Data to Inventory Upland and Wetland Change 14

Rectification of Multiple-Date Remote Sensor Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 14

Radiometric Normalization of Multiple-Date Images. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 15

Selecting the Appropriate Change Detection Algorithm 16

Change Detection Using Write Function Memory Insertion 16

Multiple-Date Composite Image Change Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 17

Image Algebra Change Detection 17

Postclassification Comparison Change Detection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 19

iii
Multiple-Date Change Detection Using a Binary Change Mask Applied to T&-1 or 1/>+1 •••.. 20

Multiple-Date Change Detection Using Ancillary Data Source as Tb • ••••••••••••••••••• 25

Manual On-screen Digitization of Change 26

Selecting Appropriate Classification Algorithms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 28

Supervised and Unsupervised Image Classification Logic 29

Selection of Training and Verification Samples for Supervised and Unsupervised

Classification 31

Use of Collateral Data in Image Classification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 32

Cartographic Portrayal of Classification and Change Detection Maps . . . . . . . . . . . . . . . . . . .. 33

The Concept of the Minimum Measurement Unit 33

Analog (Hardcopy) Cartographic Products. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 33

Chapter 4. Monitoring Submerged Land Using Aerial Photography

C-Cap Focus on Aerial Photography of Submersed Rooted Vascular Plants (SRV) 35

Ancillary Categories of Submersed Habitat. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 35

Ancillary Technologies for Collection of Submersed Habitat Data 35

Aerial Photography of SRV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 36

Film 36

Metric Photography and Photographic Scale .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 36

Flightlines, Reconnaissance Flights, and Photographic Overlap . . . . . . . . . . . . . . . . . . . . . .. 37

En~ronment~Con~derations 37

Photointerpretation of SRV ',: 38

Field Surveys. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 39

Species and Habitat at Randomly Selected Stations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 39

Signature Verification and Supplemental Spatial Data 40

Base Maps and Registration of Habitat Polygons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 40

Planimetric Base Maps. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 40

Transfer of Polygons to the Map Coordinate Projection System . . . . . . . . . . . . . . . . . . . . . .. 41

Digitization of Habitat Polygons. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 41

Change Detection with Aerial Photographic Data . . . . . . . . . . . . . . . . . . . . . . . .. 42

Recent Photography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 42

Historical Photography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 42

Change Detection of Seagrass Habitat in North Carolina .. . . . . . . . . . . . . . . . . . . . . . . . . .. 44

Chapter 5. spacial Data Q!lality Assurance and Control

Lineage 45

Positional Accuracy and Precision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 45

Generalization Versus Error 46

Attribute Accuracy and Precision. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 47

Logical Consistency. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 47

Completeness. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 47

Temporal Accuracy and Precision 47

iv
Fitness for Use 47

Recommended Accuracy Assessment Test 48

Sample Selection and Field Mapping. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 48

Accuracy Assessment for Individual Date Classification of Upland and

Wetland Habitat Data 48

Accuracy Assessment for Individual Date Classification of Water and

Submerged Land Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 49

Accuracy Assessment for Land-Cover Change Data 49

Comparison and Statistical Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 49

Chapter 6. Product Availability

Digital Product. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 51

Description and Availability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 51

Digital Product Redistribution Restrictions 51

Digital Product Liability Disclaimer 51

Digital Product Format and Contents 51

Digital Product Medium 52

Digital Product Cost 52

Digital Product Ancillary Documentation 52

Hardcopy Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 52

Upland and Wetland Habitats 52

Submersed Habitats. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 52

Chapter 7. Users and Informatian Needs

Chapter 8. Regianal Participatian

Purpose 55

Regional Project Summaries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 55

St. Croix River Estuary (Border of Maine and New Brunswick, Canada) 55

Coastal Massachusetts 55

Universities of Connecticut and Rhode Island 55

University of Delaware 55

Oak Ridge National Laboratory 56

Virginia Institute of Marine Sciences. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 56

North Carolina State University 56

Beaufort Laboratory, National Marine Fisheries Service 57

University of South Carolina 57

State of Florida, Department of Environmental Protection. . . . . . . . . . . . . . . . . . . . . . . . . .. 57

Texas Parks and Wildlife Department 58

Columbia River, Tillamook Bay, and Willapa Bay (Oregon and Washington) 58

Hubbard Glacier and Russell Fjord, Alaska. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 58

v
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 59

Literature Cited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 61

Appendices

Appendix Table 1 - U.S. Geological Survey Land-Cover Classification Scheme for Remote

Sensor Data 65

Appendix Table 2 - U.S. Fish and Wildlife Service Wetland Classification System 67

Appendix 3 - C-CAP Wetland Classification Scheme Definitions . . . . . . . . . . . . . . . . . . . . . .. 69

Appendix 4 - C-CAP Workshops 79

Appendix 5 - C-CAP Protocol Development Research 81

Appendix 6 - Workshop Participants. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 83

vi
Preface

This unique document represents a first attempt to

develop guidelines that will allow researchers and resource managers alike to quantitatively monitor changes

that are occurring in the abundance of emergent and

submergent wetlands and adjacent uplands in coastal

regions. Such information is essential in order to effectively relate changes in coastal land use to changes in

the productivity of estuaries and coastal waters on a

regional scale.

This is a document that was developed from the

input of approximately 200 research scientists and resource managers that attended five regional workshops

and several topical interagency meetings. Thus, we believe it represents a general consensus of how to approach the issue of quantifYing land-cover and wetland

change in coastal regions. Because improvement in

existing technologies and in our understanding of how

to measure habitat change on a regional scale undoubtvii

edly will occur, we intend to update this document

periodically. These updates, however, require time to

publish, so anyone planning to use these guidelines

should contact either the senior author or program

manager to obtain drafts of any revised chapters that

have not yet been published.

Finally, I would like to express my appreciation to the

authors for their fine effort and to Dr. Don Scavia,

Director of NOAA's Coastal Ocean Program, for his

support, both financial and moral, during the development ofthis document. I believe we have made a significant step in addressing an important coastal issue.

Ford A Cross

Manager, C-CAP

National Marine Fisheries Service/NOAA

Beaufort Laboratory

Beaufort, NC 28516

Executive Summary

The Coastal Change Analysis Programl (C-CAP) is developing a nationally standardized database on landcover and habitat change in the coastal regions of the

United States. C-CAP is part of the Estuarine Habitat

Program (EHP) of NOAA's Coastal Ocean Program

(COP). C-CAP inventories coastal submersed habitats,

wetland habitats, and adjacent uplands and monitors

changes in these habitats on a one- to five-year cycle.

This type of information and frequency of detection

are required to improve scientific understanding of the

linkages of coastal and submersed wetland habitats with

adjacent uplands and with the distribution, abundance,

and health of living marine resources. The monitoring

cycle will vary according to the rate and magnitude of

change in each geographic region. Satellite imagery

(primarily Landsat Thematic Mapper), aerial photography, and field data are interpreted, classified, analyzed, and integrated with other digital data in a geographic information system (GIS). The resulting landcover change databases are disseminated in digital form

for use by anyone wishing to conduct geographic analysis in the completed regions.

C-CAP spatial information on coastal change will be

input to EHP conceptual and predictive models to support coastal resource policy planning and analysis. CCAP products will include 1) spatially registered digital

databases and images, 2) tabular summaries by state,

county, and hydrologic unit, and 3) documentation.

I Formerly known as the "Coast Watch Change Analysis Project."

ix

Aggregations to larger areas (representing habitats, wildlife refuges, or management districts) will be provided

on a case-by-case basis. Ongoing C-CAP research will

continue to explore techniques for remote determination of biomass, productivity, and functional status of

wetlands and will evaluate new technologies (e.g. remote sensor systems, global positioning systems, image

processing algorithms) as they become available. Selected hardcopy land-cover change maps will be produced at local (1:24,000) to regional scales (1:500,000)

for distribution. Digital land-cover change data will be

provided to users for the cost of reproduction.

Much of the guidance contained in this document

was developed through a series of professional workshops and interagency meetings that focused on a)

coastal wetlands and uplands; b) coastal submersed

habitat including aquatic beds; c) user needs; d) regional issues; e) classification schemes; f) change detection techniques; and g) data quality. Invited participants included technical and regional experts and representatives of key State and Federal organizations.

Coastal habitat managers and researchers were given

an opportunity for review and comment.

This document summarizes C-CAP protocols and procedures that are to be used by scientists throughout the

United States to develop consistent and reliable coastal

change information for input to the C-CAP nationwide

database. It also provides useful guidelines for contributors working on related projects. It is considered a working document subject to periodic review and revision.

Chapter 1

Introduction

The Coastal Region Management

Problem

The conterminous United States lost 53 percent of its

wetlands to agricultural, residential, and commercial

land use from the 1780's to 1980's (Dahl, 1990). Oil

spills occurring throughout the world continue to devastate coastal wetlands Uensen et al., 1990; Narumalani

et al., 1993). Sea level has risen approximately 130 m in

the past 17,500 years. More abundant "greenhouse"

gases in the atmosphere may be increasing the Earth's

average temperature (Clarke and Primus, 1990) and

may, yet again, accelerate the global sea level rise, eventually inundating much oftoday's coastal wetlands (Lee

et al., 1992). Unfortunately, current projections for

U.S. population growth in coastal regions suggest accelerating losses ofwetlands and adjacent habitats, as waste

loads and competition for limited space and resources

increase (U.S. Congress, 1989). Coastal wetlands and

submersed habitats are being destroyed by erosion,

dredge and fill, impoundments, toxic pollutants,

eutrophication, and (for submersed habitats) excessive

turbidity and sedimentation. Many marine finfish and

shellfish depend on these coastal habitats for their survival. Salt marsh grasses, mangroves, macroalgae, and

submersed grasses and forbs are essential as nourishment and animal habitat. Continued loss of these wetlands may lead to the collapse of coastal ecosystems and

associated fisheries. Documentation of the loss or gain

of coastal wetlands is needed for their conservation and

effective managment of marine fisheries (Haddad and

Ekberg, 1987; Haddad and McGarry, 1989; Kiraly et al.,

1990; Kean et al. l ).

Submersed grasses and forbs include seagrasses that

require high salinity and other species of submersed

rooted vascular plants (SRV) that tolerate or require

low salinity water. Submersed grasses and forbs may be

crucial indicators of water quality and overall health of

coastal ecosystems (Dennison et al., 1993). Submersed

vegetation has the additional requirement of living at

photic depths and therefore is particularly sensitive to

water clarity (Kenworthy and Haunert, 1991). Change

(increase or decrease in areal extent, movement, consolidation or fragmentation, or qualitative change) in

I Kean, T. H., C. Campbell, B. Gardner, and W. K. Reilly. 1988.

Protecting America's wetlands: an action agenda. Final Report of

the National Wetlands Policy Forum. The Conservation Foundation, Washington, D.C.

submersed habitat may be a sensitive integrator of overall water quality and potential for change in fisheries

productivity. Submersed rooted vascular aquatic beds

define habitat critical for the support of many recreational and sport fisheries (Ferguson et al., 1980; Zieman,

1982; Phillips, 1984; Thayer et al., 1984; Zieman and

Zieman, 1989; KJemas et al., 1993). Changes in uplands, wetlands, and submersed habitats can be rapid and

pervasive. Hence, effective management requiresfrequent

monitoring of coastal regions (at least twice per decade).

It has long been suspected that a crucial factor in the

observed decline of fisheries in most coastal regions is

the declining quantity and quality of habitat. Landcover change is a direct measure of quantitative habitat

loss or gain. For many marine fisheries the habitats (i.e.

land covers) of greatest importance are saltmarsh and

seagrass. Other fisheries, such as those for salmon, depend on a variety of habitats that may include upland as

well. Land-cover change is also a direct measure of

increases or decreases in sources of pollution, sedimentation, and other factors that determine habitat quality.

Increases in developed land, for example, are accompanied by land disturbance that increases erosion and

sedimentation and by hydrologic alteration that increases runoff. Similarly, cultivated land is associated

with fertilizer and pesticide use that ultimately affects

the marine environment. Hence, land-cover change is

linked to habitat quantity and quality.

The NOAA Coastal Change Analysis Program

(C-CAP) Solution _

For these reasons, the National Oceanic and Atmospheric Administration (NOAA) Coastal Ocean Program initiated the Coastal Change Analysis Program

(C-CAP) , a cooperative interagency, State, and Federal

effort to detect coastal upland and wetland land cover

and submersed vegetation and to monitor change in

the coastal region of the United States (Cross and Thomas, 1992; Haddad, 1992). The project uses digital

remote sensor data, in situ measurement in conjunction with global positioning systems (GPS), and geographic information system (GIS) technology to monitor changes in coastal wetland habitats and adjacent

uplands. Landsat multispectral scanner (MSS) data,

Landsat Thematic Mapper (TM) data, and SPOT high

resolution visible (HRV) data have been used success-
2 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program _

fully to detect major categories of wetlands (Haddad

and Harris, 1985; Jensen et aI., 1993b; Lade et aI. 2).

However, they have not been used previously to map or

monitor wetlands for regional or national coverage.

The use ofsatellite imagery for mapping wetlands provides a number of advantages over conventional aerial

photographs including timeliness, synopticity, and reduced costs. While aerial photography may be appropriate for high resolution cartography, satellite imagery

is better suited and less costly for rapid, repeated observations over broad regions (Haddad and Harris, 1985;

Bartlett, 1987; Klemas and Hardisky, 1987; Ferguson et

aI., 1993). Although the program will stress the use of

satellite imagery, particularly for coastal wetlands and

adjacent uplands, aerial photography or a combination

of photography and satellite imagery (TM or SPOT)

will be used for mapping SRV (Orth and Moore, 1983)

and certain other habitats, as suggested by Patterson

(1986) and Lade et al. (1988). A methodology to photographically observe, analyze, and display spatial change

in habitat defined by the presence of SRV was a prerequisite to a nationwide change detection effort (Thomas

and Ferguson, 1990).

The C-CAP nationally standardized database will be

used to monitor land-cover and habitat change in the

coastal regions of the United States (Thomas and

Ferguson, 1990; Thomas et al. 1991) and to improve

understanding of coastal uplands, wetlands (e.g. salt

marshes), and submersed habitats (e.g. seagrass) and

their linkages with the distribution, abundance, and

health of living marine resources. Coastal regions of

the U.S. will be monitored every one to five years depending on the anticipated rate and magnitude of

change in each region and the availability of suitable

remote sensing and in situ measurements. This monitoring cycle will provide feedback to habitat managers

on the success or failure of habitat management policies and programs. Frequent feedback to managers will

enhance the continued integrity or recovery of coastal

ecosystems and the attendant productivity and health

of fish and other living marine resources at minimal

cost. In addition, the geographical database will allow

managers and scientists to evaluate and, ultimately, to

predict cumulative direct and indirect effects of coastal

development on wetland habitats and living marine

resources. Initially, C-CAP products will document current land-cover distribution and change that have occurred in the recent past. The database, as it increases

with each subsequent monitoring cycle, will be an invaluable baseline resource for research, evaluation of

2 Lade, P. K., D. Case,]. French, and H. Reed. 1988. Delineation and

classification ofsubmerged aquatic vegetation using SPOT satellite

multispectral digital data. Final report to the Maryland Dept. Natural Resources, Tidewater Administration, Coastal Resources Div.,

Annapolis, MD.

local, State, and Federal wetland management strategies, and construction of predictive models. C-CAP directly supports NOAA's responsibilities in estuarine and

marine science, monitoring, and management as legislated in the Fish and Wildlife Coordination Act; the

Magnuson Fishery Conservation and Management Act;

the Coastal Zone Management Act; the Clean Water

Act; the Marine Protection, Research, and Sanctuaries

Act; and the National Environmental Policy Act. Landcover change data are essential to the implementation

of a" 0 et Loss" wetlands policy.

A large community of managers, scientists, and users

were involved in developing a C-CAP protocol at the

national level. Guidance in this document was derived

from a series of professional workshops and in teragency

working group meetings which focused on

• user needs

• upland, wetland, and water classification schemes

• regional boundary issues

• cartographic datum and data structures

• selection of appropriate satellite imagery and aerial

photography

• field work and field verification methods

• satellite remote sensing of coastal wetlands and

uplands

• photo interpretation of coastal submersed habitat,

including seagrasses

• calibration among regions and scenes

• classification and change detection algorithms

• geographic information processing and analysis

• regional ecological modeling

• quality assurance and control

• product availability and distribution

• research issues

Approximately 40 scientists and environmental managers attended each major regional workshop held in

the Southeast, Northeast, Pacific Coast, and Great Lakes

regions; about 200 individuals participated in all workshops and special meetings. The community of users

and providers of coastal habitat information were given

an opportunity for review and comment. A detailed list

of workshops is provided in Appendix 4.

Although GCAP is national in scope, it is based on

procedures also applicable at local and regional levels.

Much of the content of this document is based on

C-CAP sponsored research conducted at the regional

level. For example, Klemas et al. (1993) of the College

of Marine Studies at the University of Delaware developed the "GCAP Coastal Land Cover Classification System" by investigating existing upland and wetland classification systems and then synthesizing a new system

that is practical at the regional level. Dobson and Bright

(1991, 1992, and 1993) of the Oak Ridge National
Laboratory (ORNL) developed a regional prototype to

inventory uplands and wetlands in the Chesapeake Bay

region. Jensen et al. (1993a) evaluated various change

detection algorithms for inland and coastal wetland

environments near Charleston, S. C. Ferguson et al.

(1993) developed a regional prototype to inventory

SRV in North Carolina based on protocols developed

by the Beaufort Laboratory, Southeast Fisheries Science Center, National Marine Fisheries Service (NMFS).

Khorram et al. (1992) investigated methods ofseamlessly

integrating multiple-region C-CAP databases.

The C-CAP protocol continues to evolve and improve. For example, projects underway in 1993 include

analysis of the effects of tidal stage on remote-sensing

classification, change detection accuracy assessment,

refined techniques for classification of forested wetlands, and advanced change detection techniques (Appendix 5). Research continues on functional health

indicators (e.g. biomass, productivity), plant stress (e.g.

mangrove freeze), new data-collection instruments, and

regional ecological modeling. Thus, C-CAP will continue to have a strong research and development component to improve and refine its operational techniques.

National Scope and Regional Implementation of C-CAP _

No single Federal or State organization will collect all

the information residing in the C-CAP database. Instead, regional inventories will be completed by regional experts following C-CAP guidelines. Therefore,

it is important to define the logic used to specify a CCAP region. First, regional boundaries must coincide

with the following NOAA/NMFS regions:

Northeast - Virginia through Maine, including the

Great Lakes

Southeast - Texas through North Carolina, U.S. Virgin Islands, and Puerto Rico

Northwest - Oregon, Washington, and Alaska

Southwest - California, Hawaii, Midway Islands, Wake,

Guam, Mariana Islands, American Samoa,

Johnston Atoll, Trust Territory of the Pacific Islands, Baker and Howland Islands,

and Jarvis Island.

Coastal regions may be further subdivided, as appropriate, on the basis ofState and other administrative boundaries or ecoregions as defined, for example, by Omernik

(1987).

The boundary should encompass coastal watersheds

plus offshore coral reefs, algae, and seagrass beds in the

photic zone. In keeping with the goals of C-CAP and

anticipated funding constraints, the recommended apChapter 1: Introduction 3

proach is to designate 1) standard coverage limits for

general application and 2) extended coverage limits

for regions with special needs. Standard coverage will

utilize biological and other geographical boundaries

appropriate to the needs ofspecified C-CAP users identified through the protocol workshops. Extended coverage will be defined for each regional project in collaboration with states and other regional organizations. NOAA will make every effort to identify and

accommodate research, conservation, management, and

the needs of other interests that rely on wetland maps

and data. Regional projects will be designed to identify

special needs that may require extended coverage and

to suggest sources of funds to support the additional

cost of extended coverage.

The estuarine drainage area (EDA), defined by

NOAA's National Ocean Service (NOS) as the "land

and water component of an entire watershed that most

directly affects an estuary," is an appropriate standard

coverage area for C-CAP. For the purposes of this program, all U.S. coasts are or will be defined as part of an

EDA. The boundary of each EDA basin is defined to be

consistent with U.S. Geological Survey (USGS) hydrologic units and codes.

The estuarine drainage boundary as defined by NOS

is considered a standard inland boundary for

C-CAP regional projects. Regional analysts may employ

C-CAP protocols upstream, but C-CAP funding is not

intended for coverage beyond the EDA. However,

C-CAP funding may be used to purchase satellite scenes

that extend beyond the EDA if they are necessary to

cover the coastal region. Functional definitions, such as

"limits oftidal influence," may be employed in response

to local situations justified by local user communities

and local or regional experts on a coastal region-byregion or estuary-by-estuary basis. Regional analysts

should be aware of local, State, and Federal rights and

responsibilities and should seek intergovernmental and

interagency cooperation. Because C-CAP interests include the effects of eutrophication due to development

of uplands, information from outside the EDA may be

justified in high order streams that extend beyond the

coastal region. In this case, the point where the river

enters the region will be defined as a point source for

inputs.

The offshore boundary of each region is defined as

the seaward extent ofwetlands, seagrass, coral, or other

submersed habitat detectable using remote sensing systems. The functional definition of limits of detection

normally will be based on satellite and aerial sensors

and will vary within and among regions. Both the limits

of detection and the actual bathymetric range of SRV

are based on light attenuation and, thus, will not be a

consistent bathymetric contour even within a single

region.
4 NOAA Technical Report NMFS 123: Dobson et a1.: Coastal Change Analysis Program _

Overlap ofregions, consistent with TM scene boundary overlap, is preferred so that analysts may calibrate

results from neighboring regions. A healthy exchange

between neighboring regional analysts could reconcile

differences, not only in the area of overlap, but also in

signature identification across both regions. Each regional project team will be responsible for calibrating

the relationship between remotely sensed spectral information and other information such as field measurements of biomass and photosynthetic rates. Historically, such measurements have focused on relatively few

of the many species, habitats, and land-cover types of

significance in the coastal region. Analysts should also

ensure that protocols originally developed for northern temperate latitudes are modified sufficiently to serve

well in tropical areas of the southern United States,

Caribbean, and Pacific Ocean, and in the Arctic areas

ofAlaska. It will be necessary, for example, to use different methods and sensors for coral reefs than for wetlands. Similarly, the identification of Arctic muskeg

may require different methods and sensors from those

used to identify temperate, herbaceous wetland.

Change Detection Every One to Five Years _

The frequency of change detection is a crucial issue.

For most regions in the United States, the base year

(referred to as Tb or Date 1 in the diagrams) should be

the most recent year for which acceptable satellite imagery for uplands and wetlands or aerial photographs

for submersed habitat can be obtained, and for which

sufficient in situ information is available to conduct an

error evaluation. Exceptions may occur in regions where

cloud cover is a perennial problem or where other

considerations favor aerial photographs over satellite

imagery. The choice of the second date of imagery

(Date b-1 or b+1) may be more flexible. It may be

desirable to choose a date one to five years earlier than

the base period to capture recent changes in coastal

habitats. Plans should then be made for another change

analysis no later than five years after the base time.

However, plans may be altered abruptly when natural

or human-induced events, such as hurricanes and oil

spills, occur.

Five years is the recommended frequency of change

detection for most regions, but shorter periods may be

necessary in regions undergoing rapid economic development or affected by catastrophic events. Longer periods may be necessary where funds are limited or where

change is exceptionally slow. Regional analysts are advised to evaluate rates of change and explicitly recommend the base year and change period as a part of each

regional project proposal. Unfortunately, remotely

sensed data obtained specifically for other purposes

(e.g. urban analysis, forest inventory) often are not

suitable for use in C-CAP. Aquatic beds, and even coastal

wetlands, may not be identifiable on aerial photographs

obtained for other purposes.

The Need for Standardization and

Guidelines _

C-CAP desires to create a synoptic, digital database of

coastal wetland and upland land cover by class for a

base time period and to identify change between the

base period and other time periods. The use ofsatellite

remote sensing to inventory uplands and wetlands, conventional aerial photography to inventory submerged

lands, and GIS to analyze the data are important elements of the C-CAP methodology. However, the goal of

completing an accurate change detection product overrides any given technical consideration. Therefore,

timely high-quality information from aerial photographs,

topographic maps, field experience, or other sources

may be used to prepare C-CAP products if appropriate

guidelines are followed.

By standardizing procedures at the national level,

this document will benefit not only C-CAP but also

coastal management research conducted by other State

and Federal agencies. C-CAP desires to facilitate the

exchange of standardized data among programs, decrease duplication, and improve the quality and utility

of decision support for wetlands policy, management,

and research activities. All data accepted for inclusion

and eventual distribution in the C-CAP database must

adhere to the protocol described in this manual. The

protocol is designed to allow flexibility in the use of

elemen ts of the classification scheme and in the choice

of remote sensor data, classification and change detection procedures, and other key elements that vary regionally. However, potential users must adhere to the

protocol in order to maintain high quality information

in the C-CAP database. Coastal land-cover change databases derived independently from C-CAP will be considered for dissemination as C-CAP products if originating organizations can document compliance with

C-CAP protocol and data quality standards.

General Steps Required to Conduct Regional C-CAP Projects _

The general steps required to conduct regional C-CAP

change detection projects using sateIlite remotely sensed

data are summarized in Table 1. This document is

organized according to these specific requirements and,

in certain instances, provides step-by-step instructions

to be used when conducting regional projects. One of
the first requirements of regional partIcIpants is to

precisely identify land-cover classes of interest to be

monitored and eventually placed in the C-CAP change

detection database. This must be performed in conjunction with an appropriate classification scheme. Unfortunately, no existing standardized classification

Chapter 1: Introduction 5

scheme was suitable for all C-CAP requirements. Therefore, great effort went into the development of the CCAP Coastal Land-Cover Classification System, which

can be used to inventory uplands and wetlands by using

satellite remote sensor data and to inventory SRV by

using metric aerial photography.

Table 1

General steps required to conduct regional C-CAP change detection projects to extract upland and wetland information

using satellite remote sensing systems. Each major step is listed in the order to be accomplished.

1. State the regional change detection problem

a. Define the region

b. Specify frequency of change detection (I to 5 yr)

c. Identify classes of the C-CAP Coastal Land-Cover

Classification System

2. Consider significant factors when performing change

detection

a. Remote sensing system considerations

I) Temporal resolution

2) Spatial resolution

3) Spectral resolution

4) Radiometric resolution

5) The preferred e-CAP remote sensing system

b. Environmental considerations

I) Atmospheric conditions

2) Soil moisture conditions

3) Vegetation phenological cycle characteristics

4) Tidal stage

3. Conduct image processing of remote sensor data to

extract upland and wetland information

a. Acquire appropriate change detection data

I) In situ and collateral data

2) Remotely sensed data

a) Base year (Time b)

b) Subsequent year(s) (Time b-I or Ittl)

b. Preprocess the multiple-date remotely sensed data

1) Geometric rectification

2) Radiometric correction (or normalization)

c. Select appropriate change detection algorithm from

the three C-CAP alternatives

d. Apply appropriate image classification logic if necessary

1) Supervised

2) Unsupervised

3) Hybrid

e. Perform change detection using GIS algorithms

1) Highlight selected classes using change detection

matrix

2) Generate change map products

3) Compute change statistics

4. Conduct quality assurance and control

a. Assess spatial data quality

b. Assess statistical accuracy of

1) Individual date classification

2) Change detection products

5. Distribute C-CAP Results

a. Digital products

b. Analog (hardcopy) products
6 NOAA Technical Report NMFS 123: Dobson et aI.: Coastal Change Analysis Program _
Chapter 2

The C-CAP Coastal Land-Cover Classification System

Introduction

It is essential that the coastal land-cover information

stored in the C-CAP database be taxonomically correct

and consistent with coastal wetland information derived from other agencies. The C-CAP Coastal LandCover Classification System (Table 2) includes three

Level I superclasses (Klemas et aI., 1993):

1.0-Upland,

2.0-Wetland, and

3.0-Water and Submerged Land.

These superclasses are subdivided into classes and subclasses at Levels II and III, respectively. While the categories Wetland and Water and Submerged Land constitute the primary habitats of interest to NOAA, Uplands are also included because they influence adjacent

wetlands and water bodies. The classification system is

hierarchical, reflects ecological relationships, and focuses on land-cover classes that can be discriminated

primarily from satellite remote sensor data. It was

adapted and designed to be compatible with other

nationally standardized classification systems, especially

• the U.S. Geological Survey (USGS) "Land Use and

Land Cover Classification System For Use with Remote Sensor Data" (Anderson et a!., 1976; USGS,

1992; Appendix Table 1),

• the U.S. Fish and Wildlife Service (USFWS) "Classification of Wetlands and Deepwater Habitats of the

United States" (Cowardin et a!., 1979; Wilen, 1990;

Appendix Table 2), and

• the U.S. Environmental Protection Agency (USEPA)

Environmental Monitoring and Assessment Program

(EMAP) classification system.

Dedicated workshops on the C-CAP classification system and productive discussions and reviews with represen tatives from each ofthese major agencies resulted in

a classification system that is in harmony with other

major U.S. land-cover databases. The C-CAP Coastal

Land-Cover Classification System includes upland, wetland, submerged land, and water in a single, comprehensive scheme. An attempt has been made to identifY

land-cover classes that can be derived primarily through

remote sensing and that are important indicators of

ecosystem change. Modifications were necessary to reconcile inconsistencies between Anderson et a!. (1976)

and Cowardin et a!. (1979) and to remove all land-use

categories (Dobson, 1993a). C-CAP focuses on land

cover and its relationship to other functional components oflandscape (Dobson, 1993b). Definitions of the

pertinent terms are as follows:

• land cover-vegetation, soils, rocks, water (in its various forms), and constructed materials covering the

land surface, physically present and visible.

• land use-economic and cultural activities, permitted or not, that are practiced at a place which mayor

may not be manifested as visible land-cover features.

For example, forestry land use may be visibly manifested as forest land cover, but recreational land use

may occur in many different types of land cover,

often without visible evidence of recreational use.

• landscape-the zone of in teraction and convergence

of the atmosphere, the hydrosphere, and the solid

earth. Its vertical bounds are determined by the frequency and exten t ofinteractions pertinent to a given

field of inquiry. Horizontally, landscape may be divided into areal units defined by physical or cultural

features pertinent to a field of inquiry.

While all categories of the C-CAP classification system

can be represented as two-dimensional features at the

mapping scale of 1:24,000, some features may be mapped

as lines (e.g. a Marine/Estuarine Rocky Shore) or points

(e.g. unique landmarks). Most linear and point features

will be obtained from nonsatellite sources ofinformation

(e.g. aerial photography or in situ measurement using

GPS). Those classes and subclasses that are required by CCAP and which each regional C-CAP project will include

in its database are underlined in Table 2. The underlined

classes, with the exception of aquatic beds, can generally

be detected by satellite remote sensors, particularly when

supported by surface in situ measurement

Superclasses of the C-CAP System _

Uplands

The Upland superclass consists of seven subclasses

(Table 2): Developed Land, Cultivated Land, Grassland, Woody Land, Bare Land, Tundra, and Snow/Ice.

Upland classes are adapted from Level I classes in the

USGS Land-Use and Land-Cover Classification System

(Anderson et a!., 1976; USGS, 1992; Appendix Table

1). Detailed definitions of all C-CAP classes and subclasses in Table 1 are found in Appendix 3.

7
8 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program

Table 2

C-CAP Coastal Land-Cover Classification System (Modified from Klemas et aI., 1993). C-CAP is committed to include the

underlined classes in the land cover change databases.

1.0 Upland

1.1 Developed Land

1.11 High Intensity

1.12 Low Intensity

1.2 Cultivated Land

1.21 Orchards/Groves/Nurseries

1.22 Vines/Bushes

1.23 Cropland

1.3 Grassland

1.31 Unmanaged

1.32 Managed

1.4Woody Land

1.41 Deciduous

1.411 Forest

1.412 Scrub/Shrub

1.42 Evergreen

1.421 Forest

1.422 Scrub/Shrub

1.43 Mixed

1.431 Forest

1.432 Scrub/Shrub

1.5 Bare Land

1.6 Tundra

1.7 Snow/Ice

1.71 Perennial Snow/Ice

1.72 Glaciers

2.0 Wetland

2.1 Marine/Estuarine Rocky Shore

2.11 Bedrock

2.12 Rubble

2.2 Marine/Estuarine Unconsolidated Shore

(Beach, Flat, Bar)

2.21 Cobble-gravel

2.22 Sand

2.23 Mud/Organic

2.3 Marine/Estuarine Emergent Wetland

2.31 Haline (Salt Marsh)

2.32 Mixohaline (Brackish Marsh)

2.4 Estuarine Woody Wetland

2.41 Deciduous

2.411 Forest

2.412 Scrub/shrub

2.413 Dead

2.42 Evergreen

2.421 Forest

2.422 Scrub/Shrub

2.423 Dead

2.43 Mixed

2.431 Forest

2.432 Scrub/shrub

2/433 Dead

2.5 Riverine Unconsolidated Shore (Beach, Flat, Bar)

2.51 Cobble-Gravel

2.52 Sand

2.53 Mud/Organic

2.6 Lacustrine Unconsolidated Shore (Beach, Flat, Bar)

2.61 Cobble-Gravel

2.62 Sand

2.63 Mud/Organic

2.7 Palustrine Unconsolidated Shore (Beach, Flat, Bar)

2.71 Cobble-Gravel

2.72 Sand

2.73 Mud/Organic

2.8 Palustrine Emergent Wetland (Persistent)

2.9 Palustrine Woody Wetland

2.91 Deciduous

2.911 Forest

2.912 Scrub/shrub

2.913 Dead

2.92 Evergreen

2.921 Forest

2.922 Scrub/shrub

2.923 Dead

2.93 Mixed

2.931 Forest

2.932 Scrub/shrub

2.933 Dead

3.0 Water and Submerged Land

3.1 Water

3.11 Marine/Estuarine

3.12 Riverine

3.13 Lacustrine (Basin> 20 acres)

3.14 Palustrine (Basin < 20 acres)
Chapter 2: C-CAP Coastal Land-Cover Classification System 9

Table 2 (continued)

3.2 Marine/Estuarine Reef

3.3 Marine/Estuarine Aquatic Bed

3.31 Algal (e.g., kelp)

3.32 Rooted Vascular (e.g., seagrass)

3.321 (High Salinity (~5 ppt; Mesohaline,

Polyhaline, Euhaline, Hyperhaline)

3.322 Low Salinity « 5 ppt; Oligohaline, Fresh)

3.4 Riverine Aquatic Bed

Developed Land (derived from the Anderson et al.

[1976] Urban or Built-Up class) characterizes constructed surfaces composed of concrete, asphalt, roofing, and other building materials with or without vegetation. This class has been divided into two subclasses

based on the amount of constructed surface relative to

the amount of vegetated surface present. High-Intensity Developed Land contains little or no vegetation.

This subclass includes heavily built-up urban centers as

well as large constructed surfaces in suburban and rural

areas. Large buildings (such as multiple-family housing, hangars, and large barns), interstate highways, and

runways typically fall into this subclass. Low-Intensity

Developed Land contains substantial amounts of constructed surface mixed with substantial amounts ofvegetated surface. Small buildings (such as single family

housing, farm outbuildings, and sheds), streets, roads,

and cemeteries with associated grasses and trees typically fall into this subclass.

Cultivated Land ("Agricultural Land" ofAnderson et

al. [1976]) includes herbaceous (cropland) and woody

(orchards, nurseries, vineyards, etc.) cultivated lands. Seasonal spectral signatures, geometric field patterns, and

road network patterns may help identify this land-cover

type. Always associated with agricultural land use, cultivated land is used for the production offood and fiber.

Grassland differs from "Rangeland" of Anderson et

al. (1976) by excluding shrub-brushlands. Unmanaged

Grasslands are dominated by naturally occurring grasses

and forbs which are not fertilized, cut, tilled, or planted

regularly. Managed Grasslands are maintained by human activity such as fertilization and irrigation, are

distinguished by enhanced biomass productivity, and

can be recognized through vegetative indices based on

spectral characteristics. Examples ofsuch areas include

lawns, golf courses, forest or shrub areas converted to

grassland, or areas of permanent grassland with altered

species composition. This category includes managed

pastures and pastures with vegetation that grows vigorously as fallow. Managed Grasslands are used for graz3.41 Rooted Vascular/Algal/Aquatic Moss

3.42 Floating Vascular

3.5 Lacustrine Aquatic Bed (Basin> 20 acres)

3.51 Rooted Vascular/ Algal/Aquatic Moss

3.52 Floating Vascular

3.6 Palustrine Aquatic Bed (Basin < 20 acres)

3.61 Rooted Vascular/ Algal/Aquatic Moss

3.62 Floating Vascular

ing or for growing and harvesting hay and straw for

animal feed.

Woody Land includes nonagricultural trees and

shrubs. The category alleviates the problem of separating various sizes oftrees and shrubs using satellite remote

sensor data but allows a height-based separation if high

resolution aerial photographs are available. The class may

be partitioned into three subclasses: Deciduous, Evergreen,

and Mixed. These three subclasses generally can be discriminated with satellite remote-sensing systems.

Bare Land (derived from Barren Land of Anderson

et al. [1976]) is composed of bare soil, rock, sand, silt,

gravel, or other earthen material with little or no vegetation. Anderson et al.'s Barren Land was defined as

having limited ability to support life; C-CAP's Bare Land

is defined by the absence of vegetation without regard

to inherent ability to support life. Vegetation, if present,

is more widely spaced and scrubby than that in the

vegetated classes. Unusual conditions such as a heavy

rainfall may occasionally result in growth of a shortlived, luxuriant plant cover. Wet, nonvegetated, exposed

lands are included in the Wetland categories. Bare Land

may be bare temporarily because of human activities. The

transition from Woody Land, Grassland, or Cultivated

Land to Developed Land, for example, usually involves

a Bare Land phase. Developed Land also may have temporary waste and tailing piles. Woody Land may be clearcut,

producing a temporary Bare Land phase. When it may be

inferred from the data that the lack ofvegetation is due to

an annual cycle of cultivation (e.g. plowing), the land is

not included in the Bare Land class. Land temporarily

without vegetative cover because of cropping or tillage is

classified as Cultivated Land, not Bare Land.

Wetlands

Wetlands are lands where saturation with water is the

dominant factor determining soil development and the

types of plant and animal communities living in the soil
10 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program

and on its surface (Cowardin et aI., 1979). A characteristic feature shared by all wetlands is soil or substrate

that is at least periodically saturated with or covered by

water. The upland limit of wetlands is designated as 1)

the boundary between land with predominantly hydrophytic cover and land with predominantly mesophytic

or xerophytic cover; 2) the boundary between soil that

is predominantly hydric and soil that is predominantly

nonhydric; or 3) in the case of wetlands without vegetation or soil, the boundary between land that is flooded

or saturated at some time during the growing season

each year and land that is not (Cowardin et aI., 1979).

Most wetlands are vegetated and found on soil.

Wetland in the C-CAP Coastal Land-Cover Classification System (Table 2) includes all areas considered

wetland by Cowardin et al. (1979) except for bottoms,

reefs, aquatic beds, and nonpersistent emergent wetlands. The class subdivision was adopted primarily from

the Cowardin et al. system, shown in Appendix Table 2.

At Level II, C-CAP incorporates certain Cowardin et al.

classes (e.g. Rocky Shore, Unconsolidated Shore, Emergent Wetland) or grouped Cowardin et al. classes (e.g.

Woody Wetland may be further divided into ScrubShrub and Forested categories) in combination with Cowardin et al. systems (i.e. Marine, Estuarine, Riverine, Lacustrine, Palustrine). Thus, a typical Level II class in the

C-CAP system might be Palustrine Woody Wetland.

Marine and Estuarine Rocky Shores (Cowardin et aI.,

1979) were combined into a single class, Marine/Estuarine Rocky Shore. The same logic was used to produce

Marine/Estuarine Unconsolidated Shore.

Salinity exhibits a horizontal gradient in coastal estuarine marshes. This is evident not only through the

direct measurement of salinity but in the horizontal

distribution of marsh plants (Daiber, 1986). Therefore,

the Estuarine Emergent Wetland class is partitioned

into Haline (Salt) and Mixohaline (Brackish) Marshes.

For both subclasses, the C-CAP classification system

uses the Cowardin et al. (1979) definitions. Mixohaline

salinity ranges from O.S ppt to 30 ppt, and Haline salinity is c30 ppt. Within a marsh. plant zonation is usually

quite evident. Along the Atlantic coast of orth America

the pioneer plant on regularly flooded mudflats is

saltmarsh cordgrass, Spartina alterniJlora, which often

appears in pure stands. In more elevated areas that are

flooded less frequently, saltmeadow hay, Spartina patens, often dominates. The upland interfaces are bordered by marsh elder, Iva frutescens, and groundsel tree,

Baccharis halimifolia. Thus, salt marshes may be subdivided further into High Marsh and Low Marsh, but this

distinction is not required in C-CAP regional projects.

The C-CAP Coastal Land-Cover Classification System

does not attempt to identify freshwater nonpersistent

emergent wetlands because they are invisible during

much of the year and are difficult to detect by remote

sensors. These wetlands are classified as Riverine Water

and Lacustrine Water.

Water and Submerged Land

All areas ofopen water with <30% cover oftrees, shrubs,

persistent emergent plants, emergent mosses, or lichens

are assigned to the superclass Water and Submerged Land,

whether the area is considered wetland or deepwater

habitat under the Cowardin et al. (1979) classification.

The Water class includes Cowardin et al.'s (1979)

classes Rock Bottom and Unconsolidated Bottom, and

Nonpersistent Emergent Wetlands, as well as Reefs and

Aquatic Beds that are not identified as such. Most

C-CAP products will display water as a single class. However, the major systems (Marine/Estuarine, Riverine,

Lacustrine, Palustrine) are ecologically different from

one another, and for this reason, the C-CAP system

identifies the four systems as Level III subclasses: 3.11-

Marine/Estuarine Water, 3.12-Riverine Water, 3.13-

Lacustrine Water, and 3.14-Palustrine Water. While

C-CAP does not require these subclasses, the option is

provided to participants who may have such data available from ancillary sources. Having the water subclasses

also makes the C-CAP scheme more compatible with

the Cowardin et al. (1979) system. The subclass 3.11-

Marine/Estuarine Water includes Bottoms and undetected Reefs and Aquatic Beds. The subclasses 3.12-

Riverine Water, 3.13-Lacustrine Water, and 3.14-

Palustrine Water include Bottoms and undetected

Aquatic Beds as well as Nonpersistent Emergent Wetlands. Palustrine waterbodies, defined as covering <20

acres, are smaller than Lacustrine waterbodies.

C-CAP combined Marine and Estuarine Reefs and

Aquatic Beds into two classes: Marine/Estuarine Reefs

and Marine/Estuarine Aquatic Beds. Marine/Estuarine

Aquatic Beds includes the subclass Rooted Vascular,

which is subdivided into High Salinity (cS ppt) and

Low Salinity «S ppt). The cS ppt salinity level separates

seagrasses from submersed grass and forbs that tolerate or require low salinity. Both types of plants define

aquatic beds, submersed habitats that are important to

the C-CAP project. High Salinity includes mesohaline,

polyhaline, euhaline, and hyperhaline salinity categories of Cowardin et al. (1979). Low Salinity includes

oligohaline and fresh categories «S ppt salinity).

With the noted exceptions, most of the Wetland and

Water classes have definitions similar to those contained

in Cowardin et al. (1979) so that data can be interchanged with other programs, such as the USFWS National Wetlands Inventory (NWI) program, which is based

on the Cowardin et al. (1979) classification system. Detailed definitions of all superclasses, classes, and subclasses

shown in Table 2 are provided in Appendix 3.
Chapter 3

Monitoring Uplands and Wetlands Using Satellite Remote Sensor Data

Successful remote-sensing change detection of uplands

and wetlands in coastal regions requires careful attention to 1) sensor systems, 2) environmental characteristics, and 3) geodetic control. Failure to understand the

impact of the various parameters on the change detection process can lead to inaccurate results. Ideally, the

remotely sensed data used to perform C-CAP change

detection are acquired by a remote sensor system that

holds the following factors constant: temporal, spatial

(and look angle), spectral, and radiometric. It is instructive to review each of these parameters and identifY why they have a significant impact on the success of

C-CAP remote-sensing change detection projects. Table

3 summarizes the characteristics of some of the most

important satellite remote-sensing systems.

Remote-Sensing System Considerations _

Temporal Resolution

Two important temporal resolutions should be held

constant when performing coastal change detection

using multiple dates of remotely sensed data. First, the

data should be obtained from a sensor system which

acquires data at approximately the same time of day

(e.g. Landsat TM data are acquired before 0945 h for

most of the conterminous United States). This eliminates diurnal sun angle effects which can cause anomalous differences in the reflectance properties of remotely sensed objects. Second, whenever possible it is

desirable to use remotely sensed data acquired on anniversary dates (e.g. 1 October 1988 versus 1 October

1993). Using anniversary date imagery removes seasonal sun angle differences that can make change detection difficult and unreliable Qensen et aI., 1993a).

Usually, precise anniversary date imagery is not available. The determination of acceptable near-anniversary

dates then depends on local and regional factors such as

phenological cycles and annual climatic regimes.

Spatial Resolution and Look Angle

Accurate spatial registration of at least two images is

essential for digital change detection. Ideally, the remotely sensed data are acquired by a sensor system that

collects data with the same instantaneous-field-of-view

(IFOV) on each date. For example, Landsat TM data

collected at 30 x 30 m spatial resolution (Table 3) on

two dates are relatively easy to register to one another.

Geometric rectification algorithms Qensen, 1986; Novak,

1992) are used to register the images to a standard map

projection (Universal Transverse Mercator [UTM] for

most U.S. projects). Rectification should result in the

two images having a root mean square error (RMSE) of

:":: ±0.5 pixel. RMSE ±0.5 pixel may result in the identification of spurious areas of change between the two

datasets. See "Rectification of Multiple-date Remote

Sensor Data" for a summary of C-CAP image rectification requirements.

It is possible to perform change detection using data

collected by two different sensor systems with different

IFOV's, e.g. Landsat TM data (30 x 30 m) for date 1 and

SPOT HRV data (20 x 20 m) for date 2. In such cases, it

is necessary to decide upon a representative minimum

mapping unit (e.g. 20 x 20 m) and then resample both

datasets to this uniform pixel size. This does not present

a significant problem as long as one remembers that

the information content of the resampled data can

never be greater than the IFOV of the original sensor

system (i.e. even though the Landsat TM data are

resampled to 20 x 20 m pixels, the information was still

acquired at 30 x 30 m resolution, and one should not

expect to be able to extract additional spatial detail in

the dataset) .

Some remote-sensing systems like SPOT collect data

at off-nadir look angles as much as ±20· (Table 3), i.e.

the sensors obtain data of an area on the ground from

an "oblique" vantage point. Two images with significantly different look angles can cause problems when

used for change detection purposes. For example, consider a maple forest consisting of very large, randomly

spaced trees. A SPOT image acquired at O· off-nadir will

look directly down upon the "top" of the canopy. Conversely, a SPOT image acquired at 20· off-nadir will

record reflectance information from the "side" of the

canopy. Differences in reflectance from the two datasets

can cause spurious change detection results. Therefore, the data used in a remote-sensing digital change

detection should be acquired with approximately the

same look angle whenever possible.

Spectral Resolution

A fundamental assumption of digital change detection

is that there should exist a difference in the spectral

response of a pixel on two dates if the biophysical

materials within the IFOV have changed between dates.

Ideally, the spectral resolution of the remote sensor

11
12 NOAA Technical Report NMFS 123: Dobson et at.: Coastal Change Analysis Program _

Table 3

Selected satellite remote-sensing system characteristics; abbreviations: MSS=multispectral scanner; TM=thematic mapper.

Remote sensor system

Landsat MSS 1-5

Landsat TM 4-6

Landsat TM 6, PAN2

SPOTHRV, XS

SPOT HRV, PAN

Spectral resolution Spatial resolution Temporal Radiometric

(~m) (m) resolution (d) resolution (bits)

Band 1 (0.50-0.60) 80x 80 18 8 1

Band 2 (0.60-0.70) 80x 80 18 8

Band 3 (0.70-0.80) 80x 80 18 8

Band 4 (0.80 -1.1) 80x 80 18 8

Band 1 (0.45-0.52) 30 x 30 16 8

Band 2 (0.52-0.60) 30x 30 16 8

Band 3 (0.63-0.69) 30 x 30 16 8

Band 4 (0.76--0.90) 30 x 30 16 8

Band 5 (1.55-1.75) 30 x 30 16 8

Band 7 (2.08-2.35) 30 x 30 16 8

Band 6 (10.4-12.5) 120 x 120 16 8

Band 8 (0.5-0.90) 15 x 15 16 8

Band 1 (0.50-0.59) 20 x 20 pointable 8

Band 2 (0.61-0.68) 20 x 20 pointable 8

Band 3 (0.79-0.89) 20 x 20 pointable 8

Pan (0.51-0.73) 10 x 10 pointable 8

1 Landsat MSS 1 and 2 collected data in 7 bits.

2 The panchromatic (PAN) band was found on Landsat 6, which was lost during a launch mishap.

system is sufficient to record reflected radiant flux in

spectral regions that best capture the most descriptive

spectral attributes of the object. Unfortunately, different sensor systems do not record energy in exactly the

same portions of the electromagnetic spectrum, i.e.

bandwidths (Table 3). For example, Landsat MSS

records energy in four relatively broad bands, SPOT

HRV sensors record in three relatively coarse multispectral bands and one panchromatic band, and TM

records in six relatively narrow optical bands and one

broad thermal band (Table 3). Ideally, the same sensor

system is used to acquire imagery on multiple dates.

When this is not possible, the analyst should select

bands which approximate one another. For example,

SPOT bands 1 (green), 2 (red), and 3 (near-infrared)

can be used successfully with TM bands 2 (green), 3

(red), and 4 (near-infrared) or MSS bands 1 (green), 2

(red), and 4 (near-infrared). Many ofthe change detection algorithms to be discussed do not function well

when bands from one sensor system do not match

those of another sensor system. For example, using TM

band 1 (blue) with either SPOT or MSS data is not wise.

Radiometric Resolution

Converting satellite remote sensor data from analog to

digital usually results in 8-bit brightness values with

values ranging from 0 to 255 (Table 3). Ideally, the

sensor systems collect the data at the same radiometric

precision on both dates. When the radiometric resolution of data acquired by one system (e.g. MSS 1 with 7-

bit data) are compared with data acquired by a higher

radiometric resolution instrument (e.g. TM with 8-bit

data) then the lower resolution data (e.g. 7-bit) should

be "decompressed" to 8-bit data for change detection

purposes. However, the precision of decompressed

brightness values can never be better than the original,

uncompressed data.

The Preferred C-CAP Satellite Sensor System

TM is currently the primary sensor recommended for

C-CAP image acquisition and change analysis for all

land cover except aquatic beds. Although its spatial resolution is not as good as that of a SPOT satellite or aircraft

MSS image, a TM image is generally less expensive to

acquire and process for large-area coverage. Compared

with SPOT imagery, TM has betterspectral resolution and

specific spectral bands that are more applicable to wetlands delineation (bands 5 and 7). In addition, TM is

preferred over SPOT because TM has collected data for a

longer time (since 1982, as opposed to SPOT since 1986)

and because many TM scenes of U.S. coastal regions were

systematically collected on a routine basis.
There are advantages and disadvantages to using other

sensors. Aircraft multispectral scanners are more expensive and complex to use over large regions (Jensen

et aI., 1987). However, good algorithms are now available for georeferencing, and in certain cases (e.g. when

higher spectral or spatial resolution is needed and when

unfavorable climatic conditions for satellite sensors exist) aircraft sensors may be optimum. The SPOT sensor

has a greater temporal coverage because the satellite

can collect data off-nadir. However, if off-nadir SPOT

imagery is used for C-CAP change analyses, the data

must be normalized to compensate for different look

angles that may preclude pixel-to-pixel spectral-change

analysis. Nevertheless, SPOT imagery may be a reasonable alternative in certain areas because of cloud cover

or other impediments to TM data availability.

C-CAP remains flexible to take advantage of new

sensors and other technologies that become operational

during the lifetime of the program. Regional participants should work with the C-CAP program coordinators to ensure that the sensor selection meets the following C-CAP requirements:

• Standard radiometrically corrected TM data are required, and geocoded (georeferenced) data are optional. If geocoded data are selected, the coordinate

system should be UTM.

• Regional participants must collaborate with C-CAP

managers to ensure that the exchange medium and

its format will be amenable to the processing capabilities of the participants.

• C-CAP normally will purchase and archive the raw

data in collaboration with the regional image processing center. In cases where the regional participants already have usable raw imagery or are making

their own purchases, formal agreements between

C-CAP managers and participants must address vendor licensing and other legal requirements as well as

C-CAP archiving and quality-control protocol.

Important Environmental Characteristics __

Failure to understand the impact of various environmental characteristics on the remote-sensing change

detection process can also lead to inaccurate C-CAP

results. When performing change detection it is desirable to hold environmental variables as constant as

possible. Specific environmental variables and their

potential impacts are described below.

Atmospheric Conditions

There should be no clouds, haze, or extreme humidity

on the days remote-sensing data are collected. Even a

Chapter 3: Monitoring Uplands and Wetlands 13

thin layer of haze can alter spectral signatures in satellite images enough to create the false impression of

spectral change between two dates. Obviously, 0% cloud

cover is preferred for satellite imagery and aerial photography. At the upper limit, cloud cover >20% is usually unacceptable. In addition, clouds not only obscure

terrain but the cloud shadow also causes major image

classification problems. Any area obscured by clouds or

affected by cloud shadow will filter through the entire

change detection process, severely limiting the utility of

the final change detection product. Therefore, regional

analysts must use good professional judgment to evaluate such factors as the criticality ofthe specific locations

affected by cloud cover and shadow and the availability

of timely surrogate data for those areas obscured (e.g.

perhaps substituting aerial photography interpretation

for a critical area). Even when the stated cloud cover is

0%, it is advisable to "browse" the proposed image on

microfiche at the National Cartographic Information

Center in each State to confirm that the cloud cover

estimate is correct.

Assuming no cloud cover, the use of anniversary dates

helps to ensure general, seasonal agreement between

the atmospheric conditions on the two dates. However,

if dramatic differences exist in the atmospheric conditions present on the n dates ofimagery to be used in the

change detection process, it may be necessary to remove the atmospheric attenuation in the imagery. Two

alternatives are available. First, sophisticated atmospheric transmission models can be used to correct the

remote-sensor data if substantial in situ data are available on the day of the overflights. Second, an alternative empirical method may be used to remove atmospheric effects. A detailed description of one empirical

method of image-to-image normalization is found in

"Radiometric Normalization of Multiple-Date Images.

Soil Moisture Conditions

Ideally, the soil moisture conditions should be identical

for the n dates of imagery used in a change detection

project. Extremely wet or dry conditions on one date

can cause serious change detection problems. Therefore, when selecting the remotely sensed data to be

used for change detection it is very important not only

to look for anniversary dates but also to review precipitation records to determine how much rain or snow fell

in the days and weeks prior to data collection. When

soil moisture differences between dates are significant

for only certain parts of the study area (perhaps due to

a local thunderstorm), it may be necessary to stratify

(eliminate) those affected areas and perform a separate analysis that can be added back in the final stages

of the project.
14 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program _

Vegetation Phenological Cycle Characteristics

Vegetation grows according to seasonal and annual

phenological cycles. Obtaining near-anniversary images

greatly minimizes the effects of wetland seasonal phenological differences that may cause spurious change

to be detected in the imagery. One must also be careful

about two other factors when dealing with upland seasonal agricultural crops. First, many monoculture crops

(e.g. corn) normally are planted at approximately the

same time of year. A month lag in planting date between fields having the same crop can cause serious

change detection error. Second, many monoculture

crops are composed of different species (or strains) of

the same crop, which can cause the crop to reflect

energy differently on multiple dates of anniversary imagery. These observations suggest that the analyst must

know the biophysical characteristics of the vegetation

as well as the cultural land-tenure practices in the study

area so that imagery which meets most of these characteristics can be selected for change detection.

The choice of image date is best determined by mutual agreement among remote-sensing specialists, biologists, ecologists, and local experts. The selection of

the acceptable window of acquisition will be made independently by participants in each region. No single

season will serve for all areas because of substantial

latitudinal variation extending from temperate to tropical regions. For example, coastal marshes in the midAtlantic region are best inventoried from June through

October while submersed habitats in southern Florida

may be best inventoried in November. Even within

regions, some cover types will be more easily distinguished in different seasons. For example, in the Caribbean, estuarine seagrasses can be best detected in early

January, yet marine seagrasses can be best detected in

Mayor June. Technically, these vegetation patterns

should be monitored at optimal times throughout the

year, but cost limitations usually limit the analyst to a

single date.

Effects of Tidal Stage on Image Classification

Tidal stage is a crucial factor in satellite image scene

selection and the timing of aerial surveys. Ideally, tides

should be constant between time periods, but this would

rule out synoptic satellite sensors since tidal stages are

notsynchronized within a region or even within a single

image. Alternatively, analysts should avoid selecting the

highest tides and should take into account the tide

stages occurring throughout each scene. Tidal effect

varies greatly among regions. In the Northwest, for

example, when all of the temporal, atmospheric, and

tidal criteria are taken into account, the number of

acceptable scenes may be quite small. In some regions

it may be necessary to seek alternative data such as

SPOT satellite data, aerial photographs, or other landcover databases. For most regions, mean low tide (MLT)

or lower will be preferred, one or two feet above MLT

will be acceptable, and three feet or more will be unacceptable (Jensen et aI., 1993a). Ideally, tides for aerial

photographic surveys of submersed habitat should approach low tide as predicted in NOS tide tables, but

optimal visualization of the subtidal bottom depends

on water clarity as well as depth. Two ofthe 1993 C-CAP

protocol development projects focus on improving the

C-CAP protocol for tidal effects (see AppendiX 5).

Image Processing Data to Inventory Upland

and Wetland Change _

With the classification scheme developed and the appropriate remote-sensor data selected, it is possible to

process the data to extract upland and wetland change

information. This involves geometric and radiometric

correction, selection of an appropriate change detection algorithm, classification if necessary, creation of

change detection products, and error evaluation (Table

1). A separate section (Chapter 4) describes the extraction ofinformation on SRV because aerial photography

and significantly different photogrammetric techniques

must be utilized.

Rectification of Multiple-Date Remote Sensor

Data

Georeferencing (spatial registration ofa remotely sensed

image to a standard map projection) is a necessary step

in digital change detection and cartographic representation. The following C-CAP recommendations should

be followed when rectifying the base image to a standard basemap:

• Geocoded base TM images can be purchased if preferred by regional analysts. However, participants

should be aware that some analysts have reported

undocumented variations in commercial products that

can lead to poor registration in certain regions, especially where local relief requires substantial terrain

correction. Additional registration may be necessary

to achieve the C-CAP standard precision of RMSE

±O.5 pixel. Therefore, it is recommended that each

regional project perform its own base image-to-map

rectification by using data that is radiometrically corrected but not geocoded.

• Ground control points (GCP's) used to compute rectification transformation coefficients should be rela-
tively static features in the landscape (e.g. road intersections) or should be based on new GPS measurements taken in the field. When GCP's are digitized

from USGS 7.5' (1:24,000) maps, analysts should use

the marginal information and available updates to

improve the location of control points. GCP's should

be extracted from mylar copies of the USGS maps

whenever possible to minimize system-produced digitizing error. Traditional paper maps expand and contract with changes in relative humidity and should

not be used for digitizing GCP's.

• C-CAP recommends the use of the current NAD '83

national datum. Unfortunately, most existing map

series are based on the NAD '27 datum. NAD '27 will be

acceptable on a region-by-region basis until published

maps based on NAD '83 are universally available.

• In all but the flattest coastal regions, terrain correction of imagery may be necessary to reduce image

distortion caused by local relief.

• The required coordinate system is UTM. If another

coordinate system is used (e.g. state plane), it is the

responsibility of the regional analyst to provide complete documentation and conversion equations.

• It is the responsibility of the regional analyst to understand (or seek advice concerning) the variety of

rectification-resampling algorithms (e.g. bilinear interpolation, nearest neighbor, cubic convolution) and

their impact on the data. Nearest-neighbor resampling

is recommended.

Rectification of an earlier date (TIrl) or later date

(Tbtl) to the base image (Tb) can be accomplished in

several ways. The primary concern is to accomplish the

most exact co-registration of pixels from each time

period and thus reduce a potentially significant source of

error in change analysis (Lunetta et aI., 1991). The following are minimum recommendations and requirements:

• Geocoded and terrain-corrected TM data can be ordered from commercial vendors. Two separate images can be overlaid according to like coordinates,

but this technique may introduce error if prior

geocoding was not precisely the same in both images.

The regional analyst has no control in this process,

but if high precision is accomplished by the vendor,

the analyst can significantly reduce image processing

effort at the regional facility.

• The regional analyst can geocode the image to UTM

coordinates as was done with the base image. If this

technique is adopted, it is important to use the identical GCP's and resampling algorithm that were used

to rectify the base image.

• For multiple images, the preferred technique is to

rectify nongeocoded images directly to the geocoded

base image. This technique may have the advantage

Chapter 3: Monitoring Uplands and Wetlands 15

of reducing or better controlling co-registration error among images. Selection and consistency of control points and rectification algorithms are important

to the success of this technique. Cubic convolution

algorithms normally yield the most precise spatial fit,

but cubic convolution and bilinear interpolation algorithms suffer from the disadvantage of averaging

pixel brightness values. Nearest-neighbor algorithms

are spatially less precise, but they offer the advantage

of retaining pixel brightness values through the processes of rectification and registration.

Radiometric Nonnalization of Multiple-Date

Images

The use of remotely sensed data to classify coastal and

upland land cover on individual dates is contingent

upon there being a robust relationship between remotely sensing brightness values (BV's) and actual surface conditions. However, factors such as sun angle,

Earth/Sun distance, detector calibration differences

between the various sensor systems, atmospheric condition, and sun/target/sensor geometry (phase angle)

will also affect pixel brightness value. Differences in

direct beam solar radiation due to variation in sun

angle and Earth/sun distance can be calculated accurately, as can variation in pixel BV's due to detector

calibration differences between sensor systems. Removing atmospheric and phase-angle effects requires information about the gaseous and aerosol composition of

the atmosphere and the bidirectional reflectance characteristics of elements within the scene. However, atmospheric and bidirectional reflectance information

are rarely available for historical remotely sensed data.

Also, some analysts may not have the necessary expertise to perform a theoretically based atmospheric path

radiance correction on remotely sensed data. Hence, it

is suggested that a relatively straightforward "empirical

scene normalization" be employed to match the detector calibration, astronomic, atmospheric, and phaseangle conditions present in a reference scene.

Image normalization reduces pixel BV variation

caused by nonsurface factors, so variations in pixel BV's

between dates can be related to actual changes in surface conditions. Normalization enables the use of image analysis logic developed for a base-year scene to be

applied to other scenes. This can be accomplished using techniques pioneered by the U.S. Bureau of Land

Management (Eckhardt et aI., 1990). Image normalization is achieved by developing simple regression equations between the brightness values of "normalization

targets" present in Tb and the scene to be normalized

(e.g. TIrl or Tbtl)' Normalization targets are assumed to

be constant reflectors, therefore any changes in their
16 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program _

brightness values are attributed to detector calibration,

astronomic, atmospheric, and phase-angle differences.

Once these variations are removed, changes in BV may

be related to changes in surface conditions.

Acceptance criteria for potential "normalization targets" (Eckhardt et aI., 1990) are as follows:

• Targets must be at approximately the same elevation

as the land cover of primary interest within the scene.

Most aerosols in the atmosphere occur <1000 m above

ground level (AGL). Selecting a mountain-top normalization target, thus, would be of little use in estimating atmospheric conditions near sea level. Although C-CAP projects are on the coast, many regions include areas of substantial local relief.

• Targets should contain only minimal amounts ofvegetation. Vegetation spectral reflectance can change

over time because of environmental stresses and plant

phenology. Good targets include bare soil fields and

deep, nonturbid water bodies.

• Targets must be on relatively flat terrain so that incremental changes in sun angle between dates will have

the same proportional increase or decrease in direct

beam sunlight for all normalization targets.

• Normalization targets should have approximately the

same texture over time. Changing textural patterns

indicate variability within the target, which could mean

that the reflectance of the target as a whole may not

be constant over time. For example, a mottled pattern on what had previously been a uniformly gray,

dry lake bed indicates changing surface moisture conditions, which would eliminate the dry lake bed from

consideration as a normalization target.

The mean BV's ofthe Tb targets are regressed against

the mean BV's ofthe TI>-l or Tb+\ targets for the n bands

used in the classification ofthe remote sensor data (e.g.

TM bands 2, 3, and 4). The slope and y-intercept of the

n equations are then used to normalize the Tb+l or TI>-l

Landsat TM data to the Tb Landsat TM data. Each regression model contains an additive component (y-intercept) that corrects for the difference in atmospheric

path radiance between dates and contains a multiplicative term (slope) that corrects for the difference in

detector calibration, sun angle, Earth/Sun distance,

atmospheric attenuation, and phase angle between dates.

It is customary first to normalize the remote-sensor

data and then perform image rectification (using nearest-neighbor resampling ifimage classification is to take

place). These data are then ready for individual date

classification or the application of various multi-image

change detection algorithms. Most studies that attempt

to monitor biophysical properties such as vegetation

biomass, chlorophyll absorption, and health require

atmospheric correction.

Selecting the Appropriate Change Detection

Algorithm _

C-CAP is the first Federal program to state as a primary

goal the monitoring of coastal habitat change using

satellite technology (Cross and Thomas, 1992). The

implementation and continuing evolution of the program is based on the fact that improved cartographic,

digital image processing, and photointerpretation methods must be developed for a program ofthis geographic

coverage, spatial resolution, and temporal frequency

(nationwide, 30 x 30 m pixel, every one to five years).

Initial implementation ofC-CAP will require a blend of

traditional and innovative approaches to change analysis.

Because the program has adopted a digital format, with

TM as a primary sensor, new techniques in processing can

be easily incorporated into future iterations.

The selection of an appropriate change detection

algorithm is very important (Jensen, 1986; Dobson and

Bright, 1991, 1992, and 1993; Jensen et aI., 1993a).

First, it will have a direct impact on the type of image

classification to be performed (if any). Second, it will

dictate whether important "from-to" information can be

extracted from the imagery. C-CAP requires that from-to

information be readily available in digital form suitable

for geographic analysis and for producing maps and tabular summaries. At leastseven change detection algorithms

are commonly used by the remote-sensing community:

1. Change Detection Using Write Function Memory Insertion-Example: Kittredge and Fort Moultrie, S.C.

2. Multiple-Date Composite Image Change DetectionNo example provided.

3. Image Algebra Change Detection (Band Differencing

or Band Ratioing)-No example provided.

4. Postclassification Comparison Change DetectionExample: Fort Moultrie, S.c.

5. Multiple-Date Change Detection Using a Binary Mask

Applied to TI>-l-Example: Chesapeake Bay, Md.

6. Multiple-Date Change Detection Using Ancillary Data

Source as Tb-No example provided.

7. Manual On-Screen Digitization of Change-No example provided.

It is instructive to review these alternatives, identify

those acceptable to C-CAP, and provide specific examples where appropriate.

Change Detection Using Write Function Memory

Insertion

It is possible to insert individual bands of remotely

sensed data into specific write function memory banks

(red, green, and/or blue) in the digital image process-
__________________________ Chapter 3: Monitoring Uplands and Wetlands 17

Figure I

Diagram ofMultiple-Date Change Detection using Write Function Memory

insertion (Jensen, 1994).

Image Algebra Change Detection

Multi-Date Visual Change Detection

Using Write-Function Memory Insertion

Red image plane

Green image plane

Blue image plane

Disadvantages:

• non-quantitative

• no 'from-to' change class information

1987 and 1988; Eastman and Fulk, 1993). This results

in the computation of eigenvalues and factor loadings

that are used to produce a new, un correlated PCA

image dataset. Usually, several of the new bands of

information are directly related to change. The difficulty arises when trying to interpret and label each

component image. Nevertheless, the method is valuable and is used frequently.

The advantage of the techniques is that only a single

classification is required. Unfortunately, it is often difficult to label the change classes, and no from-to change

class information is available.

Date 1 band n

Date 2 band n

Date 3 band n

Advantages:

• visual examination of 2 or 3

years of non-specific change

ing system (Fig. 1) to visually iden tify change

in the imagery (Jensen et aI., 1993b). For

example, consider two Landsat TM scenes of

the Fort Moultrie quadrangle near Charleston, SC, obtained on 11 November 1982 and

19 December 1988. Band 1 ofthe 1982 image

was placed in the green image plane; band 1

of the 1988 image, in the red image plane;

and no image, in the blue image plane (Fig.

2). All areas that did not change between the

two dates are depicted in shades of yellow

(i.e. in additive color theory, equal intensities

of green and red make yellow). The graphic

depicts numerous changes, including

• beach and sand bar accretion (red) and

erosion (green),

• new urban development (red), and

• changes in tidal stage between dates (green

and red).

Advantages of this technique include the possibility

of looking at two and even three dates of remotely

sensed imagery at one time, as demonstrated byJensen

et al. (l993b). Unfortunately, the technique does not

produce a classified land-cover database for either date

and, thus, does not provide quantitative information

on the amount of area changing from one land-cover

category to another. Nevertheless, it is an excellent

analog method for quickly and qualitatively assessing

the amount of change in a region, which might help to

select one of the more rigorous change detection techniques to be discussed.

Multiple-Date Composite hnage Change Detection

Numerous researchers have rectified multiple dates of

remotely sensed imagery (e.g. selected bands of two

TM scenes of the same region) and placed them in a

single dataset (Fig. 3). This composite dataset can be

analyzed in a number of ways to extract change information. First, a traditional classification using all n bands

(six in the example in Fig. 3) may be performed. Unsupervised classification techniques will result in the creation of "change" and "no-change" clusters. The analyst

must then label the clusters accordingly.

Other researchers have used principle component

analysis (PCA) to detect change (Jensen, 1986). Again,

the method involves registering two (or more) dates of

remotely sensed data to the same planimetric basemap

as described earlier and then placing them in the same

dataset. A PCA based on variance-covariance matrices

or a standardized PCA based on an analysis of correlation matrices is then performed (Fung and LeDrew,

It is possible to simply identify the amount of change

between two images by band ratioing or image

differencing the same band in two images that have

previously been rectified to a common basemap. Image

differencing involves subtracting the imagery of one

date from that of another (Fig. 4). The subtraction

results in positive and negative values in areas of radiance change and zero values in areas of no-change in a

new "change image." In an 8-bit (28) analysis with pixel

values ranging from 0 to 255, the potential range of

difference values is -255 to 255. The results are normally transformed into positive values by adding a constan t, C (usually 255). The operation is expressed mathematically as

D k = BVk(l) - BV (2) + C 1J 1J lJk

where

Dik = change pixel value,

B~k(l) = brightness value at Tb

,

B~k(2) = brightness value at T1>--1 or T&+-1'
18 NOAA Technical Report NMFS 123: Dobson et at.: Coastal Change Analysis Program

c = a constant (e.g. 255),

i = line number,

j = column number, and

k = a single band (e.g. TM band 4).

The "change image" produced using image differencing usually yields a BV distribution approximately

Gaussian in nature, where pixels of no BV change are

distributed around the mean and pixels of change are

found in the tails of the distribution. Band ratioing

involves exactly the same logic except a ratio is computed between Tb and Tb+-1 or Tb-1 and the pixels that

did not change have a value of "1" in the change image.

A critical element of both image-differencing and

band-ratioing change detection is deciding where to

place the threshold boundaries between "change" and

Figure 2

Example of Multiple-Date Change Detection using Write Function Memory Insertion using two dates of

Landsat Thematic Mapper imagery of Fort Moultrie, S. C. Red image plane =TM band 3,19 Dec 1988; green

image plane = TM band 3, 9 Nov 1982; blue image plane = blank.
__________________________ Chapter 3: Monitoring Uplands and Wetlands 19

Image Algebra Change Detection

Figure 3

Diagram of Multiple-Date Composite Image Change Detection (Jensen, 1994).

6 Principal

Components

2

3

4

Disadvantages:

• difficult to label change classes

• no 'from-to' change classes available

Date 1

Date 2

Traditional

Classification

Multi-Date Composite Change Detection

~---;:;r; Rectified Thematic

4 Mapper bands

Advantages:

• requires single classification

"no-change" pixels displayed in the histogram

of the change image (Jensen, 1986). Often, a

standard deviation from the mean is.selected

and tested empirically. Conversely, most analysts prefer to experiment empirically, placing the threshold at various locations in the

tails ofthe distribution until a realistic amount

of change is encountered. Thus, the amount

of change selected and eventually "recoded"

for display is often subjective and must be

based on familiarity with the study area. There

are also analytical methods that can be used

to select the most appropriate thresholds.

Unfortunately, image differencing simply

identifies those areas that may have changed

and provides no information on the nature of

the change, i.e. no from-to information. Nevertheless, the technique is valuable when used

in conjunction with other techniques such as

the multiple-date change detection using a

binary change mask to be discussed in "Multiple-Date Change Detection Using a Binary

Change Mask Applied to T1r-1 or Tb+1'"

Recoded to produce binary

'ChangelNo-change'Mask

2

3 Rectified Thematic

4 Mapper bands

2 Rectified Thematic 3

4 Mapper bands

3 Composite

3 Dataset

-----:7' Image differenced or

..c:....__-:--'-"./7 band ratioed image

ues from 1 to 81. The analyst then selected specific

from-to classes for emphasis. Only a select number of

the 72 (n2-n) possible off-diagonal from-to land-cover

Date 2

Date 1

Figure 4

Diagram of Image Algebra Change Detection (Jensen, 1994).

Advantages: Disadvantages:

• efficient method of identifying • no 'from-to' change classes available

pixels which have changed • requires careful selection of the

in brightness value between dates 'change/no-change' threshold

Postclassification Comparison Change

Detection

The most commonly used quantitative method

of change detection is postclassification comparison (Jensen, 1986; Jensen et aI., 1993a)

and may be used in regional C-CAP projects

under certain conditions. It requires rectification and classification of each of the remotely sensed images (Fig. 5). These two maps

are then compared on a pixel-by-pixel basis

by using a "change detection matrix" to be

discussed. Unfortunately, every error in the

individual date classification map will also be

present in the final change detection map

(Rutchey and Velcheck, 1993). Therefore, it

is imperative that the individual classification

maps used in the postclassification change

detection method be extremely accurate

(Augenstein et aI., 1991; Price et aI., 1992).

To demonstrate the postclassification comparison change detection method, consider

the Kittredge (40 river miles inland from

Charleston, S.C.) and Fort Moultrie, S.c. study

areas (Fig. 6) (Jensen et aI., 1993a). Nine

classes ofland cover were inventoried on each

date (Fig. 7). The 1982 and 1988 classification maps were then compared on a pixel-bypixel basis using an n X n GIS "matrix" algorithm whose

logic is shown in Figure 8. This resulted in the creation

of "change images maps" consisting of brightness val-
20 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program _

Figure 5

Diagram ofPostclassification Comparison Change Detection (Jensen, 1994).

Disadvantages:

• dependent on accuracy of individual

date classifications

• requires two separate classifications

Change map produced using

'change detection matrix' logic

applied to Date 1 and Date 2

classification maps

method of change detection is highly recommended for C-CAP regional projects. First,

the analyst selects the base image, Tb

• Date 2

may be an earlier image Tl>-l or a later image

1~Jtl' A traditional classification of Tb is performed by using rectified remote sensor data.

Next, one of the bands (e.g. band 3 in Figure

10) from both dates ofimagery are placed in a

new dataset. The two band dataset is then analyzed by using various image algebra functions

(e.g. band ratioing, image differencing, principal components analysis) to produce a new

image file. The analyst usually selects a threshold value to identify spectral change and nochange pixels in the new image as discussed in

"Image Algebra Change Detection." The spectral change image is then recoded into a binary

mask file, consisting ofpixels with spectral change

between the two dates, and these are viewed as

candidate pixels for categorical change. Great

care must be exercised when creating the

change/no-change binary mask (Dobson and

Bright, 1993;Jensen et aI., 1993a). The change

mask is then overlaid onto Tl>-l or Tbtl of the

analysis and only those pixels which were detected as having changed are classified in Tl>-l or

Tbtl' A traditional postclassification comparison

can then be applied to yield from-to change

information. Hence, many pixels with sufficient

change to be included in the mask of candidate

pixels may not qualify as categorical land-cover

change.

Dobson and Bright (1991, 1992, and 1993)

used this change detection methodology to

inventory change in the area surrounding the

Chesapeake Bay using TM imagery obtained

on 9 September 1984 and 3 ovember 1988

Multiple-date Change Detection Using a Binary

Change Mask Applied to Tb-l or Tb+l

This

Postclassification comparison change detection is

widely used and easy to understand. When conducted

by skilled image analysts it represents a viable technique for the creation ofC-CAP change detection products. Advantages include the detailed from-to information and the classification map for each year. Unfortunately, the accuracy of change detection is heavily dependent on the accuracy of the two separate classifications. Postclassification comparison is not recommended

for C-CAP regional projects except under special circumstances, such as when differen t sensors are involved

or when two separate organizations are classifying the

same region at different times.

Classification map of Date 1

Classification map of Date 1

Classification map of Date 2

.,...------;<"; Rectified Thematic

4 Mapper bands

,..-------,r~ Rectified Thematic

4 Mapper bands

Multi-Date Change Detection Using

Post-Classification Comparison

Advantages:

• provides 'from-to' change

class information

• next base year is already

completed

change classes summarized in the change matrix (Fig.

8) were selected to produce the change detection maps

(Fig. 9). For example, all pixels which changed from

any land cover in 1982 to Developed Land in 1988 were

color coded red (RGB=255, 0, 0) by selecting the appropriate from-to cells in the change detection matrix

(10,19,28,37,46,55,64, and 73). Note that the change

classes are draped over a TM band-4 image of the study

area to facilitate orientation. Similarly, all pixels in

1982 that changed to Estuarine Unconsolidated Shore

by 19 December 1988 (cells 9,18,27,36,45,54,63, and

72) were depicted in yellow (RGB=255, 255, 0). If desired, the analyst could highlight very specific changes,

such as all pixels that changed from Developed Land to

Estuarine Emergent Wetland (cell 5 in the matrix), by

assigning a unique color look-up table value (not

shown). A color-coded version of the change detection

matrix can be used as an effective from-to change detection map legend (Jensen and Narumalani, 1992).
Chapter 3: Monitoring Uplands and Wetlands 21

Kittredge. S.C. 11/09/82 12/19/88

Fort Moultrie, S.C.

Landsat Thematic Mapper Data

Bands 4,3,2 = RGB

11/09/82

H

5000

Scale

o

12/19/88

Meters

5000

Figure 6

Rectified Landsat Thematic Mapper data: (a and b) obtained for the Kittredge, S. C., 7.5' quadrangle C-CAP study

area, 9 Nov 1982 and 19 Dec 1988 Uensen et aI., 1993a) (c and d) Obtained for the Fort Moultrie, S. C., 7.5'

quadrangle study area, 9 Nov 1982 and 12 Dec 1988.
22 NOAA Technical Report NMFS 123: Dobson et aI.: Coastal Change Analysis Program

Kittredge. S. C. 11/09/82 12/19/88

Fort Moultrie. S. C. 11/09/82 12/19/88

Legend

Developed Land

Grassland

Woody Land

Palustrine Woody Wetland

c:::::J Estuarine Emergent Wetland

c:::::J Estuarine Unconsolidated Shore

c:::::J Cultivated Land

Riverine Aquatic Bed

_ Water

Figure 7

Multiple-date land-cover classification maps: (a and b) Kittredge, S. C., study area, produced from

9 Nov 1982 and 19 Dec 1988 Landsat TM data. (c and d) Fort Moultrie, S. C., study area, produced

from 9 Nov 1982 and 19 Dec 1988 Landsat TM data (Jensen et a!., 1993a).
__________________________ Chapter 3: Monitoring Uplands and Wetlands 23

"0 0

..c: t:: "0 C/l !:l t:: "0 <:i OJ

1988 </) <:i OJ "0

C <l) :9 CO <l) "0 .~

;>, </)

"0 "0 01) "0 ..... "0 ed t:: 0 t:: <l) OJ OJ 0 u E ::l t:: ..J ..J "0 0' => "From -To" t:: u.l "0 "0 "0 OJ <C <l) <l) <l) ..J <l) t:: To: 0- t:: t:: <l) t:: Change Detection Legend 0 OJ OJ ;>, 'C t:: "5 'C

0:; > Vi "0 OJ 'C </) OJ </) 0 .3 <l) ::l ::l > "3 OJ 0 > c; OJ <l) Vi ..... </)

From: 0 u CJ u.l 0.. u.l

Developed Land

Cultivated Land

1982 Grassland

Woody Land

Estuarine Emergent Wetland

Riverine Aquatic Beds

Palustrine Woody Wetland

Water

Estuarine Uncolsolidated Bottom

Color look-up table values in J-... Red 255 255 255 255 255 255 0 0 255

change detection map Green 0 255 255 255 163 o 255 0 255

Blue 0 255 255 255 0 255 255 255 0

•

No change in landcover between dates, and

not selected for display

D Change in land cover between dates,

but not selected for display

New Developed Land (cells lU.19, 28,37.

46,55.64.73) shown in red (RGB=255,O,O)

D New Estuarine Unconsolidated Shore (cells

9,18,27,36,45.54,63.72) shown in yellow

(RGB=255.255.0)

Figure 8

Change detection matrix. The basic elements of a change detection matrix may be used to

select specific "from-to" classes for display in a "postclassification comparison" change detection map. There are (n2 - n) off-diagonal possible change classes which may be displayed in the

change detection map (72 in this example) although some may be highly unlikely. The

colored off-diagonal cells in this diagram were used to produce the change maps in Figure 9.

For example. any pixel in the 1982 map that changed to Developed Land by 1988 is red

(RGB=255,O,O). Any pixel that changed into Estuarine Unconsolidated Shore by 1988 is yellow

(RGB=255,255,O). Individual cells can be color coded in the change map to identiry very

specific "from-to" changes (Jensen et aI., 1993a).
24 NOAA Technical Report NMFS 123: Dobson et aI.: Coastal Change Analysis Program

(the region centered on Metomkin Inlet is shown in

Figures 11 and 12). The 1988 base image was classified

using traditional supervised classification techniques

(Fig. 13). A change/no-change mask was derived by

performing image arithmetic on bands 3, 4, and 5 of

the two date dataset. All change pixels were combined

into a single change mask (Fig. 14). The change/nochange mask was then overlaid onto the earlier date of

t::

o

LL

DO I

o

o

o

o

lD

(])

co

u

(f)

co

co

<J)

Q)

o

o

o

lD

N

co

<J)

o

....

en

Q)

OJ

C

co

..c

U

Q)

>

o

U

"0

C

.5

c
Chapter 3: Monitoring Uplands and Wetlands 25

Figure 10

Diagram of Multiple-Date Change Detection Using a Binary Change

Mask Applied to Date 2 Qensen, 1994).

Classification map of Date 1

Traditional classification

of Date 1

Perform Post-Classification

Comparison Change Detection

or Update Date 1 map with

Date 2 change information

using GIS dominate function

Disadvantages:

• requires a number of steps

• dependent on quality of 'change/

no-change' binary mask

Image algebra to identify

change pixels, e.g. ratio of

multidate band 3 data.

Create change pixel mask

""--7i Mask out change pixels

3 in Date 2 imagery and

classify

Classification map of Date 2

::-:----73 Date 1band 3

3 Date 2band 3

,.,....---'7 Rectified Thematic

4 Mapper bands

T1Jt1 image is required. It may also be possible to update

the NWI map (Tb) with more current wetland information (this would be done using a GIS "dominate" function and the new wetland information found in the T~l

or T1Jt1 classification). The disadvantage is that the NWI

data must be digitized and generalized to be compatible with the C-CAP Coastal Land-Cover Classification

System, then converted from vector to raster format to

be compatible with the raster remote-sensor data. Any

manual digitization and subsequent conversion introduces error into the database which may not be acceptable (Lunetta et aI., 1991).

Multi-Date Change Detection Using A

Binary Change Mask Applied to Date 2

Advantages:

• may reduce change detection

errors (omission and comission)

• provides 'from-to' change

class information

imagery and only those pixels which were detected as

having changed were classified in the earlier image. A

from-to matrix similar to the one shown in Figure 9 was

then used to produce a change map of the region (Fig.

15). Summary statistics for the region are found in

Table 4. This process may be repeated with a later scene

to determine successive change.

This method may reduce change detection errors

(omission and commission) and provides detailed

from-to change class information. The technique reduces effort by allowing analysts to focus on the small

amount of area that has changed between dates. In

most regional projects, the amount of actual

change over one to five years is probably no

greater than 10% ofthe total area. The method

is complex, requiring a number of steps, and

the final outcome is dependent on .the quality

of the change/no-change binary mask used in

the analysis. A conservative threshold may exclude real change while a liberal threshold may

create problems similar to those of the postclassification comparison technique (See

"Postclassification Comparison Change Detection.")

Multiple-date Change Detection Using

Ancillary Data Source as Tb

Sometimes a land-cover data source may be

used in place of a traditional remote-sensing

image in the change detection process. For

example, the NWI is inventorying all wetlands

in the United States at the 1:24,000 scale.

Some of these data have been digitized. Instead of using a remotely sensed image as Tb

in the analysis, it is possible to substitute the

digital NWI map of the region (Fig. 16). In

this case, the NWI map would be "recoded"

to be compatible with the GCAP Coastal LandCover Classification System (Table 2). This

should not be difficult since the two systems

are highly compatible. Next, T~l or TIJt1 of

the analysis is classified and then compared

on a pixel-by-pixel basis with Tb information.

Traditional from-to information can then be

derived. As with any other postclassification

comparison, the accuracy of the change database is dependent on the accuracy of both

input databases (C-CAP and NWI).

Advantages of the method include the use

of a well-known, trusted data source (NWI)

and the possible reduction of errors of omission and commission. Detailed from-to information may be obtained by using this method.

Also, only a single classification of the T~l or
26 NOAA Technical Report NMFS 123: Dobson et aI.: Coastal Change Analysis Program _

Figure II

Rectified Thematic Mapper imagery of the Metomkin Inlet area obtained on 21 Sep 1984 (Dobson and Bright, 1992).

Manual On-Screen Digitization of Change

Considerable amounts of high resolution remote sensor data are now available (e.g. SPOT 10 x 10 m, the

aircraft moun ted Calibrated Airborne Spectrographic

Imager [CASI] ofthe National Aerial Photography Program [NAPP]). These data can be rectified and used as

planimetric basemaps or orthophotomaps. Often aerial

photographs are scanned (digitized) at high resolutions into digital image files (Light, 1993). These photographic datasets can then be registered to a common

basemap and compared to identify change. Digitized

high resolution aerial photographs displayed on a CRT

screen can be interpreted easily using standard photo

interpretation techniques based on size, shape, shadow,

texture, etc. (Ryerson, 1989). Therefore, it is becoming

increasingly common for analysts to interpret visually

both dates of aerial photographs (or other type of

remote-sensor data) on the screen, annotate the important features using heads-up on-screen digitizing, and

compare the various images to detect change (Cowen

et a!., 1991; Cheng et a!., 1992; Lacy, 1992; Wang et a!.,
Chapter 3: Monitoring Uplands and Wetlands 27

Figure 12

Rectified Thematic Mapper imagery of the Metomkin Inlet area obtained on 3 Nov 1988 (Dobson and Bright, 1992).

1992; Westmoreland and Stow, 1992). The process is

especially easy when 1) both digitized photographs (or

images) are displayed on the CRT side by side, and 2)

they are topologically linked through object-oriented

programming so that a polygon drawn around a feature

on one photograph will also be drawn around the same

feature on the other photograph. Scanning aerial photographs unavoidably reduces the spatial and spectral

resolution ofsource data. This loss may be significant in

photographs of submerged features, which are subject

to interferences from aquatic as well as atmospheric

sources. As with other new technologies, demonstration ofthe appropriateness ofinterpretation ofscanned

photographs will be a critical step in expanding the CCAP Protocol (Also see "Accuracy Assessment for Individual Date Classification ofWater and Submersed Habitat Data"). The manual on-screen approach is recommended as a useful adjunct to other change detection

methods. Its principle drawback is the time required to

cover large regions in such a labor-intensive fashion.
28 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program _

Figure 13

Classification map of 3 Nov 1988 Landsat Thematic Mapper imagery of the Metomkin Inlet area (Dobson and Bright, 1992).

Selecting Appropriate Classification

Algorithms

C-CAP requires that the classification procedures used

as part of the change detection process be approved

and documented. Classification algorithms used in each

region will be selected based on the capabilities and needs

of the regional participants. C-CAP assumes that the regional participants are experienced in image processing

and mapping. Ifnot, C-CAP will attempt to provide fundamental technical assistance on a case-by-ease basis.

The previous section indicated that these three of

the seven most commonly used change detection algorithms are acceptable for C-CAP regional projects:

• Postclassification Comparison

• Change Detection Using a Binary Change Mask Applied to T!J-l or Tbtl

• Change Detection Using Ancillary Data Source as Th

.

Each of these requires a complete pixel-by-pixel classification of one date of imagery and, at least, a partial
Chapter 3: Monitoring Uplands and Wetlands 29

Figure 14

Binary "change/no-change mask" produced by image differencing TM bands 3, 4, and 5 of each date (Dobson and Bright, 1992).

classification of an additional date. Hence, it is instructive to review the C-CAP-approved image classification

logic which may be used in the regional projects.

Supervised and Unsupervised Image

Classification Logic

The primary reaSOn for employing digital image classification algorithms is to reduce human labor and improve consistency. It is expected that regional analysts

will have sufficient expertise to assess the advantages of

alternative classification algorithms and to recognize

when human pattern recognition and other types of

intervention are necessary. In practice, it may be necessary to employ a suite of algorithms including both

supervised and unsupervised statistical pattern recognition approaches. Currently, maximum-likelihood classifiers often serve as a good first step, but new statistical

approaches are being developed and implemented on a

routine basis (Jensen et aI., 1987; Hodgson and Plews,

1989; Foody et aI., 1992). It is important for analysts to
30 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program

remain flexible with regard to procedures and algorithms.

Standard supervised and unsupervised classification

techniques have been available for more than 20 years

and are well documented in texts byJensen (1986) and

Campbell (1987). In a supervised classification, the

analyst "trains" the classifier by extracting mean and

covariance statistics for known phenomena in a single

date ofremotely sensed data (Gong and Howarth, 1990).

These statistical patterns are then passed to a minimum-distance-to-means algorithm in which unknown

pixels are assigned to the nearest class in n-dimensional

feature space, or passed to a maximum-likelihood classification algorithm that assigns an unknown pixel to

the class in which it has the highest probability of being

a member. Great care must be exercised when selecting

training samples (Mausel et aI., 1990).

In an unsupervised classification, the computer is

allowed to query the multispectral properties of the

scene by using user-specified criteria and to identify x

mutually exclusive clusters in n-dimensional feature

Figure 15

A map showing selected C-CAP change classes derived from analysis of the 21 Sep 1984 and 3 ov 1988 Landsat TM data

of the Metomkin Inlet area (Dobson and Bright, 1992).
Chapter 3: Monitoring Uplands and Wetlands 31

Multi-Date Change Detection Using

An Ancillary Data Source as Date 1

/7

Ancillary data source e.g.,

National Wetlands Inventory

Map

...--__"7 2 Rectified Thematic

3

4 Mapper bands

Classification map of Date 2

Classification map of Date 1

Perform Post-Classification

Comparison Change Detection

or Update Date 1 NWI map with

Date 2 change information using

GIS dominate function

Advantages:

• may reduce change detection

errors (omission and comission)

• provides 'from-to' change

class information

• requires a single classification

Disadvantages:

• dependent on quality of ancillary

information

Figure 16

Diagram of Multiple-Date Change Detection Using Ancillary Data Source

as Date 1 Uensen, 1994).

space (Chuvieco and Congalton, 1988). The analyst

must then convert (label) the x spectral clusters into

information classes such as those found in the C-CAP

Coastal Land-Cover Classification System. Training sites

visited in the field and identifiable in the digital imagery are also indispensable when labeling clusters in an

unsupervised classification. The following sections discuss C-CAP guidelines for collecting training and verification samples.

Selection of Training and Verification Samples

for Supervised and Unsupervised Classification

Only training sites that were actually visited on the

ground by experienced professionals should be selected

for extracting the multispectral statistical "signature" of

a specific class when performing a supervised or unsupervised classification. It is suggested that a minimum

of five training sites per land-cover class be collected.

This creates a representative training set when performing supervised classification and makes labeling

clusters much easier in an unsupervised classification.

In addition to the image analysts, the field team should

contain specialists in ecology, biology, forestry, geography, statistics, and other pertinentfields, such as agronomy.

Field samples should be stratified by land-eover type and

by various physical factors such as slope, elevation, vegetation density, species mix, season, and latitude. The polygonal boundary of all field sites should be measured

using CPS whenever possible, and the locational, temporal, and categorical information should be archived.

The collection of field training sites often requires

multiple visits to the field. Some ofthe field sites may be

used to train a classifier or label a cluster while a certain

proportion ofthe field sample sites should be held back

to be used for classification error assessment, which will

be discussed.
32 NOAA Technical Report NMFS 123: Dobson et at.: Coastal Change Analysis Program

Table 4

Statistical summary of areal change (in ha) by land-cover class for the Metomkin Inlet area shown in Figures 12-16. Read

across each row to find which categories the 1988 totals came from. Read down each column to find which categories the

1984 totals changed to. Bold numbers along the diagonal indicate the area that did not change from 1985 to 1988.

uncon. = unconsolidated.

1984 Classification

Developed Grassland/ Forest Scrub/ Palustrine Estuarine Palustrine Water/ Bare

land cultivated land shrub forest emergent emergent uncon. shore land TOTAL

1988 Classification

Developed land 1,158 85 8 0 0 4 0 0 1,256

Grassland/cultivated 0 21,341 562 0 0 2 0 0 17 21,922

Forest land 0 165 18,915 0 0 0 0 0 19,081

Scrub/shrub 0 240 562 854 0 2 0 0 0 1,658

Palustrine forest 0 20 9 0 787 0 0 0 0 816

Estuarine emergent 0 26 9 0 0 11,587 0 13 8 11,643

Palustrine emergent 0 0 0 0 0 0 0 0 0 0

Water/uncon. shore 0 4 0 0 0 2 0 37,172 144 37,322

Bare land 0 19 0 0 0 23 0 124 507 673

TOTAL 1,158 21,900 20,065 854 787 11,621 0 37,310 676 94,371

The following materials are indispensable to a successful field exercise:

• Imagery geocorrected to a standard map projection

• Topographic maps at 1:24,000 or the largest available

scale

• Global Positioning System (GPS)

• Aerial photographs

It is advisable to perform, at least, a cursory classification before initiating fieldwork. In this case, both raw

and classified data should be taken to the field. The

primary function ofthe cursory classification is to guide

field workers in targeting the covers and signatures that

are most difficult and confusing. Keep in mind that the

vast majority of all cover will be easy to identifY on the

ground and on the imagery. Efficient use of field time

provides for quick verification of easy cover types and

maximum attention to difficult, unusual, and ecologically critical cover types.

Field investigators should anticipate the need to know

not only the geodetic coordinates of training sites but

also the layout of the road network that will provide

access. It is advisable to imbed roadway information

into the raw imagery. This can be done using the Bureau ofthe Census Topologically Integrated Geographic

Encoding and Referencing (TIGER) files. Imbedding

is preferred rather than transparent-overlay techniques,

which are cumbersome and difficult to use under field

conditions.

C-CAP investigators have assembled and tested a field

station based on a color laptop computer with commercial software. At present the software supports visualization of raster imagery (e.g. satellite data, digital

orthophotographs, scanned aerial photographs) and

vector databases (e.g. TIGER road networks, NWI wetlands). A version of the software soon to be available

from commercial vendors will allow realtime input of

GPS coordinates. It will then be possible to follow field

movements directly on the image and map data. The

software also allows for completion of field forms on

screen in the field. Preliminary tests are encouraging,

but the field station is not fully operational at this time.

One shortcoming, for example, is the poor performance

of active matrix color screens in sunlight.

Use of Collateral Data in Image Classification

The overriding goal is to produce accurate individual

date classifications and accurate change detection databases. Any information or operation that enhances data

quality is generally encouraged. C-CAP does not endorse the notion that the use of collateral data in a
__________________________ Chapter 3: Monitoring Uplands and Wetlands 33

remote-sensing project is "hedging." Instead, the objective is to use collateral data innovatively to improve the

accuracy of the C-CAP database.

There are many potential sources of collateral data

including soil maps, OAA coastlines (T-sheets), timber surveys, USGS digital line graphs, and digital elevation models (for elevation, slope, and aspect). These

can be incorporated by masking, filtering, probability

weighting, or including in the signature file (Ryerson,

1989; Baker et aI., 1991). Depending on the importance of each category, analysts may use certain categories to overrule others (Jensen et aI., 1993a).

The NWI is an especially valuable collateral database

that may be of value when classifying wetlands. Regional analysts should incorporate NWI data to the

maximum extent possible. NWI data are recognized as

the most authoritative and complete source ofwetlands

land-cover data (Wilen, 1990). However, NWI maps are

not temporally synchronized in each region and are

not in a digital format for many regions. An approach

based on complementary use of NWI and imagery will

be an asset to both C-CAP and NWI. At a minimum,

NWI maps, digital data, or both should be used to

define training samples, to check intermediate results,

and to aid in the final verification of the wetlands

portion of the C-CAP maps. NWI digital data may be

used as a probability filter in the classification process.

In this approach, C-CAP recommends an "innocent

until proven guilty" attitude toward the NWI data. In

other words, the NWI category is considered correct for

a given pixel area for each time period, unless spectral

signatures or collateral data suggest that the NWI category is incorrect or a land-cover change has occurred.

Even if the NWI data were 100% correct at the time of

NWI mapping, overriding by spectral data would be

necessary to detect change over time. Ultimately in

turn, the C-CAP change detection database can assist

NWI managers in determining the need for NWI updates.

Cartographic Portrayal of Classification and

Change Detection Maps

C-CAP products must meet stringent cartographic standards. The following sections discuss the minimum

measurement unit and its proper use when aggregating

change information. Formats of classification maps and

change maps must satisfy C-CAP criteria whenever

hardcopy maps are produced.

The Concept of the Minimum Measurement Unit

The minimum measurement unit is a measure of both

the precision and accuracy of input data. For most CCAP regional projects, the input data will be 30 x 30 m

pixel data recorded by a Landsat TM sensor. The minimum measurement unit, however, combines the ability

(e.g. sensor limitations) and effort (e.g. field verification) required to measure a category with the spatial

precision and accuracy necessary to accomplish the

intended use of the data. Each land-cover category

could potentially have a different minimum measurement unit based on the size of individual parcels and

the distinctiveness of the signature. Thus, the minimum measurement unit differs from a traditional minimum mapping unit, which by definition imposes a predetermined polygon (or pixel) size for all land-cover

categories (for example, a rule that all parcels of one

hectare or larger will be mapped). This traditional approach is acceptable for manual mapping using analog

aerial photographs but is difficult to apply to raster

imagery. Regional analysts will be responsible for defining minimum measurement units, which will generally

be larger than a single pixel but no larger than three

pixel dimensions on the short axis.

Regardless of the minimum measurement unit,

change analysis will be conducted pixel by pixel. C-CAP

protocol requires that the inherent resolution of the

raw data must be retained throughout the classification

and change-analysis processes. Aggregation and filtering of pixels should occur only in regard to cartographic presentation of the completed change detection database.

Regardless of the techniques employed, the final database should be capable of representing land-cover by

class for the base time, land cover by class for each

earlier or later time, and land-cover change by class for

each change period. The final database should contain

the full change matrix (all "from" and "to" categories)

for each change period.

Analog (Hardcopy) Cartographic Products

Hardcopy maps ofthe final database are not specifically

required by C-CAP, but they are certain to be useful

when presenting results. Often it is useful to produce a

smaller scale regional map (usually requiring some pixel

aggregation) that gives an impression of the scope of

the effort and to produce several larger scale maps at

full resolution that demonstrate the level of detail and

highlight notable findings. All maps should come directly

from the final database complying with C-CAP protocols,

but overlaying or imbedding ancillary data, such as DLG

and TIGER data, is encouraged with proper notation.

If the statistical summary of changes is present on a

map, C-CAP recommends that the numbers included

in it always be calculated for the area shown on the

map. It is not acceptable to associate the summary of

changes for one area (larger or smaller) with a map of
34 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program _

another. The statistical summaries of the change detection matrix must always be calculated from the database at full resolution, rather than from the aggregated

data of the plot file. It is not advisable to allow the

numerical count of class area to float with the level of

cartographic aggregation. Unless all counts are based

on the full resolution database, some classes composed

of small features may disappear at higher levels of

aggregation. Map readers may become confused if matrix numbers change with aggregation for the same

territory.

Technically, the minimum cartographic presentation

is 1) a map for the base time, 2) a map showing gains by

class, and 3) a map showing losses by class. A full classification for the earlier or later (nonbase) time may be

useful, but it is not essential to present the matrix of

possible changes. Examples of some of these products

are found in Figures 6-13.
Chapter 4

Monituring Submerged Land

Using Aerial Photography

C-CAP Focus on Aerial Photography of

Submersed Rooted Vascular Plants (SRV) _

Photic submerged land can support submersed rooted

vascular plants (SRV) (including salt-requiring seagrasses and oligohaline and freshwater tolerant grasses

and forbs), macroalgae, and coral reefs (see "Water

and Submerged Land" and Appendix 3). The C-CAP

Coastal Land-Cover Classification (see Table 1) identifies Marine/Estuarine Aquatic Beds, specifically SRV,

of primary importance to be inventoried and placed in

the GCAP database (Klemas et aI., 1993). Many of the

steps discussed in Chapter 3 to monitor uplands and

wetlands are pertinent to monitor SRV However, there

are significant differences which cannot be ignored (Table

5). Important considerations include the following:

• mapping SRV is primarily a photogrammetric task,

rather than a satellite task, requiring an entirely different sensor system (aircraft, camera filter, and film);

• aerial photography is normally not radiometrically

(except for color balance between photographs) or

geometrically corrected;

• time of day, sensor altitude, and flightline placement

are very flexible, unlike fixed orbit satellite sensor

systems;

• numerous environmental conditions must be considered (sea state, water clarity, water depth, low altitude

atmospheric conditions) to optimize photography;

and

• aerial photographs are in analog format.

These differences are so significant that it is instructive

to focus on aerial photography of SRV.

Ancillary Categories of Submersed Habitat

Other types of submersed habitat classified by C-CAP

can be monitored with guidelines similar to those presented here for SRV. At a minimum, regional cooperators are requested to map and conduct change analysis

for SRV. Increasing the number of habitat types to be

included in the study will be based on local or regional

interest and support for the effort. For example, a

comprehensive mapping ofSRV, macroalgae, and coral

reefs is underway in the Florida Keys (see "State of

Florida, Department of Environmental Protection").

Ancillary Technologies for Collecting

Submersed Habitat Data

Some successes have been reported with satellite imagery and a number of other technologies in monitoring

photic submerged land. Presently, these technologies

supplement, and eventually may replace, aerial photography for change detection in SRV. Some of them are

briefly mentioned here.

Satellite imagery has some advantages and disadvantages compared with photography. Satellite data generally have greater spectral resolution than aerial photography but lesser spatial resolution. Satellite imagery is

already in a digital format whereas information derived

from aerial photography must eventually be digitized

to be quantitatively analyzed. Landsat and SPOT data

have been successfully used to inventory some

macroalgae such as the giant kelp, Macrocystis pyrifera,

along southern California shorelines (Jensen et aI., 1980;

Augenstein et aI., 1991). In clear shallow tropical waters

with highly reflective substrate, Landsat imagery may

discriminate sandy from coral reef or seagrass areas

(Luczkovich et aI., 1993) or provide an estimate of

biomass for unispecific beds of Thalassia testudinum

(Armstrong, 1993). In the turbid estuaries of the eastern United States, Landsat and SPOT imagery can be

used to detect some (e.g. large, dense, shallow) but not

all of the SRV that is visible in the best aerial photography. Aerial photography is, in fact, often used as "ground

truth" when interpreting satellite imagery. Because of

the fixed orbital paths of satellites, it is only fortuitous

when a satellite image is acquired under optimum conditions to inventory SRV (see "Environmental Considerations"). For these reasons, aerial photography is the

C-CAP imagery of choice for comprehensive mapping

and change detection (Ferguson and Wood, 1990; Thomas and Ferguson, 1990; Orth etal., 1991; Ferguson et

aI., 1992 and 1993). Photo interpretation supported by

surface-level signature verification and species identification is qualitatively and spatially more reliable for

SRV than are satellite-based methods.

Several technologies may provide valuable supplemental data to aerial photographic detection of habitat

change. These include closed circuit television (CCTV)

on an airplane, small boat, or remotely operated vehicle (ROV), side-scan and down-looking sonar, new

satellite sensors, airborne spectral scanners and digital

video scanners, and digitized photography. Such new

35
36 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program _

Table 5

General steps required to conduct regional C-CAP

change detection projects to extract water and submerged land information using aerial photography.

Each major step is listed in the order to be accomplished.

1. State the regional change detection problem

a. Define the region

b. SpecifY frequency of change detection (1 to 5 yr)

c. IdentifY classes of the C-CAP Coastal Land-Cover

Classification System

2. Consider significant factors when performing change

detection

a. Remote sensing system considerations

1) Spatial resolution and scale

2) Flightline considerations

3) Spectral resolution and film/filter combination

4) Temporal resolution and diurnal sun angle

5) The preferred C-CAP aerial photography system

b. Environmental considerations

1) Atmospheric conditions

2) Turbidity conditions

3) Vegetation phenological cycle characteristics

4) Tidal stage

5) Surface roughness and sun glint conditions

3. Interpret aerial photographs to extract water and

submerged land information

a. Acquire appropriate change detection data

1) In situ surface level verification and basemaps

2) Aerial photography

a) Base year (Tb)

b) Subsequent year(s) (TI>-l or Tl>tl)

b. Preprocess the multiple-date photography

1) Radiometric correction (color balance)

2) Optically register photography to planimetric

basemap

c. Select appropriate change detection algorithm

(usually postclassification comparison)

d. Image analysis

1) Monoscopic (interpretation of single photos or

orthophotographs)

2) Stereoscopic (analog or analytical)

e. Transfer polygons to planimetric basemap

f. Digitize polygons

g. Perform change detection using GIS algorithms

1) Highlight selected classes using change detection

matrix

2) Generate change map products

3) Compute change statistics

4. Conduct quality assurance and control

a. Assess spatial data quality

b. Assess statistical accuracy of

1) Individual date classification

2) Change detection products

5. Distribute C-CAP results

a. Digital products

b. Analog (hardcopy) products

technology will be incorporated into the C-CAP guidelines as it is demonstrated to meet qualitative, quantitative, resolution, and geographic positioning standards.

At present, CCTV is effective for surveil1ance applications but georeferencing and rectification fal1 short of

metric quality photography. Airborne multispectral scanners and digital cameras are technologies with applications in the demonstration stage of development.

Direct mapping of habitat borders can be performed

with differential1y correctable CPS instrumentation

when the perimeter of that habitat can be visual1y observed or detected with the aid of instruments in the

field. Differential1y corrected CPS can provide positions of surface level data at an accuracy suitable to

supplement or assess the accuracy of aerial photographic

data. With differential correction, single position fixes

with CPS are accurate to a circular error probable

(CEP)of ±5 m 50% of the time. The methodology for

using CPS in accuracy assessment and monitoring of

SRV is a current research topic funded by C-CAP.

Aerial Photography of SRV _

Film

The recommended film for aerial photography of SRV

is Aerocolor 2445 color-negative film. Second choices

are Aerochrome 2448 color-reversal and Aerographic

2405 black-and-white negative film. A haze filter should

always be used to minimize the degrading effect of haze

on photographic images. We do not recommend infrared film for delineating SRV. In our experience in

North Carolina with tandem cameras, Aerochrome 2443

false-color infrared film was much less effective than

color film at recording benthic features in shal1ow,

moderately turbid water. True color film gives more

information than black-and-white or infrared film, is

critical for initial mapping attempts in new or unfamiliar areas, and may permit identification of species in

some tropical areas. Color negative film also appears to

be better than color reversal or black-and-white film for

identification of habitat under moderately turbid or

hazy conditions. Color transparency prints are dimensional1y stable and are most amenable to il1uminating

dark areas of the photograph under magnification.

Paper prints are not as dimensional1y stable as transparencies (i.e. paper prints are subject to stretching and

shrinking) but they are more resistant than transparencies to damage from handling when used for field work.

Metric Photography and Photographic Scale

Metric-quality aerial photographs (~3° of tilt off-nadir

and including camera calibration data) are essential
and should be acquired with a protocol similar to that

employed by NOAA's Photogrammetry Branch (1980)

to produce the highest quality data possible. The need

for rectification of photography is minimized by precise control of aircraft altitude and orientation relative

to the vertical during photography and by interpretation in stereo. Photography should be obtained at a

scale appropriate to the areal extent of habitat, local

water conditions, type of habitat being studied, and

resolution requirements for the resultant data. Scale is

a compromise among resolution of signatures, coverage of habitat, inclusion of land features sufficient for

horizontal control, and cost. Photographic scale should

normally range from 1:12,000 to 1:24,000. For extensive areas of high and variable turbidity such as Chesapeake Bay and eastern North Carolina, 1:24,000 or

1:20,000 scale photographs may be adequate when the

water is clear. For chronically turbid estuarine or brackish water areas, 1: 12,000 or larger scale photographs

obtained at times of minimal turbidity may be required

for acceptable visualization of submerged features.

Small-scale photography may be necessary to bridge

habitat delineated in larger scale photographs to local

horizontal control points on adjacent land features that

are not included in the larger scale photographs. CPS

onboard the airplane for positioning photographic centers during exposure may reduce this limitation oflarger

scale photography. For extensive areas ofrelatively clear

water, such as the Florida Keys, a scale of 1:48,000 may

be sufficient and cost effective. This is a current C-CAP

research topic (see "State of Florida, Department of

Environmental Protection").

Flightlines, Reconnaissance Flights, and Photographic Overlap

Flightlines are planned with reference to aeronautical

and nautical charts to include all areas known to have,

or which potentially could have, SRV. The efficiency of

photographic missions can be optimized by minimizing

the number offlightlines and by contingency planning.

Some airspace is restricted for military or other use, for

example, and is indicated on aeronautical charts. Nautical charts provide bathymetric data useful for designating potential habitat areas when combined with local knowledge of the depth of vegetated bottoms. Reconnaissance flights can provide valuable perspective

on SRV distribution if timed to optimize visualization of

shallow bottoms (see "Environmental Considerations").

Ideally, each photograph in a flightline records cultural and shoreline features required to register the

image to the base map, about 1/3 ofthe exposure. This

permits correction of photographic scale and orientation to the external reference system. At a scale of

Chapter 4: Monitoring Submerged Lands 37

1:24,000 (1 inch = 2,000 ft), a standard 9 x 9 inch aerial

photograph has a coverage of 18,000 x 18,000 ft. Large

areas (relative to coverage of a single photograph) of

open water require parallel flightlines and bridging of

the large-scale photography to control points with the

small-scale photography, construction of towers, etc.,

to supplement horizontal control features or inflight

CPS positioning of photographic cen ters.

Overlap of photographs includes endlap of adjacent

photographs along a flightline and sidelap of photographs along parallel flightlines. Sixty percent endlap

allows stereoscopic interpretation, facilitates interpretation from the most central region ofthe photographs,

and compensates for loss of coverage due to sun glint in

the photographs. (Sun glint is the image of the sun

reflected off the surface ofthe water. See "Sun Angle.")

Sidelap of 30% ensures contiguous coverage of adjacent flightlines and produces a block of aerial photographs that may be subjected to photogrammetric

bundle adjustment if necessary.

Environmental Considerations

Knowledge of the study area that IS Important to a

successful project includes the plant species comprising SRV; morphology and phenology of these plants;

depth range and location of known habitat; locations

with water depth potentially suitable for habitat, types

and locations ofbenthic features that may confuse photo

interpretation of SRV; seasonality of turbidity, weather,

and haze; daily patterns in wind speed and direction;

and progression ofsun angle through the day. Primary

and secondary seasonal windows and the day and time

to conduct photography are selected to optimize the

visibility of habitat in the photography. Surface waters

in different locations and at different times of the year

will be more or less sensitive to turbidity from local

runoff, plankton blooms, local resuspension of sediment, and surface waves. Seasonal and daily trends for

haze, cloud cover, wind direction, wind duration, and

wind velocity should be included in planning for photography. The decisions of when to have the aircraft

arrive at the study area (within the seasonal window)

and when to collect photography are based on NOS

tide tables, local knowledge of factors affecting water

clarity and depth, observation of recent weather patterns (precipitation, wind direction, and wind speed),

and water clarity. The final decision to photograph

includes observations from the air based on the pilot's

estimate of haze, cloud cover, and overall visibility.

Primary and secondary photographic windows should

be one or two months duration to ensure optimal conditions for photography. For single day missions it may

be possible to have the plane and flightcrew fly to the
38 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program _

study area on the day of photography. In our experience in North Carolina, staging of the plane and flight

crews to the study area several times for several days was

required to complete missions involving more than one

day of actual photography.

Phenology-The best time of year to acquire photography is during the season of maximum biomass or flowering of dominant species, considering the phenologic

overlap for the entire community. This isJune for the

SRV of the Pacific Northwest and Atlantic Northeast,

April and May for eelgrass in eastern North Carolina,

and September for most of the other species of SRV in

the eastern United States.

Clouds and Haze-It is best to have no clouds and

minimal haze. Thin broken clouds or thin overcast

above the plane may be acceptable when these are

determined by visualization from the air neither to cast

shadows nor adversely affect illumination of the study

area. Haze reduces illumination and clarity of the image of benthic features being recorded in the photograph. Cooperators are referred to the "Aerial Photographers Clear Day Map," U.S. Department of Commerce, Environmental Data Service.

Turbidity-Aerial photography should be conducted

when turbidity is low. Care should be exercised in areas

adjacent to sources of suspended sediment and nutrients. Data collection should be avoided during seasonal

phytoplankton blooms or immediately following heavy

rains or persistent strong winds. Potential days for photography are those during the photographic window

when high water clarity is expected, based on local

experience, recent weather patterns, and surface level

observation. The flightcrew should confirm water clarity from the air on the day of photography.

Tides-Generally, aerial photography should be collected within ±2 hours of the lowest tide predicted by

NOS tide tables, although factors affecting water depth

and water clarity should be considered simultaneously.

In general, extreme low tide, which may be -0.5 to -1.0 m

or more around the U.S. coast is preferred, if compatible with other constraints. The significant "lag" in the

tidal stage of some estuaries should be considered for

data acquisition.

Wind and Surface Waves-No wind and no waves is best

for aerial photography. Low wind (<10 mph) may be

acceptable. The direction, persistence, fetch (the distance

that wind can blow unobstructed over water), and recent

wind events should be taken into account. Breaking waves

and associated turbidity, white caps, lines of bubbles, and

floating debris should not be visible from the air or in the

photographs. For some areas, ocean swell can be an important consideration and should not exceed 3 ft.

SunAngle-Sun angle affects the illumination of benthic

features, sun glint, and shadows from tall shoreline

features in the photographs. A sun angle of 20-250is

optimal to record benthic features (Keller, 1978). A

sun angle of 15-300is recommended by C-CAP. This

interval maximizes the time for photography considering both the illumination of submerged features and

sun glint. Sun angles above 150illuminate the bottom

sufficiently for photographic purposes. Sun glint also

increases with sun angle but precludes visualization of

benthic features where it occurs in the photograph. As

sun angle increases, sun glint also increases and moves

from the edge toward the center of the photograph.

Loss of coverage due to sun glin t at sun angles of up to

about 300is compensated (to ensure monoscopic coverage, at a minimum) by the recommended endlap of

60% (see "Flightlines, Reconnaissance Flights, and Photographic Overlap"). Eighty percent endlap will improve coverage when high sun angles cannot be avoided.

Photography at sun angles above 300is not recommended. Sun glint is minimized when the sun and land

are on the same side of the plane because sun glint

does not occur on land. Shadows from tall objects on

shore such as trees, however, can preclude visualization

of benthic features and may be a factor when the land

and sun are on the same side of the plane.

Photointerpretation of SRV _

Habitat defined by the presence of SRV can be interpreted from metric-quality aerial photographs exposed

as recommended in the previous sections. The accurate

identification of SRV in aerial photographs requires

visual evaluation of the fundamental elements ofimage

interpretation (tone, color, contrast, texture, shadow, etc.).

It also requires extensive experience at ground level in the

study area; the photographic images of habitat and

nonhabitat features vary in ways which cannot readily be

modeled, described, or communicated. Training for a

habitat change analysis effort includes literature research;

discussions with local ecologists and biologists; site visits

on foot, swimming (snorkel or scuba), or small boat;

overflights in a small plane; and examination of historical

aerial photographs of the area. Training of photo interpreters is active throughout the life of the project.

SRV are best observed by using stereo pairs of photographs and high quality stereoscopic instruments (e.g.

Wild, AVIOPRET, APT2, stereoscopes). Polygons are

traced on overlays fixed to each photograph. To be

delineated as habitat, recognizable and verified signa-
tures of SRV must be present in the photographs. SRV

(and other benthic features) in a given area will present

a variety of signatures depending upon the species

present, bottom sediment, depth, season, haze, clouds,

water clarity and surface disturbances, and sun angle at

the time of photography.

The designation of a given area as SRV is a function

of the minimum detection unit, the minimum mapping unit, and the proximity of the area to other SRV.

Assuming a photographic scale of 1:24,000, high quality optics, high resolution film, and ideal conditions

(e.g. dense clusters oflarge vigorous shoots growing on

light-colored sediment in shallow, clear, calm water), it

is usually possible to have a minimum detection unit of

approximately 1 m. All detected SRV that appear to be

in a continuum with adjacent SRV in an area exceeding

0.03 ha will be mapped as a single. polygon. The minimum mapping unit is the smallest area to be mapped as

habitat. At the C-CAP map scale of 1:24,000, the minimum mapping unit is 0.03 ha for SRV (i.e. a diameter

of about 0.8 mm on the map represents a diameter of

about 20 m or an area of about 0.03 ha on the ground).

Therefore, isolated groups ofshoots with a diameter of

less than 20 m may be detected but not mapped as

habitat. The presence of SRV signature in the photograph defines habitat if 1) the total area exceeds 0.03 ha;

2) no unvegetated discontinuities, such as dredged or

natural channels, partition the distribution into spatial

units less than 0.03 ha; and 3) unvegetated areas between plants are not large relative to the minimum

mapping unit. Unfortunately, not all areas of SRV can

be detected when photographic conditions are less than

ideal. Because of the constraint of the minimum mapping unit and the possibility of suboptimal photography, delineations of SRV will tend to be conservative.

The degree of underestimation depends upon the atmospheric and hydrographic conditions at the time of

photography, the experience of the photo interpreter,

and the nature of the subject area.

Optimizing conditions for photography will minimize underestimation of SRV, particularly in areas that

are intrinsically more difficult to interpret. Where habitat edges are clearly distinct in superior-quality photography, they may also be detected in inferior-quality

photography (e.g. high biomass of SRV along a clear

water channel with a steep bank of light-colored sediment). In other cases where the edges are not clearly

distinct in superior-quality photography, they are likely

to remain undetected in inferior photography (e.g. low

biomass of SRV growing on a shallow depth gradient of

deep, turbid water over dark-colored sediment). The

deep-water edge of habitat often will be difficult to

delineate. This edge may also be at high risk for loss due to

degradation in water quality that limits the illumination of

the bottom with photosynthetically active radiation.

Chapter 4: Monitoring Submerged Lands 39

SRV with unrecognized signatures due to poor photographic conditions cannot be mapped as habitat unless the area is rephotographed or additional sources of

data are incorporated into the database. When photo

interpretation is difficult or not possible, the preferred

option is to rephotograph the area under better conditions. Although desirable, this may not be possible.

Even under the best photographic conditions, delineation of all or part of some habitat polygons may require

additional effort in regard to surface level verification or

direct inclusion of surface level data. Polygon borders

derived from surface level data must be so designated in

the lineage database for "truth in labeling" requirements

(see "Digital Product"). Suitable surface level positioning

techniques include CPS or more traditional survey positioning techniques that can be demonstrated to provide

the positional accuracy required by C-CAP.

Within a polygon ofSRV, the extent of bottom coverage by shoots of SRV and the pattern of distribution of

the shoots or bed form (e.g. circular, doughnut-shaped,

irregular patches, or continuous cover of SRV) reflects

the interaction of biotic, physical, and anthropogenic

factors. Coverage and bed form can be estimated from

aerial photographs but is not a requirement of C-CAP.

An example of a coverage index is an adaptation of the

crown density scale originally developed to categorize

coverage by trees crowns in forests (Orth et aI., 1991).

However, coverage indices and bed-form identifications

are affected by factors such as water depth and brightness

of bottom sediments. The degree of contrast between

shoots and exposed sediment and the clarity ofthe photographic image determines the minimum detection unit of

features within SRV. Comparison of habitats with different depths, water clarity, or substrate brightness, therefore, is problematic. Analysis of change over time at a

given location may be useful but requires consistent photographic conditions and field verification. Changes in

coverage or bed form over time in a given location may

indicate changing conditions in that habitat polygon or

disturbances, such as scarring by boat propellers.

Some data including species, biomass, productivity,

functional status, and health of SRV may not be interpretable from the aerial photographs. Species identification is not possible from aerial photography in temperate areas such as North Carolina and the Chesapeake Bay. In some tropical areas, species distributions

and photographic signatures may be sufficiently distinct to discriminate by species.

Field Surveys

Species and Habitat at Randomly Selected Stations

Once selected by stratified random sampling of potential habitat, stations are observed for SRV species and
40 NOAA Technical Report NMFS 123: Dobson et at.: Coastal Change Analysis Program _

the presence or absence of aquatic beds during the

same season and preferably within one year of the

photography. Stations are stratified by water depth and

water body. Water depth determines ifsampling can be

accomplished by wading, snorkeling, or scuba diving.

Clear water with a bottom depth of~2.5 m or somewhat

shallower turbid water may require scuba. Stratification

permits flexibility in sampling intensity and effort (sampling by scuba requires special training and resources and

takes about twice the time per station). Bathymetry and

reference coordinates in NOAA nautical charts of the

study area facilitate selection and positioning of stations.

Navigation to stations is with GPS. The spatial density of

points is adjusted according to the resources and scale of

the project (e.g. an average of 1.5 to 2.5 nmi from station

to station in North Carolina). Great care is taken to include all locations of potential habitat in the surface level

survey. SRV are limited to water depths less than about 2

m at mean lower low water (MLLW) for Chesapeake Bay.

A similar depth limit was determined for that habitat in

eastern North Carolina. To determine that depth in North

Carolina, potential habitat was sampled to water depths of

10 ft MLLW (Ferguson and Wood, 1990, 1994). SRVare

not known to occur seaward of the barrier islands in

North Carolina. In sharp contrast, the maximum depth

for SRV is 9 m off the northwest coast of Florida.

The presence or absence of aquatic beds and species

of SRV are determined within an area equal to the

minimum mapping unit and centered around the nominal station location. If SRV are present, visual observations of the number, size, and distribution of groups of

plant shoots are recorded. These data are translated

into an assessment of the presence or absence of an

aquatic bed at the station considering the spatial distribution of SRV relative to the minimum detection and

mapping units. The goal is to assess presence data in a

manner relevant to photo interpretation (see "Photo

interpretation of SRV"). Ancillary data recorded are

water depth, salinity, water clarity, latitude and longitude, and descriptions of benthic sediment, algae, animals or animal shells, boulders, etc. A GPS position fix

is taken to be differentially corrected (postprocessing)

to a CEP of ±5 m. If the station data are not required to

verify photo interpretation (see below), they can be

used to estimate the accuracy of the habitat data (see

"Recommended Accuracy Assessment Test").

Signature Verification and Supplemental Spatial

Data

Locations selected from the photographs are observed

during the same season and within one year of the

photographic mission. The purpose of this survey is to

resolve uncertainties in the photographs and, if necessary, to collect surface level data for inclusion in the

spatial database. Surface level data intended to augment photo interpreted data require differentially corrected GPS positioning to a CEP of±5 m.

Base Maps and Registration of Habitat

Polygons

Accurate and up-to-date planimetric base maps of coastal

land features are essential for georeferencing (establishing of geographic location) and scaling polygons of

habitat interpreted from aerial photographs. C-CAP

recommends 1) use of the most accurate and up-todate base map available for the study area and 2) use of

the most cost-effective technology to apply local horizontal control to interpreted data by registration of the

photographs to base maps. The base map and the registration technology may vary regionally.

Planimetric Base Maps

The accuracy of the base map used for local horizontal

control places a limit on the accuracy of the C-CAP

product. The two base maps broadly available are NOAA

shoreline and USGS 7.5' topographic maps. NOS produces highly accurate shoreline maps based on tidecoordinated and fully rectified photography (Swanson,

1949; Ellis, 1978; Slamma, 1980; OAA Photogrammetry Branch, 1989; Crowell et aI., 1991). When available

and current, NOAA shoreline and coastal data should

be used for C-CAP projects (e.g. Ferguson et aI., 1991).

These data, available in graphic and digital form, are

products of the NOAA Coastal Mapping Program and

are available from NOS. Shoreline data are produced

from tide-coordinated photographic data and ground

level survey data by the Photogrammetry Branch of

NOS and meet or exceed national map accuracy standards. Horizontal ground control meets or exceeds

third-order class I specifications found in the geodetic

control standards (Federal Geodetic Control Committee, 1984). The Coastal Mapping Project of the Photogrammetry Branch provides data that depict the delineation of the mean high water line, the limit of emergent vegetation (apparent shoreline) and/or cultural

shoreline, and in some areas, e.g. orth Carolina, the

approximate MLLW line. NOS shoreline data are a

data source for NOAA nautical charts and USGS topographic maps. Coverage of the U.S. coastline is not complete, however, and for some areas the data may be dated.

In some locations, USGS 7.5' topographic maps may be

the only base maps available at a scale of 1:24,000. These

maps delineate the high tide line and cultural features

and may meet GCAP requirements. In many instances,
however, these maps are out ofdate and temporal changes

in shorelines may cause problems in the application of

local horizontal control to compile the habitat polygons

(Ferguson et aI., 1989; Ferguson and Wood, 1990). This

can reduce the positional and scaling accuracy of habitat

data which is critical for change analysis (see "Recent

Photography"). Care should be taken to determine the

effective date of coastal features in these maps. Updates of

these maps generally include cultural but not natural

changes in shoreline. Coverage ofthe coastal United States

is almost comprehensive, but dated. In some coastal areas,

1:24,000 scale orthophotoquads have been published as

an alternative to topographic maps. Orthophotoquads at

a scale of 1:24,000 are unsatisfactory for compilation from

aerial photography in remote areas. Orthophotoquads do

not have delineated shorelines, which may be needed

when the preferred cultural features are insufficient to

register the photograph to the map base.

Transfer of Polygons to the Map Coordinate

Projection System

Polygons of habitat interpreted from aerial photographs

are mapped into a standard map projection coordinate

system. The UTM projection is recommended. C-CAP

protocol allows the polygons interpreted from aerial

photography to be transferred onto planimetrically accurate basemaps using three approaches:

1) Stereoscopically interpret the photographs and

optically scale the polygons and photographic image to

fit planimetric horizontal control in the basemap with a

zoom transfer scope. This is the least expensive and

often the most reliable approach. Habitat delineations

drawn at the photographic scale through stereo viewing under magnification are transferred using camera

lucida principles from the photographic overlay directly onto the planimetric basemap.

2) Process the aerial photographs into planimetrically

accurate orthophotographs, and interpret and directly

trace habitat polygons onto the planimetric base map.

Interpretation of the orthophotographs is performed

using monoscopic airphoto interpretation techniques.

The orthophotographs must be at the same scale as the

base map or the images must be enlarged or reduced to

the map scale. This approach applies orthophotographic

rectification (Thrower and Jensen, 1976), which corrects relief displacement in the original photographs

and ensures planimetric mapping results in the database. Some loss of detail may occur since the orthophotography is a generation away from the original

aerial photography. The process is expensive but accuracy is improved in areas with substantial vertical relief.

3) Delineate and simultaneously rectify and digitize

habitat polygons by using an analytical stereo plotter.

Chapter 4: Monitoring Submerged Lands 41

The three-dimensional stereo model of the aerial photographs is leveled and scaled in the analytical plotter

(AP) and the interpreter views a three-dimensional landscape during photo interpretation. All polygonal interpretations are automatically stored in digital x,y coordinates in their proper planimetric position during photo

interpretation (Welch et aI., 1992), avoiding any error

that might arise during information transfer in methods 1 and 2 discussed above. The polygon data are

registered and digitized without the errors that are

associated with transfer in a zoom transfer scope or by

hand digitization. Unfortunately, analytical stereoplotters are expensive and their use requires special

training. Some additional expense to locate x, y, and z

control points may be necessary to successfully level the

block of aerial photography. Recent advances in softcopy photogrammetry allow analytical stereoplotter

functions to be accomplished using UNIX type workstations and image processing software (e.g. ERDAS

ORTHO-max). Therefore, this alternative will become

more affordable and attractive in the future.

An adaptation of the third approach is being tested

by NOAA and the State ofFlorida. Photo interpretation

is done as in approach 1. Registration and digitization

of the interpreted habitat polygons is completed in the

AP. Due to the high expense of AP and the specialization of AP technicians, this option may be feasible for

processing data from SRV interpreters who do not have

direct access to or training on an AP.

Digitization of Habitat Polygons _

Habitat polygons that have been transferred to the

planimetric base map according to procedure 1 or 2

above require digitization to be incorporated into the

C-CAP spatial data base. Normally, digitization is accomplished using a digitizing tablet. Polygons are digitized with a digitizing table in point mode. The overlays

are labeled according to the base map. Compilations

are checked for clear delineation and cartographic acceptability ofline work, existence of and consistency in

feature attributes, and adequacy of horizontal control

points. Compilations are checked along neat lines to

confirm edgeline match and label match for polygons

extending over adjoining maps. Any inconsistencies

are brought to the attention of the map author.

Compilations are affixed to a digitizing table for

georeferencing and data entry. The accuracy of the

reference points, the four corners of the neat line, and

no less than four internal tick marks on the overlays are

checked to ensure that control points are within ±O.02

inches. This translates to ±40 ft or ±12.2 m from its

stated location. If tolerance is exceeded on anyone

point, new control points are selected, digitized, and
42 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program _

reevaluated until all points test within tolerance. Information regarding the georeferencing error for each

control point is recorded on a documentation form. In

addition the technician records other information about

the overlay manuscript such as scale, size, media type,

source map information, and author.

Polygons are digitized with the cartographic style and

accuracy that is represented on the source manuscript. A

technician perfonns digitizing and data processing to map

completion, including matching edgelines, preparing initial check plots, and reviewing, editing, and preparing

final check plots. All linework and labeling are reviewed

using check plots produced at the source map scale. Each

arc is checked for acceptance on a light table with the final

check plot overlaid on the source map. Digitized linework

should conceal original linework with exceptions for difference in line thickness, differences in media, and subtle

differences of horizontal control on the source map and

in digital files. Unacceptable data is flagged, edited, and

reviewed prior to acceptance into the digital database. A

data layer specification form is completed for fonnal documentation at the conclusion of all digitizing.

Scan digitizing may be an acceptable alternative to

hand digitizing and could be applied at one of two

stages: 1) when polygons are positioned on overlays of

base maps or 2) when polygons are interpreted from

individual photographs. Large format scanners would

be required to scan an entire map, approximately

19 x 23 inch, in one pass. A standard desktop scanner,

8.5 x 11 inch, could scan the overlay from a single 9 x 9

inch photograph. In the latter case, geopositioning

might be accomplished digitally without the use of the

zoom transfer scope. In either case, the digital product

would have to meet the same positional tolerances described above for data entered with the digitizing tablet.

Change Detection With Aerial Photographic Data _

The C-CAP objective of site-specific change detection

places greater emphasis on accuracy and precision of

spatial data than required in one-time inventories or

regional summaries of change. Methodology for monitoring site-specific change on a statewide or regional

scale is a recent development (Ferguson and Wood,

1990; Orth et aI., 1991; Ferguson et aI., 1993). Quantitative historical data, with possible exceptions in Chesapeake Bay or spatially limited study sites, does not exist.

Recent Photography

C-CAP recommends post-classification change detection for SRV. Photographs taken in the same season of

different years are independently interpreted, verified,

and compiled to the base map. In this case independence does not mean different photo interpreters, compilers, and field personnel but rather an avoidance of

side-by-side comparison of the data until after classification is complete. Postclassification change detection

can be accomplished graphically, or polygons may be

digitized and compared by using a geographic information system to detect spatial displacement and to quantify change. Although simple in concept, the statistics

of change analysis are not well understood. Development

of consensus for statistical evaluation of qualitative or

spatial change is a subject of ongoing C-CAP research.

As an expedient to postclassification change detection, photographs from different years are compared

directly or with mapped pOlygons. By using such comparisons, areas where change may have occurred can be

identified rapidly but subjectively. Determining what constitutes significant change and how to objectively quantify

the degree of change remain to be accomplished.

Historical Photography

The earliest metric-quality aerial photographs were acquired in about 1939. Prior to 1960, virtually all aerial

photographs were black and white. Incomplete coverage, lack of coordination with tide, lack of camera

calibration data, inappropriate scale, sun angle, and

inappropriate time of year, or poor quality for visualization of benthic features often make these photographs

unacceptable for a C-CAP change analysis. Interpretation of historical photographs is likely to proceed with

limited or no concurrent surface level information for

signature verification and should be attempted only by

interpreters with extensive experience in the study area.

Unless historical photography meets the C-CAP requirements listed in "Aerial Photography of SRV" and is supported by surface level data as discussed in "Field Surveys,"

the historical presence or absence of SRV at a given location may remain an open question. Some but not all SRV

can be identified in less than optimal photography and be

confirmed in the literature or in the memory of local

residents. A visible signature for "bare bottom" or another

nonhabitat signature is required to interpret absence of

habitat at the time of photography. As a result, documentation of loss may be more likely than documentation of

gain of SRV with historical photography.

Historical photographs may contain limited but valuable information on presence of submersed habitat

other than SRY. Canopies of the giant kelp, Macrocystis

pyrifera, for example, are readily discernible in color

infrared (IR) photography because they have very high

IR reflectance against a background of water that has

no reflectance. The ease of photo interpretation of
Chapter 4: Monitoring Submerged Lands 43

some macroalgae allows historical photography to be

used to identify this and perhaps other types of habitat.

The most complete and general. (but not comprehensive) source for historical photography is the Earth

Resources Observation Systems (EROS) Data Center in

Sioux Falls, South Dakota EROS records can be searched

through the Earth SciencesInformation Center (ESIC) of

the USGS. The searcher must supply coordinates or the

names of USGS 7.5' quadrangles that locate the area of

interest. The ESIC office conducts a microfiche or computer-based search. Information produced includes latitude and longitude, emulsion, scale, month, year, source

of the photography, cloud cover, camera, and frame

numbers. Sources of this photography are USGS, National Aeronautics and Space Administration (NASA),

USFWS, Agricultural Stabilization and Conservation Sero

OCEAN

100 ACRES 100 HECTARES

ATLANTIC

MARCH APRIL

1985 988

SAV HABITAT _

SPATIAL SCALE

1

HS'12 0 2 3 4 5

MILES

0 1 2 3 4 5

KILOMETERS

f:l

34'47'+

) N

t

L- -NO-R...... TH CAROll:~~ STUCYAREA

+ 34' 35'

Ie

34' 3r +

Figure 17

Seagrass habitat in Back Sound and southern Core Sound in 1985, gray, and in 1988, green.

Pure gray indicates habitat present in 1985 but not in 1988. Light green indicates habitat in

1988 but not in 1985. The overlap of gray and green indicates the presence of habitat in

both 1985 and 1988. (A) Head of the Hole, an area where seagrass habitat decreased due to

mechanical harvesting of clams. (B) Spoil deposition island from which uncontained spoil

was released into the water and buried seagrass habitat (Ferguson et aI., 1992, 1993).
44 NOAA Technical Report NMFS 123: Dobson et aI.: Coastal Change Analysis Program _

vice (ASCS) , Soil Conservation Service (SCS), U.S. Forest

Service (USFS), and some private companies. All historical photography identified in an ESIC search is reproducible. Substantial collections of historical photography may

also be found in other Federal or State agencies, universities, or private companies. These collections of photography may be available for reproduction, distribution, loan,

or examination. Federal sources include U.S. Army Corps

of Engineers (COE), NOAA, National Archives for pre1956 photos, and the Smithsonian Institution.

Change Detection of Seagrass Habitat in North

Carolina

Ferguson et al. (1993) followed the C-CAP guidelines

in Back Sound and southern Core Sound in North

Carolina. That study demonstrated ~he feasibility of

monitoring spatial change in SRV using C-CAP guidelines for large-scale metric aerial photography, photo

interpretation, geographic positioning, and postclassification change detection techniques. Aerochrome MS

2448 color-reversal film was exposed in March 1985 at

1:20,000 and 1:12,000 scales. Aerocolor 2445 color-negative film was exposed at 1:24,000 scale in April 1988. All

aerial photography was obtained by the NOS Photogrammetry Branch. The photography was coordinated

with low tide and sun angle and was collected with

minimal haze, no clouds below the aircraft, and no

visible shadows from high clouds. Water was essentially

free of white caps and clear enough for identification

of vegetated and shallow unvegetated bottoms. Episodic wind, haze, local turbidity, and airborne pollen

often precluded photography for one or more days.

The sun angle during photography ranged from 15 to

30'. This sun angle localized sun glare to one edge ofthe

photography while presenting illumination below the water surface. The aerial photographs were interpreted stereoscopically and the polygons were transferred to planimetric NOS shoreline maps with a zoom transfer scope. A

graphical postclassification overlay approach was used to

visually identify changes between years (Fig. 17). A gray

tone in the chart indicates habitat present in 1985 but not

in 1988. Light green indicates habitat in 1988 but not in

1985. The overlay of gray and green indicates the presence ofseagrass habitat in both 1985 and 1988.

Summary statistics, obtained via automated geometric analysis of digitized video images of individual polygons (pixel size <0.03 ha) revealed that seagrass habitat

is a major resource in the study area, comprising about

35% of the subtidal land. Total area of habitat changed

less than 6%, from 7,030 ha in 1985 to 6,637 ha in 1988.

Polygons along the mainland and Harkers Island tended

to be linear and close to shore. Large broad areas of

seagrass habitat were present in the subtidal shallows

east of Browns Island, north of Shackleford Banks, and

west of Core Banks. The total number of habitat polygons was similar in the two years, 151 in 1985 and 149 in

1988. Reliability of detected change was conducted by

reinspection of the photography and is summarized in

Ferguson et al. (1993). Some areas of detected change

were confirmed by surface-level observations and two of

these were associated with known anthropogenic disturbances. Some areas of detected change were confirmed but could not be associated with potential causes.

Still others could not be confirmed, which may have

been the result of variable quality in the photography.

In a continuation of this study, the study area was rephotographed in 1992, selected polygons were mapped

with GPS at surface level during the 1992 photographic

window, and surface-level verification of signature was

completed in 1993. Data for all three years, 1985, 1988,

and 1992, will be digitized and change and positional

accuracy will be assessed in a GIS.
Chapter 5

Spatial Data Q]mlity Assurance and Control

Quality assurance and control (QA/QC) from data acquisition through final database compilation are the

responsibility of each regional project team. Acceptance of the final database into the C-CAP archive and

dissemination system are contingent upon the demonstration that the project has complied with the mandatory requirements stated in this document.

C-CAP standards of data quality are based on authoritative references (Goodchild and Kemp, 1990; Chrisman, 1991; Congalton, 1991; LunettaetaI., 1991; NIST,

1992). These documen ts recommend that producers of

data document

• Lineage-A record of the type of data sources and

the operations involved in the creation of a database.

• Positional accuracy and precision-The closeness of

locational information (in x,y coordinates) to the

true position.

• Attribute accuracy and precision-The closeness of

attribute values to their true values.

• Logical consistency-The adherence of internal data

structures to established conventions or stated rules.

• Completeness-The degree to which the data exhaust the universe of possible items.

C-CAP has added to this list

• Temporal accuracy and precision-The time over

which source materials were acquired and observations were made.

Users are responsible for determining

• Fitness for use-The degree to which the data quality

characteristics of each database and its components

collectively suit an intended application.

The C-CAP protocol also distinguishes between

• Accuracy-The closeness of results, computations, or

estimates to true values (or values accepted to be

true), and

• Precision-The number of decimal places or significant digits in a measurement.

The accuracy of the resulting land-cover database for

each time period and for change between time periods

is a crucial measure of the success of C-CAP. Several

different types of accuracy are involved, and some of

them are difficult to measure. For rigorous statistical

measures of accuracy, field-based reference data are

exclusively preferred over other data sources, including

aerial photographs.

Lineage _

The sources, scales, or resolutions, and dates of materials involved in the preparation of all regional C-CAP

databases must be documented (Lunetta et aI., 1991),

including

• satellite images or aerial photographs used in the

analysis,

• aerial photographs (including oblique photographs)

used as an aid in training or field verification if the

photographs directly influenced the identification of

land-cover types for significant portions of a given

area,

• collateral information such as NWI data or soils maps

if the information directly influenced the identification for significant portions of a given area,

• planimetric basemaps,

• state and county land-cover inventories or other surface level data, and

• sources and techniques of georeferencing, especially

for submerged land and other land where identifiable features are sparse.

Positional Accuracy and Precision _

Positional accuracy is concerned with the accuracy of

the geometric placement of points, lines, and polygon

boundaries. In land-cover databases, polygons are derived either from raster spectral data representing discrete pixels or from closed polygons delineating the

edges ofspectral signatures in photographs. In the first

case, the placement of polygon boundaries depends on

a) selection of spectral signatures for class boundaries

and b) registration of pixel locations. The second case

generally applies to C-CAP SRV projects in which the

primary intent is to delineate limits between presence

and absence of habitat classes. In this application, polygons of class 1 tend to occur as discrete objects in a

large polygon of class 0 that has specified boundaries

landward and unspecified boundaries seaward. In addition, one or more polygons of class 0 may be included

within a polygon of class 1. The placement of polygon

boundaries depends on a) limits of signatures attrib45
46 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program _

uted to the habitat class and b) registration of horizontal control points present in the base map and visible in

the photography. In both cases, signature selection directly affects attribute distribution which, in turn, affects the size and shape of polygons. This effect is most

common at polygon edges but may occur throughout

the polygon, for example, as internal voids or as circumscribed polygons of different classes. The selection

of spectral signatures for class boundaries is similar to

the task of generalization that cartographers have traditionally faced in deciding where to draw boundaries

between land-cover features.

For most remote-sensing applications, positional accuracy on the order of±I-2 pixels has not been a major

concern. Neither has positional accuracy for photographic delineations of submersed habitat been a major concern or a subject of independent verification.

For a single time period, positional errors may not

greatly affect the aggregate area of each land-cover

type. Positional errors may be difficult to detect even

when a specific polygon in the field is visited. For CCAP, however, positional accuracy is a crucial concern

(Ferguson et aI., 1992 and 1993). The change database

amounts to a comparison that will conspicuously record

positional errors of one or more pixel dimensions in

the satellite imagery and errors in excess of about 10 m

in the photographic images used to delineate submersed

habitat. This compounds the problem of recognizing

real changes, which also tend to concentrate at polygon

edges and class boundaries.

The registration of pixel locations is a purely geometric problem which has been greatly improved with recent advances in sensors, CPS, and image processing

systems. Many vendors claim a positional accuracy of

±0.5 pixel root mean square error (RMSE) for commercial image processing systems and a CEP of 3-5 m for

CPS. Selective availability (SA, the intentional distortion of CPS signals for military security purposes) reduces CPS precision to a CEP of 40 m when SA is in

operation. Differential readings by multiple receivers

can improve the quality of positional data, even when

SA is active, to a CEP of <5 m. C-CAP regional analysts

should verify vendor claims to their own satisfaction

based on sources of higher precision. Unless stated otherwise, a geometric registration of±O.5 pixel RMSE will be

assumed for all C-CAP regional databases (±15 m ifLandsat

TM data are used).

For submerged land, the registration of polygon edges

is a function of the metric quality of photographs, methods used to transfer the information to a planimetric

map projection, and quality of the digitization performed. Positional accuracy is therefore subject to the

accuracy of the base map including deviations not only

between the source photography and the base map but

also actual changes in the study area in the time between aerial photography for the base map and for the

submersed habitat. A positional accuracy that meets

national map accuracy standards is assumed for submersed habitat data. At the compilation scale of 1:24,000

this amounts to ±I3.3 m on the ground, close to the ±I5

meter precision of Landsat TM data for uplands and

wetlands.

Generalization Versus Error _

It is a tribute to the power of modern information

technologies that what we used to call generalization,

we now call error. With analog maps it has always been

necessary to use human judgment in deciding, for example, precisely where a forest becomes a field. In

reality most forests have some grass, and most fields

have some trees or shrubs. In natural circumstances the

boundary is not a precise line but rather a "fuzzy" zone

of highly variable width in which the predominan t land

cover grades from one class to another. Scale and resolution are crucial determinants of such boundaries. In

an analog map, scale limits the feasibility ofdrawing the

densities and convolutions oflines that would be necessary to represent each patch offorest or each individual

tree. Conceptually there will always be unrepresented

boundaries because, in the modern sense of fractals, a

nearly infinite number of convolutions are possible.

Digital systems are capable of representing a much

larger portion of all possible boundaries, but there are

practical limitations affecting digital systems as well. In

current technology the most often encountered limitation is the established resolution ofsatellite sensors.

While the terms "error" and "accuracy" are frequently

used in regard to generalized boundaries, conceptually

the "accurate" boundary can only be determined on

the basis of a highly specific set of criteria that goes far

beyond what can actually be implemented for large

areas. Land-cover phenomena are prime examples of

fuzzy sets. This fuzzy characteristic is explicitly recognized in the procedures of image processing (for example, the use of maximum-likelihood statistics), but

the remote-sensing community traditionally has presumed that a "right" answer or "ground truth" can be

determined if the analyst can get close enough to see

the polygon and its boundary in the field or on the

photograph. Yet different investigators "see" different

land covers, a problem that is especially troublesome

when the area is large enough to require multiple teams.

In reality, land-cover phenomena are fuzzy sets whether

viewed directly in the field or through remote sensors.

Fuzziness persists because each class is defined, not by a

discrete boundary, but by factors that grade from one

class to another-spatially, temporally, categorically, and

observationally. Also, classification and accuracy assess-
ment procedures are not always implemented in a timely

manner but often months or years after the image is

collected or analyzed.

Generalization also occurs within delineated polygons whether derived from satellite or photographic

images. In both cases a finite limit for signature detection and mapping exists. Minimum detection units are

one pixel for spectral scanner data and about one meter

for high altitude photographic images. Elimination of

"salt and pepper" and preservation of reasonable accuracy for perimeter or areal estimates requires a minimum mapping unit of 4 pixels or about 0.4 ha. At a

compilation scale of 1:24,000 the smallest polygon that

can be traced from a photograph is about 20 m in

diameter or an effective area on the ground of about

0.03 ha. Realistically, the goal for improving generalization should be to strive for cOQsistency more than

"accuracy. "

Reference data for accuracy assessment must have a

resolution and reliability that meet or exceed those of

the C-CAP remotely sensed data. The reliability, including attribute and positional accuracy, must be demonstrated prior to its qualification as reference data for CCAP. Reference data, including surface level observations, must be evaluated in accordance with C-CAP's

minimum detection unit and minimum mapping unit

for the remote data and with the classification system

used to categorize the habitat. The presence of a characteristic species or natural or cultural feature mayor

may not, in itself, establish an area as a particular type

of habitat. A number of questions need to be answered

to conclude the appropriate category of land cover to

assign based on the reference data: Does a characteristic species or feature meet the minimum detection unit

of the remote sensor? What other characteristic species

or features also are present within the minimum mapping unit? and What conclusion can be drawn from the

reference data as to the C-CAP category for a given

location based upon data generalized to the minimum

detection and minimum mapping units?

Attribute Accuracy and Precision _

Attribute accuracy is a measure of the probability that

the land-cover type for any given polygon is properly

identified according to the land-cover scheme. For example, the identification of a substantial polygon of

"High-Intensity Developed" land as "Deciduous Woody

Wetland" is a clear instance of categorical error. If 15%

of all sample polygons for this class are misclassified as

"Deciduous Woody Wetland" and other categories, the

categorical accuracy for the "High-Intensity Developed"

class is 85%. The remote-sensing literature is replete

with procedures for measuring attribute accuracy

Chapter 5: Spatial Data Quality 47

(Congalton, 1991). Generally, these procedures serve

well for current time periods and for relatively small

study areas. Past time periods, however, cannot be field

verified. Conventional procedures also are difficult to

apply to large areas. Accuracy assessment oflarge change

databases is currently infeasible due to the combination of past time period, large area, and the excessive

number of "from" and "to" classes.

Logical Consistency _

Tests for logical consistency should indicate that all row

and column positions in the selected latitude/longitude window contain data. Conversion, integration, and

registration with vector files should indicate that all

positions are consistent with earth coordinates. Attribute

files must be logically consistent. For example, when

examining the change matrix for logical consistency,

very few pixels should change from the urban category

to any other category or from water to any category

other than bare ground or marsh. The range of appropriate tests is left to the judgment and experience of

regional analysts. All attribute classes should be mutually exclusive. The criteria cannot be met if land-use

classes are included along with land-cover classes.

Completeness _

The classification scheme should be comprehensive,

containing all anticipated land covers. The C-CAP

Coastal Land-Cover Classification System is intended to

provide complete coverage, but regional analysts may

find special land covers that are not included. It is the

responsibility of regional project personnel to ensure

that all categories are included and that all pixels are

assigned a category. Regional analysts may use their

discretion in deciding at what classification system level

(0 to 3) they wish to classify. The level need not be the

same for all branches of the classification scheme.

Temporal Accuracy and Precision _

Regional analysts should document the time of data

collection for the primary input data to at least the

precision of year, day, and hour.

Fitness for Use _

C-CAP workshops have involved many discussions with

potential users and have devoted a great deal of effort

to field verification and other types of verification.
48 NOAA Technical Report NMFS 123: Dobson et aI.: Coastal Change Analysis Program _

G-CAP is confident that the databases resulting from

compliance with this document will be of sufficient

quality to support most policy and management activities as well as some regulatory, enforcement, and research activities. The spatial precision and attribute

accuracy are not sufficient for enforcement of individual small permits, but they may be useful in evaluating cumulative impacts in the vicinity of a permit site or

for evaluating individual sites larger than the minimum

mapping unit. In the southeast region of the United

States, a vast majority of the total area of coastal salt

marsh or seagrass habitat that is potentially subject to

direct loss, according to permits submitted, would be

detectable in the C-CAP data (Rivera et aI., 1992). Databases will also be of value in many applications, such as

land-use planning, unrelated to the C-CAP mission.

Ultimately, however, only the user can make the decision regarding fitness for use.

Recommended Accuracy Assessment Test __

The recommended accuracy assessment for C-CAP regional databases is a test based on comparison with

independent field samples. Independence should be

guaranteed through the use of personnel who are not

familiar with and do not have access to the results ofthe

land-cover classification (Congalton, 1991).

Sample Selection and Field Mapping

Regional analysts are responsible for selecting unbiased, statistically meaningful area samples for field verification in the accuracy assessment process.

Accuracy Assessment for Individual Date

Classification of Upland and Wetland Habitat

Data

Accepted procedures in the remote-sensing, cartographic, and geographic literature assess 1) the positional accuracy of identifiable, stable features and 2)

the categorical accuracy at the interior of class polygons. Unfortunately, the methods often neglect the

fuzzy nature ofland cover-categorically (e.g. the class

boundary between grass and marsh), spatially (e.g. the

polygon boundary between water and marsh), temporally, and observationally. Given these limitations, it is

not feasible at this time to provide a quantitative estimate of accuracy with every C-CAP regional database. A

reasonable alternative is to establish data quality objectives (DQO) designed to serve expected uses, establish

and consistently implement a set of protocols and procedures, and manage the data production process to

meet DQO's. C-CAP has conducted three workshops

on accuracy assessment and sponsored two protocol

development projects in the hope of devising new procedures that will work for accuracy assessment of large

land-cover change databases.

Nevertheless, the following material identifies sound

procedures that may be used to obtain unbiased field

information which, in turn, may be statistically evaluated to perform an accuracy assessment for a single

time period. This is a blind field test in which the field

mapping personnel will not see the C-CAP Land-Cover

and Land-Cover-Change Maps until all mapping has

been completed.

Since the field mapping personnel may be unfamiliar with C-CAP, it is advised that they be required to

submit a memorandum stating the design of the field

mapping implementation. Early in the effort, regional

analysts should review the design, in collaboration with

NOAA, and reach agreement with the field mapping

personnel regarding final implementation. Field personnel should be provided copies of the land-cover

classification scheme and should be trained in its use.

The field personnel will be responsible for ensuring

the positional accuracy and precision of each sample

site and each land-cover class boundary within each

site. Field personnel will be responsible for determining physical accessibility and obtaining permission for

legal access to the sample sites.

An early determination will be made regarding who

is responsible for acquiring the best available aerial

photographs, topographic maps, and other collateral

data to ensure an accurate mapping of each sample site

for each time period. These materials will assist in mapping land cover and land-cover change for each site at

1:24,000 scale. Positional accuracy shall comply with

national map accuracy standards. The determination

of class type will be based primarily on field observation. The determination of class areas and boundaries

will be based primarily on aerial photographs. The final

results for each sample polygon will be provided in a

digital form.

After completing field mapping, regional analysts

will compare the generated map for each sample site

with the C-CAP map for the same site. All discrepancies

will be referred back to the field mapping personnel for

a final check. The regional analyst may request a special

examination and may accompany the field mapping

personnel for a final reconciliation of any discrepancies for which field error is suspected.

The regional analysts will compile the results of all

sample polygon comparisons and conduct a statistical

analysis. The results of this analysis will be provided to

the field mapping personnel for review and comment.

At this point the field mapping personnel may also see
the C-CAP land-cover and land-cover-change maps for

the sample quadrangles.

The field mapping personnel will provide a brief

documentation of the field mapping task for inclusion

in the final accuracy assessment report to be prepared

by the regional analysts. The field mapping personnel

must be given an opportunity to comment on the results of the final statistical analysis if they so choose.

Accuracy Assessment for Individual Date

Classification of Water and Submerged Land

Data

Accuracy assessment for submersed habitat is similar to

that for emergent and upland habitat but it should be

noted that data for submersed habitat is intrinsically

vector, not raster. Positional accuracy of polygon borders and attribute accuracy of a point location can both

be assessed. Habitat polygons or areas of potential habitat should be stratified by class and region (water body)

and randomly selected. Additional sample locations

from potential habitat sites (i.e. sites of suitable depth

but apparently devoid of habitat) should be randomly

selected. Verification locations should be identified by

latitude and longitude coordinates and visited in the

field with CPS navigation. The nature of the habitat, if

present, should be documented by inspection or sampling if necessary and the position of the sample or

observation recorded to a CEP of <5 m. The entire

perimeter ofa small polygon or a section, e.g. 0.5 km of

the perimeter of a large polygon, should be positioned

by differentially corrected CPS at a point spacing of 3 to

20 m depending upon the degree of curvature in the

perimeter. Differential CPS provides CEP of <5 m for

single position fixes. C-CAP projects found that single

point differential CPS position fixes did not exceed 10

m (Ferguson, R. L.,J. A. Scope, and L. L. Wood, unpublished data). Habitats at the minimum mapping limit,

i.e. with diameters on the order of 20 m, therefore,

should be located and delineated with multiple position estimates. CPS manufacturers recommend collection of multiple position fixes for a time period of

about 4 min to achieve CEP on the order of 1 or 2 m

with differentially collected CPS. Multiple position fixes

obtained at strategic points around the perimeter of

the smallest mapped polygons would be required to

ensure mapping the polygon rather than generating a

scattered pattern of points.

Chapter 5: Spatial Data Quality 49

Accuracy Assessment for Land-Cover Change

Data

The methodological difficulties of accuracy assessment

for the final change map are significantly greater than

those for a single, current database. Remote-sensing,

cartographic, and geographic literature provide no guidance on techniques for assessing the accuracy of a

change detection map (Lunetta et al., 1991;Jensen and

Narumalani, 1992). Even for a single-time database,

existing procedures are ineffective for past land cover

since the recommended "source of higher accuracy"

cannot include actual field verification. Change detection databases compound the difficulty because they

always include a past time period and a large number of

"from" and "to" categories (potentially the square of

the number of categories for each time). This large

change matrix can make accuracy assessment more

expensive than the original classification and change

detection effort. Furthermore, if the distribution of

error is thoroughly depicted by class and position, the

accuracy database may be as large as the thematic database itself. Even worse, both the distribution of error

and the distribution of actual change tend to concentrate on the same circumstances (for example, polygon

edges and transitional classes, such as marsh and

palustrine forest). C-CAP is sponsoring workshops to

develop improved methods for assessing the accuracy

of change databases and maps.

Comparison and Statistical Analysis

The C-CAP land-cover database and the field-mapped

verification database should be compared and measured to determine differences in attributes for the

base time period and for change that can be recognized in the field. The measures obtained from this

comparison are numerical differences relating to the

sample sites only. It will then be necessary to employ

statistical algorithms to determine what the differences

reveal about the accuracy of the entire regional database. These algorithms should be designed to estimate

the attribute accuracy and positional accuracy ofthe change

database. The necessary algorithms are not currently available in the remote-sensing, cartographic, and geographic

literature (Congalton etal., 1991;Jensen and Narumalani,

1992). GCAP funded two protocol development projects

in an attempt to remedy this deficiency.
50 NOAA Technical Report NMFS 123: Dobson et aI.: Coastal Change Analysis Program _
Chapter 6

ProductAvailability

Digital Product _

Description and Availability

Regional databases generated by C-CAP partICipants

will be provided to the C-CAP project director in accordance with procedures specified in research funding

proposals (RFP) , statements of work (SOW), funding

documents, memoranda of understanding (MOU), or

other applicable documents under which each regional

project is authorized and conducted. The purpose of

this transfer is to place each regional database into a

central archive from which all data will be made available to the public. It will be the responsibility of the

regional participants to document and certify that the

data have been prepared in accordance with C-CAP

protocols. C-CAP may conduct additional data quality

and accuracy assessment tests before final submission

to the archive. The data should adhere to the Spatial

Data Transfer Standard (SDTS) proposed by the Federal Geographic Data Committee and adopted as a

Federal Information Processing Standard (FIPS) (NIST,

1992). Commercial implementations are not currently

available but will be marketed by software vendors in

the near future. At a minimum, the standard should be

considered a near-term goal with one or more de facto

standards-such as DLG, ARC, DXF (geometry only),

and ERDAS-accepted in the interim. Lineage, quality,

and format information should be transmitted with the

data disseminated to users.

The digital product for each region will be a change

matrix of land cover by class for coastal submersed

habitats, emergent coastal wetlands, and adjacent uplands. The only regional database currently completed

and available to the public is the Chesapeake Bay LandCover Change Database for 1984 and 1988-89.

Digital products are available from

National Oceanographic Data Center (NODC)

1825 Connecticut Avenue, NW

Washington, DC 20235

(202) 606-4549

When more C-CAP regions have been completed, an

on-line electronic catalog will be created for users to

browse.

Digital Product Redistribution Restrictions-The product file will contain statements defining the responsibility of the user in regard to C-CAP data. The user must

acknowledge NOAA as the source of the product whenever data are redistributed and must provide an accounting to NOAA stating who received copies of the

database. If the redistributed data are modified, an

accompanying disclaimer must acknowledge NOAA as

the source of the original data, must state the nature of

the modifications, and must relieve NOAA of responsibility for the modified data.

Liability Disclaimer-The user of C-CAP data will hold

the U.S. Government and its agencies, officers, and

employees harmless against any and all liability, including costs and expenses, which may result from or arise

out of any use of the data.

Digital Product Fonnat and Contents

The goal is to exchange the digital products in the

Federal Spatial Data Transfer Standard (SDTS) format

for raster data. Until the SDTS raster standard is available, the initial data products may not adhere to the

final standard.

Product Identifiers and Characteristics-Each data

transmittal from NODC to the user will be accompanied by documentation provided by the data producers

stating the following:

• Geographical coverage in UTM coordinates

• UTM zone number

• Computer and operating systems used to create the

file

• Precision of the computer system (e.g. 16-bit, 32-bit)

• Software used to create the file (e.g. ERDAS Imagine

8.2, ARC-Info 7.0)

• Type of file (ASCII, binary, ERDAS.IMG, ARC-Info

coverage)

• Description and format of header file

• Data record format

• Number of classes

• Class names

• Number of pixels by class and by file, including the

null class (i.e. no data in pixel)

The header file for each database will repeat the quantitative portion of this information.

Product Data Quality-The documentation will describe

the lineage, date and source of data (i.e. instrument,

5]
52 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program _

and platform), resolution, positional accuracy and precision, attribute accuracy and precision, logical consistency, completeness, and temporal accuracy and precision of the data being transferred.

Guidance Version-The product file will contain a field

indicating the "C-CAP Guidance for Regional Implementation" version used to produce the image product.

Transfer Verification Parameters-Each C-CAP product will contain unique verification parameters for the

image raster data and a confirmation algorithm that

can be applied to the image values. The algorithm tests

whether the database received by the user is equivalent

to the original. If an image has been damaged or modified, application of the algorithm will produce results

different from the master values in the original data file

maintained at the NODC. The occurrence of each class

value (including the no data class) can be tabulated

and compared with the original summary statistics.

Derived Data and QuaIity-The data product may include derived data, such as tabular summaries of land

cover and accuracy assessments for specified areas (e.g.

counties, watersheds, wildlife management areas). Data

are defined as "derived data" if they cannot be used to

reconstitute the C-CAP data at the pixel level.

Digital Data Values--The data values in raster format are

numerical values representing the land-cover categories

described in this document (see 'The GCAP Coastal LandCover Classification System"). A lookup table or other

accompanying statement will define the relationship between the stored values and the land-cover categories.

Digital Product Medium

Digital data products are available on 9-track magnetic

tapes and CD-ROM. As the completed coverage expands, these data may be available on other magnetic

and optical media.

Digital Product Cost

The organization conducting each regional project will

receive one copy of the final database as distributed by

NODC at no cost. All other users will be charged the

standard NODC reproduction fee.

Digital Product Ancillary Documentation

General Protocol-A copy of the "C-CAP Guidance for

Regional Implementation" for the version used to produce the product will be available from the NODC in

digital form for the cost of reproduction.

Specific Digital Products Documentation-Regional

analysts may provide ancillary documentation for dissemination by NODC if both the documentation and

the corresponding database are provided to NODC in a

standard digital format.

Hardcopy Products

Upland and Wetland Habitats

Hardcopy maps of uplands and wetlands for selected

areas will be produced for informational purposes, primarily to illustrate database content. At present there

are no plans to publish hardcopy maps for general sale

and distribution to the public. Requests for informational maps will be considered on a case-by-case basis.

Individuals and organizations should make their requests in writing to

Dr. Ford A Cross, Director

Beaufort Laboratory

NOAA/National Marine Fisheries Service

101 Pivers Island Road

Beaufort, NC 28516-9722

Organizations wishing to serve as value-added vendors

of hardcopy products derived from C-CAP data should

write to this address.

Submersed Habitats

Hardcopy maps are routinely produced as part of the

submersed habitat change analysis because the techniques are currently based on aerial photographic interpretation in analog form. A limited number of publication-quality maps are reproduced at the completion

of each regional task. Individuals and organizations

may request copies on a "first come, first served" basis

by writing to Dr. Ford A Cross at the address listed

above.
Chapter 7

Users and Information Needs

Table 6 presents a matrix developed by participants in

the regional concerns breakout group at the C-CAP

Rhode Island Workshop (see Appendix 4). The matrix

matches potential uses with C-CAP products and indicates the relative value of the product according to use.

Interested parties are encouraged to modify this table

from their own regional perspective and submit their

modifications to C-CAP. This will enable C-CAP to generate matrices for each region or a single national

matrix that will help ensure that C-CAP products meet

the broadest range of user needs possible.

Table 6

The potential utility of C-CAP coastal land-cover information; abbreviations: H=high; M=moderate; L=low.

Poten tial uses Map data Digital data Tables Physical boundaries Error estimation

Technical review M M L L L

Decisions M M L L L

Modeling L H H H H

Interstate coordination H H H H H

Enforcement L L L L L

Hazard response H H L L L

Policy L L H H H

Management and planning H H H H H

Education H H M M H

Citizens H L L L L

Commerce H H L L H

Research H H M M H

53
54 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program
ChapterS

Regional Participation

Purpose _

NOAA C-CAP will endeavor to cooperate with all ongoing wetlands mapping and change-detection programs

at the Federal, State, and regional levels. Priorities for

NOAA funding allocation will be

1) biogeographic diversity,

2) joint funding efforts, and

3) existing field-based studies.

Other considerations will include: .

1) areas of rapid development,

2) areas disturbed by major storms or other natural

events, and

3) areas disturbed by hazardous technologies (e.g. oil

spills) .

Regional Project Summaries _

St. Croix River Estuary (Border of Maine

and New Brunswick, Canada)

This is a cooperative effort involving the U.S. Fish and

Wildlife Service (USFWS), the Gulf of Maine Program,

and Environment Canada. A change detection analysis

was performed using TM imagery from 1985 and 1992.

The image processing and change detection analysis

was performed at Oak Ridge National Library (ORNL).

Five field verification exercises were carried out in conjunction with USFWS personnel. The C-CAP change

detection product has been completed and submitted

to NODe.

Coastal Massachusetts

This is a cooperative submerged land effort involving

the Massachusetts Department of Environmental Protection (DEP) Wetlands Conservancy Program. A pilot

project focusing on training Massachusetts personnel

in current SRV mapping techniques and adapting the

C-CAP protocol for use in Massachusetts was conducted

in the spring and summer of 1993. Photo interpretation and mapping are being performed by DEP personnel with technical assistance from the NMFS Beaufort

Laboratory. The SRV polygons will be added to wetland

data on coastal orthophoto maps.

Universities of Connecticut and Rhode Island

Faculty members of the Universities of Connecticut

and Rhode Island worked cooperatively to examine

several issues concerning coastal land-cover classification and change detection in the Northeast (Hurd et

aI., 1992). In the first part of the project, detailed GIS

data on coastal wetlands in Rhode Island derived from

aerial photography were used to establish coastal wetlands signatures for input to a digital classification of

Landsat TM imagery. This work is crucial in assessing

the extent to which an existing coastal wetlands dataset

(e.g. NWI digital data) can be used to establish a classification for a larger TM dataset. Other areas of importance to C-CAP include assessments of 1) classification

approaches bestsuited to characterize wetlands in southern New England; 2) techniques for monitoring coastal

wetlands change in the Northeast using several change

detection techniques to look at TM imagery from the

same location for 1988 and 1982; and 3) multistate,

multi-institutional collaboration in southern New England.

University of Delaware

University of Delaware faculty members at the Center

for Remote Sensing played a lead role in developing

the interagency land-cover classification system used by

C-CAP (Klemas et aI., 1993). The system was developed

during joint meetings with representatives from key

government agencies including NOAA, USGS, USEPA,

USFWS, and COE.

Currently, University of Delaware faculty members

are developing remote-sensing and field techniques for

measuring indicators of wetland condition and functional health over large wetland areas. An overview of

wetland health assessment techniques has been prepared, with special emphasis on wetland condition and

functional health indicators that can be monitored with

remote sensors (Patience and Klemas, 1993). The overview report contains a comprehensive literature search

and chapters describing the techniques and their status. A joint study has been initiated with investigators

from Louisiana State University and USEPA to work on

impaired and healthy pilot test sites in Louisiana

marshes. Field data, including measures of biomass,

soils, hydrology, chemistry, biology, and light reflectance, are being correlated with Landsat TM imagery to

assess biomass and stress indicators over large areas

with the help of modified models and techniques devel55
56 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program _

oped during previous studies. The data derived from

these investigations are crucial to C-CAP for early detection of functional change in habitat.

Oak Ridge Nationa! Laboratory

The C-CAP prototype and first regional project was

conducted in the Chesapeake Bay region by ORNL

(Dobson and Bright, 1991, 1992, and 1993). In the first

phase, a land-cover classification was completed for a

four-scene area using MSS data for 24-25 October 1978,

and change detection was completed for portions of a

scene in the vicinity of Metomkin Inlet, VA, using MSS

data for 12 September 1974, MSS data for 24 October

1978, and TM data for 18 November 1982. The Virginia

Institute of Marine Sciences (VIMS) was contracted to

assist in field verification and training-sample identification. The results of this prototype served as a proofof-principle for large-area change analysis, and the methods and techniques served as the basis for the draft

protocol presented at the first protocol development

workshop.

This initial prototype and proof-of-principle was conducted at the Oak Ridge Geographics Laboratory. All

cartographic and geographic information processing

was conducted by ORNL personnel using Oak Ridge

Geographics software. Tentative land-cover classes were

determined on the basis ofsupervised training samples

in areas of known land cover. The tentative classes were

checked with information available from other sources

such as 1:24,000 USGS topographic maps and wetlands

inventories (NWI and county marsh inventories). Investigators visited the area on 4-6 November 1985 for

field verification ofthe tentative classes and for identification of additional training samples in the Wachapreague, Metomkin Inlet, and Saxis areas of Virginia.

Land-cover classes were determined through iterative

refinement of supervised training samples. Investigators visited the area in August 1986 for field verification

of final land-cover classes in the York River estuary of

Virginia and the Tangier Island and Blackwater National Wildlife Refuge areas of Maryland. Finally, the

entire dataset was compared digitally on a cell-by-cell

basis to land-cover data from the USGS Land Use Data

Analysis (LUDA) database in order to resolve certain

classes.

In the second phase, the change detection was extended to cover the full four-scene area by using TM

data for 27 August 1984, 21 September 1984, 3 November 1988, and 10 October 1989. The final product

consisted of a classified land-cover change matrix database for the entire Chesapeake Bay area. Regional maps

at 1:500,000 scale and numerous local area maps covering individual USGS 1:100,000 and 1:24,000 quadrangle

areas were prepared to illustrate static land cover for

1984 and 1988/1989 and land-cover change between

these dates. The final database was delivered to ODC

and is available on CD-ROM for purchase by the public.

This analysis was conducted on graphics workstations

employing ERDAS image processing software, ERDAS

raster GIS software, and Oak Ridge Geographics GIS

software. Processing and verification techniques were

similar to those employed in the initial MSS/TM analysis. Investigators revisited the area in the spring and

summer of 1991 and participated in the Maryland field

verification workshop (See AppendiX 4). Finally, the

database was modified to accommodate the new C-CAP

land-cover classification scheme and to incorporate suggestions and corrections resulting from the Maryland

workshop. The protocols developed for the Chesapeake

Bay project have been incorporated into the C-CAP

protocols. Thus the final C-CAP Chesapeake Bay LandCover Change Database complies with this document.

Virginia Institute of Marine Sciences

The Virginia Institute of Marine Sciences has been

conducting photographic mapping of submersed vegetation in the en tire Chesapeake Bay beginning in 1978

and annually since 1984 (Orth et aI., 1990 and 1991).

Although not funded by C-CAP, this important work is

considered a regional C-CAP project because of the

voluntary collaboration among principal investigators.

Methodology for the Chesapeake Bay project was a

starting point for the C-CAP protocol. Data from Chesapeake Bay have been provided to C-CAP to attempt to

overlay it with the land-cover data for Chesapeake Bay

generated by ORNL. Historically, Chesapeake Bay has

suffered a dramatic decline in SRV and associated fisheries. From 1984 to 1990, however, SRV habitat increased from 15,400 to 24,313 ha.

North Carolina State University

A land-cover classification project was conducted by the

Computer Graphics Center at orth Carolina State

University (NCSU) prior to the University's involvement in C-CAP. Coincidentally, the four-scene area analyzed by CSU was contiguous with the four-scene area

analyzed by ORNL in the Chesapeake Bay project. Scene

dates are contemporaneous with the 1988 Chesapeake

Bay scenes. Faculty members ofNCSU cooperated with

ORNL research staff to investigate the potential for

merging portions ofthese two independently conducted

land-cover classifications based on TM digital data. The

goal was to merge the project areas and form a seamless

regional land-cover classification from the Chesapeake
Bay to Dare County, N.C. One of the major problems

investigated was the development of a classification

scheme adaptable to both areas. This research was a

crucial test of the C-CAP concept of regional compatibility among neighboring databases developed by different organizations.

Beaufort Laboratory, National Marine Fisheries

Service

The project in North Carolina is researching protocol

for conducting and verifying change detection in SRV,

including seagrasses and low-salinity-tolerant grasses and

forbs. Simultaneously the project is completing the first

comprehensive inventory of such habitat in North

Carolina. The project wasjointly funded by the Albemarle

Pamlico Program of EPA's National Estuary Program.

Aerial photography to delineate SRV was first commissioned in 1985 (Bogue, Back, and southern Core

Sounds). The rest of the aerial photography for all

areas of potential SRV between Bogue Inlet and the

Virginia border were taken between 1988 and 1992. All

photography was subcontracted to the NOAA Photogrammetry Unit and acquired at scales of 1:12,000,

1:20,000, 1:24,000, or 1:50,000. The smallest scale photography provided a bridge between parallel flightlines

(at 1:24,000) in eastern Pamlico Sound, where minor

dimensions of some habitat areas exceeded 3 nmi.

All aerial photography from 1985 through 1991 was

interpreted and most was compiled on base maps. The

interpretation was supported by extensive systematic

and directed sampling throughout the study area. At

the time of photography, stations were selected by

statified random sampling, visited, and sampled for

species of submersed plants and ancillary data (sediment particle size and organic content, water depth,

salinity, temperature, and Secchi depth and the presence of exposed peat deposits, shells, algae, or debris

which might confuse signature identification). Alilocations of known and potential habitat, water <6 ft MLLW

on nautical charts, were sampled by positioning a rectangular matrix of points over the nautical chart. Station

positions approximately two scaled nautical miles apart

were extracted from the chart and visited with the aid

of LORAN C, now, preferably, GPS. Mter receipt and

preliminary interpretation of the photographs, field

surveys were conducted to verify the range of habitat

signatures and confirm false signatures.

Photographs initially interpreted monoscopically are

now interpreted stereoscopically. Polygons are traced

on stable film at the photograph scale, rectified, and

transferred to base maps with a zoom transfer scope.

Base maps are NOAA shoreline manuscripts, if available, or USGS 7.5' topographic series maps, if consisChapter 8: Regional Participation 57

tent with current photographs, on stable media. The

topographic maps are virtually complete for orth Carolina (with the exception of Currituck Sound) but are

out of date (mid-forties photography with occasional

photo-revision for cultural features dated in the seventies or eighties). NOAA shoreline manuscripts are based

on 1988 or more recent photography but are not complete for North Carolina. Necessary photographs for

construction of manuscripts to complete shorelines in

North Carolina were obtained by NOAA Photogrammetry Branch in 1988-92.

Habitat polygons were coordinate digitized by State

of orth Carolina personnel and incorporated into a

statewide ARC-Info database referenced to the State

Plane Coordinate System.

Three two-color charts ofseagrass habitat at a scale of

1:36,000 and measuring about 3 x 4 ft were published

and are available at no cost.

University of South Carolina

University ofSouth Carolina faculty members performed

a detailed investigation of the geographic area centered on two 7.5' U.S. Geological Survey quadrangles

(quads) along South Carolina's coastal plain. These

quads, representative of many other quads in coastal

South Carolina, provide an opportunity to examine two

very different wetland communities. One quad is directly on the coast and contains extensive Spartina

alterniflora marsh, developed and undeveloped beach

front, and a mature maritime forest. The other quad is

40 river miles inland and contains significant inland

freshwater wetlands with extensive bottomland hardwoods. The project identified optimum parameters for

conducting accurate coastal change detection including, but not limited to, 1) an optimum wetlands classification scheme; 2) an optimum type of remotely sensed

data; 3) optimum digital image processing pattern recognition algorithms for C-CAP land-cover classification;

4) the applicability and utility of including ancillary

data (e.g. NWI digital data) in the classification process; 5) optimum change detection algorithm logic;

and 6) detailed error evaluation. Results were reported

in Jensen et al. (1993a).

State of Florida, Department of Environmental

Protection

State ofFlorida personnel are mapping submersed habitat in the Florida Keys, Florida Bay, Biscayne Bay, and

Tampa Bay. Photography was conducted in the winter

ofl991 and 1992 by the NOAA Photogrammetry Branch.

The effort in the Keys is cooperative with NOAA's Ma-
58 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program _

rine Sanctuary Program. The Keys were photographed

at 1:48,000 because of cost considerations and will demonstrate resolution of signatures of submersed habitat

at a scale smaller than that acceptable with the current

GCAP protocol. The motion-compensating camera used

in this case should enhance resolution. Photography in

Florida Bay is being interpreted and ground verified in

fiscal year 1993. C-CAP is partially funding the cost of

photography and interpretation.

Texas Parks and Wildlife Department

This agency is currently processing TM imagery, as per

the steps outlined in this document, for the entire

Texas coast with technical assistance from ORNL. Two

scenes in the Galveston Bay area for 2 December 1988

have already been classified, and a change detection

analysis was performed, comparing a November 1992

scene with the southernmost of the 1988 scenes. Classification has been aided by an abundance of ground

reference data as well as digital NWI data that are

available for most of the Texas coast.

Columbia River, Tillamook Bay, and Willapa Bay

(Oregon and Washington)

This is a cooperative effort involving cooperating agencies within the Columbia River Estuary Study Taskforce

(CREST), NMFS's Point Adams Field Station, Hammond, Oreg., and Washington State personnel. Imagery for September 1989 and 1992 has been obtained

and a change-detection analysis is being performed by

CREST and its cooperators in conjunction with ORNL.

Several field verification exercises have been performed,

and a final change detection project is expected in fall

1994. This information should be useful to a variety of

managers that are presently dealing with severely stressed

salmon stocks throughout the study area.

The Hubbard Glacier and Russell Fjord, Alaska

This is a cooperative effort involving NMFS's Auke Bay

Laboratory. A 1986 image is the only image available at

this time that meets C-CAP cloud cover specifications.

The implications of the future movements of the

Hubbard Glacier make this project unique. During 1986

the Hubbard Glacier blocked off the mouth of the

Russell ~jord and created the world's largest glacierformed lake. Within months, rising water levels caused

the glacier to burst, restoring tidal flow to the Fjord.

Glacier experts predict that there is a 90% chance that

the Hubbard Glacier will block off the mouth of the

Russell Fjord again within the next 10 years. Because

the portion of the glacier that will block off the Fjord is

bigger than the one in 1986, it is predicted that the

glacier will not burst. This would cause the rising waters

to exit the Fiord at the end opposite the glacier, flowing

into Old Situk Creek. This may significantly affect a

very important salmon fishery, crucial to the inhabitants of nearby Yakutat, Alaska. C-CAP is presently looking for another image to perform a change analysis and

provide more baseline information for future changedetection activities, should the glacier again close off

Russell ~jord. The 1986 image has been processed by

ORNL and the data has been submitted to NODC.
Acknowledgments

Many individuals contributed to this effort at regional

C-CAP workshops, at meetings, and through private

communications. We wish to thank all of them, especially the following who have helped us continuously

over several years to improve this classification system:

Michael DeMers, New Mexico State University; Francis

Golet, University of Rhode Island; Steven Hoffer,

Lockheed Engineering and Sciences Co.; Michael

Hodgson, Oak Ridge National Laboratory; Jimmy

Johnston, National Biological Survey; Bill Wilen, U.S.

Fish and Wildlife Service; Donley Kisner, Bionetics Corporation; Richard Kleckner, Kathy Lins, Keven Roth,

and Peg Rawson, U.S. Geological Survey; Mark Laustrup,

National Biological Survey; Doug Norton, U.S. Environmental Protection Agency; Robert Peplies, East Tennessee State University; and Warren Pulich, Texas Department of Parks and Wildlife. Brent Moll of Oak

Ridge National Laboratory assisted in the technical revision of a preliminary draft. The Chesapeake bay prototype project was funded in part by NOAA's Chesapeake Bay Program. The development of this document was funded by NOAA's Coastal Ocean Program.

59
60 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program
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Appendix 1

U.S. Geological Suroey Land-Cover Classification Schemefor Remote Sensor Data

Appendix Table I

Summary of Level I and Level II elements of the U.S. Geological Survey "Land Use

and Land Cover Classification System for Use with Remote Sensor Data" (Anderson et

aI., 1976; USGS, 1992).

Level Land-use and land-cover class

Urban or Built-Up Land

11 Residential

12 Commercial and Services

13 Industrial

14 Transportation, Communications, and Utilities

15 Industrial and Commercial Complexes

16 Mixed Urban or Built-up

17 Other Urban or Built-up Land

2 Agricullural Land

21 Cropland and Pasture

22 Orchards, Groves, Vineyards, Nurseries, and Ornamental Horticultural Areas

23 Confined Feeding Operations

24 Other Agricultural Land

3 Rangeland

31 Herbaceous Rangeland

32 Shrub-Brush land Rangeland

33 Mixed Rangeland

4 Forest Land

41 Deciduous Forest Land

42 Evergreen Forest Land

43 Mixed Forest Land

5 Water

51 Streams and Canals

52 Lakes

53 Reservoirs

54 Bays and Estuaries

6 Wetland

61 Forested Wetland

61 Nonforested Wetland

7 Barren Land

71 Dry Sal t Flats

72 Beaches

73 Sandy Areas Other than Beaches

74 Bare Exposed Rock

75 Strip Mines, Quarries, and Gravel Pits

76 Transitional Areas

77 Mixed Barren Land

8 Tundra

81 Shrub and Brush Tundra

82 Herbaceous Tundra

83 Bare Ground Tundra

84 Wet Tundra

85 Mixed Tundra

9 Perennial Snow or Ice

91 Perennial Snowfields

92 Glaciers

65
66 NOAA Technical Report NMFS 123: Dobson et aI.: Coastal Change Analysis Program _
Appendix 2

U.S. Fish and Wildlife Service Wetland Classificatian Scheme

Appendix Table 2

Summary ofthe classification hierarchy ofwetlands and deepwater habitats, showing systems, subsystems, and classes ofthe U.S.

Fish and Wildlife Service "Classification ofWetlands and Deepwater Habitats of the United States" (Cowardin et a!., 1979).

Rock Bottom

Unconsolidated Bottom

Aquatic Bed

Rocky Shore

Unconsolidated Shore

Emergent Wetland

Rock Bottom

Unconsolidated Bottom

AquatiC Bed

Rocky Shore

Unconsolidated Shore

E Rock Bottom

Unconsolidated Bottom

Aquatic Bed

Reef

E Aquatic Bed

Reef

Rocky Shore

Unconsolidated shore

E Rock Bottom

Unconsolidated Shore

Aquatic Bed

Reef

- Aquatic Bed

- Reef

- Streambed

- Rocky Shore

- Unconsolidated Shore

- Emergent Wetland

- Scrub-Shrub Wetland

- Forested Wetland

- Rock Bottom

- Unconsolidated Bottom

- Aquatic Bed

Streambed

- Rocky Shore

- Unconsolidated Shore

- Emergent Wetland

Tidal

-[

Subtidal

,...- Marine

Intertidal

Subsystem

Jsubtidal

f-- Estuarine I

- Intertidal

iii

«

I

a:

w

i

ltJ- g I- Lower Perennial --§ f-- Riverine -

(fJ

o

2

3:

Intermittent Streambed

-{

Limnetic

r-- Lacustrine

Littoral

-----IE Rock Bottom

Unconsolidated Bottom

Aquatic Bed

Rock Bottom

Unconsolidated Bottom

Aquatic Bed

Rocky Shore

Unconsolidated Shore

Emergent Wetland

'-- Palustrine

Rock Bottom

Unconsolidated Bottom

Aquatic Bed

Unconsolidated Shore

Moss-Lichen Wetland

Emergent Wetland

Scrub-Shrub Wetland

Forested Wetland

67
68 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program _
Appendix 3

C-CAP Coastal Land-Cover Classification System Definitions

The C-CAP Coastal Land-Cover Classification System, described in Chapter 2 (Table 2), was developed to meet C-CAP

requirements (Klemas et aI., 1993). It is intended to be compatible with other classification systems to facilitate the exchange of data among related programs, especially USGS,

WI, and EPA's EMAP. Those classes underlined in Table 2

are of greatest importance to the C-CAP program and most

can be detected by satellite sensors such as TM and SPOT.

Categories of the C-CAP Classification System

The system starts with three superclasses: 1.0-Uplands, 2.0-

Wetlands, and 3.0-Water and Submerged Land. These superclasses are subdivided into classes and subclasses at the second and third levels, respectively. Most of the classes and

subclasses in the C-CAP system are taken from Anderson et

al. (1976), Cowardin et al. (1979), and USGS (1992). However, a few definitions have been modified to resolve conflicts

between the Anderson et al. and Cowardin et al. categories,

and some finer categories have been added (e.g. High-Intensity Developed Land and Low-Intensity Developed Land).

t.O-Upland _

The superclass 1.0-Upland is divided into seven classes: 1.1-

Developed Land, 1.2-Cultivated Land, 1.3-Grassland, 1.4-

Woody Land, 1.5-Bare Land, 1.6-Tundra, and 1.7-Snow/Ice.

1.I-Developed Land

This class is composed of areas of intensive anthropogenic

use. Much of the land is covered by structures and impervious surfaces. Anderson et al. (1976) called these areas "Urban or Built-up Land" although the definition clearly included suburban and rural areas:

"Included in this category are cities; towns; villages; strip

developments along highways; transportation, power, and

communications facilities; and areas such as those occupied by mills, shopping centers, industrial and commercial complexes, and institutions that may, in some instances, be isolated from urban areas."

To clarify this apparent contradiction, C-CAP specifies all

constructed surfaces regardless ofland use. Developed Lands

are divided into two Level II groups: 1.11-High Intensity and

1. 12-Low Intensity.

l.ll-High Intensity (Solid Cover)-High-Intensity Developed

Land includes heavily built-up urban centers and large constructed surfaces in suburban and rural areas with a variety of

different land uses. The High-Intensity category contains

areas in which a significant land area is covered by concrete

and asphalt or other constructed materials. Vegetation, if

present, occupies <20% of the landscape. Examples of such

areas include apartment buildings, skyscrapers, shopping centers, factories, industrial complexes, large barns, airport runways, and interstate highways.

l.12-Low Intensity (Mixed Pixels)-Low-Intensity Developed

Land includes areas with a mixture of constructed materials

(e.g. roofing, metal, concrete, asphalt) and vegetation or

other cover. Constructed materials account for 50-79% of

total area. These areas commonly include single-family housing areas, especially in suburban neighborhoods, but may include scattered surfaces associated with all types of land use. As

the percentage of constructed material cover decreases, this

category grades into Cultivated, Grassland, Woody, and other

land-<:over classes. A large building surrounded by several acres

of grass, for example, might appear as one or more pixels of

High-Intensity Developed Land, one or more pixels of LowIntensity Developed Land, and many pixels of Grassland.

1.2-Cultivated Land

Agricultural Land in the Anderson et al. (1976) classification

system was defined as

"... land used primarily for production of food and

fiber. On high-altitude imagery, the chief indications of

agricultural activity will be distinctive geometric field

and road patterns on the landscape and the traces produced by livestock or mechanized equipment."

C-CAP renamed this class "Cultivated Land" to emphasize

land cover rather than land use. This category contains areas

that have been planted, tilled, or harvested. Pastures and

hayfields that are in a state of tilling or planting are also

included. Otherwise, pasture or hayfield with well-established

grasses are placed in the Grassland category (3.0). The Cultivated Land class is divided into three subclasses: 1.21-0rchards/

Groves/Nurseries, 1.22-Vines/Bushes, and 1.23-Cropland.

l.21-0rchards/Groves/Nurseries-This category includes

woody-stemmed crops that are dominated by single-stemmed,

woody vegetation that is unbranched 0.6 to 0.9 m (2 to 3 ft)

above the ground, having a height >3 m (10 ft). Some examples of the crops included are apple and cherry orchards,

and palm date groves. Anderson et al. (1976) states

"Orchards and groves produce the various fruits and nut

crops. Tree nurseries that provide seedlings for plantation forestry also are included."

Isolated fruit trees and other orchards substantially smaller

than the areal unit of observation are not included. Pine

69
70 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program

plantations are not included in this class; they are assigned to

the Forest, Evergreen, Woody category (1.421).

1.22-Vines/Bushes-Vines/Bushes refers to areas of multiple

stemmed, woody-stemmed crops that are shrubs <3 m (10 ft)

in height. Examples of crops included in this category are

blueberries, grapes, and other vines and bushes producing

various fruit or nut crops (Anderson et a!., 1976). This group

has a different spectral signature than other Cultivated Land

groups because of the size and spatial configuration of the

vines, shrubs, and bushes.

1.23-Cropland-This class of Cultivated Land refers to any

crop type that is planted on a regular basis. Crops may be

planted annually in the same field year after year or on a

rotating schedule. Anderson et a!. (1976) states

"The several components ofCropland now used for agricultural statistics include: cropland harvested, including

bush fruits; cultivated summer-fallow and idle cropland,

land on which crop failure occurs; and cropland in soilimprovement grasses and legumes."

C-CAP has modified this category to emphasize the instantaneous state of the land at the time of observation. Hence, for

example, Cultivated Land in a five-year rotation scheme will

be categorized as Cropland for the four years the land is

tilled and as Grassland for the one year the land is fallow and

covered by grasses. A fallow period ofseveral years may result

in a transition from Cropland to Grassland to Scrub/Shrub

and back to Cropland.

Nurseries and horticultural areas (which include floriculture,

seed, and sod areas) used perennially for those purposes are

included in this category ifwoody-stemmed plants are not grown.

Greenhouses normally fall in the Developed Land category.

1.3-Grassland

The Grassland category includes lands covered by natural

and managed herbaceous cover. Historically, grassland has

been defined as land where the potential natural vegetation

is predominantly grasses, grasslike plants, and forbs, and

where natural herbivory was an important influence in its

pre-civilization state. Anderson et a!. (1976) state

"Some grasslands have been or may be seeded to introduce or domesticate plant species. The Grassland (Herbaceous) category contains both managed and

unmanaged or natural herbaceous cover. The Grassland

(Herbaceous) category can be found in every state in the

United States along with Canada and Mexico."

The GCAP category includes lands with herbaceous cover at the

time of observation regardless of origin or potential. Pastures,

hayfields, and natural rangelands are included. Also included

are lawns and other managed grassy areas such as parks, cemeteries, golf courses, road rights-of-way, and other herbaceouscovered, landscaped areas. The Grassland class is divided into

two subclasses: 1.31-Unmanaged and 1.32-Managed.

1.31-Unmanaged-The Unmanaged, Herbaceous, Grasslands

category refers to herbaceous cover that is allowed to grow

naturally and is not fertilized, cut, or tilled and planted

regularly. This category includes, but is not limited to, the

Anderson et a!. (1976) "Herbaceous Rangeland" category:

"the tall grass (or true prairie), short grass, bunch grass

or palouse grass, and desert grass regions...Bunch grass

and desert grass are found in many locations, representing transitional situations to desert shrub. Typical occurrences of grasslands include such species as the various

bluestems (Andropogon) , grama grasses (Bouteloua) , wheatgrasses (Agropyron), needle-grasses (Stipa) , and fescues

(Festuca). This category also includes the palmetto prairie areas of south-central Florida, which consist mainly

of dense stands of medium length and tall grasses such

as wiregrass (Aristida strieta) and saw palmettos (Seronoa

ripens), in terspersed occasional palms (Sabal palmetto),

and shrubs."

Unmanaged grasslands are found throughout the United

States, often as a transitional phase in the regrowth of abandoned Cropland, c1earcut Woody Land, or land affected by

natural disturbance.

1.32-Managed-These grasslands are maintained by human

activity and include lawns, golf courses, pastures, hayfields,

and other areas of grassland in which seeding, fertilization,

or irrigation enhance biomass productivity. This category

may contain vegetation that grows as fallow ifvigorous growth

persists due to the residual effects of management practices

in the nonfallow state.

lA-Woody Land

The Woody Land class includes any species with an aerial

stem that persists for more than one season. The class is

divided into three subclasses: 1.41-Deciduous, 1.42-Evergreen,

and 1.43-Mixed.

1.41-Deciduous-The Deciduous Woody subclass includes all

forest and shrub areas having a predominance of trees and

shrubs that lose their leaves or needles at the end ofthe frostfree season or at the beginning of a dry season. Areas in this

category are composed of greater than two-thirds deciduous

trees and shrubs. The Deciduous Woody category can be

divided into two groups: 1.411-Forest and 1.412-Scrub/Shrub.

1.411-Forest-Deciduous Forest includes areas dominated

by single stemmed, woody vegetation unbranched 2-3 ft above

the ground having a height ~6 m (20 ft). Forest Deciduous

Woody areas have a tree-crown areal density (crown closure

percentage) of~lO percent, are stocked with trees capable of

producing timber or other wood products, and exert an

influence on the climate or water regime. In most parts ofthe

United States, these would be the hardwoods such as oak,

Quercus, maple, Aeer, or hickory, Carya, and the "soft" hardwoods, such as aspen, Populus tremuloides. Tropical hardwoods

are included in the Evergreen Forest (Woody Land) category

(1.421). Deciduous forest types characteristic of Wetland,
Appendix 3: C-CAP Land-Cover Classification Definitions 7l

such as tupelo, Nyssa, or cottonwoods, Populus deltoides, are

not included in this category.

1.412-Scrub/Shrub-Deciduous Scrub/Shrub includes all

areas having a predominance of shrub that lose their leaves

or needles at the end of the frost-free season or at the beginning of the dry season (Anderson et aI., 1976). This category

contains vegetation that is <6 m (20 ft) in height. The species

include true shrubs, young trees, and trees or shrubs that are

small or stunted because of environmental conditions. True

shrubs are those woody-stemmed species that exhibit several

erect, spreading, or prostrate stems and a general bushy

appearance. Shrub Lands may represent a successional stage

leading to forests or they may be relatively stable communities. Forest regrowth composed ofyoung trees <6 m tall is also

included in this category.

1.42-Evergreen-The Evergreen Woody subclass contains forests and shrubs that do not lose their leaves or needles at the

end of a frost-free season or at the beginning of a dry season.

The Evergreen Woody category is subdivided into two additional categories: 1.421-Forest and 1.422-Scrub/Shrub.

1.421-Forest-Evergreen Forest includes areas in which

~67% of the trees remain green throughout the year. Both

coniferous and broad-leaved evergreens are included in this

category. Coniferous evergreens predominate except in tropical regions where broad-leaved evergreens are indigenous.

Coniferous evergreens, often called softwoods, include such

eastern species as the longleaf pine, Pinus palustris, slash

pine, P. ellioti, shortleaf pine, P. echinata, loblolly pine, P.

taeda, and other southern yellow pines; various spruces, Picea,

and balsam fir, Abies balsamea; white pine, P. strobus, red pine,

P. resinosa, and jack pine, P. banksiana; and hemlock, Tsuga

canadensis; and such western species as Douglas fir, Pseudotsuga

menziesii, redwood, Sequoia sempervirens, ponderosa pine,

P. monticola, Sitka spruce, P. sitchensis, Engelmann spruce,

P. engelmanni, western redcedar, Thuja plicata, and western

hemlock, Tsuga heterophylla. Evergreen species commonly associated with Wetland, such as tamarack, Larix laricina, or black

spruce, P. mariana, are not included in this category.

1.422-Scrub/Shrub-Evergreen Scrub/Shrub includes areas in which ~67% of the shrubs remain green throughout

the year. Anderson et al. (1976) states

"Both coniferous and broad-leaved evergreens are included in this category. The typical Shrub Lands are

found in those arid and semiarid regions characterized

by such xerophytic vegetative types with woody stems as

big sagebrush, Artemisia tridentata, shadscale, Atriplex

confertifolia, greasewood, Sarcobatus vermiculatus, and

creosotebush, Larrea divaricata. When bottom lands and

moist flats are characterized by dense stands of typical

wetland species... they are considered Wetland. Where

highly alkaline soils are present, halophytes such as desert

saltbush, Atriplex may occur. The type, density, and association ofthese various species are useful as indicators of

the local hydrologic and pedologic environments. Also

included in this category is chaparral, a dense mixture

of broadleaf evergreen sclerophyll shrubs, and the occurrences of mountain mahogany, Cercocarpus ledifolius

and scrub oaks, Quercus."

1.43-Mixed-The Mixed Woody class includes all forest and

shrub areas where both evergreen and deciduous trees and

shrubs grow and neither predominates. When evergreen and

deciduous species each respectively occupy ~33 % of an area,

the land is classified as Mixed Woody. The Mixed Woody

category is subdivided into two additional categories: 1.431-

Forest and 1.432-Scrub/Shrub.

1.431 Forest-This class includes all forested areas where

both evergreen and deciduous trees are growing and neither

predominate.

1.432 Scrub/Shrub-This class includes all shrub areas

where both evergreen and deciduous shrubs are growing and

neither predominate.

1.5-Bare Land

The Bare Land class, modified from "Barren Land" in Anderson et al. (1976) is composed of bare rock, sand, silt, gravel,

or other earthen material with little or no vegetation regardless ofits inherent ability to support life. Vegetation, if present,

is more widely spaced and scrubby than that in the vegetated

categories. Unusual conditions, such as a heavy rainfall, occasionally may result in a short-lived, luxuriant plant cover.

Wet, nonvegetated exposed lands are included in the wetland categories.

Categories of Bare Land include Dry Salt Flats; Beaches;

Sandy Areas other than Beaches; Bare Exposed Rock; Strip

Mines, Quarries; Gravel Pits; Transitional Areas; and Mixed

Barren Land:

Dry Salt Flats are level bottoms of interior desert basins that

capture infrequent rainfall and do not qualify as Wetland.

Salt concentrations result in highly reflective surfaces.

Beaches are the smooth sloping accumulations of sand and

gravel along shorelines. The inland face is usually stable, but

the shoreward face is subject to erosion by wind and water

and subject to deposition in protected areas.

Sandy Areas other than Beaches are composed primarily of

dunes-accumulations ofsand transported by the wind. Sand

accumulations most commonly are found in deserts although

they also occur on coastal plains, river flood plains, and

deltas and in periglacial environments. When such sand accumulations are encountered in tundra areas, they are not

included here but are placed in the Bare Ground Tundra

category.

Bare Exposed Rock includes areas of bedrock, desert pavement, scarps, talus, slides, volcanic material, glacial debris,

and other accumulations of rock without vegetative cover,

with the exception ofsuch rock exposures in tundra regions.

Strip Mines, Quarries, and Gravel Pits are areas of extractive

mining activities with significant surface expression. Vegetative cover and overburden are removed to expose such deposits as coal, iron ore, limestone, and copper. Quarrying of

building and decorative stone and recovery ofsand and gravel

deposits also result in large open-surface pits. Active, inac-
72 NOAA Technical Report NMFS 123: Dobson et a1.: Coastal Change Analysis Program _

tive, and un reclaimed strip mines, quarries, borrow pits, and

gravel pits are included in this category until other cover has

been established, after which the land is classified in accordance with the resulting cover. Unused pits or quarries that

have been flooded, however, are placed in the appropriate

Water category.

Transitional Areas are dynamically changing from one land

cover to another, often because of land use activities. This

transitional phase occurs when, for example, forest lands are

cleared for agriculture and wetlands are drained for development. Often land becomes temporarily bare as construction

initiates the transition from Woody Land or Grassland to a

future cover associated with residen tial, commercial, or other

intensive land use. Lands, such as spoil banks and sanitary

landfills, that are temporarily altered by grading and filling

are considered transitional.

1.6-Tundra

Tundra is the term applied to the treeless cover beyond the

latitudinal limit of the boreal forest in poleward regions and

above the elevation range of the boreal forest in high mountains. In the United States, tundra is found primarily in

Alaska, several areas of the western high mountain ranges,

and isolated enclaves in the high mountains of New England

and northern New York.

The vegetative cover of the tundra is low and dwarfed,

often forming a continuous mat. Plant characteristics are an

adaptation to an extreme physical environment in which

temperatures may average above freezing only one or two

months of each year, strong desiccating winds may occur,

great variation exists in solar energy, and permafrost is ubiquitous beneath the surface.

The number of species in the flora is relatively small compared with typical middle- and low-latitude flora, and the

number decreases as the environment becomes more severe

with increasing latitude and elevation. The tundra vegetation

is most luxuriant near the boreal forest. Conversely, plant

density and species diversity are lowest near the boundaries

of permanent ice and snow areas, where only isolated patches

of vegetation occur on generally bare surfaces.

Tundra may be further subdivided into Shrub and Brush

Tundra, Herbaceous Tundra, Bare Ground Tundra, Wet Tundra, and Mixed Tundra (Anderson et aI., 1976).

1.7-SnowlIce

The temporal dimension is crucial in determining snow and

ice cover. Any snowfall, for example, deep enough to conceal

another land cover, no matter how briefly, comprises the

visible surface at that time and technically constitutes the

land cover for the period of its duration. As a practical matter, of course, analysts usually need to characterize the land

cover persisting for a greater portion of the year. At higher

latitudes and elevations, snow and ice persist for greater

portions of the year, and seasonal coverage becomes a more

important concern. At extreme latitudes and elevations, Perennial Snow/Ice cover is of paramount interest. A combination of environmental factors may cause snow and ice to survive

the summer melting season. Areas of Perennial Snow/Ice cover

are defined as those where snow, firn (coarse, compacted granular snow), or ice accumulation exceeds ablation. Ablation is the

combined loss of snow or ice mass by evaporation and meltwater run-off (Anderson et aI., 1976). The class Snow/Ice contains two subclasses: 1.71-Perennial Snow/Ice and l.72-Glacier.

1.71-Perennial Snow/Ice-This class contains areas covered

year-round with snow and ice but which have not accumulated sufficient ice to be considered Glaciers. Snowfields can

be extensive and thus representative of a regional climate, or

can be isolated and localized, where they are known by various terms, such as snowbanks. The regional snowline is controlled by general climatic conditions and closely parallels

the regional 32' F (0' C) isotherm for the average temperature of the warmest summer month. The use of the term

"line" is somewhat misleading because the "snowline" represents an irregular transitional boundary, which is determined

at any single location by the combination of snow accumulation, snow melt, and ablation, variables that can change rapidly within short distances because of changes in local topography and slope orientation. Snowfields normally can be

distinguished from the following Glacier subclass by their

relative lack offlow fcatures (Anderson et aI., 1976).

1.72-Glaciers-Glacial ice originates from the compaction of

snow into firn and finally into ice under the weight ofseveral

successive annual accumulations. Refrozen melt water usually contributes to the increasing density of the glacial ice

mass. With sufficient thickness, weight, and bulk, flow begins;

all glaciers exhibit evidence of present or past motion in the

form of moraines, crevasses, and other glacial geomorphic

features.

Where the snowline of adjacent ice-free areas extends across

the glacier, it is known as the firn limit, which represents the

dividing line between the glacier's two major zones, the zone

of accumulation and the zone of ablation. While glaciers

normally are recognized easily, certain glacial boundaries

may be subject to misinterpretation, even by the experienced

interpreter. Flow features up-glacier from the firn limit typically are obscured by fresh snow, forcing the image interpreter to depend on secondary information, such as valley

shapes, or to seek a more discriminating sensor. Similarly, glacial drift materials (rock and soil) may stripe the surface of a

glacier, and moraine material may cover the terminus (or snout)

because of ablation, making boundary determination in that

vicinity difficult. This later problem occasionally is compounded

by the presence ofconsiderable vegetation rooted in the insulating blanket of ablation moraine (Anderson etal., 1976).

2.0-Wetland _

Cowardin et aI. (1979) define wetlands as lands where saturation with water is the dominant factor determining soil development and the types of plant and animal communities

living in the soil and on its surface. The single feature that all

wetlands share is soil or substrate that is at least periodically
Appendix 3: C-CAP Land-Cover Classification Definitions 73

saturated with or covered by water. The upland limit for

vegetated wetlands with soil is 1) the boundary between land

with predominantly hydrophytic cover and land with predominantly mesophytic or xerophytic cover; 2) for nonvegetated wetlands with soil, the boundary between soil that is

predominantly hydric and soil that is predominantly

non hydric; or 3) in the case ofwetlands without vegetation or

soil, the boundary between land that is flooded or saturated

sometime during the growing season each year and land that

is not. Most wetlands are vegetated and are found on soil.

In the C-CAP Coastal Land-Cover Classification System

(Table 2), "Wetland" includes all areas considered wetland

by Cowardin et al. (1979) except for Wetland Bottoms, Aquatic

Beds, and Nonpersistent Emergent Wetlands. Subdivision of

the Wetlands class closely resembles the Cowardin et al. system (Appendix 2). At Level II, C-CAP uses certain Cowardin

et al. classes (e.g. Rocky Shore, Unconsolidated Shore, Emergent Wetland) or grouped Cowardin et al. classes (e.g. Woody

Wetland = Scrub-Shrub + Forested Wetland) in combination

with Cowardin et al. systems (i.e. Marine, Estuarine, Riverine,

Lacustrine, Palustrine). Thus, C-CAP Level II wetland classes

became 2.l-Marine/Estuarine Rocky Shore, 2.2-Marine/Estuarine Unconsolidated Shore, 2.3-Marine/Estuarine Emergent Wetland, 2.4-Estuarine Woody Wetland, 2.5-Riverine

Unconsolidated Shore, 2.6-Lacustrine Unconsolidated Shore,

2.7-Palustrine Unconsolidated Shore, 2.8-Palustrine Emergent

Wetland (persistent), and 2.9-Palustrine Woody Wetland.

Salinity displays a horizontal gradient in marshes typical of

coastal plain estuaries. This is evident not only through the

direct measurement of salinity but also in the horizontal

distribution of marsh plants in marshes with positive correlations between vertical rise and landward location (Daiber,

1986). Therefore Marine Estuarine Emergent Wetland was

partitioned into Haline (Salt) and Mixohaline (Brackish)

Marshes. For both subclasses, the definitions used in Cowardin

et al. (1979) were used, i.e. the salinities for Mixohaline

range from 0.5 to 30 ppt, and Haline include salinities >30

ppt. Within a marsh, plant zonation is usually quite evident.

Along the Atlantic coast of North America the pioneer plant

is saltmarsh cordgrass, Spartina alterniflora, which often appears in pure stands. Higher up the slope saltmeadow hay,

Spartina patens, becomes dominant, while the upland edge,

are bordered by marsh elder, Iva frutescens, and groundsell

tree, Baccharis halimifolia. Thus, salt marshes could be subdivided further into High Marsh and Low Marsh.

C-CAP does not attempt to identify Nonpersistent Emergent Wetlands, because they are seasonal. These wetlands are

classified as "Riverine Water" and "Lacustrine Water." Marine and Estuarine Rocky Shores were combined into a single

class, Marine/Estuarine Rocky Shore. The same logic was

applied to create Marine and Estuarine Unconsolidated

Shores, Aquatic Beds, and Water.

3.0-Water and Submerged Land _

All areas of open water with <30% cover of trees, shrubs,

persistent emergent plants, emergent mosses, lichens, or other

land cover are grouped under the heading, Water and Submerged Land, regardless of whether the area is considered

wetland or deepwater habitat under the Cowardin et al. (1979)

classification system. The Level II C-CAP Water and Submerged Land classes are modified from Cowardin et al. (1979)

(Appendix 2), and include Water, Reef, and Aquatic Beds.

Marine and Estuarine Reefs and Marine and Estuarine Aquatic

Beds are combined into two classes, 3.2-Marine/Estuarine

Reef and 3.3-Marine/Estuarine Aquatic Bed. Aquatic bed in

rivers, lakes, and streams are assigned to 3.4-Riverine Aquatic

Bed, 3.5-Lacustrine Aquatic Bed, and 3.5-Palustrine Aquatic

Bed classes. This last class also includes Cowardin et al.'s

(1979) Rock Bottom and Unconsolidated Bottoms.

Most C-CAP products will designate water as a single class

(3.1) regardless ofsystem type. It is recognized, however, that

the major systems (Marine/Estuarine, Riverine, Lacustrine,

Palustrine) are ecologically quite different from one another.

Hence, the four systems at Level III are shown as subclasses:

3.II-Marine/Estuarine, 3. 12-Riverine, 3. 13-Lacustrine, and

3.l4-Palustrine. Even though C-CAP does not commit itselfto

provide the subclass data, this option is encouraged for regional participants. Incorporating water system information

makes the C-CAP scheme more compatible with the Cowardin

et al. system. The subclass 3.ll-Marine/Estuarine includes

bottoms and undetected reefs and aquatic beds. The subclasses 3. 12-Riverine, 3. 13-Lacustrine, and 3.l4-Palustrine include bottoms and undetected aquatic beds or non-persistent emergent wetlands.

3.3-Marine/Estuarine Aquatic Beds includes the subclass

Rooted Vascular, which is broken into High Salinity (>5 ppt)

and Low Salinity «5 ppt). The break was made at 5 ppt

salinity because it separates true seagrasses that require high

salinity from low salinity species that are tolerant of or require fresh water. Both low and high salinity types of SRV are

important to the C-CAP project. High Salinity includes

Cowardin et al.'s mesohaline, polyhaline, euhaline, and

hyperhaline salinity categories. Low Salinity includes Cowardin

et al.'s oligohaline and fresh categories.

Systems and Classes of Cowardin et al.

Most of the C-CAP wetland and water definitions are taken

directly from Cowardin et al. (1979). This classification is

hierarchical, progressing from systems and subsystems, at the

most general level, to classes, subclasses, and dominance

types. Appendix Table 2 illustrates the hierarchical structure

to the class level. Modifiers for water regime, water chemistry,

and soils are applied to classes, subclasses, and dominance

types. Special modifiers describe wetlands and deepwater

habitats that have been either created or highly modified by

human or beaver activity.

Systems _

The term system refers to a complex of wetlands and

deepwater habitats that share the influence of similar hydrologic, geomorphologic, chemical, or biological factors. Systems are subdivided into subsystems.

The characteristics of the five major systems-Marine, Estuarine, Riverine, Lacustrine, and Palustrine-have been dis-
74 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program

cussed at length in the scientific literature and the concepts

are well recognized. However, there is disagreement as to

which attributes should be used to bound the systems in

space. For example, both the limit of tidal influence and the

limit of ocean-derived salinity have been proposed ,as definitions of the upstream limit of Estuarine Systems (Caspers,

1967). As Bormann and Likens (1969) affirm, boundaries of

ecosystems are defined to meet practical needs.

Marine System

Definition. The Marine system consists of the open ocean

overlying the continental shelf and its associated high-energy

coastline. Marine habitats are exposed to the waves and currents of the open ocean, and the water regimes are determined primarily by the ebb and flow of ocean tides. Salinities

exceed 30%, with little or no dilution except near the mouths

of estuaries. Shallow coastal indentations or bays without

appreciable freshwater inflow, and coasts with exposed rocky

islands that provide the mainland with little or no shelter

from wind and waves, are also considered part of the Marine

System because they generally support typical marine biota.

Limits. The Marine System extends from the outer edge of

the continental shelf shoreward to one of three lines: I) the

landward limit of tidal inundation (extreme high water of

spring tides), including the splash zone from breaking waves;

2) the seaward limit of wetland emergents, trees, or shrubs;

or 3) the seaward limit of the Estuarine System, where this

limit is determined by factors other than vegetation. Deepwater

habitats lying beyond the seaward limit ofthe Marine System are

outside the scope of this classification system.

Estuarine System

Definition, The Estuarine system consists of deepwater tidal

habitats and adjacent tidal wetlands that are usually semienclosed by land but have open, partly obstructed, or sporadic access to the open ocean, and in which ocean water is at

least occasionally diluted by freshwater runoff from the land.

The salinity may be periodically increased above that of the

open ocean by evaporation. Along some low-energy coastlines there is appreciable dilution of sea water. Offshore

areas with typical estuarine plants and animals, such as red

mangroves, Rhizophora mangle, and eastern oysters, Crassosl'rea

virginica, are also included in the Estuarine system.

Limits. The Estuarine system extends I) upstream and landward

to where ocean-<:lerived salts measure <0.5% during the period of

average annual low flow; 2) to an imaginary line closing the

mouth of a river, bay, or sound; and 3) to the seaward limit of

wetland emergents, shrubs, or trees where they extend beyond

the river mouth defined by (2). The Estuarine System also

includes offshore areas of continuously diluted sea water.

Riverine System

Definition, The Riverine system includes all wetlands and

deepwater habitats contained within a channel, except I)

wetlands dominated by trees, shrubs, persistent emergents,

emergent mosses, or lichens and 2) habitats with water containing ocean-derived salts >0.5%. A channel is "an open

conduit either naturally or artificially created which periodically or continuously contains moving water, or which forms

a connecting link between two bodies ofstanding water."

Limits. The Riverine System is bounded on the landward

side by upland, by the channel bank (including natural and

man-made levees), or by wetland dominated by trees, shrubs,

persistent emergents, emergent mosses, or lichens. In braided

streams, the system is bounded by the banks forming the

outer limits of the depression within which the braiding

occurs.

The Riverine System terminates downstream where the

concentration of ocean-derived salts in the water exceeds

0.5% during the period of annual average low flow, or where

the channel enters a lake. It terminates upstream where

tributary streams originate or where the channel originates

from a lake. Springs discharging into a channel are considered part of the Riverine System.

Lacustrine System

Definition. The Lacustrine System includes wetlands and

deepwater habitats with all ofthe following characteristics: I)

situated in a topographic depression or a dammed river

channel; 2) lacking trees, shrubs, persistent emergents, emergent mosses or lichens with >30% areal coverage; and 3) total

area >8 ha (20 acres). Similar wetland and deepwater habitats

totaling less than 8 ha are also included in the Lacustrine

system if an active wave-formed or bedrock shoreline feature

makes up all or part of the boundary, or if the water depth in

the deepest part of the basin is >2 m (6.6 feet) at low water.

Lacustrine waters may be tidal or nontidal, but ocean-derived

salinity is always <0.5%.

Limits. The Lacustrine System is bounded by upland or by

wetland dominated by trees, shrubs, persistent emergents,

emergent mosses, or lichens. Lacustrine systems formed by

damming a river channel are bounded by a contour approximating the normal spillway elevation or normal pool elevation, except where Palustrine wetlands extend lakeward of

that boundary. Where a river enters a lake, the extension of the

Lacustrine shoreline forms the Riverine-Lacustrine boundary.

Palustrine System

Definition. The Palustrine System includes all nontidal wetlands dominated by trees, shrubs, persistent emergents, emergent mosses or lichens, and all such wetlands that occur in

tidal areas where salinity due to ocean-derived salts is below

0.5%. It also includes wetlands lacking such vegetation, but

with all of the following four characteristics: I) the area is

<8 ha (20 acres); 2) active wave-formed or bedrock shoreline

features are lacking; 3) water depth in the deepest part of

basin is <2 m at low water; and 4) salinity due to oceanderived salts is <0.5%.

Limits. The Palustrine System is bounded by upland or by

any of the other four Systems.
Appendix 3: C-CAP Land-Cover Classification Definitions 75

Description. The Palustrine System was developed to group

the vegetated wetlands traditionally called by such names as

marsh, swamp, bog, fen, and prairie potl1ole, which are found

throughout the United States. It also includes small, shallow,

permanent, or intermittent water bodies often called ponds

(except in New England and New York where the term pond

often refers to substantial lakes) . Palustrine wetlands may be

situated shoreward of lakes, river channels, or estuaries on

river floodplains, in isolated catchments, or on slopes. They

may also occur as islands in lakes or rivers. The erosive forces

of wind and water are of minor importance except during

severe floods.

Classes and Subclasses _

The class is the highest taxonomic unit below the Subsystem

level. It describes the general appearance of the habitat in

terms of either the dominant life form of the vegetation or

the physiography and composition of the substrate-features

that can be recognized without the aid of detailed environmental measurements. Vegetation is used at two different

levels in the classification. Five life-forms-trees, shrubs,

emergents, emergent mosses, and lichens-are used to define classes because they are relatively easy to distinguish, do

not change distribution rapidly, and have traditionally been

used as criteria for classification of wetlands. Other forms of

vegetation, such as submersed or floating-leaved rooted vascular plants, free-floating vascular plants, submergent mosses,

and algae, though frequently more difficult to detect, are

used to define the class Aquatic Bed. Pioneer species that

briefly invade wetlands when conditions are favorable are

treated at the subclass level because they are transient and

often are not true wetland species (Cowardin et aI., 1979).

Using life-forms at the class level has two major advantages:

1) extensive biological knowledge is not required to distinguish between various life-forms and 2) various life-forms are

easily recognizable on a great variety of remote sensing products (Anderson et aI., 1976) If vegetation (except pioneer

species) covers :::-30% ofthe substrate, classes are distinguished

on the basis of the life form of the plants that constitute the

uppermost layer ofvegetation and that occupy an areal coverage :::-50% of vegetative cover. Finer differences in life-forms

are recognized at the subclass level. For example, in the

C-CAP system Estuarine Woody Wetland is divided into the

subclasses Scrub-Shrub and Forest categories, each of which

may be further characterized as Deciduous, Evergreen, and

Mixed on the basis of the predominant life-form. This differs

somewhat from the Cowardin et al. system which distinguishes

trees from shrubs at the class level.

If vegetation covers <30% of the substrate, the physiography and composition of the substrate are the principal characteristics used to distinguish classes. The nature of the substrate reflects regional and local variations in geology and the

influence of wind, waves, and currents on erosion and deposition of substrate materials. The classes Bottoms, Shores,

and Streambeds are separated on the basis of duration of

inundation. In the Riverine, Lacustrine, and Palustrine Systems, Bottoms are submerged all or most ofthe time, whereas

Streambeds and Shores are exposed all or most of the time.

In the Marine and Estuarine Systems, Bottoms are Subtidal,

whereas Streambeds and Shores are Intertidal. Bottoms,

Shores, and Streambeds are further divided at the class level

on the basis of the important characteristic of rock versus

unconsolidated substrate. Subclasses are based on finer distinctions in substrate material unless, as with Streambeds and

Shores, the substrate is covered by, or shaded by, an areal

coverage of pioneering vascular plants (often nonhydrophytes) :::-30%. The subclass is then simply "vegetated." Further detail as to the type of vegetation must be obtained at

the level of dominance type. Reefs are a unique class in which

the substrate itself is composed primarily of living and dead

animals. Subclasses of Reefs are designated on the basis of

the type of organism that formed the reef.

As shown in Appendix Table 2, the classes defined in

Cowardin et al. (1979) include

Rock Bottom (not used in the C-CAP system)

Unconsolidated Bottom (not used in the C-CAP system)

Aquatic Bed

Reef

Streambed (not used in the C-CAP system)

Rocky Shore

Unconsolidated Shore

Emergent Wetland

Scrub-Shrub Wetland

Forested Wetland

Aquatic Bed

Definition. The Aquatic Bed class includes wetlands and

deepwater habitats dominated by plants that grow principally

on or below the surface of the water for most of the growing

season in most years. Water regimes include subtidal, irregularly

exposed, regularly flooded, permanently flooded, intermittently

exposed, semi-permanently flooded, and seasonally flooded.

Description. Aquatic beds represent a diverse group of

plant communities that require surface water for optimum

growth and reproduction. They are best developed in relatively permanent water or under conditions ofrepeated flooding. The plants are either attached to the substrate or float

freely in the water above the bottom or on the surface. The

subclasses are Algal, Aquatic Moss (not used by C-CAP and

not defined here), and Rooted Vascular.

Algal-Algal beds are Widespread and diverse in the Marine

and Estuarine Systems, where they occupy substrates characterized by a wide range of sediment depths and textures.

They occur in both the Subtidal and Intertidal subsystems

and may grow to depths of 30 m (98 ft). Coastal algal beds are

most luxuriant along the rocky shores of the Northeast and

West. Kelp (MaCTocystis) beds are especially well developed on

the rocky substrates of the Pacific coast. Dominance types

such as the rockweeds Fucus and Ascophyllum and the kelp

Laminaria are common along both coasts. In tropical regions,

green algae, including forms containing calcareous particles,

are more characteristic; Halimeda and Penicllus are common

examples. The red alga Laurenciaand the green algae Caulerpa,

Enteromorpha, and Viva are also common Estuarine and Ma-
76 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program

rine dominance types; Enteromorpha and Ulva are tolerant of

fresh water and flourish near the upper end of some estuaries. The stonewort Chara is also found in estuaries.

Inland, the stoneworts Chara, Nitella, and Tolypella are examples of algae that look much like vascular plants and may

grow in similar situations. However, meadows of Chara may

be found in Lacustrine water as deep as 40 m (131 ft) where

hydrostatic pressure limits the survival ofvascular submergents

(phanaerogams). Other algae bearing less resemblance to

vascular plants are also common. Mats of filamentous algae

may cover the bottom in dense blankets, may rise to the

surface under certain conditions, or may become stranded

on unconsolidated or rocky shores.

Rooted Vascular-Rooted Vascular beds include a large array of vascular species in the Marine and Estuarine Systems.

They have been referred to as temperate grass flats (Phillips,

1974), tropical marine meadows, as well as eelgrass beds,

turtlegrass beds, and seagrass beds. The greatest number of

species occur in shallow, clear tropical, or subtropical waters

of moderate current strength in the Caribbean and along the

Florida and Gulf Coasts. Principal dominance types in these

areas include turtle grass, Thalassia testudinum, shoalgrass,

Halodule wrightii, manatee grass, Cymodocea filiformis, widgeon

grass, Ruppia maritima, sea grasses, Halophila spp., and wild

celery, Vallisneria americana.

Reef

Definition. The Reef class includes ridgelike or moundlike

structures formed by the colonization and growth of sedentary invertebrates. Water regimes are restricted to subtidal,

irregularly exposed, regularly flooded, and irregularly flooded.

Description. Reefs are characterized by being elevated above

the surrounding substrate and interfering with normal wave

flow; they are primarily subtidal, but parts ofsome reefs may

be intertidal as well. Although corals, oysters, and tube worms

are the most visible organisms and are mainly responsible for

reef formation, other mollusks, foraminifera, coralline algae,

and other forms of life also contribute substantially to reef

growth. Frequently, reefs contain far more dead skeletal material and shell fragments than living matter. The subclasses

are Coral, Mollusk, and Worm. Only the first subclass is

emphasized by C-CAP; the other two definitions are omitted.

Coral-Coral reefs are widely distributed in shallow waters of

warm seas, in Hawaii, Puerto Rico, the Virgin Islands, and

southern Florida. Odum (1971) characterized them as stable,

well-adapted, highly diverse, and highly productive ecosystems with a great degree of internal symbiosis. Coral reefs lie

almost entirely within the Subtidal subsystem of the Marine

System, although the upper part of certain Reefs may be

exposed. Examples of dominance types are the corals Porites,

Acropora, and Montipora. The distribution of these types reflects primarily elevation, wave exposure, and reef age.

Rocky Shore

Definition. The Rocky Shore class includes wetland environments characterized by bedrock, stones, or boulders which

singly or in combination have an areal cover ~75% and an

areal coverage by vegetation of <30%. Water regimes are

restricted to irregularly exposed, regularly flooded, irregularly flooded, seasonally flooded, temporarily flooded, and

intermittently flooded.

Description. In Marine and Estuarine Systems, Rocky Shores

are generally high-energy habitats that lie exposed as a result

of continuous erosion by wind-driven waves or strong currents. The substrate is stable enough to permit the attachment and growth ofsessile or sedentary invertebrates as well

as attached algae or lichens. Rocky shores usually display a

vertical zonation that is a function oftidal range, wave action,

and degree of exposure to the sun. In the Lacustrine and

Riverine Systems, Rocky shores support sparse plant and

animal communities. The subclasses are Bedrock and Rubble.

More detailed definitions are provided in Cowardin et al.

(1979).

Unconsolidated Shore (Tidal Flats)

Definition. The Unconsolidated Shore class includes all wetland habitats having three characteristics: 1) unconsolidated

substrates with < 75% areal cover of stones, boulders, or

bedrock; 2) <30% areal cover of vegetation other than pioneering plants; and 3) any of the following water regimes:

irregularly exposed, regularly flooded, irregularly flooded,

seasonally flooded, temporarily flooded, in termittently

flooded, saturated, or artificially flooded. Intermittent or

intertidal channels ofthe Riverine System and intertidal channels of the Estuarine System are classified as Streambed.

Description. Unconsolidated Shores are characterized by

substrates lacking vegetation except for pioneering plants

that become established during brief periods when growing

conditions are favorable. Erosion and deposition by waves

and currents produce a number oflandforms such as beaches,

bars, and flats, all of which are included in this class. Unconsolidated Shores may be found adjacent to Unconsolidated

Bottoms in all systems. As in the class Unconsolidated Bottoms, the particle size of the substrate and the water regime

are the important factors determining the types of plant and

animal communities present. Different substrates usually support characteristic invertebrate fauna. The subclasses are

Cobble-gravel, Sand, and Mud. More detailed definitions are

provided in Cowardin et al. (1979).

Emergent Wetland

Definition. The Emergent Wetland class is characterized by

erect, rooted, herbaceous hydrophytes (excluding mosses

and lichens) which are present for most of the growing

season in most years. These wetlands are usually dominated

by perennial plants. All water regimes are included except

subtidal and irregularly exposed.

Description. In areas with relatively stable climatic conditions, emergent wetlands maintain the same appearance year

after year. In other areas, such as the prairies of the central

United States, violent climatic fluctuations cause them to

revert to open water in some years. Emergent wetlands are
Appendix 3: C-CAP Land-Cover Classification Definitions 77

found throughout the United States and occur in all systems

except Marine. Emergent wetlands are known by many names,

including marsh, meadow, fen, prairie pothole, and slough.

Areas dominated by pioneer plants that become established

during periods of low water are not emergent wetlands and

should be classified as Vegetated Unconsolidated Shores or

Vegetated Streambeds. The subclasses in the Cowardin et al.

system are Persistent and Nonpersistent.

Woody Wetland

The Woody Wetland class includes any species with an aerial

stem that persists for more than one season. The Woody

Wetland class is divided into three subclasses: Deciduous,

Evergreen, and Mixed.

Deciduous-The Deciduous Woody Wetland subclass includes

all wetland forest and shrub areas having a predominance of

trees and shrubs that lose their leaves or needles at the end of

the frost-free season or at the beginning of a dry season. This

category contains greater than two-thirds deciduous trees

and shrubs. The Deciduous Woody Wetland category can be

divided into three categories: Forest, Scrub-Shrub, and Dead.

Forest-Definition. Forested wetland is characterized by

woody vegetation ~6 m in height. All water regimes are included except subtidal.

Description. Forested wetlands are most common in the

eastern United States and in those sections of the West where

moisture is relatively abundant, particularly along rivers and

in mountains. They occur only in the Palustrine and Estuarine Systems and normally contain an overstory of trees, an

understory of young trees or shrubs, and a herbaceous layer.

Forested wetlands in the Estuarine System, including the

mangrove forests of Florida, Puerto Rico, and the Virgin

Islands, are known by such names as swamps, hammocks,

heads, and bottoms. These names often occur in combination with species names or plant associations, such as cedar

swamp or bottom land hardwoods.

Broad-leaved Deciduous-Dominant trees typical of broadleaved deciduous wetlands, which are represented throughout the United States, are most common in the South and

East. Common dominants are species such as red maple,

American elm, Ulmus americana, the ashes Fraxinus

pennsylvanica and F. nigra, black gum, Nyssa sylvatica, tupelo

gum, N. aquatica, swamp white oak, Quercus bicolor, overcup

oak, Q. lyrata, and basket oak, Q. michauxii. These wetlands

generally occur on mineral soils or highly decomposed organic soils.

Needle-leaved Deciduous-The southern representative of

the Needle-leaved Deciduous Wetland subclass is bald cypress, Taxodium distichum, which is noted for its ability to

tolerate long periods of surface inundation. Tamarack is

characteristic of the boreal forest region, where it occurs as a

dominant on organic soils. Relatively few other species are

included in this subclass.

Scrub-Shrub-Definition. Scrub-Shrub wetland includes areas dominated by woody vegetation <6 m (20 ft) tall. The

species include true shrubs, young trees, and trees or shrubs

that are small or stunted because of environmental conditions. All water regimes except subtidal are included.

Description. Scrub-shrub wetlands may represent a successional stage leading to forested wetlands, or they may be

relatively stable communities. They occur only in the Estuarine and Palustrine Systems but are one of the most widespread classes in the United States. Scrub-shrub wetlands are

known by many names, such as shrub swamp, shrub, bog, and

pocosin. The C-CAP category includes forests composed of

young trees <6 m tall regardless of potential height at maturity.

Broad-leaved Deciduous-In Estuarine System wetlands

the predominant deciduous and broad-leaved trees or shrubs

are plants such as sea-myrtle, Baccharis halimifolia, and marsh

elder, Iva frutescens. In the Palustrine System typical dominance types are alders, Alnus spp., willows, Salix spp., buttonbush, Cephalanthus cocidentalis, red osier dogwood, Comus

stolonifera, honeycup, Zenobia pulverulenta, spirea, Spiraea

douglasii, bog birch, Betula pumila, and young trees ofspecies

such as red maple, Acerrubrum, or black spruce, Picea mariana.

Needle-leaved Deciduous-This group, consisting of wetlands where trees or shrubs are predominantly deciduous

and needle-leaved, is represented by young or stunted trees

such as tamarack or bald cypress, Taxodium distichum.

Dead-Definition. Dominated by dead deciduous woody

vegetation, these wetlands are usually produced by a prolonged rise in the water table resulting from impoundment

ofwater by landslides, human activity, or beaver activity. Such

wetlands may also result from various other factors such as

fire, salt spray, insect infestation, air pollution, and

herbicides.

Evergreen-The Evergreen Woody Wetland subclass contains

wetland forests and shrubs that do not lose their leaves or

needles at the end of a frost-free season or at the beginning of a

dry season. The Evergreen Woody Wetland category is subdivided into two additional categories: Forest and Scrub--Shrub.

Forest-Definition. Forested wetland is characterized by

woody vegetation ~6 m in height. All water regimes are included except subtidal.

Broad-leaved Evergreen-In the Southeast, broad-leaved

evergreen wetlands reach their greatest development. Red

bay, Persea borbonia, loblolly bay, Gordonia lasianthus, and sweet

bay, Magnolia virginiana, are prevalent, especially on organic

soils. This group also includes red mangrove, black mangrove, Avicenniagerminans, and white mangrove, Languncularia

racemosa, which are adapted to varying levels of salinity.

Needle-leaved Evergreen-Black spruce, growing on organic soils, represents a major dominant of the Needle-leaved

Evergreen subclass in the north. Though black spruce is

common on nutrient-poor soils, northern white cedar, Thuja

occidentalis, dominates northern wetlands on more nutrientrich sites. Along the Atlantic Coast, Atlantic white cedar,

Chamaecyparis thyoides, is one of the most common dominants

on organic soils. Pond pine, Pinus serotina, is a common

needle-leaved evergreen found in the Southeast in association with dense stands of broad-leaved evergreen and deciduous shrubs.

Scrub-Shrub-Definition. Scrub-shrub wetland includes

areas dominated by woody vegetation <6 m (20 ft) tall. The
78 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program

species include true shrubs, young trees, and trees or shrubs

that are small or stunted because of environmental conditions. All water regimes except subtidal are included.

Broad-leaved Evergreen-In the Estuarine System, vast wetland areas are dominated by mangroves (Rhizophora mangle,

Languncularia racemosa, Conocarpus erectus, and Avicennia

germinans) that are I m to <6 m tall. In the Palustrine System,

the broad-leaved evergreen species are typically found on

organic soils. Northern representatives are labrador tea, Ledum

groenlandicum, bog rosemary, Andromedaglaucophylla, bog laurel, Kalmia polifolia, and the semi-evergreen leatherleaf,

Chamaedaphne calyculata. In the south, fetterbush, Lyonia lucida, coastal sweetbells, Leucothoe axillaris, inkberry, flex glabra,

and the semi-evergreen black ti-ti, Cyrilla racemiflora, are characteristic broad-leaved evergreen species.

Needle-leaved Evergreen-The dominant species in needleleaved evergreen wetlands are young or stunted trees such as

black spruce or pond pine.

Dead-Definition. These wetland areas are dominated by

dead evergreen woody vegetation. Like dead deciduous woody

wetlands, they are most common in, or around the edges of,

impoundments and beaver ponds. The same factors that

produce dead deciduous woody wetlands produce dead evergreen woody wetlands.

Mixed-The Mixed Woody Wetland subclass includes all

forest and shrub wetland areas where both evergreen and

deciduous trees and shrubs grow and neither predominates.

When evergreen and deciduous species each occupy :::0:33% of

an area, the land is classified as Mixed Woody. The Mixed

Woody category is subdivided into two additional categories:

Forest and Scrub/Shrub.

Forest-This category includes all forested areas where

both evergreen and deciduous trees are growing and neither

predominate.

Scrub/Shrub-This category includes all shrub areas where

both evergreen and deciduous shrubs are growing and neither predominate.

Dead-Wetland areas dominated by dead mixed woody

vegetation are, like dead deciduous woody wetlands, most

common in, or around the edges of, impoundments and

beaver ponds. The same factors that produce dead deciduous

woody wetlands produce dead mixed woody wetlands.

Water

Cowardin et al. (1979) define deepwater habitats as permanently flooded lands lying below the deepwater boundary of

wetlands. Deepwater habitats include environments where

surface water is permanent and often deep, so that water, rather

than air, is the principal medium within which the dominant

organisms live, whether or not they are attached to the substrate.

As in wetlands, the dominant plants are hydrophytes. However,

the substrates are considered nonsoil because the water is too

deep to support emergent vegetation. The class Water includes

Marine/Estuarine, Lacustrine, Palustrine, and Riverine

Deepwater subclasses as defined by Cowardin et al. (1979).
Appendix 4

C-CAP Workshops

Date:

Host:

Chair:

This guidance document results from the participation of

more than 200 scientists, technical specialists, managers, and

regional experts in nine protocol-development workshops. Four

regional workshops were held in the Northeast, Southeast, West

Coast, and Great Lakes regions to address a broad range of

issues including those specific to each major coastal region.

Also, a major national workshop was held that focused specifically on mapping submerged aquatic vegetation. Participation

was encouraged across all Federal and State agencies involved in

coastal research, management, and policy and many other agencies concerned with remote sensing and land-eover analysis. The

seagrass workshop followed the same format, focusing specifically on submersed habitats. Each workshop was presented with

a draft C-CAP protocol, based initially on the Chesapeake Bay

prototype, and participants were encouraged to refine the draft

and resolve remaining issues. Issues not resolved in the five

major workshops were addressed through dedicated topical

workshops. Finally, the issues that could not be resolved through

workshops were explored through research funding proposals.

Two accuracy assessment workshops involved leading specialists in spatial error estimation who were asked to recomSoutheast Regional Workshop

Location: University of South Carolina

Columbia, SC

Dates: 29-31 May 1990

Host: University of South Carolina

Co-Chairs: Jerome Dobson

Kenneth Haddad

Seagrass Mapping Workshop

Location: Embassy Suites, Tampa Airport Hotel

Tampa, FL

Dates: 23-25 July 1990

Co-Chairs: Randolph Ferguson

Robert Orth

Accuracy Assessment Workshop

Location: National Marine Fisheries Service

Beaufort Laboratory

Beaufort, NC

25 September 1990

Beaufort Laboratory

Jerome Dobson

Northeast Regional Workshop

Location: Whispering Pines Conference Center

W. Alton Jones Campus

University of Rhode Island

Kingston, Rl

Dates: 8-10January 1991

Host: University of Rhode Island

Co-Chairs: Jerome Dobson

Kenneth Haddad

mend protocols for accuracy assessment of C-CAP TM data.

The accuracy assessment procedures outlined in this report

are a direct result of those workshops. The classification

workshop was a multi-agency group organized to develop a

classification system that would suit C-CAP needs. Through

the workshop and a long iterative process thereafter, the

classification presented in this report was developed. The

Maryland Field Reconnaissance workshop was unique in that

the organizers were not from C-CAP but from the Maryland

Department of Natural Resources and Salisbury State University. The principal objective of the workshop was to provide

recommendations concerning C-CAP products. The reconnaissance consisted of field visits to sites in the vicinity of

Salisbury, Maryland, identified in a preliminary version ofthe

C-CAP Chesapeake Bay Land-Cover Change Database.

In addition, the preliminary C-CAP data were compared

with other types of ancillary data supplied by workshop

participan ts.

Findings and recommendations from these workshops and

from other meetings of specialists (not listed here) were

crucial in the development of this document.

Classification Scheme Workshop

Location: Silver Spring, MD

Date: 12 February 1991

Co-Chairs: JamesJohnston

Vic Klemas

West Coast Regional Workshop

Location: Embassy Suites Hotel

Seattle, WA

Dates: 29 April-l May 1991

Co-Chairs: Jerome Dobson

Kenneth Haddad

Maryland Field Reconnaissance Workshop

Location: Salisbury State University

Salisbury, MD

Dates: 16-18July 1991

Host: Salisbury State University

Workshop William Burgess, Maryland Department of

Design: Natural Resources;

Edward W. Christoffers, NOAA/NMFS

Jerome Dobson, Oak Ridge National Laboratory

Randolph Ferguson, NOAA/NMFS

Adam Fisch, Virginia Council on the

Environment

K. Peter Lade, Salisbury State University

James Thomas, NOAA/NMFS

Bill Wilen, USFWS, National Wetlands Inventory.

Sponsors: Salisbury State University, Maryland Department

of Natural Resources, NOAA, and USFWS

79
80 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program

Date:

Host:

Chair:

Accuracy Assessment Workshop

Location: Oak Ridge National Laboratory

Oak Ridge, TN

1-2 August 1991

Oak Ridge National Laboratory

Jerome Dobson

Great Lakes Regional Workshop

Location: Best Western Ann Arbor Regent

Ann Arbor, MI

Dates: 19-27 August 1991

Host: NOAA Great Lakes Environmental Research Lab

Co-Chairs: Jerome Dobson

Kenneth Haddad
Appendix 5

C~CAP Protocol Development Research

C-CAP funded research to refine various aspects of the protocol based on workshop recommendations and on findings

from the upland and wetland prototype (Chesapeake Bay),

the water and submerged land prototype (North Carolina

Coast), and the Salisbury field experience. These research

projects are intended to increase the geographical coverage

of the C-CAP change detection database. The following is a

list of the institutions that performed the research and the

topics addressed in fiscal year 1991:

University of South Carolina

• Test change detection methodologies

• Identiry optimum pattern recognition algorithms

North Carolina State University

• Develop a seamless database from two independently developed land-cover databases derived from TM data

Universities of Rhode Island and Connecticut

• Test change detection methodology

• Test the use of available digital wetlands data as an aid for

c1assirying TM imagery

Beaufort, NMFS

• Develop change detection methodologies for SAV

An announcement of availability of funds for protocol development research was distributed in March 1992. The following five studies were funded:

University of South Carolina

• Determine the impact of tides on coastal change detection

North Carolina State University

• Develop methodologies for accuracy assessment for change

detection databases

University of Virginia

• Examine influence of tides on TM data with the aid of

digital elevation models

University of Maine

• Develop improved methodologies for detecting forested

wetlands

University of New Hampshire

• Develop methodologies for accuracy assessment of change

detection databases

Two other studies were also funded by C-CAP in fiscal year

1992:

Universities of Rhode Island and Connecticut

• Funded for six months to finish work started in 1991

Beaufort, NMFS

• Develop change detection methodologies for SAV using

CPS technology.

81
82 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program
Mr. Bob Ackerman

MD, DNR, FPWS

Chesapeake Bay Programs (Forestry)

Tawes State Office Building

Annapolis, MD 21401

Steve Ackleson

Bigelow Laboratory for Ocean Science

McKown Poin t

West Boothbay Harbor, ME 04575

Appendix 6

Workshop Participants

Thomas E Bigford

NOAA/National Marine Fisheries Service

Habitat Conservation Branch

One Blackburn Drive

Gloucester, MA 01930-2298

Elaine Blok

Geonex Martel, Inc.

8950 9th Street North

St. Petersburg, FL 33703

Dean Albro

Division of Freshwater Wetlands

Rhode Island Dept. of Environmen tal Managemen t

291 Promenade St.

Providence, RI 02908

Warren Alward

Dept. of Natural Resources

Land and Water Management Division

P.O. Box 30028

Lansing, MI 48909

Roy A. Armstrong

NASA, Ames Research Center

37250 Sequoia Terrace #1032

Freemont, CA 94536

Peter V. August

Environmental Data Center

Dept. of Natural Resources Science

University of Rhode Island

Kingston, RI 02881

John Banta

Director of Planning

Adirondack Park Agency

Ray Brook, NY 12977

Franklin S. Baxter

U.S. Geological Survey

Mail Stop 514

National Center

Reston, VA 22092

Al Beeton

Director

Great Lakes Enviromental

Research Lab/NOAA

2205 Commonwealth

Ann Arbor, MI 48105

Nate Boyer

EOSAT

4300 Forbes Blvd.

Lanham, MD 20706

Earl Bradley

Coastal Resources Division

Tawes State Office Bldg. B-3

Annapolis, MD 21401

James Brewer

USDA, SCS

339 Revell Highway

Annapolis, MD 21401

Douglas A. Bulthuis

Padilla Bay National Estuarine

Research Reserve

1043 Bayview-Edison Road

Mount Vernon, WA 98273

Bill Burgess

Maryland Water Resources Admin.

Tawes State Office Building, D-2

Annapolis, MD 21401

Alice Chalmers

University of Georgia

Marine Institute

Sapelo Island, GA 31327

Michael Chambers

USGS, National Mapping Division

590 National Center

Reston, VA 22092

Heather Cheshire

Computer Graphics Center

Box 7106, North Carolina State University

Raleigh, NC 27695

83
84 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program

AlexanderJ. Chester

NOAA/National Marine Fisheries Service

Beaufort Laboratory

Beaufort, C 28516-9722

icholas Chrisman

Dept. of Geography

University of Washington

Smith Hall/DP-lO

Seattle, WA 98195

Eric Christensen

Science Technology Laboratory

Lockheed 1210

Stennis Space Center, MS 39529

Edward W. Christoffers

NOAA/CBP Science Coordinator

Tawes State Office Building, C-4

Annapolis, MD 21401

Barbara Cintron

Puerto Rico Dept. of Natural Resources

P.O. Box 5887

Puerta de Pierra, PR 00906

Daniel Civco

Dept. of atural Resources

Management and Engineering, Box U-87

1376 Storrs Road

University of Connecticut

Storrs, CT 06269-4087

Elaine Collins

NOAA/NESDIS

1825 Connecticut Ave., N.W., Room 406

Washington, DC 20235

Commander

U.S. Army Corps of Engineers

Waterways Experiment Station

Attn: CEWE5-ER-W/Buddy Clarain

Vicksburg, MS 39180-6199

Russell G. Congalton

Dept. of Natural Resources

215James Hall

University of ew Hampshire

Durham, H 03824

Robert Costanza

Chesapeake Biological Laboratory

Solomons Island, MD 20688-0038

Dave Cowen

Statistical and Behavioral

Sciences Laboratory

University of South Carolina

Columbia, SC 29208

Joseph E. Costa

Massachusetts Coastal Zone Management

Buzzards Bay Project

2 Spring Street

Marion, MA 02738

Paul Crawford

Olympic National Park

ational Park Service

600 East Park Avenue

Port Angeles, WA 98362

Ford A Cross, Director

NOAA/National Marine Fisheries Service

Southeast Fisheries Science Center

Beaufort Laboratory

101 Pivers Island Road

Beaufort, NC 28516-9722

Pat Cummens

Division of Science and Research

GIS Laboratory

401 East State Street

Trenton, NJ 08625

Thomas E. Dahl

ational Wetlands Inventory

U.S. Fish and Wildlife Service

9720 Executive Center Drive

Suite 101, Monroe Building

St. Petersburg, FL 33702

Barbara D'Angelo

3WMOO U.S. EPA Region III

841 Chestnut Building

Philadelphia, PA 19107

Rick Dawson

U.S. Department of the In terior

National Park Service

Southeast Regional Office

75 Spring Street SW

Atlanta, GA 30303

Michael DeMers

ew Mexico State University

Dept. of Geography

Box MAP

Las Cruces, M 88003

Larry Deysher

Coastal Resources Assoc.

2270 Camino Vina Roble

Suite L

Carlsbad, CA 92009
Jerome E. Dobson

Oak Ridge National Laboratory

Geographic Information Systems

and Computer Modelling

P.O. Box 2008, 4500 N, MS 6237

Oak Ridge, TN 37831

Bill Dunn

USDASCS

339 Revell Highway

Annapolis, MD 21401

Sandy Wyllie Echeverria

Institute of Marine Science

University of Alaska

Fairbanks, AK 99775

Robert Emmett

NOAA/National Marine Fisheries Service

Pt. Adams Biological Field Station

P. O. Box 155

Hammond, OR 97121

William Enslin

Center for Remote Sensing

Michigan State University

302 Berkey Hall

East Lansing, MI 48824-1111

Ron Erickson

U.S. Fish and Wildlife Service

4101 E. 80th Street

Bloomington, MN 55425-1600

Maggie Ernst

NOAA/Coastal Ocean Program Office

1315 East-West Highway

Silver Spring, MD 20910

Tamra Faris

NOAA/National Marine Fisheries Service

P.O. Box 21668

Juneau, AK 99802

Robin Fegeas

U.S. Geological Survey

Mail Stop 521

National Center

Reston, VA 22092

Randolph L. Ferguson

NOAA/National Marine Fisheries Service

Southeast Fisheries Science Center

Beaufort Laboratory

101 Pivers Island Road

Beaufort, NC 28516-9722

Appendix 6: Workshop Participants 85

Don Field

NOAA/National Marine Fisheries Service

Southeast Fisheries Science Center

Beaufort Laboratory

101 Pivers Island Road

Beaufort, NC 28516-9722

J. Michael Flagg

Virginia Dept. of Conservation and Recreation

Division of Soil and Water

203 Governor St., Suite 206

Richmond, VA 23219

Ms. Bellory Fong

California Dept of Water Resources

3251 "S" Street (RM B-5)

Sacramento, CA 95816

Andrew Frank

Dept. of Civil Engineering

119 Boardman Hall

University of Maine

Orono, ME 04469

Adam Frisch

Virginia Council on the Environment

202 N. 9th Street, Suite 900

Richmond, VA 23219

Ellen Fritts

Alaska Dept. of Fish and Game

Habitat Division

P.O. Box 3-2000

1255 West 8th Street

Juneau, AK 99802-2000

Gregory Fromm

NOAA/National Ocean Service

Photogrammetry Branch

1315 East-West Highway

Silver Spring, MD 20910

Carolyn Gates

California Coastal Commission

45 Freemont St.

Suite 2000

San Francisco, CA 94105

Len Gaydos

Ames Research Center

P.O. Box 1000

Moffett Field, CA 94035-1000

Bess Gillelan

Chief

NOAA/Chesapeake Bay Office

410 Severn Ave, Suite 107A

Annapolis, MD 21403
86 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program _

Frank Golet

Dept. of Natural Resources Science

University of Rhode Island

Kingston, Rl 0288]

AJ Green

Texas Parks and Wildlife Dept.

4200 Smith School Road

Austin, TX 78745

Christie Guy

Assateaque Island National Seashore

Rt 6] ]-7206

National Seashore Lane

Berlin, MD 2]8] ]

Kenneth D. Haddad

Marine Research Institute

FL Dept. of Environmental Protection

]00 8th. Avenue SE

St. Petersburg, FL 3370]-5059

Anne Marie Hale Miglarese

South Carolina Water Resources Commission

] 201 Main Street, Suite]] 00

Columbia, SC 2920]

Larry Handley

U.S. Fish and Wildlife Service

ational Wetlands Research Center

700 Cajun Dome Blvd.

Lafayette, LA 70506

Bud Harris

University of Wisconsin at Green Bay

2420 Nicolet Drive

Green Bay, W1 543]]

Greg HeIlyer

U.S. Environmental Protection Agency

Region I, WWP 424

JFK Federal Building

Boston, MA 02203

Gary Hendrix

U.S. Department of the Interior

ational Park Service

Room] 092, Sciences and atural Resources

75 Spring Street SW

Atlanta, GA 30303

Rex C. Herron

OAA/ ational Marine Fisheries Service

Building] ] 03

Stennis Space Center, MS 39529

Carl Hershner

Virginia Institute of Marine Science

CoIlege of William and Mary

Gloucester Point, VA 23062

Michael E. Hodgson

Oak Ridge National Laboratory

Geographic Information Systems and Computer Modelling

P.O. Box 2008, 4500 . MS6274

Oak Ridge, TN 3783]

Sherman Hollander

Michigan Resource Information Program

P.O. Box 30028

Lansing, MI 48909

Bob Holman

WRRl

Box 79] 2

North Carolina State University

Raleigh, NC 27695-79] 2

Frank Horvath

Chief Scientist

Great Lakes Information System

Michigan Dept. of Natural Resources

Land and Water Management Division

Lansing, MI 48909

Karl Huber

Virginia Dept. of Conservation and Recreation

Division of Soil & Water

203 Governor Street, Suite 206

Richmond, VA 232]9

Kent Hughes

Dept. of Commerce, NOAA/ ESDIS

Federal Office Building 4]069

Washington, DC 20233

Paul G. Huray

] 06 Osborne Administration Building

University of South Carolina

Columbia, SC 29208

Merton Ingham

NOAA/National Marine Fisheries Service

ortheast Fisheries Science Center

Narragansett Laboratory

28 Tarzwell Drive

Narragansett, Rl 02882-] ] 99

Harry Iredale

OAA/ ESDIS

niversal Building

] 825 Connecticut Avenue, W

Washington, DC 20235

Eugene Jaworski

SER-GEM Cen ter

3075 Washtenaw Avenue

Ypsilanti, MI 48197
JohnJensen

Dept. of Geography

University of South Carolina

Columbia, SC 29208

LisaJipping

U.S. Army Corps of Engineers

Great Lakes Hydraulics and Hydrology Branch

P.O. Box 1027

Detroit, Ml 48231

Jimmy Johnston

National Biological Survey

National Wetlands Research Center

700 Cajun Dome Blvd.

Lafayette, LA 70506

James R. Karr

Institute for Environmental Studies

Engineering Annex, FM-12

University of Washington

Seattle, WA 98195

Dick Kelly

Maine State Planning Office

184 State Street

Augusta, ME 04333

Dick Kempka

Ducks Unlimited, Inc.

9823 Old Winery Place, #16

Sacramento, CA 95827

Siamak Khorram

Computer Graphics Center

51I2Jordan Hall, Box 7106

North Carolina State University

Raleigh, NC 27695-7106

Donley Kisner

Bionetics Corp.

P.O. Box 1575

VHFS

Warrenton, VA 22186

Bjorn KjerfVe

Baruch Institute

University of South Carolina

Columbia, SC 29208

Richard Kleckner

U.S. Geological Survey

Mail Stop 590

National Center

Reston, VA 22092

Appendix 6: Workshop Participants 87

Vic Klemas

Director

Center for Remote Sensing

College of Marine Studies

University of Delaware

Newark, DL 19716

Frederick Kopfler

U.S. Environmental Protection Agency

Gulf of Mexico Program

Bldg.1I03

Stennis Space Center, MS 39529

K Koski

NOAA/National Marine Fisheries Service

Auke Bay Lab

11305 Glacier Highway

Auke Bay, AK 99821

Rose Kress

U.S. Army Corps of Engineers

Waterways Experiment Station

3909 Halls Ferry Road

Vicksburg, MS 39180

Tom Kunneke

Geonex Martel, Inc.

8950 9th Street North

St. Petersburg, FL 33703

Kathy Kunz

U.S. Army Corps of Engineers

Seattle District

P. O. C-3755

Seattle, WA 98124-2255

Charles LaBash

Environmental Data Center

Dept. of Natural Resources Science

University of Rhode Island

Kingston, Rl 02881

K.-Peter Lade

Salisbury State University

Image Processing Center

110 Power Street

Salisbury, MD 21801

Rene Langi

Dept. of Biology

San Diego State University

San Diego, CA 92182

Lewis A. Lapine

NOAA, National Ocean Service

Photogrammetry Branch, N/CG23

1315 East-West Highway

Silver Spring, MD 20910
88 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program _

M. Larson

No Address

George Leshkevich

Great Lakes Environmental Res. Lab.

2205 Commonwealth Blvd

Ann Arbor, MI 48105-1593

Roy R. Lewis, III

Lewis Environmental Services, Inc.

P.O. Box 20005

Tampa, FL 33622-0005

ancy Liebowitz

U.S. Environmental Protection Agency

1600 S.W. Western Blvd.

Corvallis, OR 97333

Katherine Lins

U.S. Geological Survey

Mail Stop 512

ational Center

Reston, VA 22092

Robyn Loudermilk

Hawaii Office of State Planning

State Capitol - Room 406

Honolulu, HI 96813

Ross Lunetta

U.S. Environmental Protection Agency

Environmental Monitoring System

Laboratory

P.O. Box 93478

Las Vegas, NY 89193-3478

David Lusch

Center For Remote Sensing

302 Berkey Hall, Michigan State University

East Lansing, MI 48824-1111

Anne Lynn

State Biologist

USDASCS

339 Revell Highway

Annapolis, MD 21401

Andreas Ma Ir.

NOAA/National Marine Fisheries Service

Southeast Region

9450 Koger Blvd.

St. Petersburg, FL 33702

Leslie Manfull

National Park Service

AD R 470

P.O. Box 37127

Washington, DC 20013-7127

Carl Markon

.5. Geological Survey

4230 University Drive

Anchorage, AK 99508-4664

Garry Mayer

NMFS Restoration Center

1315 East-West Highway

Silver Spring, MD 20910

L. Nelson May Jr.

Coastal Fisheries Institute

Louisiana State University

Baton Rouge, LA 70803

Bruce McCammon

USDA/USFW

Ecology Range and Watershed

P.O. Box 3623

Portland, OR 97208

Keith McLaughlin

1720 Peachtree Road, .W.

Atlanta, GA 30367

Peter McRoy

Institute of Marine Science

University ofAlaska

Fairbanks, AK

or

Dept of Botany

University of Hawaii

3190 Mailae Way

Honolulu, HI 96822

Todd Mecklenberg

Geonex Martel, Inc.

8950 9th Street North

St. Petersburg, FL 33703

Norman Melvin

Plant Ecologist

USDA\sCS

339 Revell Highway

Annapolis, MD 21401

Robert E. Menzer

Director

U.S. Environmental Protection Agency

Environmental Research Laboratory

Sabine Island

Gulf Breeze, FL 32561-5299

Carolyn Merry

Dept of Civil Engineering

470 Hitchcock Hall

Ohio State niversity

2070 Neil Avenue

Columbus, OH 43210-1275
William Michner

Baruch Institute

University of South Carolina

Columbia, SC 29208

Chris Mobley

OAA/ ational Marine Fisheries Service

777 Sonoma Avenue - Room 325

Santa Rosa, CA 95404

John Mooney

NOAA/National Ocean Service

Photogrammetry Branch, N/CG2

1315 East-West Highway

Silver Spring, MD 20910

Tom Mumford

Division of Aquatic Lands

Washington Dept. of Natural

Resources, EX12

Olympia, WA 98504

Ron Myszka

U.S. Environmental Protection Agency

Great Lakes National Program Office (SGB)

230 South Dearborne Street

Chicago, IL 60604

Scott ixon

Rhode Island Sea Grant Program

Graduate School of Oceanography

arragansett, RI 02882-1197

Douglas orton

U.S. Environmental Protection Agency

WH553, Room E-7, 43D

Washington, DC 20460

Richard P. Novitzki

U.S. Environmental Protection Agency

1600 S.W. Western Blvd.

Corvallis, OR 97333

Charles E. OlsonJr.

School of Natural Resources

University of Michigan

Samuel Trask Dana Building

430 East University Street

Ann Arbor, MI 48109-1115

Chris Onuf

U.S. Fish and Wildlife Service

ational Wetlands Research Center

Campus Box 339, CCSU

6300 Ocean Drive

Corpus Christi, TX 78412

Appendix 6: Workshop Participants 89

Robert Orth

Virginia Institute of Marine Science

The College of William and Mary

Gloucester Point, VA 23062

Jacquelyn Ott

ERIM-Room C148

Box 8618

Ann Arbor, MI 48107

Keith Patterson

Geonex Martel, Inc.

8950 9th Street, North

St. Petersburg, FL 33703

Brian Pawlak

NOAA/National Marine Fisheries Service

Southeast Fisheries Science Center

Beaufort Laboratory

101 Pivers Island Rd.

Beaufort, NC 28516-9722

Ed Pendleton

U.S. Fish and Wildlife Service

180 Admiral Cochran Drive, Suite 535

Annapolis, MD 21401

Robert Peplies

Dept. of Geography

East Tennessee State University

P.O. Box 22870A

Johnson City, T 37614

Dennis Peters

U.S. Fish and Wildlife Service

WI Regional Wetland Coordinator

Eastside Federal Complex

911 NE 11th Ave.

Portland, OR 97232-4181

Ron Phillips

Beak Consultants, Inc.

12931 NE 126th Place

Kirkland, WA 98034-7716

Steve Phillips

Division of Forestry

Alaska Dept. of Natural Resources

P.O. Box 107005

Anchorage, AK 99510-7005

Larry Pomatto

Wetlands and Aquatic Protection

DNREC

P.O. Box 1401

Dover, DE 19903
90 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program _

John Posenau

Virginia Institute of Marine Science

College of William and Mary

Gloucester Point, VA 23062

Sandy Prisloe

Natural Resources Center

Dept. of Environmental Protection

Room 545

State Office Building

Hartford, CT 06106

Jane Provancha

NASA

Biomedical and Environmental Laboratories

Mail Code BIO-2

Kennedy Space Center, FL 32899

Warren PulichJr.

Texas Parks and Wildlife Dept.

Resource Protection Division

4200 Smith School Road

Austin, TX 78745

Charles Racine

U.S. Army Corps of Engineers

Cold Regions Research and

Engineering Laboratory

72 Lyme Road

Hanover, NH 03755

David Rackley

NOAA/National Marine Fisheries Service

Southeast Region

9450 Koger Blvd.

St. Petersburg, FL 33702

Ann Rasberry

Programmer/Analyst

Forest Park and Wildlife Service

Tawes State Office Bldg. B-2

Annapolis, MD 21401

Jill Reichert

Maryland Water Resources Admin.

Tawes State Office Bldg. D-2

Annapolis, MD 21401

Kenneth R. Reisinger

Div. of Rivers and Wetlands Conservation

Bureau of Water Resources Management

P.O. Box 8761

Harrisburg, PA 17105-8761

Becky Ritter

Ducks Unlimited, Inc.

One Waterfowl Way

Long Grove, IL 60047

Tony Reyer

NOAA/National Ocean Service

Strategic Environmental Assessments Div.

1305 East West Highway

SSMC-4, 9th Floor

Silver Spring, MD 20910

Steve Robb

Salisbury State University

Image Processing Center

110 Power Street

Salisbury, MD 21801

Robert Rolley

Division of Fish and Wildlife

300 West 1st Street

Bloomington, IN 47403

Charles Roman

U.S. Department of the Interior

National Park Service

Cooperative Research Unit

Graduate School of Oceanography

University of Rhode Island

Narragansett, Rl 02882-1197

Lynn Sampson

USDA/Soil Conservation Service

Manley Miles Building

1405 South Harrison

East Lansing, MI 48823

Don Scavia, Director

OAA/Coastal Ocean Program Office

1315 East-West Highway

Silver Spring, MD 20910

Charles Simenstad

Wetland Ecosystem Team

Fisheries Research Institute

University of Washington

Seattle, WA 98195

Tim Schlagenhaft

Minnesota Dept. of Natural Resources

Section of Fisheries

Box 12

500 Lafayette Road

St. Paul, MN 55155

Peter F. Sheridan

NOAA/National Marine Fisheries Service

4700 Avenue U

Galveston, TX 77551-5997
Fred Short

Jackson Estuarine Laboratory

University of New Hampshire

RFD 2

Adams Point

Durham, NH 03824

Karen Siderelis

North Carolina Center for Geographic

Information and Analysis

North Carolinia Dept. of Environment, Health

and Natural Resources

Raleigh, NC 276] ]-7687

Gene Silberhorn

Virginia Institute of Marine Science

The College of William and Mary

Gloucester Point, VA 23062

Jerry Spratt

California Dept. ofFish and Game

2201 Garden Road

Monterey, CA 93940

Louisa Squires

U.S. Environmental Protection Agency

] 600 SW Western Blvd.

Corvallis, OR 97333

Kristen Stout

Bionetics Corp.

P.O. Box ]575 (VHFS)

Warrenton, VA 22186

Judy Stout

University of South Alabama

Dauphine Island Sea Laboratory

Marine Environmental Sciences Consortium

P.O. Box 369

Dauphine Island, AL 36528

Larry Strong

U.S. Fish and Wildlife Service

Northern Prairie Wildlife Research Center

Route 1, Box 96C

Jamestown, ND 5840]

Richard P. Stumph

USGS Center for Coastal Geology

600 4th Street South

St. Petersburg, FL 3370]

Michael Sullivan

Mississippi State University

Dept. of Biological Sciences

P.O. Drawer GY

Mississippi State, MS 39762-5759

Appendix 6: Workshop Participants 91

Kevin Summers

U.S. Environmental Protection Agency

EMAP-NC Gulf Province Manager

Gulf Breeze Environmental Research Manager

Gulf Breeze, FL 3256]-5299

John Sutherland

NOAA/National Sea Grant College Program

R/OR]

1335 East-West Highway

Silver Spring, MD 209] 0

Billy Teels, National Biologist

USDA Soil Conservation Service

South Ag Bldg.

P.O. Box 2890

Room 6]44

Washington, DC 200] 3

Michael Thabault

NOAA/National Marine Fisheries Service

777 Sonoma Avenue, Rm 325

Santa Rosa, CA 95404

James P. Thomas

NOAA/National Marine Fisheries Service

Restoration Center

Office of Protected Resources, F/PR5

] 315 East-West Highway

Silver Spring, MD 209]0

Greg Tilley

Maryland Water Resources Admin.

Tawes State Office Bldg. D-2

Annapolis, MD 2]401

Ralph Tiner

U.S. Fish and Wildlife Service

NWl Regional Wetland Coodinator

300 Westgate Center Drive

Route 2 & 1]6 North

Hadley, MA 01035

Tom Tiner

Salisbury State University

Image Processing Center

110 Power Street

Salisbury, MD 21801

Bill Tippets

California Dept. of Parks & Recreation

8885 Rio San Diego Drive

Suite 270

San Diego, CA 92108

Robert Virnstein

St.Johns River Water Management District

P.O. Box 1429

Palatka, FL 32178
92 NOAA Technical Report NMFS 123: Dobson et al.: Coastal Change Analysis Program _

Joel Wagner

National Park Service - Air

12795 W. Alameda

Lakewood, CO 80225

Carl Weaver

MDE, Chesapeake Bay and

Special Programs

2500 Broening Highway

Baltimore, MD 21224

David Weaver

Massachusetts Office of Environ. Mfairs

GIS Division

20 Somerset St., 3rd Floor

Boston, MA 02108

Jeff Weber

Oregon Coastal Program

Dept. of Land Conservation & Development

320 SW Stark, Rm 530

Portland, OR 97204

Fred Weinmann

U.S. Environmental Protection Agency

(WD-138)

1200 6th Avenue

Seattle, WA 98101

Jennette Whipple

NOAA/National Marine Fisheries Service

Tiburon Laboratory

3150 Paradise Dr.

Tiburon, CA 94920

Bill Wilen

National Wetlands Inventory

U.S. Fish and Wildlife Service

400ARL-SQ

18th and C Streets, NW

Washington, DC 20240

Donald Williams

c/o Army Corps of Engineers

Planning Division

P.O. Box 1027

Detroit, MI 48231

Ken Winterberger

United States Forest Service

Forestry Science Laboratory

201 E. 9th Avenue

Suite 303

Anchorage, AK 99501

Lisa Wood

NOAA/National Marine Fisheries Service

Southeast Fisheries Science Center

Beaufort Laboratory

101 Pivers Island Road

Beaufort, NC 28516-9722

Jay Zieman

University ofVirginia

Dept. of Environmental Sciences

Clark Hall

Charlottesville, VA 22903

Kevin Zytkovicz

Minnesota Dept. of Natural Resources

Ecological Services, Box 25

500 Lafayette Road

St. Paul, MN 55155