Protocol for Landsat-Based Monitoring of Landscape 

Dynamics at North Coast and Cascades Network Parks

U.S. Department of the Interior

U.S. Geological Survey

Techniques and Methods 2–G1

Prepared in cooperation with the NORTH COAST AND CASCADES NETWORK, 

NATIONAL PARK SERVICE

Chapter 1 of Book 2,

Collection of Environmental Data

Section G, Remote Sensing

Protocol for Landsat-Based Monitoring of 

Landscape Dynamics at North Coast and 

Cascades Network Parks

By Robert E. Kennedy, Warren B. Cohen, Alan A. Kirschbaum, and Erik Haunreiter

Chapter 1 of 

Book 2, Collection of Environmental Data

Section G, Remote Sensing

Prepared in cooperation with the 

NORTH COAST AND CASCADES NETWORK, NATIONAL PARK SERVICE

Techniques and Methods 2–G1

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

DIRK KEMPTHORNE, Secretary

U.S. Geological Survey

Mark D. Myers, Director

U.S. Geological Survey, Reston, Virginia: 2007

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Although this report is in the public domain, permission must be secured from the individual copyright owners to 

reproduce any copyrighted materials contained within this report.

Suggested citation:

Kennedy, R.E., Cohen, W.B., Kirschbaum, A.A., and Haunreiter, Erik, 2007, Protocol for Landsat-based monitoring of 

landscape dynamics at North Coast and Cascades Network Parks: U.S. Geological Survey Techniques and Methods 

2-G1, 126 p.
iii

Figures

Figure 1. Graphs showing conceptual relations affecting the choice of remote-sensing

technologies and methods for monitoring different ecological phenomena ......... 3

Figure 2. A schematic view of the steps needed to convert raw remote-sensing data into

information useful to natural-resources professionals ................................. 6

Figure 3. Schematic showing conceptual description of the relation between apparent

spectral change and real, meaningful change on the ground ........................ 7

Figure 4. Tasseled-cap spectral space, showing regions where physiognomic types

approximately are consistent across ecosystems .................................... 8

Figure 5. Tasseled-cap images and associated change products for an area adjacent to

Mount Rainier National Park ............................................................... 11

Figure 6. Flow diagram showing steps in the startup phase .................................... 14

Figure 7. Flow diagram showing steps in Type 1 change detection .............................. 15

Figure 8. Flow diagram showing steps in Type 2 change detection .............................. 16

Figure 9. An example of a full error matrix used to report validity of a change product ...... 18

Tables

Table 1. Ecological monitoring goals of the North Coast and Cascades Network Parks

evaluated at three project meetings, 2004 and 2005 .................................... 4

Table 2. North Coast and Cascades Network monitoring goals grouped by change

interval needed for detection ............................................................ 5

Table 3. Satellite-to-satellite interpretation change classes .................................... 12

Table 4. Linking monitoring goals to satellite-to-satellite interpretation classes ............ 13

Table 5. Sequential phases in the monitoring program .......................................... 17

Contents

I. Background and Objectives......................................................................................................................1

II. Sampling Design........................................................................................................................................9

III. Methods...................................................................................................................................................10

IV. Data Handling, Analysis, and Reporting.............................................................................................17

V. Personnel Requirements And Training ................................................................................................18

VI. Operational Requirements....................................................................................................................19

VII. References Cited...................................................................................................................................20

SOP 1. Acquiring Landsat Imagery ...........................................................................................................23

SOP 2. Preprocessing Landsat Imagery ..................................................................................................31

SOP 3. Physiognomic Change Detection .................................................................................................67

SOP 4. Validation of Change-Detection Products ..................................................................................91

SOP 5. Data Management ........................................................................................................................117

SOP 6. Revising Protocol ..........................................................................................................................125
iv

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Protocol for Landsat-Based Monitoring of Landscape 

Dynamics at North Coast and Cascades Network Parks

By: Robert E. Kennedy,1 Warren B. Cohen1, Alan A. Kirschbaum2, and Erik Haunreiter2

1

 USDA Forest Service, Pacific Northwest Research Station, 3200 SW Jefferson Way, Corvallis, OR 97330

2 Department of Forest Service, Oregon State University, Corvallis, OR 97330

I. Background and Objectives

As part of the National Park Service’s larger goal of 

developing long-term monitoring programs in response 

to the Natural Resource Challenge of 2000, the parks of 

the North Coast and Cascades Network (NCCN) have 

determined that monitoring of landscape dynamics is 

necessary to track ecosystem health (Weber and others, 2005). 

Landscape dynamics refer to a broad suite of ecological, 

geomorphological, and anthropogenic processes occurring 

across broad spatial scales. The NCCN has sought protocols 

that would leverage remote-sensing technologies to aid 

in monitoring landscape dynamics. The parks’ personnel 

considered remote sensing necessary for several reasons 

(adapted from Woodward and others, 2002):

Monitoring must cover large areas often inaccessible 

by foot travel.

Some large-area processes are best viewed from the 

landscape perspective afforded by remote sensing.

1.

2.

Narrative

Version 1.01 (September 6, 2006)

Revision History Log:

Previous 

VersionNumber Revision Date Author Changes Made Reason for Change New Version Number

n.a. 07-05-06 REK Final version under 

agreement

1.0

1.0 09-15-06 REK Wordsmithing and 

updating to reflect 

use of current-year 

rather than historical 

PMR field data

Preparation for 

publication

1.01

I. Background and Objectives   

Some small-area events are captured more efficiently 

and consistently with repeat viewing using remote 

sensing.

At a workshop organized by Andrea Woodward (U.S. 

Geological Survey-Biological Resources Division/Forest 

and Rangeland Ecosystem Science Center), October 2002, a 

variety of remote sensing experts were convened to develop 

strategies for remote-sensing based monitoring (Woodward 

and others, 2002). Several factors were considered potential 

challenges to application of remote-sensing methods to the 

parks in the Pacific Northwest, including frequent cloud 

cover and dense coniferous vegetation. Considering costs and 

benefits of a wide range of active and passive remote-sensing 

systems, one of the central conclusions of the workshop was 

that Landsat Thematic Mapper/Enhanced Thematic Mapper 

(TM/ETM+) data should serve as the core tool for monitoring 

landscape dynamics. This protocol describes a strategy for 

monitoring a wide range of landscape dynamics using such 

data.

3.
The project was conducted under an interagency 

agreement between the U.S. Department of Agriculture Forest 

Service and the U.S. Geological Survey Biological Resources 

Division/Forest and Rangeland Ecosystem Science Ccenter 

program (Interagency Agreement 03WRPG0008), and was 

divided into four tasks. Task 1 was development of a study 

plan between January and June 2004. Between June 2004 

and February 2005, the authors did field and remote sensing 

analysis for Task 2, which was the testing and evaluation 

stage. Task 2 finished in February 2005 when the authors 

presented their findings to the National Park Service (NPS) 

and BRD personnel. Three meetings were held during Tasks 1 

and 2 to allow NPS personnel to prioritize goals and evaluate 

options for inclusion of methods in the final protocols. Task 

3 was the writing of this protocol, and Task 4 is the revision 

of the protocol according to comments gathered in the 

anonymous peer review process.

The authors’ original proposal to the USGS BRD/FRESC 

and the NPS (hereafter, the “parks”) had several themes. First, 

the remote-sensing monitoring program would be built around 

the TM and ETM+ sensors (Cohen and Goward, 2004). 

Second, the original strategy for monitoring change was to 

identify a baseline landcover map from among several already 

available at the parks, use various change detection methods 

to identify areas of change, and remap only the changed 

areas. The remapped areas could then be field tested for 

accuracy, allowing comparison across different baseline maps 

and change-detection methods. Following the first meeting 

with the parks (January 14, 2004), this approach was altered 

substantially. The resulting strategy is discussed in the section, 

“Strategy for Monitoring.”

The third major theme of the original proposal was the 

goal of articulating the cost, confidence, and utility of using 

remote-sensing technology to address specific landscape 

monitoring goals. Some attributes can be tracked by remotesensing technologies with relatively high confidence even 

at relatively low cost (Type I), while others are difficult to 

monitor even with substantial outlays of time and money 

(Type II; fig. 1A). In general, confidence in results is related 

positively to investment costs, but the confidence/cost ratio 

(the slope of the increase) varies by the attribute being tracked.

Predicting whether a given ecological phenomenon or 

landscape attribute is more likely Type I or Type II requires a 

basic understanding of core remote sensing and scale concepts. 

Remote sensing instruments measure electromagnetic 

energy emanating from or being reflected by an object. The 

sensitivity of an instrument is determined primarily by its 

engineering design, which represents a strategy to balance 

   Protocol for Landsat-Based Monitoring of Landscape Dynamics at North Coast and Cascades Network Parks

tradeoffs between sensor grain size, temporal frequency, and 

spectral response. Grain size is the spatial property describing 

the smallest footprint on the ground over which the sensor 

measures electromagnetic energy (corresponding roughly to 

the “pixel size” reported for many sensors). The smaller the 

grain size, the more sensor elements or the more measurement 

occasions are needed to map a given geographic area, which 

means that the same location can be mapped less frequently 

(with a coarser temporal grain). Spectral response describes 

both the region of the electromagnetic spectrum over which 

the sensor measurements electromagnetic energy, and the 

sensor’s physical sensitivity across that region. The larger the 

region of the electromagnetic spectrum over which a sensor 

measures energy, the more energy it can capture per time 

period, which tends to increase the signal relative to ambient 

noise levels. Taken together, these design specifications define 

an instrument’s measurement properties.

At the most basic level, a landscape phenomenon can 

be considered Type I when its physical properties match 

well with an instrument’s measurement properties. A good 

match sets the upper limit on the theoretical confidence with 

which that attribute can be sensed. This confidence level 

is diminished by grain mismatch between the sensor and 

the object (spatial, temporal), by uncoupling between the 

landscape phenomenon and its detectable physical properties, 

by inappropriateness of the analysis tools applied to the 

problem, or by poor quality of reference data used to build and 

test models. Note this important implication: Improving grain 

size match, analysis techniques, or reference data cannot 

boost detection confidence above the theoretical maximum 

level determined by an instrument’s measurement properties. 

Often, however, actual confidence levels are far lower than this 

theoretical maximum, and thus increased effort (cost) applied 

toward analysis and reference data collection can achieve 

higher confidence.

Although confidence depends to some degree on cost, 

utility to the parks may not depend entirely upon confidence 

(fig. 1B). In some cases, attributes that can be measured with 

high confidence through remote-sensing methods may not be 

useful to the parks (curve labeled “irrelevant information” in 

fig. 1B), while others may be extremely useful to monitor even 

if confidence in their accuracy is relatively moderate (curve 

labeled “relevant information” in fig. 1B). Often, it is possible 

to attain higher ultimate utility to the parks by monitoring a 

slightly different attribute than the parks might originally have 

intended, simply because its confidence can be much higher at 

a given cost than for the original attribute.
Narrowing Goals

The first phase in the project was to narrow and 

articulate monitoring goals. At the January 14th, 2004, 

meeting in Seattle, the authors aided the parks in articulating 

their monitoring goals in the framework of remote-sensing 

technology. Monitoring goals were grouped into broad 

monitoring themes: Alpine vegetation, Forested vegetation, 

Disturbance and recovery, Riparian areas and rivers, Land 

use and land cover, and Snow and ice. Key monitoring 

goals were then described in terms of their relevant spatial 

and temporal grain and extent, which were then matched 

to the appropriate remote-sensing technology. Considering 

these scale issues, as well as the authors’ recommendations 

on relative difficulty of monitoring, the parks prioritized 

which monitoring goals were to become the foci of protocol 

development testing of Task 2. These priorities were revisited 

at the May 13, 2004, meeting in Seattle, resulting in a slight 

adjustment of some priorities. The combined result of group 

prioritization of monitoring goals from the January 14 and 

May 13 meetings is listed in table 1.

Only monitoring goals identified as “Achievable 

Monitoring Goals” in table 1 were considered for testing in 

Task 2 of this study. By narrowing monitoring objectives 

to those likely to be achieved with TM/ETM+ imagery, the 

parks began defining the cost/confidence/utility envelope 

for their goals. Several monitoring goals were considered 

unachievable under the current protocol development. These 

included goals that required information at a finer grain size 

than Landsat (for example, many riparian monitoring goals) 

or involved changes with minimal spectral manifestation 

(changes between herbaceous communities in the alpine). 

Figure 1. Conceptual relations affecting the choice 

of remote-sensing technologies and methods for 

monitoring different ecological phenomena. (A) 

Confidence in an ecological remote-sensing product 

generally increases as increased resources are applied 

to imagery, processing, analysis, and collection of 

ground-truth data. Some ecological attributes can 

be mapped with high confidence at minimal expense 

(Type I), while others are extremely difficult to map 

with a given class of remote-sensing technology, even 

with significant expense (Type II). (B) Usefulness to a 

park depends, in part, on the confidence with which 

the attribute can be determined and mapped, but not 

always. Some attributes that can be derived from remote 

sensing are irrelevant for the National Park Service, 

and will be of little utility even when confidence in 

their measurement is high. Others are relevant, even at 

relatively low confidence levels.

I. Background and Objectives 

Other goals to be skipped under this protocol include those 

already being tracked by other agencies (volcanic eruptions 

and shoreline change). Encroachment of trees into the alpine, 

although a high priority, was acknowledged to be particularly 

challenging. Snow and ice monitoring are discussed separately 

in the following sections.

For additional clarity, the monitoring goals were grouped 

according to the time period of observation needed to capture 

them. Fast processes, such as disturbance, must be monitored 

yearly because post-event recovery can obscure the original 

signal if intervals of monitoring are too great. Slow processes 

require a long period to manifest themselves. Most of the 

achievable monitoring goals are regrouped in table 2.

The goals for monitoring snow, ice, and glaciers fell 

into an entirely different temporal scheme. Here, important 

monitoring questions involve attributes such as the maximum 

extent of snow cover in a year, duration of snow cover across 

the landscape, and spatial distribution of glacial snow and 

ice at the snow-minimum time of year. In all three cases, 

observation must occur many times within a single year, yet 

the time period over which these attributes change spans 

many years. Landsat TM/ETM+ data lack the high-temporal 

frequency needed to answer these questions alone. Moderate 

Resolution Imaging Spectrometer (MODIS) and Advanced 

Very High Resolution Radiometer (AVHRR) data have an 

appropriate temporal frequency, and the MODIS sensor has a 

standard product of an 8-day map of snow extent (with both 

daily and aggregate snow-cover information). The confidence 

at which fine-grained properties of snow cover could be 

mapped with MODIS was expected to be relatively low, but 

the information was important enough even at a coarse grain 

that exploration of this topic was desired (an example of 

highly “relevant information” in fig. 1B).
Table 1. Ecological monitoring goals of the North Coast and Cascades Network Parks evaluated at three project meetings, 2004 and 

2005.

[All goals are characterized in terms of spacial and temporal grain. Based on spatial and temporal grain, as well as importance to the NCCN parks, each goal was 

assigned a priority for consideration in the study plan. Priority: Topics marked “advise” indicate goals that likely were not achievable within this protocol using 

Landsat TM/ETM+ imagery, but on which the parks sought separate guidance on possible approaches. Those that were thought likely to be achievable using 

Landsat-based satellite data are noted. Abbreviations: USGS, U.S. Geological Survey; NCCN, North Coast and Cascades Network; m, meter; ?, uncertain; 

>, greater than; <, less than]

Topic Subtopic Spatial grain Temporal grain Priority Achievable 

monitoring goal?

Alpine

vegetation

Bare ground impacts 1 m 5 year Skip (need higher resolution)

Interface with forest 1 m / 30 m Decadal High/Advise Yes

Vegetation communities 1 m > Annual High Advise

Forested

vegetation

Hardwood/conifer 30 m > Annual High Yes

Forest structure (classes) 1 m / 30 m > Annual High Yes

Disturbance and 

recovery

Vegetation disturbance in

 avalanche chutes

1 m / 30 m 5–10 years High Yes

Landslides 1m / 30 m Annual / > Annual High Yes

Fire 30 m Annual / > Annual High Yes

Insect/disease 1 m / 30 m Annual / > Annual High Yes

Windthrow 1 m/ 30 m Annual / > Annual High Yes

Pollution ? ? Low (important in future; 

impacts are not extensive

enough to detect at present)

Shoreline change 1 m Annual / > Annual Skip (problematic and

already being done)

Developed areas (within

 parks)

1 m / 30 m Skip (accessible and need

higher resolution)

Forest harvest 30 m Annual High Yes

Volcanic 500 m–1,000 m > Annual Skip (already being done by

USGS)

Riparian areas 

and rivers

Riparian vegetation 1 m (30 m) High (hardwood/conifer 

distinction)

Yes (30 m)

Riparian width 1 m Advise (Landsat not useful)

Floods and channel 1 m Advise 

Wood ? 1 m Lidar ? Advise

Lake shoreline 1 m Advise

Land use 1 m Advise

Sedimentation 1 m Advise

Land use and 

land cover

Various (primarily

outside of parks,

including clearcuts)

30 m for most > Annual High Yes

Snow and ice Glaciers 1m / 30 m > Annual High Yes

Snow cover 30 m / 500–1,000 m Annual High (integrating Landsat

with MODIS)

Yes

Ice out (lakes) 1 m / 30 m Annual High Maybe

 Protocol for Landsat-Based Monitoring of Landscape Dynamics at North Coast and Cascades Network Parks
Strategy for Monitoring

The authors’ original plan relied on the identification 

of baseline maps from which change areas could be mapped 

forward in time for monitoring. At the January 14, 2004, 

meeting in Seattle, a key problem became apparent: Among 

the parks there is a sense that the mid-1990s era Pacific 

Meridian Resources (PMR) maps, which are the most detailed 

of the available maps, have inadequate detail to be useful 

to field ecologists, and that these maps should not form 

the basis of a monitoring plan. Roger Hoffman (OLYM; 

Olympic National Park) raised an idea that evolved into a 

new direction for evaluation. Rather than build a change map 

based on a particular base map, the focus should be instead a 

methodology for detecting change that is —to the maximum 

extent possible—separated from any particular baseline map. 

The goal of protocol development would shift from creation 

of new maps of landcover to development of maps strictly 

focusing on areas that have changed (see notes in app. 1 from 

the January 14, 2004, meeting). Under this “change-focused” 

strategy, the remote-sensing data would serve as a filter for the 

Parks, highlighting areas that appear to be changing and which 

can be investigated in further detail either on the ground or 

with more detailed imagery (airphotos, high resolution satellite 

data, etc.).

The change-focused approach is attractive for several 

reasons:

Errors in baseline maps are not incorporated into the 

analysis—all errors will be traceable solely to the 

change detection methodology. Although the end goal 

•

I. Background and Objectives 

Table 2. North Coast and Cascades Network 

monitoring goals grouped by change interval 

needed for detection.

Type 1: Monitor yearly

Avalanche chute clearing

Landslides

Fire

Insect/disease defoliation in forest

Windthrow

Riparian disturbance

Clearcuts

Rural development

Type 2: Monitor decadally

Alpine tree encroachment

Hardwood/conifer forest composition

Forest structure 

may be to attach the change products to a particular 

map, which will have errors, the errors will be 

separable.

By focusing on areas that have changed, field sampling 

of the landscape may be more efficient. Nevertheless, 

the need to validate areas labeled “no-change” still 

may require considerable sampling effort.

By making the change methodology as modular as 

possible, it can be “bolted on” to any map or maps 

at either the beginning or ending of a given changedetection interval. This gives substantial flexibility 

to adapt the change products to the needs of different 

users within the parks, or as new maps become 

available.

Challenges to Utilizing a Change-Focused 

Approach

There are several challenges to the change-focused 

approach. In remote-sensing technology, the changes seen 

over time are spectral, which have no inherent meaning 

ecologically, as they represent relative quantities of reflected 

light energy. While the goal of the change-focused approach 

was eliminating dependence on any particular landcover map, 

it is not possible to understand the spectral changes without 

reference to some sort of reality or truth.

•

•
The importance of “truth” data is illustrated in 

figure 2, which lists the conceptual steps needed to move 

from remote-sensing data to actual ecological information. 

The base layer in figure 2 represents the digital numbers 

returned from a remote device. These raw data have no 

inherent physical meaning or location information. They also 

incorporate undesirable effects of atmospheric conditions, 

sun illumination and surface orientation, that are often 

unrelated to the properties of the surface materials that are 

the ultimate target of sensing. Raw images must undergo 

several so-called preprocessing steps to move them from a 

raw to a clean format that better characterizes actual surface 

Figure 2. A schematic view of the steps needed to convert raw remote-sensing data into information useful to 

natural-resources professionals. Raw remote-sensing data are digital numbers arriving from satellite or other 

digital-recording devices, and have no inherent physical meaning. Cleanup of these data requires information on 

engineering specifications of the instrument and on conditions of the atmosphere, sun, and surface at time of data 

collection. These clean remote-sensing data are related to surface conditions with the aid of ancillary data from 

ground measurements or from basic models. In some cases, further application of models and rules is needed to 

infer spatial pattern and underlying processes.

   Protocol for Landsat-Based Monitoring of Landscape Dynamics at North Coast and Cascades Network Parks

conditions. These clean data are still one step removed from 

the information content required in this project, because 

they characterize the surface only in terms of its reflectance 

properties. Assigning meaningful labels to those reflectance 

qualities requires that the clean data be linked through models 

or rules to observations made at the surface. The result of this 

process is a map of surface properties, such as a landcover 

characterization, that has meaning to the end user. Change 

detection and labeling involve tracking these properties over 

time, resulting in maps of landscape change. Without reference 

to maps, some other source of truth data must be used to link 

reflectance information to surface conditions.
The processing steps illustrated in figure 2 expose 

another challenge to change-detection studies in the parks 

of the NCCN. Moving from raw to clean remote-sensing 

data in a single date requires that the effects of sensor, 

atmosphere, illumination, and topography be removed 

from the signal. As noted in the October 2002 meeting that 

preceded this study (Woodward and others, 2002), the parks 

of the NCCN experience frequent cloud cover and contain 

steep terrain. Atmospheric and terrain correction were 

considered potentially very challenging steps, but necessary. 

These challenges are amplified when changes over time are 

sought. Conceptually, when images from different dates have 

different spectral values, those different spectral values could 

be the result of artifacts in either sensor or viewing condition 

(the result of technological noise that cannot be cleaned, 

or of atmospheric, illumination, or topographic effects that 

change apparent spectral properties of the surface), the 

result of changes on the ground that are spectrally real but 

that are uninteresting for the change analysis (for this study, 

phenological changes), or the result of real changes of interest 

for the analysis (fig. 3). Separating real changes from the other 

two categories can be difficult.

A third challenge arises when change detection maps 

are completed. Since the product being tested is a map 

of change, not a map of landcover, validation requires an 

Figure 3. Conceptual description of the relation between apparent spectral change and real, 

meaningful change on the ground. Some spectral changes are artifacts caused by variation in 

illumination, atmospheric, sensor calibration, or geometric-registration conditions. Some spectral 

changes are real changes to the spectral condition of the cover on the ground, but which are not 

of interest in monitoring (i.e. phenological state at the date of image acquisition). Finally, some 

spectral changes are real and of interest to the parks.

I. Background and Objectives 

accurate assessment not only of current conditions, but also of 

conditions at some point in the past. Thus, field measurements 

of current conditions alone will not be sufficient, and other 

approaches must be included.

Finally, the parks require monitoring of a suite of 

landscape dynamics simultaneously (tables 1 and 2). The 

multiple goals of ecological monitoring contrast with much 

of the remote-sensing research literature in change detection, 

where typically only a single attribute is tracked (Bradley 

and Mustard, 2005; Cohen and others, 2002; Collins and 

Woodcock, 1996; Coppin and others, 2004; Gumbricht 

and others, 2002; Hayes and Sader, 2001; Michener and 

Houhoulis, 1997; Muchoney and Haack, 1994; Roy and 

others, 2002; Royle and Lathrop, 2002; Sader and others, 

2003; Trigg and Flasse, 2001; Viedma and others, 1997; and 

Wilson and Sader, 2002). In many methods documented in 

the literature, analytical tools appropriate for one type of 

monitoring goal would not be appropriate or useful for another 

monitoring goal. Additionally, future monitoring questions 

may require retrospective analysis of as-yet-unknown 

processes or attributes. Therefore, any tools developed for this 

protocol must be flexible and retain as much information as 

possible, even if beyond the currently recognized monitoring 

goals.
Tracking Change in Physiognomic Types

At the second project meeting in May 2004, the authors 

and the parks agreed on a general strategy for implementing 

the change-focused approach. Rather than derive truth 

information from existing landcover maps or from expensive 

new photo- or field-interpretation programs, cover-type labels 

would be defined in broad physiognomic categories that could 

be interpreted directly from spectral data alone. The strategy 

was based on the tasseled-cap transformation of TM/ETM+ 

data. The tasseled- cap transformation compresses the six 

nonthermal bands of Landsat TM and ETM+ imagery into 

three spectral axes, originally named brightness, greenness, 

and wetness (Crist and Cicone, 1984). Although spectral 

interpretation of Landsat TM and ETM+ imagery can be 

achieved by trained interpreters in any spectral space, the 

tasseled-cap transformation’s use of three standardized axes 

makes it especially useful for interpretation using standard 

three-color theory. Based on the authors’ experience with 

tasseled-cap imagery from many different biomes, it appears 

possible to make general interpretations of landcover based on 

tasseled-cap spectral character alone in every system (Cohen 

and others, 1998; Cohen and Spies, 1992; Maiersperger and 

others, 2001; Oetter and others, 2000; and Parmenter and 

others, 2003). For example, conifer and broadleaf forest, soil 

and rock, water, and snow and ice display predictable spectral 

characteristics in every ecosystem examined (fig. 4). As covertype definitions become more finely described for a particular 

ecosystem, spectral ambiguity increases. If definitions of 

physiognomic character are kept relatively generalized, 

Figure 4. Tasseled-cap spectral space, showing regions where physiognomic types 

approximately are consistent across ecosystems.

 Protocol for Landsat-Based Monitoring of Landscape Dynamics at North Coast and Cascades Network Parks

however, changes likely are to be tracked with relatively high 

confidence. The theme of the change-focused approach, then, 

becomes one of tracking changes among physiognomic types 

using general knowledge of the spectral properties of those 

types. Several broad categories of change detection based on 

tasseled-cap imagery were to be evaluated in Task 2.

A second theme of testing in Task 2 involved 

preprocessing. As figure 2 shows, each successive step in 

processing is built on the layer below it. Many monitoring 

objectives require detection of changes with very small 

footprints (windthrow) or with linear features that contrast 

with their surroundings (rivers, avalanches). Therefore, 

geometric registration was presumed to be of paramount 

importance. Additionally, the change-focused approach 

requires that the spectral space of images from the two dates 

be aligned. Radiometric correction, which accounts for 

effects that cause misalignment of spectral space, including 

engineering, sun angle, and atmospheric effects, thus also was 

expected to be critically important. Radiometric correction 

also is necessary to build consistent and robust long-term 

datasets.

Therefore, the goals of Task 2 were to develop pilot 

studies to examine approaches to implement the changefocused strategy and to investigate best practices for 

preprocessing of Landsat TM/ETM+ imagery in support of the 

parks’ monitoring goals. Additionally, because snow and ice 

questions were acknowledged to require more than Landsat 

TM/ETM+ data alone, a separate pilot study investigating the 

potential use of MODIS snow products was needed.
Key Findings of the Pilot Studies in Task 2

A brief overview of the findings of Task 2 is relevant. 

First, the authors determined that typical tolerances for 

geometric coregistration of images (Richards, 1993) are often 

inadequate for the monitoring goals desired by the parks. The 

authors have included specific recommendations for ordering 

and processing imagery standard operating procedures 1 

and 2 (SOP 1 and 2) that improve geometric properties of 

coregistered images. Included is a version of automatic tiepoint software that finds hundreds of image tie points quickly, 

allowing excellent coregistration of images. These approaches 

will minimize, but never entirely eliminate, artifacts of 

misregistration.

Second, detecting subtle changes caused by insect 

defoliation, encroachment of woody vegetation into 

herbaceous zones, and recovery or transition in forest 

type requires that radiometric and atmospheric differences 

between images be removed. The authors found that excellent 

radiometric normalization of images can be achieved with 

the MADCAL algorithm (Canty and others, 2004), an 

implementation of which is included with this protocol 

(SOP 2).

Third, spectral separation of forest age and structure 

information requires large sets of photo-interpreted reference 

data, which was determined by the parks to be beyond the 

scope of the work desired under this protocol. Therefore, 

changes in forest structure would not be modeled explicitly 

in terms of objective age or structure attributes. Rather, they 

would need to be wrapped into general change-detection 

procedures and separated in a relative sense.

Fourth, the authors found that standard change detection 

approaches in the literature must be adapted to handle the 

diversity of changes being sought. The change-detection 

method most attractive to the parks at the February 2005 

meeting was a directed change-vector approach, which 

allowed more intuitive labeling of change than the standard 

change-vector approaches in the literature (Lambin and 

Strahler, 1994 and Malila, 1980). The method ultimately was 

considered inadequate because of the lack of connection to 

real quantities on the ground. Therefore, it was used as the 

model for development of a final method for change detection 

based on fuzzy classification approaches (see “Methods” 

section below, as well as details in SOP 3).

Fifth, validation methods must be based on direct 

interpretation of satellite imagery because airphotos will not 

be available for yearly assessments, and because exhaustive 

field validation is too costly for the future budgets of the parks. 

Therefore, the authors have described an approach to validate 

directly from satellite imagery (SOP 4) that allows followup 

with airphoto or field validation.

Finally, monitoring goals for rural development and 

snow and ice extent were determined infeasible with Landsat 

technology alone. Rural development, especially near the 

smaller parks of the NCCN, likely is better achieved through 

more focused investigations that revolve around tax-lot 

information and monitoring of specific locales of interest 

to the parks. The authors showed that raw MODIS data 

may be useful for tracking regionwide temporal and spatial 

patterns of snow extent, but that the MODIS snow product 

itself was not suitable for the parks’ needs. In a pilot study 

for a year of MODIS imagery (water year 2002/MODIS 

data version 4, obtained from EDC in November 2004), the 

8-day maximum snow extent product contained significant 

false positives and false negatives distributed across the 

landscape, and these patterns of error changed for each 

8-day period visually examined, indicating that there was 

no predictable bias that could be corrected. Examination of 

individual day data indicated that cloud cover may have been 

a significant complicating factor. A detailed snow-extent 

mask was extracted for two single dates that coincided with 

Landsat 7 imagery (one in 2001 and one 2002). Relative 

to the fine-grained Landsat data, the MODIS snow product 

underestimated snow extent significantly. However, a simple 

unsupervised classification of the raw MODIS imagery from 

those same dates suggested that the snow signal was present 

in the spectral information of the MODIS imagery, indicating 

that its use may be appropriate if specialized products can 

be developed for the NCCN. Further development of snowmonitoring goals with MODIS will need to be addressed 

separately.

II. Sampling Design

Because the maps of change are produced for every pixel 

in a park, the sample used for the final product is spatially 

exhaustive. Temporal sampling of imagery depends on the 

type of monitoring goal being tracked: Type 1 monitoring 

goals require yearly sampling of imagery, while Type 2 

monitoring goals require decadal sampling.

Various parts of the processing methodology (see 

Methods below) require that sample sites be used for training 

and validation. For example, the radiometric-normalization 

approach requires identification of many thousands of pixels 

that have not changed over time and the validation approaches 

require sampling of the landscape for intensive satellite-tosatellite (S2S) validation or field sampling. These sampling 

issues are confined to the discussions within each SOP.

II. Sampling Design 
III. Methods

Overview of Methodology

At the end of the February 2005 meeting, the parks and 

the authors agreed upon a general structure for Landsat-based 

monitoring of landscape dynamics. The structure was an 

amalgam of approaches tested in Task 2, with several new 

components determined at the end of the February meeting. 

The basic purpose for the change-focused approach would 

remain as it had throughout much of the project: create maps 

of change in cover type that could be used as an alarm to 

indicate where more focused monitoring should occur. Of the 

methods tested in Task 2, the directed change-vector approach 

was attractive to the parks because it captured the complexity 

of change on the landscape in a manner that was relatively 

intuitive to understand. The primary drawback was that the 

relative changes captured by this approach had no inherent 

meaning, either in terms of physical quantities or class-based 

membership. For many management needs, resource managers 

would need summarized versions of these changes that sorted 

the information according to change type and labeled it 

according to certainty thresholds. Traditional classificationbased change methods were attractive in their ability to 

describe spectral space in terms of membership in classes with 

physical or ecological meaning. However, these approaches 

did not capture the complexity of change adequately, and were 

too constrained to allow later reinterpretation if monitoring 

needs were to change. Therefore, the desired approach was 

one that would retain the key characteristics of the directed 

change-vector approach while allowing more explicit 

characterization of the physical characteristics of the changes 

being captured.

Core Approach

After the February 2005 meeting, the authors developed 

an approach to meet those needs. Geometric and radiometric 

preprocessing steps are defined according to those found most 

useful in Task 2. Images on either end of the change interval 

are normalized using the MADCAL approach of Canty and 

others (2004). For a baseline image, expert interpretation of 

a k-means, nonparametric-classification procedure is used to 

partition tasseled-cap space into broad physiognomic classes. 

These classes can be mapped onto the landscape, and thus can 

be linked with any appropriate reference dataset (ground plot, 

airphoto interpretation, etc.) to describe the classes in terms 

deemed relevant to managers.

10 Protocol for Landsat-Based Monitoring of Landscape Dynamics at North Coast and Cascades Network Parks

More importantly, these spectral classes also are used to 

develop multivariate-normal probability distributions for the 

baseline image. Based on the multivariate-normal distribution, 

probability of membership (POM) in all physiognomic classes 

is calculated for every pixel in the baseline image. Because 

the original k-means classification is based on image data, 

classes are distributed across the entire spectral space, such 

that few points on the landscape are far in spectral space from 

a class centroid. With robust radiometric normalization, the 

second image in the change interval can be mapped directly 

into the POM space of the baseline image. By subtracting 

POM images from the two dates, changes on the landscape are 

expressed in terms of changes in the POM classes.

The method is based on well-established classification 

approaches. The POM calculation comes directly from a 

standard maximum likelihood classification process (Richards, 

1993). In the typical incarnation, however, each pixel would 

be assigned to the class in which it had the highest POM. In 

a fuzzy-classification approach for single-date mapping, the 

POM in all classes are retained, allowing pixels to take on 

fuzzy membership in several classes (Wang, 1990; Foody, 

1996). Here, we extend the single-date, fuzzy-mapping 

paradigm into two-date change mapping. When events on the 

ground result in a spectral change, those changes are expressed 

as n-dimensional vectors of changes in class membership 

likelihood, where n is the number of physiognomic classes. 

Essentially, changes in spectral space are converted to 

changes in an n-dimensional POM space, where probability 

is expressed on a 0.0 to 1.0 basis. It is important to emphasize 

our method does not ascribe a single-class label to the class 

with the highest POM; these POM values are carried forward 

for all classes. This avoids the problem of a typical hard 

classification, where change is captured only if it happens to 

cross a discrete boundary in spectral space. Because the POM 

space is defined according to easily grasped physiognomic 

classes, this method provides a descriptor that is more intuitive 

to a nonspecialist audience than descriptors based solely on 

spectral values.

One of the attractive properties of this approach is 

its ability to produce maps of change at different levels of 

probability. By accepting only changes where probability of 

class membership changes dramatically, only high-certainty 

changes are mapped. Lowering the threshold includes changes 

that are increasingly more ambiguous. An example from Mt. 

Rainier National Park is shown in figure 5, where change 

maps at two levels of thresholding are shown (figs. 5B and 

5D). As long as the POM threshold is stated explicitly, there is 

no one correct threshold for change detection. In practice, it is 

likely that only one or two thresholds are of actual interest to 

individual parks, but until this protocol has been implemented 

over time and at different parks, the authors will not prescribe 

a particular threshold for change. Regardless of the threshold 

of change chosen, it must be validated.
Figure 5. Tasseled-cap images and associated change products for an area adjacent to Mount 

Rainier National Park. (A) Tasseled-cap imagery from 1996. Aspect classes are processed 

separately; this imagery shows only northwest aspects. Tasseled-cap brightness is shown in 

red, greenness in green, and wetness in blue. Conifer forest appears as light cyan to dark blue, 

broadleaf vegetation as yellow, and open areas as red or orange. (B) The product of the fuzzychange-detection approach for the 1996 to 2002 period, shows only areas where probability of 

membership (POM) in a given class has changed by more than 70 percent. Red, green, and blue 

color guns correspond to bare soil, broadleaf, and conifer physiognomic types. Insect mortality 

results in negative conifer values and broadleaf values, leaving yellow (G+R color guns positive) 

or green (only G color gun positive) tones. (C) As in part A, but for the year 2002. (D) As in part B, 

but for a change threshold of 50 percent rather than 70 percent.

III. Methods 11
Translating POM Images

Validation of the POM change image is difficult. 

Quantitative validation of a continuous-variable, 

n-dimensional product requires independent estimates of a 

similar n-dimensional space, which is largely impractical. 

Even if these spaces could be validated, the changes in 

physiognomic class membership do not correspond directly 

to the monitoring goals of the parks listed in tables 1 and 

2. Therefore, to increase utility to the parks, physiognomic 

changes expressed in the POM difference images should be 

translated into change labels that can be validated and that can 

be linked more logically to monitoring goals.

For the purposes of both validation and linkage to 

monitoring goals, differences in POM are compressed into 15 

categories of change (table 3) that can be interpreted directly 

from interpretation of tasseled-cap data. Rules for linking 

changes in physiognomic class POM to S2S change classes 

(see below) are presented in SOP 3. Using these rules, an 

image of S2S change class is created from the difference in 

POM images. This categorical map of change labels greatly 

simplifies use of the change products for nonspecialists.

Additionally, the S2S categorical maps can be validated 

directly from satellite imagery. The classes in table 3 form 

the core of the S2S validation approach (described in SOP 4). 

Validation is achieved by visual interpretation of tasseled-cap 

imagery at the start and end of the change interval, as well as 

a tasseled-cap-difference image and a high-resolution digital 

orthoquad (DOQ). Rules are used to aid in interpretation and 

to score the confidence of the interpreter in change calls.

S2S change classes encompass two types of change. S2S 

classes two through seven all involve change to or from water. 

These changes are categorical: if water was present at one time 

period and not the other, the change falls into one of these 

classes. These changes refer entirely to changes in riparian 

areas, not to shoreline changes in stationary water bodies. The 

remaining classes describe changes occurring elsewhere on 

the landscape, and are considered to be relative-change types. 

Classes 10 and 13 are used if vegetation quantity is changing, 

but cannot be determined to be solely change in conifer or 

broadleaf types. As defined here, broadleaf types encompass 

not only broadleaf-forest vegetation, but all non-needleleaf 

herbaceous and woody vegetation.

12 Protocol for Landsat-Based Monitoring of Landscape Dynamics at North Coast and Cascades Network Parks

Table 3. Satellite-to-satellite interpretation 

change classes.

Satellite-tosatellite code Description

1 No change

2 Water to rock/soil

3 Water to partial vegetation cover

4 Water to complete vegetation cover

5 Rock/soil to water

6 Partial vegetation to water

7 Complete vegetation to water

8 Increase in broadleaf

9 Increase in conifer

10 Increase in vegetation

11 Decrease in broadleaf

12 Decrease in conifer

13 Decrease in vegetation

14 Increase in snow

15 Decrease in snow

The S2S classes listed in table 3 represent just one 

approach to labeling change from imagery, and are described 

in more detail in SOP 4. Although suggested for use in this 

protocol, future evaluation of products may lead to addition or 

subtraction of some categories.

Most of the monitoring goals in tables 1 and 2 can be 

described according to labels used in the S2S change classes 

(table 4). As table 4 lists, the physiognomic cover-type 

changes associated with many monitoring goals are the same. 

Loss of vegetation broadly describes many disturbances, for 

example. Ascribing agents of change requires analysis of 

the spatial pattern of the change and its context, as well as 

understanding of the processes that drive landscape dynamics. 

These require contextual pattern analysis and process-based 

rule building, the algorithms for which are not included as 

part of this protocol. Rather, the goal of this protocol is to 

produce maps that highlight and label changes in terms of their 

physiognomic type. These form a strong foundation on which 

to build any number of spatial analyses.
Table 4. Linking monitoring goals to satellite-to-satellite interpretation classes.

 Agent Description of change Satellite-to-satellite 

interpretation class

Group 1: Monitor yearly

Avalanche chute clearing Avalanche occurs in a chute with broadleaf shrubs ↓ Broadleaf, ↓Vegetation

Avalanche occurs in a chute with conifer stands ↓ Conifer, ↓Vegetation

Landslides Landslide clears broadleaf trees or shrubs ↓ Broadleaf, ↓Vegetation

Landslide clears conifer forest ↓ Conifer, ↓Vegetation

Fire Fire occurs in herbaceous, shrub, or broadleaf tree community ↓ Broadleaf, ↓Vegetation

Fire occurs in conifer-shrub/conifer-tree community ↓ Conifer, ↓Vegetation

Insect/disease defoliation in forest Defoliation occurs in broadleaf forest ↓ Broadleaf, ↓Vegetation

Defoliation occurs in conifer forest ↓ Conifer, ↓Vegetation

Windthrow Windthrow occurs in broadleaf forest ↓ Broadleaf, ↓Vegetation

Windthrow occurs in conifer forest ↓ Conifer, ↓Vegetation

Riparian disturbance Shifting stream channel removes or covers sandbar/rock/gravel ↓ Rock/soil →Water

Shifting stream channel builds sandbar/rock/gravel ↓ Water →Rock/soil

Shifting stream channel removes broadleaf trees or shrubs or 

conifer trees

↓ Partial vegetation →Water,

↓ Complete vegetation →Water

Shrub/grass cover quickly recovers on newly deposited sandbar ↓ Water →Partial vegetation cover

↓ Water →Complete vegetation cover

Shifting stream allows recovery of shrub cover on gravel bars ↑ Broadleaf, ↑Vegetation

Longer term recovery of conifer occurs along riparian corridor ↑ Conifer

Clearcuts Clearcut in broadleaf forest ↓ Broadleaf, ↓Vegetation

Clearcut in conifer forest ↓ Conifer, ↓Vegetation

Rural development Conversion from agriculture to developed ↓ Broadleaf, ↓Vegetation

Conversion from broadleaf to developed ↓ Broadleaf, ↓Vegetation

Conversion from conifer to developed ↓ Conifer, ↓Vegetation

Group 2: Monitor decadally

Alpine tree encroachment Broadleaf shrub encroachment into rocky glacial outwash ↑ Broadleaf, ↑Vegetation

Conifer shrub/tree encroachment into rocky areas ↑ Conifer, ↑Vegetation

Hardwood/conifer forest 

composition

Succession from broadleaf shrub to broadleaf tree ↑ Broadleaf

Succession from broadleaf shrub or tree to conifer shrub/tree ↑ Conifer

Conversion from conifer to broadleaf ↑ Broadleaf

Forest structure Transition from broadleaf shrub to broadleaf tree ↑ Broadleaf

Transition from semi-open to closed conifer canopies ↑ Vegetation

Transition from young, dense conifer stands to vertically 

structured older stands

↑ Conifer

III. Methods 13
Detailed Flow of Methods

In a general sense, the flow of methods of image 

processing is captured in the sequence of the first four SOPs 

provided with this document. Preprocessing is applied to 

images at the start and the end of the change interval, and 

includes geometric and radiometric processing (Canty and 

others, 2004; Kennedy and Cohen, 2003; Richards, 1993). 

The change interval is only 1 year for Type 1 monitoring goals 

and a decade or more for Type 2 goals. The change-detection 

approach described above is applied to the images to produce 

a map of changes in POM in nine physiognomic classes 

(SOP 3). Thresholds are applied to the map of change in 

probability, and change types are compressed into one of the 

15 S2S classes listed in table 3. Validation (SOP 4) requires 

Figure 6. Steps in the startup phase. An image from the era corresponding to the date of recent field validation (approximately 

2005) is normalized to reference images, classified, and translated into probability-of-membership (POM) images for two aspect 

classes. These baseline POM images are then used in Type 1 and Type 2 change-detection phases (figs. 7 and 8).

14  Protocol for Landsat-Based Monitoring of Landscape Dynamics at North Coast and Cascades Network Parks

independent interpretation of the imagery at a sample of areas 

across the park, and can be followed by airphoto-based or field 

validation.

The images involved in these steps will differ during 

three distinct phases of the project. The “startup” phase 

focuses on establishing a foundation image that can be 

linked with the field data collected by the NPS in the current 

era under a project designed to validate maps developed in 

the past by PMR (henceforth named “field-collection era.” 

around 2005). Figure 6 provides a flow diagram describing 

this process. First, a Landsat image most appropriate for 

linkage with the field data is identified, purchased (SOP 1), 

and is normalized to a reference ETM+ image acquired from 

the LEDAPs (using methods of SOP 2; LEDAPS reference: 

http://ledaps.nascom.nasa.gov/ledaps/products2005_new.

html). The resultant image becomes the reference image 
against which all future images will be referenced for 

radiometric and geometric normalization. This image is 

then split by aspect, clipped to the study area, and applied 

to a tasseled-cap transformation (SOP 2). For each aspect 

class, the methods of SOP 3 are applied to create a map of 

physiognomic classes for this image. These classes are then 

summarized using the field data collected in approximately the 

year 2005. The classes also are used to build the POM image 

for each aspect class for the field-collection era. These two 

POM images become the baseline images on which all further 

change detection will be based.

Once the startup phase is complete, the “Type 1 change 

detection” phase begins (fig. 7). Each year, a new image is 

purchased (SOP 1), preprocessed (SOP 2), and the POM rules 

for the baseline field-collection era image applied (SOP 3). 

Figure 7. Steps in Type 1 change detection. An image from current year is normalized and applied probability-of-membership 

(POM) algorithms developed for the baseline images. From these POM baseline images, the POM images from the prior year are 

subtracted, creating POM difference images. These are translated into satellite-to-satellite (S2S) labels, which allows direct S2S 

validation.

III. Methods 15

These POM images are compared to the existing POM images 

from the prior year for detection and labeling of Type 1 

changes, and then validated using S2S (SOP 4).

When a new airphoto mission is acquired, the “Type 2 

change detection” phase can begin (fig. 8). In this case, the 

two images being compared must match the years of airphoto 

acquisition. The image for the year corresponding to the new 

airphoto mission already will have been processed under 

ongoing Type 1 change detection. The image for the year 

corresponding to the prior airphoto mission must be processed, 

however. The most recent year of airphoto acquisition is 2003, 

2002, and 1998 for Olympic, Mt. Rainier, and NCCN parks, 

respectively. Images from these years must be identified and 

processed to POM images, and the POM images compared 

with those derived for the year of the new airphoto acquisition. 
Figure 8. Steps in Type 2 change detection. When a new airphoto mission occurs in the current year, Type 2 change detection 

occurs in addition to Type 1 change detection. The end date POM image from Type 1 change detection in the current year is 

compared to a POM image developed for the year corresponding to the prior airphoto acquisition (see text for dates of prior 

airphoto acquisition). Validation can include both S2S validation for Type 1 changes as well as airphoto validation for Type 2 

changes.

Standard S2S interpretation will be conducted on this decadal 

difference POM image (SOP 4). By matching images and 

airphotos, direct airphoto interpretation (SOP 4) also can be 

used for validation of Type 2 changes, which are by nature 

more difficult to unambiguously interpret from satellite 

imagery alone.

16 Protocol for Landsat-Based Monitoring of Landscape Dynamics at North Coast and Cascades Network Parks

The steps are summarized in table 5. Although the 

details of the imagery years may vary slightly depending on 

availability of imagery and airphotos, the flow of the overall 

monitoring program should be very similar to the steps 

detailed in table 5.
IV. Data Handling, Analysis, and 

Reporting

Because final change maps are the result of a long 

cascade of processing steps, accurate data handling is critical 

to the success of this protocol. Each step in the processing 

flow has different requirements.

Metadata

Metadata requirements for all steps in the image 

processing flow are in SOP 5. The goal of the metadata is 

to provide a means of replicating the processing steps. This 

allows better error tracking when problems arise.

IV. Data Handling, Analysis, and Reporting 17

Table 5. Sequential phases in the monitoring program.

[Abbreviations: POM, probability of membership; NA, not applicable]

Phase Description Type of change 

monitored

Baseline 

image year

POM

Start year End year

Startup Establish baseline 

image

NA 2005 

Type 1 change 

detection

Change detection Type 1 2005 2005 2006

Type 1 change 

detection

Change detection Type 1 2005 2006 2007

Type 1 change 

detection

Change detection Type 1 2005 2007 2008

Type 2 change 

detection

Change detection Type 2 2005 1998 2008

Type 1 change 

detection

Change detection Type 1, etc. 2005 2008 2009

Database Design

Image processing does not use a database design. 

However, S2S interpretation (SOP 4) occurs within the 

confines of an ArcGIS geodatabase. Data entry within this 

database contains fields that allow full reconstruction of steps. 

Details of the database setup are in SOP 4.

Error Reporting

Reporting of error is an important consideration in 

validation. Error rates are often reported as summarized 

statistics, but many of these are deceptive and do not provide 

a full picture of true accuracy of a map (Congalton and Green 

1999). Therefore, full error matrices must be reported at the 

end of S2S interpretation. These are generated in SOP 4 using 

the ERDAS Imagine accuracy assessment tool, and can be 

summarized in a grid format (error matrix) as in shown in 

figure 9. Error matrices describe the counts of occurrences 

of agreement and disagreement between two types of 

classification.
Figure 9. An example of a full error matrix used to report validity of a change product.

Data Archival Procedures

Because of the large size of images, archiving of data is 

often necessary when immediate processing has finished. Final 

products (as defined in SOP 5) should be retained in their 

original format. Original imagery and intermediate products 

should be compressed and archived rather than deleted. This 

is especially important if later comparison reveal errors or 

new comparisons that require reprocessing of part or all of the 

image stream.

V. Personnel Requirements And 

Training

This protocol involves a wide range of detailed remotesensing interpretation, image processing, data analysis, 

field measurement, and photo interpretation. The authors 

have attempted to provide sufficient detail in the steps that 

personnel with moderate familiarity with computers and 

remote sensing can undertake most steps. Many of the detailed 

18  Protocol for Landsat-Based Monitoring of Landscape Dynamics at North Coast and Cascades Network Parks

steps can thus be carried out by nonspecialists. However, the 

level of detail needed for image processing provides many 

opportunities for error that can be solved only with significant 

understanding of the satellite imagery being examined and the 

methods being used for its analysis. Moreover, integration of 

field, remote sensing, and other datasets requires significant 

understanding of each component.

Therefore, the authors envision a personnel structure that 

involves a single specialist who can provide overall guidance 

for processing teamed with other nonspecialists who can carry 

out many of the steps for each park. In the multipark setting 

of the NCCN, it may be efficient for a single group to take 

primary responsibility for technical processing of all imagery, 

and for pieces of the validation or interpretation to be carried 

out by each individual park. Because interpretation of change 

signals requires an understanding of the mechanisms causing 

the change, park ecologists, geomorphologists, and other 

specialists should be involved in the interpretation process at 

early stages.

Qualifications for a specialist overseeing this work 

include significant direct expertise in satellite-image 

processing and analysis, in geographic information-system 

processing, and in basic multivariate and univariate statistics. 
The specialist also must have significant experience in 

airphoto interpretation. Nonspecialists will need less 

direct experience with image processing, but experience 

with geographic information systems and basic statistical 

knowledge are critical. Field technicians must be familiar 

with basic map concepts, and must have enough familiarity 

with geographic information systems and aerial photography 

to be trained in the interpretation of satellite imagery for field 

analysis.

The specialist can receive sufficient training from the 

protocol itself and from the image library associated with 

the protocol. The specialist will then take responsibility 

for training nonspecialists and field technicians in image 

interpretation, which forms the core of much of the 

processing. The specialist also must train nonspecialist and 

field technicians in airphoto interpretation, to the extent that 

each will be using it.

VI. Operational Requirements

Equipment

The primary requirement of this protocol is a computing 

system that can be used to carry out the image analysis. The 

protocol requires the following software: ERDAS Imagine, 

ESRI TM ArcGIS, Microsoft Access (or equivalent), Microsoft 

Excel (or equivalent), and Microsoft Word (or equivalent). 

ERDAS Imagine should be purchased with the Vector 

Module, if at all possible. To allow processing of large image 

datasets, computers should have memory of at least 1Gb 

(gigabyte). Image processing requires the production of many 

intermediate image products, each of which can be hundreds 

of megabytes, making hard disk storage of many 10s of 

gigabytes essential. Facilities for printing of color maps are 

essential, especially for field validation efforts. Large-area 

plotters (>36 in.) are extremely useful for production of maps, 

but are not essential.

Airphoto interpretation requires facilities for storing and 

analyzing large numbers of airphotos. Stereo interpretation of 

photos is essential. Good quality stereoscopes make for good 

stereo interpretation, and are thus desirable.

Field validation requires basic field equipment (SOP 4), 

plus a Global Positioning System (GPS) and a digital 

camera. Good quality GPS units may work better under poor 

VI. Operational Requirements 19

conditions (such as in dense canopies), but even recreationalgrade GPS units can be sufficient for the accuracy of pixel 

location required here. A digital camera is needed only for 

field documentation, and thus can be an inexpensive unit 

designed for recreational use.

Startup Costs and Budget Considerations

Startup costs primarily involve purchase of data and 

software for image processing. Landsat imagery is currently 

$425 per image for historical data, and purchase of a mid1990s image likely is necessary for each park. Except for the 

extreme northwest coastal section of OLYM and, in certain 

TM images, the extreme southeast portion of North Cascades 

National Park, each park can be captured within the footprint 

of a single Landsat TM/ETM+ scene. ERDAS Imagine is an 

expensive software package, but has been purchased already 

by at least one GIS shop (Roger Hoffman, OLYM). Costs 

of airphoto acquisition may not be included as part of this 

protocol, since decadal airphoto acquisition already is planned 

for the airphoto-based monitoring program.

Annual Workload

The initial workload will be higher than the sustaining 

workload, as the specialist overseeing this work trains on each 

of the steps. After Landsat images have been acquired, initial 

processing of the first set of change images for a single park 

may take 2 months or more. Once this has occurred, several 

of the time consuming steps are reduced, and familiarity with 

the process should increase the proficiency of the specialist 

and nonspecialists in the processing. The time required for 

S2S validation will depend on the size of the park and the 

number of samples needed (determined in SOP 4). In testing 

for this project, nonspecialists trained in validation were able 

to validate eight validation box areas, each 1.5 by 1.5 km in 

size, in 1 day. Airphoto interpretation will take about twice 

as long. Final data analysis and map preparation will require 

approximately 1 month of the specialist’s time. Therefore, 

initial workloads for yearly change detection may be as much 

as four- or five-person months in the first year, but this should 

be reduced to 3 or fewer months in subsequent years. At the 

decadal scale, when airphoto interpretation is undertaken, an 

extra month or more will need to be added.
VII. References Cited

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Fundamentals.

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the Great Basin: Remote Sensing of Environment 94, p. 

204–213.

Canty, M.J., Nielsen, A.A., and Schmidt, M., 2004, Automatic 

radiometric normalization of multitemporal satellite 

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Chander, G., and Markham, B., 2003, Revised Landsat-5 

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Chavez, Jr., P.S., 1996, Image-based atmospheric 

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Civco, D.L., 1989, Topographic normalization of Landsat 

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Cohen, W.B., and Goward, S.N., 2004, Landsat’s role in 

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Cohen, W.B., Spies, T., Alig, R.J., Alig, D.R., Oetter, 

Maiersperger, T.K., and Fiorella, M., 2002, Characterizing 

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western Oregon forests with Landsat imagery: Ecosystems 

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Cohen, W.B., and Spies, T.A., 1992, Estimating structural 

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Collins, J.B., and Woodcock, C.E., 1996, An assessment of 

several linear change detection techniques for mapping 

forest mortality using multitemporal Landsat TM data. 

Remote Sensing of Environment 56, p. 66–77.

20 Protocol for Landsat-Based Monitoring of Landscape Dynamics at North Coast and Cascades Network Parks

Conese, C., Gilabert, M.A., Maselli, F., and Bottai, L., 1993, 

Topographic normalization of TM scenes through the use 

of an atmospheric correction method and digital terrain 

models: Photogrammetric Engineering & Remote Sensing 

59, p. 1745–1753.

Congalton, R.G., and Green, K., 1999, Assessing the accuracy 

of remotely sensed data: principles and practices: Lewis 

Publishers, Boca Raton, 137 p.

Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., and 

Lambin, E., 2004, Digital change detection methods in 

ecosystem monitoring: a review: International Journal of 

Remote Sensing 25, p. 1565–1596.

Crist, E.P., 1985, A TM tasseled cap equivalent transformation 

for reflectance factor data: Remote Sensing of Environment 

17, p. 301–306.

Crist, E.P., and Cicone. R.C., 1984, A physically-based 

transformation of thematic mapper data—The TM tasseled 

cap: IEEE Transactions on Geoscience and Remote Sensing 

GE 22, p. 256–263.

Egbert, D.D., and Ulaby, F.T., 1972, Effect of angles on 

reflectivity: Photogrammetric Engineering 38, p. 556–564.

Foody, G.M., 1996. Approaches for the production and 

evaluation of fuzzy land cover classifications from remotely 

sensed data: International Journal of Remote Sensing 17, p. 

1317–1340.

Foody, G.M., 2002, Status of land cover classification 

accuracy assessment: Remote Sensing of Environment 80, 

p. 185–201. 

Gu, D., and Gillespie, A., 1998, Topographic normalization of 

Landsat TM images of forest based on subpixel sun-canopysensor geometry: Remote Sensing of Environment 54, p. 

166–175.

Gumbricht, T., McCarthy, T.S., McCarthy, J., Roy, D., Frost, 

P.E., and Wessels, K., 2002, Remote sensing to detect 

sub-surface peat fires and peat fire scars in the Okavango 

Delta, Botswana: South African Journal of Science 98, p. 

351–358.

Hayes, D.J., and Sader, S.A., 2001, Comparison of changedetection techniques for monitoring tropical forest clearing 

and vegetation regrowth in a time series: Photogrammetric 

Engineering and Remote Sensing 67, p. 1067-1075.
Holben, B.N., and Justice, C.O. , 1980, The topographic 

effect on spectral response from nadir-point sensors: 

Photogrammetric Engineering & Remote Sensing 46, p. 

1191–1200.

Kennedy, R.E., and Cohen, W.B., 2003, Automated 

designation of tie-points for image-to-image coregistration: 

International Journal of Remote Sensing 24, p. 3467–3490.

Kimes, D.S., 1983, Dynamics of directional reflectance factor 

distributions for vegetation canopies: Applied Optics 22, 

p. 1364–1372.

Kriebel, K.T., 1978, Measured spectral bidirectional reflection 

properties of four vegetated surfaces: Applied Optics 17, 

p. 253–259.

Lambin, E.F., and Strahler, A.H., 1994, Change-vector 

analysis in multitemporal space: A tool to detect and 

categorize land-cover change processes using high 

temporal-resolution satellite data: Remote Sensing of 

Environment 48, p. 231–244.

Maiersperger, T.K., Cohen, W.B., and Ganio, L.M., 2001, 

A TM-based hardwood-conifer mixture index for closed 

canopy forests in the Oregon Coast Range: International 

Journal of Remote Sensing 22, p. 1053–1066.

Malila, W.A., 1980, Change vector analysis: An approach for 

detecting forest changes with Landsat, in Burroff, P.G., and 

Morrison, D.B., eds.: West Lafayette, IN, Purdue University 

Laboratory of Applied Remote Sensing, p. 326–335.

Michener, W.K., and Houhoulis, P.F., 1997, Detection of 

vegetation changes associated with extensive flooding in 

a forested ecosystem: Photogrammetric Engineering & 

Remote Sensing 63, p. 1363–1374.

Muchoney, D.M., and Haack, B.N., 1994, Change detection 

for monitoring forest defoliation: Photogrammetric 

Engineering & Remote Sensing 60, p. 1243–1251.

Norman, J.M., Welles, J.M., and Walter, E.A., 1985, Contrasts 

among bidirectional reflectance of leaves, canopies, and 

soils: IEEE Transactions on Geoscience and Remote 

Sensing GE-23, p. 659–667.

Oetter, D.R., Cohen, W.B., Berterretche, M., Maiersperger, 

T.K., and Kennedy, R.E., 2000, Land cover mapping in an 

agricultural setting using multiseasonal Thematic Mapper 

data: Remote Sensing of Environment 76, p. 139–155.

Parmenter, A.W.,Hansen, A., Kennedy, R.E., Cohen, W.B., 

Langner, U., Lawrence, R., Maxwell, B., Gallant, A., 

and Aspinall, R., 2003, Greater Yellowstone Land Cover 

Change: Ecological Applications 13, p. 687–703.

VII. References Cited 21

Ranson, K.J., Irons, J.R., and Williams, D.L., 1994, 

Multispectral bidirectional reflectance of northern 

forest canopies with the advanced solid-state array 

spectroradiometer (ASAS): Remote Sensing of 

Environment 47, p. 276–289.

Richards, J.A., 1993, Remote sensing digital image analysis: 

An introduction: Springer-Verlag, Berlin, 340 p.

Roy, D.P., Lewis, P.E., and Justice, C.O., 2002, Burned area 

mapping using multi-temporal moderate spatial resolution 

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approach: Remote Sensing of Environment 83, p. 263–286.

Royle, D.D., and Lathrop, R.G., 2002, Discriminating Tsuga 

canadensis hemlock forest defoliation using remotely 

sensed change detection: Journal of Nematology 34, p. 

213–221.

Sader, S.A., Bertrand, M., and Wilson, E.H., 2003, Satellite 

change detection of forest harvest patterns on an industrial 

forest landscape: Forest Science 49, p. 341–353.

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the slope-aspect correction of multispectral scanner data: 

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Trigg, S., and Flasse, S., 2001, An evaluation of different bispectral spaces for discriminating burned shrub-savannah: 

International Journal of Remote Sensing 22, p. 2641–2647.

Viedma, O., Meliá, J., Segarra, D., and García-Haro, J., 1997, 

Modeling rates of ecosystem recovery after fires by using 

Landsat TM data: Remote Sensing of Environment 61,p. 

383–398.

Weber, S., Woodward, A., and Freilich, J., 2005, North Coast 

and Cascades Network Vital Signs Monitoring Report: 

National Park Service.

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harvest type using multiple dates of Landsat TM imagery: 

Remote Sensing of Environment 80, p. 385–396.

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Remote Sensing for Long-term Ecological Monitoring in 

the North Coast and Cascades Network: Summary of a 

Workshop: U.S. Geological Survey, available online at: 

http://fresc.usgs.gov/olympic/research/rswkspsummary.pdf.
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22 Protocol for Landsat-Based Monitoring of Landscape Dynamics at North Coast and Cascades Network Parks
I. Choosing Landsat Imagery

For the purposes of this protocol, we assume that 

image orders are for continuation of monitoring forward 

from the year 2005, and that images prior to 2005 already 

are in the possession of the parks. Two options for future 

Landsat imagery exist: Landsat 5 and Landsat 7. Landsat 

5 has been in operation well beyond its planned mission, 

and is therefore soon to be retired. Despite its age, it still 

provides reliable imagery, although slow deterioration of its 

mechanical components has required continual monitoring 

and readjustment (see http://edc.usgs.gov/products/satellite/

tm.html). Indeed, it was taken offline for 2 months at the 

end of 2005 to deal with a solar-array problem, but has been 

reinitiated as of January 30, 2006. Landsat 7 has superior 

technical specifications, but unfortunately suffered a major 

instrument malfunction in May 2003 that diminishes its utility. 

The choice of imagery will thus depend on whether Landsat 5 

imagery is available, and on the characteristics of the available 

Landsat 7 imagery. Landsat data continuity is mandated by 

Protocol for Landsat-Based Monitoring of Vegetation at 

North Coast and Cascades Network Parks

Standard Operating Procedure (SOP) 1

Acquiring Landsat Imagery

Version 1.01 (September 15, 2006)

Revision History Log:

Previous 

VersionNumber Revision Date Author Changes Made Reason for Change New Version Number

n.a. 07-05-06 REK Final version under 

agreement

1.0

1.0 09-15-06 REK Minor wordsmithing; 

separation of figures 

for publication

Preparation for 

publication

1.01

SOP 1. Acquiring Landsat Imagery 23

statute, and plans exist for data continuity after Landsats 5 and 

7 cease functioning (http://ldcm.usgs.gov/). The Operational 

Land Imager (OLI) will be the immediate successor to Landsat 

7, and is planned for launch in 2010.

For the present, Landsat 5 imagery is preferable to 

Landsat 7 imagery because all pixels in a given scene will be 

from a single acquisition date. Imagery purchased as recently 

as 2004 has been reliable and geometrically robust. Eventually, 

Landsat 5 will no longer be functional. This could happen at 

any moment, and may be soon (although it has enough fuel 

to stay in orbit until 2008). Even if Landsat 5 imagery is still 

available, it may not be sufficiently free of clouds to be usable. 

In either case, Landsat 7 imagery likely will need to be used 

(although see note at the end of this section on Advanced Land 

Imager (ALI) and Advanced Spaceborn Thermal Emission and 

Reflection Radiometer (ASTER).

Therefore, discussion of the malfunction of the Landsat 

7 instrument is necessary. The instrument malfunction was 

the failure of the scan-line corrector of the satellite, and is 

discussed at http://landsat.usgs.gov/slc_enhancements/slc_off_

background.php. The following is taken directly from that web 

site:

This SOP explains the procedures for acquiring Landsat Thematic Mapper (TM) imagery for use in image-based 

monitoring. Procedures are provided for ordering imagery from the EROS Data Center (EDC).
“An instrument malfunction occurred onboard Landsat 7 on May 31, 2003. The problem was caused by failure of the 

Scan Line Corrector (SLC), which compensates for the forward motion of the satellite. Subsequent efforts to recover 

the SLC were not successful, and the problem appears to be permanent. Without an operating SLC, the ETM+ line of 

sight now traces a zig-zag pattern along the satellite ground track” (fig. 1).

“The Landsat 7 Enhanced Thematic Mapper Plus (ETM+) is still capable of acquiring useful image data with the SLC 

turned off, particularly within the central portion of any given scene. Landsat 7 ETM+ therefore continues to acquire 

image data in the “SLC-off” mode. Please note that all Landsat 7 SLC-off data will be of the same radiometric and 

geometric quality compared to data collected prior to the SLC failure.

“The SLC-off impacts are most pronounced along the edge of the scene and gradually diminish toward the center of 

the scene ..... The middle of the scene (approximately 22 kilometers with a L1G product) contains very little duplication or data loss, and this region should be very similar in quality to previous (“SLC-on”) Landsat 7 image data.”

Figure 1. Effect of the Landsat 7 scan-line correction error.

Taken from http://landsat.usgs.gov/slc_enhancements/slc_off_background.php.

24  Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks

USGS is continually updating approaches to deal with 

the gaps caused by the SLC failure. The general strategy 

is to merge image pixels from two or more different dates 

to provide a composite image without gaps. Because this 

results in a single image with pixels from different points 

in the growing season, it may introduce errors because of 

phenological progression of vegetation through the season. 

The effect of this gap filling is not known in advance for any 

given scene, since it depends on the images used to fill the 

gaps.

After November 18, 2004, gap filling was provided 

using the so-called “Phase 2” approach, which allows for 

filling from multiple scenes. A detailed description of this 

process is found at http://landsat.usgs.gov/documents/

L7SLCGapFilledMethod.pdf.

That document provides guidance on choosing which 

scenes should be used for gap-filling:

“The following scene selection guidelines are 

roughly in order of priority:

1. Select fill scenes that are as free of clouds as possible 

and that contain as few obvious changes in scene content 

(e.g., different snow cover, waterbody ice cover/sun glint 

differences) as possible.

2. Select SLC-off scenes that are as close in time to the 

primary scene as possible to minimize changes in 

vegetation conditions. Failing this, select fill scenes (SLCoff or SLC-on) that are as close to an anniversary date as 

possible to take advantage of the similarities in seasonalvegetation cycles.

3. Select scenes that provide good predicted gap coverage 

based on the gap phase statistics. These statistics predict 

the locations of the scan gaps in each scene relative to 

the Worldwide Reference System (WRS) scene center. 

Computing the difference between the fill scene gap phase 

and the primary scene gap phase makes it possible to 

predict (to within a few pixels) the gap overlap geometry.
4. Select SLC-off fill scenes that are within +/- 4 WRS 

cycles of the primary scene, if possible. Since the ETM+ 

scan period increases slowly with time, the primary/fill 

scene relative scan gap phase will vary across the scene 

if the acquisition dates are too far apart. Thus, even 

an apparently “perfect” scan gap phase match will not 

provide complete gap coverage.

5. For Phase 2 products with multiple SLC-off scenes, 

including an anniversary date SLC-on scene as the final 

fill scene is recommended as a way of ensuring good 

image registration performance and providing complete 

gap coverage.”

For the purposes of monitoring at the parks, gap-filled 

products may introduce several errors.

First, pixels in a single image could be from different 

acquisition dates within a season. Phenological changes 

in vegetation thus may be mingled within a single date, 

exacerbating the potential for false positives in the change 

detection.

Second, pixels in a single image may be from different 

years. This situation is to be avoided, since detecting interyear 

differences is the crux of the monitoring effort. However, at 

some parks it may be unavoidable because of frequent cloud 

cover.

Third, because of the matching algorithms used to bring 

the gap-filled pixels into radiometric consistency with the 

reference image, gap-filled pixels may introduce radiometric 

noise that will affect normalization algorithms later on in 

processing (SOP 2).

These potential problems unfortunately are unavoidable, 

but their actual effect is unknown at present for the parks of 

the NCCN. It likely will be useful to re-examine the effects 

of the gap-filling on change-detection projects in 5 years as 

research in this particular question emerges in the remote 

sensing literature. As practical knowledge is gained about 

these effects, this protocol may need to be updated (SOP 6). 

One of the more promising approaches to dealing with this 

problem may be to consider only areas in the imagery that 

does not fall in gaps; i.e. use imagery from only one date, and 

consider this to be a large sample that does not exhaustively 

characterize the parks.

Potential Use of ASTER or ALI Data

Two other sources of potentially compatible image data 

may be available: ASTER and ALI.

ASTER (http://asterweb.jpl.nasa.gov/) is an instrument 

mounted on the Terra spacecraft, which also houses MODIS 

(MODerate-resolution Imaging Spectroradiometer). ASTER 

obtains images that are similar enough to Landsat data to 

SOP 1. Acquiring Landsat Imagery 25

be comparable, although there are differences. ASTER 

obtains only two visible bands where Landsat obtains three, 

but ASTER’s pixel size is 15 m where Landsat’s is 30 m. 

ASTER collects better data in the short-wave infrared region, 

particularly in the region overlapping with Landsat’s Band 7. 

The swath width of the ASTER instrument is 60 km pointable 

on either side of center by 106 km. ASTER is a request-only 

instrument, meaning that the Parks would need to submit an 

acquisition proposal and make requests for particular image 

acquisitions.

ALI is a satellite designed to test and illustrate new 

technical approaches to imaging after the current era of 

Landsat imagers ends. To facilitate comparison with Landsat 

imagery, ALI data are designed to be spectrally and spatially 

similar to Landsat data. Additionally, ALI is a pointable 

instrument that can be tasked to acquire imagery of a 

particular location. This is necessary because the swath width 

of an image is 37 km, as compared to 185 km for a typical 

Landsat TM image. If gap filling in Landsat 7 data create 

significant problems, then ALI may be a plausible substitute.

If either ALI or ASTER are determined to be necessary, 

details of ordering and initial processing will need to be 

developed that are logically consistent with the existing 

methods described in this protocol. Currently this is not 

known, however, requests would need to be placed to acquire 

imagery at the end of the growing season for the parks. 

Because the swatch width of both instruments is less than that 

of Landsat, two or more adjacent images likely may be needed 

to image some of the parks.

II. Ordering Imagery From Eros Data 

Center

Landsat imagery is acquired at the satellite as it orbits 

the Earth, and then is downloaded to various ground-receiving 

stations. For the most recent Landsat sensor, Landsat 7 or the 

Enhanced Thematic Mapper Plus (ETM+), these images are 

gathered at a central repository in South Dakota, the EROS 

(Earth Resources Observation Systems) Data Center (EDC). 

Information on the center can be obtained at that center’s web 

site: http://edc.usgs.gov/about/background.html. Through 

EDC, various other satellite and spatial databases also are 

available. EDC also maintains a full archive of historical 

Landsat data, including Landsat 5, which is still active as of 

August 2005, but which will reach the end of its life by 2008. 

Information about the Landsat family of sensors is found at 

http://landsat.usgs.gov/. Landsat imagery is best searched and 

ordered through EDC’s “EarthExplorer” interface.
Using EarthExplorer

EarthExplorer is found at http://edcsns17.cr.usgs.gov/

EarthExplorer/, and also can be reached from EDC’s main web 

site (http://edc.usgs.gov/) by following the Products/Satellite 

Imagery Link and selecting either ETM+ or TM from the table 

of products listed at (http://edc.usgs.gov/products/satellite.

html).

All data can be ordered from EarthExplorer as a guest. 

Alternatively, users may desire to register (available as a link 

from the main EarthExplorer web page). Registration is free. 

The advantage of registration is that user address and order 

information is saved for future sessions, reducing redundancy 

later.

Steps for Ordering Imagery

1 Log in to EarthExplorer as a guest or registered user at 

http://edcsns1cr.usgs.gov/EarthExplorer/

2. In the “Data Set Selection” box, select “Landsat 4-5 TM” 

or “Landsat 7 ETM+ SLC-off,” depending on availability 

of Landsat 5 data.

a. If the availability of Landsat 5 is not known, select it 

first, continue through the steps below to determine if 

cloud-free imagery is available for the desired park. 

If not, return to this step and select Landsat 7 imagery 

instead of Landsat 5 data.

3. The next window is the “Search Criteria” window.

a. Confirm that the dataset you wanted is in the “Data 

set” window.

b. Do not enter anything in the “Spatial Coverage” 

window. Search will be by WRS path and row, below.

c. In the Acquisition Date box, select an appropriate 

date window.

i. Conditions for appropriate date windows.

(1) The date window should be for a single year.

(2) The date window should be approximately 

consistent with imagery from the year 

against which this imagery is to be 

differenced. That imagery already must exist 

in house. Typically, a good starting point 

for the parks of the NCCN is a data window 

that spans June 1 to October 1 of the year. 

If multiple cloud-free scenes are available 

(see below), then choose the date of imagery 

26 Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks

closest to the imagery already in house. The 

ideal window is late July to mid-August, 

as vegetation is in full display for most of 

the parks at most elevation in most years, 

and the sun is still high enough in the sky to 

reduce the effects of illumination-induced 

shadowing.

(3) Assuming that only 1 year has been 

selected, the button “Linear” can remain 

selected. If multiple years are selected, it 

is advantageous to select the “Seasonal” 

button, as it only will examine the date 

window within each year, rather than 

extending across the nondesired portions of 

the year in between date windows.

d. For the WRS Path and Row boxes, select the 

following according to the park for which imagery is 

to be ordered:

i. Olympic: 47/27 (for the bulk of the park) and 

47/28 (for the NW coastal section)

ii. Mt. Rainier: 46/27

iii. North Cascades: 46/26

(1) The following parks may not use this 

protocol at this time, but path/row 

information is provided for reference in the 

future.

(a) San Juan: 47/26

(b) Ebey’s Landing: 47/26

(c) Ft. Vancouver: 46/28

(d) Lewis & Clark: 47/28

e. In the “Results Restrictions” box at the bottom of the 

screen, change from 10 to 100 records.

f. Start the search.

i. The results page will run for a few seconds or 

minutes.

g. Once the “Results Summary” page has appeared and 

the “Status” column reads “Complete,” select the 

“Results” icon to see a table of the images.

i. The list of images will appear with information 

on the cloud cover of each image and the date of 

the image acquisition.

h. After viewing browse images, select one image by 

checking its box in the “Order Qty” column, and then 

selecting “Add selected items to shopping basket.”
i. It is critical that browse images be viewed, as the 

cloud-cover estimate, while relatively accurate, 

does not describe where in the image clouds 

may reside. Some images with low cloud cover 

may have clouds directly over the park, while 

others with moderate cloud cover may be cloud 

free over the parks. The goal is to pick an image 

with the most cloud-free area within the park of 

interest. In some cases, thin cirrus clouds will 

not be visible in the browse images, however, so 

if the cloud-cover estimate provided in the table 

is higher than appears obvious visually, consider 

whether another image is available (see criteria 

below). Also, it is helpful to become familiar 

with the browse-image format for many dates of 

imagery. After looking at many images spanning 

the continuum from cloud covered to absolutely 

clear, the interpreter begins to build an image of 

what the landscape looks like when clear, and 

is better able to catch the subtle effects of thin 

clouds.

ii. For many years, it may be necessary to choose 

between several nonoptimal conditions. Follow 

these guidelines on choosing images.

(1) Avoid choosing imagery from very early in 

the season (generally before mid-June), even 

if cloud free. Early in the season, too much 

of the relevant area of the parks are still 

snow covered and vegetation phenology will 

be quite different. These two factors will 

cause so many false positives in the change 

detection later on (SOP 3) that they should 

be avoided.

(a) Images from July and August are 

preferred, even if some of the park is 

obscured by clouds. Evaluate browse 

images for location of clouds over the 

parks. Accept a July or August image if 

less than approximately 10 percent of 

the park is cloud covered, as estimated 

through simple visual estimation (do not 

use the percent cloud-cover estimate for 

the whole scene).

(b) Evaluate whether images from the other 

sensor (Landsat 5 or 7) are available 

before moving forward to other dates. 

Go back to step 2 and choose the other 

sensor. If not, proceed to (c).

(c) Avoid images with “Image Quality” 

flags. 

SOP 1. Acquiring Landsat Imagery 27

(d) Images from early September are nearly 

as good as those in August, especially 

as they tend to be the best snow-free 

images, but ultimately are less desirable 

than those slightly earlier in the season 

because of sun angle considerations. 

Also, spectral artifacts caused by rapid 

fall vegetation senescence likely are 

increasingly in September. Choose 

September images if the criteria for July 

and August images are not met.

(e) Images in June or October should be 

chosen only if no other images are 

available and if these images essentially 

are cloud-free.

i. Once an image has been selected and added to the 

shopping basket, proceed to the checkout by selecting 

“View Shopping Basket.”

j. Select the appropriate product type in the next screen 

(“USGS Shopping Basket”).

i. For Landsat 5.

(1) In the USGS Shopping Basket select “L4-5 

TM LIG Single NLAPS.”

(2) On the next screen, “Processing Options 

for L4-5 TM L1G Single NLAPS,” select 

options as follows:

(a) For pixel size for the reflective bands, 

choose 28.5 m

(i) This is as close to the native pixel 

size of the sensor. This will later 

be resampled to 25 m, but ordering 

the imagery at 28.5 m maintains 

the maximum level of geometric 

integrity of the imagery.

(b) Resampling method: Use nearest 

neighbor (NN).

(i) By selecting 28.5 m and using NN, 

pixels are as close to the original 

condition as possible. Effects of 

later resampling (SOP 2) always 

can be compared back to this 

original condition to understand 

potential artifacts.

(c) Image Orientation: MAP

(d) Datum: ensure WGS84

(e) Output format: NDF

(f) Projection: UTM
(g) Zone number: leave at default (should 

be zone 10 for all parks).

(h) Submit, and proceed to item “k” in the 

outline below.

ii. For Landsat 7, select “ETM+ SLC-off gap filled 

LPGS”

(1) Once this option is selected, the browser 

will move to the screen that allows you to 

determine which scenes to use for the gap 

filling: “ETM+ SLC-off Gap-filled Products: 

Fill Scene Selection.”

(2) Instructions on the choice of scenes for 

gap-filling are available as a link from this 

window (see http://edcsns17.cr.usgs.gov/

helpdocs/landsat/instructions.html). They 

are recreated here for reference:

 Instructions for Selection of Fill Scene(s)

 For best results, any scene selected for 

gap-fill should be cloud-free and as 

close as possible to the anniversary date 

of the primary SLC-off scene. For more 

information on the influence of scene 

content on the gap-filled results, see Tips for 

choosing a fill scene.

 Section 1 - SLC-off Scenes

 The most recent potential fill scenes are 

listed in this section (“SLC-off scenes”). 

Note that the fill scenes also contain data 

gaps, so a variable number of SLC-off 

scenes (generally 2-3) will be required to 

obtain a complete fill in the final product.

 Up to four SLC-off fill scenes may be 

selected from the SLC-off section.

 “Fill Sequence” refers to the order in 

which the selected fill scene will be used to 

contribute to the final gap-filled product. 

The scene with a value of “1” will be used 

first to fill all possible gap in the original 

primary scene. The scene with a value of 

“2” will be used to fill any remaining gap, 

after initial fill has been contributed by the 

“1” scene. The scene with a value of “3” 

will be used to fill any remaining gap (after 

“1” and “2”), etc.

 Due to small potential errors in the residual 

gap estimation tool, there may still be a gap 

remaining in the final processed product. 

Selection of an additional SLC-off scene 

(after reaching a residual gap of 0 pixels) 

will help to assure complete coverage. You 

28  Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks

may also wish to select an SLC-on scene 

(Section 2) for an additional “final fill”, 

to assure that all residual gap has been 

eliminated in your final product.

 Section 2 - SLC-on Scenes

 Potential SLC-on scenes are listed in the 

second section (“SLC-on scenes”). These 

scenes were collected prior to the SLC 

failure, and the contributing data will 

therefore not contain any gaps. Selection of 

a scene from the SLC-on section will always 

insure a fully populated gap-filled image. 

Only one scene may be chosen from the 

SLC-on section.

 If the user selects an SLC-on scene in 

addition to one or more SLC-off scenes 

(from Section 1), the SLC-on scene will 

always be the last scene used for the gap-fill 

during Level 1G processing.

(a) Because of cloud-cover characteristics 

in the Pacific Northwest, it likely is 

that few scenes will have less than 

10 percent cloud cover. From the 

perspective of monitoring across years, 

prioritize obtaining fill imagery from 

the same year over obtaining cloud-free 

imagery. Where clouds exist, it will 

be explicit that no conclusions can be 

drawn. Filling those areas with cloudfree imagery from a prior year may lead 

to misleading conclusions about change.

(b) Nevertheless, it will often be necessary 

to include prior-year imagery in the 

gaps. Additionally, it will be necessary 

to include imagery from 2003 or before 

in the SLC-ON section of the window. 

Because the gap mask included with the 

imagery contains information on which 

scene was used for a given pixel, it is 

possible to later selectively eliminate 

the gap-filled data.

(c) NOTE: If purchasing imagery for the 

year 2005 onwards, do not choose the 

SLC-on scene option, because these 

scenes will be at least 2 years out of 

date. If there are gaps after other recent 

SLC-off images have been added, these 

will be areas where no conclusions 

about change can be drawn for the 

period of interest.

(d) Once images are selected, select 

“Submit fill scenes.”
(3) Once gap-filling scenes have been selected, 

proceed to the next screen, “Processing 

Options for L7 ETM+ SLC-OFF Gap-fill 

L1G Systematic LPGS.”

(a) For pixel size for the reflective bands, 

choose 28.5 m.

(i) This is as close to the native pixel 

size of the sensor. This will later 

be resampled to 25 m, but ordering 

the imagery at 28.5 m maintains 

the maximum level of geometric 

integrity of the imagery.

(b) Resampling method: Use NN.

(i) By selecting 28.5 m and using NN, 

pixels are as close to the original 

condition as possible. Effects of 

later resampling (SOP 2) always 

can be compared back to this 

original condition to understand 

potential artifacts.

(c) Image Orientation: NUP

(d) Output format: Select Fast L7

(e) Datum: ensure WGS84

(f) Projection: UTM

(g) Zone number: leave at default (should 

be zone 10 for all parks).

SOP 1. Acquiring Landsat Imagery 29

k. The next screen, USGS Shopping Basket, will show 

the results of the selections just made in the “Product 

Type” column. Confirm that image dates for the Off 

Fill are as desired.

i. For the media type, select either CD or FTP.

ii. Proceed to the payment screen.

l. On the next screen, enter contact information, if 

registered as a guest.

m. Select appropriate payment options.

n. When the order confirmation appears, select all of the 

text with details of the order, copy them using Ctrl-C 

for Windows machines, and paste into a text or wordprocessing document for archiving. The following 

information is critical to save for the order:

i. Date of image.

ii. Sensor (5 or 7).

iii. Date of gap-filling images and the order in which 

they fill the gaps.

iv. Pixel size and resampling mode. 
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30 Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
Protocol for Landsat-Based Monitoring of Vegetation at 

North Coast and Cascades Network Parks

Standard Operating Procedure (SOP) 2

Preprocessing Landsat Imagery

Version 1.0 (July 5, 2006)

Revision History Log:

Previous 

VersionNumber Revision Date Author Changes Made Reason for Change New Version Number

n.a. 07-05-06 REK Final version under 

agreement

1.0

1.0 09-15-06 REK Figures and captions 

extracted

Preparation for 

publication

1.01

Processing Datasheet

Many detailed and sometimes complicated steps are in 

this SOP. Because the analyst may need to split work between 

this work and other responsibilities, we provide table 1 that 

can be printed out separately for the analyst to keep track of 

steps that have been completed. This should facilitate easier 

recovery from interruptions.

This SOP explains the procedures for processing data acquired in SOP 1 into a geometrically robust, radiometrically 

standardized-reflectance product that can then be used for change detection (SOP 3). Preprocessing is a critical step in any 

change-detection study, but is especially so for the types of phenomena being sought by the parks.

I. Conventions For Describing Software 

Methods

Two main types of software package are discussed in 

this SOP: ERDAS Imagine and IDL-Virtual Machine (VM). 

The former is an industry standard image processing software 

package. While the work described in this protocol was done 

using ERDAS, much of the functionality it encompasses may 

be found in other software packages. However, because the 

goal of an SOP is detailed description of methodology, one 

software package has to be described in detail, and we have 

chosen ERDAS Imagine because of its widespread use and 

ease of integration with ESRI Arc products, which also are 

industry standards and in use at the parks.

SOP 2. Preprocessing Landsat Imagery  31
Table 1. Datasheet to keep track of steps in Standard Operating Procedure 2.

[Abbreviations: IMG, Erdas Imagine file; AOI, area of interest; DEM, digital elevation model; ITP, image tie point; XLS, Microsoft© Excel file; TXT, text file; 

BSQ, BINSCII-encoded file; SE, Southeast; NW, Northwest]

General information

Park being processed

Analyst

Change interval being processed

Start of processing

End of processing

Section Step Filename 

Section II Establish geometric-reference image IMG:

Establish radiometric-reference image IMG:

Section III Import subject image (if necessary) IMG:

Section IV Reproject reference-geometric image (if necessary) IMG:

Section V Define study area AOI:

Vector:

Clip geometric-reference image to study area IMG:

Clip DEM to study area DEM:

Section VI Name of input image IMG:

Run ITPFind to match input image to geometric-reference image ITP Readme: 

Terrain-corrected input image IMG: 

Clip input image to study area IMG: 

Section VII Fill out COST processing spreadsheet for subject image XLS:

Apply COST model to subject image IMG:

Section VIII Develop MADCAL runtime file TXT:

Run MADCAL on subject image BSQ:

Import MADCAL subject image IMG:

Section IX Convert radiometric-reference image to tasseled-cap values IMG:

Convert input image to tasseled-cap values IMG:

Section X Develop SE and NW aspect AOIs from DEM SE Aspect AOI:

NW Aspect AOI:

Split reference image tassel cap by aspect SE Aspect IMG:

NW Aspect IMG:

Split input image tassel cap by aspect SE Aspect IMG:

NW Aspect IMG:

32 Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
Figure 1. Main console of Imagine.

The second software package is built on the Interactive Data Language (IDL). IDL is common in remote sensing research 

labs but less common in the general image processing industry community, and also is fairly expensive (around $1,500 for a 

license currently). Because only a few key pieces of the SOP involve IDL programs written for image processing, these can 

provided as compiled code that can be run in a free version of IDL called Virtual Machine (VM). The code cannot be altered in 

the VM, but can be utilized for the specific purposes described herein. Alternatively, the image processing system Environment 

for Visualizing Images (ENVI) could be purchased instead of ERDAS Imagine, since ENVI is built on IDL and includes IDL 

in its shipping package. This latter step is less conventional, though, and thus the current protocol is for the use of Imagine for 

image processing and IDL-VM for specific tasks. The code for IDL-VM is supplied as a part of this protocol.

Figure 2. Subset window.

Conventions for describing steps in ERDAS Imagine (from here forward, simply referred to as “Imagine,” with a capital 

“I”) are straightforward. Imagine is a menu-driven software package that begins with a “Main Console,” shown in figure 1.

The buttons arranged horizontally refer to the main modules of the program. Actions are taken by selecting a button, which 

then either starts an action or brings up a pull down menu with several options, some of which have pull down options of their 

own. Actions will be referred to by the box in the main console, followed by a forward slash (“/”) and the name of the option 

from the pulldown menu. A second slash and pulldown option will be listed as necessary. For example, image subsetting is in 

a pulldown menu from a pulldown menu from the Interpreter button on the main console, referred to as “Interpreter/Utilities/

Subset.”

Once an action is selected, an action window typically appears 

that provides a suite of options for a given action. The “Subset” 

action window, for example, is shown in figure 2.

Instructions for filling in boxes and checking options on 

these action windows are referenced by the name on the window 

followed by a colon. For example, to switch the default Output type 

in the subset window above to Signed 16 bit, the instruction in this 

SOP would read “Output: select Signed 16-bit.”

Instructions for IDL-VM code is described in detail as it is 

relevant. Go to http://www.rsinc.com/idlvm/index.asp to download 

the current version of IDL-VM. When installed, the virtual 

machine will be represented as an icon on the desktop of the 

computer. Programs developed by the authors are provided as VMenabled code that is simply dragged with the pointing device on top 

of the IDL-VM desktop icon to execute the code.

SOP 2. Preprocessing Landsat Imagery  33
II. Establish Reference Images

Two categories of preprocessing are to be carried out in 

this SOP: geometric and radiometric. In each category, the 

user must define an image acquired on a specific date to be 

a reference towards which all other images will be matched. 

Thus, a reference geometric image and a reference radiometric 

image are needed.

The geometric-reference image is one whose geometric 

properties are well characterized. Each pixel in the image can 

be linked directly with a map coordinate on the ground, in 

coordinates determined by a map projection and an associated 

spheroid and datum. The spheroid represents a model of the 

Earth and the datum a set of coordinates developed through 

actual measurements of points on the ground.

A key step in linking the geometric properties on the 

ground to a given image is accounting for the potential 

distortion effects of topography and the curvature of the Earth. 

When Landsat TM measures reflectance in a particular pixel at 

the center of the image, it is viewing that pixel straight down, 

but when it measures reflectance at the edge of the image, it is 

viewing that pixel at an angle. In the latter case, the elevation 

of the land can cause distortion in the apparent position of 

the pixel from the perspective of the sensor: pixels at the tops 

of mountains will appear to be farther away than they really 

are, and pixels in valleys will appear closer. The curvature of 

the Earth also interacts with the view angle to affect apparent 

position of the pixel. Therefore, a geometric model that 

includes both curvature and the effects of surface topography 

is needed to link the observed pixels with their actual locations 

on the ground. Once the effects of topography and view angle 

have been accounted for, the image is said have been terrain 

corrected.

The NASA sponsored GeoCover project resulted in 

orthoimagery of Landsat TM for the entire globe. Only one 

image for the 1990 era and one image for the 2000 era are 

available under this project. For the United States, versions 

of these GeoCover images can be downloaded easily and 

cheaply from the NASA-sponsored project Landsat Ecosystem 

Disturbance Adaptive Processing System (LEDAPS) available 

at http://ledaps.nascom.nasa.gov/ledaps/ledaps_NorthAmerica.

html). The authors recommend using the appropriate 2000-era 

Landsat ETM+ image as the geometric-reference image (in the 

Startup Phase of monitoring; see Narrative).

The radiometric-reference image is one whose 

radiometric properties will anchor the radiometric properties 

of later images. The radiometric properties simply refer to 

how the spectral reflectance of objects on the ground surface 

are represented as numbers in the image, and takes into 

account both the sensor engineering design and the state of 

the atmosphere that interferes with the reflected light from the 

ground as it travels to the satellite. The radiometric-reference 

image is one whose radiometric properties are well defined.

In practice, choice of a good radiometric-reference 

image comes down to a clear atmosphere during image 

acquisition and a date of acquisition similar to that of most 

of the other images for which the image is to be used as a 

reference. This latter condition is useful because a key step 

in preprocessing is normalizing later images to the spectral 

properties of the radiometric-reference image. Differences 

caused by phenological state of the vegetation could confuse 

the normalization if the reference image were obtained during 

a point in the season completely unusual relative to the other 

images desired for use.

Therefore, for the parks of the NCCN, a radiometric 

reference image should be chosen from the same late-summer 

date-windows that are recommended for all images (SOP 1). 

The clarity of the atmosphere for a given image typically 

cannot be known, but can be inferred. First, minimal cloud 

cover is preferred, since cloud cover indicates presence 

of increased levels of moisture in the atmosphere. A dry 

atmosphere is preferred. Cloud-free images can still have 

significant levels of atmospheric haze, which also is to 

be avoided. Haze levels can be inferred from the spectral 

response of pixels across the six visible bands of the Landsat 

imagery. Haze affects the bands at shorter wavelengths more 

than longer wavelengths because of the relation between 

the size of the particle causing the haze and the wavelength 

of light passing through it. Therefore, images with the least 

inferred scattering in Band 1 (the blue band of Landsat, 

with the shortest wavelength) are the images least affected 

by atmospheric haze. Thus, if there is a choice of several 

images for potential radiometric reference image, then it is 

recommended that the images be processed through to the 

level of COST processing, detailed below, and then the image 

with the lowest overall dark-object value in Band 1 be used as 

the radiometric-reference image.

III. Importing Images Into Imagine

For the methods described in this protocol, all images, 

whether reference or not, must be converted into the ERDAS 

Imagine format. The Imagine format is indicated with the 

filename extension “.img” and is a proprietary, hierarchical 

spatial data format. Conversion from other file formats into 

imagine format is best done within Imagine, where the process 

is named “importing” of external image formats.

34  Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
Figure 3. Button used to access the import/export action window.

Once the import/export button is selected, the Import/

Export dialog box will pop up (fig. 4).

To import an image, first choose the type from the 

pulldown menu “Type:”. If files are purchased from EDC and 

are Landsat 7 images, then the appropriate choice should be 

Landsat 7 Fast-L7A EROS. If images are taken from other 

sources, then other options may be appropriate. For example, 

many images from NASA sources are provided in an opensource hierarchical-data format (HDF), which has various 

options on the Imagine import/export dialog. If images are 

from the LEDAPS site, they are in HDF format, which is a 

public-format heirarchical data type used by NASA. Select 

“HDF Scientific Data Format (Direct Read)” as the data type.

Once the appropriate input file type has been chosen, 

fill in the “Input file:” box, either by manually typing the full 

path name, or navigate with the mouse using the open folder 

button just to the right of the blank input-file field. Clicking 

the “Open folder” button to the right of each filename brings 

up a file finder window (fig. 5) that allows quick access of 

files.

The “recent” and “goto” buttons on the right are 

particularly useful, as they allow quick reference to recently 

accessed files and folders, respectively.

Setting the default data directories: If the same directory 

folder is to be used often, it is advantageous to set that 

directory as the default directory in Imagine. To do this, 

select “Session/Preferences” from the Imagine main console 

to bring up the “Preference Editor” window. Select “User 

Interface & Session” from the “Category” list, and then 

manually type in the desired path name into the Default Data 

Directory and Default Output Directory fields. Choose “User 

Save” to register these preferences for this user for this and 

all future sessions.

When the input filename is chosen, Imagine will place 

a default output filename in the “Output File” field of the 

Import/Export window. Unless specifically overridden, this 

file will be in the default directory folder. Since the default 

directory is not typically the desired folder for the output 

image, it will often be necessary to navigate in the output 

file field to the desired directory, and then retype the name of 

the output file. A workaround for this process is to navigate 

in the Output File field to the desired directory before the 

input file is chosen, and to simply type in a dummy filename 

in the output file field, and thereafter to select the input file 

as desired. This will ensure that the default name chosen 

by Imagine for the output file will be placed in the desired 

output directory.

Figure 4. Import/export action window.

Figure 5. File finder window.

Images from a wide range of sources can be imported into Imagine through the Imagine Import/Export 

action window. This action window is accessed through the second box on the main console (fig. 3).

SOP 2. Preprocessing Landsat Imagery  35
Figure 6. Open-file folder icon, circled.

IV. Reprojecting and Resampling 

Images

It is critical that all images and vector coverages be 

in the same projection, spheroid, and datum before any 

radiometric or change-detection processing is begun!

Although Imagine will tolerate different projections and 

spheroids when processing, it is best not to allow Imagine to 

reproject “on-the-fly” without knowing explicitly that images 

are aligned as they should be. Only by viewing spatial data 

and ensuring that spatial relations are as expected can robust 

results be achieved.

The geometric reference image is the base of all 

geometric processing, and thus it is important that it have the 

appropriate map properties before any further processing is 

done. Therefore, after the reference image has been imported 

to the Imagine format, it frequently must be reprojected and 

resampled.

The NCCN standard spheroid/datum pair is GRS80/

NAD83, which should be used for all spatial data. Although 

many existing Digital Elevation Models and some vector 

coverages are in the Clarke 1866/NAD27 Spheroid/Datum 

combination, those are based on an older model of the Earth, 

a poorer datum-measurement network, and thus should be 

avoided as baselines for future monitoring. The conversion 

between NAD27 and NAD83, although not perfect, can be 

achieved with accuracy more than sufficient for Landsat-based 

investigations.

All Landsat images are to be resampled to the 25 m pixel 

size. Although the native resolution of the imagery is 28.5 m, 

experience has shown that the 25 m size is far easier to work 

with, both for processing and for eventual field validation.

Therefore, it is necessary as a first step to check the 

condition of the reference image and reproject it if necessary.

1. Start Imagine if that has not been done already.

a. If Imagine asks whether a Classic viewer or a 

geospatial light table is desired, select the classic 

viewer. It may be desirable to choose not to be asked 

this question again.

2. In Viewer #1, load the geometric-reference image.

a. Click on the folder icon directly under File on the 

viewer menu (fig. 6).

b. Navigate to the folder with the reference image and 

open it.

i. Note: This assumes that the reference image has 

been imported into Imagine’s format. If not, go 

back to section III above.

ii. The image should come up in the viewer.

(1) Because of the tilt of the Landsat image 

relative to the earth, the upper left portion 

of the image area likely is black; because 

the viewer defaults to viewing this area, 

the initial visible area may be black. To see 

more of the image, navigate around with the 

sidebar and bottom bars.

(2) Alternatively, right-click in the viewing 

space of the viewer to see the viewer options 

menu, and select “Fit image to window.” 

This option will resize the entire image to 

fit within the viewing space of the viewer. It 

may take several seconds for the resizing to 

finish.

36 Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
Figure 7. ImageInfo button, circled.

3. Note the geometric properties of this image.

a. Select the ImageInfo icon (a white box with a lowercase “i” in it (fig. 7).

b. An ImageInfo window will pop up.

i. On the front panel of this window, labeled as the 

“General” tab, the geometric information about 

the reference image is shown.

c. At the bottom of the ImageInfo box is the Projection 

Info:

i. Ensure that the image is in Universal Transverse 

Mercator (UTM), in the correct zone (zone 10 

for all of the parks of the NCCN), and that the 

spheroid and datum are as desired (NAD83, 

unless compelling reasons exist to use a 

different datum).

(1) See Bolstad (2002) for more information on 

datums and spheroids.

ii. Ensure that the pixel size is 25.0 m.

(1) If the reference image has been acquired 

recently, it was recommended in SOP 1

that the image be purchased at 28.5 m. 

Therefore, it is expected that the pixel 

size will be 28.5 m, which will need to be 

resampled to 25.0 m.

d. If either the map-projection information or the 

pixel size is not as desired, then reprojection will be 

necessary.

4. Reprojecting and resampling images in Imagine

a. If the reference-geometric image is in a projection 

or a spheroid/datum other than desired, it is 

straightforward to reproject the image in Imagine. 

Reprojection also can be achieved in Arc programs, 

but details of the Imagine-based approach are 

sufficient.

b. Steps:

i. Ensure that the image to be reprojected is open 

in a viewer.

(1) See step 2 above if not.

ii. From within that viewer, select “Raster/

Geometric Correction”

(1) An action window named “Set Geometric 

Model” will popup, with a list of options.

iii. Scroll down and select “Reproject” from the list.

(1) A “Reproject Model Properties” window 

will pop up, as well as a “Geo Correction 

Tools” window.

(2) In the Reproject Model Properties window:

(a) Change grid sampling X and Y to 30 

each.

(i) Note that the number 30 is not 

the size of pixels. This number 

refers to the density of a grid that 

Imagine will construct to solve 

the reprojection equation between 

different projections. The number 

30 in X and Y means that a 30 

by 30 grid (i.e. with 900 points) 

will be used to project between 

projections.

(b) Select the “Projection” tab.

(c) Click on “Add/Change Projection.”

(i) A “Projection Chooser” window 

will come up.

(d) Select the desired projection into which 

the image is to be projected.

(e) Click on OK button.

(3) In the Geo Correction Tools window, select 

the “Resample” button, which is a box with 

four smaller multicolored boxes inside it, 

tilted on its side (fig. 8).

(a) The “Resample” dialog will pop up.

(4) For the Output File, navigate to the desired 

output folder before typing in the name of 

the resampled output file.

(a) Follow naming conventions in SOP 5

Data Management when reprojecting a 

file.

SOP 2. Preprocessing Landsat Imagery  37
Figure 8. Geo Correction Tools window, with the 

resample icon circled.

(5) For the resample method, select “Cubic 

Convolution.”

(a) Note: Nearest-neighbor (NN) 

resampling is often recommended 

to maintain the radiometric integrity 

of the imagery, since it leaves the 

spectral response within a pixel 

unchanged. However, experience in 

monitoring at the NCCN parks has 

shown that the geometric distortions 

introduced by the NN resampling 

cause artifacts in later change 

detection. Therefore, it is important 

that the cubic-convolution approach 

be used. Cubic convolution (see 

Richards (1993) for a discussion) 

retains the geometric properties of the 

image much better, especially when 

pixel size is changing as in this case, 

but does introduce some spectral 

anomalies at the edges of contrasting 

pixels that must be considered in later 

steps.

(6) For the output cell sizes, manually type in 

25 for both X and Y fields.

(7) Select “Ignore Zero in Stats.”

(a) This will make later display of the 

image work better. Without selecting 

this option, the large area of no-data 

around the margins of the imagery 

is included in the statistics used by 

Imagine to display the image. Images 

will appear “washed-out.”

(8) Click on OK button.

(a) A progress window will appear and 

notify when the job is complete.

(9) Close all windows except the Viewer 

#1, and do NOT save the changes or the 

geometric model used for the resampling.

(10) The resultant 25 m image will be clipped 

to the study area in section V below.

5. Reprojecting the Digital Elevation Mode (DEM)

a. A DEM is necessary for a variety of steps in the 

protocol, and must be in the same projection and 

datum as the geometric-reference image. Because 

DEMs typically exist in the NAD27 datum at 30 

m, it is often necessary to reproject these into the 

desired NAD83 and 25 m condition.

i. NOTE: If the DEM is in NAD27 and is a grid 

coverage, reproject it in an ESRI Arc software 

product using the NADCON option for 

reprojection. This method will be more precise 

than using Imagine’s approach in steps b. and c. 

below.

b. Open the DEM in Viewer #1.

i. Properties of the DEM:

(1) A digital-elevation model for the areas around 

the parks has been included by the authors, 

and thus this step will not be necessary unless 

new DEMs become available, or if the study 

area around the parks is changed. These steps 

are provided for that case.

(2) If a digital-elevation model is acquired 

from another source, or if an in-house DEM 

from the park is used, it must have metadata 

describing the map-projection and datum 

information, pixel size, and units of the 

vertical dimension (meters or feet).

ii. Open the DEM in a viewer.

c. Reproject the DEM to NAD83 and 25 m.

i. Follow steps in section 4 above in this section, but 

use the DEM as the base rather than the geometric 

reference image.

d. If reprojection is done in an Arc software product, 

it will be necessary to import the reprojected DEM 

GRID into Imagine. See section III above and follow 

the instructions for importing images into imagine. 

The GRID format is listed as one of the import format 

options.

i. After importing, ensure that the DEM has 

imported successfully by viewing it in a viewer 

(see step 2 in this section above).

38  Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
V. Defining Study Area, Clipping 

Images and DEMs to the Study Area

This protocol focuses on monitoring in and around 

the parks, and as such does not require that processing be 

conducted on an entire Landsat image. Those images are 

approximately 180 by 180 km, and are located in a manner 

completely irrelevant to the parks. Therefore, before any 

processing is done, it is necessary to define a study area in 

which all image-processing and change-detection activities 

will take place.

The end goal of study area delineation is to have two 

geographic coverages representing the same study area 

boundary: an Imagine-format Area of Interest (AOI) and an 

ESRI Arc-format vector coverage. Both can be created in 

Imagine, or, if the study area has been defined elsewhere and 

is currently in a vector format, Imagine can be used to make 

an AOI of the vector coverage.

The delineation of the study area is an important 

step in the long-term success of this protocol, as the study 

area defined here will be used for reference when creating 

summary metrics of change over time. Therefore, this step 

should involve the input of as many interested parties at the 

parks as possible. As of this writing, there was no agreed-upon 

rules by which such a designation could be made generic, 

but there are several components to consider. First, of course, 

the study area should include the entire park. There may be 

interest in creating separate study areas in some parks (such 

as the coastal region at OLYM), but this decision should be 

approached carefully as it requires more processing. Second, 

the study area may extend beyond a park by either an arbitrary 

distance or by some distance defined by ecological or 

economic factors. It may be difficult to predict these factors at 

the current time, which argues for a conservatively large study 

area that could allow future contraction. It will be significantly 

easier to make the study area smaller in the future than it 

would be to make it larger.

If the desired study area already exists as a vector layer, 

the following steps are required:

1. Start Imagine and load the geometric reference image in a 

viewer (see section IV, steps 1 and 2, if this has not been 

done already). Ensure that this is the geometric reference 

image with 25 m pixels.

2. In the viewer, choose “File/Open/Vector Layer” and 

navigate to the directory where the new study area vector 

layer is housed.

a. Open the vector layer, and ensure that it matches with 

the reference image as expected.

b. Use the ImageInfo (section IV, step 3 above) to 

ensure that the vector coverage is in an appropriate 

projection and datum for the geometric-reference 

image.

i. Assuming that the geometric reference will be 

UTM and NAD83, respectively, then the vector 

coverage must be in UTM, NAD83.

(1) NOTE: If a vector coverage is in the Clarke 

1866/NAD 27 projection/datum space, it 

must be reprojected. It is recommended 

that this projection process occur in 

ESRI Arc software, using the NADCON 

transformation approach. Arc/Info 

documentation on “Projections” provides 

more information. However, if this approach 

is not possible, the steps for reprojecting a 

vector coverage in Imagine are given below.

(2) If the coverage is not in the appropriate 

datum, it will be necessary to reproject it 

into the correct datum. This can be done in 

various ESRI Arc products and in Imagine. 

The steps for doing this in Imagine are 

detailed below. GIS professionals also 

may prefer to do this in Arc workstation or 

ArcTools.

ii. Reprojecting a vector coverage in Imagine.

(1) In a blank viewer, open the vector coverage.

(2) Click on the ImageInfo icon (section IV, step 

3 above).

(a) The window associated with the 

“General” tab will pop up.

(3) Select “Edit/Reproject the coverage.”

(a) A “reproject coverage coordinates” 

window will pop up.

(4) Name the output vector in such a manner 

that the distinction in datums can be 

inferred. Follow data-management 

considerations in SOP 5 Data Management.

(5) Click on “Define new projection.”

(a) The “Projection chooser” window will 

pop up.

(6) Use the pulldown menu for the spheroid 

name field to select NAD83.

(7) Use the pulldown menu for “Datum name” 

to pick the NAD83 datum from the list.

(8) Ensure that zone is 10 and “North or South” 

is set to north.

(9) Click OK, then click OK again in the 

“reproject coverage coordinates” window.

(a) Save the coverage.

SOP 2. Preprocessing Landsat Imagery  39
Figure 9. Vector viewing properties for test window.

Figure 10. Vector-attributes window.

3. With the vector coverage and the geometric-reference 

image open in the same viewer, select “Vector/Viewing 

properties.”

a. The “Properties for test” window for the vector 

coverage will pop up (fig. 9).

b. When vector layers are first shown in Imagine, the 

default is to display them as arcs, not polygons. To 

manipulate polygons, as will be done below, the 

polygons must be displayed.

c. First, uncheck the box for “Arcs” by clicking it.

d. Then check the box to the left of “Polygon.”

i. To change the border and fill properties of the 

polygon, select the box to the right of the colored 

field to the right of the polygon (in cyan in 

fig. 9).

ii. Keep this window open for now.

4. Then from the viewer with the vector coverage in it, select 

“Vector/Attributes.”

a. The “Attributes” window will pop up.

i. Each polygon will have a row in the table 

of attributes. Select all the rows that include 

polygons that are desired to defined the study 

area. Typically, this should be a small number of 

polygons – often only one – because the study 

area should be a large, internally connected 

space.

(1) To select a row, click on the row number 

in the left-hand side of the window (the 

“Record” column in fig. 10).

ii. To select multiple rows, click and drag down all 

rows.

iii. Because polygons are displayed, selecting these 

rows will change all of the polygons on the 

viewer to the selection color, which defaults to 

yellow.

5. With these polygons selected, select “AOI/Copy selection 

to AOI” from the viewer.

a. Note: If the coverage and the image are not in the 

same projection, this step will fail. It is not sufficient 

to simply remove the image from the viewer to avoid 

this failure, because the AOI must be associated with 

the correct datum of the image for all later steps. 

Therefore, if the coverage is not in the correct datum, 

it must be either reprojected or the datum must be 

overruled, as noted in step 2 above.

b. This step creates a new AOI in the viewer, anchored 

to the geometric reference image.

6. Save the AOI.

a. In the viewer, select “File/Save/AOI Layer as.”

b. Navigate to the appropriate directory and save the 

AOI layer. Follow the SOP 5 on naming and file 

location conventions.

7. This AOI is now the base AOI for all subsequent steps 

that involve clipping of imagery to the study-area 

boundary, including clipping the DEM.

 If the study area does not exist in vector form and must be 

created, follow steps 8 through 11.

40 Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
8. In a viewer, open the geometric-reference image.

a. See section IV, steps 1–2 if this is not done already.

9. Draw the study area as an AOI.

a. Select “AOI/Tools” from the viewer, and then select 

the polygon drawing button (fig. 11).

b. When the cursor is moved across the viewer, it will 

take the form of a crosshair. Clicking with the left 

mouse button will create a vertex in the developing 

polygon. Define the vertices that will define the 

polygon desired to encompass the study area. 

Double-click when the last desired vertex has been 

placed to close the polygon-creating process.

i. NOTE: The study area for ecological monitoring 

may well extend beyond the boundaries of the 

parks proper.

10. Save the AOI as the study area AOI.

a. In the viewer, select “File/Save/AOI layer as.”

b. Navigate to the appropriate directory and save the 

AOI layer. Follow the SOP 5 on naming and filelocation conventions.

c. This AOI is now the base AOI for all subsequent 

steps that involve clipping of imagery to the study 

area boundary, including clipping the DEM.

Figure 11. AOI tools window, 

with the polygon drawing button 

circled. Figure 12. Arrange layers window.

11. Copy this AOI to a vector coverage.

a. NOTE: This step is designed to create a vector 

coverage of the study area for use in displaying data 

in ESRI software. It is not strictly necessary, as it 

will not be used for analysis in any other part of this 

protocol.

b. It is first necessary to create a blank vector coverage.

i. In the viewer, ensure that the geometricreference image is displayed.

ii. Select “File/New/Vector layer.”

(1) A “Create a New Vector Layer” window will 

pop up.

iii. Navigate to the appropriate directory and type in 

the name of the coverage in the Filename field.

(1) Follow the conventions in SOP 5 Data 

Management for placement and naming of 

the coverage.

iv. Select “single precision” when given the choice 

between single and double precision.

c. Once this blank vector layer is open, the viewer 

should show “Vector” in the list of options at the top 

of the viewer.

d. In the AOI tools window, click on the selection arrow 

icon (upper left icon in fig. 12).

i. Click in the viewer on the space of the AOI to 

select it.

(1) A white box will appear around the outside 

perimeter of the study area AOI. This 

indicates that the AOI is selected.

SOP 2. Preprocessing Landsat Imagery  41
ii. Select “Vector/Copy selection to vector.”

(1) Little will seem to have happened. Do not be 

alarmed.

iii. Select “File/Save/Top layer.”

e. Confirm that the vector coverage is as expected by 

removing the study area AOI from the viewer.

i. Select “View/Arrange Layers.”

(1) The “Arrange layers” window will pop up.

(2) Right-click on the top layer, the AOI layer. 

Select “Delete layer” from the list. Then 

click on the “Apply” button in the lower left. 

This will remove the AOI from the viewer. 

Do not save the AOI layer, since this has 

been done already in step 10 above.

(3) After the AOI is removed, the outline of 

the study area drawn on the screen should 

remain.

f. Build the vector layer.

i. In the main imagine console, select the Vector 

layer utility.

(1) Note: If the Vector layer utility is not 

included in the Imagine installation being 

used, this layer can be built in ArcGIS or 

Arc workstation. See the documentation for 

those software packages for assistance on 

building polygon coverages.

ii. Select “Build Vector Layer Topology”

(1) Select the vector coverage just defined.

(2) Click OK.

Once the study area AOI has been defined, the 

geometric-reference image and the DEM must be clipped 

to that area.

12. From the main Imagine icon panel (fig. 1 above), click on 

the “Intepreter” button.

a. The “Image Interpreter” window will pop up.

b. Click on the “Utilities” button from this window.

i. The “Utilities” window will pop up.

ii. Select “Subset.”

(1) The “Subset” window will pop up (fig. 13).

iii. In the “Input File” field, navigate to the directory 

with the geometric-reference image and select it.

(1) Ensure that the selected image is the one 

with 25 m pixels and in the NAD83 datum.

iv. In the “Output File” field, navigate to the 

appropriate directory for the geometric-reference 

image.

Figure 13. Subset window.

(1) Follow the conventions in SOP 5 for 

locating and naming the reference image.

v. Then click on the “AOI” button at the bottom of 

the Subset window (fig. 13).

(1) In the “Choose AOI” window, select “AOI 

File.”

(2) Use the folder button on the right of the AOI 

file field to navigate to the study area AOI 

created in step 6 or 10 above in this section.

vi. Then click on OK button in the Subset window 

to start the clipping process.

13. Conduct the same process on the DEM (step 12), except 

use the DEM file as the input file in the Subset window 

instead of the geometric-reference image.

At the end of the steps in this section, there should be the 

following:

a. A study area AOI that defines the bounds of the area 

where all subsequent work will be done.

b. A vector coverage of the same area.

c. A geometric-reference image clipped to the study area.

d. A DEM clipped to the study area.

42 Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
VI. Geometrically Registering Images 

to the Reference Image

Images must be aligned so that the information in a 

pixel at a given coordinate in an image corresponds to the 

same point on the ground over multiple dates of imagery. 

To do this, all images must be geometrically aligned to 

the reference image. Because that image is assumed to be 

geometrically accurate and registered to a map projection with 

known coordinates, geometrically registering other images 

to that reference will result in those new images also being 

registered to map coordinates. This allows not only direct 

comparison between images, but also allows connection with 

other spatially registered datasets, such as GIS layers with 

information on roads, streams, and trails.

Registration of one image to the reference image is a 

multistep process (Richards, 1993). First, tie points are in 

each image. A tie point is a location that can be identified 

unambiguously in both the reference image and the dependent 

image, here known as an “Input” image. After a sufficiently 

large number of tie points is found linking the coordinates 

in the input image to those in the reference image, a 

mathematical transformation in the form of a linear equation 

can be solved that projects a coordinate in the input image into 

the map system of the reference image. Because of the need 

to terrain-correct imagery, the process of solving this equation 

also must take into account the viewing geometry of the sensor 

and the topography of the land being imaged. Finally, once 

the equation for transforming coordinates is solved, all of 

the coordinates of the input image are transformed into their 

coordinates of the reference image, and resampled to place 

them on a regular grid in the reference-image map system.

The first step in this process, finding tie points between 

images, can be done manually or in an automated fashion. 

Manual location of tie points is time consuming, subject to 

the interpretation of the individual interpreter and thus not 

repeatable, and typically results in tie-point populations that 

are small. Therefore, an automated approach is desirable, and 

is provided as a module in IDL Virtual Machine (VM) called 

“ITPFind.”

Locating Image Tie Points Using ITPFind

ITPFind uses image correlation to locate coordinates 

of image tie points (ITPs) that best match the pixel patterns 

of small windows in the reference and the input images. It is 

described in detail in Kennedy and Cohen (2003).

The program is supplied as a module to be run in the 

IDL-VM. IDL-VM must be installed on a computer before 

this module can be used (see http://www.rsinc.com/idlvm/

index.asp, as noted above, for downloading instructions). Once 

IDL-VM has been installed, the program can be run by simply 

clicking and dragging the ITPF module onto the IDL-VM 

icon. Before this is done, several preparatory files must be 

setup. The program is named “ITPFind.sav.”

ITPFind requires text files as inputs. These files describe 

the location and properties of the images to be registered and 

the parameters used by the program for choosing windows of 

correlation, grid spacing of tie points, and thresholds for tiepoint acceptance. The authors have provided examples of each 

of these files as part of this protocol, along with the ITPFind 

VM module. These files must be set up before the program 

can be used to register images.

1. The first file needed is named “squeet_master.txt”. This 

file provides the pathnames for all subsequent text files 

needed by the program. This file can be located anywhere, 

but it is best to place it in a directory relevant for the 

files of interest so its location can be found easily. When 

the ITPFind module is run, it will query the user for 

the location of this file. The file contains the path and 

filenames of the two other pointer files required for the 

program to begin functioning.

a. One pointer file contains the list of available 

parameter files (its contents are described in (2.) 

below). Identify the name of this pointer file using the 

following syntax: <path/file_here_without_brackets>.

2. The other pointer file contains the list of available image 

files (its contents are described in (3.) below). Identify 

the name of this pointer file using the following syntax: 

<path/file_here_without_brackets>.

b. File of available parameter files — The filename for 

this file is specified in squeet_master.txt. This file 

contains the list of filenames of candidate parameter 

files. The list of candidate-parameter files starts at the 

beginning of the file, one file per line separated by 

carriage returns, with no leading text in the file or on 

any line. The contents of the parameter files will be 

outlined below.

3. File of available image files — The filename for this file 

is specified in squeet_master.txt. This file contains the 

list of available images and their vital characteristics. For 

each image file, you must include critical information 

for the program to run. The following is an example. 

SOP 2. Preprocessing Landsat Imagery  43
Each line will be described below the example. Note that 

each line ends with a carriage return. Also, there must 

be a colon and a space between the colon (:) and the first 

character of the associated information (i.e. Rotation: 0, 

not Rotation:0 or Rotation 0). Text that does not exactly 

match the text before the colons will be ignored and will 

cause errors.

EXAMPLE:

Filename: images/exampleInpImage.img

File codename: inputimage

Start point:(x,y): 388, 663

Pixel center to pixel center distance:(x,y): 25, 25

Rotation: -6

Layer to use: 4

Ignore: 0

Mask below: 10

Mask above: 250

Description of Each Line:

Filename:

Type in the full pathname of the image without quotes. 

Currently, the image formats that are accepted are ERDAS 

Imagine format, or a flat-binary file denoted with a “.bsq” 

suffix. If the flat-binary option is used, however, there MUST 

be an accompanying file that describes the geometric qualities 

of the image. See “Supported Image Types” below.

Currently, the software is designed to accept either 

ERDAS Imagine 8.3–8.7 images or flat-binary files. Imagine 

types supported are: Unsigned 8 bit, Unsigned or Signed 16 

bit, and IEEE Float.

Flat-binary files must be stored in band sequential 

format, and the filename must end with “.bsq”. Also, 

such files must have an accompanying file named the 

same as the image file, except with “.hdr” swapped in 

the place of “.BSQ” (i.e. image.bsq and image.hdr). 

The .hdr file must contain at least the following entries 

in the following order:

BANDS:

ROWS:

COLS:

DATATYPE:

UL_X_COORDINATE:

UL_Y_COORDINATE:

LR_X_COORDINATE:

LR_Y_COORDINATE:

PIXEL_WIDTH:

PIXEL_HEIGHT:

Text on other lines will be ignored.

BANDS: the number of bands in the image

ROWS: the number of rows (lines) in the image

COLS: the number of columns (pixels) in each line of 

the image

DATATYPE: use 3 for 8-bit unsigned, 5 for 16-bit 

unsigned, and 9 for 32-bit IEEE floating point.

 <***>_COORDINATE: All coordinates can be 

written as decimal values or integer values. These 

must be in the coordinate system units of the image. 

If that system is unknown, assume that each pixel is 

offset from the next by a value of 1.0. It is critical, 

however, that the coordinates be referenced as if they 

were a UTM-type map coordinate system for the 

northern hemisphere, where Y-value increases from 

bottom to top. File-coordinate system coordinates 

typically work in reverse (Y increases from top to 

bottom), and this should be avoided.

 PIXEL_WIDTH, _HEIGHT: These should be 

reported in the units of the map system for the 

image, and should be consistent with the UL_, LR_

COORDINATES given above. For example, if the 

coordinates given above are in the UTM system with 

meters as the basis, then pixel dimensions also must 

be in meters (i.e. 28.5 for raw LANDSAT TM data, 

or 1000.0 for resampled 1km AVHRR data). Again, if 

the coordinate system of the image is not known, use 

1.0 for both X and Y pixel sizes.

File Codename:

This codename will be incorporated in the output ITP 

files. It allows later identification of which images were used 

to find the ITPs. Use some short, unique identifier string 

without quotes. Because the input and the reference images 

are stored in this file and there is no distinction in how they 

are stored, it is sometimes useful to include as part of the 

codename a reminder about which image is to be used as a 

reference and which as an input image.

Start Point:(x,y):

The map coordinates of the ‘start point’ of this image. 

It is simply the approximate location of a point in this image 

that will match with the ‘Start point’ of its complementary 

reference or input image. This start point must be in the 

coordinate system of this image: i.e. in the coordinates of the 

input image for the input image, and in the coordinates of the 

reference image for the reference image. Note that the method 

for location of these coordinates is described in Running the 

ITPFind Module section below.

44  Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
NOTE: Typically the Input image has no map coordinates 

associated with it, since this is the image to be reprojected. 

Because of a glitch in Imagine, it is necessary to go through 

the following steps for images that have no map coordinates 

associated with them before the start point is in the viewer.

Open the input image in a viewer (section IV, steps 1–2 

above).

Open the ImageInfo for that image (section IV, step 3 

above).

Select “Edit/Change Map Model.” The Change Map 

Model window will appear. Simply click on OK button 

once this has been done.

Accept changes when prompted.

Close the viewer.

Why do this? For some reason, Imagine has a 

disconnect between the map info that is used in the 

viewer when an image is opened that has no geometric 

information and the map information that is passed to 

the ITPFind program and to the GCP editor of Imagine 

(further down in this SOP). By opening the Edit/

Change Map model dialog, this forces Imagine to make 

geometric properties consistent across modules.

Pixel Center to Pixel Center Distance:(x,y):

The distance in a COMMON REFERENCE SYSTEM 

between the center of adjacent pixel centers in this image. 

When ITPFind runs, this value will be compared to the 

parallel measure in the complementary reference or input 

image to determine how far the program should jump between 

ITPs. It must be in the same units for both the reference 

and the input images in a given run of the program, typically 

in units of the reference image. Since the geometric-reference 

image is a LANDSAT TM image with pixel size 25 by 25 

m, its pixel center to pixel center distance should be listed as 

25, 25 m. However, assuming that the input image has been 

ordered from EDC with 28.5 m pixels, its pixel center to pixel 

center distance should be 28.5 m, even if no map information 

is associated with the image. Note that if the input image 

coordinate system is unknown (a likely case, given that ITPs 

are being sought), the “pixel size” of the input image may be 

reported in the ImageInfo of ERDAS Imagine as 1.0, 1.0 – but 

the Pixel center to pixel center distance:(x,y) is still 28.5, 28.5.

Rotation:

The rotation, in degrees clockwise, of this image relative 

to a stable reference direction. It typically is easiest to set 

the reference image rotation to zero, and set the input image 

relative to the reference image. To determine whether the 

rotation of the input image relative to the reference image is 

positive or negative, use the following approach:

1. Imagine an arrow pointing north on the ground in the 

reference image.

2. Imagine an arrow pointing north on the ground in the 

input image.

3. If the head of the input image arrow is to the left of the 

reference arrow, the input image rotation is negative. If 

the head of the input arrow is to the right of the reference 

arrow, the input image rotation is positive.

Layer to Use:

The layer number of the image to use for the analysis. 

Only one layer is used. In all likelihood, this should be the 

same layer number as will be used in the complementary 

reference or input image. Layers begin with 1. It is best to 

use a layer of the image with high contrast, such as the near 

infrared bands. For TM images, band 5 is often good.

Ignore:

If the image file contains unwanted background, give the 

value of the background here. This is often zero. The programs 

will recognize background areas and will skip past them, 

improving performance. Moreover, this method can be used to 

“work around” popcorn-type clouds or other drastic changes 

between images. A quick unsupervised classification of the 

cloudy image can be used to identify the bulk of the clouded 

areas and the shadows, and all of these can be set to the Ignore

value. The program will use only areas between the clouds 

for calculation of correlation. Note that if this route is chosen, 

the window size parameter (see below) likely SHOULD be 

increased to ensure that an adequate number of pixels remain 

for calculation of correlation when the cloud pixels are 

removed.

Mask Below, Mask Above (Both Optional)

As of version 2.1, a more useful approach to screening 

out unwanted areas was added by inclusion of the Mask Below 

and Mask Above keywords for an image. These are optional 

– if not used, do not include the keyword on the line (i.e. 

simply omit the entire line, rather than retaining the keyword 

“Mask below” and setting it blank). The number attached to 

either mask below or mask above is the value below or above 

which pixels will not be used for correlation matching. This 

was designed for use in cloudy areas, where a mask above 

value eliminates much of the cloud area and the mask below 

value eliminates much of the deep cloud shadow. The values 

must be chosen by the user, so it is best to just look at your 

image in your favorite image processing system and get a 

rough guess for the digital number (DN) values of the clouds 

and shadows. As with the ignore keyword, it is best to increase 

the window size you are using if you think that a fair portion 

of each window will be masked out with this feature.

SOP 2. Preprocessing Landsat Imagery  45
Parameter Files

1. Parameter files are pointed to in the “File of available 

parameter files” (see above). Each run of the software 

requires a single-parameter file. An example of the 

required information is given below, followed by detailed 

descriptions of each line.

EXAMPLE:

Original Parameters File for use in ITPFind program

Window Size: 150,150

Window Spacing: 400, 400

Number of Iterations: 1

Pixel Aggregations: 1

Search Neighborhoods: 5

Threshold min. steepness: .35

Zoom Factors: 2

Maximum move: 0

Nudgefactor: .20

Postfilter_rms: .025

Window Size:

The size, in pixel counts of the reference image, of the 

window extracted for matching. It is in x,y format. Note that 

this dimension is not in the units of any coordinate system, but 

referenced by the number of reference pixels. For example, 

if the reference is a TM image with pixels 28.5 m on a side, 

then setting the window size to 100,100 means that a window 

of 100 by 100 pixels will be extracted, equivalent to an area 

2,850 by 2,850 m. The equivalent sized window in the input 

image is calculated by the software based on the Pixel center 

to pixel center distance:(x,y) given for the reference image 

and the input images. If they are not the same, the program 

will resample the input image to match the Pixel center to 

pixel center distance:(x,y) of the reference image.

Window Spacing:

The approximate desired grid spacing between ITPs, 

in pixel counts of the reference image, between ITPs. 

Again, the relation between the Pixel center to pixel 

center distance:(x,y) of the reference and input images will 

determine the jump in pixels of the input image. Note that 

the rotation of the input image also will be calculated in the 

jumping between points. Thus, if the user is unsure of the 

rotation value of the input image, it is safer to give a smaller 

window-spacing value, since the error in the rotation value 

will be multiplied over a shorter distance.

Number of Iterations:

The number of times that the program will refine the ITPs 

coordinate pair at each point. The next five parameters must 

each have this many entries, each separated by a comma. For 

example, if Number of Iterations is set to 2, then each line 

of the next five must have the keyword parameter followed by 

“: <n1>, <n2>”, where <n1> and <n2> are the values for that 

parameter.

Typically, this can be set to 1. If set to 2 or higher, the 

program engages in an iterative process. Using the first 

entry in each of the next 5 parameters, the program finds an 

approximate match for the ITP pair. Using that approximate 

match derived in the first iteration, the program uses the 

second entry (the number after the first comma) to find a better 

match for the ITP pair. Typically, this only makes sense if the 

Pixel Aggregations value is lower for the second iteration 

than for the first iteration.

Pixel Aggregations:

The number of reference pixels by which to aggregate 

both images before floating the input image over the reference 

image. Note that this aggregation occurs AFTER the input 

image is resampled to match the grain size of the reference 

image.

If set to 2.0, for example, then the reference image 

is first aggregated such that a 2 by 2 set of original pixels 

becomes 1 pixel in an aggregated image. That image—with 

its doubled pixel center to pixel center distance—is used to 

develop correlation surfaces and find an ITP. Because of the 

aggregation, this ITP will have a lower precision than if the 

ITP were calculated on the images at native resolution, but this 

aggregation allows a larger area, with more potential for strong 

spatial pattern, to be used to calculate correlations. Used in 

conjunction with two or more iterations (set with the Number 

of iterations parameter above), this can efficiently hone in on 

ITPs in difficult-to-match image pairs.

Search Neighborhoods:

The number of pixels to float the input image is relative 

to the reference image. This value is in units of the aggregated 

pixels—i.e. if the Pixel Aggregations for this iteration were 

2, then a search neighborhood of 10 for this iteration actually 

would mean that the input image has floated an equivalent 

of 20 original pixels in all directions relative to the reference 

image.

This parameter greatly influences performance of the 

software, since increases in the search neighborhood result in 

squared increases in the number of comparisons necessary. It 

is thus desirable to keep this number as low as possible. Values 

between 5 and 7 are optimal, although values as high as 10 or 

12 may be necessary.

46 Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
Threshold Minimum Steepness:

This value is the threshold against which a potential 

peak in correlation is tested, based on the relation between the 

peak of correlation and the plane at the “base” of the peak. If 

the observed value is below this Threshold min. steepness

value, the point will be thrown out. A value of 0.35 has been 

robust across most image situations. If it seems that erroneous 

ITPs are being accepted too often, increasing this parameter 

value may help—however, generally it is better to attempt 

altering other parameters first, i.e. the window size or the 

search neighborhoods,

Zoom Factors:

The portion of the originally extracted window 

(determined by the window size) is used for the determination 

of covariance. A value of 1 means that the entire extracted 

window is used to determine covariance. A value of 2 means 

that the window used for covariance calculations will be 

one-half the size of the original image in both the X and the 

Y dimensions; i.e. it will be one-quarter the area. The smaller 

window is extracted from the center of the originally extracted 

window.

Consider the following example: The window size is set 

to [400,400], number of iterations set to 2, pixel aggregations 

set to [2,1], and zoom factors set to [1,2]. In iteration 1, the 

400 by 400 window is read from the file, and then aggregated 

in 2 by 2 blocks to create a 200 by 200 image that captures 

the spatial patterns across the entire area represented in the 

original [400,400] image. The spatial matching is run on 

that image and a preliminary ITP pair located. In the second 

iteration, the zoom factor of 2 takes the original 400 by 400 

image and extracts the 200 by 200 area around the preliminary 

ITP pair found in iteration 1, and this image (unaggregated, 

because the pixel aggregation is set to 1 for this iteration) is 

used to derive a more refined ITP pair.

Maximum Move:

If this value is non-zero, every time the program cannot 

find a valid covariance peak at a given point, it will try again 

by searching nearby rather than skipping the point altogether. 

“Nearby” is defined by the next parameter, the Nudgefactor.

The pattern of searching nearby is circular; i.e. it will attempt 

to find a point just to the “right” of the first attempt, then 

slightly right and down if no match was found there, then 

down if no match is found, etc., until a complete circle has 

been traversed. If the Maximum move is 2, this process will 

continue on a second circle with greater radius.

In most cases, it is more efficient to set this value to zero, 

since factors that cause difficulty locating peaks at a given 

location (for example, a bank of clouds in one image) are more 

likely to exist nearby than far away. However, under certain 

circumstances the user may require that ITPs be located as 

close to full-grid pattern as possible, and this option allows 

this.

Nudgefactor

This value determines how far away from the original 

point the program should search for covariance values if it 

cannot find a covariance peak on an initial try. The value is 

a proportion of the window-size value. If the window size is 

[400, 400] and the Nudgefactor is 0.5, then the program will 

extract new windows at a radius of 200 reference pixels away 

from the original point tried.

Postfilter_rms (Optional)

Some tie points will still be odd, despite the various 

filters that occur during the search for each individual point. 

By setting this parameter, the program calculates a simple 

first-order transformation after all points have been found, 

and iteratively removes any points whose removal results in 

an improvement in the overall root-mean-square (RMS) error 

of the solution. The value of this parameter is the threshold 

for determining whether improvement has occurred, and 

corresponds to the proportional improvement in RMS error. 

Thus, setting it to 0.025 means that points will be removed 

when their removal improves the RMS error by 2.5 percent or 

more. This tends to remove the points that clearly are errors 

(i.e. points over water, etc.). This feature was added with 

version 2.1.

At a minimum, the squeet_master.txt file must exist, and 

it must point to a squeet_images.txt file and a squeet_params.

txt file. The squeet_images.txt file must contain information 

for at least two images, one to be used as reference image 

and one to be used as the input image. The squeet_params.

txt file must point to at least one parameter file with all of the 

necessary parameter fields filled in.

Running the ITPFind Module

To find a good start point of the input and reference 

images, load each image into an Imagine Viewer (follow steps 

1 and 2 of section IV above). In the reference image, use the 

side bars on the viewer to navigate to approximately the center 

of the image, and visually locate a feature on the landscape 

that is recognizable and likely to change little over the time 

period of the two images. In the viewer, select the crosshair 

icon (it looks like a plus sign, and is just to the left of the 

hammer symbol) to start the coordinate-crosshair tool.

SOP 2. Preprocessing Landsat Imagery  47
The X and Y coordinates of the crosshair are shown in 

the X and Y fields (fig. 14). Grab the center of the crosshairs 

by clicking with the mouse point, and drag it to the feature 

desired. In the viewer for the input image, use the sidebars to 

navigate to approximately the same area of the image, locate 

the landscape feature that was identified in the reference 

image, start a coordinate-crosshair tool in the input image, 

and click and drag its center point to approximately the same 

location on the landscape as the crosshairs in the reference 

image.

Record the X and Y position of the respective crosshairs 

in the squeet_images.txt file for the reference and the input 

images.

Once pointer files, image files, and parameter files have 

been defined, drag the ITPFind icon over the IDL-VM icon to 

start the program. It will query immediately for the location 

of the squeet_master.txt file that was created above. Navigate 

in the file-finding dialog box to the appropriate directory and 

select the file.

An action window will pop up. The list of available 

image files will be on the left, the list of available parameter 

files on the right. On the bottom is an empty table. This 

table is where reference and input images will be paired and 

associated with a parameter file.

Click on an available image that is the referencegeometric image. Its name will appear in the ‘Currently 

Selected’ box. Then click in the first box (first row) of the 

reference column (the left-hand column) in the bottom table.

For the reference image in ITPFind, use the 25-m 

geometric-reference image that has been clipped to the 

study area (section IV, step 12 above). This will ensure that 

tie points are located only within the study area where terrain 

information in the form of the DEM also is available.

Pick the input image from the same available image list 

in the same manner and place it in the middle column below, 

and finally pick a parameter file and place in the parameter 

column below. At this point, the ITP program can be run by 

clicking on the ‘Submit for processing’ button.

Figure 14. Coordinate-crosshair window. 

Alternatively, additional sets of reference/input image and 

parameter files can be placed in the second row of the bottom 

table. After the ITP program has found ITPs for the first set, it 

will move to the second set, third set, in order.

While the program is running, it will display two 

windows: one shows the growing grid of ITPs, the second 

shows the image chunks and correlation surfaces for the points 

being processed currently.

The program will output five text files: one file for X 

coordinates of the input image, one for Y coordinates of the 

input image, one for X coordinates of the reference image, one 

for Y coordinates of the reference image, and one readme file. 

All files are placed in the directory of the INPUT IMAGE. 

They will be named with an identical root based on the 

image codenames used to find the ITPs and the time at 

which the program commences. The files with the X and 

Y coordinates will be imported into the ground-control-point 

(GCP) editor of Imagine below (see step 4.b. below).

These files are only the coordinates of the ITPs. From this 

point, Imagine must be used to conduct the terrain correction.

Conducting Terrain Correction

1. Load the geometric-reference image that has been clipped 

to the study area.

a. The image was created in section IV, step 12 above.

b. To load the image into the viewer, see section IV, 

steps 1 and 2 above.

2. In the main icon panel of Imagine, select the viewer 

button (fig. 15).

a. Viewer #2 will come up.

i. Open the Input image in Viewer #2.

(1) Again, click on the folder icon of the viewer 

and navigate to the appropriate folder. This 

assumes that the input image has been 

imported into Imagine format. If not, it 

is necessary to import this image into the 

Imagine format (section III above).

Figure 15. A portion of Imagine’s main icon window, 

with the button that starts Viewers circled.

48  Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
3. From within Viewer #2 (with the input image), select 

“Geometric correction.”

a. An action window named “Set Geometric Model” 

will pop up, with a list of options. Another action 

window labeled “Geo Correction Tools” will pop up 

(see fig. 8 above).

b. In the Set Geometric Model window, select Landsat 

and click on the OK button.

i. The “Landsat Model Properties” window will 

pop up (fig. 16).

c. For sensor type, ensure that TM is selected, and that 

the Landsat number: field is correct.

i. The information on which Landsat sensor 

acquired an image can be found in the header 

file (a file ending with .h1) that came with the 

original Landsat image from EDC (see SOP 1

Ordering Imagery).

d. In the “Elevation File: (*.img)” field, use the folder 

icon to navigate to the DEM that has been clipped to 

the study area (this step is discussed in section V, step 

13, above).

e. Ensure that the elevation units are correct.

i. This information should be found in the metadata 

associated with the original DEM file.

ii. If there is no such information on the vertical 

units of the DEM, it can be inferred in many 

cases.

(1) Open the DEM in a viewer (see section IV 

steps 1–2).

(2) Open the ImageInfo for that viewer (see 

section IV step 3).

(3) In the Statistics info section of the 

ImageInfo window, note the value of 

the “Max:” field. This value should 

approximately represent the maximum 

elevation in your study area. Since units of 

feet and meters differ by a factor of three, 

this maximum value should indicate which 

unit is used for the DEM. Other vertical 

units (decimeters) have sometimes been 

used; if neither feet nor meters seem likely, 

then it will be necessary to track down 

the source of the DEM and determine the 

appropriate units.

f. Leave the rest of the fields in the default position.

4. Setup the coordinates for the terrain correction by 

importing the image tie-point text files that were created 

by ITPFind above.

a. Click on the “Projection” tab of the Model properties 

page.

i. Click on the button labeled “Set Projection from 

GCP Tool.”

(1) The “GCP tool reference setup” window 

will pop up, with the “Existing viewer” line 

selected with a circular radio button. This 

is the desired choice, so simply click on the 

OK button.

(a) The “Viewer selection instructions” 

window will pop up, instructing user to 

left-click the mouse in the viewer with 

the reference image. Place the mouse 

in Viewer #1 and left-click the mouse 

button.

(b) The Reference Map information 

window will pop up, with a list of the 

geometric properties of the reference 

image. Click on the OK button.

(c) Imagine will then rearrange windows 

and add windows.

b. All of the steps below occur in the “GCP Tool” 

window.

i. First, turn off the automatic calculation and 

display mode by clicking on the “Toggle fully 

automatic GCP editing mode” button on the lefthand side of the window (fig. 17). Figure 16. Landsat model properties page, used to reproject 

images acquired by the Landsat sensors.

SOP 2. Preprocessing Landsat Imagery  49
(1) With the mouse over the title of the “X 

Input” column, left-click on the mouse to 

select that column. The first cell should be 

highlighted and the shaded title box of the 

column inverted, indicating that the column 

has been selected.

(2) Right click from the same position over the 

title box to see the column options window. 

From the list of options, select “Import.”

(a) The “Import column data” window will 

appear. Navigate to the directory where 

the Input image used in the ITP find 

program above is stored. This is where 

ITPFind puts tie-point files.

(i) Select the file whose filename ends 

with “..._xinput.txt” and click on 

the OK button.

(1) Note: The first part of the 

file name corresponds to the 

image code names that were 

included in the available image 

text files used as input to the 

ITP program, the second part 

corresponds to the time stamp 

when the ITP program was 

run.

(ii) The X coordinates of the input 

image tie points should appear in 

the X-input column.

(3) Repeat this process for the Y input column, 

this time selecting the filename ending with 

“..._yinput.txt”.

(4) Repeat for the X ref and Y ref columns as 

well.

(5) When all the tie points have been located, 

the elevation values in the Z ref column 

should appear. If not, click on the “Z” icon 

in the GCP Tool window to load them. If no 

values appear, return to the Landsat Model 

properties dialog (fig. 16) and ensure that 

the DEM has been identified properly, and 

then click on the Apply button again.

(a) If no values appear after this step, 

open new viewers and ensure that the 

coordinates of the DEM correspond to 

the same area as the image.

(i) Open a new viewer.

(ii) Load the reference image.

(iii) Load the DEM, but when loading 

the image in the “Select Layer to 

Add” dialog (fig. 18), click on the 

“Raster Options” tab.

(iv) Uncheck the “Clear display” box. 

Now the DEM will load into the 

same viewer as the reference 

image.

(v) Once the DEM has loaded, rightclick in the viewer and select 

“Fit image to window” from the 

pulldown list.

(vi) The viewer should redraw. The 

image should appear first, then 

the DEM should appear over 

it. If the two are in entirely 

different portions of the viewer, 

this indicates that coordinates or 

projections have been in error. 

Double-check all resampling and 

reprojecting steps above.

Figure 17. GCP tool, with the automatic GCP mode button 

circled.

Figure 18. Window where files are chosen to add to a Viewer.

50 Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
(6) Calculate the solution for the reprojection.

(a) Hit the sigma symbol on the left-hand 

side of the GCP Tool window.

(i) The columns X residual, Y 

residual, RMS error, and Contrib. 

all will be populated. Additionally, 

the field in the center top of the 

GCP tool will display the “Control 

point error” for X, Y, and total. This 

is the total RMS error. See Richards 

(1993) for more information on the 

calculation of the RMS error. These 

will be in units of the input image. 

If the input image is without map 

information and has pixels of size 

1.0, for example, then a total error 

of 0.5 would indicate a RMS error 

of half a pixel.

(7) Screen out any tie points that are outside the 

edge of the DEM.

(a) With the mouse over any row in the 

GCP tool, right click to get the rowselection tool. Select “Criteria” to bring 

up the “Selection Criteria” window 

(fig. 19).

(b) Using the mouse and the left mouse 

button, select from the Columns: field 

the “Z Ref.” row.

(i) Imagine will place $“Z Ref.” in the 

“Criteria:” field at the bottom of the 

window, and will continue to build 

the criterion expression as more 

values are selected below.

(c) Select from the “Compares” field the 

double-equal-sign (==).

(d) Select from the keypad the number 0.

Figure 19. Selection criteria window for the GCP tool.

(i) Ensure that the final expression is 

$“Z Ref.” == 0.

(e) Click on the Select button. All of the 

rows in the GCP tool where Z Ref. is 

zero will be highlighted in yellow.

(f) Ideally, there should be none of these 

rows. But in case there are, place the 

cursor over any row number in the lefthand side of the GCP tool and rightclick. Select “Delete selection.” This 

will eliminate any points where the 

elevation is zero.

(i) Why? Elevations of zero would 

otherwise be incorporated in the 

general solution relating the images 

to each other, and false zeros will 

cause large distortions in the 

solution.

(8) Recalculate the solution for the reprojection.

(a) This must be done twice, once before 

and once after taking out points with 

zero elevation, or else Imagine will 

crash.

(9) The control-point error should be well below 

1 pixel in size, ideally less than one-half of 

a pixel. If it is significantly greater than this, 

examine the control points in the table to see 

if there are any extremely unusual points, 

as indicated by the value in the “Contrib.” 

column.

(a) These can be selected using the criterion 

window, as in step 7 above, but in this 

case select “Contrib. >2.0,” etc. to find 

the points.

(i) To view individual points, select 

the two binocular icons to have 

the viewers zoom to the selected 

tie points to examine particularly 

unusual points. If a visual 

examination shows that a given 

tie-point pair is wrong – i.e. that 

the points correspond to entirely 

different points on the landscape, 

then right-click on the row of the 

offending point and select “Delete 

selection” to remove it. This will 

delete all the points currently 

selected, so make sure that only the 

desired point is selected.

SOP 2. Preprocessing Landsat Imagery  51
(ii) After deleting a point, recompute 

the solution by hitting the sigma 

function button again. This 

will compute the overall error 

without the offending point, and 

will redistribute error among 

the remaining points. Check for 

unusual points again.

(1) This iterative process is done 

automatically in the ITPFind 

program, so this process likely 

should be unnecessary.

(c) Once the reprojection solution has been 

computed from the tie points, the input 

image can be resampled.

(i) First, record the Control Point 

Errors computed in the GCP Tool 

window, both for X and Y and for 

the total. These should be attached 

to the metadata for the resampled 

input image. See SOP 5 Data 

Management.

(ii) Click on the resampled icon in the 

“Geo Correction Tools” window 

(see fig. 8 above).

(iii) Follow the instructions in section 

IV, step 4.b. iii.(3)–(9) above for 

resampling.

(d) When resampling has concluded, 

close all windows and do not save the 

projection parameters or model. These 

would become attached to the images, 

which causes confusion if later images 

are to be registered to these same 

images.

At this point, the input image should be in the same 

projection as the geometric-reference image. To check this, 

follow these steps.

5. Checking for geometric matching of images.

a. Open a viewer.

b. Open the reference image using the standard file 

opening procedure (section IV, steps 1–2 above).

c. In the same viewer, use the open folder icon to start 

the “Select Layer to Add” window (fig. 18) and 

navigate to the input image and select it, but do NOT 

click on OK.

i. Click on the “Raster Options” tab in the Select 

Layer to Add window, and uncheck the box next 

to “Clear display.”

ii. Click on OK button.

d. Both images should be displayed in the same viewer.

e. In the viewer, select “Utility/Flicker.”

i. The Viewer Flicker dialog will open.

ii. Hit the “Manual filter” button to toggle between 

the two images in the viewer.

iii. Alternatively, click the box “Auto Mode” to have 

the toggling occur automatically.

iv. When the two images are toggled, there should 

be minimal apparent “jumping” of pixels back 

and forth across dates. The flickering image 

should appear geometrically stable, although 

radiometric changes may cause the illusion of 

some motion as the images flicker.

v. Scroll around the image and ensure that the 

image is stable across the entire range.

vi. Zoom in on some locations and view the flicker 

closely.

vii. Pay particular attention to mountainous areas. 

If the automatic flicker mode is on, if there is a 

problem with the terrain correction, the flicker 

will reveal distortions that are correlated with 

the position of hills and mountains, creating a 

pseudo-three-dimensional effect as flickering 

occurs.

(1) If this is the case and terrain correction has 

been done in Imagine, this suggests that 

either the terrain correction was inaccurate, 

or that the original reference image was in 

fact NOT terrain corrected.

(a) Check the elevation units of the 

DEM and make sure these are in the 

appropriate units. If not, redo the terrain 

correction of the input image.

(b) Check the history of the geometric 

reference image to confirm that it was 

terrain corrected or orthorectified.

(c) Repeat the reprojection of the input 

image process to the point where tie 

points have been loaded in the GCP 

tool, and check carefully for unusual tie 

points. Check each tie point with high 

RMS error and ensure that all points 

make sense visually.

Once the input image has been reprojected to the 

geometric properties of the geometric reference, and once it 

has been confirmed to be accurate, the input image must be 

clipped to the study area. Follow the steps in section V to 

clip the input image. Use the naming conventions in SOP 5

(Data Management) to name the clipped, terrain-corrected 

input image.

52 Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
VII. Compensating for Sensor and 

Atmospheric Influences in Reference 

and Input Images

The atmosphere affects the reflectance signal that 

impinges on the sensor from the surface of the Earth. 

Differences in atmospheric conditions between dates of 

imagery will cause differential artifacts in the images that 

will confuse later change detection. Therefore, all reasonable 

efforts must be made to compensate for these effects. The 

first step is to bring both images into a common system of 

measurement, reflectance.

The units of a Landsat image are simply digital numbers 

(DNs), corresponding to the magnitude of energy being 

measured by each of the sensor elements in the satellite. 

Because the engineering properties of the sensor are known, 

the DNs can be converted into physical units of radiance. 

Knowing the emission spectrum of the sun entering the 

atmosphere, these units of radiance can further be quantified 

as a proportion of the incoming radiation ranging from 0 to 

1.0. This is known as “top-of-atmosphere reflectance.” It does 

not take into account the effects of the atmosphere.

Without direct measurements of atmospheric absorption 

at the moment of image acquisition, it is impossible to know 

this atmospheric effect directly, but it can be approximated. To 

approximate the effects of the atmosphere, it must be assumed 

that there are some objects on the surface with little to no 

reflectance. These so-called “dark objects” should indicate 

a reflectance of zero or near-zero. Because the atmosphere 

introduces scattering between the objects and the sensor, 

the apparent reflectance of these objects from the sensor’s 

perspective is non-zero and positive. The additive effect of 

this scattering can be removed by simply subtracting the offset 

above these dark objects from all pixels in the image. The 

multiplicative effects of the atmosphere can be approximated 

simply by a correction factor that scales with the path length 

through the atmosphere, which requires only that the sun angle 

and elevation be known. All of these steps can be incorporated 

in a single-transformation process and placed in a graphic 

model in Imagine. Because these were best described in 

(Chavez, 1996) as COS-theta or COST methods, these steps 

are referred to here as COST processing.

1. Selecting dark-object values for each band:

a. The first part of the COST processing is selection of 

dark-object values for each of the six visible bands in 

Landsat.

b. An Excel spreadsheet is in the appendixes and serves 

as a template for which the relevant values for COST 

processing can be stored.

i. This spreadsheet file is named “COST_

processing_template.xls.”

ii. Save this Excel file under a new name that 

corresponds to the name of the image, so that it 

can be connected easily with that image in the 

future.

iii. Make a note of this filename in the metadata 

associated with this image.

(1) Follow the conventions in SOP 5 Data 

Management for naming convention.

c. It is helpful to use the entire image area, not just the 

area within the study area of the park, for locating 

potential dark objects because the population of 

potential targets is much higher. Therefore, for a 

given image, go back to the first version of the image 

that includes the entire footprint of the original 

Landsat image directly after importing into Imagine 

format.

i. This image should be the one that was imported 

in section III above. It need not be geometrically 

referenced for selection of dark-object values.

d. Open the image in a viewer using the Pseudocolor 

option:

i. Use the open-folder icon to bring up the “Select 

layer to Add” window (shown in fig. 18 above).

(1) Navigate to the desired image folder, select 

the image from the available files list so that 

its name appears in the “File name:” field. 

Do NOT click on OK yet.

ii. Click on the “Raster options” tab.

(1) In the field labeled “Display as:” select 

“Pseudo color.”

(2) Choose the desired layer. If just starting, 

select 1 for band 1, which corresponds to the 

blue wavelengths in Landsat.

(3) Retain defaults for the other options.

iii. Click on OK button.

iv. Right-click in the viewer and select “Fit image to 

window.”

v. Select “Raster/Attributes.”

(1) The Raster Attribute Editor will popup.

(a) There are three columns: Histogram, 

Color, and Opacity. The important 

columns for this exercise are the 

Histogram and the Color columns.

(b) The Histogram column indicates the 

count of pixels that have the value 

indicated in the row value on the lefthand side of the attribute editor. For 

Landsat images, these range from 0 to 

255.

SOP 2. Preprocessing Landsat Imagery  53
(c) There is a high histogram count in the 

row with value 0. These are the pixels 

outside the edge of the image area, and 

should be ignored.

(d) For bands 1–3 and often 4, the row 

number with the lowest non-zero 

histogram value is not the row 1, but 

rather another row with a higher value. 

This indicates that even the darkest 

objects in the scene have non-zero 

reflectance, and indicates the level of 

atmospheric scattering. It is not wise 

to simply take the row number of the 

lowest non-zero histogram count as the 

dark-object value, however, because 

these low values could be artifacts 

of processing upstream of image 

acquisition or even of steps conducted 

in Imagine.

(i) It is most robust to use images that 

only have been resampled using the 

NN method, which is why SOP 1

recommends ordering imagery 

using the NN option from EDC.

2. Start with the lowest non-zero histogram value and 

examine the location of the pixels corresponding to that 

value using step 1.a.i.–vii. below, and step sequentially to 

higher values until a pixel value is identified whose pixels 

reside in the landscape, and which correspond to features 

that are expected to be dark-objects step 1.b.i.–ii. below.

a. To examine where pixels at a given pixel value reside 

on the image, do the following:

i. Select the row with the desired pixel value. For 

example, select the row with the first non-zero 

histogram value.

ii. Then place the mouse over the box in the column 

labeled “Color” for this row, and right-click.

(1) A small window with a variety of color 

options will pop up. Select a color that 

allows easy contrast with the grey tones of 

the image, perhaps red or yellow.

iii. Look in the viewer at the image and see where 

the red pixels reside.

iv. Evaluate these pixels according to the criteria in 

step b. below.

v. If these pixels are not considered valid dark 

objects, select “Edit/Undo Last Edit” in the 

Raster Attribute Editor to change the color patch 

back to its original nonhighlighted shade. Then 

move to a pixel value one step higher and repeat 

this process.

vi. If these pixels are considered legitimate dark 

objects, then take this value as the approximate 

dark-object value.

(1) Because few objects are truly nonreflective, 

it is typical to assume that the observed 

dark objects have an actual reflectance on 

the ground of roughly 1 percent. Therefore, 

the true value for zero reflectance will be 

somewhat lower than the observed value 

in the image. The correction factor can be 

approximate, since these dark objects are 

an approximation of the actual atmospheric 

effects. Therefore, take the tentative dark 

object value and adjust it as follows to 

calculate the actual dark-object value for the 

band:

(a) For bands 1–3 and band 7, subtract 1 

from the observed value.

(b) For bands 4 and 5, subtract 2 from the 

observed value.

(i) In both cases, however, the 

minimum allowed dark-object 

value is zero.

vii. Enter this value in the dark-object value column 

for the row corresponding to the band in the 

Excel spreadsheet saved in step 1.b. above.

b. Evaluating dark-object pixels.

i. Characteristics of valid dark-object-type pixels.

(1) The pixels reside on the landscape, not on 

the margins of the scene area.

(2) The pixels are in areas where dark 

reflectance is expected:

(a) Water bodies.

(b) Topographic shadow.

(c) Deep shadow on shaded aspects of 

forests.

(ii) Characteristics of dark objects that are artifacts:

(1) The pixels reside on the very margin of the 

active area of the image.

(a) These typically are formed because of 

bilinear or cubic convolution of zero 

values outside the image with non-zero 

values inside the image.

(2) The pixels reside directly next to a very 

bright pixel and the image has been 

subjected to cubic convolution resampling 

beforehand.

54  Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
(a) Cubic convolution can depress values 

of pixels neighboring bright objects, 

and thus can force otherwise dark 

objects to even darker values that are an 

exaggeration of the actual dark-object 

value.

c. Repeat this process for all six of the visible bands in 

the Landsat image. Record each dark-object value in 

the Excel file from step 1.b. above.

i. When this has been done for all bands, evaluate 

the relation between dark-object values across 

bands.

(1) The effect of haze is greatest at shorter 

wavelengths in the blue bands of the image, 

and tapers off as band number increases. 

Therefore, the dark-object values should be 

highest in band 1, lower in band 2, etc. The 

dark-object values of each band should be 

no higher than the band number lower than 

it. If this is not the case, then it indicates 

that the dark-object value for the lowernumbered band may have been too low. 

Review the dark-object pixels for that band.

(2) Bands 5 and 7 may have almost no 

scattering, and therefore may have darkobject values of 1 or 0.

3. Identifying sun elevation and angle:

a. When an image is ordered from EDC, the associated 

header contains information on the sun elevation and 

sun azimuth.

i. For Landsat 7 (ETM+) imagery, the gains and 

offsets are listed for each band in the header 

file. These should be entered into the Excel 

spreadsheet.

ii. For Landsat 5 imagery ordered after May 4, 

2003, the gains and offsets in the header can be 

used and entered into the Excel spreadsheet.

iii. For Landsat 5 imagery that was acquired by the 

satellite before 1989 and processed before May 

4, 2003, use the gains and offsets (table 2) found 

in Chander and Markham, 2003.

4. Entering data into an Imagine spatial modeler

a. An Imagine graphic model appears in the appendix 

that will apply the values collected in steps 1–3 to 

an image to create a COST-processed version of 

the image. This model is named applying_cost_

template.gmd

i. From Imagine’s main icon panel, select the icon 

labeled “Modeler.”

ii. Select “Model Maker.”

iii. In the Model Maker, use the open folder to 

navigate to the template model and open it 

(fig. 20).

iv. Save this file under a name that links it to the 

image being processed. Do this using File/Save 

As. Use the naming conventions of SOP 5 Data 

Management.

b. Conventions of the spatial modeler in Imagine.

i. See figure 20.

Figure 20. Graphic modeler in Imagine, with the template for COST 

processing.

b. These values should be entered into the 

excel spreadsheet noted above. Note that the 

formula for the COST processing requires 

the sun zenith, which is 90 degrees minus the 

sun elevation.

4. Identifying gains and offsets:

a. The DN values reported in a band from 

Landsat do not correspond to actual physical 

quantities. To convert the DNs to physical 

values of radiance, the gains and offsets of 

the sensors in the satellite must be taken 

into account. The gain of a sensor is akin to 

the amplification factor needed to convert 

it from one set of units into another—the 

slope of the line relating the DNs to radiance 

values, in this case. The offset or bias is 

simply the equivalent of the intercept of the 

same line. As a sensor ages, these translation 

factors will drift, and thus it is necessary to 

know them for each specific image being 

processed.

SOP 2. Preprocessing Landsat Imagery  55
ii. All spatial data and actions applied to it are 

represented as shapes linked with arrows that 

indicate the flow of processing.

iii. Double-click on any shape to see information 

about the spatial data, numerical data, or function 

contained in the shape.

iv. Click on the hammer symbol to show a window 

of tools that can be used to update or build a 

graphic model.

v. Circles correspond to functions. For a function to 

operate, it must have an arrow leading to it from 

some data object, such as an image or a table.

vi. Stacked oblique squares represent spatial data. 

These are used as inputs to functions.

vii. Squares correspond to data that must be entered 

manually or that is not spatial, such as tables 

taken from external text files.

c. Enter data accumulated in the Excel spreadsheet into 

the modeler.

i. In the upper-left of the model, double-click on 

the image-stack symbol labeled “Image to be 

converted.” The Raster information layer will 

pop up (see fig. 21).

ii. In the filename field, use the open folder icon to 

navigate to the appropriate image and select it.

(a) NOTE: Even though the dark objects were 

chosen using the entire original Landsat 

image footprint, the actual correction model 

should be applied to the image that has been 

clipped to the study area! The use of dark 

objects outside the study area is justified on 

the simplifying assumption that atmospheric 

conditions are stable across the entire image, 

even though this assumption is technically 

false. However, dark objects are critical to 

this correction and also are rare, so the use 

of the entire image allows inclusion of more 

dark objects, which outweighs the potential 

problems in variable atmospheric conditions. 

If enough dark objects are viewed across 

the image, then these conditions should 

approximately average out.

iii. All of the other values in the window can be left 

in their default conditions.

d. In the box near the top center labeled “Sun Zenith 

Angle,” enter the sun zenith angle from the Excel 

spreadsheet. Recall that this is 90 degrees minus 

the sun elevation, which typically is what is listed 

in the header for the file.

e. In the box on the right-hand side of the model labeled 

“Gains and Offsets,” double-click and enter the gains 

and offsets recorded in the Excel file. Note that the 

matrix window that appears will have six rows. Rows 

1–5 correspond to bands 1 through 5, while row 6 

corresponds to band 7 of Landsat.

f. In the box labeled “Min Vals for Subtraction,” enter 

the dark-object values from the excel spreadsheet that 

were determined in step 1 in this section above.

g. At the bottom of the model, click on the image stack 

shape labeled “Output COST image.”

h. Save the model, ensuring that the name can be linked 

easily to the image on which the processing is to be 

conducted.

i. Run the model by clicking on the red lightning bolt 

icon in the icon row at the top of the graphic model 

window.

Figure 21. Raster information layer that is used to specify image 

files for modeling.

Table 2. Gains and offsets for Landsat 5 imagery acquired before 

1989.

[From Chander and Markham (2003)]

 Gain Bias

Band 1 0.602431 -1.52

Band 2 1.1751 -2.84

Band 3 0.805765 -1.17

Band 4 0.814549 -1.51

Band 5 0.108078 -0.37

Band 7 0.05698 -0.15

56 Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
5. The result of this process is an image in units of apparent 

reflectance on the surface of the Earth. It likely will not 

correspond to true reflectance on the ground because 

the approximation for atmospheric effects is not perfect, 

and because the illumination angle of slope facets on the 

landscape is not considered. Nevertheless, the gross effects 

of atmospheric scattering and atmospheric patheffects 

have been addressed. This image will be referred to as the 

COST version of an image.

a. Note that the units of this image are in scaled 

reflectance. True reflectance is considered a 

proportion from 0 to 1.0. Representing these values 

requires the use of the floating-point data type, which 

requires twice as much space on the disk as the 

integer type. Therefore, all of the reflectances have 

been scaled to run from 0 to 1000, with 1000 equal to 

1.0 reflectance. Note also that some pixels will have 

reflectances less than zero—these correspond to the 

pixels that were noted as artifacts in the dark-objectidentification step above.

VIII. Radiometric Normalization

In most cases, COST processing will not take care of all 

of the atmospheric effects that could cause confusion when 

change detection is conducted later. Two COST-processed 

images likely will differ somewhat in their claimed reflectances 

for a given pixel, even if no real change has occurred, because 

the true nature of the atmosphere above every pixel is not 

known. Because some of the parks’ monitoring goals require 

distinction of subtle spectral difference, even these slight 

artifacts could be problematic. Therefore, it is necessary to 

further normalize images before change detection can take 

place.

Many approaches exist in the remote sensing literature to 

normalize the radiometric and spectral qualities of one image 

to another. The authors tested a variety of these approaches 

and determined that a recently published approach known as 

Multiple Alteration Detection Calibration (MADCAL) works 

extremely well for the parks of the NCCN, and has the great 

advantage of being automated.

The authors have supplied IDL-VM software that will 

perform the MADCAL normalization between two images. 

Follow the steps below for normalization.

1. Identify the radiometric-reference image and the 

radiometric-dependent image.

a. As noted above in section II, the radiometric-reference 

image need not be the same image as the geometricreference image. However, both the reference 

and dependent images must have been registered 

geometrically to the reference- geometric image.

b. The reference-radiometric image ideally should be as 

close to clear-sky conditions as possible.

2. To run the MADCAL software, a text file with run 

parameters must be constructed.

a. An example file has been provided, named “madcal_

runfile_template.txt”. Save this file under a new name 

that will identify it specifically to the images being 

used. It is advisable that the runtime file be placed 

in the same folder as the dependent image, since it is 

that image that will be altered.

b. The following are the fields that must be present in 

that file:

Reference file: 

D:\NPS\sites\mora\geospatial_data\mora_1996_2002\

radiometric\tm_mora_14aug2002_nad83_cc_rcost.img

Dependent file: 

D:\NPS\sites\mora\geospatial_data\mora_1996_2002\

radiometric\tm_mora_21aug1996_nad83_cc_rcost.img

Run name: test1

Subset size in pixels: 1000

Subset upper left coordinate: 569949, 5192068

Subset upper left coordinate: 576724, 5217368

Subset upper left coordinate: 602174, 5217368

Subset upper left coordinate: 604374, 5187943

c. Key phrases are those that appear to the left of the 

colon (:) symbol. They must exist exactly as shown 

above, followed immediately by the colon symbol, 

followed by a space, followed by the relevant field 

information. To view the file and set the parameters, 

use a generic text editor such as Notepad.

d. Description of key phrases above.

i. Reference file:

(1) Provide the full pathname to an imagineformat file that corresponds to the 

radiometric-reference image.

ii. Dependent file:

(1) Provide the full pathname to an imagineformat file that corresponds to the image that 

will be matched to the radiometric-reference 

image.

iii. Run name:

(1) Provide a unique name for this run that will 

identify outputs in case other runs are done 

on the same dependent image. This run 

name will be attached to the file name of key 

files to distinguish it from other runs.

SOP 2. Preprocessing Landsat Imagery  57
iv. Subset size in pixels:

(1) The MADCAL program extracts a subset 

from the two images to be matched and 

conservatively identifies pixels that have 

not changed substantially between the two 

images. Because of the calculations needed 

to identify the no-change pixels, memory 

sizes may constrain the size of the subset 

that can be extracted. We have found that a 

subset size of 1000 pixels works well.

v. Subset upper left coordinate:

(1) Because of the limitation of individual 

subset size, our implementation of the 

MADCAL approach allows for multiple 

subsets. Pixels that have not changed in each 

subset are identified, and then all of the nochange pixels are combined to produce a 

large no-change pixel sample that is used to 

develop bandwise regressions that link the 

reference and dependent images.

(2) Therefore, the position of the different 

subsets must be provided to the program. To 

identify these subsets, do the following:

(a) Open a viewer in Imagine (see section 

IV, steps 1 and 2).

(b) Load the radiometric-reference image 

that has been clipped to the study area 

for the park.

(c) Anywhere in the viewer space, rightclick and select “Fit image to window” 

from the pulldown menu.

(d) Anywhere in the viewer space, rightclick and select “Inquire Box” from the 

pulldown menu.

(i) A small window with coordinates 

for the inquire box will popup 

(fig. 22).

(3) The goal is to use this inquire box to 

visually delineate the areas on the image 

that are to be used for the MADCAL 

subsets. Therefore, the first step is to make 

the inquire box equal in size to the subsets 

that will be used in the MADCAL routines. 

If the recommended size of 1000 pixels is 

used, follow these steps. Alter the number 

1000 if a different subset size is chosen.

(a) In the inquire box, from the “Type” 

pulldown menu select “File”, rather 

than the default “Map” value.

(i) The coordinates change from map 

coordinates to file coordinates.

(b) With the mouse, highlight the value in 

upper left field box labeled ULX: and 

type 0 to overwrite the value to 0.

(c) Do the same for the ULY value, setting 

it to 0.

(d) For the LRX and LRY fields, set each 

field to 1000.

(e) Click on the Apply button.

(i) The inquire box should be resized 

to the size of the subset, and should 

reside in the upper left of the 

viewer.

(f) In the inquire box, from the “Type” 

pulldown menu select “Map.” The box 

will be of the same size, but now the 

coordinates will be map coordinates.

(g) With the mouse, click and drag in the 

center of the inquire-box area to move 

it across the image. Do NOT resize the 

box by pulling the corners, as this will 

require repeating steps (a)–(e) above.

(4) Move the inquire box over the reference 

image and locate subsets. When an area has 

been located, note the values in the inquire 

box in the ULX and ULY fields. Ensure 

that these are in map coordinates, not file 

coordinates.

(a) Enter these values in the MADCAL 

control text file for the Subset upper 

left coordinate field. The format is <X 

coordinate>, <Y coordinate>. 

(b) Repeat this process for as many subsets 

as desired. Four such subsets are shown 

in the example above, but any number 

Figure 22. Inquire-box window. from 1 to as many is allowed.

58  Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
(c) If more than four subsets are chosen, 

copy the key phrase “Subset upper left 

coordinate: ” text to a new line directly 

under the prior subset.

(d) If fewer than four subsets are chosen, 

make sure to erase the entire line for 

the nonused subsets. The key phrase 

“Subset upper left coordinates: ” only 

should appear if valid coordinates 

follow it on the same line.

(5) Tips on locating subsets.

(a) The subsets should be well 

distributed across the area of the 

image.

(b) Make sure that subsets represent all 

of the types of landcover conditions 

present in the image.

(c) Avoid subsets that have a majority 

of their area in conditions that 

likely are to change drastically 

between images, such as cloudy 

areas in one image or large snow 

areas.

3. Once the MADCAL run parameters have been entered, 

the program can be run.

a. Locate the “madcal.sav” file that has been provided 

with this protocol.

b. Click and drag this file over the IDL-VM.

c. The program will produce a “splash screen” 

advertising IDL. Click on it to make it go away.

d. The program will then prompt for the location of the 

MADCAL control file.

i. Navigate to the file created in step 2 and click on 

the OK button.

e. The program will then run.

i. A window will appear noting that MADCAL has 

begun, and noting the location of the diagnosis 

file. This diagnosis file is a text file that keeps 

track of programmatic flow, in case of errors.

ii. There will be no other initial indicator of 

progress for the file.

(1) The diagnosis file will be updated as the 

program runs, so occasional checks of this 

file’s time stamp, or re-opening of this file, 

will provide an idea that the program is still 

running.

iii. Eventually, a cascade of new windows will pop 

up, each with a scatterplot of the reference and 

dependent image pixels, as well as the regression 

line fit to them, for each band, for the first subset.

iv. This will repeat for each of the subsets identified 

in the MADCAL control file

v. Finally, a set of similar windows will pop up that 

will cycle through the bands more slowly. This is 

the set that combines all of the no-change pixels 

across subsets, and takes longer for each set.

vi. When the last one is done, the windows will 

disappear. This indicates that the program is 

complete.

f. View the log file. It defaults to the C:\temp directory. 

Open this file with a simple text editor.

i. In that file, the location of the MADCAL results 

file is noted. This is the file that contains all of 

the relevant information on the run, including 

correlation coefficients for the regressions 

between the reference and dependent images.

(1) It is listed in the log file as:

Setting up results file:

D:\NPS\sites\mora\geospatial_data\mora_1996_2002\

radiometric\tm_mora_21aug1996_nad83_cc_rcosttest1_

madcaloutputs.txt

g. Open the results file.

i. Listed at the top of this file are the reference 

image, dependent image, and subset coordinates. 

These should be included with metadata on the 

dependent file. See SOP 5 Data Management for 

components of the metadata to be included with 

each file.

ii. Information on the fitting of subsets is provided 

for reference, but can be ignored for the final 

image file because the subset-specific fitting 

parameters are not used.

iii. The important fitting information begins in the 

file after the point labeled as follows:

 “Performing final calibration 

using all no-change pixels.”

h. The program will write a calibrated version of the 

dependent image. The name of this file is noted at 

the end of the results file, and is based on the original 

name of the dependent file, with “_madcal.bsq” 

added at the end of the filename.

i. This file is a “flat binary” file. It must be imported 

SOP 2. Preprocessing Landsat Imagery  59
into Imagine.

4. Importing the MADCAL adjusted image into Imagine 

(referenced in SOP 3, Probability Differencing).

a. Open the header file associated with the calibrated 

image. This header file has the same name as the 

file noted in h) above, but with the extension “.hdr” 

instead of “.bsq”.

i. This is a text file with information on the flat 

binary file that is relevant for importing into 

imagine. An example is shown here:

BANDS: 6

ROWS: 2930

COLS: 3228

DATATYPE: S16

UL_X_COORDINATE: 557599.00

UL_Y_COORDINATE: 5228568.0

LR_X_COORDINATE: 638274.00

LR_Y_COORDINATE: 5155343.0

PIXEL_WIDTH: 25.000000

PIXEL_HEIGHT: 25.000000

b. See section III above (Importing images into 

Imagine). Open the Import/Export dialog

i. Select “Generic Binary” from the “Type” 

pulldown menu.

ii. In the Input File field, navigate to and select the 

madcal calibrated image with extension “...bsq”, 

noted in step 3.h. above.

iii. In the Output File field, navigate to the same 

directory and type in the desired name of the 

output file.

(1) Follow the conventions in SOP 5 Data Management.

iv. Click on the OK button. The “Import Generic 

Binary Data” window will popup (fig. 23).

c. Use the information in the header file to fill in the 

appropriate fields.

i. Data Format: Select “BSQ.”

ii. Data Type: Select “Signed 16 Bit.”

iii. # Rows: Type in the value from the “Rows” line 

in the header file.

iv. # Cols: Type in the value from the “Cols” line in 

the header file.

v. # Bands: Type in the value from the “Bands” line 

in the header file.

vi. Click on the “Import Options” button.

(1) The Import Options window will appear (not 

shown).

(2) Select “Run-Length Encoding (ESRI)” from 

the “Output Data Compression” field. This 

will conserve significant file space, since 

it reduces the large areas of background to 

compressed data.

(a) Do not select “Ignore zero in stats.”

(3) Click on OK button.

vii. In the “Import Generic Binary Data” window, 

click on the OK button.

(1) A progress meter will show the progress on 

the import.

(2) When it is done, click on the OK button.

d. Update the map model and geometric information for 

the imported image.

i. The imported image has no geographic 

information. Therefore, it is necessary to 

manually enter this once the image has been 

imported. It is identical to the map model and 

geometric information for the dependent image. 

Therefore, use it to fill in the appropriate fields.

ii. Open a viewer and load the dependent image 

(the original, noncalibrated image, entered in the 

MADCAL control file in step 2d above in this 

section). This image will serve as the template 

for entering geographic information in the 

recently imported image.

(1) Open the ImageInfo for this viewer (see 

fig. 7 above to find the appropriate icon to 

start the ImageInfo).

iii. Open a second viewer and load the calibrated 

Figure 23. Import generic binary data window.

60 Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
image that was just imported.

(1) Open the ImageInfo for this viewer. Arrange 

the two ImageInfo windows so that both can 

be seen on the screen at the same time.

iv. In the ImageInfo for the calibrated image, select 

“Edit/Change Map Model.”

(1) The Change Map Info window will pop 

up. Manually fill in the fields, using 

the ImageInfo of the original image as 

reference:

(a) Upper Left X: Use the value of Upper 

Left X in the ImageInfo of the original 

dependent image.

(b) Upper Left Y: Again, use the value from 

the Upper Left Y in the ImageInfo of 

the original dependent image.

(c) Pixel size X and Y: Use the values 

from the fields named the same in the 

ImageInfo of the dependent image.

(d) Units: meters

(e) Projection: UTM

(f) Click on OK button, and then accept the 

changes for all layers when prompted.

v. In the ImageInfo for the calibrated image, select 

“Edit/Add/Change Projection.”

(1) Again, use the information in the “Projection 

Info” of the original dependent image to 

update the spheroid, datum, UTM zone, and 

north/south fields.

e. For these changes in the map model to take effect, the 

image must be closed. Close all of the viewers.

At the end of this section, the dependent image will be 

as radiometrically close to the radiometric reference image as 

possible. Both images will be in units of scaled reflectance, for 

all six nonthermal bands of Landsat thematic mapper. These 

images must then be converted to tasseled-cap values, and then 

split by aspect, before preprocessing is complete and change 

detection can commence.

IX. Converting Imagery to Tasseled-Cap 

Values

The tasseled-cap transformation is a linear transformation 

of the six-dimensional space of the original Landsat bands into 

three new bands known as brightness, greenness, and wetness. 

(Note: The original tasseled cap actually converts into six 

new bands, but in practice only the first three bands are used 

because they capture more than 90 percent of the variation of 

the 6-band images.) The tasseled-cap space can be interpreted 

in physiognomic terms in most terrestrial systems, and thus 

forms the core of the change-focused approach to monitoring 

that is the central theme of this protocol.

The authors have included an Imagine graphic model 

that converts reflectance-based COST-processed images into 

tasseled-cap values. Note that the tasseled-cap coefficients 

for reflectance band inputs, such as those resulting from 

the COST processing, are different from those originally 

published. The reflectance coefficients were published in a 

later article (Crist, 1985).

To run the tasseled cap, follow these steps.

1. Open Imagine’s graphic modeler.

a. In the main icon panel, select “Modeler,” and then 

“Model Maker.”

b. When the blank spatial modeler window appears, 

use the open-folder icon and navigate to the graphic 

model that is included in the appendix for this 

protocol.

i. The graphic model is entitled Landsat_refl_tasselcap.gmd.

c. When the model is open, click on the red lightning 

bolt to run the model.

d. The graphic model will prompt for the name of the 

input file by creating a dialog window with the title 

“Select Existing File for Raster: n1_Prompt_user.”

i. Navigate to the COST-processed image, select it, 

and click on the OK button.

e. The graphic model will then create a nearly identical 

looking window titled “Enter New File for Raster: 

n9_Prompt_user.”

i. Navigate to the desired directory, and type in the 

name of the desired output file.

(1) Use the conventions in SOP 5 Data 

Management for naming and location 

conventions.

ii. Click on OK button once and the appropriate 

folder and name will be entered.

SOP 2. Preprocessing Landsat Imagery 61
f. The model will run and show a progress meter.

g. When the model is complete, click on the OK button.

2. Run the tassel-cap model on both dates of imagery that 

will be used for change detection.

a. These images need not be the geometric nor the 

radiometric reference images. However, it is 

imperative that these images be referenced to the 

designated geometric and radiometric reference 

images as detailed in sections II through VIII above.

3. Reset the default band combinations in Imagine for 3-

band images

a. The three color guns on a color screen or monitor 

are red, green, and blue. When displaying images, 

Imagine must be told which band of an image is 

to be displayed in each of the three colors. NOTE: 

Unless otherwise specified, Imagine will default 

to displaying 3-band tasseled-cap images with 

brightness in blue, greenness in green, and wetness 

in red. This is the reverse of the color tones to be 

used throughout the rest of the protocol, and thus the 

defaults must be changed.

i. From the main console, select “Session/

Preferences.” In the “User interface & session” 

category of the Preference Editor, scroll down on 

the right-hand portion of the panel to the point 

where 3-Band Image defaults are listed (see 

fig. 24).

ii. Set the 3-band Image Red Channel Default to 1, 

Green Channel Default to 2, and Blue Channel 

Default to 3.

iii. Click “User Save.”

iv. Close the preference editor.

X. Splitting Imagery by Aspect

The Landsat Thematic Mapper passes over a given point 

in the northern hemisphere in late morning, around 10:30 

a.m. local sun time. At this time of day, the sun azimuth 

approximately is southeast, depending on the day of the year. 

Because of this, hillslopes that face southeast are illuminated 

directly with the maximum sunlight, while hillslopes that 

face northwest are least illuminated. In the remote sensing 

literature, various approaches have been proposed to 

equilibrate the effects of terrain-related variable illumination.

The key challenge in these approaches is that the 

bidirectional reflectance-distribution function, which 

describes variation in reflectance as a function of viewing 

and illumination angle and on which illumination correction 

must depend, differs substantially by type of material being 

illuminated (Egbert and Ulaby, 1972; Kimes, 1983; Kriebel, 

1978; Norman and others, 1985; Ranson and others, 1994). 

Thus, an illumination-angle correction must apply a different 

correction for every type of surface, and the correction 

must be based on detailed measurements made in those 

conditions. Approximations exist, but few have emerged as 

generically applicable. Moreover, atmospheric effects change 

the ambient illumination, which varies by image acquisition. 

For this reason, topographic correction has remained a 

challenging prospect (Civco, 1989; Conese and others, 1993; 

Holben and Justice, 1980; Teillet and others, 1982). For 

this project and for prior projects, the authors evaluated a 

promising recent entry in this field (Gu and Gillespie, 1998), 

but found it unacceptable for the variety of surface and 

illumination conditions encountered in the parks of the NCCN. 

Overcompensation was a common outcome of illumination 

correction (as has been found in most of the papers cited 

previously), resulting in reflectance values whose true 

meaning was even less well known than those resulting from 

sections II through IX above.

More importantly, no illumination correction can 

compensate for the simple fact that slopes with lower 

illumination have less reflective signal to be measured (Conese 

and others, 1993). For Landsat TM images, hillslopes facing 

northwest have lower signal-information content relative to the 

constant levels of background noise. Even if an illumination 

correction were to be developed that could produce images 

that appear visually appealing, they would imply that the 

image-information content were consistent across the entire 

area, which is false.

Therefore, all change detection for this protocol must be 

split into two broad aspect classes: those that face the sun at 

the time of image acquisition, and those that face away from 

the sun at the time of image acquisition. This approach makes 

explicit the fact that the two aspect classes inherently have 

different signal-to-noise ratios. This section describes how the 

images produced thus far can be split in preparation for the 

change detection.

Figure 24. Setting bank-display preferences for viewing 

tasseled-cap image with brightness in red, greenness in green, 

and wetness in blue.

62 Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
1. First, the DEM must be split into different aspect classes.

a. Create an aspect image.

i. From the main icon panel in Imagine, click 

on the Image Interpreter button, and select 

“Topographic Analysis/Aspect.”

ii. In the “Surface Aspect” window that appears 

next, do the following:

(1) In the Input DEM field, navigate to the 

DEM that was clipped to the study area in 

section V, step 13 above.

(2) In the Output file field, navigate to the 

same directory, and name the output file 

according to the conventions in SOP 5 Data 

Management.

b. Create a slope image

i. From the main icon panel in Imagine, click 

on the Image Interpreter button, and select 

“Topographic Analysis/Slope.”

ii. In the “Surface Aspect” window that appears 

next, do the following:

(1) In the Input DEM field, navigate to the 

DEM that was clipped to the study area in 

section V, step 13 above.

(2) In the Output file field, navigate to the 

same directory, and name the output file 

according to the conventions in SOP 5 Data 

Management.

c. The authors have provided an Imagine graphic model 

to create the aspect class masks that will be used to 

split the images.

i. The model provided is named: make_aspect_

masks_mora.gmd. See figure 25.

(1) This model was applied to the DEM at 

MORA on the computer system of the 

authors. Therefore, all file names and paths 

must be adapted when the parks conduct this 

processing.

(2) First, save the graphic model under a new 

name using File/Save As. Follow the naming 

conventions in SOP 5 Data Management.

(3) Changing filenames:

(a) Double-click the image stack icon in 

the upper left of the graphic model, 

and enter the appropriate name in the 

filename field. The file is the aspect 

image created in section X, step 1a 

above.

(b) Do the same procedure to change the 

name of the input slope image in the 

image stack icon in the upper right of 

the model.

(c) Double-click on the icon on the righthand side of the image. Enter a name 

in the filename field for the mask 

corresponding to southeast aspects. It is 

recommended that a naming convention 

similar to that provided be used, where 

the filename includes the name of the 

park and the fact that the mask is for the 

southeast aspects.

(d) Double-click on the icon on the bottom 

of the mask, and name the output for the 

NW aspects.

(4) Run the model by clicking on the lightning 

bolt icon in the modeler icon row.

d. Convert the masks into AOIs.

i. Open a viewer, and open the NW aspect mask 

that was just created.

Figure 25. Imagine graphic model for making aspect 

mask images at MORA.

SOP 2. Preprocessing Landsat Imagery 63
ii. In the viewer, select “Raster/Attribute editor.”

(1) The Raster attribute editor window will 

open. There will be two rows, 0 and 1.

iii. Select the row with the value 1 by left-clicking 

on the shaded box with the value “1” in it on the 

left-hand side of the table in the window.

(1) This selects all of the pixels that have value 

1, which are those that are within the NW 

aspect mask.

iv. In the Viewer, select “AOI/Copy selection to 

AOI.”

(1) This will copy those pixels to a new, 

unnamed AOI.

v. In the Viewer, select “File/Save/AOI layer 

as,” and save the AOI with a name based on 

the conventions described in SOP 5 Data 

Management.

(1) This mask AOI will be relevant to all 

processing done at a given park, so it is 

critical that it be named and located for 

further use.

(2) NOTE: In some cases, the authors have 

found that Imagine will not know how to 

use an AOI if the parent image from which 

it is drawn does not exist. This is even the 

case when the parent image exists but simply 

has been moved from its original location. It 

is therefore important to retain the original 

pixel-level aspect masks in their original 

directory. This also makes it advantageous 

for the user to refer back to them to recreate 

the masks if needed.

vi. Repeat this process for the SE aspect mask.

e. The result of this process are two image masks, one 

for each of the two aspect classes. In the mask image, 

a value of 1 indicates membership in the aspect class 

and a value of 0 indicates nonmembership. These 

images can be multiplied by any other image on 

a pixel by pixel basis to eliminate areas not in the 

mask, which is the next step below.

2. Splitting images by aspect mask.

a. The authors have provided a graphic model to 

multiply tasseled-cap images by the aspect masks 

to create tasseled-cap images split by aspect (named 

split_imagery_by_aspect.gmd). (see fig. 26)

i. This model was applied to imagery and aspect 

masks at MORA on the computer system of 

the authors. Therefore, all file names and paths 

must be adapted when the parks conduct this 

processing.

ii. First, save the graphic model under a new 

name using File/Save As. Follow the naming 

conventions in SOP 5 Data Management.

iii. Changing file names:

(1) Double-click the image stack icon in the 

upper left of the graphic model, and enter 

the name of one of the two tasseled-cap, 

study-area clipped images from section IX 

above.

(2) Rename the files for the top-center and topright image stacks. These correspond to the 

aspect images that were created in step 1 

above in this section. The top center image 

is for the NW aspects mask and the top right 

for the SE aspects mask.

(3) Follow the conventions in SOP 5 Data 

Management for naming the output files.

iv. Run the model by clicking the lightning bolt icon 

in the modeler icon row.

3. Repeat step 2 for all tasseled-cap images that are to be 

used in change detection.

Figure 26. Graphic model to split tasseled-cap 

imagery by aspect class.

64  Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
XI. Summary

The processing steps described in this document take 

Landsat TM imagery from its original state when purchased 

from EDC (at the end of SOP 1) to the state that can be used in 

change detection (SOP 3). The following files are to be used in 

later steps:

1. Study area AOI and vector coverages (section V).

2. DEM clipped to the study area (section V).

3. Aspect masks (both raster and AOI) for the NW and SE 

aspects (section X).

4. Tasseled-cap imagery, split by aspect, for the study area 

(section X).

SOP 2. Preprocessing Landsat Imagery 65
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66 Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
Protocol for Landsat-Based Monitoring of Vegetation at 

North Coast and Cascades Network Parks

Standard Operating Procedure (SOP) 3

Physiognomic Change Detection

Version 1.0 (July 5, 2006)

Revision History Log:

Previous 

VersionNumber Revision Date Author Changes Made Reason for Change New Version Number

n.a. 07-05-06 REK Final version under 

agreement

1.0

This SOP explains the procedures for detection changes in the physiognomic properties of the surface over time. It follows 

and relies on outputs from SOP 2 (Preprocessing Imagery). 

This SOP describes three phases in change detection. In the startup phase, the spectral space of a reference image for the 

parks is converted into probability-of-membership in physiognomic classes, and this forms the basis for all change detection in 

all subsequent years. For the type 1 phase of change detection, yearly changes are converted to that reference probability-ofmembership space and converted to S2S classes for interpretation (SOP 4). For the type 2 phase of change detection, images 

from years corresponding to dates of airphoto acquisition are referenced to that probability-of-membership space, differenced, 

converted to S2S classes, and then optionally backed up with direct airphoto interpretation. 

SOP 3. Physiognomic Change Detection 67
Processing Datasheet

There are many detailed and sometimes complicated steps in this SOP. Because the analyst may need to split work between 

this work and other responsilities, we provide table 1 that can be printed out separately for the analyst to keep track of steps that 

have been completed. This should facilitate easier recovery from interruptions

Table 1. Datasheet to keep track of steps in Standard Operating Procedure 3. 

[Abbreviations: SOP, standard operating procedure; TC, tassel cap; IMG, Erdas Imagine File; POM, probability of membership; CSV, comma-delimited text 

file; TXT, text file; PMR, Pacific Meridian Resources; S2S, satellite to satellite]

General information

Park being processed

Analyst

Change interval being processed

Start of processing

End of processing

Section Step Filename 

SE

Section II (Startup Phase Only) Identify baseline TC image from SOP 2 IMG:

Create 25-class unsupervised classification image IMG:

Section III (Startup Phase Only) Collapse 25-class image into 9-class physiognomic image IMG:

Section IV Develop POM spreadsheets (Startup Phase only) CSV:

Set up POM runfile template TXT:

Section V Calculate POM difference image IMG:

NW

Section II (Startup Phase Only) Identify baseline TC image from SOP 2 IMG:

Create 25-class unsupervised classification image IMG:

Section III (Startup Phase Only) Collapse 25-class image into 9-class physiognomic image IMG:

Section IV Develop POM spreadsheets (Startup Phase only) CSV:

Set up POM runfile template TXT:

Section V Calculate POM difference image IMG:

Merged aspects

Section VI Merge aspects IMG:

Section VII Tabulate plot data from PMR database (Startup phase only) Plot Database:

Link PMR data with physignomic classes (Startup phase only) Summarized data:

Section VIII Create S2S images IMG:

Section IX Sieve S2S images IMG:

Section X Convert to vector layer Vector:

68  Protocol for Landsat-Based Monitoring of Landscape Dynamics at North Coast and Cascades Network Parks
I. Required Input Imagery From SOP 2

Several of the images and associated spatial datasets from SOP 2 are required for SOP 3. They are listed here again for 

clarity:

1. Study area AOI and vector coverages (section V).

2. DEM clipped to the study area (section V).

3. Aspect masks (both raster and AOI) for the NW and SE aspects (section X).

4. Tasseled-cap imagery, split by aspect, for the study area (section X). One image must be defined as the baseline image (the 

early image) and one as the changed image (see Narrative, table 5, for more detail). 

Confirm that these files exist and that the locations are known. 

Figure 1. Unsupervised classification window.

II. Unsupervised Classification of Baseline 

Image

The baseline image must first be classified according to the spectral 

variation present in the image. This step will occur only in the Startup 

Phase of monitoring, and should be applied only to the baseline image 

(see Narrative, Methods section). Unsupervised classification using 

the k-means classifier is a common nonparametric approach to split a 

multivariate space into an arbitrary number of regions. For conventions 

on describing the steps and methods in Imagine, see section I of SOP 2. 

Carry out the steps in this section separately for both NW and SE 

aspects. 

1. From the main Imagine icon panel, select “Classification” and then 

“Unsupervised Classification” from the list of options. 

a. NOTE: The following assumes that the tasseled-cap image has 

been displayed in your viewer with brightness in red, greenness 

in green, and wetness in blue. If you did not change the 

defaults for display of three-layer images in SOP 2, section IX, 

change the default first, and then restart the classification. 

b. The “Unsupervised Classification” window will pop up (fig. 1).

c. In the “Input Raster File:” field, navigate to the aspectappropriate baseline tasseled-cap image.

i. This image is the baseline tasseled-cap image created in 

the Startup Phase of monitoring (fig. 5 in the Narrative). 

Ensure that this image has been clipped to the study area 

and aspect masks (SOP 2). 

d. In the Output Cluster Layer field, navigate to the desired directory and name the output file.

i. Follow the conventions in SOP 5 Data Management for naming and locating this file. 

e. Uncheck the box for “Output signature set.”

f. Click on the “Initializing Options” button.

i. Select “Initialize from Principal Axis.”

(1) This option finds the dominant axis of variation in the spectral data and seeds the classes along that axis. 

ii. Other options can be left as default values.

SOP 3. Physiognomic Change Detection 69
g. Click on “Color Scheme Options.”

i. Select “Approximate True Color” and leave the 

band combinations as default. 

h. In the Number of Classes field, type “25”.

i. This will create 25 classes in this spectral space. 

The choice of 25 classes is somewhat arbitrary, 

since these classes will be collapsed later, 

but experience shows that it is a large enough 

number to separate real classes on the ground 

and yet small enough to interpret and integrate 

later. 

i. In the Maximum Iterations field, type in “20”.

j. Leave the convergence threshold at 0.950.

k. Click on the AOI button.

i. In the window that appears, select the button for 

“AOI File.”

(1) The file-field dialog will become active. 

(2) Navigate to the NW aspects AOI that was 

created in SOP 2, section X. 

l. Click on OK button. 

i. The progress meter will run as Imagine processes the 

image. It will take several minutes.

2. Repeat this process with the baseline image, but use the 

SE aspects AOI instead of the NW aspects AOI, and name 

the output file accordingly. All other options are the 

same. 

3. The result of this process will be two images, each with 

25 spectral classes. 

III. Collapsing Spectral Classes Into 

Simple Physiognomic Classes

The 25 spectral classes in section I must be compressed 

down to 9 simple physiognomic types (table 2). 

These types occupy distinct regions of tasseled-cap 

spectral space. To collapse the 25 classes into the simplified 

classes, the tasseled-cap spectral space must be visualized, 

nearby classes noted, and then classes recoded into 

physiognomic classes. 

Conduct the following steps for both aspect classes. The 

examples below show the NW aspects case. 

Figure 2. Create Feature Space Images window. 

Table 2. Physiognomic classes for the North Coast and 

Cascades Network Parks.

Class number Class description

1 Water/deep shade

2 Closed-canopy conifer

3 Conifer-broadleaf mix

4 Dense broadleaf/grass

5 Broadleaf tree/shrub

6 Mixed

7 Open: dark

8 Open: bright

9 Snow and ice

1. Viewing spectral space of NW aspects using feature space 

images:

a. From the main icon panel in Imagine, click the 

Classification button, and then select “Feature space 

image” from the list of options that appears. 

i. The “Create Feature Space Images” window will 

pop up (fig. 2). 

b. Input raster layer field:

i. Use the open-folder icon to navigate to the 

baseline tasseled-cap image that has been 

clipped to the study area and to NW aspects.

(1) Recall that the baseline image is the one at 

the beginning of the desired change interval. 

(2) This image was created in section X of 

SOP 2. 

70 Protocol for Landsat-Based Monitoring of Landscape Dynamics at North Coast and Cascades Network Parks
c. Output root name:

i. Imagine will produce a root name in the default output directory, which likely is not where it belongs. Thus, it is 

necessary to specifically indicate the directory to which the output feature space image should be placed. 

(1) Navigate to a desired output directory. 

Follow the conventions of SOP 5 Data 

Management for placement of the output 

file.

(2) Click on OK button. 

d. Click on the AOI button

i. Select “AOI File.”

(1) The “Select the AOI file” field will become 

active. 

ii. Navigate to the NW aspects AOI created in 

SOP 2 section X. 

e. Left-click and drag down through the three rows in 

the table at the bottom of the Create Feature Space 

Images window. All three rows (label 1,2,3) should 

be highlighted in yellow. 

f. Click on the OK button. 

g. Examine the image outputs. For example, the 

feature space image for bands 1 and 2 – tasseled-cap 

brightness and greenness – are shown in figure 3.

h. In figure 3, brightness (band 1) values are arranged 

Figure 3. An example of a greenness/brightness feature space 

image.

from left to right, and greenness values are arranged 

from bottom to top. Color intensity corresponds 

to pixel count—red indicates the greatest number 

of pixels, magenta the fewest. Snow and ice are 

relatively rare and are high in brightness but low 

in greenness, and thus occupy a sparse population 

spread out in a tail from left to right in figure 3. 

Most of the pixels in this image (taken from the 

area around Mt. Rainier National Park) are forested 

and fall along a diagonal running from lower left to 

upper center in figure 3. 

2. View the 25 spectral classes in this spectral space. 

a. From the main icon panel in Imagine, click the 

Figure 4. Create thematic feature space images window.

“Classification” button, then select “Feature Space Thematic” from the list that appears. 

i. The “Create Thematic Feature Space Images” window will popup (fig. 4). 

b. Thematic Input File:

i. Use the folder icon to navigate to the unsupervised classification image for the NW aspects that was created in 

section II above. 

c. Feature Space Files: 

i. Use the folder icon to navigate to the brightness-greenness feature space image created in step 1 above in this 

section.

(1) The brightness-greenness feature space image is noted by the phrase “_1_2.fsp.img” in the file name. 

SOP 3. Physiognomic Change Detection  71
Figure 5. Feature-space thematic image corresponding to the 

feature space image in figure 3. 

Figure 6. Approximate locations of physiognomic cover 

classes in the brightness/greenness space.

d. Output Root Name:

i. Use the folder icon to open the file navigation dialog. 

ii. As in step 1c above, it is useful to copy the default “output root name” onto the clipboard before navigating to the 

desired output folder. 

iii. Navigate to the same folder in which the feature 

space images were placed. 

iv. Follow naming and file location conventions in 

SOP 5 Data Management. 

e. In the Feature Space Files field, navigate and select 

the greenness-wetness feature space image

i. This is the feature space image with “_2_3.fsp.

img” in the filename.

ii. When this is selected, a second row of filenames 

should appear in the table at the bottom of the 

window.

(1) This second set of files will be placed in 

the correct directory, assuming it has been 

changed in step 2.d. above prior to this step. 

f. Click on the AOI button and navigate to the AOI for 

NW aspects. Click OK. 

g. In the “Create Thematic Feature Space images” 

window, select both rows by clicking and dragging the 

numbers on the left-hand side of the table. 

h. Click OK. 

i. View the results.

i. In a viewer, open the feature space 

thematic image. Figure 5 shows an 

example of the output for the same image 

shown in the feature-space image in 

figure 3.

j. The colors in figure 5 correspond to the colors 

of the classes in the 25-class unsupervised 

classification image from section I. 

k. To see which class numbers correspond to a 

given color in the feature space thematic, use 

the cursor utility.

i. Click the plus sign icon in the viewer (just 

to the left of the hammer symbol; fig. 5). 

The cursor utility window will pop up. 

ii. A crosshair will appear in the viewer. 

Click and drag at the center of that 

crosshair to move it through the feature 

space. The class number will be reported 

in the “Pixel Value” column of the cursor 

window. 

72 Protocol for Landsat-Based Monitoring of Landscape Dynamics at North Coast and Cascades Network Parks
Figure 7. Approximate locations of physiognomic cover classes in 

the greenness/wetness space (greenness on X axis)

3. Identifying classes to combine. 

a. Each of the nine physiognomic classes listed 

in table 2 will be composed of several spectral 

classes. A table of the nine classes and the spectral 

classes that make them up must be determined by 

the interpreter. Two tools can be brought to bear on 

this. 

i. The shape of the brightness-greenness feature 

space in figures 3 and 5 is typical. The 

physiognomic classes can be identified based 

on their approximate locations in this feature 

space. Indeed, the relative stability of these 

broad physiognomic classes across different 

systems is one of the key reasons that the 

tasseled-cap transformation was chosen as the 

base for change detection. Figure 6 shows the 

approximate regions of these physiognomic 

classes in brightness/greenness space, while 

figure 7 shows the same in greenness/wetness 

space. These general locations serve as the starting point for determining how to aggregate spectral classes. 

ii. In addition to noting the position in spectral space of the physiognomic classes, it also is useful to use direct 

interpretation of the imagery to aid in labeling the spectral classes.

(1) Load the image of the 25 spectral classes into a new Imagine viewer.

(2) Select Raster/Attributes.

(a) A Raster Attribute window with a table of the 25 spectral classes will appear, with a column for the color 

of each class in the image. 

(b) To see where on the landscape a given class falls, use the left-mouse button to select the row of the given 

class, and then right-click on the color patch of that row to change the class colors. Pick a new color that 

is unlike most of those already on the image–often white or black are useful. 

(c) Examine where on the landscape the class falls to aid in interpreting the class. Take into consideration 

known elevation patterns, as well as a general knowledge of the cover types in the system. See the section 

in SOP 4 Validation on S2S interpretation (section VI) for tips on interpreting directly from tasseled-cap 

images.

(d) When done with viewing the class, select “Edit/Undo last edit” to return the class to its original colors. 

(i) When exiting the raster attribute editor, do not save changes. 

(3) If desired, link the 25-class image with the original tasseled-cap image from which it was formed to aid in 

interpretation.

(a) In a separate viewer, load the tasseled-cap image for the NW aspects.

(b) Right-click anywhere in the classified-image viewer, and select “Geo. Link/Unlink.”

(i) Left-click in the viewer with the tasseled-cap image. 

(c) Open a cursor window in the classified image, if one is not open already.

(i) Select the “+” symbol in the viewer with the 25-class spectral image. 

(ii) Click the center of the crosshairs in the viewer to move the crosshairs around, and note spectral 

patterns in the tasseled-cap image to aid in interpretation of the imagery.

SOP 3. Physiognomic Change Detection  73
Figure 9. Recode window, where the 25-spectral class image is 

recoded into 9 physiognomic classes.

Figure 8. An example of record 

keeping for linking the 25-class spectral 

image with 9 physiognomic classes. 

(see table 2).

(d) Use the interpretation aids in the supplied “spectral library” 

that the authors have provided in an appendix to see examples 

of different cover classes on airphotos, in the field, and on 

imagery. 

iii. In practice, class calls will not always be clear cut, especially for the 

physiognomic classes that involve mixtures of types. While good class 

calls will improve the quality of later change detection, the method 

does not assume that the physiognomic classes will be perfect. The 

classes will be converted later into a statistical representation based on 

these classes, adjusting the boundaries of the classes, and that statistical 

representation will further be described by ground and/or airphoto 

interpretation data to characterize within-class variability. Finally, the 

method works on directional variation in spectral change and assumes 

overlap of classes. Therefore, the precise definitions of the classes are 

less critical than in a project where the classified image was the single 

outcome. Nevertheless, the definitions of the class groupings may affect 

outcomes, and it is assumed that lessons learned about class grouping 

for each park eventually will need to be incorporated in a later revision 

of this Protocol. See the Narrative section for a broader discussion of 

this topic. 

b. Keep track of the link between the original spectral class number (1 through 25) and the new physiognomic classes (1 

through 9). 

i. This can be done in a simple word processing document, or in a spreadsheet. An example in Excel is shown in 

figure 8. 

4. Merging classes.

a. Once a physiognomic class is associated with each of the 25 spectral classes, the 25-class image can be recoded into the 

9 physiognomic classes (table 2).

i. Open the 25-class image in a Viewer, if not open already.

(1) NOTE: This is the geographic image of the 25 classes, not the feature space image of the type shown in 

figure 5. 

74  Protocol for Landsat-Based Monitoring of Landscape Dynamics at North Coast and Cascades Network Parks
ii. Save this image under a new name that includes 

“phys9,” or a similar designation that will 

distinguish it from its 25-class parent

(1) Use “File/Save top layer as” to give the 

image a new name

(2) Follow the naming conventions in SOP 5

Data Management.

iii. Select “Raster/Recode” from the viewer menu. 

(1) The “Recode” window will pop up (fig. 9).

(2) Keep the row 0 the same.

(3) Starting with row 1, use the table or 

document where the class linkages 

were stored to type in the appropriate 

physiognomic class for a given spectral 

class.

(a) The spectral class is the “Old Value” 

column.

(b) The physiognomic class is the “New 

Value” column. 

(4) After typing in the values in the New Value 

column, click on the Apply button. 

(a) The colors on the image should change. 

The color scheme will remain the same 

as in the original 25-class image, but 

now the new classes only occupy the 

first 9 classes. Therefore, the colors of 

the first 9 classes in the 25-class image 

will be “painted” across the entire 

image. 

(5) Select “File/Save Top Layer”

(a) Imagine will warn that pixel values 

could change permanently; this is okay. 

Continue. 

(b) NOTE: To see the effects of the 

change in the raster attribute editor, it 

is necessary to reload the image into 

the viewer; only then will statistics be 

calculated for the new physiognomic 

classes.

b. The image at this stage is a representation of the 

physiognomic classes on the landscape. It may be 

desirable to view this image and confirm that the 

patterns on the landscape make sense, using direct 

image interpretation and knowledge of the landscape 

as a guide. Follow step 3.a.ii. above to link with the 

original tasseled-cap imagery. 

i. While it is important that the general patterns 

on the landscape make sense, it must be kept in 

mind that these represent broad physiognomic 

classes, and that there is often a mixture of a 

variety of land types within one physiognomic 

class. See note 3.a.iii. above. 

5. If it is thought necessary to change or regroup the 25 

classes differently, repeat step 3, adjusting class linkages 

as deemed necessary. Repeat steps 1 through 4 for the SE 

aspects. 

a. Where the NW aspects AOI is indicated, choose the 

SE aspects instead. 

b. Where the NW aspects classification or tasseled-cap 

image is indicated, choose the SE aspects version of 

the same instead. 

IV. Probability Surfaces for 

Physiognomic Classes

The change-detection approach used here relies on 

the concept of likelihood of class membership, a concept 

well established in the common “maximum-likelihood” 

classification process. In the maximum-likelihood 

classification process, training samples are used to characterize 

the spectral properties of each of several classes, on the 

assumption that class membership is a Gaussian probability 

surface in the multiple spectral bands of the training image. 

Each pixel in a spectral image can then be scored according 

to its probability on that probability surface for each class; 

a pixel is labeled according to the class in which it has the 

maximum probability of membership. See Richards (1993) 

for details on the maximum-likelihood approach. Rather 

than using the maximum-likelihood probabilities directly, 

we consider each pixel to have the potential for partial 

membership in several classes, which makes the method an 

essentially fuzzy classification approach (Foody, 1996). 

Although maximum-likelihood classification is a builtin component in Imagine, the built-in module cannot be 

used here. First, for each pixel Imagine provides the single 

or, in the case of fuzzy classification, multiple-class calls, 

but does not provide a multilayer POM image. Imagine will

provide a single “distance image,” the Mahalanobis distance. 

The Mahalanobis distance is a component of the likelihood 

function, and could be converted to a POM using fairly 

straightforward linear-algebra techniques implemented in an 

Imagine graphical model. Unfortunately, the distance image 

provided by Imagine represents the distance of each pixel 

from the class to which it was assigned, so class-level-distance 

images can be achieved only if the classification is done with a 

single training class at a time. This is cumbersome, but would 

be manageable if it were not for the second challenge: the 

SOP 3. Physiognomic Change Detection  75
Figure 10. An example of the covariance-matrix 

template file to keep track of the spectral properties of 

the nine physiognomic classes. 

probability calculations in a typical likelihood calculation are 

the likelihood of the unique point in spectral space being in 

a given class, which is a very small number because a point 

occupies a very small portion of the space. What is envisioned 

in our change-detection approach is to evaluate where a given 

pixel resides on a cumulative-probability surface ranging 

from 0 to 1.0, with the probability of 1.0 indicating the mean 

spectral position of a class. This requires an integration of 

the probabilities of all points in spectral space at a similar 

probability contour level, relative to all probabilities greater 

than the value at that point. Such an integration is not achieved 

easily in Imagine. 

Therefore, the process for producing likelihood 

surfaces was developed in IDL and transferred to IDL-VM 

for implementation. It involves several steps. First, spectral 

signatures for the nine physiognomic classes are gathered in 

the signature editor of Imagine. These are then copied and 

pasted into a comma-delimited text file for use as input to 

the IDL-VM routine. The IDL procedure takes the signature 

information and produces a lookup table that assigns a 

cumulative probability of membership in each of the nine 

classes for a given combination of tasseled-cap values. A 

second IDL procedure then applies that lookup table to any 

image, either the baseline image or a changed image, to 

produce an image of probabilities. This image is then imported 

back into Imagine, where it can be differenced and further 

processed. 

1. Characterize the statistical properties of each of the nine 

physiognomic classes. Do this process for the NW aspect 

images first, then the SE aspect images. 

a. Open in a viewer the nine-physiognomic-class image 

from section III. 

b. Open the tasseled-cap image from which the 25-class 

image was derived (see section I). 

c. Open the template spreadsheet for recording the 

covariance matrix information. 

i. This file has been provided with this protocol, 

and is named:

(1) “Physclass_covariance_matrix_template.

csv”.

(a) The file is comma-delimited, which 

means it can be viewed in any text 

editor, but is most easily manipulated in 

Excel, where each cell can be entered 

separately. 

d. Save this file – AS COMMA DELIMITED FORMAT 

– under a new name for the particular park, year, and 

aspect class for which the physclass image above was 

created. 

i. Follow the naming and file locations in SOP 5

Data Management. 

ii. Note the following example to understand the 

key components of this file (fig. 10).

(1) The covariance-matrix file must have the 

same format as noted above to be intelligible 

to the IDL routines later on. 

(a) Each class is noted by a cell labeled 

“class <x>”, where <x> is 1, 2, 3, etc. 

Note the lack of capitalization. 

(b) In the cell immediately to the right of 

that cell, the name of the class can be 

entered, of any arbitrary length, but be 

careful NOT to include any commas in 

the description! 

(c) The next row should include cells in this 

sequence:

(i) Blank, “band 1”, “band 2”, “band 

3”, “mean vector”.

(d) The first cell in the next row must have 

the name “band 1” followed by four 

cells of numbers (to be entered below).

(e) The first cell in the next row must have 

the name “band 2”, followed by four 

cells of numbers (to be entered below).

(f) The first cell in the next row must have 

the name “band 3” followed by four 

cells of numbers (to be entered below).

76 Protocol for Landsat-Based Monitoring of Landscape Dynamics at North Coast and Cascades Network Parks
e. From the main Imagine icon panel, select 

“Classification/Signature Editor”.

i. The “Signature Editor” window will pop up 

(fig. 11).

f. Select “Edit/Image Association.” 

i. A window will appear prompting for the name of 

the associated image file. Navigate to the tasseledcap image noted in step 1.b. above. Be careful to 

select the tasseled-cap image for the same aspect 

group (NW or SE aspects). 

g. Next, have Imagine extract the spectral properties for Figure 11. Signature Editor window. 

each of the nine physiognomic classes:

i. Select “Edit/Extract” from thematic layer.

ii. The “Signature Extract from Layer” dialog will 

pop up (fig. 12). 

iii. Use the folder icon to navigate to the nine 

physiognomic-class image (created in section III 

above) for the desired aspect class. 

iv. Name the output-signature file according to the 

conventions in SOP 5 Data Management.

(1) Note this file name for use in the probability 

calculations later. 

v. Click on the “AOI” button.

(1) Navigate to the AOI for this aspect class, 

created in SOP 2, section X. 

(a) Recall that an AOI is an area of interest 

in Imagine terminology. This confines 

Figure 12. Window to specify signature extraction from thematic 

layers. 

the signature gathering to only the NW 

or SE aspects. 

vi. Click on OK button to run the signature 

collection.

h. In the Signature Editor, select “File/Open.”

i. Use the folder icon to navigate to the signature 

file just created. 

(1) Recall that the “Recent” button in the file 

navigation window can be used to quickly 

access recently used files (fig. 13). 

i. Click on Ok button to load the signatures. 

i. The Signature Editor should now show nine rows 

of signatures, with colors corresponding to the 

colors chosen for the nine physiognomic classes 

in the classified image (section III above). See 

figure 14 for an example. 

Figure 13. Screen capture with drawn circle iIllustrating the 

Recent button in the file-navigation dialog, which allows quick 

access to recently opened Imagine files.

SOP 3. Physiognomic Change Detection  77
Figure 14. Signature editor window, with spectral signatures from nine 

physiognomic classes.

j. The goal now is to transfer the covariancematrix and mean-spectral values from each 

of these nine classes into the covariancematrix file created in step 1.d. above. 

i. In the Signature Editor, note the column 

to the left of the Signature Name. The 

column is headed with a greater-than 

sign (>). In the first row, there also is a 

greater-than sign, indicating that the first 

class is the active class. 

ii. Select View/Statistics from the 

Signature Editor

(1) The “Statistics” window will open 

Figure 15. Class statistics window, from which values will be 

entered into the covariance-matrix spreadsheet. 

showing with the name of the active class in the title bar (typically class 1 on startup). See figure 15. 

iii. First, copy and paste the covariance-matrix values into the spreadsheet.

(1) Select all three rows in the covariance matrix in the bottom half of the window (fig. 15) by clicking and 

dragging the grey boxes on the left-hand side of the table. The rows should be highlighted. 

(2) Select all three columns in the same table by clicking and dragging left to right across the boxes at the top of 

the table. The columns should be highlighted (fig. 16). 

Figure 16. Highlighting of covariance-matrix cells.

(3) Right-click in any of the three column header number boxes (1, 2, or 3)

(a) Select ‘copy.’ 

iv. In the Excel spreadsheet from section 1.d. above (fig. 10), click and drag to select the 3 by 3 set of cells in the 

class 1 section of the spreadsheet corresponding to bands 1, 2, and 3 (fig. 17). 

(1) Right-click in those cells and select “paste.”

(a) Confirm that the values in the spreadsheet match those in the covariance matrix in the Imagine Statistics 

window. 

78  Protocol for Landsat-Based Monitoring of Landscape Dynamics at North Coast and Cascades Network Parks
Figure 17. Selecting the cells in the covariance matrix 

of Class 1 to be written over by the values calculated in 

the Statistics window.

v. In the Signature Editor, left-click in the “>” 

column in row 2 for the second class. The “>” 

symbol should move to that cell, indicating that 

class 2 is now the active class. The statistics 

window should be updated to show the statistics 

for class 2. 

(1) Because the cells are still selected in the 

Statistics window for the covariance matrix, 

simply right-click in the column header of 

any of the three columns of the covariance 

matrix, and select “copy.”

vi. Paste these into the 3 by 3 set of cells of the 

covariance matrix spreadsheet (follow steps iv. 

and v. above). 

vii. Repeat this for all nine classes.

viii. Then return to class 1 in the signature editor.

(1) Click in the “>” column next to class 1. 

ix. In the Statistics window, select all three rows 

in the top half of the window this time (the 

“Univariate” half).

x. Select just the single column title “Mean.”

xi. Copy and paste these cells into the class 1 “Mean 

Vector” column of the spreadsheet. 

xii. Repeat this process for all nine classes.

xiii. Save the file. Excel will alert the user about .csv 

having properties that may be saved in the file. 

Save anyway. 

xiv. This covariance-matrix file will be used by the 

IDL VM program “PROBDIFF” to calculate 

POM in each of the nine classes. 

2. Set up the text format “runfile” in preparation for running 

the IDL-VM program.

a. The authors have provided a template runfile for 

PROBDIFF program. 

i. The runfile template is named: probdiff_

runfile_template.txt.

ii. This file controls the PROBDIFF program, 

telling it where to find files, the covariance 

matrix, etc. 

b. Before making changes, first save this file under a 

new name that allows unique connection with the 

park, aspect class, and occasion of this run.

i. The occasion refers to the current 

implementation of change detection, recognizing 

that in future years a new change-detection 

run will be done, and thus will need to be 

distinguished from the current run. 

c. The file must have these fields. Each file must end 

with a colon, and have exactly one space following 

the colon. 

i. NOTE: It is critical that you use exactly the 

same names for the key phrases on each line (i.e. 

Apply to file: must be written exactly as shown), 

that a single space follow the colon of each key 

phrase, and that the filename, etc. entered for that 

phrase be entirely on the same line in the file – 

do NOT press return/enter anywhere between the 

phrase and the end of the characters specifying 

the filename. 

ii. “Covariance matrix file: ”

(1) This must have the full pathname of 

the comma-delimited file to which the 

covariance-matrix values have been copied 

in step 1. 

iii. “Output probability lookup file: ”

(1) This file will retain the IDL-code 

designating the lookup values to link 

spectral values with the cumulative POM. It 

can be used only by the IDL program, but 

the naming should be determined by the user 

to include as a reference in the metadata. 

SOP 3. Physiognomic Change Detection  79
iv. “Run name: ”

(1) This is a string identifier that will be 

attached to the image file names to name the 

output nine-layer probability images, and to 

the current occasion of the change detection.

v. “Apply to file: ”  repeated for as many 

images as desired, with a separate entry on each 

subsequent line

(1) This is the full pathname of the tasseledcap images for which this lookup table is to 

apply.

(2) At a minimum, the baseline and the changed 

image should be listed here. But there is 

no limit to the number of images to which 

this can be applied. Thus, several years of 

imagery could be processed in succession, 

all referenced to the baseline-image 

condition. At present, this protocol simply 

assumes that two images, one at each end of 

a change interval, are being processed. 

(3) NOTE: It is critical that you use exactly 

the same names for the key phrases on each 

line (i.e. Apply to file: must be written 

exactly as shown), that a single space follow 

the colon of each key phrase, and that the 

filename, etc. entered for that phrase be 

entirely on the same line in the file – do 

NOT press return/enter anywhere between 

the phrase and the end of the characters 

specifying the file name. 

3. Run the PROBDIFF program

a. Ensure that the IDL-VM has been installed on the 

computer. 

i. See SOP 2 section VI for information on 

installing Virtual Machine.

b. Find the PROBDIFF.sav file provided in the 

appendices. 

i. Drag the file onto the IDL-VM icon. 

ii. On the IDL-VM splash screen, click OK to 

continue. 

iii. The PROBDIFF program will notify you that 

it is beginning by showing a small information 

screen. Click OK to start the program.

c. A window will pop up asking for the location of the 

runfile-template text file. 

i. This is the text file that was just created in the 

prior step. 

d. The program will begin. 

i. There will be no direct indicator icon showing 

progress of the program. However, the program 

continually writes out a diagnosis file for error 

analysis, should the program cease functioning. 

This diagnosis file is placed by the program in 

the same directory as the PROBDIFF control 

file that was used to run it. The name is based on 

the PROBDIFF control file name, but has added 

a unique time stamp identifier and the word “_

diag.txt”. By watching this file (either reloading 

it over time or simply refreshing a Windows 

Explorer window viewing the file and its time 

stamp), the user can ensure that the program is 

running. As long as the file is updated frequently, 

the file is running. If there are any problems, the 

text file can be used to determine where in the 

program flow the problem occurred. 

ii. The program first uses the covariance-matrix 

data from the nine classes to build a lookup table 

for the POM for each combination of spectral 

values in the baseline image from which the 

covariance matrix was built. This program will 

take significant time to run. 

iii. Then the program applies this lookup table to all 

of the files that are listed in the runfile. These 

are the files listed as “Apply to file:” noted in 

step 2.c.iv. above. 

4. When the program is done, import the probability images 

into Imagine. These images are named Probability of 

Membership (POM) images, because they describe the 

POM in each of the nine physiognomic classes.

a. See details on importing BSQ images into Imagine 

in SOP 2, section VII, step 4. Briefly, use Imagine’s 

Import/Export dialog to navigate to the directory of 

each of the “Apply to file” images. The probability 

image name will follow the name of the image from 

which it is calculated, with a modifier associated with 

the unique name provided in the runtime file. 

i. Name the Imagine format file according to the 

naming conventions in SOP 5 Data Management. 

b. There also will be a file with the extension “.hdr”. 

i. Open that as a text file, and use it to find the 

appropriate fields for the Imagine importer. 

ii. Once the image is imported, use that file to 

fill out the map-projection information in the 

ImageInfo of the imported image. 

5. The result of this process is an aspect-appropriate POM 

80 Protocol for Landsat-Based Monitoring of Landscape Dynamics at North Coast and Cascades Network Parks
image with nine layers (one for each class listed in 

the covariance-matrix file) for each image listed in the 

PROBDIFF control file. For a given year of image, the 

nine values associated with each pixel correspond to the 

probabilities of that pixel belonging in each of the nine 

physiognomic classes for that year. The next step is to 

subtract probabilities of class membership across years to 

identify areas of change. 

a. NOTE: These images represent probabilities scaled 

by 100 and transferred into byte type (to make the file 

sizes manageable). Thus, the probabilities will range 

from 0 to 100. 

At this point, repeat all of the steps in this section for the 

SE aspect-image class. 

V. Calculating Difference Image

At this stage, we focus on POM images for the baseline 

and the change image for one aspect class. In section IV, 

POM in each of the physiognomic classes was calculated for 

each pixel. In this section, the POM values for each class are 

subtracted on a pixel-wise basis to identify changes in POM 

over the change interval. Here again, we refer to the earlier 

date image as the “baseline” image and the later date image as 

the “change” image. Again, conduct every step here for the 

NW aspects, then repeat for the SE aspects. 

1. Confirm that the map information entered for the 

imported images is correct (these steps referred to several 

other points earlier in the SOPs)

a. In two viewers, open the two POM images that will 

be subtracted. The images should be from the same 

aspect class (either NW or SE). One image should be 

the POM image associated with the baseline year and 

one the POM image associated with the change year. 

b. In a third viewer, open the tasseled-cap version of the 

baseline image. This will serve as a visual reference. 

c. In the tasseled-cap viewer, right-click and select “Geo 

Link/Unlink,” and link with one of the other viewers, 

then do it again and link with the last viewer. 

d. Open a cursor (click on the “+” cursor icon in either 

viewer), and then click and drag it around, making 

sure that all three images are lined up geographically. 

i. If not, use the ImageInfo to examine the 

geographic-map model and projection 

information. Use discrepancies to trace back to 

the source of the potential mismatch. Since these 

are all derived from the same images, errors are 

most frequently associated with mistypings, etc. 

in the manual importing of the images. 

e. Once these have been confirmed, close all viewers. 

2. In Imagine’s main console, select the “Image Interpreter” 

button, then select “Utilities\Operators”

a. The “Two Input Operators” window will pop up 

(fig. 18). 

b. The “Input File #1” image is the POM image created 

by PROBDIFF for the “Change image” (the later of 

the two dates). 

c. The “Input File #2” image is the POM image created 

by PROBDIFF for the “Baseline image.”

d. Name the output file according to the SOP 5 Data 

Management. 

e. Change the “Operator” to the minus sign. 

f. Change the “Output” to “Signed 16-bit.”

g. Select the AOI button.

i. Navigate to the aspect AOI (nwaspects.aoi 

or seaspects.aoi) file corresponding to this 

aspect image. This AOI was created in SOP 2,

section X.

h. Click on OK button to run the differencing. 

i. This image will take the two 9-layer POM 

images, subtract each band from the same band, 

which will result in a 9-layer difference-in-POM 

image. 

3. Examine the difference image to make sure it makes 

sense.

Figure 18. Two input operators window, used to 

subtract two images. 

SOP 3. Physiognomic Change Detection  81
a. In two new viewers, open the baseline and change 

tasseled-cap images on which the POM images were 

based.

i. These images are the tasseled-cap images listed 

in the “Apply to file:” lines of the PROBDIFF 

control file (see section IV, step 2.c.v. above). 

b. In two additional viewers, open the predifferencing 

POM images.

i. These are the nine-layer images corresponding to 

the POM in each of the physiognomic classes. 

c. In a fifth viewer, open the POM difference image

d. In any of the viewers, select “View/Tile Viewers” to 

better distribute all five viewers on the screen. 

e. Geolink all viewers to the POM difference image.

i. Right-click in the POM difference image, select 

Geo Link/Unlink, and then select another viewer. 

Repeat for all four other viewers. 

f. Start a cursor using the “+” icon. 

g. Move the cursor around and ensure that the patterns 

in the difference image make sense. 

h. How to interpret the images:

i. There are nine bands in these POM images, 

only three of which are shown. Depending on 

the configuration of Imagine, the three shown 

initially may differ, but are typically bands 

4, 3 and 2 for Red, Green, and Blue on the 

screen, respectively. Each band corresponds to a 

physiognomic class, with band 1 corresponding 

to the first physiognomic class. The order of 

the classes is the same as the order they were 

entered in the covariance matrix file passed to 

the PROBDIFF program.

(1) The choice of which bands are displayed can 

be changed using Raster/Band Combinations 

in the viewer. There, the bands associated 

with each color gun (red, green, and blue) on 

the screen can be chosen.

(2) No-change: If an area has not changed in 

any of the three bands currently displayed 

in the viewer, it will appear grey because 

all three bands are zero. Grey is roughly 

midway between the negative and positive 

ranges of the changes seen across the image. 

(3) Change: Colors other than grey involve 

some change in the combined probabilities 

of the three classes being displayed. 

Because the color reflects potential positive 

and negative changes in each of the three 

displayed classes as well as no-change, the 

exact interpretation requires some careful 

thought. 

(a) Consider the case where classes 1, 2, 

and 3 are represented by the red, green, 

and blue color guns on the screens. 

(b) If a pixel shows up red, it could be 

the result of two distinct processes. 

The more intuitive process is where 

class 1 has increased in POM, and 

classes 2 and 3 are unchanged. The 

less intuitive process is where classes 2 

and 3 have decreased dramatically, and 

class 1 has remained the same. Thus, 

the color combination represents the 

relative change among the three classes 

currently displayed. 

(4) It is safest to use the colors as a preliminary 

indicator of change, and then to use the 

cursor (crosshairs) to query individual pixels 

in a changed area to confirm the direction of 

change (positive or negative) of each class. 

If the difference image appears to have many geometric 

artifacts, it may be that the map information was entered 

incorrectly, or that the registration of the images was 

insufficient. 

(5) In the case of the latter problem, it is 

necessary to confirm that the terrain 

correction in the geometric registration was 

done correctly, and that the tie points were 

located correctly. See SOP 2, section VI on 

geometric correction. 

ii. If there appear to be many false positives—areas 

where change is claimed in the difference image 

but where there is no apparent cause in the 

source tasseled-cap images—then the patterns of 

the false positives must be examined to develop 

hypotheses about what might be going wrong. 

(1) Note that low probability changes in class 

membership are often due to normal noise in 

illumination, processing, etc. False positives 

should be considered as those changes 

where a high probability (>50) of change 

is indicated, but where there is no obvious 

change in the parent images. 

(2) If false positives appear to have no 

82 Protocol for Landsat-Based Monitoring of Landscape Dynamics at North Coast and Cascades Network Parks
relationship to the patterns on the actual 

landscape, it may be that the radiometric 

normalization has not been achieved 

appropriately. Go back to the SOP 2 section 

VIII on radiometric normalization and check 

the output images to make sure that the 

normalization has not failed. 

(3) If false positives are restricted to particular 

features on the landscape, first check to 

determine if those features are always 

located in one of the physiognomic classes. 

If so, first check to make sure that the 

covariance matrices of the classes were 

entered correctly into the covariance matrix 

comma-delimited file (see section IV, step 1 

above). 

The steps in this section need to be repeated in their 

entirety for the other aspect class.

The difference POM images at this point can be used in 

one of three ways. First, they can be used directly as visual 

estimates of the continuous-variable probability of change 

across all classes. This directly fulfills one of the objectives 

of the protocol, and makes this image a final product (see 

SOP 5). Secondly, these nine-layer probability-of-change 

images can be masked to only include areas where a certain 

threshold of change has occurred, to simplify viewing and 

interpretation. Finally, these continuous-variable images can 

be categorized according to rules deemed as useful for an 

individual park’s needs. This will be described in section VIII 

below. 

VI. Merging Aspects

Sections II through V must be done separately for each 

of the two aspect classes (SE and NW). Once this has been 

done, the POM difference images should be merged for further 

analysis. 

In Imagine’s main icon panel, click on the Interpreter 

button, and select “Utilities/Operators.” The operator window 

will popup. Enter the SE aspect image as Input File #1, 

the NW aspect image as Input File #2. Retain the “+” as 

the operator. This will add the two POM difference images 

together on a pixel-wise basis. Because the two images have 

zeros where their aspect class is not present, this approach 

is a simple approach to merge the two aspect classes. Name 

the output according to the conventions in SOP 5 Data 

Management. 

VII. Using Pacific Meridian Resources 

Data to Describe Physiognomic Class

The physiognomic classes created in section IV above 

were based on a direct understanding of the physical basis 

for the distribution of reflectances in spectral space. Broadly 

speaking, regions of spectral space describe vegetation 

physiognomic properties anywhere in the world. While 

changes in these classes can often be used to describe where 

disturbance is occurring and generally how that disturbance 

affected the vegetation, the physiognomic classes contain no 

information on park-specific vegetation-community types that 

they represent. 

In the parks of the NCCN, field data were recorded 

for the purposes of Landsat-based mapping by the private 

consulting company Pacific Merdian Resources (PMR) in 

the mid-1990s. While these field data do not cover enough 

of the spectral space to be used as the basis for statistical 

class building, they can be used as a first approximation of 

the park-specific vegetation within each of the physiognomic 

classes. This step only need be taken during the startup phase 

of monitoring, when the image from the era near collection of 

ground data from PMR is being processed. 

Using the PMR field data to describe the physiognomic 

classes consists of three steps. First, the database with the 

field data must be summarized to the plot level with summary 

statistics that are desirable to the user. An example is 

described below, but any statistics could be used. The key 

is simply that each plot with a unique geographic location 

have a set of numeric descriptors. This is done in Microsoft© 

Access, or could be done in any similar database-software 

package. The second step is to extract the physiognomic class 

associated with each plot. This is done in Imagine using the 

geographic coordinates in the database. Finally, the plots are 

grouped by physiognomic class and summarized to provide a 

description of that class. 

1. Summarizing PMR data in the database. 

a. This can be done to any level of detail desired. 

Below, we provide a sketch of the process. 

b. The PMR data were provided to the authors 

in the form of an MS Access database called 

“PMRPlotData.” Each tree or each cover component 

is stored as a separate entry. The goal is to collapse 

all tree observations and cover observations into a 

single entry per plot. 

i. A query with all plots and measures for the 

NCCN existed in that database. From it, a 

simple query was used to develop a new table 

with the plots and measures for a single park. 

SOP 3. Physiognomic Change Detection  83
Figure 19. Pixel to Table window. 

(1) Record cleaning involved filling in values in 

the FIXED column for trees where no entry 

was made. The FIXED value was inferred 

from the DBH of the observed tree. 

(a) if DBH is between 4 and 18, assign 40.

(b) if DBH is between 18 and 36, assign 10.

(c) if DBH is >36, assign 5.

(2) A select query was then used to extract only 

the live-tree observations. 

(3) Conifer and broadleaf components were 

separated.

(a) The broadleaf and conifer species were 

assigned manually in the species table 

of the database, and linked to the livetree observations. 

(b) This was then linked to the result of 

the query of live tree observations (step 

(2)). 

(c) A query could then be made on conifer 

and hardwood designation. In this 

query, the DBH was converted to area 

per acre using the expansion factor (the 

FIXED value) in the formula: [exp_fact

or]*((0.5*[DBH])^2)*3.14159/144. 

(i) A new column with area per acre 

was created in the query. 

(d) The results were two queries, one for 

broadleaf and one for conifer, where 

each tree’s DBH was scaled to a plotlevel basal area. 

(e) Then a crosstab query was used to add 

up the total basal area by broadleaf and 

conifer per plot. 

ii. For all components where the COMPCODE was 

not LT (live tree), a crosstab query was used to 

add the cover percentages by plot number. 

iii. This was combined with the conifer and 

broadleaf tree components to result in a single 

table that had all plot coordinates from the 

original plot file, along with the broadleaf and 

conifer basal area and the percentages of the 

nontree components. 

2. Extracting these points in Imagine.

a. Copy the X,Y coordinates of the plots into a separate 

spreadsheet. 

i. In the database program, select all of the plot 

X and Y coordinates. In Access, the column 

headers X_coord and Y_coord are selected, and 

the Edit/Copy function selected. 

(1) NOTE: It is critical that these coordinates 

be listed in the same coordinate system as 

the image (i.e. NAD83, if standard NCCN 

conventions are used). The images in the 

PMR database are in NAD83 already, so this 

should not be an issue. 

ii. Paste these into an empty Excel spreadsheet. 

iii. Remove any leading text rows, so that only two 

columns have data. 

iv. Save this as a comma-delimited file. 

v. In the Windows Explorer, change the extension 

of this file to .dat (this is so that Imagine will 

accept it). 

b. In Imagine’s main console, select “Utilities/Pixels to 

ASCII.”

i. The Pixel to Table window will pop up (fig. 19).

ii. The Input Image is the nine-class physiognomic 

image created by recoding the 25-class image in 

section III above. 

(1) Once the file is located and shows in the 

Input File field, the “Add” button must be 

clicked. 

iii. Under “Type of Criteria:” select “Point File,” and 

then find the “.dat” file just created. 

iv. The Output File (ending in .asc) should be placed 

in this park’s folder. 

v. Click on OK. 

84  Protocol for Landsat-Based Monitoring of Landscape Dynamics at North Coast and Cascades Network Parks
c. Copy the extracted class values back into the 

database. 

i. In a Windows Explorer window, change the 

extension name of the .asc file to “.txt.”

ii. Open the file in Excel.

(1) Fixed width formatting will be necessary. 

iii. Copy and past the column under “B1” (band 

1). It should contains numbers from 1 to 9, 

maximum (the nine physiognomic classes). 

It likely is that some of the classes will be 

underrepresented or absent because those regions 

of spectral space were not field sampled. 

iv. Paste the values into a new column in the 

database.

3. Group by the class.

a. The easiest approach is to conduct a crosstab query 

in the database software to summarize by the class of 

each plot. 

i. Summarize both by mean and variance of each of 

the plot metrics (conifer DBH, broadleaf DBH, 

percent cover of live vegetation, etc.). 

VIII. Converting Change to Satellite-toSatellite Classes

Changes in the physiognomic classes can be viewed 

directly as continuous-variable probability-change images, 

which provide insight into the range of spectral change 

occurring. However, these continuous-variable images are 

difficult to summarize visually or statistically. Moreover, 

they are difficult to validate directly without substantial, 

quantitative airphoto interpretation that is beyond the budget 

or scope of the NCCN parks. Validation of year-to-year 

changes must occur without photos, and thus for this protocol, 

direct interpretation of satellite images will form the core of 

validation (see SOP 4). The authors have developed a structure 

for S2S image interpretation of changes (SOP 4) that relies on 

15 classes of interpretable change and no-change (see table 3). 

To achieve later validation, then, the continuous-variable, 

nine-layer POM images must be converted into classified S2S 

images. 

To link changes in POM to S2S, a matrix of pairwise 

increases and decreases POM of the nine physiognomic 

classes is developed (table 4). 

Each cell in table 4 can be viewed as an hypothesis of 

change. For example, the greater the positive change in POM 

for the closed-canopy conifer and the greater the negative 

change in POM for the open: dark conditions, the more likely 

that S2S change class 9 is occurring (“Increase in conifer,” 

see table 3). By adding the positive POM change for the 

physiognomic class in each cell’s column header with the 

negative of the POM change for the physiognomic class in 

the cell’s row header, a score for that cell is calculated that is 

more positive as support for that cell’s hypothesis grows. This 

score is the total probability change in support of a given cell’s 

hypothesis.

For each pixel in the study area, all cells from table 4

that support a given S2S change-class hypothesis are 

compared, and the single best score is taken as the score for 

the hypothesis of that type of S2S change occurring in a given 

pixel. This is done for all 14 S2S change classes for each 

pixel, resulting in a 15-layer image with a 15-dimensional 

S2S score for each pixel. Then, a separate algorithm is used 

to identify the highest score and use it to label the type of 

S2S change occurring. If that highest score does not pass a 

minimum threshold, then no-change is likely to have occurred 

and the S2S class 1 is assigned.

These steps of detecting and labeling changes have been 

distilled into two graphic models in Imagine. Therefore, 

the steps for conducting this process are achieved easily in 

Imagine using graphic models supplied by the authors. 

1. Calculate scores for the S2S change classes by applying 

the rules from table 4 to the POM difference image 

created in section VI.

a. In the main Imagine icon panel, click on the modeler 

button, and select “Model Maker.”

b. Load the graphic model named “assign_s2s_labels_

to_9physclass_difference.gmd”.

c. The model is shown in figure 20. 

Table 3. Satellite-to-satellite interpretation change classes.

Satellite-to-satellite code Description

1 No change

2 Water to rock/soil

3 Water to partial vegetation cover

4 Water to complete vegetation cover

5 Rock/soil to water

6 Partial vegetation to water

7 Complete vegetation to water

8 Increase in broadleaf

9 Increase in conifer

10 Increase in vegetation

11 Decrease in broadleaf

12 Decrease in conifer

13 Decrease in vegetation

14 Increase in snow

15 Decrease in snow

SOP 3. Physiognomic Change Detection  85
Table 4. Linking observed increases and decreases in probability of membership with satellite-to-satellite change classes.

[na, not applicable]

Inceasing

 No increases Water/deep shade Closed-canopy conifer Conifer-broadleaf mix Dense broadleaf/grass Broadleaf tree/shrub Mixed Open: dark Open: bright Snow and ice

Decreasing

No decreases 1 1 9 10 8 8 10 1 1 14

Water/deep shade 1 na 4 4 4 4 3 2 2 14

Closed-canopy conifer 12 7 na 8 8 8 12 12 12 14

Conifer-broadleaf mix 13 7 9 na 8 8 13 13 13 14

Dense broadleaf/grass 11 7 9 9 na 11 11 11 11 14

Broadleaf tree/shrub 11 7 9 9 8 na 11 11 11 14

Mixed 13 6 9 10 8 8 na 13 13 14

Open: dark 1 5 9 10 8 8 10 na 1 14

Open: bright 1 5 9 10 8 8 10 1 na 14

Snow and ice 15 15 15 15 15 15 15 15 15 na

Figure 20. Model used to calculate satellite-to-satellite scores based on probability-of-membership 

difference-image values. 

86 Protocol for Landsat-Based Monitoring of Landscape Dynamics at North Coast and Cascades Network Parks
d. Save this model under a different name that is 

specific to the park and year of change being 

investigated. 

i. Follow guidelines in SOP 5 Data Management 

for naming and placing the model. 

e. Update filenames in the model.

i. In the upper left, double-click on the raster object 

and change the name to the difference image 

created in section VI.

ii. In the lower right, change the filename to the 

appropriate filename for the “POM to S2S class 

image” listed in SOP 5, Data Management. 

f. Run the model by clicking on the lightning bolt. 

2. In the model, select “File/Open” and open the second 

model in the sequence: “detect_and_label_s2s.gmd” 

(fig. 21). 

a. Save this model under a new name, again specific 

to the park and years of the images used for change 

detection. 

i. In the raster layer at the top of the model, change 

to the file just created by the model in step 1 (the 

14-layer S2S score image). 

ii. On the left is a box containing the scalar value 

that will be used as the threshold of change for 

change labeling. This value can be changed. 

It currently is set at 80, which means that any 

pixels where the combined POM change exceeds 

80 for any cell in table 4 will be labeled as 

change. 

(1) NOTE: As noted in the Narrative, there 

is no single threshold of change value that 

is “correct.” As long as maps of change 

explicitly describe the threshold used to 

make them, and as long as the error of a 

given map has been assessed using S2S 

(SOP 4) or other similar methods, they 

are legitimate. It may be useful for parks 

to initially experiment with different 

probability thresholds to determine which 

thresholds appear useful. In practice, 

the parks may find that some threshold 

values are more useful over time for actual 

implementation and use by the various users 

at the parks. Additionally, the parks may 

find that some high thresholds are useful for 

some monitoring goals and lower thresholds 

for others. In either case, suggested values 

for change thresholds could be added to this 

protocol later (see SOP 6). 

iii. At the bottom are two raster layers that should 

be changed. The lower-left image will be a 

single-layer file that records with ones and 

zeros if change has occurred or not, based on 

the threshold value. The lower-right image is 

the labeled change image. Name this image to 

include the park, the year of the change interval, 

and the string “_S2Slabel_”. 

(1) Follow guidelines in SOP 5 Data 

Management for location of the images. 

b. Run the model. 

Figure 21. Model used to create a change-label layer. 

SOP 3. Physiognomic Change Detection  87
Figure 23. Clump window. 

IX. Sieving Out Small Changes Based 

on Satellite-to-Satellite Class

The result of the change thresholding and labeling 

process is an image that often contains “speckle”: individual 

pixels that pass the threshold-of-change rule spectrally, but 

whose change likely is the result of small misregistration 

errors. Typically, change-detection products are sieved to a 

minimum mapping unit that is large enough to eliminate most 

of these small occurrences, often 2 ha or more. In this case, 

many monitoring goals require that small events be retained. 

Therefore, a minimum mapping size of only 6 (0.375 ha) 

pixels is recommended. 

Because there are 14 classes of S2S change, patches 

where change has occurred often contain a mosaic of similar 

S2S labels. For example, regrowing clearcuts may include 

pixels labeled as increasing in broadleaf, conifer, and general 

vegetation. When individual pixels lie in a matrix of similarly 

themed pixels, they should not be sieved away. Therefore, the 

14 S2S classes are first grouped before sieving. The original 

labels will be reassigned after sieving. 

1. Group S2S labels by theme.

a. Open the graphic model “group_s2s_for_sieving.

gmd” (fig. 22).

i. Save this model under a new name specific 

for the image being sieved. See SOP 5 Data 

Management for details. 

b. Change the name of the two files. 

i. The top file is the S2S change-label image from 

the prior section. 

ii. The output will be a classified image with four 

classes. These four classes are regroupings of the 

15 S2S classes into common thematic elements: 

no-change, increase in any vegetation type, 

decrease in any vegetation type, and conversion 

of snow. Place this in a directory below the level 

of the change-detection files named “sieving” 

(create this directory if it has not been done 

before). This file will be used only for sieving. 

(1) Click on the AOI button, and navigate to 

the study area AOI (the entire study area, 

not just one of the two aspects) created in 

SOP 2. 

(a) NOTE: If this AOI step is not done, the 

entire area outside of the study area will 

be assigned a value of 1, indicating nochange. 

c. Run the model.

2. Filter out pixel groups that are smaller than 6 pixels. 

a. Hit the Image Interpreter button in the main Imagine 

icon panel. 

i. Select “GIS Analysis/Clump.”

(1) The Clump window will pop up (fig. 23). 

(2) The input file is the image created in step 1. 

(3) The output file should be placed in the 

subdirectory named “sieving.” 

(a) This is a temporary image that can be 

erased after this SOP is complete. 

Figure 22. {Screen capture showing} model to group 

satellite-to-satellite labels by theme.

88  Protocol for Landsat-Based Monitoring of Landscape Dynamics at North Coast and Cascades Network Parks
(4) Click on OK. 

(a) This will create an image where all 

connected pixels are assigned a clump 

number. 

ii. Select “GIS Analysis/Sieve.”

(1) The Sieve window will pop up. (fig. 24).

(a) The input file is the clumped image 

from the prior step. 

(b) The output file should be placed in the 

same “sieving” folder.

(c) Set the minimum size to 6 pixels.

(i) This is equivalent to a minimum 

mapping unit of 0.375 ha. 

(d) Click on OK. 

(2) The result of this process is an image with 

pixels labeled with the clump number. This 

number is uninteresting. But any pixel that 

has a non-zero value is one that survived the 

clumping. By treating this as a mask, the 

original S2S class image can be sieved using 

a simple graphic model. Areas that did not 

have more than six adjacent S2S change 

pixels will be assigned a value of zero. By 

multiplying this image by the original S2S 

class image, the zeros will eliminate any 

pixels in the original 15-class S2S image 

that are isolated. This multiplication is 

carried out in the next step. 

3. Apply the sieve filter to the original S2S image.

a. Open the model “sieve_s2s_labels.gmd” (fig. 25). 

i. Save this model using naming conventions in 

SOP 5 Data Management. 

ii. This model multiplies the mask resulting from 

the sieve process by the S2S change-labeled 

image. Where individual pixels have been 

masked out in the sieved image, they are erased 

from the change-labeled image. 

iii. Change the input files. The upper-left file is the 

change labeled image from section VIII. The 

upper-right file is the product of step 2 (the 

sieved image). 

iv. Change the output file (at the bottom of the 

model). 

(1) Name the output file using the conventions 

in SOP 5 Data Management. 

(2) NOTE: The output type should be forced to 

thematic.

b. Run the model. 

Figure 24. Sieve window. 

Figure 25. Model to sieve Satellite-to-Satellite 

labels. 

SOP 3. Physiognomic Change Detection  89
The result of these steps is an image of S2S change 

labels. Pixels that are labeled with one of the 14 S2S change 

labels are those that meet the threshold of change criterion 

determined in the “detect_and_label_s2s.gmd” model and that 

are labeled according to the observed spectral changes. This 

product will be validated in SOP 4. Keep track of the name 

and location of this file. NOTE: This file directly fulfills one 

of the objectives of the protocol, and as such is considered a 

final product (see SOP 5). 

Errors in this product will be caused by several important 

factors. Illumination differences, caused by changes in the 

date of image acquisition, will cause the change labeling 

approach to indicate changes to or from the water/shadow 

class when no real change has occurred. Geometric 

misregistration will cause false positives in all classes 

when contrasting spectral type are adjacent to each other. 

Phenological change will cause the broadleaf classes to either 

increase or decrease in one image, resulting in labels that 

indicate changes in those classes. Note that this effect will be 

widespread if the cause is a phenological difference; therefore, 

if an individual patch shows a label of increase or decrease in 

broadleaf component, and surrounding patches do not, then 

the label likely is real. 

X. Converting to ArcInfo Polygon 

Formats

When change products are to be used in the field or in 

conjunction with other GIS data, it is convenient to convert 

them into an ArcInfo polygon-vector coverage. 

1. Click on the Vector Utilities button in the main icon panel 

of Imagin.

a. Select “Raster to vector.” 

i. For the Input Raster, select the file created at 

the end of section IX—the labeled and sieved 

thematic layer.

ii. Name the output vector layer and click on OK.

iii. The Raster to Arc/Info Coverage window will 

popup (fig. 26).

iv. Click on OK. 

This is the final-change product in this SOP. 

XI. Summary

The steps described in this SOP 3 take tasseled-cap 

imagery derived from SOP 2 to a state that describes change 

on the landscape in several ways. Key products in this SOP 

are:

1. POM lookup values. 

2. Nine-layer POM difference images from the two dates of 

imagery.

3. S2S-labeled change image(s).

Figure 26. Window for converting an Imagine image to 

a polygon coverage.

90 Protocol for Landsat-Based Monitoring of Landscape Dynamics at North Coast and Cascades Network Parks
Protocol for Landsat-Based Monitoring of Vegetation at 

North Coast and Cascades Network Parks

Standard Operating Procedure (SOP) 4

Validation of Change-Detection Products

Version 1.0 (July 5, 2006)

Revision History Log:

Previous 

VersionNumber Revision Date Author Changes Made Reason for Change New Version Number

n.a. 07-05-06 REK Final version under 

agreement

1.0

This SOP explains the steps for validating polygon-based maps of vegetation change and disturbance created in SOP 3. 

Because reference sources are variable among parks and depend on unknown future budgets, the validation strategy chosen 

is hierarchical. Direct validation from satellite imagery is the minimum validation that is necessary for change products, and 

requires minimal additional financial outlay. This approach allows yearly validation for the group of monitoring goals that must 

be monitored on a yearly or near-yearly basis (type 1 monitoring objectives; see Narrative). Photo and field validation of the 

those satellite-based validation steps can follow and provide a better understanding of actual error rates. Field validation is best 

for type 1 monitoring goals, photo validation for type 2 validation. 

Note that SOP 3 will produce a variety of continuous-variable-change products that precede the steps needed to produce 

polygons of change. Validation of such continuous-variable products would require many more field- or photo-validation plots 

than the methods for polygon-based validation, and thus are not considered part of this protocol. 

I. Overview

Validation is the process of determining how accurately 

the product from SOP 3 detects and labels changes. Typically, 

an accuracy assessment is conducted using a large number 

of sample plots where the labels from a change product are 

compared with labels determined by another, more reliable 

method. For single-date landcover products, accuracy 

assessment can be achieved through a random sample of small 

plots. This is not necessarily the case for change detection. 

Most of a landscape does not change, so randomly placed 

small plots would primarily occur in no-change areas. To 

adequately test whether a change-detection method is accurate, 

a sample design balanced across change and no-change areas 

is desirable. 

Our approach is to use direct human intepretation of 

imagery to exhaustively label change within boxes that are 

much larger than the grain size of most disturbances on the 

landscape. Here, we use boxes 1.5 km by 1.5 km in size. Most 

of the area in these validation boxes will be unchanged, but the 

large size of the boxes will increase the chance of capturing 

change events. Once these events are digitized, random 

samples can be drawn equally from the change and no-change 

areas and used for actual accuracy assessment. 

SOP 4. Validation of Change-Detection Products  91
Exhaustive description in 2.25 km2

 areas is difficult by 

most means. Field validation over such a large area essentially 

is impossible. Airphoto interpretation was tested as a means 

of doing such an exhaustive survey, but was found by the 

authors to be unreliable using typical airphoto-interpretation 

procedures. Instead, the authors found that direct interpretation 

of the satellite images, using methods borrowed from airphoto 

interpretation, proved to be a quick and reliable means of 

identifying changes on a landscape. We call the method S2S, 

shorthand for satellite-to-satellite interpretation. The key 

advantage of this approach is the availability of a difference 

image (an image of the mathematical difference in spectral 

values) to highlight areas of potential change. This necessarily 

limits the accuracy assessment to those changes that can be 

detected and labeled from spectral-imagery alone. In theory, 

many small and subtle changes may go undetected. By testing 

against airphotos, we found that most of the changes of 

interest to the parks were, in fact, captured. 

Once S2S is used to identify change or no-change over 

a set of 2.25 km2

 boxes, small sample plots for accuracy 

assessment can be drawn from those areas using random 

sampling. Additionally, these plots can be validated 

independently from airphotos if such photos are available. 

II. Required Products From Prior SOPs

This SOP requires imagery from SOP 2 and final 

products from SOP 3. 

From SOP 2, the following are needed:

1. Study AOI and vector coverages (section V).

2. DEM clipped to the study area (section V).

3. Aspect masks (both raster and AOI) for the NW and SE 

aspects (section X).

4. Tasseled-cap imagery, split by aspect, for the study area 

(Section X). 

Confirm that these files exist and that the locations are 

known. 

From SOP 3, the following are needed:

1. Final polygon-based S2S change products (section X). 

Note that if two or more thresholds for change are used 

to create change products in SOP 3 (for example, one at a high 

threshold and one at a lower threshold), each must be validated 

independently. Currently, this protocol considers validation of 

only a single product. 

III. Software Used

In this SOP, geospatial-database visualization is more 

important than in prior SOPs. Therefore, here we introduce the 

use of ESRI TM ArcGIS software for parts of the SOP. ArcGIS 

provides easy onscreen digitizing and database updating. 

Although these functions can be achieved in ERDAS Imagine, 

they are more efficient in ArcGIS. Moreover, ArcGIS is 

a software package familiar to most parks because of its 

ubiquitous use in GIS applications. Therefore, relative to 

SOP 2 and SOP 3, parts of this SOP will contain less detail 

about ArcGIS commands and strategies, as local expertise for 

ArcGIS problem-solving likely is more robust than could be 

reported effectively in this document. NOTE: Within this SOP, 

we also assume that the user has installed ArcGIS components 

(here, version 9.0), specifically ArcCatalog and ArcMap. 

IV. Setting Up Validation Database 

Validation occurs within validation boxes that are 1.5 km 

by 1.5 km on a side. The interpreter will use standardized rules 

(section VI) to fully label the entire 2.25 km2 area as either 

no-change or one of 14 other change categories. Interpretation 

will be based on the satellite image from the two dates of the 

change interval, a difference image of those images, and a 

high-resolution digital orthoquad for reference. All of these 

steps will occur within ArcMap, a module of ArcGIS. The 

first step is setting up a database within ArcGIS to record 

and label digitized changes. This involves creation of a 

geodatabase and the feature datasets that will be used to record 

interpreted change calls. 

1. Set up the database and feature set.

a. In ArcCatalog, navigate to the desired folder.

i. Follow guidelines in SOP 5 Data Management 

for naming that folder. 

b. Right-click on the folder.

i. Select “New/Personal geodatabase.”

(1) Name the geodatabase.

(a) This database will house all of the S2S 

interpretation for this park. Feature 

datasets will be created that specify the 

years within which change is validated. 

Thus, the name should indicate only the 

name of the park, and the fact that it is 

for validation.

(b) Follow guidance in SOP 5 Data 

Management for naming and placement 

of this database. 

92 Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
c. Create an S2S feature dataset for the specific years of 

change detection.

i. This feature dataset will house the feature classes 

(polygon coverage-like datasets) that will house 

the accuracy assessment for this park and year. 

Therefore, this dataset should have a name that 

indicates the park name and the years of change. 

Because the accuracy assessment may include 

both S2S and airphoto and other accuracy 

assessments, the name should only include the 

park and the years of the validation. See SOP 5. 

ii. Right-click on the new geodatabase.

iii. Select “New/Feature dataset.”

(1) Name the feature dataset.

(a) Follow guidelines in SOP 5 Data 

Management for naming. 

(2) Assign geographic properties to this dataset.

(3) Click the Edit button.

(a) In the Spatial Reference Properties 

dialog box, select “Import.”

(i) Navigate to either of the tasseledcap images that were used for the 

difference-image processing in 

SOP 3. We use these images as the 

reference because digitizing must 

match the pixel location of these 

images. Do not use another GIS 

coverage for this purpose. 

(b) Click OK to exit the Edit session.

iv. Click OK to approve the new dataset.

v. The new-feature dataset should be listed in the 

Contents pane of the ArcCatalog window, as 

shown in figure 1. 

Figure 2. New feature class window.

d. In later steps, new feature classes must be created 

or imported into this feature dataset. The steps for 

creating a new feature class are provided here for 

later reference. 

e. Steps for creating a new feature class within the 

feature dataset:

i. Right-click on the feature dataset, select “New/

Feature Class.”

(1) Name this dataset to uniquely identify it by 

park, year of change detection, and the fact 

that this is an S2S dataset. 

(a) Follow guidelines in SOP 5 Data 

Management for naming. 

(b) Type remains default (simple features).

(c) Click next.

ii. Use default configuration.

iii. In the next window, new fields can be entered 

into the database. See figure 2 for an example of 

the starting point, before user fields have been 

entered. 

Figure 1. ArcCatalog window showing the contents of a newly 

created geodatabase.

SOP 4. Validation of Change-Detection Products   93
1. New fields can be added by typing directly into empty 

boxes. Field names indicate the kind of data that will be 

entered into the fields. When new-feature classes are to 

be added later, the field name and the data type will be 

specified, e.g.:

a. Field name = box_id

i. Data type = short integer

2. New fields can be added to an existing feature class in 

ArcMap as follows:

a. Open the layer in ArcMap.

b. Right-click on the layer and select “Attributes.”

c. In the Options box of the Attributes window, select 

“Add Field.”

d. The field name and data type are specified in a popup window.

e. NOTE: The new fields cannot be created if the 

feature class is open, or if it has been opened recently, 

in ArcCatalog. It may be necessary to quit from 

ArcCatalog if ArcMap will not allow a new field to 

be added because the dataset is locked. 

V. Creating Validation Boxes 

Validation boxes will be chosen by one of two methods: 

(1) Grid placement, or (2) placement based on location of 

airphotos for later validation. 

Use method 1 if no airphoto validation is planned or 

possible. This will be the case more often than not, as airphoto 

validation will be possible only when appropriate airphotos 

exists on both ends of the change intervals. Assessments 

of yearly change-detection products for type 1 validation 

processes (disturbance, etc.; see Narrative) will use method 1. 

Use method 2 if two-date airphoto validation is planned. 

This will occur only when airphotos are available for both 

dates of the period.

If aerial photos are available only for a small portion 

of the desired area of monitoring, it may be necessary to use 

both methods. Method 2 would be used to create a core set 

of validation boxes where photo interpretation can be used to 

validate the S2S interpretation method, while Method 1 can be 

used to expand the area over which the S2S interpretation can 

be used to validate the change-detection image from SOP 3. 

Method 1: Creating Random Grid-Based 

Validation Boxes. 

1. Create a new map in ArcMap.

a. Launch ArcMap.

b. Start ArcMap with a “New empty map” when 

prompted. 

c. Immediately save this new map under a new name.

i. It is recommended that a single-map project 

be used for all validation at a given park. This 

map ultimately will incorporate layers for 

image interpretation and validation for many 

years. Therefore, the name should include the 

park name and the indication that it is a map for 

validation purposes. 

ii. Follow guidelines in SOP 5 Data Management 

for naming. 

d. Load the following layers and images:

i. Early date tasseled-cap image.

(1) This typically is the “baseline” image in the 

change-detection methods (SOP 3).

(2) For ease of interpretation, use the full image, 

rather than the aspect-split images. 

(3) Be sure to use the radiometrically 

normalized images in all cases.

ii. Late date tasseled-cap image.

(1) This typically is the “changed” image 

defined in the change-detection process 

(SOP 3).

iii. Study area vector layer.

(1) This was defined in SOP 2, section V.

2. Create a grid of potential validation boxes.

a. Open ArcToolbox.

i. Within ArcMap or ArcCatalog, click on the red 

toolbox icon. 

b. In the ArcToolbox, select Conversion Tools/To 

Raster/Feature to Raster.

i. Input feature: The study area vector layer.

ii. Field: id field.

iii. Output raster: Name this according to the 

conventions in SOP 5 Data Management.

iv) Output cell size: Type in 1500 (for 1500 m).

94  Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
c. Convert the grid coverage to a polyline.

i. In ArcToolbox, select Conversion Tools/From 

Raster/Raster to Polyline.

(1) Input raster: The grid just created.

(2) Output polyline: <park>_1500pl.

d. Convert the polyline to a coverage.

i. In ArcToolbox, select Conversion Tools/To 

Coverage/Feature Class to Coverage.

(1) Input Feature Class: The polyline dataset 

just created.

(2) Output coverage: <park>_1500arc

e. Build topology for the Arc coverage.

i. This approach uses Arc Workstation—the 

command line oriented version of Arc that 

comes with ArcGIS. If Arc Workstation was not 

installed with ArcGIS, consult documentation on 

installing it. Alternatively, users of ArcGIS may 

be aware of other means of building the topology 

for the coverage. 

ii. Start Arc Workstation.

iii. Change the workspace to the one where the 

coverage just created exists. 

iv. Type: build <park>_1500arc poly

f. In ArcMap, load the coverage into the same map 

composition. 

i. Confirm that the boxes fall within the study area. 

3. Select a random subset of those grids.

a. The process above created a polygon coverage where 

each 1.5-km box has a unique ID. Those IDs can 

then be used to develop a random subset. 

b. Calculate a new column for the IDs.

i. In the build in Arc, the polygon IDs were 

assigned to an item (column header) with the 

name of the coverage and a # at the end. We 

want to lock those numbers into the column with 

the coverage name followed by “-ID”, which will 

be stable (fig. 3). Right now, it is set to zeros. 

ii. In ArcMap: With the coverage open in ArcMap, 

right-click on the name of the coverage in the list 

of layers on the left of the ArcMap interface, and 

select “Show Attributes.”

(1) The table with the attributes for all of the 

1.5 km boxes will pop up. (fig. 3). 

iii. Left-click on the title of the column with the 

ending “-ID”.

(1) The column should be selected (see 

figure 3).

iv. Right-click on the column header and choose 

“Calculate.” 

(1) Answer “Yes” when queried about editing 

outside of the editor.

(2) A Field Calculator window will pop up 

(fig. 4)

(3) In the “Fields:” area, simply click on the 

field label with the # symbol at the end.

(a) In figure 4, it is the field “MORA_

1500_PL#”.

(b) The field should show up in the bottom 

area, indicating that the “-ID” field will 

be calculated to be equal to the “#” 

field. 

Figure 3: A portion of an attribute window, showing the 

appropriate column to select.

Figure 4. Field Calculator window.

SOP 4. Validation of Change-Detection Products   95
c. Export the table

i. Still in the Attributes window, select “Options/

Export.” (Options is a button near the bottom on 

the right.)

(1) Select “All Records” from the pulldown at 

the top of the window.

(2) Click on the folder icon to change the name 

and type of the output.

(a) Change the type from .dbf to text.

(b) Change the name and location of the 

file to one such as: <park>_1500arc.txt

(c) Do not add the table to the current map 

when prompted. 

(1) Optional: In a file explorer, change the name 

of the exported file.

(a) Although ArcMap calls this file a text 

file, and assigns the extension .txt, it 

actually is a comma-delimited file. 

(b) Using a file explorer, change the 

filename extension from .txt to .csv

ii. In Excel or a similar spreadsheet:

(1) Open the text file. 

(a) If the extension was changed to .csv, it 

can be read directly into Excel, but if 

the filename extension is not changed, 

the import dialog will guide user 

through the steps. 

(b) Five columns will appear. 

(c) The column with increasing sequential 

numbers is the one from which random 

samples will be chosen. 

(2) Create a column of random numbers.

(a) In a sixth column, type “random_

number” in the first cell.

(b) In Excel: Type and paste to all cells the 

function RAND()

(i) Because this number changes each 

time it is pasted, it must be captured 

once.

1. Once all cells have had a random 

number generated, copy the entire 

column, paste special, and select 

“Paste Values Only.” 

a. The random numbers will 

become locked. 

(3) Create a column with the rank of the random 

numbers.

(a) In a seventh column, type “Rank” in the 

first cell.

(i) Copy and paste the rank formula into 

all cells.

1. In Excel: Use ANK(<cell>, <all 

cells>, 1) to sort ascendingly.

a. Or use “Insert/Function,” type

 Rank in the search window, and

 use the help wizard to select the

 correct cells.

b. Regardless of the method used

 for the first cell, the address for

 the range of cells against which

 to rank must be locked.

i. Put dollar signs ($) before

 the number components of the

 beginning and ending of 

 the range of cells for the rank 

 calculation. 

 Example: change 

 =RANK(F2,F2: F1786,1) to 

 =RANK(F2,F$2: F$1786,1).

(4) Determine how many validation boxes will 

be used for validation.

(a) Because the size of the change events 

is small and unknown, and the rate of 

change is unknown, it is difficult to 

know how many validation boxes are 

necessary. 

(b) Aim for a 5 percent sample. If there 

are 2,000 boxes in the spreadsheet, this 

would mean 100 boxes for sampling. 

(c) All boxes with a rank from the random 

number generation less than 101 will be 

used for validation. 

(5) Create an indicator for those boxes that will 

be used for validation.

(a) In a new column, type “Use_it” in the 

first cell.

(b) Insert a conditional formula into the 

next cell .

96 Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
i. In Excel: use the formula =IF(rank < 

101, 1, 0)

1. This will set the Use_for_

validation column to 1 if the box 

is to be used for validation, and 0 

otherwise. 

2. Copy the entire column and paste 

special, values only, to lock in the 

values. 

3. Delete all columns except for the 

column with unique IDs and the 

column “Use_it”

that was just created. 

(6) At this point, the file should have two 

columns – one for the unique identifier of 

the boxes, and one with the “use label.” 

(7) Save this file (maintain it in the .csv format).

(8) Close the file in Excel.

(a) This is important! ArcMap will not 

allow joins if the file is open in the 

spreadsheet program. 

(b) This Excel file will then be linked to the 

coverage, once the coverage has been 

imported into the geodatabase. 

4. Import the coverage of boxes into the feature dataset 

created in section IV.c. above in the geodatabase. 

a. Start ArcCatalog.

i. Navigate to the directory with the geodatabase 

created in section IV.c. above. 

(1) Expand the database to see the feature 

dataset associated with this change interval 

(i.e. “mora_96_02”). 

(a) Right-click on the feature dataset, and 

select “Import/Feature Class (single).

(b) Navigate to the polygon coverage 

just created in steps 1–3, and select 

the polygon-feature class from that 

coverage to import.

(c) For simplicity, name the Output Feature 

Class Name in a manner that allows 

identification of the park, the year of the 

change detection, and the fact that this 

coverage is of the validation boxes. 

b. In ArcMap, remove the stand-alone polygon coverage 

from display by right-clicking on it in the layers 

window, and then select Delete. This will prevent 

later confusion. 

c. In ArcMap, bring in the newly imported feature class.

5. Add new fields to this feature class.

a. See section IV, step 1.e. for how to add an item to a 

feature class (table 1).

i. Add these items and types: 

6. Then join this feature class to the Excel file indicating 

which polygons should be used for validation.

a. In ArcMap:

i. Join the .csv data back to the original boxes.

ii. Right-click on the polygon coverage of the 1.5 

km boxes, and select “Joins and relates/Join.”

(1) The Join Data dialog will pop up (fig. 5).

iii. Choosing answers to the three fields needed.

(1) The field in this layer that the join will be 

based on is the one with unique IDs for each 

box. This is the column whose values were 

calculated in step 3.b. above. 

(2) The table to join to this layer is the 

spreadsheet just created. 

(3) The field in the table should be the same 

name as in the first field, since it was created 

from the layer 

in part 1. 

Figure 5. Join data dialog window in ArcMap.

SOP 4. Validation of Change-Detection Products   97
b. After this join, the column with the label “Use It” 

will contain ones and zeros. The feature class will 

then be subset so that only the polygons with a use-it 

value of 1 remain.

i. In ArcToolbox, select Analysis Tools/Extract/

Select.

ii. The input feature is the feature class on which 

the join was just done.

iii. The output feature class is the name of the 

layer that will be used to do the actual S2S 

interpretation. Name it with the park, the year of 

the change interpretation, and “S2S” to indicate 

that it is for the S2S.

iv. Use the Expression to select only the polygons 

that have a Use It value of 1.

(1) Click on the button with the letters 

“SQL” on it. In the expression builder, 

select the “Use It” column by doubleclicking (to make it appear in the 

expression window in the bottom half 

of the window), then add “= 1” after it. 

Click OK. 

(2) This will create a new layer with only 

the randomly selected polygons. 

c. Load this layer into ArcMap to ensure that only 

a subset of polygons remains. (See fig. 6 for an 

example at MORA.)

d. Indicate in each box the baseline condition of No 

Change (S2S type 1).

i. Enable editing on the layer just created (Editor/

Enable Editing). 

ii. Open the attribute editor for the layer.

(1) Click on the first cell in the “Change_type” 

column, which currently should have the 

<Null> value. 

(a) Type in the number 1 in this cell. 

(b) Use the down arrow to select the next 

cell down, and type a 1. Do this for all 

of the boxes. This sets the background 

value of 1 to be no-change for all of the 

boxes that will be interpreted. 

Method 2: Selecting Validation Boxes Based on 

Airphoto Availability 

If airphotos are available and are to be used later to 

validate the S2S process, then S2S must be conducted at 

locations where airphotos can be used for such a validation. 

1. Determine candidate airphoto pair.

a. If airphotos are to be used to validate the S2S 

process, airphotos from the same areas must be 

available in both the early and late image dates. 

Because of missing airphotos, difficulty matching 

flightlines, etc., this is not possible for every single 

photo in a flightline. 

b. Determining the appropriate set of airphotos will be 

highly dependent on the particular airphotos available 

at a given park, and thus cannot be written in specific 

detail. Rather, we present here a sketch of the typical 

steps. 

c. Locate the center points of all airphotos in both years 

(early year and late year). 

i. This already has been done for most parks, as 

digitized flightlines are available for most current 

airphoto acquisitions. If not, photo-center points 

must be digitized by hand into a point coverage 

where the airphoto name and unique identifier on 

the photo itself are stored as one of the fields in 

the point-coverage database. 

d. Methodically search through all of the airphotos in 

1 year and determine which photos in the prior year 

cover the same area. These will be candidates from 

which to draw samples for S2S validation. 

i. The airphoto validation of the S2S process will 

be matched to the size of the S2S validation 

boxes, which are 1.5 km on a side. This size was 

chosen to capture a large enough area on most 

1:12,000 airphotos, but not so large as to require 

sampling on the edges of individual photos, 

where distortions are greatest. 

Figure 6. An ArcMap session showing the distribution of 

randomly selected validation boxes.

98  Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
ii. Therefore, when looking for overlap areas 

between years, prioritize areas of overlap that are 

nearer to the centers of photos from at least one 

of the 2 years. 

(1) It likely will be useful to produce a mylar 

overlay with a 1.5-km box on it for 

reference. It typically is easiest to draw a 

box in a graphics program where the size 

of the box in centimeters can be specified, 

print the box on a piece of paper, then use a 

photocopier to copy the box onto a mylar or 

overhead transparency sheet. 

(2) The size of the box will be determined by 

the scale of the airphotos. For a 1:12,000 

airphoto set, the length of 1,500 m on 

the ground corresponds to a line 12.5 cm 

long, or a box 12.5 cm on a side. Note that 

the scale of a given photo will be slightly 

different than the desired scale of the 

acquisition for the entire photoset, since 

the elevation under the plane will change. 

This will be handled later in the image 

interpretation, so the point of the mylar 

box is simply to provide a guide for the 

approximate area being interpreted later. 

iii. If at all possible, stereo viewing will be used 

for validation later, so it is best in this process 

to consider areas where stereo viewing can be 

achieved in both image dates. This requires that 

two sequential airphotos along a flightline are 

needed for an area to be a candidate for selection. 

2. Develop a spreadsheet of possible airphoto-candidate 

pairs. 

a. Each area determined in step 1 will occupy a single 

row in the spreadsheet. For each of the two airphoto 

years, keep two columns to list the two photo IDs that 

will be used for the stereo interpretation of that year. 

b. It may be advantageous to include another column 

that will allow this area to be found easily again 

on the landscape, because once the photo areas are 

subsetted, 1.5-km boxes will be drawn manually for 

the area of overlap. Thus, the area used to determine 

possible candidate-airphoto pairs should be stored in 

some manner. 

i. The flightline information from each photo may 

be sufficient.

ii. Or the X and Y coordinates of the upper-left 

corner of the 1.5-km box may be useful as well. 

To do this, the airphotos would need to be on 

a Geo-referenced dataset, such as one of the 

tasseled-cap images develop in SOP 2. 

(1) Open the tasseled-cap image.

(2) Open the digitized flightline.

(3) Use the flightline information to find the 

point on the landscape corresponding to the 

candidate area, and then use the cursor to 

note the X and Y coordinates of the upperleft corner of the box. 

(4) Store the X and Y coordinates in columns 

for that purpose. 

3. Determine how many areas can be photointerpreted under 

budget considerations. 

a. Again, with no sense for the rate of disturbance, 

it is impossible to know how many samples will 

be needed to characterize the accuracy of the S2S 

process. More is always better, of course, and if 

only 1 change is captured per validation box, then 

a sample smaller than 30 likely will be inadequate 

for anything but the coarsest estimates of error 

rate. In determining this number, recall that only a 

small number of plots on any given airphoto will be 

photointerpreted, so the time per photo is relatively 

small. 

b. A sample covering approximately 5 to 10 percent of 

the park is an excellent target. 

4. From the spreadsheet, use the random number generation 

and ranking process described in section 1 to pick 

a random subset of the candidate airphoto pairs for 

validation. 

a. In the spreadsheet developed in step 2 in this section, 

follow the sequence in method 1, steps 3c.(ii) above. 

5. Create 1.5-km boxes for the validation areas.

a. Because the grid of boxes is not available as it was in 

section 1 above, each 1.5-km box must be drawn in a 

new feature class in the geodatabase.

i. Create a new feature class in the S2S feature 

dataset. 

(1) The S2S feature dataset was created in 

section IV.1.c.

(2) Follow the steps for creating a new S2S 

feature class within this dataset.

(3) Name this feature class with components of 

the name that indicate the park, S2S, and the 

years for which change is occurring. This 

feature class will first be used to conduct the 

standard S2S interpretation, and then a copy 

will be made for later interpretation with 

airphotos. 

SOP 4. Validation of Change-Detection Products   99
Table 2. Satellite-to-satellite interpretation change 

classes.

Satellite-tosatellite code Description

1 No change.

2 Water to rock/soil.

3 Water to partial vegetation cover.

4 Water to complete vegetation cover.

5 Rock/soil to water.

6 Partial vegetation to water.

7 Complete vegetation to water.

8 Increase in broadleaf.

9 Increase in conifer.

10 Increase in vegetation.

11 Decrease in broadleaf.

12 Decrease in conifer.

13 Decrease in vegetation.

14 Increase in snow.

15 Decrease in snow.

(4) When creating the feature class, add the 

same 7 fields that are listed in table 1

above (in method 1). 

b. Bring a single date tasseled-cap image into ArcMap 

for reference. 

c. Bring the new feature class into the ArcMap session 

and make it editable. 

i. Select the Editor pulldown menu from the 

ArcMap session (see fig. 7), and select “Enable 

Editing.” Ensure that the new feature class 

is selected as active in the layers list before 

enabling editing (i.e. its name is in the “Target:” 

window).

d. For each area selected for validation, do the 

following:

i. Draw a 1.5-km box to delineate the interpretation 

area. As noted above, because these areas must 

be found from the spreadsheet of candidate areas, 

it is useful that those candidate areas have a tag 

or a coordinate pair that allows easy location.

(1) To draw the box, ensure that the “Task:” 

window shows “Create new feature.” 

(2) Click on the Pencil icon for sketching. 

(3) Left-click on the image to position the first 

vertex of the box that will define the first 

corner.

Table 1. Fields to add to feature classes used 

in satellite-to-satellite interpretation.

Field name Field type

Change_type Short integer

Dist_agent Short integer

Certscore_change Short integer

Certscore_dist Short integer

Box_id Short integer

Event_id Long integer

Comments Text

Interpreter Text

(4) Click on the first point of the box. Rightclick and select Distance and Direction 

fields to ensure a square box with lengths of 

exactly 1,500 m. 

(a) Tip: When the third vertex is drawn, 

right-click and select ‘Square and 

Finish’ to draw the fourth vertex. 

(5) Open the Attribute window for this layer (if 

not already open), and find the box that was 

just created (its row will be highlighted). 

(a) Click on the cell corresponding to the 

“change_type” column. Type in the 

number 1 in this box, indicating that 

the baseline condition of the box is nochange (see S2S codes in table 2). 

ii. Save after each box is coded. 

iii. At the end of this process, all of the boxes 

that will be used for S2S validation and the 

subsequent airphoto validation will be digitized 

and saved into the feature class within the 

accuracy assessment geodatabase. 

Figure 7. ArcMap interface, with a red oval indicating the area with 

editing options referred to in this section. 

100 Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
VI. Satellite to Satellite 

(S2S) Interpretation of 

Changes

Once validation boxes have been 

located and entered into the geodatabase, 

interpretation of changes can begin. S2S 

interpretation is based on the principles 

applied to airphoto interpretation. The 

human brain brings in spatial context to 

help interpret patterns of shape, color, 

and text to determine what is happening 

in an image. 

The differences between aerial 

photo and satellite interpretation are the 

scale of the analysis and the spectral 

depth of the respective image types. In 

high-resolution aerial photos, individual 

elements on a landscape (i.e. trees, 

small patches of rock, or small streams) 

are interpretable at a scale familiar to a 

human. In satellite imagery at the scale 

of Landsat TM/ETM+, such detailed 

features individually are not discernible. 

This requires some degree of retraining 

for interpretation. Satellite imagery 

provides an important advantage over 

aerial photos in its increased spectral 

depth. Landsat imagery provides spectral 

information about the surface in the 

near infrared and midinfrared regions, 

both of which are extremely useful for 

distinguishing different cover types. The 

tasseled-cap transformation captures the 

variation in the six visible, near-infrared, 

and midinfrared bands of TM with just 

three axes, allowing display on standard 

The S2S process also requires that the interpreter 

understand how to interpret spectral patterns in images of the 

mathematical differences between two tasseled-cap images. 

Table 3 provides an interpretation key for difference imagery. 

This assumes that the image is displayed in a viewer with 

brightness in red, greenness in green, and wetness in blue. See 

SOP 2 if this has not been set as the default for viewing. 

The interpretations in table 3 provide a first 

approximation of the likely change that has occurred. The 

interpreter must then apply knowledge of the conditions 

before and after the change (using direct interpretation of the 

tasseled-cap images), knowledge of the ecosystem, and spatial 

context to further hone the interpretation. Finally, reference to 

a single-date, high-resolution digital orthoquad can improve 

understanding of the potential land-use condition in which 

Table 3. Interpreting tasseled-cap difference images when early image is subtracted 

from late image.

Color

Direction of tassledcap change: 

early to later

Interpretation

Grey No change No change

Bright red Increase in 

brightness

Dramatic loss in vegetation, either because of 

disturbance or because of phenology; OR presence 

of cloud in later date.

Dark red Subtle increase in 

brightness

Often associated with loss of vegetation from areas 

that were very bright and green at early date—a 

good example is a disturbance in shrub field or 

young hardwood/conifer plantation

Light blue 

to white

Increase in all 

bands, wetness 

especially

Increase in vegetation, especially conifer. Common 

in clearcuts that were vegetated partially in early 

date and which are increasing in HW and conifer; 

OR an increase in snow from a relatively vegetated 

starting point; OR presence of cloud in early date 

and conifer in later date.

Dark blue Increase in wetness, 

potential decrease 

in brightness

Increase in conifer percent cover. Common in 

clearcuts transitioning from hardwood or shrub 

dominance to conifer dominance; OR increase 

in shading in later date (take into account date of 

image); OR presence of cloud in later date; OR 

increase in water (channel changes).

Bright 

yellow

Increase in 

brightness and 

greenness

Typically, increase in broadleaf component, either 

shrub, grass or hardwood tree, caused either by 

recovery from disturbance or by phenology; OR 

presence of cloud shadow in the early date and 

vegetation in the later date.

Bright green Increase in 

greenness

Increase shrub/grass/hardwood cover from a 

condition that was bright before, but not green—

typically found when river beds come back to 

vegetation, but also common where snow was 

present before and now is gone. 

digital monitors in the three color guns (red, green, and blue). 

Color-infrared photography, when available, could provide 

insight into the near-infrared reflectance of the surface, but 

not typically into the midinfrared reflectance. The added 

spectral depth and interpretability afforded by the tasseledcap transformation of TM/ETM+ imagery requires further 

training to interpret, but increases the ability of an interpreter 

to describe conditions. 

Because of the need for training, the authors have 

provided an image library to be used in conjunction with the 

S2S interpretation process. Before S2S interpretation begins, 

interpreters should use this library to gain familiarity with 

the main cover types and their representations in tasseled-cap 

space. The library associated with this version of the protocol 

is “NCCN_Image_library_v1.0.ppt”.

SOP 4. Validation of Change-Detection Products 101
Table 4. Satellite-to-satellite disburbance agents.

Satellite-to-satellite 

disturbance

agent code

Agent

1 Landslide.

2 Avalanche chute.

3 Fire.

4 Insect.

5 Fire or insect.

6 River disturbance.

7 Clearcut.

8 Unknown.

Table 5. Certainty scoring for change classes.

[Score is assigned by the relative degree of agreement with the conditions 

statement, with zero indicating that the condition is not found. Change call:

Total certainty score ranges from 0 to 4; Disturbance agent: Total certainty 

score ranges from 0 to 3]

Condition Certainty points

Change call

Spectral change vector is distinct from change 

vector of similar starting types in surrounding 

area

0, 1, or 2

Area of spectral change is large and consistent 

within “patch”

0 or 1

Spectral condition of endpoints is interpretable 

and consistent with change call

0 or 1

Disturbance agent

Shape is consistent with disturbance agent 0 or 1

Size is consistent with disturbance agent 0 or 1

Landscape position and context are consistent 

with disturbance agent

0 or 1

interpretation is occurring. A final change call is made and 

categorized into one of 15 categories, listed in table 2. This 

table can be printed out separately to provide an easy reference 

when onscreen editing is done. 

In addition to determining the change class, the 

interpreter makes a call about the disturbance agent (table 4). 

This is drawn from the combination of the spatial context and 

the observed spectral change. 

In assignment of both change class and disturbance agent, 

the interpreter may have varying degrees of certainty. Degrees 

of certainty can be quantified using simple rules that describe 

the elements of the decision-making process when a change 

call and disturbance agent are identified. Table 5 lists the 

conditions and scoring for certainty scores in the change calls 

and the disturbance agents. 

A variety of factors could lead to false positives in the 

automated change algorithms, and these must be filtered 

out by the interpreter. To achieve this, the interpreter must 

be aware of the factors that can lead to false positives in the 

automated algorithms. The following is a partial list of four 

major effects that the interpreter can take into consideration 

when evaluating the spatial patterns of spectral change. 

Geometric Misregistration

Even with excellent image-wide geometric registration 

of two images, local distortions can remain. These are 

mostly inconsequential, but if they occur at a boundary 

with sharply contrasting spectral types, even a small 

geometric shift can result in large apparent shifts in 

spectral value. Thus, misregistration will manifest 

itself most strongly around small patches and linear 

features that contrast with their surrounding matrix 

(i.e. avalanche chutes, rivers, or small openings 

in the forest). A difference image will show that 

large change has occurred, but examination of the 

two source images separately will show that the 

shape and color of the area has not changed. This 

indicates that misregistration has caused the spectral 

change. Another clue comes from the shape of the 

apparent spectral change in the difference image. If 

the difference is only at the margins of a small patch, 

and the complementary spectral change occurs on 

the other side of the patch, then misregistration also 

is implicated. In cases where it is unclear whether 

misregistration plays a role, indicate the change type 

but give a correspondingly low confidence score. 

Illumination Differences:

After the summer solstice, the sun is lower at the time 

of each successive Landsat image acquisition, so the 

shadows get longer. Landsat images are acquired at 

approximately10:30 a.m. local sun time, meaning that 

the sun is approximately in the southeast, although 

the azimuth of the sun also varies with the seasons. 

The illumination variability caused by the different 

angles of the sun is sometimes subtle, but can cause 

some shadowing effects on the northwest aspects of 

slopes. For example, a small opening in the forest in 

1 year may appear to go away in another year, simply 

because the patch has fallen into shadow in the latter 

date. This can happen even with a difference of just 

a week or 10 days in the date of image acquisition, 

especially as days change more rapidly in late August 

and September. If it seems likely that shadow may have 

caused difference, do not include it.

102 Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
The decision is not always straightforward. 

Illumination will cause bulk changes in reflectance for 

entire hillsides, but so can other effects. For example: 

Insect infestation can affect entire hillsides in forested 

areas. 

If it is difficult to tell, label the change, but score 

the certainty appropriately. In this case, because the 

spectral condition is not interpretable easily and 

because the signal may or may not be similar to other 

conditions, table 5 would indicate a certainty score of 

2. 

Year-to-Year Phenology Changes

Because the date at which cloud-free imagery can 

be acquired varies from year to year, and because of 

climatic variations between years, the phenological 

state of deciduous vegetation often varies from image 

to image. This is true especially at the alpine/subalpine 

interface, where snowpack duration drives much of 

the timing from year to year. Changes in phenological 

state will have spectral manifestations that, although 

corresponding to an actual change in the state of the 

vegetation, do not represent a change in cover type 

or quality that is of interest in this work. Automated 

spectral change-detection methods will be fooled by 

this spectral change, but a human interpreter can often 

account for such phenological change by comparing 

the spectral changes in deciduous vegetation in one 

location with the overall spectral changes of similar 

vegetation in other similar parts of the landscape. 

If spectral changes are definitely associated with 

phenology, change should not be labeled. If the 

change call is difficult, the certainty score should 

be adjusted accordingly. If all of the vegetation in a 

given type is experiencing similar changes, then the 

first change-call condition in table 5 is not met, so the 

maximum certainty score is 2, with likely lower scores 

because the other conditions are not met. 

Problems With Radiometric Normalization

Radiometric normalization with the MADCAL 

algorithm appears to be quite robust, but no automated 

normalization process is perfect. The magnitude of 

errors in the fit between the two image years often 

scales with the brightness of the target. Thus, small 

errors in radiometric normalization can lead to spectral 

differences that are quite evident in the difference 

imagery used for interpretation. When the two endpoint images also are referenced, however, there is 

often no apparent difference in the condition, because 

each image is displayed according to its own display 

scaling equation that compensates for the scaled error. 

Thus, it is often possible to rule out the apparent 

changes that are caused by radiometric error. 

VII. Imagery Setup and Satellite-toSatellite Interpretation Process

The actual setting up of imagery and S2S interpretation 

process is as follows:

1. Create a tasseled-cap difference image for interpretation.

a. In ERDAS Imagine, do the following steps:

i. From the main icon panel, click the Image 

Interpreter button. 

(1) This is the icon with the magnifying glass 

over a small raster box.

ii. Select Utilities/Operators

(1) The Two Input Operators dialog window 

will pop up (fig. 8)

(2) The “Input File #1:” is the LATER date 

image (the changed image).

(a) Use the entire study area, tasseled-cap 

image used in SOP 3 for the change 

detection (not the aspect-subsetted 

image). 

Figure 8. Window used to create difference images. 

SOP 4. Validation of Change-Detection Products 103
(3) “Input File #2” is the EARLY date image 

(the baseline image).

(4) The output file should be named to indicate 

both parent images, and should include the 

word “minus” in it, to clearly indicate that it 

is a difference image.

(a) This image is the “Difference Image” 

referred to subsequently in this section. 

(5) Change the “Operator:” to the minus sign.

(6) Change the output type to Signed 16-bit.

(7) Click OK. 

2. All of the interpretation is done in ArcMap. 

a. Ensure that the following layers are present in the 

ArcMap project.

i. The baseline tasseled-cap image (the baseline 

image was defined in SOP 3). 

ii. The changed tasseled-cap image (the image from 

the later year). 

iii. The difference image. 

iv. A DOQ specific for the area being interpreted. 

(1) Install the Terraserver add-in to ArcMap.

(a) Terraserver provides free access to 

digital orthoquads that will be used to 

aid the interpreter to understand landuse 

in an area.

(i) If Terraserver is not already installed 

in ArcMap, see information at the 

ESRI web site for instructions. 

(ii) Generally, the steps for ArcGIS 9.0 

are as follows:

1. At the ESRI support website 

(support.esri.com), click on 

downloads and search for

Terraserver.

2. Download and install according to 

ESRI instructions

3. This will involve the .NET 

architecture, which, if not already 

installed, also will 

need to be installed. 

4. Once Terraserver is installed, it 

can be viewed within ArcMap 

using View/Toolbars/Terraserver.

v. The feature class that holds the validation boxes. 

(1) Select this layer and then enable editing on 

this layer, if not done already.

(a) Select Editor/Enable editing.

b. Begin with the first validation box where 

interpretation is to begin (created either by method 1 

or 2). It likely is that a systematic interpretation from 

top to bottom or left to right in the study area will be 

easiest. 

i. Open the attribute table in the feature class that 

holds the validation boxes.

(1) Locate the current validation box in the 

attribute table.

(a) The selection tool in ArcMap (a small 

white arrow next to a square with three 

small polygons inside) can be used to 

click and drag over the area of the box, 

selecting it. 

(2) Fill-in the information on the box itself.

(a) Label the box with a unique numerical 

sequential ID, beginning with 1.

(b) There is no event ID for the box, which 

is no-change. 

(c) Assign a change type of 1 (no-change; 

see table 2) by typing directly into the 

“change_type” cell for the row of this 

box, if the number is not already there.

(d) Assign a certainty score of 4. 

(i) This sets the background to the nochange value. 

c. With the difference image as the visible layer 

(checkmark in the Layers window), methodically 

work through the entire 1.5 km by 1.5 km box 

looking for spectral evidence of change. 

i. In the difference image, no-change is shown with 

grey tones. 

ii. Bright and colored areas in the difference image 

indicate large spectral change between years. 

Examine each patch of such potential change. 

(1) Zoom into the area with the potential 

change. 

(2) Turn off the difference image by unchecking 

the box. Note which of the two tasseled-cap 

images is now visible – it will be the image 

that is higher up in the list of the layers. 

(3) Then turnoff that image by checking its box 

104  Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
off, and flicker between the two tasseled-cap 

images by checking and unchecking that 

box. 

(4) Evaluate whether the change corresponds 

to real change, or is caused likely by one 

of the confounding factors listed above 

(geometric misregistration, illumination 

difference, year-to-year phenology change, 

or radiometric-normalization problems). 

iii. Ultimately, the interpretation of change must 

occur by viewing the before and after images, 

not the difference image. The difference image 

is useful as a means of focusing on areas that 

may have changed, but because of the many 

possible ways that spectral change may occur, it 

cannot be used to make the change call. 

iv. Disturbance agents (tables 4 and 5) are 

interpreted using spatial context and the shape 

of the area experiencing change. Note that S2S 

classes that involve an increase in vegetation 

amount are not assigned a disturbance agent, 

under the assumption that a disturbance agent 

is used only to describe agents that remove or 

diminish vegetation. 

v. If change has occurred, digitize it on the screen.

(1) Select the pencil icon from the ArcMap icon 

panel just to the right of the Editor pulldown 

menu. 

(2) Use the pencil to draw the vertices of a 

polygon that captures, as precisely as 

possible, the feature that has changed.

(a) These features will be later turned 

into a raster layer. Therefore, the 

polygon boundary should be drawn to 

incorporate all pixels where change has 

occured. 

(3) Double-click on the last vertex. 

vi. The new feature will appear in the attribute table 

for the feature class.

(1) Enter the box number in the box cell. The 

box number is the same for all changes that 

occur within a validation box. 

(a) If method 1 was used to create boxes, 

then the box number can be taken 

from the Object ID of the layer. If 

method 2 was used, then box numbers 

are assigned sequentially as they are 

interpreted. 

(2) The event number is the number of the 

change event occurring within the box. 

Restart this numbering for each new 

validation box. Thus, each box with a 

change event will have a polygon with the 

box number and an event ID of 1. 

(3) Type in the change type, disturbance agent, 

and certainty scores in each of the boxes. 

(4) Type in comments on the change call. In 

particular, reasons for low certainty scores 

are quite useful for later interpretations. 

vii. In the Editor pulldown menu, select “Save Edits” 

before moving on to the next change. 

d. Repeat this process for all of the validation boxes. 

e. Select “Edit/Stop Editing” to stop editing the feature 

class. 

VIII. Converting S2S Polygons to 

Raster

The polygons digitized in section VII will be converted to 

raster layers so that accuracy assessment can be run. 

1. In ArcToolbox, select “Conversion Tools/To Raster/

Feature to Raster.”

a. The input feature is the feature class that was just 

populated with S2S interpretation calls.

b. The output location is the folder where the S2S 

interpretation exists. This typically should be in the 

same folder that holds the parent database. 

c. The name of the output grid should identify the park, 

the years, and the fact that this is an S2S product. 

The total name of grids must be 13 characters or 

fewer. 

i. Example: mora96_02_s2s

d. Select the cell size to be 25 m. This will match with 

the pixel size of the imagery. 

e. The value for the grid is the change type. 

SOP 4. Validation of Change-Detection Products 105
VIX. Accuracy Assessment of Change 

Detection

The detection of change is the first step in monitoring. 

Therefore, the first need for accuracy assessment is 

determining whether the automated change-detection 

algorithms from SOP 3 separate areas that have changed from 

a background of areas that have not changed. To assess this 

accuracy, both the S2S and the change images from SOP 3

must be collapsed into a change/no-change state, and then a 

standard accuracy assessment performed. 

From the population of cells in the gridded S2S change 

coverage, a sample of small, 2 by 2 pixel plots will be used 

to compare the S2S calls to the change product produced in 

SOP 3. The sampling process will be conducted in ERDAS 

Imagine. 

1. First, the grid must be imported into Imagine.

a. Click on the Import icon in the main Imagine icon 

panel.

b. Select the type to be “Grid” from the pulldown list.

c. Navigate to the folder where the grid from section 

VIII was placed in both the input and output-file 

areas.

d. Select the grid from the list for the input file.

i. Imagine automatically will fill-in the name of 

the output file. NOTE: This is the same as the 

folder that houses the original grid, so it actually 

will change folders without warning. Manually 

navigate backup one folder level and manually 

type in the name of the output file, including 

the .img, to avoid this automatic change of 

directories. 

e. Click OK.

i. In the Import Grid window, select “Import 

Options,” and change the Output Data 

Compression to “Run Length Compression.”

(1) This will greatly reduce the size of the file, 

since most of the file is background zero 

area that can be represented with small file 

size. 

ii. Change the output-data type to unsigned 8-bit.

(1) This will allow the image to be viewed 

as a thematic layer in Imagine, which is 

necessary for Imagine’s accuracy assessment 

tool. 

iii. Click OK in the Import Options window.

iv. In the Import Grid window, click OK to start the 

import. 

2. Change the type of the image from continuous to thematic

a. Open the file in a viewer.

b. In the viewer, click the ImageInfo button to bring up 

an ImageInfo button.

c. Select Edit/Compute statistics.

i. Change the skip factor to 1 for both X and Y

ii. Click OK.

d. Once the statistics have been calculated, the layer can 

be changed to a thematic type.

i. Select Edit/Change Layer Type. 

ii. Confirm that the “Type:” field in the Layer Info 

section of the ImageInfo window now reads 

“Thematic”.

iii. Close the ImageInfo window. 

3. Recode all of the change categories into one of two 

categories: no-change or change.

a. Save a copy of the thematic image under a new name.

i. In the viewer, select File/Save Top Layer As, 

and save it under the same base name, but add 

“_chnoch” in the file name.

b. In the viewer of the change/no change file, select 

“Raster/Recode.”

i. For all rows 2 through 15, change the New value 

to 2.

(1) Change is now indicated with the number 2. 

No-change has a value of 1, and areas that 

were not interpreted remain 0. 

c. In the Viewer, select “File/Save Top Layer,” and 

agree when it asks whether the file should be saved 

since it will change pixel values. 

d. Then reopen the file, using the file open dialog, to 

force Imagine to recognize the change. 

4. Open the change/no-change image in an Accuracy 

Assessment window.

a. From the main icon panel, select the “Classifier” 

icon, then “Accuracy Assessment” from the pulldown 

menu.

i. The Accuracy Assessment window will pop-up 

(fig. 9).

b. Select File/Open, and open the classified image file 

of the change_types that was just created above. 

i. No obvious change will take place. 

106 Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
5. Select validation points.

a. Set-up the options. 

i. In the Accuracy Assessment window, select 

“Edit/Class Value Assignment Options.”

ii. The Assignment Options window will appear 

(fig. 10). 

iii. Retain the window size of 3.

iv. Change the Window Majority Rule to “Majority 

Threshold” and set the “Threshold:” to 4 (as 

shown in fig. 10). This defines the minimum 

number of pixels within the 3 by 3 window 

needed to achieve “majority” class status. By 

setting to 4, only 4 of 9 pixels need be the same 

for a potential accuracy assessment plot to be 

labeled and used. 

b. Have Imagine find the points.

i. Add in the Accuracy Assessment window, select 

“Edit/Add Random Points.”

ii. The “Add Random Points” interface will pop up. 

(1) The number of points should be as large as 

possible. It will be guided by the number 

of change events captured in the S2S 

interpretation above. The goal is a balanced 

sample of change and no-change classes. 

If there are relatively few areas that have 

changed, there will simply not be much 

“room” to place change samples. 

(a) Select the largest number of points 

that reasonably will fit within the S2S 

interpreted change areas. 

(2) Change the distribution parameters to 

“Equalized Random.”

(3) Click the “Select Classes” button. 

(a) A Raster Editor will appear. Click and 

drag on the rows for values 1 and 2 (not 

value 0) to select only the change and 

no-change classes for picking random 

points.

(4) The routine will search for points. If it runs 

out of trials before the desired number of 

points is reached, it will ask if more runs 

should be done. Repeat this process until 

the desired number of points is run, or until 

the increase in points per run is negligible. 

c. Display the class values for the random points.

i. In the Accuracy Assessment window, select 

“Edit/Show Class Values.”

d. Display the points on the images.

i. In the Accuracy Assessment window, select 

“View/Select Viewer.”

(1) Click in the viewer with the change/nochange image in it. 

(2) The points should appear as a set of labeled 

crosshairs. 

e. Export the points to a file.

i. In the Accuracy Assessment tool, select all of the 

rows.

Figure 9. Accuracy assessment window in Imagine.

Figure 10. Class-value assignment 

options window. 

SOP 4. Validation of Change-Detection Products 107
(1) Right-click on any of the greyed-rownumber boxes on the left of the table, and 

select “Select All.” All of the rows should 

be highlighted (in yellow unless the user has 

changed preferences). 

ii. In the Accuracy Assessment tool, select all of the 

columns.

(1) Click and drag across all five columns 

(Name, X, Y, Class, Reference). Now the 

entire matrix should be highlighted in a new 

color. 

iii. Export the points.

(1) Right-click anywhere in the column-headers 

and select “Export.”

(2) Change the format to comma-delimited.

(a) Click on the Options button and change 

the separator character to “comma”.

(3) Save the points in the same folder where the 

accuracy assessment is being conducted.

(a) Name the file with the park, year of 

change, and include a component of the 

filename “_s2s_validationpts”.

iv. Change the extension of the file from .dat to .csv

(1) Do this in the Windows Explorer file. 

Single-click on the filename to highlight it, 

click the F2 button, and then manually type 

in “csv” instead of “dat”.

f. Make a copy of the file with the coordinates only.

i. Users will need a version of the file with the 

coordinates to extract the values from the change 

image. 

ii. In the Windows file explorer, single-click on the 

filename, right-click and select “copy.” Click 

elsewhere in the Windows folder and paste it. 

(1) Rename the file (in the same manner that it 

was changed to .csv in the previous step), 

adding in “_xyonly_” to the filename. 

iii. Open the “_xyonly_” file in Excel or a similar 

spreadsheet.

iv. Select and delete all of the columns except the X 

and Y coordinates.

v. Save the file.

vi. In the Windows Explorer, change the filename 

extension on the _xyonly_ file to “.txt” instead of 

“.csv”, now that it has been changed. This will 

allow Imagine to read it. 

g. CLOSE the Accuracy Assessment window (File/

Close). 

i. This is necessary to get a fresh start on the 

difference image.

6. Convert the change image created in SOP 3 into a change/

no-change image.

a. The change/no-change image from S2S will be used 

to assess how well the product from SOP 3 detects 

change. To compare like products, the product from 

SOP 3 must be collapsed to a change/no-change 

image. 

i. Follow steps 2 and 3 above, only apply them to 

the classified change image from SOP 3. 

ii. This image will be referred to as the “SOP 3

change/no-change image”

7. Extract change calls from SOP 3 change/no-change 

image. 

a. Open the SOP 3 change/no-change image in a new 

viewer in Imagine. 

b. Open the Accuracy Assessment tool. 

c. Select “File/Open” and select the SOP 3 change/nochange image. 

d. Select “Edit/Import User Defined Points.”

i. Navigate to the _xyonly_ file created in step 

5 above. Once selected the “Import Options” 

window will appear (fig. 11). 

ii Change the “Separator Character:” to a comma. 

iii Imagine will then import the points and add its 

own IDs. 

(1) Confirm that the IDs assigned automatically 

here are the same as those already saved in 

the “s2s_validationpts.csv” file from step 

5.e. above. Check a few random points 

through the list in both files and ensure that 

the X and Y coordinates are the same for a 

given ID number. 

(a) If not, this indicates an error in the 

steps above. Repeat the process and 

make sure not to resort or otherwise 

manipulate the points in the spreadsheet 

or the cell arrays in Imagine. 

e. Extract the values from the SOP 3 change/no-change 

image.

i. Change the class assignment options

(1) Select “Edit/Class Value Assignment 

Options.”

108  Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
(a) Change the majority rule to 4 pixels. 

ii. Extract the points.

(1) Select “Edit/Show Class Values.”

8. Copy the S2S derived values for change/no-change into 

the Accuracy Assessment tool.

a. In the Excel spreadsheet showing the “validationpts.

csv” file (step 5.e. above), select and copy the entire 

column of change/no-change values that were 

extracted from the S2S image. 

b. In the Accuracy Assessment tool, left-click on the 

column labeled “Reference,” then right-click and 

select “Paste.” The column should be filled with the 

values just copied from the Excel spreadsheet. 

9. Develop an accuracy assessment report.

a. In the Accuracy Assessment tool, select “Report/

Accuracy Report.”

b. Imagine will produce an accuracy assessment text 

file. 

c. This report will include standard accuracy assessment 

metrics: Users Accuracy, Producers Accuracy, and 

the Kappa Statistic. 

i. See the following two references for information 

on interpreting and reporting accuracy 

assessments of classified maps: Congalton and 

Green (1999) and Foody (2002).

X. Accuracy Assessment of Change 

Labeling

Ideally, the process in SOP 3 will not only detect change 

well, but will label it correctly. The process of determining 

accuracy for the change labels is identical to the process 

in section IX, except that the full 15-class S2S and change 

products will be used. 

Often, many of the 14 classes of change will have few–if 

any–examples. Thus, the accuracy-assessment matrix will be 

sparse, and the accuracy of some classes will be based on very 

few examples. These sparse classes must be interpreted with 

care. 

1. Close any viewers, Excel files, and accuracy assessment 

tools that are open, to prevent confusion. 

2. In a new viewer, open the S2S image imported in step 1 of 

section IX.

3. Open the Accuracy Assessment tool.

a. Select “Open” and find the S2S image from step 2. 

4. Select Edit/Import User Defined points.

a. Load the “_xyonly_” points from step 5.f. in section 

IX. 

b. Change the Options to a majority of 4 for class 

assignment (step 5.a. in section IX).

c. Select “Edit/Show Class Values.”

5. Export these points (with the class values from the full 

15-class S2S image now) to a new excel file, with a part 

of the file name “_s2svalidationpts.”

a. See step 5.e. in section IX for instructions. 

6. Close the Accuracy Assessment tool.

7. In a new viewer, open the SOP 3 change image (with all 

15 S2S classes). 

8. Open a new Accuracy Assessment tool.

a. Load the “_xyonly_” points again. 

b. Change the Options to a majority of 4 for class 

assignment (step 5.a. in section IX).

c. Select “Edit/show class values,”

i. The SOP 3 change image values for change are 

now in the Class column. 

d. For the Reference column, copy and paste the 

reference values from the “_s2svalidationpts” 

spreadsheet developed from step 5. 

9. Have Imagine conduct a new accuracy assessment. 

Again, note that the accuracy of classes with few points 

can be difficult to interpret because a single point labeled 

correctly or incorrectly plays a large proportionate role. 

Figure 11. Import options window for bringing coordinates into the 

accuracy assessment tool. 

SOP 4. Validation of Change-Detection Products 109
XI. Photo-Based Accuracy Assessment

If airphotos are available for both dates of the change 

interval, an airphoto interpretation is feasible under budget 

constraints by using the following steps. 

1. Overview of process:

a. Use the validation points developed in section IX as 

locations for airphoto validation. 

i. Save the points from section IX as an annotation 

layer.

(1) In the Accuracy Assessment tool linked 

to the S2S validation image, with the 

coordinates and IDs of the validation points 

visible in the cell array of the window, select 

“File/Save as Annotation.”

ii. Load this annotation layer into a viewer with the 

tasseled-cap image from either the early or later 

year of the change interval. 

iii. The validation points will appear on the image, 

along with their IDs, in the viewer. 

b. Visit each point in-turn, and record the photointerpreted condition of the change in a spreadsheet 

where each row corresponds to a single-validation 

point. 

i. Locate the points on the image, and then locate 

the appropriate photos for validation (both the 

early and later photos). Because the validation 

boxes were selected with method 2 (section 

V above), all of the boxes should be readily 

linked with an airphoto pair that can be used for 

validation. 

ii. The change calls that are being validated with 

the airphoto interpretation should follow the S2S 

change categories. See step 2 below for more 

information. 

c. Once the points have been assigned S2S-type calls 

from the airphoto interpretation, the column with 

the S2S-type calls can be copied and pasted into the 

Accuracy Assessment tool along with the same calls 

made from the satellite image (those loaded in step a. 

above). 

d. Accuracy assessment and reporting follows the 

approaches described in section IX. 

i. As described here, the accuracy statistics 

represent the assessment of the accuracy of 

the S2S interpretation, not the original change 

product. 

2. Suggested rules for photo interpretation.

a. For consistency with the S2S format, it is suggested 

that the same 15 classes of change and 8 classes of 

disturbance agent (tables 2 and 4, respectively) be 

determined from the airphoto interpretation. 

b. As with the S2S interpretation, it is not always 

possible to be certain about some types of change. 

In fact, the process of determining change with 

airphotos brings with it new uncertainties. Therefore, 

additional considerations must be taken into account. 

i. First, it is sometimes difficult to take a plot on 

an image and find its location on an aerial photo. 

This leads to some uncertainty in position, which 

can be scored. 

ii. Second, cover type and condition can be 

ambiguous based on tone alone, sometimes 

context is needed. Moreover, some changes in 

condition or quality can be inferred simply from 

ecological knowledge provided by the highresolution detail of the spatial context of change. 

But the certainty of type change versus condition 

change requires that the S2S change calls be 

separated into two broad categories: those that 

involve changes in land-cover type (water to 

vegetation) and those that involve change in 

land-cover quality (increase in proportions of 

broadleaf cover). Table 6 lists how the original 

15 classes of S2S change are classified according 

to type or quality. 

iii. Given this background, table 7 provides a set of 

rules for assigning certainty scores to changes 

interpreted from airphotos. 

Table 6. Classifying satellite-to-satellite interpretation change 

classes by type or quality change.

[–, not applicable]

Satellite-tosatellite code Description Type or 

quality?

1 No change –

2 Water to rock/soil Type

3 Water to partial vegetation cover Type

4 Water to complete vegetation cover Type

5 Rock/soil to water Type

6 Partial vegetation to water Type

7 Complete vegetation to water Type

8 Increase in broadleaf Quality

9 Increase in conifer Quality

10 Increase in vegetation Quality

11 Decrease in broadleaf Quality

12 Decrease in conifer Quality

13 Decrease in vegetation Quality

14 Increase in snow Type

15 Decrease in snow Type

110 Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
iv. For changes in cover percentages, grid-based 

photointerpretation of percent cover may be 

desired. The following methods are suggested:

(1) With reference to the scale of the 

aerial photos, develop square plots on 

transparencies. These plots should be small 

enough to capture a relatively homogeneous 

cover condition, usually <0.5 ha, but large 

enough to be located unambiguously on both 

imagery and photos. 

(a) The square plots should include a grid 

of small points at which cover type 

is photo interpreted. The number of 

grid points that can fit within a square 

depends on the scale of the photos. 

For 1:12,000 photos, a grid of 20 

points (arranged in a 5 by 4 matrix) is 

sufficient. 

(2) For each plot, use the 

transparency to label cover type 

at each of the grid points. 

(a) Cover types depend on the 

park, but should at least 

separate soil/rock, conifer, 

hardwood trees, shrubs and 

grass. Further distinction 

of type may be desired, 

but would be more than 

needed for the S2S change 

validation. 

(b) Record the total count of 

each cover type for each 

plot. 

(c) Do this for the early date 

photo and the late date 

photo. 

(d) Record these values in a 

spreadsheet for archiving. 

(e) Based on changes on 

cover type, ascribe the 

appropriate S2S change 

class call to the plot. 

v. After all photo-interpreted changes have 

been recorded, the accuracy assessment 

can be conducted to validate the S2S 

interpretation. 

3. As noted in the introduction to section V, if 

the aerial photos do not cover enough of the 

desired area, S2S interpretation may need 

to be applied both to areas where photos 

are available and to a larger area where the 

airphotos are not available. If this is the 

case, then S2S interpretation for the larger 

area serves as an estimate of the accuracy of 

the change product from SOP 3, while the 

smaller airphoto-interpretation set provides 

an insight into the accuracy of the S2S 

process. 

Table 7. Certainty scoring for airphoto-based change detection.

[Total score incorporates a score for location uncertainty (1–3) and for either type or quality]

Apply to which type of plots? Certainty statements Score

All plots Plot can be located only by reference to 

relatively distant features.

1

Plot can be located within a patterned 

area, but not entirely clear how the 

satellite patterns match with photos.

2

Plot can be unambiguously located in 

both satellite and photos, because of 

unique patterns in both image and 

photos.

3

For “type” changes only Cover type is ambiguous, and tone and 

context help only slightly.

1

Cover type is somewhat ambiguous in 

tone, but context helps make a call.

2

Cover type is unambiguous in terms of 

tone and patterns, and is confirmed 

by context.

3

For “quality” changes only Direct observation of changes in 

cover condition is not possible, 

but ecological knowledge of type 

indicates that change is likely.

1

Direct observation of changes in 

cover condition is not possible, but 

knowledge of ecological conditions 

indicates that change is probable.

2

Cover change is visible directly either 

in terms of percent cover or size of 

individual trees/shrubs in plot.

3

SOP 4. Validation of Change-Detection Products 111
XI. Field-Based Validation

Field validation is expensive and thus not considered a 

primary means of validation for this protocol. True random 

sampling of the entire park would be prohibitively expensive. 

Rather, field validation of change calls should be conducted 

on an opportunistic basis as field crews visit the park for 

other reasons, or as small funds become available for limited 

field visits. Because field sampling will be limited to 

small geographic areas and will be conducted under a time 

constraint, maximal use of field time is required. This has two 

implications. First, all areas labeled as change by the methods 

of SOP 3 should be considered possible validation targets, 

rather than a random subset of those targets that would be 

encouraged with random sampling. Second, in the interest 

of acquiring the largest sample size possible, field sampling 

should focus on areas that are either relatively easy to access 

or which can be validated from a distance. 

Field validation only can be used reliably to validate 

change products that track changes occurring in the prior 

1–2 years. Author pilot studies confirmed that validation of 

changes that occurred long ago is essentially impossible, as 

those changes are often obscured by later recovery or later 

disturbance events. 

Field-based change detection validation typically will 

be limited to changes that can be detected in a short change 

detection window (i.e. type 1 monitoring goals). 

1. Preparation of validation.

a. Electronic products needed: 

i. Polygon form of the change product from SOP 3

(section XI in SOP 3).

ii. Tasseled-cap imagery from the most recent time 

period possible.

iii. Digitized aerial photos for the areas where field 

visits will occur.

iv. GIS coverages of park trails and roads.

v. DEMs.

b. Field materials needed (see table 8).

i. Some of these materials will require preparation 

before the field season. 

(1) Prior to the first field season, the most 

recent set of airphotos should be scanned. 

Scanning these photos ahead of time will 

allow for more time in the field (validating 

during the field season). A digital copy of 

these photos will greatly reduce the time 

spent finding and scanning relevant photos. 

(2) Datasheets also should be developed 

and printed out ahead of time, including 

25 percent on Rite in the Rain® paper.

(a) An example datasheet is shown in 

figure 12. 

(3) Make sure that the digital camera is fully 

operational and that download cable works. 

(4) Make sure GPS unit works and that 

download cable works. Check that the GPS 

displays in the correct datum (NAD83). 

c. Determination of field routes

i. To maximize use of field crew time, routes 

should be chosen for validation that will 

encounter as many change polygons as possible. 

The polygon-form change product from SOP 3

should be overlaid on the recent tasseled-cap 

imagery, along with DEMs and path and road 

GIS layers, to determine which paths and roads 

likely will give field personnel the opportunity to 

either directly visit polygons labeled as change, 

or to view those polygons from overlooks or 

distance vantage points.

ii. Master printouts of the change polygon number 

and its associated SOP 3 derived change type 

should be printed out on Rite in the Rain paper. 

When each polygon is visited in the field, the 

change call produced by the SOP 3 methods 

will be compared against conditions noted in the 

field. 

Table 8. Field equipment for field validation.

Number Description

Req.

1 Compass (with declination set for particular park)

1 Digital camera

1 Binoculars

1 GPS unit for navigating to plots

Variable Data sheets for the number of plots to sample

3 Pencils for marking datasheets

3 Sharpie® marker for making notes on airphoto or imagery

1 Clipboard for holding and marking datasheets

1 First aid kit

1 Sunscreen

1 Insect repellent

1 Watch

1 5-m D-tape for measuring tree diameters

Several Reference books for tree and plant identification

112 Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
d. Map making:

i. Before going into the field, maps should be 

produced with some key characteristics. These 

include labeled roads and trails, in the same 

coordinate system as imagery, to assist fieldvalidation crew in finding the correct trail or 

waXXXXX_fig0X

Park OLYM Watershed (circle one)

Polygon ID Aspect: SE / NW Investigator aak

Pictures taken Time of day

Oblique view yes no Sample Date

obstructed view

too small

too similar

Direct visit no yes

too far gps w aypoint

couldn't find identifiable on airphoto yes no

dangerous

other....

Type of change correct? yes no maybe

landslide disturbance unlikely disturbance possible but not evident

fire cover type w ould show cover type does not disallow change

insect/disease change, but it is not

avalanche evident

river disturbance if no,speculate why it w as

other identifed as change:

percent coverage age (young/old)

deciduous tree young / old deciduous tree yes / no

conifer young / old conifer yes / no

shrub young / old shrub yes / no

rock large / med / small rock yes / no

other (describe) other (describe)

additional comments

(location of area, unique features)

Polygon description

quinault / hoh / queets / n.fk. quinault

how long to access point?

minutes (roundtrip)

Area surrounding polygon

disturbance agent

Figure 12. Sample data sheet.

road. Other helpful layers are isolines for slope 

and elevation interpretation, scale for distance, 

labeled polygons with waypoints, and imagery 

dates for any further interpretation of possible 

phenology or shadowing problems with the 

imagery. Scales for maps vary from 1:10000 to 

1:20000, depending on scope or AOI. 

SOP 4. Validation of Change-Detection Products 113
ii. Maps should include tasseled-cap imagery from 

both the early and later years for reference. 

This will help the crew to understand what was 

there beforehand and what is there currently, 

and to better diagnose whether false positives 

in the change product were caused potentially 

by misregistration or other effects discussed in 

section VI of this SOP. 

(1) If time permits, it is extremely helpful for 

the field crew to obtain basic training in the 

interpretation of the tasseled-cap imagery, 

and to have color printouts of the Image 

Library for reference. 

iii. Airphotos prove to be very helpful provided 

there is enough detail. To this end, airphotos for 

use in the field should be at a scale ranging from 

1:4000 to 1:12000. Airphotos with detail less 

than this are not as helpful because of difficulty 

identifying specific polygons on the landscape 

and the inability to distinguish relative height of 

trees or area of polygons. After airphotos have 

been scanned and printed, polygons should be 

drawn and labeled on the photos with a black 

Sharpie® to aid in field identification. 

2. Field validation

a. Direct visits:

i. In most cases, a direct visit will be the preferred 

method to confidently ascertain the accuracy 

of the change call. Access time to polygons 

will depend on distance, slope, vegetation, 

and GPS coverage. On average, in NOCA it 

took 35 minutes to access plots off trail, while 

in OLYM the average was much lower at 11 

minutes, partly due to large river valleys and 

areas being validated. In OLYM, it was possible 

to hike up the river channel, whereas NOCA 

had less river channel access and more polygons 

on slopes off trail. GPS coverage is crucial in 

effectively navigating to plots. If GPS coverage 

is unreliable or spotty, it will be necessary to 

note distance and direction of polygon, then 

pace and shoot a bearing to polygon. Hopefully 

you will regain GPS coverage along the way, in 

which case re-assess your position, distance, and 

direction to target. 

ii. Once at a polygon, record a waypoint, fill out 

field-validation datasheet (fig. 12) and take 

multiple photos of area. Digital photos will 

aid in long-term storage of index areas and 

provide useful information in the instance 

the imagery does not explain data collected 

during the field visit. Important characteristics 

of photos include a photo directly overhead, 

oblique photo of understory, and any closeups of 

disease or infection. Record this photo number 

on datasheet for future reference. It may be 

advantageous to include in the frame of the photo 

a handheld whiteboard with key site identifier 

information (date, ID of change polygon, park 

name, etc.) for easier labeling later. If there are 

any other notable features of this plot or area 

make sure to note these in the ‘comments’ space 

of datasheet. 

iii. Determination of change call accuracy:

(1) The change type defined as an S2S class 

is one of physiognomic change, while the 

condition viewed in the field at a single 

point in time is one of cover type and 

condition. Linking the two is necessary. 

(a) If the S2S type indicates that a loss of 

vegetation occurred, then the agent of 

change should still be visible, and must 

be consistent with the proposed type of 

change. 

(b) If the S2S type indicates a gain in 

vegetation, then the process that causes 

that change (recovery from disturbance, 

for example) must still be in evidence. 

Typically, this will require inference of 

process from the current pattern seen 

on the ground. Notes regarding the 

inferences used to make the change call 

are necessary. 

(2) If the polygon-change product indicates 

change, but none has occurred, then the 

field crew should speculate why the area 

was identified as change. With both the 

prechange and postchange interval tasseledcap imagery in the maps, the crew can often 

determine if false positives are caused by 

geometric misregistration, phenological 

change, or illumination effects. 

b. Oblique views:

i. If change polygons are very far from access 

routes, they may be viewed from vantage points 

that allow a reasonable assessment of conditions 

on the ground. 

114  Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
(1) First, assess whether the correct area is 

being viewed.

(a) Locate the current observation point on 

the photo.

(i) Visually reference the airphoto to the 

satellite image on the printed map, 

and use the trail and access-road 

layers as reference. 

(b) Locate the desired change polygon on 

the photo and on the image on the map. 

(c) On the satellite image, which has true 

(map) north oriented up, estimate the 

compass bearing of the change polygon 

from the current location. 

(d) Use that bearing to sight into the 

polygon in the field. 

(i) Consider obstructions and elevation 

when making this sightline. 

ii. Because field validation is used for type 1 

monitoring goals (see Narrative), most of the 

changes can be observed from a distance. 

Oblique views are aided by binoculars. Spotting 

scopes, while useful, were tested and deemed of 

little value for their extra weight and bulk. 

c. Direct field visits for distance plots:

i. If a plot is far from access, and if an oblique 

view is not feasible because of obstructed views, 

distance plots may need to be visited directly. 

ii. Because the spectral data used for the change 

detection are consistent across large areas, 

changes that are far away from access, but which 

are spectrally similar to other changes nearby, 

have limited value for direct visiting. Because 

this is difficult to judge in the field, however, it is 

suggested that direct visits be made to plots that 

are distant only a part of the time. An easy way 

to determine this is to flip a coin twice, and only 

visit a distance plot if heads appear twice (one of 

four times). 

iii. Distant plots that are to be visited still may be 

inaccessible because of rivers or topography. In 

that case, record for that polygon that the plot 

was not verified because it was dangerous to 

access. 

3. Data entry.

a. Field-validation calls should be entered into a fieldvalidation database. Fields in the database should 

include:

i. Park name.

ii. Filename of early year image used for change 

detection.

iii. Filename of late year image use for change 

detection.

iv. Filename of polygon-based, change-detection 

product used for field validation

v. Polygon number.

vi. Polygon centroid X coordinate in NAD83 UTM 

coordinates.

vii. Polygon centroid Y coordinate in NAD83 UTM 

coordinates.

viii. Photo number of photo used in field to locate 

change polygon.

ix. Direct visit in field? 

(1) Yes or no.

x. Oblique view?

(1) Yes or no.

xi. If no visit, reasons.

(1) Dangerous.

(2) Not selected by random. 

xii. S2S change call provided by SOP 3 methods. 

xiii. Additionally:

(1) Include all of the other fields on the fieldverification datasheet shown in figure 12.

4. Analysis and reporting.

a. Analysis is straightforward, and need only involve 

calculation of simple proportion statistics from the 

database. 

b. Summary statistics derived from the database should 

include at least the following:

i. Percent of change/no-change calls from the 

SOP 3 product that were correct. 

ii. Breakdown of the correct and incorrect calls by 

S2S change label.

c. Additionally, it is helpful to quantify the reasons 

that the field crew speculated the change call was 

incorrect. 

SOP 4. Validation of Change-Detection Products 115
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116 Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
Protocol for Landsat-Based Monitoring of Vegetation at 

North Coast and Cascades Network Parks

Standard Operating Procedure (SOP) 5

Data Management

Version 1.0 (July 5, 2006)

Revision History Log:

Previous 

VersionNumber Revision Date Author Changes Made Reason for Change New Version Number

n.a. 07-05-06 REK Final version under 

agreement

1.0

This SOP describes the steps needed to ensure that images, image processing steps, and interpreted data are maintained and 

documented correctly. 

I. Journaling

It is recommended that an electronic text file journal 

be maintained to keep track of all filenames and decisions 

made during the steps of this protocol. This journal should 

be maintained in chronological order, with the date of each 

entry noted. Whenever a processing step is conducted, all 

of the relevant information about that step is entered into 

the journal. Enough detail should be given to fully replicate 

the step, including the names of all input and output files, 

the software module and tool used for the analysis, and all 

processing options (buttons selected, data types, etc.). The use 

of hyperlinks to other files also can be a useful feature because 

it provides full pathnames of files that can be used later for 

interpretation, even if the hyperlinks themselves become out of 

date because files are moved. 

Additionally, the journal should be used to record 

rationale for making decisions and for noting observations 

about imagery or datasets that may be useful later for error 

tracking or evaluation of unexplained results. 

Although the key steps leading up to any geospatial 

product also will be stored in the metadata, this journaling 

process allows much more flexibility in keeping track of 

details. Most of the time, these details are not needed later, but 

occasionally they are the difference between a quick fix and 

starting from scratch. 

II. Metadata

Good metadata will allow complete replication of a 

product. The processing of imagery requires many steps, each 

of which requires documentation to be replicable. In table 1, 

the major steps in image processing through this protocol are 

listed, along with the resultant image and the information that 

must be stored in the metadata. These metadata requirements 

are in addition to those that NPS specifies in its current version 

of “Specifications for GIS Products and Deliverables” (current 

version dated August 22, 2005: http://www1.nature.nps.gov/

im/units/nccn/dm_docs/NCCN_GIS_Product_ Specifications.

pdf). 

The validation process also requires metadata. Validation 

windows must be identified according to the method by which 

they were created. If validation windows are defined using 

method 2 (based on airphoto flightlines), then the spreadsheet 

developed to track all possible photo pairs, as well as those 

randomly selected for the validation, must be noted. S2Sinterpretation codes, disturbance-agent codes, and certaintyscoring rules also must be linked to the interpretation database, 

either by hyperlinking to the files or by inclusion of the text of 

the rules with each database. 

SOP 5. Data Management 117
Table 1. Image-processing steps and their metadata requirements.

[Abbreviations: TM, Thematic Mapper; ETM, Enhanced Thematic Mapper Plus; EDC, EROS Data Center; NN, nearest neighbor; RMS, root-mean square; 

COST, cosine theta; MADCAL, MAD calibration; NW, northwest; SE, southeast; AOI, area of interest; DEM, digital elevation mode; POM, probability of 

membership; S2S, satellite to satellite] 

Step Resultant image Required metadata

Image purchasing 7-band Landsat TM or ETM+ image Date of image acquisition; date image was ordered; source 

for image (EDC, etc.); cell size ordered; resampling format 

(NN); plus all header information included with image.

Geometric rectification Terrain corrected image Name of parent image and of terrain-corrected image used 

for reference; Number of image tie points found; means 

of finding tie points (automated; manual); RMS Error 

X, Y, and total; resampling method (NN, etc.); order of 

polynomial model for resampling; digital elevation model 

used for terrain; software used to do terrain correction.

Atmospheric correction COST corrected image Dark-object values used for calculations; bandwise gains and 

offsets used for scaling; sun elevation and azimuth; sun 

distance; illumination values by band; name of graphic 

model used to process model.

MADCAL Radiometrically normalized image Name of parent image and of radiometric-reference image; 

calculated gains and offsets relating each band in parent 

image to reference image; location and size of image 

subsets used to identify no-change pixels; name of control 

file used to run MADCAL algorithm.

Application of tasseled-cap 

coefficients

Tasseled-cap image Name of parent image; coefficients used for tasseled-cap 

calculations.

Splitting by aspect NW or SE aspect tasseled-cap image Name of parent image; name of AOI file used to split by 

aspect; name of DEM used to develop aspect-AOI.

Unsupervised classification 25-spectral class image Name of parent image; source of original classes (principal 

components or diagonal axis); number of iterations for 

classification; threshold for stability.

Calculation of feature-space images Two feature-space images Name of parent tasseled-cap image.

Assignment of classes to feature space Two feature-space images Name of parent tasseled-cap image; name of 25-spectral class 

image.

Recoding to nine physiognomic types Physiognomic-class image Name of parent tasseled-cap image; name of 25-spectral 

class image; name of Excel file to store from- and to-class 

numbers; names of physiognomic classes. 

Calculation of POM images POM image Name of parent tasseled-cap image; name of physiognomic 

class image; filename of PROBDIFF control file; date of 

PROBDIFF run.

Calculation of POM change POM difference image Name of two parent POM images.

Conversion to S2S classes S2S class image Name of POM parent image; name of S2S classes; name of 

graphic model used to convert the image.

Thresholding S2S images for change S2S change-detection image Name of S2S class image; threshold of change used for 

detection; name of graphic model used to threshold change.

Labeling S2S images S2S labeled image Name of S2S class image; threshold of change used for 

detection; name of graphic model used to threshold change.

118  Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
III. File Structure and Naming

File naming should follow the conventions of the current 

version of the NCCN “GIS Naming Conventions” document 

(current version: August 3, 2005; http://www1.nature.nps.gov/

im/units/nccn/dm_docs/ NCCN_GIS_Naming_Conventions.

pdf). Final and intermediate products must follow slightly 

different conventions. The key images and their final or 

intermediate status are listed in table 2. 

During processing, all of the images in table 2 will retain 

intermediate file-naming conventions. Also, there are a host of 

other images developed during processing that should follow 

intermediate file-naming conventions. After completion of all 

processing, a copy of the final-image products listed in table 2

will be saved under a new name that conforms to the final 

product naming conventions. 

A variety of images, other spatial data, and control 

files are used throughout this protocol. These are listed in 

tables 3 through 5, along with the naming conventions that 

should be used when creating the files. Intermediate naming 

conventions specify a 13 character filename with the 10.3 file 

naming structure as follows: cxxxxxxxxc9.ext. The first “c” 

corresponds to park name, and is referenced in the NCCN 

naming conventions documentation. The second “c” lists 

Table 2. Image products listed by final or intermediate status.

[Abbreviations: TM, Thematic Mapper; ETM, Enhanced Thematic Mapper Plus; COST, cosine theta; MADCAL, MAD 

calibration; NW, northwest; SE, southeast; POM, probability of membership; S2S, satellite to satellite] 

Step Resultant image Final or 

intermediate?

Image purchasing 7-band Landsat TM or ETM+ image Intermediate

Geometric rectification Terrain corrected image Intermediate

Atmospheric correction COST corrected image Intermediate

MADCAL Radiometrically normalized image Intermediate

Application of tasseled-cap coefficients Tasseled cap image Final 

Splitting by aspect NW or SE aspect tasseled-cap image Intermediate

Unsupervised classification 25-spectral class image Intermediate

Calculation of feature space images Two feature-space images Intermediate

Assignment of classes to feature space Two feature-space images Intermediate

Recoding to nine physiognomic types Physiognomic-class image Intermediate

Calculation of POM images POM image Intermediate

Calculation of POM change POM difference image Final

Conversion to S2S classes S2S class image Intermediate

Thresholding S2S images for change S2S change-detection image Intermediate

Labeling S2S images S2S labeled image Final

image type, which in the case of image is always “r”. The 

number in the location occupied by the “9” indicates version 

number for the file. In the middle of these are seven character 

locations to uniquely identify the filename. Underscores are 

used to indicate unused portions of the name. 

According to the August 3, 2005, version of the “GIS 

Naming Conventions” document (http://www1.nature.nps.gov/

im/units/nccn/dm_docs/NCCN_GIS_Naming_Conventions.

pdf), final products must follow a different format. Thus, for 

normalized imagery (“tc_96__” in the intermediate naming 

stage), POM difference images (“pdn0296” and “pds0296”), 

and S2S labeled images (“sln0296” and “sls0296”), formatting 

should follow the final-product naming conventions. First, 

a project code must be established in the NCCN tracking 

database, with a separate subcode for each of these three 

products. Each code/subcode name will contain five 

characters. Final names will then follow a 10.3 naming 

format, beginning with a 4-character prefix code for the park 

name, the five-character project code/subcode, and a singlecharacter code to separate successive occasions of change 

detection. This latter single-character identifier begins with 

“a” and continues up the alphabet. Each year that change 

detection is carried out, a new letter is added. It is unclear how 

the name will change when 26 years have passed. 

SOP 5. Data Management 119
Table 3. Codes for intermediate-image products.

[7-digit code: Full filename is of format cxxxxxxxc9.ext, where the first “c” is the park letter abbreviation, the “x” is the 7-character core, the second “c” is the 

format of the file, the number is the version of the file, and the extension depends on the type of image (“.img” for the images in this table). Here, the digits “96” 

are used as a place holder to represent a single year image from 1996, and “02” to represent the year 2002. Any time both occur in the same filename, the file 

represents combined information from both years. If two filenames are listed, they correspond to different aspect images. If four are listed, they correspond to 

two instances of files for two aspects each. Abbreviations: SOP, standard operating procedure; TM, Thematic Mapper; ETM, Enhanced Thematic Mapper Plus; 

COST, cosine theta; MADCAL, MAD calibration; NW, northwest; SE, southeast; POM, probability of membership; S2S, satellite to satellite] 

SOP Section Process Resultant image 7-digit code

1 II Image purchasing Orthorectified TM image (geometric reference) 

25 m grain size

tmo02__

1 II Image purchasing 7-band Landsat TM or ETM+ image (Subject 

image)

tmp96__

2 V Clipping to study area Reference Image clipped to study area tmo02st

2 VI Geometric rectification Image terrain corrected and registered to 

orthorectified image

tmg96__

2 VI Clipping to study area Subject Image clipped to study area tmg96st

2 VII Atmospheric correction COST corrected image tma96__

2 VIII MADCAL Radiometrically normalized image tmm96__

2 IX Application of tasseled-cap coefficients Tasseled-cap image tc_96__

2 X Splitting by aspect NW or SE aspect tasseled-cap image tcn96__, tcs96__

3 II Unsupervised classification 25-spectral class image ucn9625, ucs9625

3 III Calculation of feature-space images Two feature space images for each of two aspect 

classes

fsn96bg, fsn96gw, fss96bg, 

fss96gw

3 III Assignment of classes to feature space Two thematic feature-space images for each of 

two aspect classes

fun96bg, fun96gw, 

fus96bg, fus96gw

3 III Recoding to nine-physiognomic types Physiognomic class image p9n96__, p9s96__

3 IV Calculation of POM images POM image pmn96__, pms96__

3 V Calculation of POM change POM difference image pdn0296, pds0296

3 VI Merging aspects Final, aspect-merged nine-layer POM difference 

image

pdf0296

3 VIII Conversion to S2S classes POM to 15-layer S2S image p2s0296

3 VIII Thresholding S2S images for change Single-layer S2S change-detection image s2c0296

3 VIII Labeling S2S images Single-layer S2S labeled image (with speckle) s2l0296

3 IX Sieving speckle 4-layer S2S themed image s240296

3 IX Sieving speckle S2S class sieved s2p0296

120 Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
Table 4. Codes for other intermediate products.

[Full filename is of format cxxxxxxxc9.ext, where the first “c” is the park letter abbreviation, the “x” is the 7-character core, the second “c” is the format of the 

file, the number is the version of the file, and the extension depends on the type of image (“.img” for the images in this table). Here, the digits “96” are used as 

a place holder to represent a single year image from 1996, and “02” to represent the year 2002. Any time both occur in the same filename, the file represents 

combined information from both years. If two filenames are listed, they correspond to different aspect images. Abbreviations: SOP, standard operating 

procedure; DEM, Digital Elevation Model; NAD83, North American Datum of 1983; AOI, area of interest; S2S, satellite to satellite] 

SOP Section Process Resultant image 7-digit code

2 IV Reprojecting DEM 25-m DEM in NAD83 dem_n83

2 V Defining study area Study area AOI starea__ 

2 V Defining study area Study area vector coverage starea__

2 V Subsetting DEM 25-m DEM clipped to study area demst__

2 X Splitting by aspect DEM aspect mask images aspnmsk, aspsmsk

2 X Splitting by aspect AOI for each of the aspect classes aspnmsk, aspsmsk

3 IV Calculating probability of membership 

in physiognomic classes

Probability lookup file pmn96lu, pms96lu

4 IV Creating S2S geospatial database Geodatabase validat

4 IV Creating S2S geospatial database Feature dataset within geodatabase for all 

validation within a given year interval

val0296

4 IV Creating S2S geospatial database Feature class for S2S interpretation of 

change

s2s0296

4 V Selecting random 1.5 km boxes Grid of 1,500-m by 1,500-m boxes for 

later random selection (use for grid, 

polyline, and polygon formats)

gd_0296

4 V Selecting random plots Text file with box labels, Excel file with 

random numbers 

gd_0296

SOP 5. Data Management 121
Table 5. Codes for control files and models.

[Full filename is of format cxxxxxxxc9.ext, where the first “c” is the park letter abbreviation, the “x” is the 7-character core, the second “c” is the format of the 

file, the number is the version of the file, and the extension depends on the type of image (“.img” for the images in this table). Here, the digits “96” are used as 

a place holder to represent a single year image from 1996, and “02” to represent the year 2002. Any time both occur in the same filename, the file represents 

combined information from both years. If two filenames are listed, they correspond to different aspect images. Abbreviations: SOP, standard operating 

procedure; COST, cosine theta; MADCAL, MAD calibration; DEM, Digital Elevation Model; POM, probability of membership; S2S, satellite to satellite]

SOP Section Process Resultant image 7-digit code*, **

2 VII COST calculations Spreadsheet to keep track of COST 

numbers

tma96cs

2 VII COST calculations Graphic model to apply COST to 

imagery

tma96cs

2 VIII MADCAL MADCAL control file mad0296

2 X Splitting by aspect Graphic model to split DEM by aspect spDMasp

2 X Splitting by aspect Graphic model to split one tasseled-cap 

image by aspect

sp96asp

3 III Recoding 25 to 9 classes Spreadsheet to document-conversion 

codes

p9n96cc, p9s96cc

3 IV Assigning POM Spreadsheet to describe statistics for 

physiognomic classes

p9n96st, p9s96st

3 IV Extracting spectral signatures for ninephysiognomic classesImagine-signature file p9n96sg, p9s96sg

3 IV Calculating POM in physiognomic 

classes

PROBDIFF control file pmn96cc, pms96cc

3 VIII Converting nine-POM layers to S2S 

class scores

Graphic model to convert 9-layer 

difference in POM image into 

15-layer S2S class image

p2s0296

3 VIII Detecting and labeling change Graphic model to convert 15-layer 

S2S class image into single-layer 

classified S2S image

s2s0296

3 IX Sieving speckle Graphic model to group S2S classes for 

sieving

s240296

3 IX Sieving speckle Graphic model to sieve S2S labeled 

image

s2p0296

122 Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
During image processing, file folders and subfolders 

should provide clear separation of different steps in the image 

processing. Modules used to create images (graphic models, 

files to control software packages, etc.) should be placed 

in the same folders as the files they are used to create. The 

following structure is suggested for intermediate processing 

(fig. 1). Each year of imagery contains its own sequence of 

<Park>

images96

Change96to02

Study_area

DEM

raw

Aspect_split

geo

cost

madcal

unsup25

POM

POM_diff

S2S_class

S2S_label

images02 As above

S2S_valid

<Park>

images96

Change96to02

Study_area

DEM

raw

Aspect_split

geo

cost

madcal

unsup25

POM

POM_diff

S2S_class

S2S_label

images02 As above

S2S_valid

<Park>

images96

Change96to02

Study_area

DEM

raw

Aspect_split

geo

cost

madcal

unsup25

POM

POM_diff

S2S_class

S2S_label

images02 As above

S2S_valid

Figure 1. Suggested directory structure for image processing.

processing steps in separate folders. Datasets that do not 

change from year to year (DEMs, Study Area, etc.) are static 

folders at the same level as the image-year folders. All steps 

that involve combinations of images from different years 

(such as difference images, etc.) must be placed in a separate 

folder clearly labeled with both years over which change is 

occurring. 

SOP 5. Data Management 123
124  Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks

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Protocol for Landsat-Based Monitoring of Vegetation at 

North Coast and Cascades Network Parks

Standard Operating Procedure (SOP) 6

Revising Protocol

Version 1.0 (July 5, 2006)

Revision History Log:

Previous 

VersionNumber Revision Date Author Changes Made Reason for Change New Version Number

n.a. 07-05-06 REK Final version under 

agreement

1.0

This SOP explains how to make changes to the protocol for Landsat-based Monitoring of Vegetation at the NCCN Parks. 

Observers asked to edit the Protocol Narrative or any one of the SOPs need to follow this outlined procedure in order to 

eliminate confusion in how data are collected and analyzed. All observers should be familiar with this SOP in order to identify 

and use the most current methodologies.

SOP 6. Revising Protocol 125

Procedures

1. This protocol attempts to incorporate the best and 

most cost-effective methods and technologies in order 

to produce results that internally are consistent and 

externally are comparable to other Landsat-based 

monitoring programs. As new methods and equipment 

become available these should be incorporated in a timely 

manner with the appropriate reviews.

2. All edits require review for clarity and technical 

soundness. Small changes or additions to existing 

methods will be reviewed by in-house GIS and NPS 

remote-sensing specialists. However, if a complete 

change in methods is sought, then a full outside review 

is required. Experts within and out of the NPS in remote 

sensing will be utilized as reviewers. 

a. Changes in software are likely. When these changes 

do not alter the fundamental tasks being conducted, 

in-house review is sufficient. When sensors change, 

or software packages disappear or become outdated, 

full reviews are required. 

3. Major edits and protocol versions should be documented 

in the Revision History Log that accompanies the 

Protocol Narrative and each SOP. Changes should be 

noted only in the Protocol Narrative or SOP being edited. 

Version numbers increase incrementally by hundredths 

(e.g. version 1.01, version 1.02 ...etc) for minor changes. 

Major revisions should be designated with the next 

whole number (e.g., version 2.0, 3.0, 4.0 ...). Record the 

previous version number, date of revision, author of the 

revision, identify paragraphs and pages where changes are 

made if major changes are undertaken, and the reason for 

making the changes along with the new version number.

4. Inform the NCCN Data Managers about changes to the 

Protocol Narrative or SOP so the new version number can 

be incorporated in the metadata of the project database. 

The database may have to be edited by Data Managers to 

accompany changes in the Protocol Narrative and SOPs.

5. Post new versions on the internet and forward copies to 

all individuals with a previous version of the affected 

Protocol Narrative or SOP.
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126 Protocol for Landsat-Based Monitoring of Landscaped Dynamics at North Coast and Cascades Network Parks
Manuscript approved for publication July 20, 2006

Prepared by the U.S. Geological Survey Publishing Network

Patricia Revitzer

Bobbie Jo Richey

Sharon L. Wahlstrom

For more information concerning the research in this report, contact the 

Director, Forest and Rangeland Ecosystem Science Center, 

U.S. Geological Survey, 777 NW 9th Street 

Corvallis, Oregon 97330

http://fresc.usgs.gov
Kennedy and others— Protocol for Landsat-Based Monitoring of Landscape Dynamics at North Coast and Cascades Network Parks—Techniques and Methods 2—G1

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