Thematic Vector and Raster Maps
A thematic map is a vector or raster map of themes such as land-cover types, soil types, land use, or forest types. Thematic map classes are discrete, not continuous. A thematic map covers the entire area of the landscape and labels everything into thematic classes. Figure 2.6 is an example of a thematic map of land-cover types for an area of the Coastal Watershed in southeastern New Hampshire. Thematic maps are created through manual interpretation of imagery or semiautomated image classification.
Feature Maps
A subset of thematic maps is feature maps. Rather than label the entire landscape, feature maps identify only a single object type, resulting in a binary map in which the feature is located and identified, and everything else is mapped as null; not that feature. Often, the feature of interest is a very specific type of object such as an airplane, military vehicle, or other unique entity that is out of place and unexpected in a particular environment. Sometimes, the objects of interest are common objects such as water bodies, roads, or buildings. Feature extraction is usually performed manually, but computer algorithms have also been developed to automatically extract features. Usually, automated feature extraction results in a number of false positives (i.e., the location of points that are not the feature of interest), which are then manually reviewed and corrected.
Imagery Workflows
Incorporating imagery in a GIS requires first deciding how you want to use the imagery. Is it as a base image, as an attribute of a feature, or to make a map? If your goal is to make a map, you must relate the objects on the imagery to features on the ground. To do so, four steps must be completed. You must
1 1.understand and characterize the variation on the ground that you want to map,
2 2.control variation in the imagery not related to the variation on the ground,
3 3.link variation in the imagery to variation on the ground, and
4 4.capture the variation in the imagery and other data sets as your map information.
First, you must decide how you want to characterize the phenomena on the earth that you want to identify, analyze, and display on the map; i.e., you need to understand the variation on the ground that you want to capture on the map. Once you understand the variation on the ground, you will need to create a set of rules that classify the variation on the ground into meaningful categories for your proposed uses of the map. It is the map categories and proposed uses that will drive your choice of what type of imagery to acquire for your project. Knowing how to best make that choice is the objective of chapters 3 and 4. Knowing how to build a rigorous classification scheme is the objective of chapter 7.
Next, you must work with your imagery in your GIS, register it to the ground, and remove or manage any spurious variation in the imagery caused by clouds, cloud shadows, or atmospheric conditions that could likely lead to map errors; i.e., you need to control unwanted variation in the imagery. Chapter 5 reviews working with imagery in ArcGIS, and chapter 6 discusses registering imagery to the ground and dealing with unwanted image variation.
Third, you must understand the variation in the imagery and how it relates to the variation you want to map; i.e., you must link variation in the imagery to variation on the ground. To do so, you will inspect the imagery to understand how the image object elements of color/tone, shape, size, pattern, shadow, texture, location, context, height, and date vary across the landscape. There are analytics you can perform on the imagery to discover how well the imagery varies with the classes you want to map, and you may decide to manipulate the imagery data to produce indices or derivative bands that help derive more information from the imagery. You may discover that some of the variation on the ground that you want to map cannot be derived from the imagery. In that case, you must discover other data sources (i.e., ancillary data), such as DEMs, that will help you make the map. Creating DEMs and their derivatives is the topic of chapter 8. Understanding how to link variation in the imagery to variation on the ground is the learning objective of chapter 9.
Fourth, you will classify the imagery to create maps of digital elevation, feature locations, or thematic landscape classes by capturing the variation in the imagery and ancillary data that is related to your map classes. This work may be performed manually or with the help of a computer. There are many methods of classifying imagery. Explaining those methods and describing how to choose which method to use are the objectives of chapters 10 and 11. Once the image is classified into a map, you will want to assess the map’s accuracy, which is the topic of chapter 12. Finally, you may want to publish your imagery and maps, which is the topic of chapter 13.
Chapter 3
Imagery Fundamentals
Introduction
Imagery is collected by remote sensing systems managed by either public or private organizations. It is characterized by a complex set of variables, including
collection characteristics: image spectral, radiometric, and spatial resolutions, viewing angle, temporal resolution, and extent; and
organizational characteristics: image price and licensing and accessibility.
The choice of which imagery to use in a project will be determined by matching the project’s requirements, budget, and schedule to the characteristics of available imagery. Making this choice requires understanding what factors influence image characteristics. This chapter provides the fundamentals of imagery by first introducing the components and features of remote sensing systems, and then showing how they combine to influence imagery collection characteristics. The chapter ends with a review of the organizational factors that also characterize imagery. The focus of this chapter is to provide an understanding of imagery that will allow the reader to 1) rigorously evaluate different types of imagery within the context of any geospatial application, and 2) derive the most value from the imagery chosen.
Collection Characteristics
Image collection characteristics are affected by the remote sensing system used to collect the imagery. Remote sensing systems comprise sensors that capture data about objects from a distance, and platforms that support and transport sensors. For example, humans are remote sensing systems because our bodies, which are platforms, support and transport our sensors—our eyes, ears, and noses—which detect visual, audio, and olfactory data about objects from a distance. Our brains then identify/classify this remotely sensed data into information about the objects. This section explores sensors first, and then platforms. It concludes by discussing how sensors and platforms combine to determine imagery collection characteristics.
A platform is defined by the Glossary of the Mapping Sciences (ASCE, 1994) as “A vehicle holding a sensor.” Platforms include satellites, piloted helicopters and fixed-wing aircraft, unmanned aerial systems (UASs), kites and balloons, and earth-based platforms such as traffic-light poles and boats. Sensors are defined as devices or organisms that respond to stimuli. Remote sensors reside on platforms