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Spatial Analysis for Landscape Changes
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Spatial Analysis for Landscape Changes

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: closed (15 September 2021) | Viewed by 16367

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Dario Gioia

E-Mail Website
Guest Editor
Consiglio Nazionale delle Ricerche—Istituto di Scienze del Patrimonio Culturale (ISPC), Tito Scalo, Potenza, Italy
Interests: tectonic geomorphology; landscape evolution; drainage network morphometry; geomorphological mapping; sediment yield; landslide analysis; geoarchaeology
Special Issues, Collections and Topics in MDPI journals
Dr. Maria Danese

E-Mail Website
Guest Editor
Institute of Heritage Science, National Research Council (ISPC CNR), I-85050 Tito, Potenza, Italy
Interests: GIS; geocomputation; remote sensing; geophysics; cultural heritage; landscape archaeology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Landscape is the backcloth over which environmental and cultural events occur, with changes to the landscape itself also involved. At the same time, the last few years have also seen a great improvement in the availability of high-resolution DEMs, GIS tools and of landscape data in general. This has promoted the development and application of spatial analyses (from map algebra to geostatistics, from machine learning to location-based cellular automata) for the quantitative evaluation of landscape changes in many geomorphological, territorial and archaeological applications. This Special Issue aims to collect contributions concerning the application of traditional and innovative methods in all application fields that are connected to these changes, such as geomorphology, urban and territorial systems and archaeology. We would like to invite you to submit articles about your recent work, experimental research or case studies dealing with the quantitative analysis of landscape changes in a variety of application fields and at different spatial and temporal scales. Relevant topics for the SI include:

  1. Multitemporal analysis of DEMs and reconstruction of short- and long-term topographic changes;
  2. Extraction of parameters and indexes to investigate landscape changes and related surface processes;
  3. Two- and three-dimensional reconstructions of historical and archaeological landscapes;
  4. Semi-automatic or unsupervised classification of landforms/landscapes;
  5. Application of quantitative methods and models to estimate landscape modification and their impact on urban systems;
  6. Analysis of geomorphic processes and rates by the multitemporal acquisition of high-resolution topographic data and spatial statistics;
  7. GIS tools and spatial statistics for the analysis of natural hazards and human impact on the landscape.

Review articles about the limitations, recent developments and new approaches of this research field are also welcomed.

Dr. Dario Gioia
Dr. Maria Danese
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

spatial analysis; high-resolution DEMs; landscape archaeology; landscape evolution model (LEM); past landscape reconstruction; soil consumption; geomorphological processes; natural hazard

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Published Papers (7 papers)

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Editorial

Jump to: Research, Review

2 pages, 164 KiB  
Editorial
Spatial Analysis for Landscape Changes
by Dario Gioia and Maria Danese
Appl. Sci. 2021, 11(24), 11924; https://doi.org/10.3390/app112411924 - 15 Dec 2021
Cited by 1 | Viewed by 1146
Abstract
Landscape is the backcloth over which environmental and anthropic events occur, and recent increasing trends of natural and anthropic processes, such as urbanization, land-use changes, and extreme climate events, have a strong impact on landscape modification [...] Full article
(This article belongs to the Special Issue Spatial Analysis for Landscape Changes)

Research

Jump to: Editorial, Review

21 pages, 8800 KiB  
Article
Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction
by Subhashree Subudhi, Ramnarayan Patro , Pradyut Kumar Biswal and Fabio Dell’Acqua
Appl. Sci. 2021, 11(22), 10876; https://doi.org/10.3390/app112210876 - 17 Nov 2021
Cited by 4 | Viewed by 1733
Abstract
In the processing of remotely sensed data, classification may be preceded by feature extraction, which helps in making the most informative parts of the data emerge. Effective feature extraction may boost the efficiency and accuracy of the following classification, and hence various methods [...] Read more.
In the processing of remotely sensed data, classification may be preceded by feature extraction, which helps in making the most informative parts of the data emerge. Effective feature extraction may boost the efficiency and accuracy of the following classification, and hence various methods have been proposed to perform it. Recently, Singular Spectrum Analysis (SSA) and its 2-D variation (2D-SSA) have emerged as popular, cutting-edge technologies for effective feature extraction in Hyperspectral Images (HSI). Using 2D-SSA, each band image of an HSI is initially decomposed into various components, and then the image is reconstructed using the most significant eigen-tuples relative to their eigen-values, which represent strong spatial features for the classification task. However, instead of performing reconstruction on the whole image, it may be more effective to apply reconstruction to object-specific spatial regions, which is the proposed objective of this research. As an HSI may cover a large area, multiple objects are generally present within a single scene. Hence, spatial information can be highlighted accurately by specializing the reconstruction based on the local context. The local context may be defined by the so-called superpixels, i.e., finite sets of pixels that constitute a homogeneous set. Each superpixel may undergo tailored reconstruction, with a process expected to perform better than non-spatially-adaptive approaches. In this paper, a Superpixel-based SSA (SP-SSA) method is proposed where the image is first segmented into multiple regions using a superpixel segmentation approach. Next, each segment is individually reconstructed using 2D-SSA. In doing so, the spatial contextual information is preserved, leading to better classifier performance. The performance of the reconstructed features is evaluated using an SVM classifier. Experiments on four popular benchmark datasets reveal that, in terms of the classification accuracy, the proposed approach overperforms the standard SSA technique and various common spatio-spectral classification methods. Full article
(This article belongs to the Special Issue Spatial Analysis for Landscape Changes)
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Figure 1

Figure 1
<p>Flowchart of the proposed method.</p> Full article ">Figure 2
<p>Moving window across the image <math display="inline"><semantics> <msup> <mi>H</mi> <mi>b</mi> </msup> </semantics></math> to create the trajectory matrix <math display="inline"><semantics> <msup> <mi mathvariant="fraktur">Z</mi> <mi>b</mi> </msup> </semantics></math>.</p> Full article ">Figure 3
<p>Implementation of 2D-SSA on a HSI scene (<b>a</b>) Original scene at 667 nm. (<b>b</b>) 1st component grouping. (<b>c</b>) 1–5th component grouping. (<b>d</b>) 1–10th component grouping, where <math display="inline"><semantics> <msub> <mi>M</mi> <mi>x</mi> </msub> </semantics></math> = 5, and <math display="inline"><semantics> <msub> <mi>M</mi> <mi>y</mi> </msub> </semantics></math> = 5.</p> Full article ">Figure 4
<p>Possible Region of Interest (ROI) around the superpixel segment. <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>r</mi> <mo>,</mo> <mi>u</mi> <mo>)</mo> </mrow> </semantics></math> denotes the location of pixel <span class="html-italic">i</span> in superpixels. <math display="inline"><semantics> <msub> <mi>r</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>r</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </semantics></math> represents the minimum and maximum row index, and <math display="inline"><semantics> <msub> <mi>u</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>u</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </semantics></math> are the corresponding <span class="html-italic">min</span> and <span class="html-italic">max</span> column indices.</p> Full article ">Figure 5
<p>(<b>a</b>) False Color Composite Image, (<b>b</b>) Ground Truth Image and (<b>c</b>) Class names for the Indian Pines Dataset.</p> Full article ">Figure 6
<p>(<b>a</b>) False Color Composite Image, (<b>b</b>) Ground Truth Image and (<b>c</b>) Class names for the Pavia University Dataset.</p> Full article ">Figure 7
<p>(<b>a</b>) False Color Composite Image, (<b>b</b>) Ground Truth Image, and (<b>c</b>) Class names for the Salinas Dataset.</p> Full article ">Figure 8
<p>(<b>a</b>) False-Color Composite Image, (<b>b</b>) Ground Truth Image, and (<b>c</b>) Class names for the Houston-2018 Dataset.</p> Full article ">Figure 9
<p>Effect of window size variation for different number of superpixels on the classification performance for the (<b>a</b>) Indian Pines (<b>b</b>) Pavia University, and (<b>c</b>) Salinas, and (<b>d</b>) Houston 2018 datasets.</p> Full article ">Figure 10
<p>Effect of training sample variation on the classification performance for the (<b>a</b>) Indian Pines (<b>b</b>) Pavia University, (<b>c</b>) Salinas, and (<b>d</b>) Houston 2018 datasets.</p> Full article ">Figure 11
<p>(<b>a</b>) Ground Truth Image, Classification Maps of (<b>b</b>) SVM (<b>c</b>) EPF (<b>d</b>) SCMK (<b>e</b>) R2MK (<b>f</b>) ASMGSSK (<b>g</b>) MsuperPCA (<b>h</b>) 2D-SSA (<b>i</b>) 2D-MSSA (<b>j</b>) SP-SSA for Indian Pines dataset.</p> Full article ">Figure 12
<p>(<b>a</b>) Ground Truth Image, Classification Maps of (<b>b</b>) SVM (<b>c</b>) EPF (<b>d</b>) SCMK (<b>e</b>) R2MK (<b>f</b>) ASMGSSK (<b>g</b>) MsuperPCA (<b>h</b>) 2D-SSA (<b>i</b>) 2D-MSSA (<b>j</b>) SP-SSA for the Indian Pines dataset.</p> Full article ">Figure 13
<p>(<b>a</b>) Ground Truth Image, Classification Maps of (<b>b</b>) SVM (<b>c</b>) EPF (<b>d</b>) SCMK (<b>e</b>) R2MK (<b>f</b>) ASMGSSK (<b>g</b>) MsuperPCA (<b>h</b>) 2D-SSA (<b>i</b>) 2D-MSSA (<b>j</b>) SP-SSA for Salinas dataset.</p> Full article ">Figure 14
<p>(<b>a</b>) Ground Truth Image, Classification Maps of (<b>b</b>) SVM (<b>c</b>) EPF (<b>d</b>) SCMK (<b>e</b>) R2MK (<b>f</b>) ASMGSSK (<b>g</b>) MsuperPCA (<b>h</b>) 2D-SSA (<b>i</b>) 2D-MSSA (<b>j</b>) SP-SSA for Houston 2018 dataset.</p> Full article ">Figure 15
<p>(<b>a</b>) Cameraman image (<b>b</b>) 2D-SSA Reconstructed image [MSE = 115.8865] (<b>c</b>) SP-SSA reconstructed image [MSE = 93.0468] (<b>d</b>) Test image (<b>e</b>) 2D-SSA Reconstructed image [MSE = 287.5323] (<b>f</b>) SP-SSA reconstructed image [MSE = 237.1038].</p> Full article ">Figure 16
<p>(<b>a</b>) Original scene at band 667 nm (<b>b</b>) Reconstructed scene by 2D-SSA (<b>c</b>) Reconstructed scene by SP-SSA (<b>d</b>) Difference image for the 2D-SSA reconstructed scene (<b>e</b>) Difference image for the SP-SSA reconstructed scene.</p> Full article ">
19 pages, 7809 KiB  
Article
Comparison of Satellite and Drone-Based Images at Two Spatial Scales to Evaluate Vegetation Regeneration after Post-Fire Treatments in a Mediterranean Forest
by Jose Luis Martinez, Manuel Esteban Lucas-Borja, Pedro Antonio Plaza-Alvarez, Pietro Denisi, Miguel Angel Moreno, David Hernández, Javier González-Romero and Demetrio Antonio Zema
Appl. Sci. 2021, 11(12), 5423; https://doi.org/10.3390/app11125423 - 10 Jun 2021
Cited by 18 | Viewed by 3354
Abstract
The evaluation of vegetation cover after post-fire treatments of burned lands is important for forest managers to restore soil quality and plant biodiversity in burned ecosystems. Unfortunately, this evaluation may be time consuming and expensive, requiring much fieldwork for surveys. The use of [...] Read more.
The evaluation of vegetation cover after post-fire treatments of burned lands is important for forest managers to restore soil quality and plant biodiversity in burned ecosystems. Unfortunately, this evaluation may be time consuming and expensive, requiring much fieldwork for surveys. The use of remote sensing, which makes these evaluation activities quicker and easier, have rarely been carried out in the Mediterranean forests, subjected to wildfire and post-fire stabilization techniques. To fill this gap, this study evaluates the feasibility of satellite (using LANDSAT8 images) and drone surveys to evaluate changes in vegetation cover and composition after wildfire and two hillslope stabilization treatments (log erosion barriers, LEBs, and contour-felled log debris, CFDs) in a forest of Central Eastern Spain. Surveys by drone were able to detect the variability of vegetation cover among burned and unburned areas through the Visible Atmospherically Resistant Index (VARI), but gave unrealistic results when the effectiveness of a post-fire treatment must be evaluated. LANDSAT8 images may be instead misleading to evaluate the changes in land cover after wildfire and post-fire treatments, due to the lack of correlation between VARI and vegetation cover. The spatial analysis has shown that: (i) the post-fire restoration strategy of landscape managers that have prioritized steeper slopes for treatments was successful; (ii) vegetation growth, at least in the experimental conditions, played a limited influence on soil surface conditions, since no significant increases in terrain roughness were detected in treated areas. Full article
(This article belongs to the Special Issue Spatial Analysis for Landscape Changes)
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Figure 1

Figure 1
<p>Location (<b>a</b>) and aerial map (<b>b</b>) of the study area, catchment area (<b>c</b>) and hillslope area (<b>d</b>) in Sierra de los Donceles forest (Castilla La Mancha, Spain).</p> Full article ">Figure 2
<p>Plot location and distribution of soil conditions in the catchment (<b>a</b>,<b>b</b>) and hillslope (<b>c</b>,<b>d</b>) areas in Sierra de los Donceles forest (Castilla La Mancha, Spain). Log erosion barriers (“LEB”), contour-felled log debris (“CFD”), burned and no action (“BNA”), unburned (“UB”).</p> Full article ">Figure 3
<p>Photos of the construction of contour-felled log debris (<b>a</b>) and log erosion barriers (<b>b</b>) in Sierra de los Donceles forest (Castilla La Mancha, Spain).</p> Full article ">Figure 4
<p>Examples of satellite (<b>a</b>) and drone (<b>b</b>) images caught in Serra de Los Donceles forest (Castilla-La Mancha, Spain).</p> Full article ">Figure 5
<p>Vegetation cover (mean ± standard deviation of 62 plots) in plots under four land conditions (UB = unburned; BNA = burned and no action; CFD = contour-felled log debris; LEB = log erosion barriers) after the wildfire of 2012 in Sierra de Los Donceles forest (Castilla-La Mancha, Spain). Mean values that do not share a lower case letter (top of graph) are significantly different from each other (HSD, <span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 6
<p>Spatial distribution of VARI surveyed by satellite (<b>a</b>) and UAV (<b>b</b>) images of 2016 among four land conditions after the wildfire of 2012 in Sierra de Los Donceles forest (Castilla-La Mancha, Spain). Legend: Fajinas = log erosion barriers; cordones = contour-felled log debris; sin tratamiento = burned and no Action; sin fuego = unburned.</p> Full article ">Figure 7
<p>Correlations between VARI and vegetation cover at catchment (<b>a</b>), and hillslope (<b>b</b>) scales surveyed by LANDSAT8 and UAV images (2016) in Sierra de Los Donceles forest (Castilla-La Mancha, Spain).</p> Full article ">Figure 8
<p>Mean values of VARI surveyed by LANDSAT8 (<b>a</b>) and UAV (<b>b</b>) images (2016) among four land conditions after the wildfire of 2012 in Sierra de Los Donceles forest (Castilla-La Mancha, Spain).</p> Full article ">Figure 9
<p>Spatial distribution of land slope (<b>a</b>) and terrain roughness (<b>b</b>) surveyed by UAV images of 2016 among four land conditions after the wildfire of 2012 in Sierra de Los Donceles forest (Castilla-La Mancha, Spain). Legend: Fajinas = log erosion barriers; cordones = contour-felled log debris; sin tratamiento = burned and no action; sin fuego = unburned.</p> Full article ">Figure 9 Cont.
<p>Spatial distribution of land slope (<b>a</b>) and terrain roughness (<b>b</b>) surveyed by UAV images of 2016 among four land conditions after the wildfire of 2012 in Sierra de Los Donceles forest (Castilla-La Mancha, Spain). Legend: Fajinas = log erosion barriers; cordones = contour-felled log debris; sin tratamiento = burned and no action; sin fuego = unburned.</p> Full article ">Figure 10
<p>Scatterplots of vegetation regeneration (measured by VARI by UAV images of 2016) versus land slope (<b>a</b>) and terrain roughness (<b>b</b>) among four land conditions after the wildfire of 2012 in Sierra de Los Donceles forest (Castilla-La Mancha, Spain).</p> Full article ">
19 pages, 5196 KiB  
Article
Modeling Short-Term Landscape Modification and Sedimentary Budget Induced by Dam Removal: Insights from LEM Application
by Dario Gioia and Marcello Schiattarella
Appl. Sci. 2020, 10(21), 7697; https://doi.org/10.3390/app10217697 - 30 Oct 2020
Cited by 9 | Viewed by 2231
Abstract
Simulation scenarios of sediment flux variation and topographic changes due to dam removal have been investigated in a reservoir catchment of the axial zone of southern Italy through the application of a landscape evolution model (i.e.,: the Caesar–Lisflood landscape evolution models, LEM). LEM [...] Read more.
Simulation scenarios of sediment flux variation and topographic changes due to dam removal have been investigated in a reservoir catchment of the axial zone of southern Italy through the application of a landscape evolution model (i.e.,: the Caesar–Lisflood landscape evolution models, LEM). LEM simulation highlights that the abrupt change in base level due to dam removal induces a significant increase in erosion ability of main channels and a strong incision of the reservoir infill. Analysis of the sediment dynamics resulting from the dam removal highlights a significant increase of the total eroded volumes in the post dam scenario of a factor higher than 4. Model results also predict a strong modification of the longitudinal profile of main channels, which promoted fluvial incision upstream and downstream of the former reservoir area. Such a geomorphic response is in agreement with previous analysis of the fluvial system short-term response induced by base-level lowering, thus demonstrating the reliability of LEM-based analysis for solving open problems in applied geomorphology such as perturbations and short-term landscape modification natural processes or human impact. Full article
(This article belongs to the Special Issue Spatial Analysis for Landscape Changes)
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Figure 1

Figure 1
<p>Geological map of the study area (modified from [<a href="#B36-applsci-10-07697" class="html-bibr">36</a>]). Legend: (<b>1</b>) Clay of lacustrine environment (lac, Holocene) (<b>2</b>) Landslide deposits (lan, Holocene) (<b>3</b>) Holistolits made by decametric blocks of limestone (pa, Upper Miocene); (<b>4</b>) Silt and marly clay (CVT2, Upper Miocene); (<b>5</b>) Coarse- to medium-grained sandstone with rare intercalation of lens of polygenic conglomerate (CVT1, Upper Miocene); (<b>6</b>) Calcareous breccia and grey shale (FYRa, Lower Cretaceous-Oligocene); (<b>7</b>) Alternance of chert, marly clay, calcarenites and calcareous breccia (FYR1, Lower Cretaceous-Oligocene); (<b>8</b>) Light-grey and greenish shale with intercalation of marls and limestone (FYG, Lower Cretaceous); (<b>9</b>) Alternance of calcarenite, calcilutite and varicoloured clay (FMS, Upper Cretaceous-Eocene); (<b>10</b>) Varicoloured clay (AVF, Lower Cretaceous); (<b>11</b>) High-angle fault (dashed if uncertain); (<b>12</b>) Thrust (dashed if uncertain); (<b>13</b>) Stratigraphic contact. (<b>A</b>) Geographical location of the study area.</p> Full article ">Figure 2
<p>(<b>A</b>) DEM of the pre-dam removal landscape and drainage network of the study area. Hierarchization follows the Strahler’s scheme. Numbering of the catchments is shown in the frame to the left. (<b>B</b>) Land-use map. Legend: (<b>1</b>) Anthropic surfaces and roads; (<b>2</b>) Arable lands; (<b>3</b>) Sclerophyllous vegetation; (<b>4</b>) Broad-leaved and mixed forests; (<b>5</b>) Natural grasslands; (<b>6</b>) Water courses and water bodies. (<b>C</b>) Isopachs of soil thickness derived by a GIS-based interpolation of the results of a field-survey analysis. Modified after [<a href="#B34-applsci-10-07697" class="html-bibr">34</a>].</p> Full article ">Figure 3
<p>Hillshades representing the initial DEMs used for the modeling of the different simulation scenarios: (<b>A</b>) pre-dam removal, initial topography (PreDR-T0); (<b>B</b>) post-dam removal, simulation period: 1 year (PostDR-T0).</p> Full article ">Figure 4
<p>(<b>A</b>) Elevation difference map for the pre-dam removal scenario (simulation period: 20 years); (<b>B</b>) Numbering of the three sub-basins of the study area.</p> Full article ">Figure 5
<p>Erosion/deposition classes in the catchment deriving from the analysis of the altitude difference map of <a href="#applsci-10-07697-f004" class="html-fig">Figure 4</a>. In the frame: distribution of the eroded volumes from the three sub-basins of the study area (numbering is shown in <a href="#applsci-10-07697-f004" class="html-fig">Figure 4</a>).</p> Full article ">Figure 6
<p>(<b>A</b>) Landscape evolution models (LEM)-based elevation difference map for the post-dam removal scenario (simulation period: 20 years). (<b>B</b>) Numbering of the post-dam removal catchments.</p> Full article ">Figure 7
<p>Statistical distribution of the altitude difference map of <a href="#applsci-10-07697-f006" class="html-fig">Figure 6</a> (Post-dam removal scenario). In the frame: distribution of the eroded volumes from the three sub-basins of the study area (numbering is shown in <a href="#applsci-10-07697-f006" class="html-fig">Figure 6</a>).</p> Full article ">Figure 8
<p>Map showing the planimetric changes of the main channels from the present-day landscape (initial DEM, Pre-DR-T0 in <a href="#applsci-10-07697-t003" class="html-table">Table 3</a>) to the final stage of the post-dam removal scenario. To the bottom: comparison of longitudinal river profiles of the three main channels (channel 1, 2 and 3 in the map) deriving from the simulation scenarios. River profile analysis highlights the amount of incision related to the base-level fall as well as the development of pronounced knickpoints in the upper and lower reaches of the main channels. Higher rates of knickpoint retreat are associated with the lower reach of the channel 2.</p> Full article ">Figure 9
<p>Topographic profiles for the different simulation scenarios (location of the profile is reported in the map) showing the landscape modification resulting from the simulation. Higher rates of fluvial incision occurred in the upper and lower reaches of the catchment (about 3 m over the 20-year simulation period, see for example profile a-a’ and i-i’). A deep incision of the reservoir top can be also observed.</p> Full article ">
16 pages, 9999 KiB  
Article
Analysis of the Use of Geomorphic Elements Mapping to Characterize Subaqueous Bedforms Using Multibeam Bathymetric Data in River System
by Ge Yan, Heqin Cheng, Lizhi Teng, Wei Xu, Yuehua Jiang, Guoqiang Yang and Quanping Zhou
Appl. Sci. 2020, 10(21), 7692; https://doi.org/10.3390/app10217692 - 30 Oct 2020
Cited by 5 | Viewed by 2322
Abstract
Riverbed micro-topographical features, such as crest and trough, flat bed, and scour pit, indicate the evolution of fluvial geomorphology, and have an influence on the stability of underwater structures and overall scour pits. Previous studies on bedform feature extraction have focused mainly on [...] Read more.
Riverbed micro-topographical features, such as crest and trough, flat bed, and scour pit, indicate the evolution of fluvial geomorphology, and have an influence on the stability of underwater structures and overall scour pits. Previous studies on bedform feature extraction have focused mainly on the rhythmic bed surface morphology and have extracted crest and trough, while flat bed and scour pit have been ignored. In this study, to extend the feature description of riverbeds, geomorphic elements mapping was used by employing three geomorphic element classification methods: Wood’s criteria, a self-organization map (SOM) technique, and geomorphons. The results showed that geomorphic element mapping can be controlled by adjusting the slope tolerance and curvature tolerance of Wood’s criteria, using the map unit number and combination of the SOM technique and the flatness of geomorphons. Relatively flat bed can be presented using “plane”, “flat planar”, and “flat” elements, while scour pit can be presented using a “pit” element. A comparison of the difference between parameter settings for landforms and bedforms showed that SOM using 8 or 10 map units is applicable for land and underwater surface and is thus preferentially recommended for use. Furthermore, the use of geomorphons is recommended as the optimal method for characterizing bedform features because it provides a simple element map in the absence of area loss. Full article
(This article belongs to the Special Issue Spatial Analysis for Landscape Changes)
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Figure 1
<p>(<b>a</b>) Location of the two bathymetric survey areas in Yangtze River, (<b>b</b>) geomorphologic environment surrounding Chizhou Reach, and (<b>c</b>) that of the Yangtze Estuary. (<b>d</b>) Sample data for Chizhou Reach and (<b>e</b>) Yangtze Estuary. Six cross-sections were set in the sample zones and used to compare the water depth profiles with the distribution of mapped geomorphic elements.</p> Full article ">Figure 2
<p>Wood’s criteria-based distribution of geomorphic elements with different slope tolerance in rows and curvature tolerances in columns for the sample data of (<b>a</b>) Chizhou Reach of the Yangtze River and (<b>b</b>) the Yangtze Estuary.</p> Full article ">Figure 3
<p>Water depth profiles overlaid by Wood’s criteria-based elements for (<b>a</b>) Cross-sections 1, 2, 4, and 5 under various slope tolerances, and for (<b>b</b>) Cross-sections 3 and 6 under various curvature tolerances.</p> Full article ">Figure 3 Cont.
<p>Water depth profiles overlaid by Wood’s criteria-based elements for (<b>a</b>) Cross-sections 1, 2, 4, and 5 under various slope tolerances, and for (<b>b</b>) Cross-sections 3 and 6 under various curvature tolerances.</p> Full article ">Figure 4
<p>Distribution of map units in the feature space with different numbers and arrangement map units using the self-organization map (SOM) technique for the sample data of (<b>a</b>) Chizhou Reach and (<b>b</b>) the Yangtze Estuary.</p> Full article ">Figure 5
<p>SOM technique-based distribution of geomorphic elements for the sample data of (<b>a</b>) Chizhou Reach and (<b>b</b>) the Yangtze Estuary.</p> Full article ">Figure 6
<p>Water depth profile overlaid by SOM technique-based elements for (<b>a</b>) Cross-section 1 and (<b>b</b>) Cross-section 4.</p> Full article ">Figure 7
<p>Geomorphons-based distribution of geomorphic elements with different flatness degrees for the sample data of (<b>a</b>) Chizhou Reach of the Yangtze River and (<b>b</b>) the Yangtze Estuary.</p> Full article ">Figure 8
<p>Water depth profile overlaid by geomorphons-based elements for Cross-sections 1–6 under various flatness degrees.</p> Full article ">Figure 9
<p>Strategy for describing subaqueous bedforms features using geomorphic elements in a river system.</p> Full article ">
17 pages, 6906 KiB  
Article
Characterization of Evolution Stages, Groundwater and Soil Features of the Mud Forest Landscape at Qian-an (China)
by XiangJian Rui, Lei Nie, Yan Xu, Chao Du, FanSheng Kong, Tao Zhang, YuanYuan He and YuZheng Wang
Appl. Sci. 2020, 10(21), 7427; https://doi.org/10.3390/app10217427 - 22 Oct 2020
Cited by 1 | Viewed by 2308
Abstract
The research on geological landscape has received more and more attention worldwide. The National Geological Park of Qian-an mud forest, located in Qian-an Country, Songyuan City (Jilin Province, China) is a rare natural geological landscape formed by erosion. Mud forest landscape has undergone [...] Read more.
The research on geological landscape has received more and more attention worldwide. The National Geological Park of Qian-an mud forest, located in Qian-an Country, Songyuan City (Jilin Province, China) is a rare natural geological landscape formed by erosion. Mud forest landscape has undergone long-term geological processes, and it is still in continuous evolution due to subsurface erosion. In the process of the mud forest landscape formation and evolution, distinct stages have been recognized. The subsurface erosion factors of the mud forest area were identified by groundwater and soil samples characterization, and the mechanism of the formation of the mud forest is studied. Results show that the occurrence of subsurface erosion is controlled by four factors: (1) The head difference of terrace increases due to geological structure, (2) The dry and cold paleoclimate increases the accumulation of soluble salts. Concentrated precipitation in the short term also promotes subsurface erosion. (3) The high content of sodium ions in groundwater promotes the dispersion of soil, and (4) Loess-like soil is characterized by high porosity, low plasticity, and dispersibility. Full article
(This article belongs to the Special Issue Spatial Analysis for Landscape Changes)
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<p>Location of the National Geological Park of Qian-an mud forest and mud forest landscape.</p> Full article ">Figure 2
<p>Dabusu Lake area plan.</p> Full article ">Figure 3
<p>A schematic geological evolution diagram of the mud forest landscape area.</p> Full article ">Figure 4
<p>Methods used in the experimental analysis: (<b>a</b>) Piper diagrams. (<b>b</b>) Gibbs model. (<b>c</b>) Sherard diagram.</p> Full article ">Figure 5
<p>Geomorphologic evolution and different periods of the mud forest landscapes: (<b>a</b>) the infant stage; (<b>b</b>) the juvenile stage; (<b>c</b>) the youth stage; and (<b>d</b>) the old stage.</p> Full article ">Figure 6
<p>Geomorphologic features of the infant stage of mud forest evolution. (<b>a</b>) The lack of vegetation on a small area of the earth’s surface leaves the loess-like soil exposed; (<b>b</b>) Many holes are shaped by subsurface erosion; (<b>c</b>) Vertical sinkhole are formed by subsurface erosion.</p> Full article ">Figure 7
<p>Piper diagrams of major ions in groundwater sample in the mud forest area.</p> Full article ">Figure 8
<p>Plot of the major ions within the Gibbs model for groundwater in study area.</p> Full article ">Figure 9
<p>Grain size distribution of loess-like soil.</p> Full article ">Figure 10
<p>Relationships between clay dispersibility (susceptibility to colloidal dispersion) and salt composition (expressed through the PS, TDS and SAR parameters defined in <a href="#applsci-10-07427-t007" class="html-table">Table 7</a>).</p> Full article ">Figure 11
<p>Profile of Dabusu Lake and its banks.</p> Full article ">Figure 12
<p>Groundwater flow and precipitation infiltration in mud forest area.</p> Full article ">

Review

Jump to: Editorial, Research

13 pages, 3749 KiB  
Review
Spatial Analysis for Landscape Changes: A Bibliometric Review
by Maria Danese and Dario Gioia
Appl. Sci. 2021, 11(21), 10078; https://doi.org/10.3390/app112110078 - 27 Oct 2021
Cited by 2 | Viewed by 2237
Abstract
The main aim of this study is to analyze from a bibliometric point of view the research trend in spatial analysis for landscape changes using the records published in the Web of Science database in the last twenty years. Several parameters such as [...] Read more.
The main aim of this study is to analyze from a bibliometric point of view the research trend in spatial analysis for landscape changes using the records published in the Web of Science database in the last twenty years. Several parameters such as documents published per year, sources of documents, number of citations as well as VOSviewer software and GIS are used for the analysis of different metrics such as the number of citations, co-authorship network, and keyword occurrences. Analysis of the number of papers, their keywords, and authorships countries shows the research trend in the specific topics of the spatial analysis for landscape changes and consequently can constitute a benchmark for researchers who approach this research topic. Full article
(This article belongs to the Special Issue Spatial Analysis for Landscape Changes)
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Figure 1
<p>Diagram representing the number of citations and publications reported by Web of Science from 2000 to 2020.</p> Full article ">Figure 2
<p>Tree map of the first twenty Web of Science categories where the works about spatial analysis for landscape archaeology are published.</p> Full article ">Figure 3
<p>Spatial distribution of the countries of article authorship (<b>a</b>) in the World and (<b>b</b>) in Europe. World background taken from @ naturalearthdata.com. The graph was extracted using QGIS software.</p> Full article ">Figure 4
<p>Co-authorship cluster map. Graph obtained by VOSviewer software.</p> Full article ">Figure 5
<p>(<b>a</b>) Co-occurrence map (period: 2001–2020); (<b>b</b>) overlay visualization between key terms and their citation year. Graphs made with VOSviewer software.</p> Full article ">Figure 5 Cont.
<p>(<b>a</b>) Co-occurrence map (period: 2001–2020); (<b>b</b>) overlay visualization between key terms and their citation year. Graphs made with VOSviewer software.</p> Full article ">Figure 6
<p>Density map. Obtained with VOSviewer.</p> Full article ">Figure 7
<p>Occurrences and related histograms for the main keywords observed in the 2001–2020 period in order to observe their trend in the four periods.</p> Full article ">Figure 8
<p>Overlay map for the types of spatial analysis more occurrent in s.a.l.c. research topic. Made with VOSviewer.</p> Full article ">
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