and their ability to fly at high altitudes with large payloads results in large operational costs, but this can be offset by their broad spatial coverage abilities and fast data collection (Abdullah et al., 2004). Single-engine platforms are lighter and have fewer logistical concerns and lower operational costs, while twin-engine platforms offer more power and weight for larger payloads (Abdullah et al., 2004). Many low-altitude platforms employ a dual sensor configuration for collecting multiple types of data (e.g., lidar and optical), but aircraft with less powerful engines are less likely to be able to carry multiple sensors because the power requirements are too high and the combined payload becomes too heavy for the plane. However, over the last 10 years the weight, size, and power requirements of many sensors have rapidly decreased, making multiple sensor configurations more feasible.
Collection Characteristics
The components of sensors and the features of platforms combine to determine the collection characteristics of an image: its spectral resolution, radiometric resolution, spatial resolution, viewing angle, temporal resolution, and extent. Table 3.1 provides definitions of commonly used categories of the three most important collection characteristics: spatial, spectral, and temporal resolution.
Table 3.1. Commonly used categories of imagery collection characteristics
Spectral Resolution
The spectral resolution of an image is determined by the sensor and refers to the following:
The number of bands of the electromagnetic spectrum sensed by the sensor
The wavelengths of the bands
The widths of the bands
Panchromatic sensors capture only one spectrally wide band of data, and the resulting images are shades of gray, regardless of the portion of the spectrum sensed or the width of that portion. Panchromatic bands always cover more than one color of the electromagnetic spectrum. Multispectral sensors capture multiple bands across the electromagnetic spectrum. Hyperspectral sensors collect 50 or more narrow bands. Traditionally, multispectral bandwidths have been quite large (usually 50 to 400 micrometers), often covering an entire color (e.g., the red portion). Conversely, hyperspectral sensors measure the radiance or reflectance of an object in many narrow bands (usually 5 to 10 micrometers) across large portions of the spectrum, similar to imaging spectroscopy in a chemistry laboratory.
Film images are stored as negative or positive film or paper prints. Remotely sensed digital data files are stored in a raster or rectangular grid format. When imaging, each picture element, or pixel, collects a digital number (DN) corresponding to the intensity of the energy sensed at that pixel for each specific band of the electromagnetic spectrum. Panchromatic data is stored in a single raster file. Figure 3.13 shows example infrared DNs for a small area.
Figure 3.13. Example infrared digital number (DN) values
Multispectral images store each band as a separate raster. Each band is monochromatic, but when they are combined they can be displayed in color. Figure 3.14 shows four separate bands of airborne digital imagery collected over a portion of Sonoma County, California. Each band is monochromatic. Figure 3.15 combines the bands to create true color and color infrared displays.
Figure 3.14. Red, green, blue, and near infrared bands of airborne multispectral imagery captured over Sonoma County, California (esriurl.com/IG314)
Figure 3.15. True color and infrared combination of bands of airborne multispectral imagery collected over Sonoma County, California (esriurl.com/IG315)
The bands shown in figures 3.14 and 3.15 are in the red, green, blue, and near-infrared portions of the electromagnetic spectrum. Each pixel of the imagery contains four numbers, one for the DN recorded in each of the four bands. Table 3.2 presents the range of DN values for each band of the different land-cover types depicted in figure 3.15.
Table 3.2. Range of sample DN values
Notice how water is significantly lower in the infrared band than are the other land-cover types. Also, urban has high values in all bands relative to the other classes. Riparian vegetation and water are similar in the red, green, and blue bands, but significantly different in the infrared band, indicating that without the infrared band it might be difficult to distinguish the greenish water from the green vegetation.
At this point, we can begin to see how variations in land-cover types can be related to variations in spectral responses, and it becomes straightforward to group the similar pixels of the image sample in figure 3.14 together into land-cover classes, as depicted in figure 3.16. Of course, it is never quite this straightforward to turn image data into map information, which is why chapters 7 to 9 thoroughly examine the methods and tools for image interpretation and classification.
Figure 3.16. Infrared DN values from figure 3.13 combined into land-cover classes
Radiometric Resolution
Radiometric resolution is the minimum variation in electromagnetic energy that a sensor can detect, and therefore determines the information content of an image. Like spectral resolution, radiometric resolution is determined by the sensor.
In film systems, radiometric resolution is determined by the contrast of the film. Higher-contrast films will have higher radiometric resolutions than low-contrast films. In digital sensors, the potential range of DN values that can be recorded for each band determines the sensor’s radiometric resolution. The larger the number of bits or intensities discernible by the sensor, the higher its radiometric resolution and the better the sensor can detect small differences in energy. In general, higher radiometric resolution increases the ability to more finely distinguish features on the imagery. Discerning objects within shadowed areas or extremely bright areas is particularly enhanced by higher radiometric resolution.
Digital
