Prepared By
the Image Resolution
Assessment and Reporting
Standards (IRARS) Committee
1. Overview
2. Imagery Interpretability Rating Scales
3. Scope of this Effort
4. The Multispectral Imagery Interpretability Rating Scale
The Imagery Resolution Assessment and Reporting Standards Committee would like to thank the following organizations for providing imagery used in the development of the MS IIRS.
MS IIRS Reference Guide
iii
MS IIRS Level 1
Distinguish between urban and rural areas.
Identify a large Wetland (greater than 100 acres).
Detect meander flood plains (characterized by features such as channel scars, oxbow lakes, meander scrolls).
Delineate coastal shoreline.
Detect major highway and rail bridges over water (e.g., Golden Gate, Chesapeake Bay).
Delineate extent of snow or ice cover.
MS IIRS Level 2
Detect multilane highways.
Detect strip mining.
Determine water current direction as indicated by color differences (e.g., tributary entering larger water feature, chlorophyll or sediment patterns).
Detect timber clear-cutting.
Delineate extent of cultivated land.
Identify riverine flood plains.
MS IIRS Level 3
Detect vegetation/soil moisture differences along a linear feature (suggesting the presence of a fenceline).
Identify major street patterns in urban areas.
Identify golf courses.
Identify shoreline indications of predominant water currents.
Distinguish among residential, commercial, and industrial areas within an urban area.
Detect reservoir depletion.
MS IIRS Level 4
Detect recently constructed weapon positions (e.g., tank, artillery, self-propelled gun) based on the presence of revetments, berms, and ground scarring in vegetated areas.
Distinguish between two-lane improved and unimproved roads.
Detect indications of natural surface airstrip maintenance or improvements (e.g., runway extension, grading, resurfacing, bush removal, vegetation cutting).
Detect landslide or rockslide large enough to obstruct a single-lane road.
Detect small boats (15-20 feet in length) in open water.
Identify areas suitable for use as light fixed-wing aircraft (e.g., Cessna, Piper Cub, Beechcraft) landing strips.
MS IIRS Level 5
Detect automobile in a parking lot.
Identify beach terrain suitable for amphibious landing operation.
Detect ditch irrigation of beet fields.
Detect disruptive or deceptive use of paints or coatings on buildings/structures at a ground forces installation.
Detect raw construction materials in ground forces deployment areas (e.g., timber, sand, gravel).
MS IIRS Level 6
Detect summer woodland camouflage netting large enough to cover a tank against a scattered tree background.
Detect foot trail through tall grass.
Detect navigational channel markers and mooring buoys in water.
Detect livestock in open but fenced areas.
Detect recently installed minefields in ground forces deployment area based on a regular pattern of disturbed earth or vegetation.
Count individual dwellings in subsistence housing areas (e.g., squatter settlements, refugee camps).
MS IIRS Level 7
Distinguish between tanks and three-dimensional tank decoys.
Identify individual S5-gallon drums.
Detect small marine mammals (e.g., harbor seals) on sand/gravel beaches.
Detect underwater pier footings.
Detect foxholes by ring of spoil outlining hole.
Distinguish individual rows of truck crops.
MS IIRS Reference Guide
iv
The Multispectral Imagery Interpretability Rating Scale (MS IIRS) has been developed by the U.S. Government's Imagery Resolution Assessment and Reporting Standards (IRARS) Committee to help people quantify and communicate the potential interpretability of multispectral imagery (MSI). This metric is an exploitation task-based scale consisting of seven graduated levels.
The form of this scale is similar to that of the National Imagery Interpretability Rating Scale (NIIRS), after which it was modeled.1 A series of image and criteria scaling evaluations provided the understanding and components necessary to construct the metric. The validation of this metric demonstrates that it exhibits the desirable properties of an imagery interpretability rating scale. The complete development of the scale is documented in the Multispectral IIRS Development - Final Report.2
Use of this interpretability scale assumes user familiarity with MSI exploitation. While the scale and accompanying Reference Guide are designed to be informative, the scale does not attempt to tell users which spectral bands or band combinations to use. IRARS felt the scale should not impose these constraints on users, the exploitation experts, as these decisions are subjective and often site- and task-specific. The objective of the scale is to provide a knowledgeable multispectral user the means to communicate MSI interpretability needs and observations.
The MS IIRS was developed with one goal in mind: to provide a structure for distinguishing varying levels of multispectral imagery interpretability, which may in turn be used to:
This Reference Guide has been assembled to present the MS IIRS and assist users in applying the scale. In addition, the guide aims to familiarize users with existing sources of MSI and to foster a better understanding of MSI applications. The purpose of this document is not to train users in the exploitation of MSI.
This document consists of six sections:
1 NIIRS is the government's standard nine-level, task-based scale for rating panchromatic imagery. This is further described in Section 2.
2 Multispectml llRS Development - Final Report, available from Scales Development Manager, NEL/IAD, (703) 799-3462. A synopsis is published as "Quantifying Multispectral Imagery Interpretability" in the proceedings of the International Symposium on Spectral Sensing Research, July 1994.
MS IIRS Reference Guide
1
Three appendices provide supplemental information. Appendix I lists evaluated criteria not appearing in the scale. These criteria are presented to better familiarize users with information that can be extracted from varying interpretability levels of MSI. The spectral regions most commonly used in exploiting various features or phenomenologies are summarized in Appendix II. Appendix III provides tables that compare basic characteristics of 60 MS imaging systems, including the spectral regions in which they collect, estimates of the quality of imagery they provide, the span of archival coverage available, and sources to contact for more information.
The commercial and scientific communities have recognized the value of MSI as evidenced by the many applications utilizing imagery from the U.S. Landsat and French SPOT satellites. Defense and Intelligence Community users have also identified requirements for MSI, and already utilize data from commercial satellites to satisfy some of these. When the MS IIRS development began (1993), new government sponsored MSI imaging systems were programmed.
To respond to projected needs for MSI information, the feasibility of developing an MSI interpretability metric was evaluated from a scale development perspective. Because the NIIRS format is well established and has been successfully used by a broad community, it was adopted as the framework for this effort. The need to address both spatial and spectral aspects of MSI was recognized as a factor that could complicate the generation of an MS IIRS. An approach was developed that has resulted in the scale described here. Before the MS IIRS is described, however, a brief introduction f the format and application of imagery interpretability scales is presented.
Imagery interpretability rathing scales are tools for making quantitative judgments about the potential interpretability of an image. The NIIRS is such a scale and is used by imagery analysts (IAs) to assign a number indicating the interpretability of a given image. This process is called rating an image.
The NIIRS is a task-based scale consisting of nine graduated levels. At each level, representative exploitation tasks (termed criteria) indicate the level of information that can be extracted with an image of a given interpretability level. For example, with a NIIRS 2 image, IAs should be able to Detect large hangars at an airfield, while with a NIIRS 8 image, they should be able to Identify the rivet lines on bomber aircraft. Thus, at a higher NIIRS level, more detailed information can be obtained from an image.
The NIIRS measures the information potential, i.e., interpretability of an image. This means that specific objects listed in the criteria need not be present, but the physical attributes of the image are such that the image would have the specified information value if the necessary content were present. The NIIRS relies on the experience of IAs to be able to extrapolate, or imagine, how well criteria would be rendered if those features were present in the image to be rated. Because each rating refers to specific tasks the IA can or could do, it has a more precise meaning than, for example, when such subjective words as good or fair are used. Thus, judgments become more uniform, because IAs can use the same yardstick to measure interpretability.
The NIIRS accounts for the major factors that affect image interpretability. Image resolution, measured as ground resolved distance (GRD) or ground sample distance (GSD), has a significant effect on interpretability. Spatial resolution alone does not determine the NIIRS of an image, as sharpness, noise, and contrast also influence the NIIRS. These effects may be due to system paramenters (e.g., optical quality, focal plane characteristics), acquisition conditions (e.g., sun angle, atmospheric haze), and exploitation conditions (e.g., duplicate film quality, softcopy monitor quality). By design, the NIIRS is independent of an particular imaging system.
MS IIRS Reference Guide
2
Figure 2.1 illustrates why GSD alone is not a good measure of image interpretability. All the images have the same spatial resolution (GSD) but they differ in interpretability as measured by the NIIRS. The upper left image represents the product of a well-designed panchromatic imaging system, exhibiting good contrast and low noise. The upper right image shows how high noise and low contrast reduce the image interpretability. The lower two images illustrate the same effect for multispectral imagery.
While a goal of the MS IIRS development was to produce a scale similar in concept and function to the NIIRS, the scale presented here is not a sanctioned community standard as is the NIIRS. It is a first step in developing an MSI quality metric and is offered as such. The IRARS is distributing this baseline to assist others in specifying requirements for current and future products. Future studies could be done to further investigate user perceptions of image quality and their effects on exploitation.
Further development is not planned at this time due to funding priorities and community circumstances. Because MSI user experience varies greatly and applications are still being developed, construction of a highly refined scale to meet diverse needs is not currently warranted.
The metric presented here has been developed with significant input from diverse segments of the intelligence and broader multispectral user community. Numerous organizations were consulted in planning the effort, and over 200 images and 500 exploitation tasks were evaluated by dozens of IAs experienced in the exploitation of MSI.
The development of this scale focused on the manual exploitation of minimally enhanced MSI. This is not meant to overlook or diminish the importance of automated machine processing. Imagery exploitation through machine processing fundamentally differs from the visual analysis performed by an IA. Deriving an applicable metric for machine processing would require a different approach. While the scale is not designed to address machine processing, it may aid in the assessment of these derived products if used to measure the incremental gain in interpretability afforded by additional processing. The scale does not address hyper- or ultraspectral imagery, as these data are almost exclusively exploited by machine.
The meaning of the term "multispectral" varies greatly by user, and some definitions were considered much too inclusive for this exploratory effort. For the MS IIRS, "multispectral" is defined to be several bands of co-registered imagery acquired in the reflective portion of the electromagnetic spectrum (0.4 - 2.5 mu m). Consequently, the scale presented in this guide is not designed to apply to multiple band images that include thermal or radar data.
The MS IIRS is a seven-level scale composed of exploitation tasks, or criteria, that indicate the level of information that can be extracted from MSI. Criteria were derived primarily from the DOD MSI Requirements Survey, although aerial survey texts and keys were also consulted. Members of several civilian government organizations also offered requirements. Although nine interpretability levels were desired (to match the NIIRS), only seven levels could be consistently distinguished within the range of data evaluated.
Five or six criteria define each level, and the spacing between levels is constant. Obviously, the limited number of criteria representing the scale do not account for all tasks that can be accomplished through MSI exploitation. They represent a sampling of the population of MSI tasks. Additional criteria which were evaluated and calibrated to MS IIRS are listed in Appendix I. These supplementary criteria establish a more
MS IIRS Reference Guide
3
 |  |
 |  |
MS IIRS Reference Guide
4
robust understanding of the seven interpretability levels.
The traditional order-of-battle criteria categories (air, electronic, ground, missile, and naval) found in the NIIRS are not present in the MS IIRS. Five new categories more relevant to MSI exploitation were developed: military, urban/industrial/lines of communication, vegetation/agricultural, terrain, and water resources. An attempt was made to represent at least one criterion from each of these categories at each level of the MS IIRS.
In addition to accommodating all the traditional parameters that affect image interpretability: spatial resolution, edge sharpness, noise, and contrast, an MSI interpretability metric must also consider spectral characteristics. The MS IIRS does not treat spectral information as a variable that ranges from no spectral information at one extreme (a panchromatic image), to a hyper- or ultraspectral image at the other. Rather, a representation of data from across the reflective portion of the spectrum (nominally blue, green, red, near-infrared - NIR, and shortwave infrared - SWIR) is assumed at all levels of the scale. As such, the scale is not a case study of the utility of MSI compared to panchromatic imagery.
To use the MS IIRS, it is necessary to consider all the bands of a particular image as part of a single package or unit of information. MSI bands are collected to be used together rather than as individual images. Therefore, the scale design assumes that a multiple-band image is given a single rating.
A single rating for an MS image is desired for logistical reasons as well. One of the aims of an interpretability metric is to facilitate communication, in part by transforming complex parameter relationships into simpler terms. Creating a scale with independent ratings for every possible three-band composite would have been self-defeating. For example, the seven bands of the Landsat Thematic Mapper could be displayed as 210 different composites. If every composite had a different rating, the scale would hinder rather than facilitate communication. Therefore, the MS IIRS characterizes MSI as a package of data (multiple bands) with a single inherent interpretability.
Selecting the spectral bands that best discriminate the materials or features of interest is of utmost importance. There are few rules for this selection, as every case is unique. The choice of bands depends on the features to be discriminated and their immediate surroundings. This selection will vary by feature, locality, season, time of day, and task. It is left to the IA, the exploitation expert, on a case-by-case basis to determine which composite best assists feature discrimination.
Once the preferred bands for maximizing spectral contrast have~been selected, the color display presentation does not significantly influence interpretability. As illustrated in Figure 4.1, if a feature/background contrast exists, it will be apparent in all presentations using those bands even though there may be a subjective preference for one presentation over another. For example, while the colors differ in all six permutations of the three-band composite shown in this figure, the large buildings can be spectrally distinguished from the trees, reservoir, or concrete in any of them. The concrete, however, is not very different spectrally from the dried grass in any of these bands. Therefore, the color of these two features is similar in all six representations.
MS IIRS addresses the spectral nature of the imagery in two ways. First, it assumes that at every level, imagery is available in the blue, green, red, NIR, and SWIG portions of the spectrum. Secondly, the criteria that compose the scale are tasks that benefit from the exploitation of spectral information. As such, the scale should not be
MS IIRS Reference Guide
5
confused with the NIIRS, which does not address spectral information. Since spectral bands are available and the criteria are spectrally oriented, the MS IIRS responds to both the spectral and spatial nature of MSI.
Consider, for example, the criterion Identify swimming pools. If only spatial characteristics of an image were considered, satisfaction of this criterion would require an image of high interpretability. The analyst would have to resolve the size and shape of the pool, and would require other details such as lane markings, diving boards, or the presence of swimmers to distinguish a pool from other similarly shaped features. With the addition of spectral information, the analyst needs only to resolve the pool and note the characteristic aqua color in the visible spectrum coupled with the lack of response in the reflective infrared to be able to identify a swimming pool. This can be done using MSI of coarser spatial resolution.
The MS IIRS is intended to provide IAs with uniform and systematic points of reference for judging the relative image interpretability of MSI. To rate an image, regardless of display medium, IAs must associate the written criteria directly with the image.
In applying the MS IIRS, an IA should:
The following guidelines allow the MS IIRS to be used as a means to communicate image interpretability, to evaluate system performance, and to plan for new systems:
A major use of the MS IIRS is to rate the interpretability of imagery as described in the previous section. Associating an MS IIRS rating with an image indicates to others the interpretability of imagery exploited, and helps establish a level of credibility for the report.
The scale can also be used to specify intelligence requirements or information needs. Tasks can be related to the criteria representing the scale to determine what imagery interpretability is required to accomplish specific missions. The MS IIRS assumes that imagery
MS IIRS Reference Guide
6
 RGB:snr |
 RGB:nsr |  RGB:nrs |
 RGB:rns |
 RGB:rsn |
 |
MS IIRS Reference Guide
7
representing all five spectral regions within the reflected portion of the spectrum is required or is available. If the requirements for spectral coverage are not as restrictive as this, a user may indicate which regions are required or desired, as shown in Figure 4.2. Appendix II summarizes the spectral regions most commonly used in exploiting various features or phenomenologies.
MSI users may utilize the MS IIRS to obtain or task imagery. The scale establishes the interpretability of the imagery required. Appendix II identifies the spectral regions commonly required to satisfy the user's needs. Appendix III then can be used to determine which sensors offer the spectral bands and interpretabilities required, as well as which systems might provide historical archival data. Sources of this imagery are listed when available.
As a metric of image interpretability, the MS IIRS can also be used to measure the performance of MS imaging systems and exploitation devices. In addition, it can be used in the engineering tradeoff studies that accompany the development of future MS imaging systems.
On the following pages, two image examples have been selected to represent each of the seven levels of the MS IIRS. The intent of these examples is not to illustrate the specific features of the criteria. The images do not include all the features or observables called out at the level the image represents. Rather, the images are representative of the interpretability needed to satisfy the criteria for that level. If the features were present in the image, the IA should be able to accomplish the tasks indicated.
The examples were selected from over 200 images evaluated in the development of the MS IIRS. The scale development included imagery from the following three orbital sensors and five airborne imaging systems:
The images are presented here in the same layout as evaluated and rated by IAs participating in the scale's development. The analysts, however, were rating image transparencies in 35-mm super slides on a light table equipped with zoom optics. The prints included in this guide are for collateral use only, and not for image rating.
The question of how to represent multiple bands of imagery on a single slide was given much attention. It was decided that the five major spectral regions in the reflective portion of the spectrum for which this scale is designed could be represented in two commonly used three-band combinations.
A natural color rendition, composed of blue, green, and red bands, provides a familiar literal representation of the scene and is good for water penetration tasks. A false color composite including SWIR, NIR, and red bands illustrates
| MS | Spectral Coverage* | |||||
|---|---|---|---|---|---|---|
| Task | IIRS | Blue | Green | Red | NIR | SWIR |
| Detect alfalfa harvest. | 4 | D | R | R | R | D |
| Detect leaks in pipeline irrigation. | 5 | D | R | R | D | |
| Identify dredged channels in harbor. | 3 | R | R | R | ||
MS IIRS Reference Guide
8
the information available from the reflective spectrum that the human eye cannot see. This composite is useful for such tasks as vegetation discrimination, identifying land/water interfaces, and observing soil moisture content. A majority of the image chips were formatted so the slides were divided in half, allowing these two composites to be displayed side by side.
In some instances, a different composite was included, either because bands were not available to create the two standard composites or because it enhanced the interpretability. This composite was presented in the lower left quadrant of the slides. All images were contrast/brightness enhanced, and a conservative sharpening filter was applied. No other image processing was performed.
Before viewing the images, the reader is advised to fold out pages 10 and 25 so that the MS IIRS criteria may be consulted. The images are captioned with MS IIRS level, the geographic location of the image, the acquisition date, the collection imaging system, and an explanation of the band composite presentation.
MS IIRS Reference Guide
9
MS IIRS Level 1
Distinguish between urban and rural areas.
Identify a large wetland (greater than 100 acres).
Detect meander flood plains (characterized by features such as channel scars, oxbow lakes, meander scrolls).
Delineate coastal shoreline.
Detect major highway and rail bridges over water (e.g., Golden Gate, Chesapeake Bay).
Delineate extent of snow or ice cover.
MS IIRS Level 2
Detect multilane highways.
Detect strip mining.
Determine wafer current direction as indicated by color differences (e.g., tributary entering larger water feature, chlorophyll or sediment patterns).
Detect timber clear-cutting.
Delineate extent of cultivated land.
Identify riverside flood plains.
MS IIRS Level 3
Detect vegetation/soil moisture differences along a linear feature (suggesting the presence of a fenceline).
Identify major street patterns in urban areas.
Identify golf courses.
Identify shoreline indications of predominant water currents.
Distinguish among residential, commercial, and industrial areas within an urban area.
Detect reservoir depletion.
MS IIRS Level 4
Detect recently constructed weapon positions (e.g., tank, artillery, self-propelled gun) based on the presence of revetments, berms, and ground scarring in vegetated areas.
Distinguish between two-lane improved and unimproved roads.
Detect indications of natural surface airstrip maintenance or improvements (e.g., runway extension, grading, resurfacing, bush removal, vegetation cutting).
(continued on page 25)
MS IIRS Reference Guide
10
MS IIRS Level 4 (continued)
Detect landslide or rocicslide large enough to obstruct a single-lane road.
Detect small boats ( 15-20 feet in length) in open water.
Identify areas suitable for use as light fixed-wing aircraft (e.g., Cessna, Piper Cub, Beechcraft) landing strips.
MS IIRS Level 5
Detect automobile in a parking lot.
Identify beach terrain suitable for amphibious landing operation.
Detect ditch irrigation of beet fields.
Detect disruptive or deceptive use of paints or coatings on buildings/structures at a ground forces installation.
Detect raw construction materials in ground forces deployment areas (e.g., timber, sand, gravel).
MS IIRS Level 6
Detect summer woodland camouflage netting large enough to cover a tank against a scattered tree background.
Detect foot trail through tall grass.
Detect navigational channel markers and mooring buoys in water.
Detect livestock in open but fenced areas.
Detect recently installed minefields in ground forces deployment area based on a regular pattern of disturbed earth or vegetation.
Count individual dwellings in subsistence housing areas (e.g., squatter settlements, refugee camps).
MS IIRS Level 7
Distinguish between tanks and three-dimensional tank decoys.
Identify individual 55-gallon drums.
Detect small marine mammals (e.g., harbor seals) on sand/gravel beaches.
Detect underwater pier footings.
Detect foxholes by ring of spoil outlining hole.
Distinguish individual rows of truck crops.
MS IIRS Reference Guide
25
This MS IIRS Reference Guide is the culmination of an IRARS project to investigate the feasibility of developing an interpretability scale for MSI. The results of this effort were more promising than anticipated at the outset. Analysis of data collected as part of an MS IIRS validation effort indicates the metric possesses the qualities desired in an imagery interpretability scale. Consistent results have been obtained in the rating of many sources and qualities of MSI and from numerous individuals representing diverse organizations and backgrounds.
Analysts have demonstrated the ability to use the textual rating scale to convey image interpretability in much the same way they compare images to each other. User surveys indicate the criteria representing each rating level are of equivalent difficulty, and the scale is linear in that each level is thought to be equidistant from the adjacent levels. Consequently, IRARS elected to provide these results to the MSI community by way of this Reference Guide. Publication of this document, however, does not imply that all image quality issues related to MSI have been resolved. The MS IIRS represents a first step toward quantifying multispectral image interpretability in a manner that can be used by the community. Numerous questions remain, and IRARS is interested in suggestions for future efforts in this arena.
The MS IIRS provides a means for specifying the interpretability or information potential of MSI which can be used by IAs, system operators, and system engineers. There are at least five principal applications for this scale:
For the MSI community, this final item may prove the most important in the near term. A wide variety of multispectral sensors can be deployed on various platforms, and MS IIRS offers a method for quantifying the image interpretability of the products from these systems. If system designers and decision makers are contemplating modifications or enhancements to a sensor, MS IIRS enables them to assess the exploitation consequences of these changes to image interpretability.
The appendices contain information that should help users apply the MS IIRS to their work. Appendix I lists numerous criteria that are not included in the scale, but may help in better matching information-gathering tasks with image interpretability requirements. Appendix II provides information that may aid the less experienced user in determining the most relevant spectral regions for assorted applications. Appendix III serves as an MSI customer assistance directory, listing dozens of imaging sensors, the available image characteristics, and sources for additional information.
There are several reasons why the MS IIRS is not a community sanctioned NIIRS product. Some parameters which may affect MSI interpretability have not been evaluated in the efforts that have been undertaken to date. While significant evaluation of the scale has already taken place, it is recognized that the community's
MS IIRS Reference Guide
26
understanding and use of MSI is still developing. The number of experienced MSI users is small, and their training and experience level varies greatly. The amount of higher interpretability imagery available is still limited at this point. Furthermore, MSI applications within the community are in their infancy. All these factors make the creation of a definitive metric difficult, and perhaps any effort to do so at this time is premature.
The MS IIRS is available now and is felt to be useful until further study can be directed toward a more thorough understanding of MSI interpretability. Briefings of this scale development effort at various community forums have been met with a high level of interest. The scale provides the community with a validated framework for binning information requirements. It can be used as a reporting mechanism to specify the performance of current and future multispectral imaging systems and as an indicator of the interpretability of imagery used in deriving information. When the scale is used in conjunction with tables in the reference guide, it can assist users in obtaining/tasking the imagery needed to satisfy their requirements. IRARS recommends that the scale be used only in conjunction with this reference guide.
Comments or requests for additional copies should be directed to Technical Director of IRARS, at (703) 799-3462.
MS IIRS Reference Guide
27
MS IIRS Reference Guide
I-1
This appendix to the Reference Guide provides additional criteria and their associated MS IIRS levels. IRARS is providing these criteria in the interest of representing a wider variety of exploitation tasks, since many useful tasks fall between the integer levels of the scale. These additional criteria should enable analysts to find tasks that closely resemble their specific interests.
The listing is separated into three general categories:
As with the NIIRS, each criterion represents a single imagery exploitation task that an analyst might perform. Associated with each criterion is an MS IIRS value that represents the level of image interpretability necessary to perform the task. The values assigned to these additional criteria are "decimal" MS IIRS. These differ from the standard MS IIRS where criteria are associated with whole number values. The decimal values were derived from formal evaluations conducted as part of the MS IIRS development effort. Because a small variability in ratings is associated with the decimal MS IIRS values, users should not attach great importance to small differences (e.g., one or two tenths) in the MS IIRS values assigned to these criteria.
MS IIRS Reference Guide
I-2
Level 1
1.2 Identify general land use (e.g., urban, forest, cultivated, large bodies of water).
1.4 Detect lines of transportation (either road or rail, but do not distinguish between).
1.8 Detect powerline cuts through forested areas.
Level 2
2.3 Determine relative soil moisture.
2.3 Delineate extent of strip mining activity.
2.4 Distinguish between areas of high and low population density within an urban setting.
2.4 Delineate extent of flood waters in an urban area.
2.4 Detect flooding of rice paddies.
2.5 Detect marijuana harvest based on absence of green vegetation in known marijuana fields.
2.5 Detect deforestation in known narcotics production area.
2.5 Detect large buildings.
2.7 Detect large individual buildings within an urban area.
2.7 Detect bare earth airstrips.
2.8 Identify quarry operations.
2.8 Detect spoil material and slag heaps in known mining areas.
Level 3
3.0 Detect large area ground clearing (e.g., groundbreaking for construction of new facility or expansion of existing facilities).
3.1 Detect two-lane unimproved roads.
3.1 Detect athletic complexes (e.g., stadiums, arenas).
3.1 Detect vegetation stress/senescence in narcotics crops in reported eradication area.
3.3 Detect major breaks in lines of communication (e.g., collapsed bridges, washed out roads, inundation due to natural disasters).
3.3 Identify ski areas.
3.3 Detect terraced agricultural areas.
3.4 Detect effluent discharge into water from industrial facility.
3.4 Detect indications of natural surface airstrip maintenance or improvements (e.g., runway extension, grading, brush removal, vegetation cutting).
3.4 Detect a sequence of circular clearings indicating utility tower sites in forested regions.
3.7 Identify overpasses.
3.7 Detect ore processing facility.
3.8 Distinguish between rail line and road.
3.8 Detect airborne effluent from industrial facilities.
3.8 Detect new oil drilling sites/pads near a known oil field.
3.9 Identify power plants by their configuration.
Level 4
4.1 Detect trains or strings of standard rolling stock on railroad tracks (not individual cars).
4.4 Detect control tower at airfield.
4.6 Distinguish between overpasses and level crossroads.
MS IIRS Reference Guide
I-3
Cultural (continued)
4.8 Detect off-shore oil exploration by presence of drilling vessel.
4.9 Determine extent of building destruction in urban areas from natural disasters.
4.9 Count all large free-standing smokestacks and/or cooling towers at major power plants.
Level 5
5.0 Distinguish between harvested and unharvested beet fields.
5.2 Identify individual tract houses.
5.2 Identify areas of crops blown down by high winds.
5.3 Detect small buildings (e.g., sheds, shacks) in or near coca growing areas.
5.3 Identify extent of fire (e.g., number of destroyed houses) in an urban area.
5.3 Identify subsistence housing areas (e.g., squatter settlements, refugee camps).
5.3 Detect basketball court, tennis court, volleyball court in urban areas.
5.3 Identify an Olympic-size swimming pool.
5.4 Detect major external structural damage to urban buildings from major storms.
5.4 Detect individual trees in an orchard.
5.4 Distinguish between mature and immature coca fields.
5.5 Detect herds of large domesticated herds (e.g. leg horses, cows, reindeer) in open areas.
5.8 Identify log decks (e.g. stacks of recently cut logs).
5.8 Identify truck crop fields (e.g., tomatoes, lettuce, peppers) as such.
5.8 Detect presence of agricultural product in silage pit.
Level 6
6.0 Detect presence, absence of leaves on coca drying patios.
6.1 Detect individual rows in a vineyard.
6.3 Detect individual large domesticated animals (e.g. horses, cattle) in pastures.
6.4 Identify individual haystacks/strawricks.
6.5 Detect a canoe (15-20' in length) on shore.
6.5 Identify group of about ten 55 gallon drums.
6.5 Count cows in a herd.
6.5 Determine row spacing of field (e.g., corn, soybean) crops.
6.6 Identify individual lines painted on paves roads, aprons, parking lots.
6.7 Identify two-person tents at established recreational camping areas.
6.9 Identify individual small bales of hay/straw.
Level 7
7.4 Detect narcotic intercropping based on color, texture, and plant leaf shape.
MS IIRS Reference Guide
I-4
MS IIRS Criteria
Level 1
0.8 Identify coastal plains.
0.9 Identify major landforms (e.g., canyons, mountains, mesas).
1.4 Delineate sea ice edge.
1.5 Detect vegetation stress over a large area.
1.6 Identify abandoned meander section in a flood plain.
1.6 Identify extent of flood waters.
1.8 Detect timber clearcutting of about 100 acres.
1.9 Determine moisture content/health of vegetation to determine fire vulnerability.
1.9 Identify extent of vegetation damage from forest fires.
Level 2
2.0 Detect ground clearing (e.g., vegetation clearing, ground scarring).
2.0 Identify extent of vegetation damage due to volcanic eruption.
2.1 Identify tidal flats.
2.2 Identify water drainage patterns.
2.2 Delineate water features to support fire vulnerability assessment.
2.4 Delineate extent of fire damage in open grasslands.
2.4 Detect salines (salt marsh/pond) in a playa region.
2.4 Detect small water sources (e.g., springs, creeks, seeps) in arid or semi-arid regions through vegetative indicators.
2.4 Identify areas of open water (greater than 1/4 acre) in marsh/swamp.
2.5 Detect alluvial deposits (left where steep mountain streams emerge on lands with low slopes).
2.6 Identify main (or low water) stream channels.
2.8 Detect breaches in levees along a large river (e.g., Mississippi, Ohio, Danube).
2.8 Identify drainage ditches in tidal areas based on the presence of a linear pattern.
2.9 Delineate extent and direction of lava flows.
Level 3
3.0 Detect vegetation infestation.
3.3 Detect windbreaks/hedgerows between fields.
3.3 Identify major rock types (e.g., sandstone, shale, granite).
3.4 Detect emergent vegetation in shallow water areas.
3.4 Determine current direction as indicated by water color differences.
3.5 Identify inland waterways navigable by medium boats (e.g., 30' cabin cruisers).
3.6 Detect soil erosion in known narcotics production area.
3.6 Detect irrigation ditches when water is present.
3.7 Delineate inland waterways navigable by barges.
3.9 Detect gullies (gullies vs. rills or ravines) in open areas.
MS IIRS Reference Guide
I-5
Level 4
4.1 Detect areas where hays has been moved.
4.3 Detect indications of slash and burn field abandonment by observation of natural regrowth.
4.3 Identify surface material (e.g., rock, sand, soil, conglomerate).
4.9 Identify the extent of oil washed up on a rock beach.
Level 5
5.2 Detect shallow water obstacles (e.g., coral heads, dragon teeth) in clear water through direct observation or water surface patterns.
5.3 Detect buffalo wallows (depressions for localized surface drainage) in alluvium areas.
5.5 Detect fallen trees obstructing a two lane road.
5.6 Detect individual trees with indications of vegetation stress.
5.6 Identify areas of crops damaged by hail.
5.6 Detect row dumps of hay or straw.
5.6 Detect fallen trees in inland waterways.
Level 6
6.1 Distinguish individual trees in a closed canopy deciduous forest.
6.1 Identify individual trees when in a group.
MS IIRS Reference Guide
I-6
MS IIRS Criteria
Level 2
2.6 Detect areas of open water in sea ice large enough for submarine surfacing.
Level 3
3.0 Detect vegetation clearing for scratch built mobile missile bases.
3.3 Detect earth grading for runway expansions at airfields.
3.3 Identify vegetation type (e.g., grass, brush, wetland, agricultural crop) in potential landing zones.
3.3 Detect wetland areas unsuitable for mobile Intercontinental Ballistic Missile (ICBM) traffic.
3.4 Detect a fixed Surface to Air Missile (SAM) site (e.g., SA-2, SA-3, SA-5) based on road pattern and overall site configuration.
3.6 Detect forest clearings and open fields large enough for medium helicopter (e.g., Huey, Iroquois, Blackhawk) landings.
3.6 Detect cleared security strip around a sensitive facility.
3.7 Detect spoil piles from possible underground construction or mining activity.
3.8 Detect snow clearing activity on runways and aprons at air facilities.
3.8 Detect effluents in water bodies near suspect underground production facility.
3.8 Identify large Petroleum/Oil/Lubricants (POL) storage tanks.
3.9 Identify marshes and saturated soil conditions within a road mobile deployment area.
Level 4
4.2 Detect large threat force concentrations (e.g., armored division, mechanized infantry division, motorized rifle division).
4.3 Detect dirt/gravel runway against bare soil background.
4.3 Identify terrain suitable for use as aircraft landing zones.
4.5 Detect large ground forces equipment in convoy (e.g. tanks, self-propelled guns - SP-guns).
4.6 Distinguish between functional areas at a ground forces installation (e.g., vehicle storage, barracks, administration).
4.7 Detect trenches or berms in ground forces deployment area.
4.7 Identify ammunition storage buildings at known storage facilities.
4.7 Detect possible explosives production plant based on presence of berms around several buildings.
4.7 Detect jeep trails through grassland/scrub (brush).
4.8 Detect barriers/obstacles (e.g., barrels, logs, vehicles) on runways.
Level 5
5.0 Detect presence/absence of armored vehicles (e.g., tanks, armored personnel carrier) at a ground forces installation or storage area.
5.0 Detect jeep trails across sand/loose soil.
MS IIRS Reference Guide
I-7
Level 6
6.1 Detect disruptive or deceptive use of paints or coatings on ground forces equipment (e.g., tanks, trucks, engineering equipment).
6.1 Identify foundations for single-bay garages.
6.3 Detect an individual concrete barrier/obstacle (e.g., dragon's teeth, Jersey barrier).
6.4 Distinguish between camouflage netting and environmental canvas coverings (area sufficient to conceal a tank).
6.6 Detect natural obstructions (e.g., rocks, boulders) in forest clearings and open fields.
Level 7
7.2 Distinguish between wooden and rubber ground equipment decoys and ground equipment.
MS IIRS Reference Guide
I-8