Spectral Unmixing Applied to Desert Soils for the - Naval ...

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The spectral regions most often exploited by remote sensing scientists are the visible and infrared (Halvatzis, 2002). These regions have wavelengths that typically fall between 0.4 and 15 micrometers (400–1500 nm), though different applications will utilize different portions of this range (Goetz and Rowan, 1981; Halvatzis, 2002). The wavelength range utilized is based on the material being analyzed and which spectral region will most easily distinguish this material from the others in the imagery (Jensen, 1983). It is widely agreed that the most appropriate regions for remotely sensing vegetation, soils, and rocks is the 0.4–2.5 micrometer (400–2500 nm) range; because of the detailed information on the unique properties of surface materials it can provide, even at the sub-pixel level (Goetz and Rowan, 1981; Jensen, 1983; Kruse et al, 2004). C. IMAGING SPECTROMETRY Imaging Spectrometry data measures reflectance or emissivity of surface materials using up to hundreds of spectral bands (Figure3) (Goetz et al., 1985; Kruse et al., 2003). This technique allows for the collection of an entire spectral signature for every surface material within that image on a picture element (pixel) by pixel basis (Goetz et al., 1985). Spectral features are a response to chemical bonds specific to a given material based on chemistry and structure, known as electronic and vibrational processes (Figure 2) (Clark, 1999) called absorption features (Goetz et al., 1985). Absorption features are the phenomena in which a collected spectrum from a material will have varying maxima and minima across the associated wavelength range. Minima are related to absorption bands that are somewhat unique to the material and contribute to its characteristic spectrum (Goetz et al., 1985; Jensen, 2007; Clark, 1999). 6
Figure 3. From Green et al. (1998), this figure shows the concept behind imaging spectroscopy and how it measures a spectrum for each image component (pixel) in a satellite image. 1. Electronic Processes Electronic processes are the result of changes in energy states of electrons when they are emitted or absorbed by some material. The most common of these are related to the unfilled electron shells of transition elements such as Iron (Fe), also known as the Crystal Field Effect (Clark, 1999). For transition elements, energy levels are split if an atom is in the crystal field, allowing an electron to jump from a lower to higher energy state when an photon with enough energy to make up the difference between energy states is absorbed (Clark, 1999). Because the crystal field varies with the atomic structure of the material, the splitting energy will also vary causing spectral signatures unique to an individual material (Clark, 1999; Jensen, 2007). Other material specific electronic processes are associated with color centers, charge transfers, and conduction bands (Clark, 1999; Jensen, 2007). Charge transfers occur when there are inter-element 7
Page 1 and 2: NAVAL POSTGRADUATE SCHOOL MONTEREY,
Page 3 and 4: REPORT DOCUMENTATION PAGE Form Appr
Page 5 and 6: Approved for public release; distri
Page 7 and 8: ABSTRACT Desert areas cover approxi
Page 9 and 10: TABLE OF CONTENTS I. INTRODUCTION..
Page 11 and 12: LIST OF FIGURES Figure 1. The above
Page 13 and 14: spectrum by atmospheric effects. Re
Page 15 and 16: emoved function showing an absorpti
Page 17 and 18: LIST OF TABLES Table 1. This table
Page 19 and 20: LIST OF ACRONYMS AND ABBREVIATIONS
Page 21 and 22: I. INTRODUCTION A study published b
Page 23 and 24: II. THE PHYSICS BEHIND REMOTE SENSI
Page 25: sensitive a given sensor is to diff
Page 29 and 30: analyzing imagery spectra, it is mo
Page 31 and 32: After data have been converted to r
Page 33 and 34: Collins et al. (1997) was able to s
Page 35 and 36: These purposes include, but are not
Page 37 and 38: III. DESERT ECOSYSTEM CHARACTERISTI
Page 39 and 40: sagebrush of Utah, Montana, and the
Page 41 and 42: in desert regions include argids, o
Page 43 and 44: 2. Biological Soil Crusts (BSCs) Bi
Page 45 and 46: 2004), especially in cases where ma
Page 47 and 48: IV. STUDY SITES The focus area of t
Page 50 and 51: Figure 13. This figure illustrates
Page 52 and 53: Following the uplift that occurred
Page 54 and 55: the Mazourka Canyon OHV park betwee
Page 56 and 57: wavelengths being analyzed to obtai
Page 58 and 59: 2. Field Spectroscopy An Analytical
Page 60 and 61: A spectral library was then built a
Page 62 and 63: after atmospherically correcting th
Page 64 and 65: where: is the mean corrected and no
Page 66 and 67: also be seen in Figure 23. The leve
Page 68 and 69: Figure 24. This figure is a compari
Page 70 and 71: Figure 25. This figure shows ASD co
Page 72 and 73: Looking at Figure 25 it is apparent
Page 74 and 75: A. IMAGERY DERIVED ENDMEMBERS The i
Page 76 and 77:
Figure 28. The above shows some of
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spectrometer, reflectance values we
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such an inference can be made (Ben-
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While this is lower than the hoped
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While the lower value would initial
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Figure 33. This figure shows the ad
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Inset C of Figure 35 is the same da
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However, the presences of BSCs are
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A B C Figure 37. Inset A shows the
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A B C Figure 38. Inset A shows a co
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differences in the studies by other
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small concentrations making them un
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area making it possible to tell wha
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Clark, R. N., Swayze, G. A., Livo,
Page 104 and 105:
Kruse, F. A., Boardman, J. W., and
Page 106 and 107:
Sharp, R. P., and Glazner, A. F., (
Page 108:
INITIAL DISTRIBUTION LIST 1. Defens
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