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22 pages, 36164 KiB  
Article
Development of an Extensional Fault System and Its Control on Syn-Rift Sedimentation: Insights from 3D Seismic Interpretation of the Weixinan Depression, Northern South China Sea
by Jie He, Chunyu Qin, Yuantao Liao, Tao Jiang, Entao Liu, Si Chen and Hua Wang
J. Mar. Sci. Eng. 2024, 12(8), 1392; https://doi.org/10.3390/jmse12081392 - 14 Aug 2024
Abstract
The impacts of the growth and linkage of fault segments on sedimentation in a lacustrine rift basin, the Weixinan Depression, the Beibuwan Basin, in the northern South China Sea, which has been demonstrated to have huge petroleum potential, are elucidated on the basis [...] Read more.
The impacts of the growth and linkage of fault segments on sedimentation in a lacustrine rift basin, the Weixinan Depression, the Beibuwan Basin, in the northern South China Sea, which has been demonstrated to have huge petroleum potential, are elucidated on the basis of well-constrained 3D seismic data. Two main fault systems, the No. 1 boundary fault system and the No. 2 fault system, were developed in the Weixinan Depression. The evolution of the lower basement is based on the No. 1 fault system, which controls the distribution of depocenters (ranging from 450–800 m) within the lower structural layer. It includes the five fault segments isolated at the initial stage, the interaction and propagation stage, the linkage stage, and the decline stage. The No. 2 fault system governs the deposition of the upper structural layer with a series of discrete depocenters in the hangingwall. Initially, it comprises several right-order echelon branching faults. Each branch fault rapidly reached the existing length and maintained a constant length while establishing soft links with each other in the subsequent displacement accrual. The development of topographic slopes, transition zones, transverse anticlines, and related fault troughs and gullies related to the activity of the No. 1 boundary fault system is the main controlling factor that induces the differential development of the western, middle, and eastern sections of steep slope fans. The differential subsidence effect along the No. 2 fault system is responsible for the multiple ‘rising-stable’ stage changes in the relative lake level during the development of axial delta deposits. This study will help elucidate the different controls of extensional fault systems on associated sedimentation, as well as rift basin development in the South China Sea and similar areas throughout the world. Full article
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Figure 1

Figure 1
<p>(<b>A</b>) Major sedimentary basins in the South China Sea. (<b>B</b>) Simplified structural map indicating the location of Weixinan Depression and other units in the Beibuwan Basin, as shown in (<b>A</b>). (<b>C</b>) Geological map of the Weixinan Depression. (<b>D</b>) Representative seismic profile aa’ perpendicular to the No. 1 and No. 2 fault systems in the Weixinan Depression, respectively. Locations of the profile are shown in (<b>C</b>). The uninterpreted seismic profile is provided in <a href="#app1-jmse-12-01392" class="html-app">Supplementary Materials Figure S1</a> [<a href="#B37-jmse-12-01392" class="html-bibr">37</a>].</p>
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<p>Stratigraphic framework, sedimentary sequence, and major tectonic stages of the Weixinan Depression, Beibuwan Basin [<a href="#B37-jmse-12-01392" class="html-bibr">37</a>].</p>
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<p>(<b>A</b>) Fault map showing the No. 1 fault system at stratigraphic levels of T86. (<b>B</b>) Profiles displaying fault activity rates at the stratigraphic levels of T86, T83, and T80. Four fault segment linkage points A1, A2, A3, and A4 are identified. (<b>C</b>) Fault dip angle profiles at the stratigraphic levels of T86. (<b>D</b>) Seismic cross-sections along the strike of the No. 1 fault system indicate the transverse anticlines formed at the fault segment linkage points. The location of the profile is shown in (<b>A</b>). The uninterpreted seismic profile is provided in <a href="#app1-jmse-12-01392" class="html-app">Supplementary Materials Figure S2</a>.</p>
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<p>Cross-sections (<b>A</b>–<b>F</b>) illustrating the structural configurations of the No. 1 fault system by inline 3130, inline 3660, inline 3880, inline 4490, inline 4770 and inline 5330, respectively (refer to its locations in <a href="#jmse-12-01392-f003" class="html-fig">Figure 3</a>A). The uninterpreted seismic profiles are provided in <a href="#app1-jmse-12-01392" class="html-app">Supplementary Materials Figure S3</a>.</p>
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<p>(<b>A</b>) Fault map illustrating the splay segmented faults and basement fault of the No. 2 fault system. (<b>B</b>–<b>D</b>) Profiles of fault displacement at the T86, T84, and T80 horizons. (<b>E</b>,<b>F</b>) Three-dimensional visualization of the No. 2 fault system.</p>
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<p>Interpreted seismic sections (<b>A</b>–<b>D</b>) perpendicular to the No. 2 fault system and their corresponding throw-depth plots. Locations of the sections are shown in <a href="#jmse-12-01392-f005" class="html-fig">Figure 5</a>A. The uninterpreted seismic profiles are provided in <a href="#app1-jmse-12-01392" class="html-app">Supplementary Materials Figure S4</a>.</p>
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<p>Fault evolution (<b>A1</b>–<b>D1</b>) and thickness maps (<b>A2</b>–<b>D2</b>) show the spatial and temporal development of the No. 1 and No. 2 fault systems and the migration of depocentres from T86, T83, T80 to T72, respectively.</p>
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<p>Balanced cross section of inline 4491 from Ls3 to present. The approximate location of inline 4491, which is close to inline 4490, is indicated in <a href="#jmse-12-01392-f003" class="html-fig">Figure 3</a>. The percentage values in the top right corner indicated the shortening percentage.</p>
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<p>(<b>A</b>) The superimposed map of the paleogeographic pattern and distribution of fans in the steep slope zone of the Ls1 Formation in the Weixinan Depression. (<b>B</b>) Representative attribute maps of fans in the steep slope zone of the Ls1 Formation in the Weixinan Depression. The arrows indicate the long axis of fans. (<b>C</b>) The seismic characteristics of fans that developed in the western, central, and eastern sections of the steep slope zone of the Ls1 Formation in the Weixinan Depression. The uninterpreted seismic profile are provided in <a href="#app1-jmse-12-01392" class="html-app">Supplementary Materials Figure S5</a>.</p>
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<p>Depositional model of fans developed in the hangingwall of the No. 1 boundary fault system.</p>
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<p>(<b>A</b>) Plane distribution characteristics of axial meandering river delta-turbidite deposition along the hangingwall of the No. 2 fault system in the Ls1 Weixinan Depression (after [<a href="#B37-jmse-12-01392" class="html-bibr">37</a>]). (<b>B</b>,<b>C</b>) Seismic foreset reflection characteristics and stage division of the axial delta system. The uninterpreted seismic profile is provided in <a href="#app1-jmse-12-01392" class="html-app">Supplementary Materials Figure S6</a>.</p>
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24 pages, 9918 KiB  
Article
Morphological Characteristics and Development Rate of Gullies in Three Main Agro-Geomorphological Regions of Northeast China
by Zhengyu Wang, Mingchang Shi, Mingming Guo, Xingyi Zhang, Xin Liu and Zhuoxin Chen
Remote Sens. 2024, 16(16), 2905; https://doi.org/10.3390/rs16162905 - 8 Aug 2024
Viewed by 285
Abstract
Gully erosion poses a significant global concern due to its role in land degradation and soil erosion, particularly pronounced in Northeast China’s diverse agro-geomorphic regions. However, there is a lack of comprehensive studies on gully characteristics, development rates, and the topographic threshold of [...] Read more.
Gully erosion poses a significant global concern due to its role in land degradation and soil erosion, particularly pronounced in Northeast China’s diverse agro-geomorphic regions. However, there is a lack of comprehensive studies on gully characteristics, development rates, and the topographic threshold of gully formation in these areas. To address this gap, we selected three different agro-geomorphic watersheds, named HL (Hailun), ML (Muling), and YKS (Yakeshi), with areas of 30.88 km2, 31.53 km2, and 21.98 km2, respectively. Utilizing high-resolution (2.1 m, 2 m) remote sensing imagery (ZY-3, GF-1), we analyzed morphological parameters (length, width, area, perimeter, etc.) and land use changes for all permanent gullies between 2013 and 2023. Approximately 30% of gullies were selected for detailed study of the upstream drainage area and gully head slopes to establish the topographic threshold for gully formation (S = a·A−b). In HL, ML, and YKS, average gully lengths were 526.22 m, 208.64 m, and 614.20 m, respectively, with corresponding widths of 13.28 m, 8.45 m, and 9.32 m. The gully number densities in the three areas were 3.14, 25.18, and 0.82/km2, respectively, with a gully density of 1.65, 5.25, and 0.50 km km−2, and 3%, 5%, and 1% of the land has disappeared due to gully erosion, respectively. YKS exhibited the highest gully head retreat rate at 17.50 m yr−1, significantly surpassing HL (12.24 m yr−1) and ML (7.11 m yr−1). Areal erosion rates were highest in HL (277.79 m2 yr−1) and lowest in YKS (105.22 m2 yr−1), with ML intermediate at 243.36 m2 yr−1. However, there was no significant difference in gully expansion rate (0.37–0.42 m yr−1) among the three areas (p > 0.05). Differences in gully development dynamics among the three regions were influenced by land use, slope, and topographic factors. The topographic threshold (S = a·A−b) for gully formation varied: HL emphasized drainage area (a = 0.052, b = 0.52), YKS highlighted soil resistance (a = 0.12, b = 0.36), and the parameters a and b of ML fell within the range between these of HL and YKS (a = 0.044, b = 0.27). This study has enriched the scope and database of global gully erosion research, providing a scientific basis for gully erosion prevention and control planning in Northeast China. Full article
(This article belongs to the Special Issue Soil Erosion Estimation Based on Remote Sensing Data)
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Figure 1

Figure 1
<p>Location of study area. Note: Subfigure (<b>a</b>) is the HL study area, subfigure (<b>b</b>) is the ML study area and subfigure (<b>c</b>) is the YKS study area.</p>
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<p>Changes in gully morphology in two periods and construction of S = a·A<sup>−b</sup> model. Note: The orange line represents the gully in 2013.</p>
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<p>Flow chart of this study.</p>
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<p>The distribution of gully morphological parameters. Note: Red curves represent the cumulative percentage of gullies. Note: Subfigure (<b>a</b>,<b>f</b>,<b>k</b>) show the gully length in HL, ML and YKS, Subfigure (<b>b</b>,<b>g</b>,<b>l</b>) show the gully width in HL, ML and YKS, Subfigure (<b>c</b>,<b>h</b>,<b>m</b>) show the gully perimeter in HL, ML and YKS, Subfigure (<b>d</b>,<b>i</b>,<b>n</b>) show the gully area in HL, ML and YKS, Subfigure (<b>e</b>,<b>j</b>,<b>o</b>) show the SI in HL, ML and YKS.</p>
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<p>S-A model for the three study areas.</p>
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<p>Differential rates of gully erosion in the three study areas: rate of headcut retreat (<b>a</b>), rate of gully area erosion (<b>b</b>), and rate of gully bank expansion (<b>c</b>). Different lowercase letters represent significant (<span class="html-italic">p</span> &lt; 0.05) differences in gully erosion rates between regions.</p>
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<p>Land use change in HL, ML, and YKS study area from 2013 to 2023. Note: Subfigure (<b>a</b>–<b>c</b>) show the 2013 HL land use, 2023 HL land use and land use change in HL, Subfigure (<b>d</b>–<b>f</b>) show the 2013 ML land use, 2023 ML land use and land use change in ML, Subfigure (<b>g</b>–<b>i</b>) show the 2013 YKS land use, 2023 YKS land use and land use change in YKS.</p>
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<p>Differences in gully head retreat rate (<b>a</b>), gully expansion rate (<b>b</b>), and areal gully erosion rate (<b>c</b>) among different land uses and study areas. Note: Different capital letters represent significant differences (<span class="html-italic">p</span> &lt; 0.05) in gully erosion rate among different study areas for same land use. Different small letters represent significant differences (<span class="html-italic">p</span> &lt; 0.05) among different land uses in a given study area. DFL is dry farmland, GL is grassland, WL is woodland.</p>
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<p>Differences in gully head retreat rate (<b>a</b>), gully expansion rate (<b>b</b>) and areal gully erosion rate (<b>c</b>) among different slope classes and study areas. Note: Different capital letters represent significant differences (<span class="html-italic">p</span> &lt; 0.05) in gully erosion rate among different study areas for same slope class, and different small letters represent significant differences (<span class="html-italic">p</span> &lt; 0.05) among different slope classes in a given study area.</p>
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<p>Differences in gully head retreat rate (<b>a</b>), gully expansion rate (<b>b</b>) and areal gully erosion rate (<b>c</b>) among different slope aspects and study areas. Note: Different capital letters represent significant differences (<span class="html-italic">p</span> &lt; 0.05) in gully erosion rate among different study areas for the same slope aspect, and different small letters represent significant differences (<span class="html-italic">p</span> &lt; 0.05) among different slope aspects in a given study area.</p>
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<p>Modeling of gully criticality in different study areas.</p>
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<p>Ten years (2013–2023) annual average temperature of three study areas (HL, ML, and YKS).</p>
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20 pages, 19235 KiB  
Article
Utilizing Machine Learning Algorithms for the Development of Gully Erosion Susceptibility Maps: Evidence from the Chotanagpur Plateau Region, India
by Md Hasanuzzaman, Pravat Kumar Shit, Saeed Alqadhi, Hussein Almohamad, Fahdah Falah ben Hasher, Hazem Ghassan Abdo and Javed Mallick
Sustainability 2024, 16(15), 6569; https://doi.org/10.3390/su16156569 - 31 Jul 2024
Viewed by 506
Abstract
Gully erosion is a serious environmental threat, compromising soil health, damaging agricultural lands, and destroying vital infrastructure. Pinpointing regions prone to gully erosion demands careful selection of an appropriate machine learning algorithm. This choice is crucial, as the complex interplay of various environmental [...] Read more.
Gully erosion is a serious environmental threat, compromising soil health, damaging agricultural lands, and destroying vital infrastructure. Pinpointing regions prone to gully erosion demands careful selection of an appropriate machine learning algorithm. This choice is crucial, as the complex interplay of various environmental factors contributing to gully formation requires a nuanced analytical approach. To develop the most accurate Gully Erosion Susceptibility Map (GESM) for India’s Raiboni River basin, researchers harnessed the power of two cutting-edge machine learning algorithm: Extreme Gradient Boosting (XGBoost) and Random Forest (RF). For a comprehensive analysis, this study integrated 24 potential control factors. We meticulously investigated a dataset of 200 samples, ensuring an even balance between non-gullied and gullied locations. To assess multicollinearity among the 24 variables, we employed two techniques: the Information Gain Ratio (IGR) test and Variance Inflation Factors (VIF). Elevation, land use, river proximity, and rainfall most influenced the basin’s GESM. Rigorous tests validated XGBoost and RF model performance. XGBoost surpassed RF (ROC 86% vs. 83.1%). Quantile classification yielded a GESM with five levels: very high to very low. Our findings reveal that roughly 12% of the basin area is severely affected by gully erosion. These findings underscore the critical need for targeted interventions in these highly susceptible areas. Furthermore, our analysis of gully characteristics unveiled a predominance of V-shaped gullies, likely in an active developmental stage, supported by an average Shape Index (SI) value of 0.26 and a mean Erosivness Index (EI) of 0.33. This research demonstrates the potential of machine learning to pinpoint areas susceptible to gully erosion. By providing these valuable insights, policymakers can make informed decisions regarding sustainable land management practices. Full article
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Figure 1
<p>Study area. (<b>a</b>) Location of India, (<b>b</b>) location of West Bengal, (<b>c</b>) location of testing and training dataset in the Rainoni River basin.</p>
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<p>Workflow diagram of the present study.</p>
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<p>Distribution of twenty-four key factors used in this research: (<b>a</b>) elevation, (<b>b</b>) slope, (<b>c</b>) slope length, (<b>d</b>) slope aspect, (<b>e</b>) curvature, (<b>f</b>) drainage density, (<b>g</b>) distance from the river, (<b>h</b>) distance from lineament, (<b>i</b>) TWI, (<b>j</b>) distance from the road, (<b>k</b>) NDVI, (<b>l</b>) rainfall, (<b>m</b>) lithology, (<b>n</b>) geomorphology, (<b>o</b>) LULC, (<b>p</b>) soil organic density, (<b>q</b>) bulk density, (<b>r</b>) clay content, (<b>s</b>) coarse fragments, (<b>t</b>) sand, (<b>u</b>) silt, (<b>v</b>) carbon exchange capacity, (<b>w</b>) nitrogen, and (<b>x</b>) soil organic carbon.</p>
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<p>Parameters describing the cross-sectional morphology of the gully (note: width of the one-fourth depth (WQD), width of the half depth (WHD), total width (WT), depth of the half right side (DRH), depth of the half left side (DLH), average depth (D) (source: based on Deng et al. [<a href="#B42-sustainability-16-06569" class="html-bibr">42</a>]).</p>
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<p>Final gully erosion susceptibility maps using: (<b>a</b>) the RF and (<b>b</b>) XGBoost models.</p>
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<p>Evaluation of the accuracy of the XGBoost and RF models using ROC analysis.</p>
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<p>Photographs captured of the gullies during the subsequent field investigations: (<b>a</b>–<b>c</b>) during the gully geometrical parameters survey; (<b>d</b>,<b>e</b>) rock exposure areas caused by deforestation and human activity (REABDHA); (<b>f</b>) agriculture practices in the gully; and (<b>g</b>–<b>i</b>) fallow lands (FL).</p>
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<p>The gully-dominant area of the Raiboni River Basin and the selected gully for measuring geometric parameters.</p>
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22 pages, 6787 KiB  
Article
Nutrient Composition, Physical Characteristics and Sensory Quality of Spinach-Enriched Wheat Bread
by Ritnesh Vishal Prasad, Sushil Dhital, Gary Williamson and Elizabeth Barber
Foods 2024, 13(15), 2401; https://doi.org/10.3390/foods13152401 - 29 Jul 2024
Viewed by 559
Abstract
Food innovation that utilises agricultural waste while enhancing nutritional value is important for waste valorisation and consumer health. This study investigated incorporating spinach (Spinacia oleracea), as a model leafy agricultural waste, into wheat bread. We analysed the nutrient content, colour, texture, [...] Read more.
Food innovation that utilises agricultural waste while enhancing nutritional value is important for waste valorisation and consumer health. This study investigated incorporating spinach (Spinacia oleracea), as a model leafy agricultural waste, into wheat bread. We analysed the nutrient content, colour, texture, sensory attributes and purchase/consume intention ratings. Adding 10–40% spinach (w/w) yielded loaves with similar heights but significantly different colour and texture (p < 0.05) from white bread. Increasing spinach decreased total carbohydrates (including starch) while significantly increasing other nutrients (protein, fibre, iron, magnesium, potassium, zinc, calcium, vitamins A, C, E, folate, niacin, pyridoxine, nitrate/nitrite and polyphenols) (p < 0.05). Spinach addition increased bread porosity, linked to higher pasting parameters (peak, trough, breakdown, final and setback viscosity) with reduced pasting time and temperature. Texture analysis resulted in decreased hardness, chewiness, gumminess and firmness while increasing cohesiveness, with maximum resilience at 20% spinach enrichment. Sensory analysis with 21 untrained panellists revealed decreased visual appeal, less preferred taste, odour and overall liking (p < 0.05) with increasing spinach, with no significant difference in texture acceptance, but the 20% enrichment had comparable acceptance to white bread. Enriching staple foods like bread with leafy vegetable waste offers a promising approach for increasing daily vegetable intake. Full article
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<p>Bread formulation and subsequent analyses. (<b>1</b>) Bread formulation process, (<b>2</b>) physicochemical and nutritional analysis, (<b>3</b>) sensory analysis, <span class="html-italic">n</span> = 21 panellists. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>. “<a href="https://app.biorender.com/illustrations/66612687f10e1aa131a6a97f" target="_blank">https://app.biorender.com/illustrations/66612687f10e1aa131a6a97f</a> (accessed on 29 June 2024)”.</p>
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<p>Sensory evaluation of bread. (<b>A</b>) Samples presented on a tray with three-digit codes with water. (<b>B</b>) Room with red lighting used to conduct sensory evaluation.</p>
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<p>Dough expansion and bread characteristics. (<b>A</b>) Dough fermented for 0, 30 and 60 min with 0%, 20% and 40% spinach enrichment. (<b>B</b>) Bread crumb and crust characteristics for 0%, 10%, 20%, 30% and 40% spinach-enriched bread. Created with <a href="http://Biorender.com" target="_blank">Biorender.com</a>. “<a href="https://app.biorender.com/illustrations/66a1a1c7fddd4fb6c3081b2e" target="_blank">https://app.biorender.com/illustrations/66a1a1c7fddd4fb6c3081b2e</a> (accessed on 29 June 2024)”.</p>
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<p>Bread height with and without spinach enrichment. Different letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05. Values are presented as mean ± SD, <span class="html-italic">n</span> = 3.</p>
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<p>Pasting properties of wheat flour with and without enrichment with 10%, 20%, 30% and 40% freeze-dried spinach powder (FSP). Values are the mean of duplicate samples.</p>
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<p>(<b>A</b>) Texture profile analysis and texture analysis parameters (<b>B</b>) firmness (g), (<b>C</b>) gumminess (g), (<b>D</b>) chewiness (g), (<b>E</b>) hardness (g), (<b>F</b>) springiness (%), (<b>G</b>) resilience (%), (<b>H</b>) cohesiveness (%) of control white bread and spinach-enriched bread at various concentrations from 10% to 40% (<span class="html-italic">w</span>/<span class="html-italic">w</span>). Different letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05. Values are the mean of duplicate samples.</p>
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<p>The colour analysis of bread with and without the addition of spinach at 10%, 20%, 30% and 40% (<span class="html-italic">w</span>/<span class="html-italic">w</span>). (<b>A</b>) L* value: lightness/brightness (0: black to 100:white), (<b>B</b>) a* value: redness (+a*) or greenness (−a*), (<b>C</b>) b* value: yellowness (+b*) or blueness (−b*) and (<b>D</b>) ΔE* value: overall colour difference. Different letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05. Values are mean ± SEM, <span class="html-italic">n</span> = 3.</p>
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<p>Sensory analysis of control white bread compared with spinach-enriched bread at varying concentrations of 10–40% (<span class="html-italic">w</span>/<span class="html-italic">w</span>), assessing (<b>A</b>) appearance, (<b>B</b>) odour, (<b>C</b>) taste, (<b>D</b>) texture and (<b>E</b>) overall liking between scores 1–9. Different letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05. The middle line in the bar graph represents the mean (<span class="html-italic">n</span> = 21).</p>
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<p>Purchase or consume rating test of control white bread compared to spinach-enriched bread at varying concentrations from 10% to 40% (<span class="html-italic">w</span>/<span class="html-italic">w</span>) between scores 1–7. Different letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05. The middle line in the bar graph represents the mean (<span class="html-italic">n</span> = 21).</p>
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16 pages, 5495 KiB  
Article
Effect of W-OH Material on Water/Fertilizer Retention and Plant Growth in the Pisha Sandstone Area of China
by Zhishui Liang, Yue Sun, Xiuwen Fang, Bo Pan, Yuan Xiao, Haiying Gao and Zhiren Wu
Sustainability 2024, 16(15), 6369; https://doi.org/10.3390/su16156369 - 25 Jul 2024
Viewed by 453
Abstract
The Pisha sandstone area in the Yellow River Basin is one of the regions with the most severe soil erosion in China and globally, and its erosion is particularly challenging to control. W-OH, a hydrophilic polyurethane material, possesses controllable degradation properties. It can [...] Read more.
The Pisha sandstone area in the Yellow River Basin is one of the regions with the most severe soil erosion in China and globally, and its erosion is particularly challenging to control. W-OH, a hydrophilic polyurethane material, possesses controllable degradation properties. It can react with water to achieve soil stabilization and erosion resistance during the curing process. The material has been successfully utilized in erosion control in Pisha sandstone areas. This study aims to investigate the impact of W-OH material on water/fertilizer retention and plant growth through experiments on soil hardness, permeability, soil evaporation, soil column leaching, pot tests, and a small-scale demonstration in practical engineering applications. The results indicate that different concentrations of W-OH solution can effectively permeate Pisha sandstone, solidifying the particles to create a flexible and porous consolidation layer on the surface with a specific depth. As the W-OH concentration (3%, 4%, and 5%) increases, the harnesses of the consolidation layer also increase but remain below 1.5 kPa, which does not impede plant root growth. The soil evaporation rate decreased by approximately 45.2%, 45.8%, and 50.3% compared to the control group. The reduction rates of cumulative total nitrogen (TN) content are around 43.57%, 48.14%, and 63.99%, and, for cumulative total phosphorus (TP), are approximately 27.96%, 45.70%, and 61.17% under the 3%, 4%, and 5% concentrations of W-OH solution, respectively. In the pot tests, concentrations of W-OH solution below 5% are suitable for germination and growth of monocotyledons, while the optimal concentration for dicotyledons is around 3%. In the demonstration, the vegetation coverage of the treated gully increases by approximately 11.35%. This research offers a promising and effective approach to enhance ecological restoration in Pisha sandstone areas. Full article
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Figure 1
<p>The Erlaohu Gully watershed and its monitoring point.</p>
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<p>Schematic diagram of the surface hardness test.</p>
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<p>Simulated soil columns in the leaching test.</p>
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<p>The spraying equipment system.</p>
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<p>Effects of different W-OH concentrations on permeation depth in Pisha sandstone.</p>
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<p>The variation in the deformation index under different W-OH concentrations.</p>
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<p>The moisture content in different samples.</p>
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<p>Total N content changed with time in leaching water samples. (<b>a</b>) TN and (<b>b</b>) cumulative TN.</p>
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<p>Total TP content changes with time in leaching water samples: (<b>a</b>) TP and (<b>b</b>) cumulative TP.</p>
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<p>The germination amount of the different seeds: (<b>a</b>) <span class="html-italic">Astragalus adsurgens Pall</span>, (<b>b</b>) <span class="html-italic">Medicago sativa</span> L., (<b>c</b>) <span class="html-italic">Buchloe dactyloides</span>, and (<b>d</b>) <span class="html-italic">Lolium perenne</span> L.</p>
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<p>The germination amount of the different seeds: (<b>a</b>) <span class="html-italic">Astragalus adsurgens Pall</span>, (<b>b</b>) <span class="html-italic">Medicago sativa</span> L., (<b>c</b>) <span class="html-italic">Buchloe dactyloides</span>, and (<b>d</b>) <span class="html-italic">Lolium perenne</span> L.</p>
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<p>Comparison before and after the treatment. (<b>a</b>) Topical Pisha sandstone slope, (<b>b</b>) the Erlaohu Gully, (<b>c</b>) treated Pisha sandstone slope after 6 months, and (<b>d</b>) vegetation coverage of Erlaohu Gully after 2 years of treatment.</p>
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<p>The structure of the original Pisha sandstone particles.</p>
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<p>The structure of the Pisha sandstone and the consolidation body by W-OH. (<b>a</b>) Original weathered Pisha sandstone particles, (<b>b</b>) consolidation body, (<b>c</b>) cementation pattern.</p>
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<p>The structure of the curing body at 15× magnification. (<b>a</b>) Surface and (<b>b</b>) cross-section.</p>
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27 pages, 29974 KiB  
Article
Evidence of Dextral Strike-Slip Movement of the Alakol Lake Fault in the Western Junggar Based on Remote Sensing
by Wenxing Yi, An Li, Liangxin Xu, Zongkai Hu and Xiaolong Li
Remote Sens. 2024, 16(14), 2615; https://doi.org/10.3390/rs16142615 - 17 Jul 2024
Viewed by 422
Abstract
The NW-SE-trending dextral strike-slip faults on the north side of the Tian Shan, e.g., the Karatau fault, Talas–Fergana fault, Dzhalair–Naiman fault, Aktas fault, Dzhungarian fault, and Chingiz fault, play an important role in accommodating crustal shortening. The classic viewpoint is that these strike-slip [...] Read more.
The NW-SE-trending dextral strike-slip faults on the north side of the Tian Shan, e.g., the Karatau fault, Talas–Fergana fault, Dzhalair–Naiman fault, Aktas fault, Dzhungarian fault, and Chingiz fault, play an important role in accommodating crustal shortening. The classic viewpoint is that these strike-slip faults are an adjustment product caused by the difference in the crustal shortening from west to east. Another viewpoint attributes the dextral strike-slip fault to large-scale sinistral shearing. The Alakol Lake fault is a typical dextral strike-slip fault in the north Tian Shan that has not been reported. It is situated along the northern margin of the Dzhungarian gate, stretching for roughly 150 km from Lake Ebinur to Lake Alakol. Our team utilized aerial photographs, satellite stereoimagery, and field observations to map the spatial distribution of the Alakol Lake fault. Our findings provided evidence supporting the assertion that the fault is a dextral strike-slip fault. In reference to its spatial distribution, the Lake Alakol is situated in a pull-apart basin that lies between two major dextral strike-slip fault faults: the Chingiz and Dzhungarian faults. The Alakol Lake fault serves as a connecting structure for these two faults, resulting in the formation of a mega NW-SE dextral strike-slip fault zone. According to our analysis of the dating samples taken from the alluvial fan, as well as our measurement of the displacement of the riser and gully, it appears that the Alakol Lake fault has a dextral strike-slip rate of 0.8–1.2 mm/a (closer to 1.2 mm/a). The strike-slip rate of the Alakol Lake fault is comparatively higher than that of the Chingiz fault in the northern region (~0.7 mm/a) but slower than that of the Dzhungarian fault in the southern region (3.2–5 mm/a). The Chingiz–Alakol–Dzhungarian fault zone shows a gradual decrease in deformation towards the interior of the Kazakhstan platform. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)
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<p>(<b>a</b>) The digital elevation model shows the distribution of the main Quaternary faults in the northern Tian Shan region (modified after Xu et al., 2016) [<a href="#B34-remotesensing-16-02615" class="html-bibr">34</a>]. Blue arrows show the GPS measurements from Wang and Shen (2020) [<a href="#B23-remotesensing-16-02615" class="html-bibr">23</a>]. The blue dashed lines (A–A’) show the locations of the GPS profiles. The white circles show the major cities. The black dashed boxes show the locations of <a href="#remotesensing-16-02615-f002" class="html-fig">Figure 2</a>. DZF—Dzhungarian fault; ALF—Alakol Lake fault; CF—Chingiz fault; KSHF—Kashihe fault; ETF—East Tacheng fault; TLF—TuoLi fault; and DF—Daerbute fault. (<b>b</b>) The global digital elevation model shows the tectonic location of the research area (<b>a</b>). (<b>c</b>) Swath GPS profile A–A’ shows the velocity components parallel to (blue dots) the profile striking N320°W [<a href="#B20-remotesensing-16-02615" class="html-bibr">20</a>]. The brown line and gray shadow show the mean value and range of elevation with 50 km width along the profile A–A’. The blue-shaded rectangles are the visually fitted range of the GPS velocities. The blue letters and numbers represent the GPS observation stations’ abbreviations.</p>
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<p>The extension of the Alakol Lake fault is shown on Google Earth. The red lines show the location of the fault trace. The red triangular arrows indicate dextral strike-slip movement. The black boxes are the study sites. The white circles show the major cities.</p>
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<p>Field collection locations of the OSL samples: (<b>a</b>) sample AL-01; (<b>b</b>) sample AL-02; (<b>c</b>) sample AL-03; and (<b>d</b>) sample AL-04. (<b>e</b>) The field site shows a dextral alluvial fan and the fault scarp, which is also where the sample AL-04 was collected. (<b>f</b>) Sample AL-05. (<b>g</b>) The field site indicates the fault trace and fault scarp, which is also where the sample AL-05 was collected. The red triangular arrows indicate the fault trace.</p>
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<p>Site 1: (<b>a</b>) The hillshade map, which was built from a high-resolution UAV DEM using ArcGIS 10.8, shows structural and geomorphological characters of the Alakol Lake fault. The red lines are the fault trace. The red dotted lines show that the fault traces are either unclear or covered. The red triangular arrows indicate dextral strike-slip movement. The black solid line (A–A’) marks the location of the profile in (<b>c</b>). Four stages of alluvial fans (T1–T4) are developed along the stream channel, and the shadows with different colors depict the corresponding alluvial fans. The white dashed lines and the pink dashed lines represent the fit lines of the T4 riser. The white dotted box represents the range of (<b>b</b>). (<b>b</b>) The image of the hillshade map displays a detailed view of the geomorphic surface in (<b>a</b>). The white and pink dashed lines in the image represent the fit lines. The white and pink arrows indicate the preferred offset. (<b>c</b>) Topographic profile across the fault extracted from the UAV DEM was used to measure the vertical offset. The blue dashed lines are fit lines. (<b>d</b>) The field photo shows the fault trace and alluvial fans. The white oval highlights the house for scale. (<b>e</b>) The field photo was shot in the northwest. The red dashed line represents the fault. Q represents the alluvial fan deposits (probably middle–upper Pleistocene), N represents the Neogene sandstone, and C represents the Carboniferous granite.</p>
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<p>Site 2: (<b>a</b>) The hillshade map image, which was made using the high-resolution UAV DEM, shows the geomorphological expression of the Alakol Lake fault. The red lines are the fault trace. The red triangular arrows indicate dextral strike-slip movement. The two blue curves represent the horizontal offset of the gullies. The orange curve represents the dextrally displaced ridge. The black solid lines (A–A’ and B–B’) indicate the location of the extracted fault scarp. All of the white dashed lines represent the fit lines. The white arrows indicate the preferred offset. (<b>b</b>) Two topographic profiles across the fault extracted from the UAV DEM were used to measure the vertical offset. The blue dashed lines are fit lines. (<b>c</b>) The field photo shows the dextrally displaced ridge. The dotted orange lines show the location of the ridge. The red triangles indicate the fault trace. The blue dashed line indicates the dextral channel. The white ovals highlight the people for scale.</p>
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<p>Site 3: (<b>a</b>) The hillshade map image, which was made using the high-resolution UAV DEM, shows the geomorphological expression of the Alakol Lake fault. The red lines are the fault trace, and the scales indicate the slope direction of the fault scarp. The red triangular arrows indicate dextral strike-slip movement. The black dotted box represents the range of Figure c. The orange shadows represent T1, and purple shadows represent T2. The blue curves represent the dextrally displaced edge of the alluvial fans, and the white dashed lines represent the fit lines. The white arrows indicate the preferred offset. The black solid lines (A–A’ and B–B’) indicate the location of the extracted fault scarps. (<b>b</b>) Topographic profiles across the fault extracted from the UAV DEM were used to measure the vertical offset. The blue dashed lines are fit lines. (<b>c</b>) A photo taken by the drone shows the fault traces and a pull-apart basin.</p>
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<p>Site 4: (<b>a</b>) The hillshade map image, which was made using the high-resolution UAV DEM, shows the geomorphological expression of the Alakol Lake fault. The red line is the fault trace, and the scales indicate the slope direction of the fault scarp. Two red triangular arrows indicate dextral strike-slip movement. The orange shadow represents T1, and the purple shadows represent T2. The blue curves with arrows represent the dextrally displaced gullies, and the white dashed lines represent the fit lines. The white arrows indicate the preferred offset. The spring symbol composed of the blue circle and blue curve indicates the location of the fault spring. The black solid lines (A–A’, B–B’) indicate the location of the extracted fault scarps. The black dotted box represents the range of (<b>b</b>,<b>c</b>). (<b>b</b>) The enlarged hillshade map image shows the more detailed geomorphic surface in (<b>a</b>). Four identical red triangles indicate the fault trace. (<b>c</b>) A field photo of the range corresponding to (<b>b</b>). (<b>d</b>) Topographic profiles across the fault extracted from the UAV DEM were used to measure the vertical offset. The blue dashed lines are fit lines. (<b>e</b>) The field photo shows the dextrally displaced gully and fault scarp. (<b>f</b>) The field photo shows the fault scarp. The dotted white line indicates the geomorphic surface. Two red arrows show the fault scarp. (<b>g</b>) The picture shows the fault spring in the field.</p>
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<p>Site 5: (<b>a</b>) The hillshade map image, which was made using the high-resolution UAV DEM, shows the geomorphological expression of the Alakol Lake fault. Different colored shadows represent different alluvial fans. The red lines indicate clear fault traces, while the red dotted lines show fault traces that are either not visible or are covered. The white arrows indicate the preferred offset. The red triangular arrows indicate dextral strike-slip movement. The scales indicate the slope direction of the fault scarp. The white dashed lines represent the dextrally displaced T2 alluvial fan. The black solid lines (A–A’, B–B’) indicate the location of the extracted fault scarps. (<b>b</b>) Topographic profiles across the fault extracted from the UAV DEM were used to measure the vertical offset. The blue dashed lines are fit lines. (<b>c</b>) The field photo shows the fault trace and the different alluvial fans. The red arrows indicate the fault scarp and fault trace, and the white circle represents the iron tower as a reference.</p>
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<p>Site 6: (<b>a</b>) View of the Pleiades DEM shows the geomorphological expression of the Alakol Lake fault. (<b>b</b>) Hillshade map image, which was made using the high-resolution UAV DEM, shows the enlarged geomorphic surface. The red lines are the fault traces, and the red dotted lines show that the fault traces are not clear or are covered. The red triangular arrows indicate dextral strike-slip movement. The scales indicate the slope direction of the fault scarp. Different colored shadows represent different alluvial fans. The green curve represents the dextrally displaced alluvial fan. The blue curve represents the dextrally displaced gullies. All of the white dashed lines represent the fit lines. The white arrows indicate the preferred offset. (<b>c</b>,<b>d</b>) The fault breccia indicated by the white arrow revealed in the field.</p>
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<p>Site 7: (<b>a</b>) View of the Pleiades DEM shows some displaced alluvial fans. The translucent green shades are the alluvial fans. The red lines are the fault traces, and the red triangular arrows indicate dextral strike-slip movement. The blue curves represent the stream channels. (<b>b</b>) Hillshade map image, which was made by the high-resolution UAV DEM, shows the geomorphic surface of enlarged alluvial fan 2. (<b>c</b>) The outline of alluvial fans identified based on the texture and color characteristics in the Pleiades satellite image.</p>
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<p>Site 8: (<b>a</b>) View of the Pleiades DEM shows the fault trace and the displaced geomorphic surface. The red triangles indicate the fault trace. The blue dashed lines indicate the displaced gullies, while the yellow dashed lines indicate the displaced T3 riser. (<b>b</b>) View of the enlarged geomorphic surface shows the displaced gullies. The red triangular arrows indicate dextral strike-slip movement. (<b>c</b>) The back-slipped view of the gullies.</p>
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<p>Site 9: (<b>a</b>) The location of site 9 is shown on Google Earth. (<b>b</b>) The fault trace and geomorphic surface are shown on Google Earth. Six identical red triangles indicate the fault trace. Two red triangular arrows indicate dextral strike-slip movement. The black solid lines (A–A’ and B–B’) show where the topographic profiles were extracted. The white boxes represent the viewing areas of (<b>d</b>,<b>e</b>). (<b>c</b>) Topographic profiles (A–A’ and B–B’) across the fault extracted from the DEM were used to measure the vertical offset. The blue dashed lines are fit lines. (<b>d</b>,<b>e</b>) Some images of the displaced gullies taken on Google Earth. The blue dashed lines indicate the displaced gullies. The white dashed lines represent the fit lines. The white arrows indicate the preferred offset.</p>
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<p>(<b>a</b>) Simplified geological model map of the new fault in the western Junggar. The white arrows indicate the relative movement directions of the Tacheng Basin, Junggar Alatau, and Junggar Basin. DZF—Dzhungarian fault; ALF—Alakol Lake fault; CF—Chingiz fault; LF—Lepsy fault; KSHF—Kashihe fault; DF—Daerbute fault; TLF—TuoLi fault; ETF—East Tacheng fault; and NTF—North Tacheng fault. (<b>b</b>) Evolutionary model map of Lake Alakol and the Alakol Lake fault.</p>
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20 pages, 13509 KiB  
Article
Responses of Soil Moisture to Gully Land Consolidation in Asian Areas with Monsoon Climate
by Mingyi Lin, Jing Zhang, Guofan Cao, Hao Han, Zhao Jin, Da Luo and Guang Zeng
Water 2024, 16(14), 2001; https://doi.org/10.3390/w16142001 - 15 Jul 2024
Viewed by 447
Abstract
Groundwater resources are essential for sustaining ecosystems and human activities, especially under the pressures of climate change. This study employed Electrical Resistivity Tomography (ERT) to assess the impact of Gully Land Consolidation (GLC) engineering on the groundwater hydrological field of small watersheds in [...] Read more.
Groundwater resources are essential for sustaining ecosystems and human activities, especially under the pressures of climate change. This study employed Electrical Resistivity Tomography (ERT) to assess the impact of Gully Land Consolidation (GLC) engineering on the groundwater hydrological field of small watersheds in the China Loess Plateau (CLP). Results revealed ample subsurface water storage in backfilled areas, primarily migrating along the original river path owing to topographical limitations. Although the distribution patterns of soil moisture in each backfilling block varied slightly, the boundaries of soil moisture content and variation mainly appeared at depths of 8 m and 20 m underground. Significant moisture variation occurred across the 0–20 m underground layers, suggesting the 8–20 m layer could function as a groundwater collection zone in the study area. Human activities could disturb groundwater, altering migration pathways from the original river path. An optimized “Drainage–Conveyance–Barrier” system is proposed to enhance GLC sustainability, involving upstream groundwater level control, midstream soil moisture management, and downstream hydrological connectivity improvement. These findings carry substantial implications for guiding the planning and execution of GLC engineering initiatives. The novelty of this study lies in its application of ERT to provide a detailed spatial and temporal understanding of soil moisture dynamics in the GLC areas. Future research should focus on factors such as soil types and topographical changes for a comprehensive assessment of GLC’s impact on small watershed groundwater hydrology. Full article
(This article belongs to the Section Soil and Water)
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<p>Location map of GLC in Gutun Watershed on the Loess Plateau of China (CLP) (<b>a</b>). (<b>b</b>) elevation map of the study area; (<b>c</b>) third sampling sub-region; (<b>d</b>) first sampling sub-region; (<b>e</b>) second sampling sub-region; (<b>f</b>) fourth sampling sub-region.</p>
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<p>Meteorological conditions at the study site throughout the hydrological year (2021–2022). Daily precipitation (mm), temperatures (°C), and relative humidity (HR). The annual mean temperature is depicted by the red dashed line.</p>
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<p>Soil moisture distribution in the Fa1 backfilled plot at different temporal and spatial scales.</p>
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<p>Soil moisture distribution in the Fa2 backfilled plot at different temporal and spatial scales.</p>
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<p>Soil moisture distribution in the Fa3 backfilled plot at different temporal and spatial scales.</p>
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<p>Soil moisture distribution in the Fa4 backfilled plot at different temporal and spatial scales.</p>
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<p>Soil moisture content and rate of change across depth profiles in Fa1 (<b>a</b>), Fa2 (<b>b</b>), and Fa4 (<b>c</b>).</p>
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<p>Spatial distribution of high soil moisture content points.</p>
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<p>The subsurface hydrological migration model in the GLC area.</p>
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21 pages, 62211 KiB  
Article
Damage Law and Reasonable Width of Coal Pillar under Gully Area: Linking Fractal Characteristics of Coal Pillar Fractures to Their Stability
by Zhaopeng Wu, Yunpei Liang, Kaijun Miao, Qigang Li, Sichen Liu, Qican Ran, Wanjie Sun, Hualong Yin and Yun Ma
Fractal Fract. 2024, 8(7), 407; https://doi.org/10.3390/fractalfract8070407 - 11 Jul 2024
Viewed by 471
Abstract
The coal pillar is an important structure to control the stability of the roadway surrounding rock and maintain the safety of underground mining activities. An unreasonable design of the coal pillar size can result in the failure of the surrounding rock structure or [...] Read more.
The coal pillar is an important structure to control the stability of the roadway surrounding rock and maintain the safety of underground mining activities. An unreasonable design of the coal pillar size can result in the failure of the surrounding rock structure or waste of coal resources. The northern Shaanxi mining area of China belongs to the shallow buried coal seam mining in the gully area, and the gully topography makes the bearing law of the coal pillar and the development law of the internal fracture more complicated. In this study, based on the geological conditions of the Longhua Mine 20202 working face, a PFC2D numerical model was established to study the damage characteristics of coal pillars under the different overlying strata base load ratios in the gentle terrain area and the different gully slope sections in the gully terrain area, and the coal pillar design strategy based on the fractal characteristics of the fractures was proposed to provide a reference for determining the width of the coal pillars in mines under similar geological conditions. The results show that the reliability of the mathematical equation between the overlying strata base load ratio and the fractal dimension of the fractures in the coal pillar is high, the smaller the overlying strata base load ratio is, the greater the damage degree of the coal pillar is, and the width of the coal pillar of 15 m under the condition of the actual overlying strata base load ratio (1.19) is more reasonable. Compared with the gentle terrain area, the damage degree of the coal pillar in the gully terrain area is larger, in which the fractal dimension of the fracture in the coal pillar located below the gully bottom is the smallest, and the coal pillar in the gully terrain should be set as far as possible to make the coal pillar located below the gully bottom, so as to ensure the stability of the coal pillar. Full article
(This article belongs to the Special Issue Applications of Fractal Analysis in Underground Engineering)
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<p>Overview of the study area: (<b>a</b>) geographic location of the northern Shaanxi mining area; (<b>b</b>) location of the Longhua Mine; (<b>c</b>) gully terrain on the surface; (<b>d</b>) 20202 large mining height working face.</p>
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<p>20202 working face roadway layout.</p>
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<p>Borehole histogram of 20202 working face.</p>
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<p>UAV observation of the topography of the study area: (<b>a</b>) Mine Geological Assurance System; (<b>b</b>) location demarcation of the study area (satellite map); (<b>c</b>) UAV assembly and navigation system debugging; (<b>d</b>) UAV observation; (<b>e</b>) remote control screen and parameter display.</p>
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<p>Observations of the topography of the study area: (<b>a</b>) gully terrain area and gentle terrain area; (<b>b</b>) typical gully terrain; (<b>c</b>) surface height difference observation; (<b>d</b>) gentle terrain area with thick loess formation.</p>
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<p>Schematic representation of coal pillar states at different damage levels: (<b>a</b>) steady state; (<b>b</b>) semi-steady state; (<b>c</b>) critical yield state; (<b>d</b>) yield state.</p>
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<p>PFC<sup>2D</sup> numerical model and measurement points layout: (<b>a</b>) roadway excavation period; (<b>b</b>) first mining period; (<b>c</b>) second mining period; (<b>d</b>) measurement points layout scheme.</p>
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<p>Distribution characteristics and fractal dimensions of fractures in coal pillars under different overlying strata base load ratios: (<b>a</b>) base load ratio: 0.5; (<b>b</b>) base load ratio: 1; (<b>c</b>) base load ratio: 1.5; (<b>d</b>) base load ratio: 2; (<b>e</b>) base load ratio: 2.5; (<b>f</b>) base load ratio: 3.</p>
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<p>Distribution characteristics and fractal dimensions of fractures in coal pillars under different overlying strata base load ratios: (<b>a</b>) base load ratio: 0.5; (<b>b</b>) base load ratio: 1; (<b>c</b>) base load ratio: 1.5; (<b>d</b>) base load ratio: 2; (<b>e</b>) base load ratio: 2.5; (<b>f</b>) base load ratio: 3.</p>
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<p>Number of fractures within coal pillars under different overlying strata base load ratios.</p>
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<p>Porosity distribution within the coal pillars at different overlying strata base load ratios: (<b>a</b>) base load ratio: 0.5; (<b>b</b>) base load ratio: 1; (<b>c</b>) base load ratio: 1.5; (<b>d</b>) base load ratio: 2; (<b>e</b>) base load ratio: 2.5; (<b>f</b>) base load ratio: 3.</p>
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<p>Fitting equation for overlying strata base load ratios to fractal dimensions of fractures within coal pillars.</p>
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<p>Numerical simulation results and the distribution characteristics of the fractures within the coal pillar for an overlying strata base load ratio of 1.19: (<b>a</b>) numerical simulation results; (<b>b</b>) distribution of fractures within the coal pillar and binarization; (<b>c</b>) fractal dimension of the fractures within the coal pillar; (<b>d</b>) porosity distribution within the coal pillar.</p>
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<p>PFC<sup>2D</sup> numerical modeling schemes: (<b>a</b>) coal pillar below the peak of the gully; (<b>b</b>) coal pillar below the gully bottom; (<b>c</b>) coal pillar below the upslope section of the gully; (<b>d</b>) coal pillar below the downslope section of the gully.</p>
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<p>Distribution characteristics and fractal dimensions of fractures within the coal pillars when located below different gully slope sections: (<b>a</b>) coal pillar below the peak of the gully; (<b>b</b>) coal pillar below the gully bottom; (<b>c</b>) coal pillar below the upslope section of the gully; (<b>d</b>) coal pillar below the downslope section of the gully.</p>
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<p>Distribution characteristics and fractal dimensions of fractures within the coal pillars when located below different gully slope sections: (<b>a</b>) coal pillar below the peak of the gully; (<b>b</b>) coal pillar below the gully bottom; (<b>c</b>) coal pillar below the upslope section of the gully; (<b>d</b>) coal pillar below the downslope section of the gully.</p>
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<p>Number of fractures and corresponding states within coal pillars when located below different gully slope sections.</p>
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<p>Porosity distribution within the coal pillar at different gully slope sections: (<b>a</b>) coal pillar below the peak of the gully; (<b>b</b>) coal pillar below the gully bottom; (<b>c</b>) coal pillar below the upslope section of the gully; (<b>d</b>) coal pillar below the downslope section of the gully.</p>
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<p>Process for determining reasonable coal pillar width design strategy.</p>
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18 pages, 7989 KiB  
Article
Assessment of Phenological Dynamics of Different Vegetation Types and Their Environmental Drivers with Near-Surface Remote Sensing: A Case Study on the Loess Plateau of China
by Fengnian Guo, Dengfeng Liu, Shuhong Mo, Qiang Li, Jingjing Meng and Qiang Huang
Plants 2024, 13(13), 1826; https://doi.org/10.3390/plants13131826 - 3 Jul 2024
Viewed by 542
Abstract
Plant phenology is an important indicator of the impact of climate change on ecosystems. We have continuously monitored vegetation phenology using near-surface remote sensing, i.e., the PhenoCam in a gully region of the Loess Plateau of China from March 2020 to November 2022. [...] Read more.
Plant phenology is an important indicator of the impact of climate change on ecosystems. We have continuously monitored vegetation phenology using near-surface remote sensing, i.e., the PhenoCam in a gully region of the Loess Plateau of China from March 2020 to November 2022. In each image, three regions of interest (ROIs) were selected to represent different types of vegetation (scrub, arbor, and grassland), and five vegetation indexes were calculated within each ROI. The results showed that the green chromatic coordinate (GCC), excess green index (ExG), and vegetation contrast index (VCI) all well-captured seasonal changes in vegetation greenness. The PhenoCam captured seasonal trajectories of different vegetation that reflect differences in vegetation growth. Such differences may be influenced by external abiotic environmental factors. We analyzed the nonlinear response of the GCC series to environmental variables with the generalized additive model (GAM). Our results suggested that soil temperature was an important driver affecting plant phenology in the Loess gully region, especially the scrub showed a significant nonlinear response to soil temperature change. Since in situ phenology monitoring experiments of the small-scale on the Loess Plateau are still relatively rare, our work provides a reference for further understanding of vegetation phenological variations and ecosystem functions on the Loess Plateau. Full article
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<p>The study area located at Chunhua County, Shaanxi Province, China, on the southern Loess Plateau. The red triangle represents the position of the flux tower.</p>
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<p>As an example, four images from different dates in 2021, the fields contained in yellow lines represent the region of interest (ROI). The vegetation types within ROI 1, ROI 2, and ROI 3 are scrub, arbor, and grassland, respectively.</p>
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<p>Variations of daily PhenoCam GCC (<b>a</b>), RCC (<b>b</b>), VARI (<b>c</b>), ExG (<b>d</b>), and VCI (<b>e</b>) from ROI1. The red dashed lines and blue dashed lines represent the start of season (SOS) and the end of season (EOS) for each year, respectively. SOS derived from RCC in 2021 cannot be accurately extracted. In addition, SOS and EOS also cannot be extracted in the VARI series.</p>
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<p>Variations of daily PhenoCam GCC (<b>a</b>), RCC (<b>b</b>), VARI (<b>c</b>), ExG (<b>d</b>), and VCI (<b>e</b>) from ROI2. The red dashed lines and blue dashed lines represent the start of season (SOS) and the end of season (EOS) for each year, respectively. For RCC and VARI, we extracted only the phenological metrics derived from RCC for the year 2022.</p>
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<p>Variations of daily PhenoCam GCC (<b>a</b>), RCC (<b>b</b>), VARI (<b>c</b>), ExG (<b>d</b>), and VCI (<b>e</b>) from ROI3. The red dashed lines and blue dashed lines represent the start of season (SOS) and the end of season (EOS) for each year, respectively. For RCC and VARI, we extracted only the phenological metrics for the year 2020. Due to the lack of raw data, we were unable to obtain phenological metrics information for 2022.</p>
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<p>Variations of green chromatic coordinate (GCC) derived from different ROIs, as well as daily precipitation (P) and evapotranspiration (ET) processes. Different colored line segments represent the length of growing season while the numbers indicate the days.</p>
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<p>Effects of environmental factors on GCC derived from ROI1. Effect of (<b>a</b>) net radiation (Rn), (<b>b</b>) sunshine hours (Sunh), (<b>c</b>) wind speed (U), (<b>d</b>) relative humidity (RH), (<b>e</b>) vapor pressure deficit (VPD), (<b>f</b>) soil temperature (Ts), (<b>g</b>) upper soil water content (SWCu), (<b>h</b>) medium soil water content (SWCm), and (<b>i</b>) deep soil water content (SWCd) on the variation of GCC. The <span class="html-italic">y</span>-axis represents the partial effects of each driver. The orange line represents the smoothed fitted curve for explanatory variables. The gray shaded area represents the 95% confidence interval. The numbers in brackets in the <span class="html-italic">y</span>-axis labels are the effective degrees of freedom.</p>
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<p>Effects of environmental factors on GCC derived from ROI2. Effect of (<b>a</b>) net radiation (Rn), (<b>b</b>) sunshine hours (Sunh), (<b>c</b>) wind speed (U), (<b>d</b>) relative humidity (RH), (<b>e</b>) vapor pressure deficit (VPD), (<b>f</b>) soil temperature (Ts), (<b>g</b>) upper soil water content (SWCu), (<b>h</b>) medium soil water content (SWCm), and (<b>i</b>) deep soil water content (SWCd) on the variation of GCC. The <span class="html-italic">y</span>-axis represents the partial effects of each driver. The orange line represents the smoothed fitted curve for explanatory variables. The gray shaded area represents the 95% confidence interval. The numbers in brackets in the <span class="html-italic">y</span>-axis labels are the effective degrees of freedom.</p>
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<p>Effects of environmental factors on GCC derived from ROI3. Effect of (<b>a</b>) net radiation (Rn), (<b>b</b>) sunshine hours (Sunh), (<b>c</b>) wind speed (U), (<b>d</b>) relative humidity (RH), (<b>e</b>) vapor pressure deficit (VPD), (<b>f</b>) soil temperature (Ts), (<b>g</b>) upper soil water content (SWCu), (<b>h</b>) medium soil water content (SWCm), and (<b>i</b>) deep soil water content (SWCd) on the variation of GCC. The <span class="html-italic">y</span>-axis represents the partial effects of each driver. The orange line represents the smoothed fitted curve for explanatory variables. The gray shaded area represents the 95% confidence interval. The numbers in brackets in the <span class="html-italic">y</span>-axis labels are the effective degrees of freedom.</p>
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19 pages, 8758 KiB  
Article
Assessing the Susceptibility of the Xiangka Debris Flow Using Analytic Hierarchy Process, Fuzzy Comprehensive Evaluation Method, and Cloud Model
by Yan Li, Jianguo Wang, Keping Ju, Shengyun Wei, Zhinan Wang and Jian Hu
Sustainability 2024, 16(13), 5392; https://doi.org/10.3390/su16135392 - 25 Jun 2024
Viewed by 806
Abstract
The seasonal Xiangka debris flow, breaking out frequently in Xinghai County, Qinghai Province, poses a serious threat to resident safety, has significant potential economic impacts, and inflicts severe damage on the geological environment, vegetation, and land resources in the area. Therefore, a susceptibility [...] Read more.
The seasonal Xiangka debris flow, breaking out frequently in Xinghai County, Qinghai Province, poses a serious threat to resident safety, has significant potential economic impacts, and inflicts severe damage on the geological environment, vegetation, and land resources in the area. Therefore, a susceptibility assessment is crucial. Utilizing data from field investigations, meteorology, and remote sensing, this study devised an assessment system using 10 evaluation factors with pronounced regional characteristics as susceptibility indices. Based on data processing using ArcGIS 10.7 and MATLAB R2016B, this study assessed the susceptibility of the Xiangka debris flow using AHP, the fuzzy comprehensive evaluation method, and a cloud model. The analysis results show that, based on AHP, the primary index affecting the occurrence of Xiangka debris flow is mainly source factor (0.447). The secondary indices are mainly the length ratio of the mud sand supply section (0.219), fractional vegetation cover (FVC, 0.208), and watershed area (0.192). Combined with the actual characteristics, it can be seen that the formation conditions of the Xiangka debris flow primarily encompass the following: sources such as slope erosion and accumulation at gully exits, challenging topography and terrain conducive to the accumulation of water and solid materials, and water source aspects like surface runoff from intense rainfall. Based on the fuzzy mathematical method—fuzzy coordinate method—cloud model, it is concluded that the degree of susceptibility is mild-to-moderate. The combination of these methods provides a new idea for the evaluation of debris flow susceptibility. This study can provide a theoretical basis for the layout of treatment engineering and geological disaster prevention in this area and promote the sustainable development of the ecological environment. Full article
(This article belongs to the Section Hazards and Sustainability)
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<p>(<b>a</b>) Topographic map of Xinghai County. (<b>b</b>) Topographic map of Ziketan Town. (<b>c</b>) Diagram of Xiangka debris flow.</p>
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<p>Technology roadmap.</p>
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<p>Evaluation index system for the susceptibility of the Xiangka debris flow.</p>
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<p>Diagram showing the classification of debris flow susceptibility evaluation factors. (<b>a</b>) DEM. (<b>b</b>) Average slope. (<b>c</b>) NDVI. (<b>d</b>) 24 h maximum rainfall. (<b>e</b>) Land use type.</p>
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<p>A comment set model for evaluating the susceptibility of the Xiangka debris flow.</p>
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<p>Fuzzy coordinate system and calculation results.</p>
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<p>Comparison of the cloud model and the comment set model of the evaluation results for the susceptibility of the Xiangka debris flow.</p>
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17 pages, 1529 KiB  
Article
Impacts of Integrated Watershed Management Interventions on Land Use/Land Cover of Yesir Watershed in Northwestern Ethiopia
by Abebaw Andarge Gedefaw, Mulutesfa Alemu Desta and Reinfried Mansberger
Land 2024, 13(7), 918; https://doi.org/10.3390/land13070918 - 24 Jun 2024
Viewed by 501
Abstract
Since 2002, numerous sustainable land management (SLM) interventions have been implemented in Ethiopia, such as agroforestry, area closure, forage development, gully rehabilitation, and conservation agriculture. In addition, watershed-based developments contributed comprehensively to a better use of existing natural resources. This study determined the [...] Read more.
Since 2002, numerous sustainable land management (SLM) interventions have been implemented in Ethiopia, such as agroforestry, area closure, forage development, gully rehabilitation, and conservation agriculture. In addition, watershed-based developments contributed comprehensively to a better use of existing natural resources. This study determined the impact of Integrated Watershed Management (IWM) on land use/land cover for the Yesir watershed in Northern Ethiopia. Supervised image classification algorithms were applied to a time series of Landsat 5 (2002) and Landsat 8 (2013 and 2022) images to produce land use/land cover maps. A Geographic Information System was applied to analyze and map changes in land use/land cover for settlements, agricultural land, grazing land, and land covered with other vegetation. In focus group discussions, the time series maps were analyzed and compared with the integrated watershed management practices to analyze their impacts. The results document that integrated watershed management practices have contributed to a significant change in land use/land cover in the study area over the past 20 years. The quantitative analysis of land use/land cover between the years 2002 and 2022 only revealed a downward trend in agricultural land. Considering the value of the Normalized Difference Vegetation Index (NDVI) as a biophysical feature for the increase of green mass, this indicator also documents an improvement in land use/land cover with regard to sustainable land management and consequently poverty alleviation. Full article
(This article belongs to the Special Issue Advances in Land Use and Land Cover Mapping)
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<p>Map of study area.</p>
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<p>Land use/cover maps of the Yesir watershed for 2002, 2013, and 2022.</p>
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<p>NDVI maps of the Yesir watershed for 2002, 2013, and 2022.</p>
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21 pages, 12788 KiB  
Article
Unveiling Deep-Seated Gravitational Slope Deformations via Aerial Photo Interpretation and Statistical Analysis in an Accretionary Complex in Japan
by Teruyuki Kikuchi, Satoshi Nishiyama and Teruyoshi Hatano
Sustainability 2024, 16(13), 5328; https://doi.org/10.3390/su16135328 - 22 Jun 2024
Viewed by 544
Abstract
The objective of this study was to identify the locations of deep-seated gravitational slope deformations (DGSDs) and define the numerical characteristics of these deformations to contribute to the sustainable management of social infrastructure in the event of an increased disaster. The topographic features [...] Read more.
The objective of this study was to identify the locations of deep-seated gravitational slope deformations (DGSDs) and define the numerical characteristics of these deformations to contribute to the sustainable management of social infrastructure in the event of an increased disaster. The topographic features of the DGSDs were quantitatively characterized based on their surface morphologies. Topographic features indicative of gravitational deformation in pre-slide topographic maps, such as terminal cliff failures, irregular undulations, and gullies, suggest that progressive deformation occurred over a prolonged period. To track the gravitational deformation over time, we interpreted aerial photographs of DGSDs from 1948 and 2012 associated with deep-seated landslides on the Kii Peninsula induced by Typhoon Talas on 2–5 August 2011. Corresponding numerical analysis of the gravitational deformations using 1 m digital elevation models reveals that landslide areas exhibit eight characteristic influencing factors, demonstrating that characteristic morphologies exist in areas that eventually experience landslides. One such morphological feature is the existence of a gently sloping area in the upper section of the deep-seated landslide mass, which comprises a catchment basin without a corresponding valley or gully. These findings suggest that rainwater penetrates the ground, and degrades and deforms the rock within the landslide mass, causing the slope to fail after torrential rainfall. This study holds great significance for advancing sustainable infrastructure development and management and mitigating environmental changes. Full article
(This article belongs to the Special Issue Sustainability in Natural Hazards Mitigation and Landslide Research)
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<p>Location of the study area relative to other collapsed areas in the vicinity.</p>
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<p>(<b>A</b>) Contour map obtained before landslide. (<b>B</b>) Image of the slope before landslide: darker shade indicates steeper slope. (<b>C</b>) Geological map plotted on an image of the slope after the landslide.</p>
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<p>Research workflow used in this study.</p>
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<p>Photograph of deep-seated landslide (DL). (<b>A</b>) Overall view of collapsed area showing the main scarp bracketing the central area of the landslide mass. The north flank comprises alternating strata dominated by sandstone, whereas the south flank comprises alternating strata dominated by shale. The two flanks show a prominent fault surface and bedding plane, respectively. (<b>B</b>) The side view of the north flank shows discoloration due to weathering deep into the profile, indicating advanced degradation within the landslide mass. (<b>C</b>) Crown and main scarp. The main scarp has an N-S strike and shows prominent high-angle oblique joint surfaces with an N75E strike.</p>
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<p>Delimitation of morphological zones in the study area based on pre-slide aerial photographs. (<b>Left</b>) Contour map created based on data from LiDAR in 2009; (<b>Right</b>) Aerial photograph captured in 2004 (No. 10 in <a href="#sustainability-16-05328-t002" class="html-table">Table 2</a>). A, B, C, and D indicate areas divided according to topographical features of the DL area. (a) Main scarp of the continuous crown on the eastern margin; (b) minor scarps at boundary between Zones A and B; (c,d) Minor scarp of convex upslope; (e) arcuate scarp facing the riverside.</p>
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<p>Pre-slide aerial photograph time series of the study area. The notations (a)–(e) are topographical features of the DL area, shown in the same locations as in <a href="#sustainability-16-05328-f005" class="html-fig">Figure 5</a>.</p>
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<p>Numerical analysis results. (<b>A</b>) Slope, (<b>B</b>) wavelet, (<b>C</b>) Eigenvalue ratio (EV), (<b>D</b>) curvature, (<b>E</b>) overground openness, (<b>F</b>) underground openness, (<b>G</b>) topographical wetness index (TWI), (<b>H</b>) elevation; (<b>I</b>) NDVI.</p>
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<p>Histograms and box plots of digital elevation model (DEM) inside and outside the landslide.</p>
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<p>Graphic representation of statistical characteristics inside and outside the landslide. (<b>A</b>) Images of the slope and topographical wetness index (TWI). Darker is steeper. Blue indicates TWI ≥ 1 and a large catchment area. Brown indicates a small catchment area. (<b>B</b>) Underground openness and eigenvalue ratios. The gray areas indicate values below 8.0, representing a convex terrain. White indicates values above 8.0, which are statistically significant inside the landslide and represent flat terrain regardless of the slope. The eigenvalue ratio (EV) is shown in green for 4.5–5.5 and red for 5.5–8.0. These values were statistically significant for the landslide area. Above 8.0, white indicates a wide triangulated irregular network (TIN) with low-density DEM values due to vegetation. (<b>C</b>) Composite images of TWI, EV, and underground openness.</p>
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<p>Block diagram showing features inside and outside the landslide. (<b>A</b>) Initial stage of displacement. A sliding surface has developed, but a distinct landslide body is not yet apparent at the surface. (<b>B</b>) Rainwater does not flow over the slope surface, but rather penetrates the ground via fissures and cracks, causing weathering and alteration of the landslide body. Accordingly, marshy terrain does not form on the ground surface. Because the landslide body concentrates more water than the surrounding area, the landslide body undergoes selective degradation. Terminal cliff failure causes the deformation of the degraded landslide body. Minor scarps appear on the landslide body surface. (<b>C</b>) Occurrence of large-scale deep-seated landslide following this progressive destabilization combined with the torrential rains.</p>
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17 pages, 4174 KiB  
Article
Gully Erosion Development in Drainage Basins: A New Morphometric Approach
by Ugo Ciccolini, Margherita Bufalini, Marco Materazzi and Francesco Dramis
Land 2024, 13(6), 792; https://doi.org/10.3390/land13060792 - 4 Jun 2024
Viewed by 438
Abstract
The formation and evolution of management gullies is a highly intense process of soil erosion often overlooked in policies and river basin strategies. Despite the worldwide spread of the phenomenon, our ability to assess and simulate gullying and its impacts remains limited; therefore, [...] Read more.
The formation and evolution of management gullies is a highly intense process of soil erosion often overlooked in policies and river basin strategies. Despite the worldwide spread of the phenomenon, our ability to assess and simulate gullying and its impacts remains limited; therefore, predicting the development and evolution of these river reaches represents a significant challenge, especially in areas where the loss of productive soil or the hazards linked to landslides or floods represent critical factors. Our study demonstrates how an exclusively morphometric approach, based on the construction of the hypsometric curve and applied to small hydrographic basins that are lithologically homogeneous and hierarchized according to the Strahler classification method, is able to predict the triggering height of the gullies; this height corresponds to the mean elevation of the basin and the inflection point of the hypsometric curve itself, confirming the hypothesis that this point coincides with the point at which a sudden change in surface runoff energy occurs, The study also shows that the portion of the basin necessary to trigger these intense erosive processes is always within a small range, regardless of the size and morphology (slope) of the basin itself. Such an approach, which is quick and relatively easy to apply, could help develop hydrogeological hazard mitigation practices in land planning projects. Full article
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<p>(<b>a</b>) Geological sketch of the Italian area with the location of the basins investigated; (<b>b</b>) typical gullies visible from satellite image (top, source Google Earth) and the ground (bottom).</p>
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<p>(<b>a</b>) Geological sketch of the US area with the location of the basins investigated; (<b>b</b>) typical gullies visible from satellite image (top, source Google Earth) and the ground (bottom).</p>
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<p>(<b>a,b</b>) stream order definition in the basins “b_3 USA” and “b_6 ITA”, respectively, using a 10 m resolution DTM; (<b>c</b>) stream order definition in the basin “b_6 ITA” using a 1 m resolution DTM.</p>
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<p>(<b>a</b>) Example of a frequency distribution histogram construction.(<b>b</b>) Example of basin with the points of intersection between the vector file of the hydrographic network and the vector file of the contour lines extracted from the DTM.</p>
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<p>Example of a hypsometric curve with the indication of value and location of the relative oblique inflection points (OIPs).</p>
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<p>Two examples of flow contribution curves: the highest elevation with the relative highest number of 3rd-order reaches is highlighted in each graph.</p>
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<p>(<b>a,b</b>) mapping of gullies from aerial photointerpretation in the basins “b_3 USA” and “b_2 ITA”, respectively.</p>
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<p>(<b>a</b>) Pearson’s “r” correlation coefficient; (<b>b</b>) Pearson’s “<span class="html-italic">p</span>” value; (<b>c</b>,<b>d</b>) standard deviation σ calculated among the “heights” evaluated with different methods in the Italian and the US basins, respectively.</p>
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<p>(<b>a</b>–<b>e</b>) Satellite images of the basins with a standard deviation greater than 15 (for the explanation, see the text).</p>
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22 pages, 62731 KiB  
Article
Early Identification and Characteristics of Potential Landslides in Xiaojiang Basin, Yunnan Province, China Using Interferometric Synthetic Aperture Radar Technology
by Xiaolun Zhang, Shu Gan, Xiping Yuan, Huilin Zong, Xuequn Wu and Yanyan Shao
Sustainability 2024, 16(11), 4649; https://doi.org/10.3390/su16114649 - 30 May 2024
Viewed by 522
Abstract
The Xiaojiang Basin ranks among the global regions with the highest density of geological hazards. Landslides, avalanches, and debris flows represent significant threats to the safety of residents and their properties, impeding sustainable development. This study utilized three InSAR techniques to monitor surface [...] Read more.
The Xiaojiang Basin ranks among the global regions with the highest density of geological hazards. Landslides, avalanches, and debris flows represent significant threats to the safety of residents and their properties, impeding sustainable development. This study utilized three InSAR techniques to monitor surface deformations in the basin, using the standard deviation of these measurements as a stability threshold to identify potential landslides. A systematic analysis of landslide development characteristics was then conducted. Key findings include the following: (1) The annual average deformation velocity in the basin from 2018 to 2021 ranged from −25.36 to 24.40 mm/year, identifying 212 potential landslides. (2) Deformation analysis of a typical landslide in Caizishan showed consistent detection of significant surface changes by all three InSAR methods. Seasonal deformation linked to summer rainfall exacerbates the movement in elevated landslides. (3) Landslides predominantly occur in fragile geological formations such as sandstone, mudstone, and kamacite on slopes of 20° to 40°. These landslides, typically covering less than 0.1 km2, are mostly found on barren and grassland terrains adjacent to lower debris gullies, with a relative elevation difference of under 300 m and an aspect range of 90° to 270°. A high kernel density value of 0.3 or higher was noted, with 86.8% influenced by regional tectonic activities, including fault zones. The results demonstrate that natural environmental factors primarily drive landslides in the Xiaojiang Basin, which pose significant threats to the safety of nearby residents. This study’s insights and outcomes provide valuable references for safeguarding local populations, disaster prevention, and promoting regional sustainable development. Full article
(This article belongs to the Section Hazards and Sustainability)
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<p>Overview map of the study area.</p>
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<p>Map of the three deformation monitoring results: (<b>a</b>) represents the D-InSAR surface deformation monitoring result; (<b>b</b>) is the PS-InSAR surface deformation monitoring result; and (<b>c</b>) is the SBAS-InSAR surface deformation monitoring result.</p>
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<p>Spatial distribution of landslides in the Xiaojiang Basin.</p>
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<p>Map of the Caizishan landslide deformation monitoring and field investigation. (<b>a</b>) SBAS-InSAR results; (<b>b</b>) PS-InSAR results; (<b>c</b>) D-InSAR results; (<b>d</b>) field survey.</p>
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<p>Figure of cumulative deformation of feature points with rainfall. PF1, PF2, and PF3 are the deformation results of points F1, F2, and F3 processed by PS-InSAR technique, respectively. SF1, SF2, and SF3 are the deformation results of points F1, F2, and F3 processed by SBAS-InSAR technique, respectively.</p>
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<p>Kernel density map of landslides in the Xiaojiang Basin.</p>
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<p>Statistical distribution of landslides in slope.</p>
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<p>Statistical distribution of landslides in elevation difference.</p>
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<p>Radar map of landslides in aspect.</p>
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<p>Geological structure map and lithologic groups of Xiaojiang Basin.</p>
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<p>Statistical distribution of landslides in lithology.</p>
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<p>River buffer map.</p>
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<p>Statistical distribution of landslides in land use.</p>
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<p>Statistical distribution of landslide development area.</p>
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34 pages, 9559 KiB  
Article
Chaff Cloud Integrated Communication and TT&C: An Integrated Solution for Single-Station Emergency Communications and TT&C in a Denied Environment
by Lvyang Ye, Yikang Yang, Binhu Chen, Deng Pan, Fan Yang, Shaojun Cao, Yangdong Yan and Fayu Sun
Drones 2024, 8(5), 207; https://doi.org/10.3390/drones8050207 - 18 May 2024
Viewed by 837
Abstract
In response to potential denial environments such as canyons, gullies, islands, and cities where users are located, traditional Telemetry, Tracking, and Command (TT&C) systems can still maintain core requirements such as availability, reliability, and sustainability in the face of complex electromagnetic environments and [...] Read more.
In response to potential denial environments such as canyons, gullies, islands, and cities where users are located, traditional Telemetry, Tracking, and Command (TT&C) systems can still maintain core requirements such as availability, reliability, and sustainability in the face of complex electromagnetic environments and non-line-of-sight environments that may cause service degradation or even failure. This paper presents a single-station emergency solution that integrates communication and TT&C (IC&T) functions based on radar chaff cloud technology. Firstly, a suitable selection of frequency bands and modulation methods is provided for the emergency IC&T system to ensure compatibility with existing communication and TT&C systems while catering to the future needs of IC&T. Subsequently, theoretical analyses are conducted on the communication link transmission loss, data transmission, code tracking accuracy, and anti-multipath model of the emergency IC&T system based on the chosen frequency band and modulation mode. This paper proposes a dual-way asynchronous precision ranging and time synchronization (DWAPR&TS) system employing dual one-way ranging (DOWR) measurement, a dual-way asynchronous incoherent Doppler velocity measurement (DWAIDVM) system, and a single baseline angle measurement system. Next, we analyze the physical characteristics of the radar chaff and establish a dynamic model of spherical chaff cloud clusters based on free diffusion. Additionally, we provide the optimal strategy for deploying chaff cloud. Finally, the emergency IC&T application based on the radar chaff cloud relay is simulated, and the results show that for severe interference, taking drones as an example, under a measurement baseline of 100 km, the emergency IC&T solution proposed in this paper can achieve an accuracy range of approximately 100 m, a velocity accuracy of 0.1 m/s, and an angle accuracy of 0.1°. In comparison with existing TT&C system solutions, the proposed system possesses unique and potential advantages that the others do not have. It can serve as an emergency IC&T reference solution in denial environments, offering significant value for both civilian and military applications. Full article
(This article belongs to the Special Issue UAV Trajectory Generation, Optimization and Cooperative Control)
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<p>Simulation of power spectrum and autocorrelation characteristics of commonly used modulation signals in the fields of IC&amp;C. (<b>a</b>) Power spectrum characteristics. (<b>b</b>) Autocorrelation characteristics.</p>
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<p>DWAPR&amp;TS system measurement principle and time sequence relationship.</p>
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<p>Schematic diagram of angle measurement based on a single baseline.</p>
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<p>Schematic diagram of optimal deployment strategy based on transmission loss.</p>
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<p>Schematic diagram of the composition and evaluation system of the single-station chaff cloud emergency IC&amp;T system.</p>
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<p>Radar chaff and cloud RCS simulation results. (<b>a</b>) Single radar chaff RCS. (<b>b</b>) Average RCS of the radar chaff cloud.</p>
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<p>Simulation of the diffusion state of a radar chaff cloud at t = 0 s, 300 s, and 600 s. (<b>a</b>) 30 radar chaffs. (<b>b</b>) 300 radar chaffs. (<b>c</b>) 3000 radar chaffs. (<b>d</b>) 30,000 radar chaffs.</p>
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<p>The density distribution of radar chaff clouds under different number after diffusing over different time periods. (<b>a</b>) Number of chaffs = 30 and t = 0 s, 300 s, and 600 s. (<b>b</b>) Number of chaffs = 300 and t = 0 s, 300 s, and 600 s. (<b>c</b>) Number of chaffs = 3000 and t = 0 s, 300 s, and 600 s. (<b>d</b>) Number of chaffs = 30,000 and t = 0 s, 300 s, and 600 s.</p>
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<p>Simulation results of communication and TT&amp;C link transmission losses based on radar chaff clouds. (<b>a</b>) Free space path loss. (<b>b</b>) Lognormal shadow path loss.</p>
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<p>Simulation results of BER of BPSK signals in AWGN, Rayleigh, and Rician channels.</p>
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<p>Simulation results of CT-RMS with the expected CNR and the rate of change in CT error with respect to the CNR of common BPSK-modulated signals. (<b>a</b>) CT-RMS. (<b>b</b>) CT relative CNR change rate.</p>
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<p>Pseudo-code multipath error envelope of common BPSK (n) signals under pseudocode anti-multipath model.</p>
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<p>Carrier multipath error envelope of common BPSK-(<span class="html-italic">n</span>) signals under carrier anti-multipath model.</p>
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<p>Ranging simulation results. (<b>a</b>) <span class="html-italic">v</span> = 20 m/s, ranging results. (<b>b</b>) <span class="html-italic">v</span> = 20 m/s, ranging errors. (<b>c</b>) <span class="html-italic">γ</span> = 0.002, ranging results. (<b>d</b>) <span class="html-italic">γ</span> = 0.002, ranging errors.</p>
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<p>Velocity measurement simulation results. (<b>a</b>) <span class="html-italic">v</span> = 20 m/s, velocity measurement results. (<b>b</b>) <span class="html-italic">v</span> = 20 m/s, velocity measurement errors. (<b>c</b>) <span class="html-italic">γ</span> = 0.002, velocity measurement results. (<b>d</b>) <span class="html-italic">γ</span> = 0.002, velocity measurement errors.</p>
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<p>Angle measurement simulation results. (<b>a</b>) <span class="html-italic">v</span> = 20 m/s, angle measurement results. (<b>b</b>) <span class="html-italic">v</span> = 20 m/s, angle measurement errors. (<b>c</b>) <span class="html-italic">γ</span> = 0.002, angle measurement results. (<b>d</b>) <span class="html-italic">γ</span> = 0.002, angle measurement error.</p>
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<p>Angle measurement simulation results. (<b>a</b>) <span class="html-italic">v</span> = 20 m/s, angle measurement results. (<b>b</b>) <span class="html-italic">v</span> = 20 m/s, angle measurement errors. (<b>c</b>) <span class="html-italic">γ</span> = 0.002, angle measurement results. (<b>d</b>) <span class="html-italic">γ</span> = 0.002, angle measurement error.</p>
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