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Volume 6
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Geosciences, Volume 6, Issue 4 (December 2016) – 16 articles

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6457 KiB  
Article
A Mineralized Alga and Acritarch Dominated Microbiota from the Tully Formation (Givetian) of Pennsylvania, USA
by John A. Chamberlain, Rebecca B. Chamberlain and James O. Brown
Geosciences 2016, 6(4), 57; https://doi.org/10.3390/geosciences6040057 - 19 Dec 2016
Cited by 7 | Viewed by 8174
Abstract
Sphaeromorphic algal cysts, most probably of the prasinophyte Tasmanites, and acanthomorphic acritarch vesicles, most probably Solisphaeridium, occur in a single 20 cm thick bed of micritic limestone in the lower part of the Middle Devonian (Givetian) Tully Formation near Lock Haven, Pennsylvania. Specimens [...] Read more.
Sphaeromorphic algal cysts, most probably of the prasinophyte Tasmanites, and acanthomorphic acritarch vesicles, most probably Solisphaeridium, occur in a single 20 cm thick bed of micritic limestone in the lower part of the Middle Devonian (Givetian) Tully Formation near Lock Haven, Pennsylvania. Specimens are composed of authigenic calcite and pyrite crystals about 5–10 µm in length. Some specimens are completely calcitic; some contain both pyrite and calcite; and many are composed totally of pyrite. The microfossils are about 80 to 150 µm in diameter. Many show signs of originally containing a flexible wall composed of at least two layers. Some appear to have been enclosed in a mucilaginous sheath or membrane when alive. The acanthomorphic forms have spines that are up to 20 µm in length, expand toward the base, and are circular in cross-section. The microflora occurs with microscopic molluscs, dacryoconarids, the enigmatic Jinonicella, and the oldest zooecia of ctenostome bryozoans known from North America. The microalgal horizon lacks macrofossils although small burrows are present. Microalgae and acritarchs have been preserved via a complex preservational process involving rapid, bacterially-mediated post-mortem mineralization of dead cells. The microfossil horizon, and possibly much of the Tully Formation at Lock Haven with similar lithology, formed in a relatively deep, off-shore basin with reduced oxygen availability in the substrate. Full article
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Figure 1

Figure 1
<p>Locality Maps. (<b>A</b>) Map of Pennsylvania showing the location of Lock Haven. The main outcrop belt of the Tully Formation and its lateral equivalents is shown in the blue line; (<b>B</b>) Detailed map of the Lock Haven area. Star shows the site of the Tully exposure from which the microfossils described here derive. PA664 and PA120 refer to Pennsylvania state highways. US220 identifies a US federal highway. North is toward the top of the page in both maps.</p> Full article ">Figure 2
<p>Stratigraphic column for the Lock Haven exposure of the Tully Formation. Star indicates the position of the microfossils described here. B indicates the position from which the index brachiopods Camarotoechia mesocostale, Rhyssochonetes aurora, and Emanuella subumbona were recovered. Disconformities: 1—disconformity at Tully/Moscow contact; 2—inferred position of disconformity representing the maximum flooding surface of Baird and Brett [<a href="#B38-geosciences-06-00057" class="html-bibr">38</a>,<a href="#B39-geosciences-06-00057" class="html-bibr">39</a>] separating the Lower and Middle Tully Formation; 3—Heckel’s [<a href="#B36-geosciences-06-00057" class="html-bibr">36</a>,<a href="#B37-geosciences-06-00057" class="html-bibr">37</a>] disconformity defining the base of the Upper Tully Formation.</p> Full article ">Figure 3
<p>Microfossil outcrop, Lock Haven, PA. (<b>A</b>) Lower Tully Formation looking east along PA route 644. The lowermost knobby micritic calcilutites referred to in the text form the prominent ledge in the left foreground. Star shows the position of the microfossil horizon. Below these beds the exposure is heavily vegetated, but float indicates that the underlying rock is calcareous shale. The Tully-Windom contact lies past the utility pole at the right margin of the figure; (<b>B</b>) Close-up of the microfossil bed (between the white hachures) showing its irregular, knobby appearance. The top of the uppermost finely laminated, fissile calcareous shale typical of the beds extending down to the Tully-Windom contact can be seen below the microfossil bed. Hammer handle is about 25 cm long.</p> Full article ">Figure 4
<p>Texture of the Lower Tully Formation microfossil bed. (<b>A</b>) thin section (TYT-11) cut parallel to bedding showing burrow cross-section. The burrow appears as the slightly darker, more microcrystalline region in the center of the picture (periphery highlighted by black arrows). Small black masses of pyrite can be seen widely disseminated within the burrow. Also inside the burrow are a small number of circular objects (white arrows), and numerous sparry structures, some of which are shell fragments (gray arrows). Scale bar = 1 mm; (<b>B</b>) thin section (TYT-23) cut perpendicular to bedding showing a pyrite-rich burrow. Scale bar = 750 μm.</p> Full article ">Figure 5
<p>Microalgal cell (TYT-05) viewed in white light. (<b>A</b>) specimen in cross-section surrounded by micrite. Scale bar = 100 μm; (<b>B</b>) enlargement of the upper portion of specimen showing a multi-layered body wall defined by three thin, dark bands. Scale bar = 20 μm; (<b>C</b>) enlargement of lower left portion of specimen showing multi-layered body wall. Scale bar = 20 μm.</p> Full article ">Figure 6
<p>Microalgae in thin section showing progressive degrees of pyritization. All scale bars = 100 μm. (<b>A</b>) specimen (TYT-11) with crystalline calcite halos, as in <a href="#geosciences-06-00057-f004" class="html-fig">Figure 4</a>. Micrite fills most of the interior. Body wall is pyritized near white arrow; (<b>B</b>) specimen (TYT-04) with pyritized wall and small masses of pyrite in the central body; (<b>C</b>) specimen (TYT-09) with numerous small masses nearly filling the central body; (<b>D</b>) (TYT-01) specimen nearly totally composed of pyrite but retaining a surrounding halo of calcite crystals. Original, body wall is visible near white arrows.</p> Full article ">Figure 7
<p>SEM images of non-spinose, sphaeromorphic pyritized microalgae extracted from rock matrix. Scale bar in A, B, and C = 50 μm. Scale bar in D = 5 μm. (<b>A</b>) specimen (TYS-14) with smooth surface split open in upper left; (<b>B</b>) specimen (TYS-02) with smooth surface covering small portions or upper right and lower left quadrants. White arrows indicate locations where smooth regions overlie the roughened area at the center of the specimen; (<b>C</b>) specimen (TYS-23) with a surface composed of pseudopolygonal “tiles” overlain in upper left (white arrow) by a thin, smooth, fine-grained covering. This covering becomes progressively broken into tiles toward the lower right (white arrow); (<b>D</b>) Close-up of tiled surface of TYS-23 in cross-section showing that this surface consists of several thin parallel layers 1 to 2 µm thick. In places, (white arrows) narrow, columnar or pillar-like structures extend between the layers.</p> Full article ">Figure 8
<p>SEM images of pyritized spinose, acanthomorphic specimens extracted from rock matrix. Scale bar in A = 40 µm. Scale bar in B = 25 µm. Scale bar in C, D = 10 µm. (<b>A</b>) spheroidal specimen (TYS-29) with large crack showing arrangement of spines; (<b>B</b>) ovoid specimen (TYS-38) with spines; (<b>C</b>,<b>D</b>) Detail of spines in two specimens. White arrows point to flat-topped spines that have hollow interiors. C: TYS-32. D: TYS-41.</p> Full article ">Figure 9
<p>Composition of specimens extracted from rock matrix. Scale bar in A = 50 µm. Scale bar in B: 10 µm. Scale bar in C, D = 5 µm. Scale bar in E = 20 µm (<b>A</b>) SEM image of incompletely preserved sphaeromorphic specimen (TYS-16) showing an interior filled with cubic and pyramidal crystals of pyrite. Box outline is the perimeter of the enlargement shown in B; (<b>B</b>) Detail of crystalline surface showing large euhedral pyrite crystals and smaller pyrite framboids (white arrows); (<b>C</b>) Detail of a large open space within the interior of a sphaeromorphic specimen (TYS-17) showing clusters of framboids lining the space (white arrows) and large euhedral crystals at the bottom of the opening; (<b>D</b>) Spheroidal framboid with individual crystallites of 1 µm or less growing from the surface of an acanthomorphic specimen (TYS-45); (<b>E</b>) Acanthomorphic specimen (TYS-41) filled with euhedral pyrite crystals. Star shows the target area of the x-ray beam producing the spectrograph illustrated in F; (<b>F</b>) X-ray signature of smooth surface at position of star in E. Main peaks are those of iron and sulfur.</p> Full article ">Figure 10
<p>Deformed specimens. Scale bar in A, B, and C = 100 μm. (<b>A</b>) Thin section of a specimen (TYT-11) with a portion of the body wall split away from the main body. The fragment has an inwardly convex curvature; (<b>B</b>) SEM image of a sphaeromorphic specimen (TYS-15) with an inwardly convex depression in its body wall; (<b>C</b>) SEM image of a sphaeromorphic specimen (TYS-04) with a sharp, crease-like fold in its body wall.</p> Full article ">Figure 11
<p>Microalgae having a thin, coating or layer at the surface. All scale bars = 10 μm. (<b>A</b>) SEM image of an acanthomorphic specimen (TYS-40) having a thin surface layer which has split away from the surface. The edges of this layer turn up, and in places form long tube-like rolls; (<b>B</b>) SEM image of an acanthomorphic specimen (TYS-51) showing that this layer contains numerous ovate or circular pits similar in appearance to collapsed air bubbles; (<b>C</b>) SEM image of an acanthomorphic specimen (TYS-44) which has split open revealing pyrite crystals within the interior. A well-developed thin, pitted layer is visible covering the surface of the specimen at the lower right and upper left, and extending across the tips and exposed surfaces of the interior crystals.</p> Full article ">Figure 12
<p>Close-up of several spines of a Tully acanthomorph (TYS-43).Arrows point to the same spine in both A and B. (<b>A</b>) Spines viewed from directly above vesicle surface. Spine indicated by arrow contains a stack of large pyrite crystals growing up from vesicle interior. Scale bar = 10 µm; (<b>B</b>) Close-up of this spine shows the open spine interior filled with pyrite crystals inside it, and with no obvious vesicle wall or partition within the spine. Scale bar = 10 µm.</p> Full article ">Figure 13
<p>SEM images of microorganisms co-occurring with the microflora. Scale bar in J = 40 μm. All other scale bars = 100 μm. (<b>A</b>) TYSV-01; paleotaxodont bivalve, probably <span class="html-italic">Nuculoidea corbuliformis</span>; (<b>B</b>) TYSG-01; highspired loxonematacean gastropod, possibly <span class="html-italic">Palaeozygopleura</span> sp.; (<b>C</b>) TYSG-02; Euomphalacean gastropod, possibly <span class="html-italic">Euomphalus</span> sp.; (<b>D</b>) TYSC-01; Longiconic nautiloid. Numbers identify the positions of the first six septa; (<b>E</b>) TYSO-01; ostracode, probably <span class="html-italic">Ulrichia</span> sp.; (<b>F</b>) TYSO-02; spiny ostracode, probably belonging to either to the Aechminidae or Aechminellidae. White arrow points to the base of a broken lateral spine; (<b>G</b>) TYSD-01; Dacryoconarid, probably <span class="html-italic">Viriatellina</span> sp.; (<b>H</b>,<b>I</b>) TYSZ-01; TYSZ-02; ctenostome bryozoan zooids. c—collar; s—stolon; (<b>J</b>) enlargement of area at base of lateral stolon in I; (<b>K</b>) TYSJ-01; <span class="html-italic">Jinonicella kolebabi</span>, ribbed morph; (<b>L</b>) TYSJ-02; <span class="html-italic">Jinonicella kolebabi</span>, unribbed morph.</p> Full article ">Figure 14
<p>Taphonomy of the Tully microflora. Model for the mineralization of algal and acritarch cells preserved in the Lower Tully Limestone at Lock Haven, PA. Arrows indicate passage between preservational stages. They do not indicate that only dead cells rather than cysts give rise to mineralized material in Stage 3. (<b>STAGE 1</b>) Live planktonic algal and acritarch cells, some with a mucilaginous envelope, dispersed in the oxygenated surface waters of the Appalachian Seaway; (<b>STAGE 2</b>) Settling through the water column of empty cysts and dead cells, and incorporation into the substrate; (<b>STAGE 3</b>) Mineralization within the sediment. Formation of halo of sparry calcite crystals, micrite and pyrite infills <b>A</b>–<b>F</b>: different end members of Tully mineralization processes: (<b>A</b>) specimen completely pyritized and filled with pyrite; (<b>B</b>) pyritized specimen with a hollow interior space; (<b>C</b>) pyritized specimen filled primarily with pyrite and a small micrite mass. (<b>D</b>) Specimen with pyritized wall and pyrite-micrite infill; (<b>E</b>) Specimen with pyritized wall and micrite infill interspersed with globular pyrite clusters; (<b>F</b>) Specimen with little or no pyritization and filled entirely with micrite.</p> Full article ">
6434 KiB  
Article
Anomaly Detection from Hyperspectral Remote Sensing Imagery
by Qiandong Guo, Ruiliang Pu and Jun Cheng
Geosciences 2016, 6(4), 56; https://doi.org/10.3390/geosciences6040056 - 12 Dec 2016
Cited by 19 | Viewed by 6052
Abstract
Hyperspectral remote sensing imagery contains much more information in the spectral domain than does multispectral imagery. The consecutive and abundant spectral signals provide a great potential for classification and anomaly detection. In this study, two real hyperspectral data sets were used for anomaly [...] Read more.
Hyperspectral remote sensing imagery contains much more information in the spectral domain than does multispectral imagery. The consecutive and abundant spectral signals provide a great potential for classification and anomaly detection. In this study, two real hyperspectral data sets were used for anomaly detection. One data set was an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data covering the post-attack World Trade Center (WTC) and anomalies are fire spots. The other data set called SpecTIR contained fabric panels as anomalies compared to their background. Existing anomaly detection algorithms including the Reed–Xiaoli detector (RXD), the blocked adaptive computation efficient outlier nominator (BACON), the random selection based anomaly detector (RSAD), the weighted-RXD (W-RXD), and the probabilistic anomaly detector (PAD) are reviewed here. The RXD generally sets strict assumptions to the background, which cannot be met in many scenarios, while BACON, RSAD, and W-RXD employ strategies to optimize the estimation of background information. The PAD firstly estimates both background information and anomaly information and then uses the information to conduct anomaly detection. Here, the BACON, RSAD, W-RXD, and PAD outperformed the RXD in terms of detection accuracy, and W-RXD and PAD required less time than BACON and RSAD. Full article
(This article belongs to the Special Issue Mapping and Assessing Natural Disasters Using Geospatial Technologies)
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Figure 1

Figure 1
<p>(<b>a</b>) AVIRIS image covering the World Trade Center (WTC) in New York City; (<b>b</b>) ground-truth map indicating spatial locations of hot spot fires, available from the United States Geological Survey.</p> Full article ">Figure 2
<p>(<b>a</b>) The SpecTIR hyperspectral image. The anomalies were highlighted by black ellipses; (<b>b</b>) Ground-truth information of the anomalies.</p> Full article ">Figure 3
<p>Detection results created by different algorithms with the WTC data.</p> Full article ">Figure 4
<p>ROC curves corresponding to the detection results presented in <a href="#geosciences-06-00056-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 5
<p>Detection results produced by different algorithms with the SpecTIR data.</p> Full article ">Figure 6
<p>Binary images created after thresholding the results in <a href="#geosciences-06-00056-f005" class="html-fig">Figure 5</a> using empirically selected thresholds. The ratio of anomaly was set to 1%.</p> Full article ">Figure 7
<p>ROC curves corresponding to the detection results from the SpecTIR data presented in <a href="#geosciences-06-00056-f005" class="html-fig">Figure 5</a> and <a href="#geosciences-06-00056-f006" class="html-fig">Figure 6</a>.</p> Full article ">
1576 KiB  
Article
Network Modelling of the Influence of Swelling on the Transport Behaviour of Bentonite
by Ignatios Athanasiadis, Simon Wheeler and Peter Grassl
Geosciences 2016, 6(4), 55; https://doi.org/10.3390/geosciences6040055 - 8 Dec 2016
Cited by 1 | Viewed by 5558
Abstract
Wetting of bentonite is a complex hydro-mechanical process that involves swelling and, if confined, significant structural changes in its void structure. A coupled structural transport network model is proposed to investigate the effect of wetting of bentonite on retention conductivity and swelling pressure [...] Read more.
Wetting of bentonite is a complex hydro-mechanical process that involves swelling and, if confined, significant structural changes in its void structure. A coupled structural transport network model is proposed to investigate the effect of wetting of bentonite on retention conductivity and swelling pressure response. The transport network of spheres and pipes, representing voids and throats, respectively, relies on Laplace–Young’s equation to model the wetting process. The structural network uses a simple elasto-plastic approach without hardening to model the rearrangement of the fabric. Swelling is introduced in the form of an eigenstrain in the structural elements, which are adjacent to water filled spheres. For a constrained cell, swelling is shown to produce plastic strains, which result in a reduction of pipe and sphere spaces and, therefore, influence the conductivity and retention behaviour. Full article
(This article belongs to the Special Issue Geological Disposal of High Level Radioactive Waste - The Relationship between Engineered and Natural Barriers)
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Figure 1

Figure 1
<p>Bentonite fabric: (<b>a</b>) macro-scale of heavily-compacted unsaturated bentonite (adapted from [<a href="#B1-geosciences-06-00055" class="html-bibr">1</a>]) and (<b>b</b>) schematic presentation of four Voronoi polyhedra and the corresponding Delaunay tetrahedron. The spheres and pipes corresponding to a Voronoi polyhedron face are also presented. The Voronoi grains are scaled down to improve the clarity of the presentation (in the actual network, the grains are in contact).</p> Full article ">Figure 2
<p>Retention behaviour: A simple 2D network subjected to (<b>a</b>) drying and (<b>b</b>) wetting. The hatched areas represent the wetting fluid. The drying and wetting fluid for cases (<b>a</b>) and (<b>b</b>), respectively, is allowed to enter the network from Pipe 1.</p> Full article ">Figure 3
<p>Structural network: (<b>a</b>) structural element with nodal degrees of freedom and displacement discontinuities at the mid-cross-section and (<b>b</b>) the spherical yield surface for the modelling of plastic deformation.</p> Full article ">Figure 4
<p>Coupling between the structural and transport network: (<b>a</b>) swelling strain is present if one of the spheres at mid-cross-section is water filled, and (<b>b</b>) the meniscus generates extra forces acting on the grain interaction if all spheres at the mid-cross-section are empty.</p> Full article ">Figure 5
<p>Coupling between structural and transport network: influence of plastic strains on (<b>a</b>) the pipes and (<b>b</b>) the spheres.</p> Full article ">Figure 6
<p>Periodic cell: (<b>a</b>) transport elements (blue) with cross-section structural elements (yellow) and (<b>b</b>) structural elements (yellow) with cross-section transport elements (blue). For the reference of the colours, the reader is referred to the online version of the manuscript.</p> Full article ">Figure 7
<p>Constrained swelling analysis: retention in the form of saturation <math display="inline"> <semantics> <msub> <mi>S</mi> <mi mathvariant="normal">r</mi> </msub> </semantics> </math> versus capillary suction <math display="inline"> <semantics> <msub> <mi>P</mi> <mi mathvariant="normal">c</mi> </msub> </semantics> </math>. The labels a, b, c and d mark stages at which the spatial distribution of quantities within the cell is investigated.</p> Full article ">Figure 8
<p>Constrained swelling analysis: conductivity versus capillary suction for the large network. The labels a, b, c and d mark stages at which the spatial distribution of quantities within the cell is investigated.</p> Full article ">Figure 9
<p>Constrained swelling analysis: swelling pressure <math display="inline"> <semantics> <msub> <mi>P</mi> <mi mathvariant="normal">s</mi> </msub> </semantics> </math> versus capillary suction <math display="inline"> <semantics> <msub> <mi>P</mi> <mi mathvariant="normal">c</mi> </msub> </semantics> </math>. The labels a, b, c and d mark stages at which the distribution of quantities within the cell are discussed later.</p> Full article ">Figure 10
<p>Constrained swelling analysis: spatial distribution of pipes filled with wetting fluid at the four stages of capillary suction marked in <a href="#geosciences-06-00055-f007" class="html-fig">Figure 7</a> to <a href="#geosciences-06-00055-f009" class="html-fig">Figure 9</a>. The capillary suction values of the four stages are (<b>a</b>) <math display="inline"> <semantics> <mrow> <mn>6</mn> <mo>.</mo> <mn>61</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>3</mn> </msup> </mrow> </semantics> </math>, (<b>b</b>) <math display="inline"> <semantics> <mrow> <mn>2</mn> <mo>.</mo> <mn>37</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>3</mn> </msup> </mrow> </semantics> </math>, (<b>c</b>) <math display="inline"> <semantics> <mrow> <mn>1</mn> <mo>.</mo> <mn>43</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>3</mn> </msup> </mrow> </semantics> </math> and (<b>d</b>) <math display="inline"> <semantics> <mrow> <mn>0</mn> <mo>.</mo> <mn>36</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>3</mn> </msup> </mrow> </semantics> </math> Pa.</p> Full article ">Figure 11
<p>Constrained swelling analysis: spatial distribution of pipes with <math display="inline"> <semantics> <mrow> <msub> <mi>r</mi> <mi mathvariant="normal">p</mi> </msub> <mo>&gt;</mo> <mn>5</mn> </mrow> </semantics> </math> <math display="inline"> <semantics> <mi mathvariant="sans-serif">μ</mi> </semantics> </math>m at the four stages marked in <a href="#geosciences-06-00055-f007" class="html-fig">Figure 7</a> to <a href="#geosciences-06-00055-f009" class="html-fig">Figure 9</a>. The capillary suction values of the four stages are (<b>a</b>) <math display="inline"> <semantics> <mrow> <mn>6</mn> <mo>.</mo> <mn>61</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>3</mn> </msup> </mrow> </semantics> </math>, (<b>b</b>) <math display="inline"> <semantics> <mrow> <mn>2</mn> <mo>.</mo> <mn>37</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>3</mn> </msup> </mrow> </semantics> </math>, (<b>c</b>) <math display="inline"> <semantics> <mrow> <mn>1</mn> <mo>.</mo> <mn>43</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>3</mn> </msup> </mrow> </semantics> </math> and (<b>d</b>) <math display="inline"> <semantics> <mrow> <mn>0</mn> <mo>.</mo> <mn>36</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>3</mn> </msup> </mrow> </semantics> </math> Pa.</p> Full article ">
5543 KiB  
Article
Magnetic Materials: Novel Monitors of Long-Term Evolution of Engineered Barrier Systems
by Simon L. Harley, Nicola Rigonat and Ian B. Butler
Geosciences 2016, 6(4), 54; https://doi.org/10.3390/geosciences6040054 - 7 Dec 2016
Cited by 1 | Viewed by 5165
Abstract
Most safety cases for the deep geological disposal of radioactive waste are reliant on the swelling of bentonite in the engineered barrier system as it saturates with groundwater. Assurance of safety therefore requires effective monitoring of bentonite saturation. The time- and fluid-dependent corrosion [...] Read more.
Most safety cases for the deep geological disposal of radioactive waste are reliant on the swelling of bentonite in the engineered barrier system as it saturates with groundwater. Assurance of safety therefore requires effective monitoring of bentonite saturation. The time- and fluid-dependent corrosion of synthetic magnets embedded in bentonite is demonstrated here to provide a novel and passive means of monitoring saturation. Experiments have been conducted at 70 °C in which neo magnets, AlNiCo magnets, and ferrite magnets have been reacted with saline (NaCl, KCl, CaCl2) solutions and alkaline fluids (NaOH, KOH, Ca(OH)2 solutions; pH = 12) in the presence of bentonite. Nd-Fe-B magnets undergo extensive corrosion that results in a dramatic change from ferromagnetic to superparamagnetic behaviour concomitant with bentonite saturation. AlNiCo magnets in saline solutions show corrosion but only limited decreases in their magnetic intensities, and ferrite magnets are essentially unreactive on the experimental timescales, retaining their initial magnetic properties. For all magnets the impact of their corrosion on bentonite swelling is negligible; alteration of bentonite is essentially governed by the applied fluid composition. In principle, synthetic magnet arrays can, with further development, be designed and embedded in bentonite to monitor its fluid saturation without compromising the integrity of the engineered barrier system itself. Full article
(This article belongs to the Special Issue Geological Disposal of High Level Radioactive Waste - The Relationship between Engineered and Natural Barriers)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The essence of the MMM (Magnetic Monitoring Materials) concept. Yellow segments form the bentonite buffer ring/sleeve in the HLW (high level waste) EBS (engineered barrier system). Orange arrows join the parts of this from the larger scale (full segmented sleeve, 1.5–2 m radius) to the scale of a single bentonite segment, both without and with embedded magnets (Nd-Fe-B magnets in this example). The blue disc is a magnified view of a single axially magnetised N42 Nd-Fe-B magnet.</p> Full article ">Figure 2
<p>Post-extraction picture of the samples of NdB set. (<b>a</b>) For the sample reacted in deionized water the magnet has been pushed upwards by the swelling bentonite matrix and there is the presence of precipitated orange-coloured material; (<b>b</b>) The three samples reacted in alkaline solutions show maximum clay swelling, tensile fractures, and no reaction haloes; (<b>c</b>) The three samples reacted in saline solutions feature partially swollen matrix and the presence of black-coloured material precipitated at the solution/bentonite interface. The butyl septa are deformed and swollen, indicating the presence of hydrogen gas formed by the corrosion of the magnets.</p> Full article ">Figure 3
<p>Hysteresis loops (magnetisation vs applied field) of NdFeB permanent magnets reacted with alkaline solutions (<b>left</b>), deionised water (<b>right</b>), and saline solutions (<b>right</b>). The sample reacted with a KOH solution features a slightly wider hysteresis loop, typical of a pseudo-single domain material, whereas all other hysteresis loops show the classic “wasp-waisted” shape of a superparamagnetic material.</p> Full article ">Figure 4
<p>HPFU (hydrogens per formula unit) vs. Curie temperature plot. The samples of the NdB set, in which HPFU has been calculated from powder X-ray diffraction (PXRD) spectra, are plotted and compared with pure NdFeB hydrides (Isnard, 1995 [<a href="#B23-geosciences-06-00054" class="html-bibr">23</a>]: points 0, 1, 2, 3, 4, and 5). The relative scatter of the samples from this study reflects their impure chemical compositions which causes shifts in temperature.</p> Full article ">Figure 5
<p>Post-extraction picture of the samples of AlB set. (<b>a</b>) Samples reacted with deionized water and alkaline solutions, showing complete swelling of the bentonite matrix, with formation of tensile fractures; (<b>b</b>) The three samples reacted with saline solutions, showing abundant quantities of red/orange precipitates at the solution/air interface and at the solution-bentonite interface. The bentonite matrix is not completely swollen and presents an orange colour at the interface, gently fading towards a yellow/pale-olive-green colour towards the basal part of the matrix.</p> Full article ">Figure 6
<p>Hysteresis loops of AlNiCo permanent magnets reacted with alkaline (<b>left</b>) and saline (<b>right</b>) solutions. All have a pseudo-single-domain behaviour.</p> Full article ">Figure 7
<p>(<b>a</b>) Ferrite reacted with Ca(OH)<sub>2</sub> solution; (<b>b</b>) Ferrite reacted with CaCl<sub>2</sub> solution. These are representative of all the experiments involving ferrite, which show negligible magnet corrosion and no detectable bentonite alteration.</p> Full article ">Figure 8
<p>Hysteresis loops of ferrite permanent magnets reacted with alkaline (<b>left</b>) and saline (<b>right</b>) solutions. All have single domain behaviour.</p> Full article ">Figure 9
<p>M<sub>rs</sub>/M<sub>s</sub> vs. H<sub>cr</sub>/H<sub>c</sub> plot after Day et al. [<a href="#B18-geosciences-06-00054" class="html-bibr">18</a>] with domain state limits drawn after Dunlop [<a href="#B20-geosciences-06-00054" class="html-bibr">20</a>]. Values for the raw materials, as given by manufacturers, are shown as filled symbols. Experimentally reacted magnets are shown by open symbols. SD = single domain; PSD = pseudo-single domain; MD = multi-domain.</p> Full article ">Figure 10
<p>(<b>a</b>) Air-dried and (<b>b</b>) glycol-solvated PXRD traces of clay samples from the AlB set. The 2.4–10°2θ range is chosen to emphasise the changes occurring to the 001 diffraction peak. (Ms: muscovite).</p> Full article ">Figure 11
<p>(<b>a</b>) Air-dried and (<b>b</b>) glycol-solvated PXRD traces of clay samples from the FeB set. The 2.4–10°2θ range is chosen to emphasise the changes occurring to the 001 diffraction peak. (Ms: muscovite).</p> Full article ">
152 KiB  
Editorial
Developing Lyell’s Legacy: Contributions to the Geosciences of the Anthropocene
by Carlos Alves
Geosciences 2016, 6(4), 53; https://doi.org/10.3390/geosciences6040053 - 30 Nov 2016
Cited by 1 | Viewed by 3918
Abstract
In this new edition of the Geoscience of the Built Environment [1], we hope to continue our contribution to the development of Geosciences studies in the Anthropocene, considering classical issuesthatareatleastasoldasCharlesLyell’smajorworks[2,3],whichcanbeconsideredthefounding literary works of modern Geology.[...] Full article
(This article belongs to the Special Issue Geoscience of the Built Environment 2016 Edition)
23217 KiB  
Review
Mineral Mapping for Exploration: An Australian Journey of Evolving Spectral Sensing Technologies and Industry Collaboration
by Thomas Cudahy
Geosciences 2016, 6(4), 52; https://doi.org/10.3390/geosciences6040052 - 29 Nov 2016
Cited by 44 | Viewed by 17419
Abstract
This paper describes selected results from over a dozen collaborative projects led by Commonwealth Scientific and Industrial Research Organisation (CSIRO) in Australia spanning a 30-year history of developments in satellite, airborne, field and drill core sensing technologies and how these can assist explorers [...] Read more.
This paper describes selected results from over a dozen collaborative projects led by Commonwealth Scientific and Industrial Research Organisation (CSIRO) in Australia spanning a 30-year history of developments in satellite, airborne, field and drill core sensing technologies and how these can assist explorers to measure and map valuable mineral information. The exploration case histories are largely from Australian test sites and describe how spectral sensing technologies have progressed from early “niche creation” systems, such as the field PIMA-II (Portable Field Mineral Analyzer) and airborne Geoscan, HyMap™ and OARS-TIPS (Operational Airborne Remote Sensing – Thermal Infrared Profiling Spectrometer) systems and drill-core HyLogger™ systems, to the current expanding array of pubic and commercial mineral mapping sensors, including the ASTER (Advanced Spaceborne Thermal Emission and Reflectance Radiometer) satellite system which has acquired imagery spanning the entire Earth’s land surface (<83° latitude). These sensors are delivering voluminous spectral data from different parts of the visible to the thermal infrared (400 to 14,000 nm) spectrum at different spectral, radiometric and spatial resolutions. Two critical exploration challenges are central to the case histories, namely: (i) can surface cover, such as vegetation, regolith or transported materials, be characterized and accounted for so that the target geology is accurately revealed; and (ii) does this revealed geology show evidence of alteration footprints to potential economic mineralization. Spectrally measurable minerals important to solving these challenges include white micas, kaolinite and garnets, with measurement of their respective physicochemistries being key. For example, kaolin disorder is useful for mapping transported versus weathered in situ materials, while the chemical substitution in white micas and garnets provide vectors to potential economic mineralization. Importantly, appropriate selection of the optimum sensor/data type for a given geological application depends primarily on the level of detail/accuracy of the mineral information required by the user. A major opportunity is to now harness the many sensor/data types and deliver to users consistent, accurate mineral information products, that is, creation of a number of valuable global mineral product standards. As part of this vision, CSIRO has been developing improved sensor/data calibration processes and information extraction methods that for example, unmix the target mineralogy from green and dry vegetation cover in remote sensing data sets. Emphasis to date has been on generating public spectral-mineral product standards, especially at ASTER’s limited but geologically-valuable spectral resolution. The results are showing that scalable, global, three-dimensional (3D) mineral maps are achievable which will only improve our ability to more accurately characterize regolith and geological architecture, increase our understanding of formative processes and assist the discovery of new economic mineral systems. Full article
(This article belongs to the Special Issue Advances in Lithological and Structural Mapping Using Earth Observation Data)
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Figure 1

Figure 1
<p>Schematic mineral-regolith models. (<b>a</b>) Cross section of a lateritic profile showing changes in a range of fresh rock and weathering minerals, including a zone of metasomatic alteration that persists from fresh rock through to the surface [<a href="#B44-geosciences-06-00052" class="html-bibr">44</a>]; (<b>b</b>) Block diagram that includes different types of parent rock as well as erosional dissection through the lateritic profile. Areas of white mica in weathered mafic or ultramafic rocks (“A”) are spectral-sensing exploration targets [<a href="#B44-geosciences-06-00052" class="html-bibr">44</a>]; (<b>c</b>) Dissected lateritic profile over different parent rock types showing diagnostic variations in kaolin physicochemistry [<a href="#B47-geosciences-06-00052" class="html-bibr">47</a>].</p> Full article ">Figure 2
<p>Airborne Geoscan Mk II results (~1992) for Olary, South Australia (scene center approximately −32.0981° latitude 140.3228° longitude). (<b>a</b>) KWIK residual RGB color composite of SWIR bands 14 (2180 nm): 15 (2224 nm): 18 (2358 nm); (<b>b</b>) Inverted de-correlation stretch RGB color composite of TIR bands 19 (8600 nm): 21 (9660 nm): 23 (10,710 nm); (<b>c</b>) Published 1:50,000 scale geology plus legend; (<b>d</b>) Selected Geoscan Mk II KWIK residual pixel-spectra from field sites A–H.</p> Full article ">Figure 3
<p>Airborne MIRACO<sub>2</sub>LAS results (~1992) for the Broken Hill area, New South Wales, Australia. (<b>a</b>) Map of the distribution of Broken Hill style base metal deposits and their Mine Sequence Suite host rocks (green) [<a href="#B57-geosciences-06-00052" class="html-bibr">57</a>], together with field sample locations and the tracks of airborne MIRACO<sub>2</sub>LAS surveys which have both been color-coded with respect to garnet TIR reflectance trough wavelength. Scene center is approximately −31.9669° latitude 141.44134° longitude; (<b>b</b>) Segment of MIRACO<sub>2</sub>LAS flight-line “13DBH10T” from over the Broken Hill Main lode open pit and surrounding areas and processed to show the “unmixed” proportion of almandine (blue) and spessartine (orange) garnets as well as the wavelength position of the garnet reflectance trough at approximately ~10,700 nm (10.7 µm; brown); (<b>c</b>) MIRACO<sub>2</sub>LAS pixel spectra of almandine-rich (A; reflectance trough at 10,700 nm (10.71 µm)) and spessartine-rich (B; emissivity peak at 10,800 nm (10.80 µm) areas shown in (<b>b</b>)); (<b>d</b>) Laboratory CO<sub>2</sub> laser spectra of garnet-bearing field surface samples—field localities shown in (<b>a</b>); (<b>e</b>) Scatter-gram of the positions of the laboratory X-ray Diffraction d-spacing peak at 2.58 Angstrom units (10<sup>−8</sup> cm) (garnet 420 hkl peak) and laboratory CO<sub>2</sub> laser reflectance trough at 10,700 nm (10.7 µm).</p> Full article ">Figure 4
<p>Airborne TIPS results from Munni Munni, Pilbara Craton, Western Australia. (<b>a</b>) Field photograph of websterite (foreground) and gabbro (background) outcrops. Note the extensive cover of Spinifex grass; (<b>b</b>) Close-up view of both natural weathered rock surfaces and a broken surface of diopside-rich websterite (locality MU21); (<b>c</b>) Field “MicroFTIR” emissivity spectra of diopside-bearing fresh rock samples. Field localities shown in (<b>e</b>); (<b>d</b>) Field “MicroFTIR” emissivity spectra of diopside-bearing weathered rock surfaces. Field localities shown in (<b>e</b>); (<b>e</b>) Published 100K geology of the Munni Munni area together with the locations of selected field samples of diopside-rich websterite. Scene center approximately −21.1236° latitude 116.8420° longitude; (<b>f</b>) Interpolated, airborne TIPS processed partial unmixing “diopside” image (~10 m pixel) generated from the red spectrum in (<b>h</b>); (<b>g</b>) Interpolated airborne TIPS 10,400 nm (10.4 µm) continuum-depth image (blue in large depth); (<b>h</b>) Airborne TIPS pixel spectra selected and showing the diopside related emissivity lows at ~10.4 µm for the red and green curves. Blue spectrum is of a quartz-rich pixel.</p> Full article ">Figure 5
<p>Blast-hole pulp (166 samples, 10 m depth composite) results (~1996) for the −230 m bench of Birthday South, Superpit, Kalgoorlie, Western Australia. Approximate position −30.7715° latitude 121.5021° longitude. (<b>a</b>) Interpolated mine bench map of Au content (ppm); (<b>b</b>) Interpolated mine bench map of S content (ppm); (<b>c</b>) Scatter-gram of Au versus S contents; (<b>d</b>) Interpolated mine bench map of PIMA maximum reflectance value (%) with lower albedo yielding warmer tones; (<b>e</b>) Interpolated mine bench map of PIMA 2200 nm absorption continuum depth; (<b>f</b>) Scatter-gram of the wavelength position of the PIMA peak reflectance versus the S content; (<b>g</b>) Interpolated mine bench map of the depth of the continuum-removed 2250 nm absorption (~chlorite mineral content); (<b>h</b>) Interpolated mine bench map of the wavelength of the continuum-removed 2200 nm absorption (~white mica Tschermak substitution); (<b>i</b>) Scatter-gram of the wavelength of the 2200 nm absorption versus electron-microprobe analyses of the white mica octahedral sheet Al, Fe and Mg cations (Al<sub>oct</sub>/[Fe<sub>oct</sub> + Mg<sub>oct</sub>]) for 13 Birthday South samples showing effects of coupled Tschermak substitution.</p> Full article ">Figure 6
<p>Airborne AMS 1998 mineral mapping results for the Superpit, Kalgoorlie, Western Australia. Approximate position −30.7715° latitude 121.5021° longitude. (<b>a</b>) AMS visible albedo image of the Superpit together with an overlay of the Black Flag Group (magenta), which in theory spans the Golden Mile Fault; (<b>b</b>) Map of gold grade modelled from blast hole drill analyses for the approximate date of the AMS survey (courtesy of Kalgoorlie Consolidated Gold Mines, Australia. Major linear zones of Au highlighted by black arrows; (<b>c</b>) Processed AMS image of the continuum-depth of the 2260 nm absorption (~chlorite content). Green polygon of long wavelength 2200 nm feature (<b>e</b>) shown for cross-reference; (<b>d</b>) Processed AMS image of the continuum-depth of the 2200 nm absorption (includes white mica). Green polygon of long wavelength 2200 nm feature (<b>e</b>) shown for cross-reference. (<b>e</b>) Processed AMS image of the wavelength minimum of the continuum-removed 2200 nm absorption (~white mica Tschermak substitution but kaolin has not been masked/unmixed). Examples of major linear zones of phengite are highlighted by white arrows.</p> Full article ">Figure 7
<p>Airborne and satellite mineral mapping results for the Archaean Panorama volcanic massive sulfide, Pilbara, Western Australia. Scene center approximately −21.1980° latitude 119.2216° longitude. (<b>a</b>) Published geology [<a href="#B75-geosciences-06-00052" class="html-bibr">75</a>]; (<b>b</b>) Airborne HyMap™ (from 1998) image of white mica composition based on the wavelength minimum of the continuum-removed 2200 nm absorption (~Tschermak substitution). Similarly processed field data are embedded. Upper and lower boundaries of the volcanic pile are shown; (<b>c</b>) Satellite Hyperion (from 2001) of white mica composition based on the wavelength minimum (first derivative of a fourth-order polynomial fit) of the continuum-removed 2200 nm absorption (~Tschermak substitution); (<b>d</b>) Satellite ASTER image (from 2001) of Al-clay composition based on the index B<sub>5</sub>/B<sub>7</sub> and masked using (B<sub>5</sub> + B<sub>7</sub>)B6 &gt;2; (<b>e</b>) Airborne HyMap™ image based on partial unmixing (RGB) of three white mica rich image endmembers overlaid with a threshold partially unmixed chert endmember product (white) and interpreted magmatic (white) seawater (blue to red) fluid flow lines.</p> Full article ">Figure 8
<p>Garnet composition results for the Yerington copper porphyry–skarn system, Nevada, USA. Approximate scene center −38.9544° latitude −119.2669° longitude. (<b>a</b>) Airborne SEBASS TIR image of garnet Fe–Al chemistry based on the wavelength of the 10,600 nm emissivity peak. Similarly processed field validation data; (<b>b</b>) Selected SEBASS pixel spectra showing evidence of Ca-rich garnets (grandite); (<b>c</b>) Selected field MicroFTIR™ spectra of grandite-rich rocks from (<b>a</b>); (<b>d</b>) Scatter-grams of laboratory electron-microprobe (Fe–Al) analyses and (<b>e</b>) XRD 2D spacing of the garnet 420 hkl peak versus the MicroFTIR 10.6 µm peak wavelength position of field samples ranging in composition from andradite to grossular garnets sampled from (<b>a</b>); (<b>f</b>) Map of the andradite component (Fe) of the garnet composition based on published electron-microprobe analyses of field samples [<a href="#B98-geosciences-06-00052" class="html-bibr">98</a>].</p> Full article ">Figure 9
<p>Regolith mineralogy of the Kalgoorlie region, Western Australia, spanning a 2500 km<sup>2</sup> area (26 airborne flight-lines @ 5 m pixel resolution) centered over Kalgoorlie city [<a href="#B100-geosciences-06-00052" class="html-bibr">100</a>]. (<b>a</b>) Airborne HyMap™ (~2005) image of kaolin disorder based on the relative continuum-depths of the 2160 nm and 2200 nm absorption (well-ordered kaolin is a warmer color) masked to include only those pixels that show these kaolin features together with similarly processed field sample spectral data (colored dots); (<b>b</b>) Published regolith map showing only depositional (transported) versus in situ units together with the locations of the Superpit (<a href="#geosciences-06-00052-f006" class="html-fig">Figure 6</a>) and Binduli (<a href="#geosciences-06-00052-f010" class="html-fig">Figure 10</a>); (<b>c</b>) Selected airborne HyMap™ pixel and the United States Geological Survey (USGS) mineral library spectra of samples rich in kaolin of different disorder (crystallinity); (<b>d</b>) Scatter-gram of ASD spectra of kaolin-rich field samples comparing the depth of the 2160 nm depth measured at 8 nm versus at 18 nm spectral resolution; (<b>e</b>) Field sample data comparing the continuum-depth of the 2160 nm absorption versus laboratory XRD data processed using Partial Least Squares (PLS) to predict this same spectral feature.</p> Full article ">Figure 10
<p>Regolith-alteration maps of the Binduli area, ~15 km WSW of Kalgoorlie, Western Australia (see <a href="#geosciences-06-00052-f009" class="html-fig">Figure 9</a>). (<b>a</b>) Airborne HyMap™ B&amp;W visible image together with drill-core locations that are color coded to show “depth to fresh rock” (<a href="#geosciences-06-00052-f001" class="html-fig">Figure 1</a>). White polygons show float/subcrop of felsic volcaniclastic rocks [<a href="#B102-geosciences-06-00052" class="html-bibr">102</a>]—all other areas, except mine workings, are published as alluvium/colluvium and lacustrine sediments; (<b>b</b>) Airborne HyMap™ image of white mica composition, masked to remove all pixels with detectable 2160 nm kaolin absorption, together with similarly processed bottom-of-hole fresh rock spectral measurements.</p> Full article ">Figure 11
<p>Alteration mineralogy of the Kalgoorlie region, Western Australia, spanning a 50 × 50 km area (25 airborne HyMap™ flight-lines @ 5 m pixel resolution) centered over Kalgoorlie city. (<b>a</b>) Airborne HyMap™ (~2005) derived image of white mica composition based on the wavelength minimum (first derivative of a fitted fourth-order polynomial) of the continuum-removed 2200 nm absorption between 2120 and 2250 nm and masked to only include pixels with a detectable 2200 nm absorption. X marks the Superpit and Y is Kanowna Belle gold mines; (<b>b</b>) Predicted mineral suite with changing pH and REDOX [<a href="#B104-geosciences-06-00052" class="html-bibr">104</a>] at greenschist facies. Note prediction paragonite and pyrophyllite in more acid/reduced conditions; (<b>c</b>) Selected HyMap™ pixel spectra and USGS library spectra showing the diagnostic major absorption feature at 2160 nm and minor one at 2320 nm for pyrophyllite.</p> Full article ">Figure 12
<p>Airborne mineral mapping results for the Starra IOCG Cu–Au–Mo–Re–U mineral system, Mount Isa Bock, Queensland Australia. Approximate scene center −21.6555° latitude 140.4841° longitude. (<b>a</b>) Location and geological legend; (<b>b</b>) Published geology map [<a href="#B112-geosciences-06-00052" class="html-bibr">112</a>] including the potential sources of hydrothermal fluids (Gin Creek and Mount Dore granites) and possible fluid pathways (Starra and Mount Dore line Shear Zones, dashed and dotted lines respectively); (<b>c</b>) Published airborne magnetics image [<a href="#B113-geosciences-06-00052" class="html-bibr">113</a>]. Shear zones are annotated; (<b>d</b>) Published airborne K-Th-U radiometrics image [<a href="#B113-geosciences-06-00052" class="html-bibr">113</a>]; (<b>e</b>) Airborne HyMap™ white mica abundance map based on the continuum-depth (fitted fourth-order polynomial) of the 2200 nm absorption and masked to remove pixels with kaolin and Al-smectite; (<b>f</b>) Airborne HyMap™ opaque mineral map (includes carbon black) based on the index R<sub>450</sub>/R<sub>1650</sub> (where R<sub>X</sub> is the reflectance of the spectral band at the indicated wavelength in nm) and with a mask that includes only those pixels with 1650 nm reflectance &lt;30%; (<b>g</b>) Airborne HyMap™ white mica composition map based on the wavelength of continuum-removed (fitted fourth-order polynomial) 2200 nm absorption and masked to remove pixels with kaolin (2160 nm feature) and Al-smectite (no 2350 nm feature). Also shown are the published mineral deposits/occurrences, extent of the reduced zone defined by the opaque product (<b>f</b>), interpreted fluid flow path and the location (yellow rectangle) of the Merlin Co–Mo–Re deposit (<b>h</b>).</p> Full article ">Figure 13
<p>Airborne mineral mapping results for the Broken Hill block, Australia. Approximate scene center −31.9669° latitude 141.44134° longitude. (<b>a</b>) Airborne HyMap white mica composition map based on the wavelength of continuum-removed (fitted fourth-order polynomial) 2200 nm absorption (masked to remove pixels with kaolin and Al-smectite) together with similarly processed field sample data (dots) and published prograde and retrograde metamorphic isograds and selected faults/shears [<a href="#B126-geosciences-06-00052" class="html-bibr">126</a>]. A selection of National Virtual Core Library (NVCL) drill core locations referred to in the text are labelled as “<span class="html-italic">DDxx</span>” where <sub>XX</sub> is diamond drill core number; (<b>b</b>) Scatter-gram of the wavelength of the 2200 nm absorption measured from field samples (dots in (<b>a</b>) versus their corresponding HyMap pixel spectra; (<b>c</b>) Zoom of the northeast white rectangle shown in (<b>a</b>) which covers a retrograde shear zone; (<b>d</b>) HyMap derived kaolin content product of the Broken Hill block together with location of retrogression-related base metal deposits (brown diamonds) and a selection of NVCL drill core locations referred to in the text; (<b>e</b>) HyMap derived kaolin content of the SW white rectangle in (<b>a</b>) together with retrogression base metal deposits (magenta diamonds); (<b>f</b>) HyMap derived white mica composition of the SW white rectangle in (<b>a</b>).</p> Full article ">Figure 14
<p>Rocklea Inlier, Pilbara WA three-dimensional (3D) mineral models. Scene center is approximately −22.8216° latitude 117.4652° longitude. (<b>a</b>) A southwest oblique 3D view of the Rocklea study area showing kaolin disorder measured using airborne HyMap™ (surface) and drill core HyLogger™ (colored vertical pegs) reflectance data. Warmer colors (well-ordered kaolin) relate to weathered, in situ bedrock, while cooler colors (poorly-ordered kaolin) relate to transported (alluvium/colluvium) materials. The interpolated model of the base of the channel iron system calculated using the 3D kaolin disorder map is shown by the shaded grey surface. The channel iron ore deposit, which was calculated using an estimate of %FeO generated from the drill core spectral data [<a href="#B133-geosciences-06-00052" class="html-bibr">133</a>], is shown by a shaded red volume. A white straight line shows the location of the cross-section (A–B) presented in (<b>b</b>); (<b>b</b>) Cross section A–B in (<b>a</b>) of the %FeO measured from the drill core and airborne imagery, which was vegetation unmixed [<a href="#B134-geosciences-06-00052" class="html-bibr">134</a>].</p> Full article ">Figure 15
<p>(<b>a</b>) ASTER V1 Ferric Oxide Composition map of Australia [<a href="#B135-geosciences-06-00052" class="html-bibr">135</a>] with similarly processed field validation ASD measurements of the field NGSA surface samples (0–10 cm depth) [<a href="#B136-geosciences-06-00052" class="html-bibr">136</a>] (colored dots); (<b>b</b>) Oblique view from south of the Australian ASTER ferric composition and Mohorovičić Discontinuity (MOHO) maps [<a href="#B137-geosciences-06-00052" class="html-bibr">137</a>]. The edge of a high in the MOHO is marked by a magenta line which mirrors a change in the surface iron oxide information; (<b>c</b>) ASTER V1 Al-clay content map of Australia together with rainfall and field validation NGSA %clay data; (<b>d</b>) ASTER false color image of the Mt Carbine area, NE Queensland. Scene center is approximately −16.4104° latitude 144.6905° longitude; (<b>e</b>) ASTER green vegetation index B<sub>3</sub>/B<sub>2</sub>; (<b>f</b>) ASTER dry vegetation index; (<b>g</b>) ASTER Al-clay content before vegetation unmixing; (<b>h</b>) ASTER Al-clay content after vegetation unmixing; (<b>i</b>) Published geology, which shows granites rimmed by contact metamorphic hornfels aureoles where minerals such as muscovite were replaced by biotite.</p> Full article ">Figure 16
<p>(<b>a</b>) Three-dimensional oblique view (up from the NE) of the Mt Turner porphyry–epithermal system near Georgetown. Area center is approximately −18.2479° latitude 143.4473° longitude. Surface map is airborne HyMap™ convolved to ASTER bandpass configuration and then processed to generate a vegetation-unmixed Al-clay composition map before draping over a digital elevation model. The similarly color-coded vertical pillars are drill-core HyLogger™ data which also have been convolved to ASTER bandpass configuration and then processed to generate the same Al-clay composition product. The interpreted boundary between advanced argillic and phyllic alteration zonation is shown on both the HyMap™ (single line) and HyLogger™ (double line) results. Also shown is the vertical cross-section of a seismic line with pink lines showing granite contacts and red lines showing major faults; (<b>b</b>) Detailed mineralogy of the four HyLogger™ drill cores shown in (<b>a</b>) and generated using “The Spectral Assistant” in TSG™ software. The interpreted boundary between advanced argillic (comprises well-ordered kaolinite ± alunite) and phyllic (comprises muscovite/illite) alteration is shown by a double black line. Note that the NVCL drill cores start at fresh rock ~300 m below surface.</p> Full article ">
4758 KiB  
Article
Electrical Resistivity Tomography and Induced Polarization for Mapping the Subsurface of Alluvial Fans: A Case Study in Punata (Bolivia)
by Andres Gonzales Amaya, Torleif Dahlin, Gerhard Barmen and Jan-Erik Rosberg
Geosciences 2016, 6(4), 51; https://doi.org/10.3390/geosciences6040051 - 16 Nov 2016
Cited by 51 | Viewed by 8513
Abstract
Conceptual models of aquifer systems can be refined and complemented with geophysical data, and they can assist in understanding hydrogeological properties such as groundwater storage capacity. This research attempts to use geoelectrical methods, Electrical Resistivity Tomography and Induced Polarization parameters, for mapping the [...] Read more.
Conceptual models of aquifer systems can be refined and complemented with geophysical data, and they can assist in understanding hydrogeological properties such as groundwater storage capacity. This research attempts to use geoelectrical methods, Electrical Resistivity Tomography and Induced Polarization parameters, for mapping the subsurface in alluvial fans and to demonstrate its applicability; the Punata alluvial fan was used as a case study. The resistivity measurements proved to be a good tool for mapping the subsurface in the fan, especially when used in combination with Induced Polarization parameters (i.e., Normalized Chargeability). The Punata alluvial fan characterization indicated that the top part of the subsurface is composed of boulders in a matrix of finer particles and that the grain size decreases with depth; the electrical resistivity of these deposits ranged from 200 to 1000 ?m, while the values of normalized chargeability were lower than 0.05 mS/m. The bottom of the aquifer system consisted of a layer with high clay content, and the resistivity ranged from 10 to 100 ?m, while the normalized chargeability is higher than 0.07 mS/m. With the integration of these results and lithological information, a refined conceptual model is proposed; this model gives a more detailed description of the local aquifer system. It can be concluded that geoelectrical methods are useful for mapping aquifer systems in alluvial fans. Full article
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<p>(<b>a</b>) Location of the study area in the central part of Bolivia and regional geology overview, modified from SERGEOMIN [<a href="#B28-geosciences-06-00051" class="html-bibr">28</a>]; (<b>b</b>) Cross section of the study area, from UNDP-GEOBOL [<a href="#B26-geosciences-06-00051" class="html-bibr">26</a>].</p> Full article ">Figure 2
<p>Electrical Resistivity Tomography (ERT) surveys performed in the Punata alluvial fan. The green dots stand for boreholes with drilling reports and the diamonds represent geophysical well-loggings.</p> Full article ">Figure 3
<p>Typical stratigraphy in the Punata alluvial fan. (<b>a</b>) The soil profile displays a layer with three different soils (B, C, and D); while (<b>b</b>) shows a pit with a soil type A (coarse soil, composed by boulders and gravels).</p> Full article ">Figure 4
<p>ERT surveys with a multiple gradient array protocol for Lines 8, 16’, and 21; graphs on the left show inverted ERT and graphs on the right show normalized chargeability. The lithological description of available boreholes close to the surveys and the groundwater level (GWL) are included. Note different scales in axis <span class="html-italic">X</span> and <span class="html-italic">Y</span>.</p> Full article ">Figure 5
<p>ERT resistivity is plotted versus the normalized chargeability and the resistivity from well-logging at the same position. (<b>a</b>) Line 8 and borehole P009; (<b>b</b>) Line 16’ and borehole P079.</p> Full article ">Figure 6
<p>(<b>a</b>) Interpretation and 3D visualization of ERT profiles; (<b>b</b>) Interpretation and 3D visualization of normalized chargeability profiles. In both cases the lines are 21, 22, and 23.</p> Full article ">Figure 7
<p>(<b>a</b>) Calculated resistivity along the line L16; the dashed lines are the proposed limits for the different units. (<b>b</b>) Proposed conceptual model and aquifer system in the Punata alluvial fan. The aquifer units are composed of boulders, gravel, and sand (blue pattern in <a href="#geosciences-06-00051-f007" class="html-fig">Figure 7</a>b).</p> Full article ">
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Article
When the Crime Scene Is the Road: Forensic Geoscience Indicators Applied to Road Infrastructure and Urban Greening
by Pier Matteo Barone, Carlotta Ferrara, Rosa Maria Di Maggio and Luca Salvati
Geosciences 2016, 6(4), 50; https://doi.org/10.3390/geosciences6040050 - 4 Nov 2016
Cited by 4 | Viewed by 5376
Abstract
Common to most cities with tree-lined roads, streets, and sidewalks is damage to paved surfaces caused by the growth of roots over time. Sub-surface root growth creates potential hazards for people driving motor vehicles and pedestrian traffic. In large urban centers like Rome [...] Read more.
Common to most cities with tree-lined roads, streets, and sidewalks is damage to paved surfaces caused by the growth of roots over time. Sub-surface root growth creates potential hazards for people driving motor vehicles and pedestrian traffic. In large urban centers like Rome (Italy), roads are vital infrastructure ensuring the mobility of citizens, commercial goods, and information. This infrastructure can become a crime scene when serious injuries or deaths result from the poor monitoring and management of urban trees. Sustainable management of road infrastructure and the associated urban greening is supported by a forensic geoscientific approach. In particular, the use of the GPR (Ground Penetrating Radar) technique allows (i) to control and detect anomalies in the root architecture beneath asphalt in a non-destructive way; and (ii) to plan actions to repair and avoid the possibility of further catastrophic scenarios and need for forensic investigations. Full article
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<p>Upward trend of serious accidents where tree root cracking or lifting of asphalt pavement resulted in injured and deceased victims in the city of Rome from 2001 to 2014 [<a href="#B22-geosciences-06-00050" class="html-bibr">22</a>].</p> Full article ">Figure 2
<p>Recent examples of serious crashes with victims where falling trees (<b>a</b>) or root cracks (<b>b</b>) have been the responsible agent of injury, death, or vehicle damage (<b>c</b>,<b>d</b>) on the roads of Rome.</p> Full article ">Figure 3
<p>The upper image shows geometry of the most common mode of GPR data acquisition, referred to as “reflection mode” coverage. In (<b>a</b>), single transmitter (Tx) and receiver (Rx) antennae are transported over the ground surface in a fixed configuration above a buried target, resulting in hyperbolic time-distance behavior. The lower images show, in (<b>b</b>), a typical radargram (B-scan or GPR section). In (<b>c</b>), the relative depth map (C-scan or GPR depth-slice).</p> Full article ">Figure 4
<p>This figure shows the area of investigation along the SS8bis (<b>a</b>), with damage to asphalt due to the pine root growth and pavement uplifting (<b>b</b>). Several hyperbolic events and red elongated anomalies within the first 0.4 m on both the radargram (<b>c</b>) and depth slide map (<b>d</b>) are due to the root architecture beneath the asphalt.</p> Full article ">Figure 5
<p>In (<b>a</b>), damaged asphalt along Viale delle Terme di Caracalla is shown. This tree-lined street was constructed in 1940s, as shown in (<b>b</b>). Strong hyperbolic events and red elongated anomalies in the first 0.4 m, observed on both the radargram (<b>c</b>) and depth map (<b>d</b>) image the root architecture beneath the asphalt.</p> Full article ">Figure 6
<p>The upper images show Via Cristoforo Colombo in recent times (<b>a</b>) and during the 1950s (<b>b</b>). The lower images clearly show the very shallow root architecture of the pine trees beneath the asphalt road surface (hyperbolic events in (<b>c</b>) and yellow elongated anomalies in (<b>d</b>). Note, however, that the asphalt surface is not yet damaged by root growth.</p> Full article ">Figure 7
<p>The upper images illustrate the critical situation of the plane trees between the embankments of the Tiber River and Lungotevere Street (<b>a</b>,<b>b</b>). The lower images illustrate the GPR results collected around the leaning tree (<b>c</b>,<b>d</b>) and highlight critical conditions in the first 0.3 m beneath the pavement. Note that the grey area in (d) represents the area with no GPR data parallel to the embankment.</p> Full article ">
5681 KiB  
Article
Maya Lime Mortars—Relationship between Archaeomagnetic Dating, Manufacturing Technique, and Architectural Function—The Dzibanché Case
by Luisa Straulino Mainou, Sergey Sedov, Ana María Soler Arechalde, Teresa Pi Puig, Gerardo Villa, Sandra Balanzario Granados, María-Teresa Doménech-Carbó, Laura Osete-Cortina and Daniel Leonard
Geosciences 2016, 6(4), 49; https://doi.org/10.3390/geosciences6040049 - 4 Nov 2016
Cited by 6 | Viewed by 7219
Abstract
Researchers have related the manufacturing technique of plasters and stucco in the Maya area with their period of production but not with their architectural function. In this paper, we establish a relationship between those three features (manufacturing technique, age, and architectural function) in [...] Read more.
Researchers have related the manufacturing technique of plasters and stucco in the Maya area with their period of production but not with their architectural function. In this paper, we establish a relationship between those three features (manufacturing technique, age, and architectural function) in the plasters of the Maya site of Dzibanché in southern Quintana Roo. Dzibanché has abundant remains of stuccos and plasters found mainly in three buildings (Plaza Pom, Pequeña Acrópolis, and Structure 2). We used thin sections, SEM and XRD, and archaeomagnetic dating processes. The pictorial layer of Structure 2 was the earliest (AD 274–316 and the stuccoes and plasters of the other two buildings were dated to the Middle Classic (AD 422–531), but we obtained different archaeomagnetic dates for the red pigment layers found in the buildings of the Pequeña Acrópolis and thus we were able to determine their chronological order of construction. The raw materials and proportions were carefully chosen to fulfil the mechanical necessities of the architectonic function: different proportions were found in plasters of floors, in the external walls, and inside the buildings; differences between earlier and later plasters were also detected. Full article
(This article belongs to the Special Issue Geoscience of the Built Environment 2016 Edition)
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<p>Location of Dzibanché and the studied buildings.</p> Full article ">Figure 2
<p>Plasters and stuccoes in Pequeña Acrópolis, Plaza Pom, and Structure 2.</p> Full article ">Figure 3
<p>Location of the sample for radiocarbon dating within Structure 2.</p> Full article ">Figure 4
<p>Some inclusions: (<b>a</b>) sparitic inclusions (xpl); (<b>b</b>) heterogeneous inclusions (xpl); (<b>c</b>) clays (xpl); (<b>d</b>) soil particle (ppl); (<b>e</b>) left, chert (xpl); (<b>f</b>) top right, shell (xpl).</p> Full article ">Figure 5
<p>Micritic inclusions types. (<b>a</b>) type 1; (<b>b</b>) type 2; (<b>c</b>) type 3; (<b>d</b>) type 4; (<b>e</b>) type 5; (<b>f</b>) type 6.</p> Full article ">Figure 6
<p>Types of plaster based on the manufacturing technique. (<b>a</b>) type 1; (<b>b</b>) type 2; (<b>c</b>) type 3; (<b>d</b>) type 4.</p> Full article ">Figure 7
<p>Thin sections microphotographs of organic inclusions. (<b>a</b>) woody tissue; (<b>b</b>) vegetal, non woody, tissue; (<b>c</b>) vegetal charcoal; (<b>d</b>) cyanobacteria; (<b>e</b>) possible sporangium; (<b>f</b>) possible lichen.</p> Full article ">Figure 8
<p>Ternary plot showing the proportions of inclusions, matrix, and porosity of the thin layers within the plasters.</p> Full article ">Figure 9
<p>Ternary plot showing the proportions of inclusions, matrix, and porosity of medium layers in the plasters.</p> Full article ">Figure 10
<p>Ternary plot showing the proportions of inclusions, matrix, and porosity in the plasters of thick layers.</p> Full article ">
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Article
Identifying Spatio-Temporal Landslide Hotspots on North Island, New Zealand, by Analyzing Historical and Recent Aerial Photography
by Daniel Hölbling, Harley Betts, Raphael Spiekermann and Chris Phillips
Geosciences 2016, 6(4), 48; https://doi.org/10.3390/geosciences6040048 - 2 Nov 2016
Cited by 32 | Viewed by 8907
Abstract
Accurate mapping of landslides and the reliable identification of areas most affected by landslides are essential for advancing the understanding of landslide erosion processes. Remote sensing data provides a valuable source of information on the spatial distribution and location of landslides. In this [...] Read more.
Accurate mapping of landslides and the reliable identification of areas most affected by landslides are essential for advancing the understanding of landslide erosion processes. Remote sensing data provides a valuable source of information on the spatial distribution and location of landslides. In this paper we present an approach for identifying landslide-prone “hotspots” and their spatio-temporal variability by analyzing historical and recent aerial photography from five different dates, ranging from 1944 to 2011, for a study site near the town of Pahiatua, southeastern North Island, New Zealand. Landslide hotspots are identified from the distribution of semi-automatically detected landslides using object-based image analysis (OBIA), and compared to hotspots derived from manually mapped landslides. When comparing the overlapping areas of the semi-automatically and manually mapped landslides the accuracy values of the OBIA results range between 46% and 61% for the producer’s accuracy and between 44% and 77% for the user’s accuracy. When evaluating whether a manually digitized landslide polygon is only intersected to some extent by any semi-automatically mapped landslide, we observe that for the natural-color images the landslide detection rate is 83% for 2011 and 93% for 2005; for the panchromatic images the values are slightly lower (67% for 1997, 74% for 1979, and 72% for 1944). A comparison of the derived landslide hotspot maps shows that the distribution of the manually identified landslides and those mapped with OBIA is very similar for all periods; though the results also reveal that mapping landslide tails generally requires visual interpretation. Information on the spatio-temporal evolution of landslide hotspots can be useful for the development of location-specific, beneficial intervention measures and for assessing landscape dynamics. Full article
(This article belongs to the Special Issue Mapping and Assessing Natural Disasters Using Geospatial Technologies)
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<p>Location of the study area in southeastern North Island, New Zealand.</p> Full article ">Figure 2
<p>Landslides caused by a heavy rainstorm in June 2015 (Photo © Harley Betts).</p> Full article ">Figure 3
<p>Object-based landslide mapping workflow. DEM, digital elevation model; RGB, red, green, blue.</p> Full article ">Figure 4
<p>Semi-automatically mapped landslides using the object-based image analysis (OBIA) approach (in yellow color) and manually mapped landslides (in red color) for the aerial photograph from 2005 for the whole study area (below). The results show that, apart from the northeastern part, the study area is heavily affected by landslides. A subset showing the two mapping results for a couple of landslides is shown above. The black rectangular indicates the location of the subset.</p> Full article ">Figure 5
<p>Subset from the southwestern part of the study area showing the aerial photographs (<b>left</b>), the OBIA (<b>middle</b>) and manual (<b>right</b>) landslide mapping results for each date. The figure shows how this sub-area is affected by landslides over time. Most landslides disappeared when comparing an aerial photography to the subsequent one, since vegetation started to grow again. The distribution of the detected landslides is very similar for both methods.</p> Full article ">Figure 5 Cont.
<p>Subset from the southwestern part of the study area showing the aerial photographs (<b>left</b>), the OBIA (<b>middle</b>) and manual (<b>right</b>) landslide mapping results for each date. The figure shows how this sub-area is affected by landslides over time. Most landslides disappeared when comparing an aerial photography to the subsequent one, since vegetation started to grow again. The distribution of the detected landslides is very similar for both methods.</p> Full article ">Figure 6
<p>Manually identified landslide scars and debris tails compared to OBIA mapping results for a subset of the aerial photograph from 2011. Landslide source areas (scars) were well detected by the object-based approach, but several landslide tails were missed due to a lack of distinct spectral, spatial, or morphological characteristics that could have been used during semi-automated mapping.</p> Full article ">Figure 7
<p>Comparison of landslide hotspot maps based on the manual landslide mapping results (<b>left</b>) and on the OBIA results (<b>right</b>) for 2005 displayed on a shaded relief.</p> Full article ">Figure 8
<p>Comparison of landslide hotspot maps based on the manual landslide mapping results (<b>left</b>) and on the OBIA results (<b>right</b>) for the years 2011, 1997, 1979, and 1944 displayed on a shaded relief for a subset from the northwestern part of the study area.</p> Full article ">Figure 8 Cont.
<p>Comparison of landslide hotspot maps based on the manual landslide mapping results (<b>left</b>) and on the OBIA results (<b>right</b>) for the years 2011, 1997, 1979, and 1944 displayed on a shaded relief for a subset from the northwestern part of the study area.</p> Full article ">
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Article
Identifying the Risk Areas and Urban Growth by ArcGIS-Tools
by Omar Hamdy, Shichen Zhao, Mohamed A. Salheen and Youhansen Y. Eid
Geosciences 2016, 6(4), 47; https://doi.org/10.3390/geosciences6040047 - 26 Oct 2016
Cited by 12 | Viewed by 6742
Abstract
Abouelreesh is one of the most at risk areas in Aswan, Egypt, which suffers from storms, poor drainage, and flash flooding. These phenomena affect the urban areas and cause a lot of damage to buildings and infrastructure. Moreover, the potential for the further [...] Read more.
Abouelreesh is one of the most at risk areas in Aswan, Egypt, which suffers from storms, poor drainage, and flash flooding. These phenomena affect the urban areas and cause a lot of damage to buildings and infrastructure. Moreover, the potential for the further realization of dangerous situations increased when the urban areas of Abouelreesh extended towards the risk areas. In an effort to ameliorate the danger, two key issues for urban growth management were studied, namely: (i) estimations regarding the pace of urban sprawl, and (ii) the identification of urban areas located in regions that would be affected by flash floods. Analyzing these phenomena require a lot of data in order to obtain good results, but in our case, the official data or field data was limited so we tried to obtain it by accessing two kinds of free sources of satellite data. First, we used Arc GIS tools to analyze (digital elevation model (DEM)) files in order to study the watershed and better identify the risk area. Second, we studied historical imagery in Google Earth to determine the age of each urban block. The urban growth rate in the risk areas had risen to 63.31% in 2001. Urban growth in the case study area had been influenced by house sizes, because most people were looking to live in bigger houses. The aforementioned problem can be observed by considering the increasing average house sizes from 2001 until 2013, where, especially in risky areas, the average of house sizes had grown from 223 m2 in 2001 to 318 m2 in 2013. The findings from this study would be useful to urban planners and government officials in helping them to make informed decisions on urban development to benefit the community, especially those living in areas at risk from flash flooding from heavy rain events. Full article
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<p>Buildings are destroyed in Abouelreesh village after a torrent of floodwater in 2010.</p> Full article ">Figure 2
<p>Roads and buildings affected by floodwater torrents in Egypt.</p> Full article ">Figure 3
<p>The study area in relation to Egypt and the world.</p> Full article ">Figure 4
<p>Digital elevation model (DEM) file and water streams. (<b>a</b>) Study area DEM file; (<b>b</b>) Study area water streams network.</p> Full article ">Figure 5
<p>A map showing the current status of the village of Abouelreesh (with the years in which construction occurred designated according to color).</p> Full article ">Figure 6
<p>A map showing the current status of the land use in and around the village of Abouelreesh.</p> Full article ">Figure 7
<p>Map showing the at risk areas, the safe areas, and the watershed boundaries for the study area.</p> Full article ">Figure 8
<p>Urban areas from 2001 to 2013.</p> Full article ">Figure 9
<p>Proportion of safe and at risk areas developed according to year.</p> Full article ">Figure 10
<p>Annual rate of increase in developed safe and at risk areas.</p> Full article ">Figure 11
<p>This graph shows the average area occupied by building units in safe areas, at-risk areas, and the overall urban area.</p> Full article ">Figure 12
<p>Distribution pre-existing and proposed urban area.</p> Full article ">
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Review
Geoengineering in the Anthropocene through Regenerative Urbanism
by Giles Thomson and Peter Newman
Geosciences 2016, 6(4), 46; https://doi.org/10.3390/geosciences6040046 - 25 Oct 2016
Cited by 27 | Viewed by 11907
Abstract
Human consumption patterns exceed planetary boundaries and stress on the biosphere can be expected to worsen. The recent “Paris Agreement” (COP21) represents a major international attempt to address risk associated with climate change through rapid decarbonisation. The mechanisms for implementation are yet to [...] Read more.
Human consumption patterns exceed planetary boundaries and stress on the biosphere can be expected to worsen. The recent “Paris Agreement” (COP21) represents a major international attempt to address risk associated with climate change through rapid decarbonisation. The mechanisms for implementation are yet to be determined and, while various large-scale geoengineering projects have been proposed, we argue a better solution may lie in cities. Large-scale green urbanism in cities and their bioregions would offer benefits commensurate to alternative geoengineering proposals, but this integrated approach carries less risk and has additional, multiple, social and economic benefits in addition to a reduction of urban ecological footprint. However, the key to success will require policy writers and city makers to deliver at scale and to high urban sustainability performance benchmarks. To better define urban sustainability performance, we describe three horizons of green urbanism: green design, that seeks to improve upon conventional development; sustainable development, that is the first step toward a net zero impact; and the emerging concept of regenerative urbanism, that enables biosphere repair. Examples of green urbanism exist that utilize technology and design to optimize urban metabolism and deliver net positive sustainability performance. If mainstreamed, regenerative approaches can make urban development a major urban geoengineering force, while simultaneously introducing life-affirming co-benefits to burgeoning cities. Full article
(This article belongs to the Special Issue Geoscience of the Built Environment 2016 Edition)
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<p>Urbanization population projections and unbuilt urban areas.</p> Full article ">Figure 2
<p>The three horizons of green urbanism.</p> Full article ">Figure 3
<p>Conceptual diagram of the carbon reduction elements of Green Design.</p> Full article ">Figure 4
<p>Conceptual diagram of the carbon reduction elements of Sustainable Development (<b>B</b>) and Regenerative Urbanism (<b>C</b>).</p> Full article ">Figure 5
<p>Singapore Garden City (source, Peter Newman).</p> Full article ">Figure 6
<p>Park Royal Hotel in Singapore shows the city’s commitment to biophilic urbanism, a component of the regenerative city agenda (source, Peter Newman).</p> Full article ">
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Article
Feasibility Study of Land Cover Classification Based on Normalized Difference Vegetation Index for Landslide Risk Assessment
by Thilanki Dahigamuwa, Qiuyan Yu and Manjriker Gunaratne
Geosciences 2016, 6(4), 45; https://doi.org/10.3390/geosciences6040045 - 20 Oct 2016
Cited by 38 | Viewed by 6104
Abstract
Unfavorable land cover leads to excessive damage from landslides and other natural hazards, whereas the presence of vegetation is expected to mitigate rainfall-induced landslide potential. Hence, unexpected and rapid changes in land cover due to deforestation would be detrimental in landslide-prone areas. Also, [...] Read more.
Unfavorable land cover leads to excessive damage from landslides and other natural hazards, whereas the presence of vegetation is expected to mitigate rainfall-induced landslide potential. Hence, unexpected and rapid changes in land cover due to deforestation would be detrimental in landslide-prone areas. Also, vegetation cover is subject to phenological variations and therefore, timely classification of land cover is an essential step in effective evaluation of landslide hazard potential. The work presented here investigates methods that can be used for land cover classification based on the Normalized Difference Vegetation Index (NDVI), derived from up-to-date satellite images, and the feasibility of application in landslide risk prediction. A major benefit of this method would be the eventual ability to employ NDVI as a stand-alone parameter for accurate assessment of the impact of land cover in landslide hazard evaluation. An added benefit would be the timely detection of undesirable practices such as deforestation using satellite imagery. A landslide-prone region in Oregon, USA is used as a model for the application of the classification method. Five selected classification techniques—k-nearest neighbor, Gaussian support vector machine (GSVM), artificial neural network, decision tree and quadratic discriminant analysis support the viability of the NDVI-based land cover classification. Finally, its application in landslide risk evaluation is demonstrated. Full article
(This article belongs to the Special Issue Mapping and Assessing Natural Disasters Using Geospatial Technologies)
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<p>Architecture of a neural network with a single hidden layer.</p> Full article ">Figure 2
<p>Geographical location of the selected site in the state of Oregon.</p> Full article ">
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Article
Measuring Beach Profiles along a Low-Wave Energy Microtidal Coast, West-Central Florida, USA
by Jun Cheng, Ping Wang and Qiandong Guo
Geosciences 2016, 6(4), 44; https://doi.org/10.3390/geosciences6040044 - 19 Oct 2016
Cited by 18 | Viewed by 8582
Abstract
Monitoring storm-induced dramatic beach morphology changes and long-term beach evolution provides crucial data for coastal management. Beach-profile measurement using total station has been conducted along the coast of west-central Florida over the last decade. This paper reviews several case studies of beach morphology [...] Read more.
Monitoring storm-induced dramatic beach morphology changes and long-term beach evolution provides crucial data for coastal management. Beach-profile measurement using total station has been conducted along the coast of west-central Florida over the last decade. This paper reviews several case studies of beach morphology changes based on total-station survey along this coast. The advantage of flexible and low-cost total-station surveys is discussed in comparison to LIDAR (light detection and ranging) method. In an attempt to introduce total-station survey from a practical prospective, measurement of cross-shore beach profile in various scenarios are discussed, including: (1) establishing a beach profile line with known instrument and benchmark locations; (2) surveying multiple beach profiles with one instrument setup; (3) implementation of coordinate rotation to convert local system to real-earth system. Total-station survey is a highly effective and accurate method in documenting beach profile changes along low-energy coasts. Full article
(This article belongs to the Special Issue Mapping and Assessing Natural Disasters Using Geospatial Technologies)
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Full article ">Figure 1
<p>Study area along the coast of west-central Florida.</p> Full article ">Figure 2
<p>Study area under normal weather condition (<b>A</b>), as well as under Tropical Storm Debby in 2012 (<b>B</b>).</p> Full article ">Figure 3
<p>Survey procedures include the use of an electronic level-and-transit total station and a 4 m survey rod.</p> Full article ">Figure 4
<p>Example of survey line including instrument and benchmark.</p> Full article ">Figure 5
<p>Example profile from the North Sand Key project area at R61.</p> Full article ">Figure 6
<p>Example profile from the area of no fill along Belleair Shore between North Sand Key and Indian Rocks, at R67.</p> Full article ">Figure 7
<p>Example profile from Indian Rocks, R80.</p> Full article ">Figure 8
<p>Example profile from the Headland, R84.</p> Full article ">Figure 9
<p>Example profile from North Redington Beach, R105.</p> Full article ">Figure 10
<p>Pre- and post-storm surveyed beach profiles at (<b>A</b>) R80; (<b>B</b>) R87; and (<b>C</b>) R105.</p> Full article ">
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Article
Applying a Hybrid Model of Markov Chain and Logistic Regression to Identify Future Urban Sprawl in Abouelreesh, Aswan: A Case Study
by Omar Hamdy, Shichen Zhao, Taher Osman, Mohamed A. Salheen and Youhansen Y. Eid
Geosciences 2016, 6(4), 43; https://doi.org/10.3390/geosciences6040043 - 11 Oct 2016
Cited by 39 | Viewed by 7067
Abstract
Urban sprawl has become a very complex process, because it has many factors affecting its directions and values. The study of relative research shows that the driving forces that lead and redirect future urban sprawl require the application of a statistical method. In [...] Read more.
Urban sprawl has become a very complex process, because it has many factors affecting its directions and values. The study of relative research shows that the driving forces that lead and redirect future urban sprawl require the application of a statistical method. In our study, logistic regressions were used to analyze and class the driving forces for urban sprawl. Identifying the driving forces, which is the most important step in predicting the future of urban sprawl in 2037, was performed using the cellular automata models. This study takes the Aswan area as a case study in the period from 2001 to 2013 by analyzing the official detailed plan and Google Earth historical imagery. Almost all data was prepared for logistic regression analysis using ArcGIS software and IDRISI® Selva. In our study, a hybrid model of the Markov chain and logistic regression models was applied to identify future urban sprawl in 2037. The findings of this paper simulate the increase in urban area over 24 years from 1.85 to 2.59 km2. These findings highlight the growing risks of urban sprawl and the difficulties opposing the sustainable urban development plans officially proposed for this area. Full article
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<p>Underlying UGPM algorithms sorted by popularity (percentage of 156 manuscripts, a manuscript may contain multiple algorithms) [<a href="#B43-geosciences-06-00043" class="html-bibr">43</a>].</p> Full article ">Figure 2
<p>The study area in relation to Egypt and the world.</p> Full article ">Figure 3
<p>Digital elevation model (DEM) file for the case study area.</p> Full article ">Figure 4
<p>Urban area in 2001 and 2009.</p> Full article ">Figure 5
<p>Prediction of future urban sprawl workflow.</p> Full article ">Figure 6
<p>Changes in land use between 2001 and 2009.</p> Full article ">Figure 7
<p>Urban sprawl in 2009.</p> Full article ">Figure 8
<p>Transition of land use.</p> Full article ">Figure 9
<p>The increase of urban area 2013–2037. (<b>a</b>) Urban 2013 (1.85 km<sup>2</sup>); (<b>b</b>) Urban 2021 (2.02 km<sup>2</sup>); (<b>c</b>) Urban 2029 (2.31 km<sup>2</sup>); (<b>d</b>) Urban 2037 (2.59 km<sup>2</sup>).</p> Full article ">Figure 10
<p>The total predicted urban areas, 2013–2037.</p> Full article ">
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Article
Assessing Floods and Droughts in Ungauged Small Reservoirs with Long-Term Landsat Imagery
by Andrew Ogilvie, Gilles Belaud, Sylvain Massuel, Mark Mulligan, Patrick Le Goulven and Roger Calvez
Geosciences 2016, 6(4), 42; https://doi.org/10.3390/geosciences6040042 - 27 Sep 2016
Cited by 18 | Viewed by 6304
Abstract
Small reservoirs have developed across semi-arid areas as a low cost solution for millions of rural small holders to harvest scarce water resources. Studies have highlighted limited agricultural water use and low water availability on individual reservoirs, but no information exists on the [...] Read more.
Small reservoirs have developed across semi-arid areas as a low cost solution for millions of rural small holders to harvest scarce water resources. Studies have highlighted limited agricultural water use and low water availability on individual reservoirs, but no information exists on the drought patterns of multiple small reservoirs. Their small size and dispersion prevents individualised hydrological monitoring, while hydrological modelling suffers from rainfall variability and heterogeneity across data sparse catchments and reservoirs. A semi-automated original approach exploiting free, archive Landsat satellite images is developed here for long-term monitoring of multiple ungauged small water bodies. Adapted and tested against significant hydrometric time series on three lakes, the method confirms its potential to monitor water availability on the smallest water bodies (1–10 ha) with a mean RMSE of 20,600 m3 (NRMSE = 26%). Uncertainties from the absence of site-specific and updated surface-volume rating curves were here contained through a power relationship adapted over time for silting based on data from 15 surrounding lakes. Applied to 51 small reservoirs and 546 images over 1999–2014, results highlight the ability of this transposable method to shed light on flood dynamics and allow inter annual and inter lake comparisons of water availability. In the Merguellil upper catchment, in Central Tunisia, results reveal the significant droughts affecting over 80% of reservoirs, confirming the need for small reservoirs to maintain a supplementary irrigation objective only. Full article
(This article belongs to the Special Issue Mapping and Assessing Natural Disasters Using Geospatial Technologies)
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<p>Estimated number of small reservoirs (SR) for several countries (based on national, non-exhaustive assessments in [<a href="#B6-geosciences-06-00042" class="html-bibr">6</a>]).</p> Full article ">Figure 2
<p>Location of Merguellil upper catchment and its small reservoirs and those with the stage data and surface-volume relationships employed here.</p> Full article ">Figure 3
<p>Decision tree for the inventory of small reservoirs.</p> Full article ">Figure 4
<p>Availability of Landsat images for the Merguellil upper catchment.</p> Full article ">Figure 5
<p>Chain of treatments applied to 546 Landsat images (adapted from [<a href="#B12-geosciences-06-00042" class="html-bibr">12</a>]).</p> Full article ">Figure 6
<p>Relationship between surface area and volume based on rating curves from 11 small reservoirs and the <span class="html-italic">interSR</span> relation (blue line) derived from linear regression.</p> Full article ">Figure 7
<p><span class="html-italic">B</span> and <span class="html-italic">β</span> parameters for each of the 15 lakes.</p> Full article ">Figure 8
<p>Lake-specific S-V power relations against the <span class="html-italic">interSR</span> power relation (in grey).</p> Full article ">Figure 9
<p>Modelled evolution (black line) of the <span class="html-italic">B</span> and <span class="html-italic">β</span> parameters over time.</p> Full article ">Figure 9 Cont.
<p>Modelled evolution (black line) of the <span class="html-italic">B</span> and <span class="html-italic">β</span> parameters over time.</p> Full article ">Figure 10
<p>Lake- and date-specific surface-volume relation against the <span class="html-italic">interSR</span> power relation adapted for silting over time (lake Gouazine).</p> Full article ">Figure 11
<p>Daily volumes for Gouazine lake over 2000–2014 based on: (i) observed stage data; (ii) Landsat data using a site-specific rating curve; (iii) Landsat data using the power relation adapted for silting; (iv) Landsat data using the power relation not adapted for silting.</p> Full article ">Figure 12
<p>Correlation on daily water volumes in three lakes between field data and remotely-sensed data using the power relation adapted for silting.</p> Full article ">Figure 13
<p>Correlation on mean annual volumes for three lakes between field data and remote sensing data using the power relation adapted for silting.</p> Full article ">Figure 14
<p>Error in the number of days water levels fall below a given volume for Gouazine (1999–2014).</p> Full article ">Figure 15
<p>Landsat-derived volumes for the subset of 12 ungauged lakes over 1999–2014. (<b>a</b>) Daily volumes; (<b>b</b>) mean daily volumes per year.</p> Full article ">Figure 16
<p>Mean interannual availability over 2007–2014 per lake over the whole year displayed as the mean ± 1 standard deviation.</p> Full article ">Figure 17
<p>Number of days for each lake that water volumes fell below a designated volume (up to 150,000 m<sup>3</sup>).</p> Full article ">Figure 18
<p>Number of drought days per lake (here, drought refers to days below 5000 m<sup>3</sup> over the dry season). Pale red refers to results with silting, while dark red represents a best case scenario without silting.</p> Full article ">Figure 19
<p>Relationship between the initial capacity of each lake and water availability parameters. (<b>a</b>) Correlation between initial maximum capacity of lakes and mean daily volumes over April–September; (<b>b</b>) correlation between the initial maximum capacity of lakes and the mean number of drought days/year (here, droughts refer to volumes &lt;5000 m<sup>3</sup>).</p> Full article ">Figure 19 Cont.
<p>Relationship between the initial capacity of each lake and water availability parameters. (<b>a</b>) Correlation between initial maximum capacity of lakes and mean daily volumes over April–September; (<b>b</b>) correlation between the initial maximum capacity of lakes and the mean number of drought days/year (here, droughts refer to volumes &lt;5000 m<sup>3</sup>).</p> Full article ">Figure 20
<p>Mean water availability of lakes within the Merguellil upper catchment.</p> Full article ">
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