humans classify the continuous stream of data received by our sensory system—our eyes, ears, tongue, nose, and skin. We receive the data and our brains turn it into information. For example, if we see a four-legged animal, shorter than 1 meter, with a long snout and canine teeth, we might identify it as a dog, wolf, or coyote. If we determine it is a dog and the dog is growling, with its hackles up and its teeth bared, we know it is a threatening dog. If the dog is wagging its tail and lowering its body into a submissive posture, we know that it is a friendly dog. Dog, wolf, coyote, threatening, and friendly are all categories of information our brains determine from the data we receive.
When we see an image, our brains immediately start to explore and classify it. We identify features and note how they are related to one another. In a GIS system, when the data of an image is “classified,” it is converted from continuous data into either continuous or categorical information and a map is created. Table 2.1 below provides an overview of the differences between continuous data such as an image, and continuous and categorical information which are derived from imagery.
Table 2.1. Overview of the differences between continuous data, continuous information, and categorical information
Types of Maps Created from Imagery
Three types of maps are produced from the classification of imagery: digital elevation models (DEMs) and their derivatives, thematic raster and vector maps, and maps of feature locations.
Digital Elevation Models
DEMs provide continuous information about the elevation of the earth—either its bare surface without vegetation or structures, or the elevation of its terrain including the height of the vegetation and structures. DEMs can be created from survey point data or from points collected from imagery. The ability to create DEMs across large areas from imagery offers distinct advantages over using much more labor-intensive and expensive ground surveys to produce DEMs. DEMs and their derivatives, such as slope and aspect, are among the most commonly used geospatial data layers.
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
