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

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also be seen in Figure 23. The leveling off of the pixel purity index curve at approximately 2000–3000 pixels is the basis that determines the maximum number of pixels to be used in the visualization step. To be sure that the best subset possible was used, 3000 pixels were set for the maximum. Once the purest pixels were identified and previewed, they were then extracted as endmembers. Two options for endmember extraction are possible, the first is imagery derived endmembers, which intuitively means that the endmembers are extracted from the imagery itself and is generally thought of as the best option (Kruse and Boardman, 2011, Kruse and Perry, 2009). The second option is to input user supplied endmembers, which means one can input field or laboratory collected spectra into the algorithm and try to detect those as target material. Both options were run on the two data sets with the highest spatial resolution for the Canyonlands site. The data sets were AVIRIS flight logs f110512t01p00r07 and f110512t01p00r08 with 2.9 m and 3.3 m spatial resolution, respectively. In some cases a third data set with 15.2 m resolution was utilized (f110623t01p00r10). This was done for two reasons; the first is because the BSC component does not necessarily take up large areas and the bigger the pixel, the less likely it is that BSCs will be detectable in the imagery. To test how the BSCs and other endmembers might be distributed in the imagery, they were compared to the results using imagery derived endmembers. The second reason it was run on two data sets was for a repeatability test with the same endmembers at the same success rate. To try and create the most similar conditions, the data sets with the most similar spatial resolution and topography were selected; the third data set was used when further testing was required. Figure 24 shows some of the 48 endmembers derived from the imagery sets compared to the spectra collected using the ASD imaging spectrometer in the Owens/Death Valley area and input as user supplied endmembers. MF and infeasibility scores were then used to determine areas with high MF and low infeasibility scores. These correspond to areas with high abundance and feasible mixtures. These areas were then analyzed to see if the endmembers could actually be related to any features within the imagery and potentially mapped as areas of interest. 46
Figure 23. This figure illustrates the number of times a pixel is marked as pure and shows the leveling off of pixels at around 2000. Analysis of this graph helps to determine how many pixels to use in visualization and endmember derivation. 47
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 and 26: sensitive a given sensor is to diff
Page 27 and 28: Figure 3. From Green et al. (1998),
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 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
Page 78 and 79: spectrometer, reflectance values we
Page 80 and 81: such an inference can be made (Ben-
Page 82 and 83: While this is lower than the hoped
Page 84 and 85: While the lower value would initial
Page 86 and 87: Figure 33. This figure shows the ad
Page 88 and 89: Inset C of Figure 35 is the same da
Page 90 and 91: However, the presences of BSCs are
Page 92 and 93: A B C Figure 37. Inset A shows the
Page 94 and 95: A B C Figure 38. Inset A shows a co
Page 96 and 97: differences in the studies by other
Page 98 and 99: small concentrations making them un
Page 100 and 101: area making it possible to tell wha
Page 102 and 103: 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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