ANSWERS¶
ANSWERS¶
` <>`__13-1: As will be mentioned later on this page, prior to obtaining the imagery, the prime task will be to become familiar with the major features in the scene with the intent of picking some specific locations and examples for use as training sites presuming that classification is your principal objective. After the classification is done on the Landsat image, it is essential to return to the field to check out a number of places where classes have been defined in order to test the overall classification accuracy of the computer-based mapping. **BACK**
` <>`__13-2: Actually, a strong case can be made for on-site measurements of any of the first 8 on this list. Most of these can be measured automatically by instruments in place. The trick is to get the resulting information to you, the user. This is usually simple, but not cheap. The measurement station can be equipped with a radio transmitter that relays the data from ground to a satellite and then to a receiving station (either a central one such as at NASA or an agency, or to you directly). This is the basis for the concept of Data Collection Platforms (DCPs) described later in this Section. **BACK**
` <>`__13-3: Optimal classes for lower resolution systems are those that are homogeneous over relatively large areas. Several that come to mind are large water bodies; clouds; vast forests; deserts; snow fields. Those that provide poor samples when resolution is low include residential areas (unless no attempt is made to define this category in terms of individual buildings but just as a general class) and multi-use terrain (e.g., a power plant near a river with adjacent fields and forests). **BACK**
` <>`__13-4: The first thing you should notice is how variable most scenes are. The question of signature acquisition has a rather complex answer. Signature of what? This depends heavily on your choice of category or class and how general it can be. If you want a signature of a house, how close should you be. If too close, you only get part of any house. If the whole house fills the field of view, then how meaningful is the signature for a second house, likely to be notably different from the first. The yards and landscaping will change from place to place. What about the land behind the house(s) off in a distance. This varies from one view to the next. Spectral signatures of the real world are hard to specify in terms of “typical”. Spectral signatures work well mainly when the target is uniform: such as a rock outcrop, a field of some particular crop, a wetlands, a water body. **BACK**
` <>`__13-5: The center left pixel has wheat, corn, and grasslands - all vegetation but it would be hard to try to extract which class should be cited. In the center pixel, the low resolution pixel has five classes: hardwoods, corn, shrubby meadows, rock outcrop, pines. In that pixel, the lower right pixel at higher resolution (smaller surface area) is mixed with the same five classes: In this case, even with the improved resolution, the mixed pixel problem does not go away. By chance, that pixel straddled a place on the ground where several classes all met. Analysis of that pixel in a classification will likely prove a problem, with an unreliable identification. The center right pixel has four classes (if pine is considered).**BACK**
` <>`__13-6: This is a reasonable pictorial scenario: There will be a cluster of 2, 3, or more actual factories - usually elongate buildings, with connecting walkways and shipping paths and roads. There may be a separate power plant. There should be an administrative or business building, commonly by itself. There may be landscaping - a lawn, trees, leading to the plant’s frontage. One or more parking lots surround the complex. There may be a gatehouse, for admittance. There could be a pond for water supply or a water tower. There is likely either a rail sideline or a place where trucks can park and load. Thus, the category “factory complex” is just that, a congregation of structures of diverse appearance and use. **BACK**
` <>`__13-7: In the overall classification, the accuracy is simply calculated as the sum of the number of correct identifications for each class as a percentage of the total number of units (pixels) which consists of the sum of the correctly identified pixels for a class plus the pixels representing errors of commission and omission associated with that class. These errors may significantly increase the denominator to numbers greater than the actual total for any one class. **BACK**
` <>`__13-8: An error of commission is a measure of the ability to discriminate within a class and occurs when the classfier incorrectly commits pixels of the class being sought to other classes. In this agricultural example, the commission error for corn stems from improperly calling other classes corn, so that seven pixels labeled as corn are really a composite of other classes. An error of omission measures between class discrimination and results when one class on the ground is misidentified as other class(es) by the observing sensor and/or the classifier. Thus, Landsat fails to recognize and correctly identify all 43 pixels of corn as such, and labels 18 of these pixels as other classes. **BACK**
` <>`__13-9: A partial list: Training site mislocated; Inadequate training samples (number too few and/or sites non-uniform (inhomogeneous); Mixed pixel effect; Class improperly defined; Ground truth inaccurate; Signature extension invalid; Stage of growth not considered; Temporal difference between ground truth and date of scene overpass by satellite. **BACK**
` <>`__13-10: Viewing conditions can be controlled; Reflectances can be quantified, using calibration targets; Different surface can be examined to get averaged values; Measurements can be repeated under the same or other conditions; Targets can be sampled (collected) directly to determine composition and other properties affecting reflectances. **BACK**
` <>`__13-11: Bands at 0.67 (chlorophyll absorption) and 0.87 µm (cell wall reflectance). A third channel at 0.56 µm is sensitive to “greenness”. **BACK**
` <>`__13-12: As the viewing angle relative to the surface increases, the reflectance ratio decreases (in numerator [IR] greater than denominator [red]); variations to a lesser degree occur as the direction of look at the sample varies relative to north. **BACK**
` <>`__13-13: The normalization (use of calibration targets) applied to the field spectra removes certain external influences, such as atmospheric absorption, and provides a truer indication of the natural reflectances of the materials themselves. (In lab conditions, the samples are illuminated with air around them [a vacuum could be used, but usually is not], so that water and gas absorption troughs are not eliminated.) `BACK <Sect13_040.html#13-13>`__
` <>`__13-14: 1) A standard reflectance target could be set up, with a small radiometer aimed at it, so that when the satellite is passing over the scene, a ground-based value for reflectance under the general illumination conditions acting on the control area within the scene can be measured; 2) seismometers, for detecting earthquake precursors; 3) a floating platform that gives sea surface temperatures; the platform can be anchored or it can float, with its position determined by GPS. Many other possibilities could be mentioned. **BACK**
` <>`__13-15: Any number of possibilities come to mind. Yours is probably valid and sensible. To give an example of what a good answer might be, try this: The problem is to monitor a region’s anticipated harvest of its staple crop, winter wheat. The platforms your choose would be a geostationary satellite to monitor weather conditions, a Landsat to assess growth stages, and a radar satellite to monitor soil moisture. Another satellite, with multiband thermal, would also be helpful. All of these will gather data over time. As the growing season progresses, observers (county agents or the farmers themselves) in the field send crop information. Periodically, you and/or they can bring portable sensors to get on-the-spot spectral data. The final result should be a quantitative estimate of crop status in terms of total yield, quality of crop, and any disease problems. All of the “multi’s” have been brought to bear on this task. **BACK**
` <>`__13-16: Hyperspectral remote sensing provides a continuous, essentially complete record of spectral responses of materials over the wavelengths considered. For relatively “pure” materials, e.g., individual minerals or tree species, it is possible to construct a spectral curve from the hyperspectral data that can then be matched with spectral signatures of individual materials collected from laboratory or field measurements and available in data banks. Specific reflectance peaks and absorption troughs can be read directly from these curves to allow precise identification of a material, class, or feature. With careful analysis (using Fourier procedures), mixtures of two or even three different materials, etc. can be identified as the components of the compound spectral curve. **BACK**
` <>`__13-17: First, a distinction should be made between “skylight” and “sunlight”. The latter refers to all the radiation entering the Earth’s atmosphere from the Sun and includes wavelengths extending from the high frequency UV to the lower frequencies in the infrared. Skylight is a term that signifies the scattering of sunlight by atmospheric gases and particles; scattering occurs to varying degrees at different wavelengths. Rayleigh scattering, causes by the gaseous molecules in the atmosphere, affects preferentially the shorter wavelengths. These include the blue, so that the sky appears to be the source of that color since, in fact, it is - the molecules selectively scatter blue but pass longer wavelengths. Sunsets are red because that is the color remaining after the shorter wavelengths (through greens) have been further scattered (by Mie scattering - from dust particles, etc. - in addition to Rayleigh scattering), plus the effect of a lower Sun angle (near the horizon) that increases the atmospheric path length. The sky from the Moon is black - that is just the result of there being no atmosphere at all to scatter the sunlight. Thus, sunlight illuminates the lunar surface but not anything in the near-vacuum of space beyond. **BACK**
` <>`__13-18: Three absorption bands, at 1.3 - 1.5 µm, 1.8 - 2.0 µm, and 2.5 - 3.0 µm, should be avoided whenever remote sensing is conducted through the atmosphere - unless the objective is to study atmospheric properties themselves. **BACK**
` <>`__13-19: A paired absorption band for Goethite near 6 µm is distinct from the single band for Hematite at 7 µm. **BACK**
` <>`__13-20: When the four plant types are analyzed in a spectrometer, only a small part of the plant is taken as a sample. It is likely that each of the types will have its own characteristic leaf or frond shape and that, taken as a whole, the overall appearance of any type will differ geometrically from most other types. Thus, oat hay and potato as crops are clearly dissimilar in the way they look in bulk. Thus, the combination of spectral response and diversity of shape will produce slightly different signatures, mainly in the depth of any absorption features. **BACK**
` <>`__13-21: Probably not - the band is too broad. Distinction and identification is much better in the 8 - 12 µm thermal interval. Of course, a hyperspectral sensor can separate the two rock types, based on their principal mineralogy, in the 2.3 µm region. `BACK <Sect13_060.html#13-21>`__
` <>`__13-22: Yes, the visual differences also show up as absorption bands in the spectral curves. They would be distinguishable. BACK
` <>`__13-23: The Bronzite’s absorption bands will affect TM Bands 4 and 5; the Diopside doesn’t have bands in these spectral intervals but possesses a moderate absorption band that will reduce Band 7 reflectance. BACK
` <>`__13-24: As the Aluminum in octahedral coordination increases, the absorption band near 2.2 µm shifts towards slightly longer wavelengths. **BACK**
` <>`__13-25: Two factors control this effect: the larger grains allow more absorption and the smaller grains provide a higher proportion of surface area available as reflectors. **BACK**
` <>`__13-26: For n = 1, sin theta = lambda/d. d = 1/5000 lines/cm = 2 x 10-4 cm/line = 2 x 10-6 meters. For red light, sin theta = 650 x 10-9 meters/2 x 10-6 meters = 0.325; theta therefore is 19°. For blue light, sin theta = 450 x 10-9/2 x 10-6 = 0.225; theta is 13°. The dispersion in the visible is therefore 6°. The wavelength continuum in this spectral range (and beyond) is therefore spread out, and separated, to be either recorded on a photographic plate or by light-sensitive detector/counters. BACK
` <>`__13-27: The detector interval is 10 nanometers, or 0.01 µm, which is a small, but still finite interval in the spectral continuum. 10 nanometers = 100 angstroms; a discrete emission line on a photographic plate is quantified as a fraction of a single angstrom, e.g., a diagnostic line for sodium (Na) when excited in an emission spectroscope is at 5889.953 angstroms (in the yellow-orange, the color of a sodium-vapor lamp used in street lighting). BACK
` <>`__13-28: Each of the Landsat bands, either MSS or TM, are spectrally broader. Thus, the green MSS Band 5 actually includes some colors that contain bluish or yellowish contributions. They are not as pure, so that color composites may not be as precise. Thus if a red object on the ground is rendered in a TM natural color image, its particular shade of red may contain some yellow tones. In AVIRIS, if the individual channel chosen for the red component in the composite is close to the “true” red color of some object, its representation in the AVIRIS image will nearly match that of its actual shade. However, overall, the AVIRIS and Landsat natural and false color composites appear similar because the range of colors in nature is usually greater than those discriminated at the 10 nanometer level. **BACK**
` <>`__13-29: Very little direct evidence. One might miss the abundant knowledge about this diverse alteration if only Landsat type images were examined. However, Landsat TM bands 5 and 7 will likely show at least some of this alteration. But, these AVIRIS images of Cuprite clearly demonstrate the power of being able to select channels that lie close to the peaks and troughs of spectral curves rather than lose some essential detailed information when these curve features are subsumed into broader bands. **BACK**
` <>`__13-30: Nothing mapped means the computer classification could not match the material that is rendered black with any of the pre-selected signatures. It may be another crop type not designated by a name (i.e., was not picked during ground truthing) or is a soil variant. **BACK**
` <>`__13-31: The hot vents (in blue-white) show up better in the thermal image. The areas in red are probably basalt or andesite flows that were laid down during one or more recent eruptions (Etna is the most active of the Mediterranean volcanos.) **BACK**