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Land
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Assessment and Monitoring of Land Degradation: Current Trends and Future...
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Assessment and Monitoring of Land Degradation: Current Trends and Future Directions

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land, Soil and Water".

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 5462

Special Issue Editor

Dr. Xuchao Zhu

E-Mail Website
Guest Editor
Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
Interests: soil erosion management; cultivated land quality evaluation and improvement; soil physical properties variation

Special Issue Information

Dear Colleagues,

Human society relies on land for survival and development; most of the earth’s soil resources, however, are in fair, poor or very poor condition at present. Land degradation is thus still an important challenge to human beings. Land degradation generally refers to the decline of land quality and productivity caused by natural forces and unreasonable development and utilization by human beings, and there are some certain causes and many manifestations. For example, drought, flood, freeze–thaw, string wind, heavy rain, sea tide and other natural forces can lead to land desertification, soil erosion, salinization, etc. Inappropriate human reclamation, indiscriminate logging, unreasonable planting systems and irrigation methods, improper use of pesticides and fertilizers, etc., will cause land desertification, soil erosion, salinization, fertility decline, soil pollution, etc. Although land degradation assessment and monitoring has received extensive attention, there is still a lack of new and effective methods for land degradation assessment and monitoring with the development of science and technology, and there is still insufficient research on the trends, driving mechanisms, control measures and future research directions for different types of land degradation. Therefore, we intend to organize this Special Issue to compensate for this deficiency.

The goal of this Special Issue is to collect papers (original research articles and review papers) to give insights into the status, monitoring and evaluation of land degradation.

This Special Issue will welcome manuscripts that link the following themes:

  • Causes and manifestations of land degradation;
  • Land degradation evaluation: method, current status and future prospects;
  • Land degradation monitoring: new method or technology, status and perspectives;
  • Prevention and control of land degradation: measures, technology, effectiveness evaluation.

We look forward to receiving your original research articles and reviews.

Dr. Xuchao Zhu
Guest Editor

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. Land is an international peer-reviewed open access monthly 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 2600 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

  • soil erosion
  • soil desertification
  • soil salinization
  • soil fertility decline
  • soil pollution
  • land degradation evaluation
  • land degradation monitoring
  • current status
  • future prospects

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

16 pages, 5926 KiB  
Article
Ecological Status Assessment of Permafrost-Affected Soils in the Nadym Region, Yamalo-Nenets Autonomous District, Russian Arctic
by Wenjuan Wang, Timur Nizamutdinov, Aleksander Pechkin, Eugeniya Morgun, Gensheng Li, Xiaodong Wu, Sizhong Yang and Evgeny Abakumov
Land 2024, 13(9), 1406; https://doi.org/10.3390/land13091406 - 1 Sep 2024
Viewed by 576
Abstract
Permafrost-affected regions in the Russian Arctic are a critical study area for studying the sources of metal elements (MEs) in soils originating from geological/pedogenic processes or from anthropogenic sources via atmospheric transport. In the Nadym region of the Yamalo-Nenets Autonomous District, we investigated [...] Read more.
Permafrost-affected regions in the Russian Arctic are a critical study area for studying the sources of metal elements (MEs) in soils originating from geological/pedogenic processes or from anthropogenic sources via atmospheric transport. In the Nadym region of the Yamalo-Nenets Autonomous District, we investigated the contents of soil organic carbon (SOC), total nitrogen (TN), and MEs across different soil types and horizons, explored the source apportionment of MEs, and assessed local ecological risks of potentially toxic elements (PTEs). The results showed that (1) the contents of SOC and TN in Histic Cryosols (8.59% and 0.27%) were significantly higher than in Plaggic Podzols (Arenic, Gelic, and Turbic) (2.28% and 0.15%) and in Ekranic Technosols (Umbric) (1.32% and 0.09%); (2) the concentrations of MEs in the Nadym region were lower than in other Arctic regions; (3) the primary sources of MEs were identified as geological processes (36%), atmospheric transport (23%), agricultural activities (21%), and transportation (20%); and (4) the permafrost-affected soils in the Nadym region exhibited low ecological risks from PTEs. These results underscore the critical role of geological and anthropogenic factors in shaping soil conditions and highlight the relatively low ecological risk from PTEs, providing a valuable benchmark for future environmental assessments and policy development in Yamal permafrost regions. Full article
(This article belongs to the Special Issue Assessment and Monitoring of Land Degradation: Current Trends and Future Directions)
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Figure 1

Figure 1
<p>The location of the study areas. (<b>a</b>) Global map highlighting the study region. (<b>b</b>) Detailed map of the Nadym region in the Yamalo-Nenets Autonomous District. (<b>c</b>–<b>e</b>) Photographs of the three sampling sites in the Nadym region (TD-tundra, AF-abandoned farmland, and UA-urban area). (<b>f</b>–<b>h</b>) Soil profiles from the sampling sites in the Nadym region.</p> Full article ">Figure 2
<p>Physicochemical properties in permafrost-affected soils of the Nadym region.TD—tundra; AF—abandoned farmland; UA—urban area; (<b>a</b>) SOC—soil organic carbon. (<b>b</b>) TN—soil total nitrogen. (<b>c</b>) C/N—the mass ratio of SOC to TN. (<b>d</b>) pH; (<b>e</b>) Clay (&lt;0.002 mm). (<b>f</b>) Silt (0.002–0.05 mm). (<b>g</b>) Sand (&gt;0.05 mm).</p> Full article ">Figure 3
<p>The concentrations (mg kg<sup>−1</sup>) of metal elements (MEs) in permafrost-affected soils of the Nadym region. TD—tundra; AF—abandoned farmland; UA—urban area. (<b>a</b>–<b>h</b>) The concentrations of eight MEs (Fe, Mn, Zn, As, Cr, Ni, Cu, and Pb) in soil depths and horizons.</p> Full article ">Figure 4
<p>Regression models of metal elements (MEs). TD—tundra; AF—abandoned farmland; UA—urban area. (<b>a</b>) Clay (&lt;0.002 mm). (<b>b</b>) Silt (0.002–0.05 mm). (<b>c</b>) Sand (&gt;0.05 mm). (<b>d</b>,<b>g</b>) SOC—soil organic carbon. (<b>e</b>,<b>h</b>). TN—soil total nitrogen. (<b>f</b>,<b>i</b>) C/N—the mass ratio of SOC to TN. **: significance level of <span class="html-italic">p</span> &lt; 0.01; *: significance level of <span class="html-italic">p</span> &lt; 0.05. The grey shadowed areas represent the 95% confidence interval. Only statistically significant results are shown here.</p> Full article ">Figure 5
<p>Source apportionment of metal elements (MEs) in the Nadym region. (<b>a</b>) The percentage of contribution for each factor by PMF model. (<b>b</b>) Factor profiles of MEs in permafrost-affected soils derived from PMF model. (<b>c</b>) The correlations of MEs by combining Pearson analysis and PMF model, **: significance level of <span class="html-italic">p</span> &lt; 0.01 and *: significance level of <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 6
<p>Ecological state of potentially toxic elements (PTEs) in permafrost-affected soils of the Nadym region. (<b>a</b>) Geoaccumulation index (I<sub>geo</sub>), Class 0: I<sub>geo</sub> ≤ 0 (no pollution) and Class 1: 0 &lt; I<sub>geo</sub> ≤ 1 (no contamination to slight pollution). (<b>b</b>) Enrichment factor (EF), Class 1: EF &lt; 2 (no enrichment) and Class 2: 2 ≤ EF &lt; 5 (moderate enrichment).</p> Full article ">Figure 7
<p>Potential ecological risk index in permafrost-affected soils of the Nadym region. E<sub>r</sub>—potential ecological risk index of the i-th element. RI—potential ecological risk index for all potentially toxic elements (PTEs), including Ni, As, Cu, Pb, Cr, and Zn.</p> Full article ">
19 pages, 11538 KiB  
Article
Redefining Benggang Management: A Novel Integration of Soil Erosion and Disaster Risk Assessments
by Xiqin Yan, Shoubao Geng, Hao Jiang, Zhongyu Sun, Nan Wang, Shijie Zhang, Long Yang and Meili Wen
Land 2024, 13(5), 613; https://doi.org/10.3390/land13050613 - 2 May 2024
Viewed by 989
Abstract
In the granite regions of southern China, benggang poses a substantial threat to the ecological environment due to significant soil erosion. This phenomenon also imposes constraints on economic development, necessitating substantial investments in restoration efforts in recent decades. Despite these efforts, there remains [...] Read more.
In the granite regions of southern China, benggang poses a substantial threat to the ecological environment due to significant soil erosion. This phenomenon also imposes constraints on economic development, necessitating substantial investments in restoration efforts in recent decades. Despite these efforts, there remains a notable gap in comprehensive risk assessment that integrates both the erosion risk and disaster risk associated with benggang. This study focuses on a representative benggang area in Wuhua County, Guangdong province, employing transformer methods and high-resolution imagery to map the spatial pattern of the benggang. The integrated risk of benggang was assessed by combining soil-erosion risk and disaster risk, and cultivated land, residential land, and water bodies were identified as key disaster-affected entities. The machine-learning Segformer model demonstrated high precision, achieving an Intersection over Union (IoU) of 93.17% and an accuracy (Acc) of 96.73%. While the number of large benggang is relatively small, it constitutes the largest area proportion (65.10%); the number of small benggang is more significant (62.40%) despite a smaller area proportion. Prioritization for benggang management is categorized into high, medium, and low priority, accounting for 17.98%, 48.34%, and 33.69%, respectively. These priorities cover areas of 30.27%, 42.40%, and 27.33%, respectively. The findings of this study, which offer benggang management priorities, align with the nature-based solutions approach. Emphasizing the importance of considering costs and benefits comprehensively when formulating treatment plans, this approach contributes to sustainable solutions for addressing the challenges posed by benggang. Full article
(This article belongs to the Special Issue Assessment and Monitoring of Land Degradation: Current Trends and Future Directions)
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Figure 1

Figure 1
<p>Location map of the study area.</p> Full article ">Figure 2
<p>Comprehensive risk assessment framework for benggang. The combination of erosion risk (III) and disaster risk of benggang (IV) in the red dashed rectangle can be obtained (V) integrated risk of benggang.</p> Full article ">Figure 3
<p>The verification of GF_1 image and UAV survey of benggang. (<b>a</b>) is the bottom map of the GF_1 image; the red polygon is the benggang range recognized based on machine learning, and the blue Roman numerals are the 3 benggang points for UVA (unmanned aerial vehicle) field verification; (<b>b</b>–<b>d</b>) are the images of benggang taken by UAV.</p> Full article ">Figure 4
<p>The spatial distribution of benggang types and topographic characteristics in Huacheng Town. (<b>a</b>) is the area of benggang, and (<b>b</b>–<b>d</b>) are the superposition diagrams of the area of benggang and the elevation, slope, and development of landform value, respectively, of Huacheng Town.m<sup>2</sup>.</p> Full article ">Figure 5
<p>Area and quantity ratio of three categories of benggang.</p> Full article ">Figure 6
<p>Relationship between benggang area and topographic characteristics. (<b>a</b>) is the relationship between the benggang area and elevation, (<b>b</b>) is the slope, and (<b>c</b>) is the HI (development of landform value).</p> Full article ">Figure 7
<p>Spatial distribution and histogram of the normalized value of benggang density in Huacheng Town. (<b>a</b>) is the spatial distribution of the normalized benggang density index, and (<b>b</b>) is the histogram of the normalized benggang density index.</p> Full article ">Figure 8
<p>Disaster risk of benggang for cultivated land, residential land, and water bodies in Huacheng Town. (<b>a</b>) is the spatial distribution of the disaster risk of benggang for cultivated land, and (<b>e</b>) is the proportion of high, medium, and low disaster risk for cultivated land; (<b>b</b>,<b>f</b>) is the disaster risk of benggang for residential land; (<b>c</b>,<b>g</b>) is the disaster risk of benggang for water bodies; and (<b>d</b>,<b>h</b>) is integrated disaster risk of benggang.</p> Full article ">Figure 9
<p>Integrated risk of benggang for cultivated land, residential land, and water bodies in Huacheng Town. (<b>a</b>) is the spatial distribution of the integrated risk of benggang for cultivated land, and (<b>e</b>) is the proportion of high, medium, and low integrated risk for cultivated land; (<b>b</b>,<b>f</b>) is the integrated risk of benggang for residential land; (<b>c</b>,<b>g</b>) is the integrated risk of benggang for water bodies; and (<b>d</b>,<b>h</b>) is integrated risk of benggang.</p> Full article ">Figure 10
<p>Differentiated priority benggang for various vulnerable entities. (<b>a</b>) is the spatial distribution of the priority of benggang control for cultivated land, (<b>b</b>) is the priority of benggang control for residential land, (<b>c</b>) is the priority of benggang control for water bodies, and (<b>d</b>) is the priority of benggang control.</p> Full article ">Figure 11
<p>Percentage of count and area of differentiated priority benggang for various vulnerable entities. (<b>a</b>) is the percentage of count and area of the priority of benggang control for cultivated land, (<b>b</b>) is the priority of benggang control for residential land, (<b>c</b>) is the priority of benggang control for water bodies, and (<b>d</b>) is the priority of benggang control.</p> Full article ">Figure 12
<p>Differentiated priority benggang. (<b>a</b>) is the spatial distribution of the priority of benggang control, and (<b>c</b>–<b>d</b>) show the features of high, medium, and low priority in high-resolution images (GF-1), respectively.</p> Full article ">Figure 13
<p>Management strategies of priority benggang based on nature-based solutions. (<b>a</b>) is a biological measure of benggang control based on the natural restoration of <span class="html-italic">Dicranopteris dichotoma</span>, (<b>b</b>) is a biological measure based on Miscanthus of benggang/flood fan, and (<b>c</b>) is a biological measure for the control of benggang based on bamboo.</p> Full article ">
16 pages, 6173 KiB  
Article
Temperature Mainly Determined the Seasonal Variations in Soil Faunal Communities in Semiarid Areas
by Zhiyong Li, Xi Yang, Wei Long, Ranran Song, Xuchao Zhu, Tongchuan Li, Ming’an Shao, Mingyu Chen and Miao Gan
Land 2024, 13(4), 505; https://doi.org/10.3390/land13040505 - 12 Apr 2024
Viewed by 954
Abstract
The implementation of the Grain for Green Project has increased vegetation coverage and provided suitable habitats and food resources for soil fauna, thereby promoting the development of soil faunal communities. Studying seasonal variations in soil fauna communities in different vegetation areas can improve [...] Read more.
The implementation of the Grain for Green Project has increased vegetation coverage and provided suitable habitats and food resources for soil fauna, thereby promoting the development of soil faunal communities. Studying seasonal variations in soil fauna communities in different vegetation areas can improve our understanding of the mechanisms that drive soil fauna recovery. We selected five typical artificially restored vegetation habitats, including Populus simonii (POS), Pinus tabulaeformis (PIT), Caragana korshinskii (CAK), Stipa bungeana (STB), and Medicago sativa (MES), and one farmland (Zea mays, FAL) habitat on the Loess Plateau. In this study, soil fauna communities and environmental factors were investigated during spring (May), summer (August), and autumn (November). Among the habitats, the STB habitat had the largest seasonal variation in soil faunal density (from 1173 ind·m−2 in May to 10,743 ind·m−2 in August), and the FAL habitat had the smallest (from 2827 ind·m−2 in August to 5550 ind·m−2 in November). Among the restored vegetation habitats, Acarina (44.89–88.56%) had the highest relative abundance of all taxa. The redundancy analysis (RDA) results showed that among the factors driving seasonal variation in soil animal communities, temperature (47.41%) was the most important, followed by precipitation (22.60%). In addition, the dominant groups, Acarina and Collembola, played an influential role in seasonal variations in soil faunal density. Temperature mainly determined the seasonal variations in soil faunal communities. Seasonal factors should be considered when conducting soil fauna research, as they contribute to biodiversity conservation and regional ecological management in the Loess Plateau. Full article
(This article belongs to the Special Issue Assessment and Monitoring of Land Degradation: Current Trends and Future Directions)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Location of the study site in Liudaogou catchment, Loess Plateau region, China.</p> Full article ">Figure 2
<p>Season variations of diversity index (<b>A</b>), richness index (<b>B</b>), dominance index (<b>C</b>), evenness index (<b>D</b>), number of groups (<b>E</b>), and density (<b>F</b>) for the soil faunal community (mean ± SE). Capital letters on the bars indicate significant temporal differences within habitats at the <span class="html-italic">p</span> &lt; 0.05 level, while lowercase letters on the bars indicate spatial differences within seasons at the <span class="html-italic">p</span> &lt; 0.05 level (Tukey test). POS, <span class="html-italic">Populus simonii</span> habitat; PIT, <span class="html-italic">Pinus tabulaeformis</span> habitat; CAK, <span class="html-italic">Caragana korshinskii</span> habitat; STB, <span class="html-italic">Stipa bungeana</span> habitat; MES, <span class="html-italic">Medicago sativa</span> habitat; FAL, farmland habitat.</p> Full article ">Figure 3
<p>The proportion of soil faunal density in a certain group to the total group density in May (<b>Upper left</b>), August (<b>Upper right</b>), and November (<b>Bottom left</b>). POS, <span class="html-italic">Populus simonii</span> habitat; PIT, <span class="html-italic">Pinus tabulaeformis</span> habitat; CAK, <span class="html-italic">Caragana korshinskii</span> habitat; STB, <span class="html-italic">Stipa bungeana</span> habitat; MES, <span class="html-italic">Medicago sativa</span> habitat; FAL, farmland habitat.</p> Full article ">Figure 4
<p>Principal component analysis of soil faunal communities in May, August, and November with the habitats as an overlay. Species arrows: each arrow points in the direction of the steepest increase of the values for corresponding species. POS, <span class="html-italic">Populus simonii</span> habitat; PIT, <span class="html-italic">Pinus tabulaeformis</span> habitat; CAK, <span class="html-italic">Caragana korshinskii</span> habitat; STB, <span class="html-italic">Stipa bungeana</span> habitat; MES, <span class="html-italic">Medicago sativa</span> habitat; FAL, farmland habitat. Abbreviations: Araneae (Ara.), Acarina (Aca.), Geophilomorpha (Geo.), Lithobiomorpha (Lit.) Symphyla (Sym.) Protura (Pro.), Collembola (Col.), Diplura (Dip.), Isoptera (Iso.), Hemiptera (Hem.), Corrodentia (Cor.), Thysanoptera (Thy.), Coleoptera larvae (Cl.), Coleoptera adult (Ca.), Lepidoptera larvae (Ll.), Diptera larvae (Dl.), Hymenoptera (Hym.), Homoptera (Hom.).</p> Full article ">Figure 5
<p>Principal component analysis of the soil faunal communities in POS, PIT, CAK, STB, MES, and FAL with the sampling periods as an overlay. POS, <span class="html-italic">Populus simonii</span> habitat; PIT, <span class="html-italic">Pinus tabulaeformis</span> habitat; CAK, <span class="html-italic">Caragana korshinskii</span> habitat; STB, <span class="html-italic">Stipa bungeana</span> habitat; MES, <span class="html-italic">Medicago sativa</span> habitat; FAL, farmland habitat. Abbreviations: Araneae (Ara.), Acarina (Aca.), Geophilomorpha (Geo.), Lithobiomorpha (Lit.) Symphyla (Sym.) Protura (Pro.), Collembola (Col.), Diplura (Dip.), Isoptera (Iso.), Hemiptera (Hem.), Corrodentia (Cor.), Thysanoptera (Thy.), Coleoptera larvae (Cl.), Coleoptera adult (Ca.), Lepidoptera larvae (Ll.), Diptera larvae (Dl.), Hymenoptera (Hym.), Homoptera (Hom.).</p> Full article ">Figure 6
<p>Redundancy analysis (RDA) showing the relationship between soil faunal composition (blue lines) and environment factors (red lines) (POS, <span class="html-italic">Populus simonii</span> habitat; PIT, <span class="html-italic">Pinus tabulaeformis</span> habitat; CAK, <span class="html-italic">Caragana korshinskii</span> habitat; STB, <span class="html-italic">Stipa bungeana</span> habitat; MES, <span class="html-italic">Medicago sativa</span> habitat; FAL, farmland habitat). Abbreviations: soil bulk density (SBD), soil water content (SWC), soil pH (pH), soil organic carbon (OC), soil total nitrogen (TN), soil total phosphorus (TP), soil nitrate nitrogen (NN), soil ammonium nitrogen (AN), soil Olsen phosphorus (OP), temperature (AT), precipitation (AP). Araneae (Ara.), Acarina (Aca.), Geophilomorpha (Geo.), Lithobiomorpha (Lit.), Symphyla (Sym.), Protura (Pro.), Collembola (Col.), Diplura (Dip.), Isoptera (Iso.), Hemiptera (Hem.), Corrodentia (Cor.), Thysanoptera (Thy.), Coleoptera larvae (Cl.), Coleoptera adult (Ca.), Lepidoptera larvae (Ll.), Diptera larvae (Dl.), Hymenoptera (Hym.), Homoptera (Hom.).</p> Full article ">
17 pages, 2915 KiB  
Article
Application of Soil Multiparametric Indices to Assess Impacts of Grazing in Mediterranean Forests
by Picazo Córdoba Marta Isabel, García Saucedo Francisco, Wic Baena Consolación, García Morote Francisco Antonio, López Serrano Francisco Ramón, Rubio Eva, Moreno Ortego José Luis and Andrés Abellán Manuela
Land 2024, 13(4), 411; https://doi.org/10.3390/land13040411 - 23 Mar 2024
Viewed by 945
Abstract
In this study, the effects of different stocking rates were quantified in three study areas in a Mediterranean forest (Cuenca, Spain) by applying a multiparametric soil quality index (SQI) developed from undisturbed forest soils (>40 years). The main objective was to advance the [...] Read more.
In this study, the effects of different stocking rates were quantified in three study areas in a Mediterranean forest (Cuenca, Spain) by applying a multiparametric soil quality index (SQI) developed from undisturbed forest soils (>40 years). The main objective was to advance the development and application of multiparametric indices that allow for soil condition assessment. To fulfill this objective, the effectiveness of the developed multiparametric soil quality index (SQI) was analyzed as an indicator of livestock impacts on soil in the Mediterranean forest. The control areas without livestock activity were forest stands of different ages (a thicket forest stand of <30 years; a high-polewood forest stand of 30–60 years; and an old-growth forest stand of >60 years), which were compared with areas subjected to various grazing intensities (areas with permanent livestock passage: a sheepfold that had been inactive for 2–3 years and an active sheepfold; areas with intermittent livestock passage: a bare-soil area, a pine stand and a scrubland). The applied multiparametric soil quality index (SQI) was sensitive to changes in forest ecosystems depending on the stocking rates. However, to obtain greater precision in the assessment of the effects of stocking rates, the multiparametric index was recalibrated to create a new index, the Soil Status Index by Livestock (SSIL). The correlation between the quality ranges obtained with both indices in different study areas suggests that the SSIL can be considered a livestock impact reference indicator in Mediterranean forest soils. Full article
(This article belongs to the Special Issue Assessment and Monitoring of Land Degradation: Current Trends and Future Directions)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Study areas and sampling plots.</p> Full article ">Figure 2
<p>Mean soil quality index (<b>SQI</b>) value in each study area. <b>Ash</b>, active sheepfold; Ish, inactive sheepfold; <b>BS</b>, bare soil; <b>Scr</b>, scrubland; <b>Pst</b>, pine stand; <b>Tfst</b>, thicket forest stand; <b>Hfst</b>, high-polewood forest stand; and <b>Ofst</b>, old-growth forest stand (n = 168, units shown on the planes of the axes).</p> Full article ">Figure 3
<p>(<b>a</b>) Diagram showing the eigenvectors for each one of the twelve parameters (shown as lines) on the first two principal component axes. Longer lines indicate parameters that relate strongly to the axes, and the closer they are plotted, the stronger the correlations between the parameters (n = 168, units shown on the planes of the axes). (<b>b</b>) Scatter plot of the principal component scores of the standardized data. Abbreviations: Ash, active sheepfold; Ish, inactive sheepfold; BS, bare soil; Scr, scrubland; Pst, pine stand; Tfst, thicket forest stand; Hfst, high-polewood forest stand; Ofst, old-growth forest stand; TOC, total organic carbon; N, total nitrogen; M, moisture; pH, soil acidity; BR, basal soil respiration; MBC, microbial biomass carbon; APA, phosphatase activity; β-GLU, β-glucosidase activity. (<b>c</b>) Principal component analysis (2PCA) performed using the eight selected parameters. The eigenvector for each of the eight parameters is plotted on the plane. (<b>d</b>) Principal component analysis (3PCA) performed using the eight selected parameters, with axes 3PC1 and 3PC2. M, moisture; pH, soil acidity; MBC, microbial biomass carbon; UA, urease activity.</p> Full article ">Figure 4
<p>Average value of <b>SSI<sub>L</sub></b> (Soil Status Index by Livestock) in each study area. <b>Ash</b>, active sheepfold; <b>Ish</b>, inactive sheepfold; <b>BS</b>, bare soil; <b>Scr</b>, scrubland; <b>Pst</b>, pine stand; <b>Tfst</b>, thicket forest stand; <b>Hfst</b>, high-polewood forest stand; and <b>Ofst</b>, old-growth forest stand (n = 168, units shown on the planes of the axes).</p> Full article ">Figure 5
<p>Ranges for <b>RSQI</b> and <b>RSSI<sub>L</sub></b> in each study area. <b>Ash</b>, active sheepfold; <b>Ish</b>, inactive sheepfold; <b>BS</b>, bare soil; <b>Scr</b>, scrubland; <b>Pst</b>, pine stand; <b>Tfst</b>, thicket forest stand; <b>Hfst</b>, high-polewood forest stand; and <b>Ofst</b>, old-growth forest stand (n = 168, units shown on the planes of the axes).</p> Full article ">
24 pages, 12940 KiB  
Article
Automatic Extraction for Land Parcels Based on Multi-Scale Segmentation
by Fei Liu, Huizhong Lu, Lilei Wu, Rui Li, Xinjun Wang and Longxi Cao
Land 2024, 13(2), 158; https://doi.org/10.3390/land13020158 - 30 Jan 2024
Cited by 3 | Viewed by 1206 | Correction
Abstract
Different land parcels possess unique microclimates, soils, and biological conditions, which in turn significantly influence the land parcels themselves, impacting biodiversity, hydrological relationships, land degradation, geological disasters, and other ecological environments. Therefore, researching an efficient and accurate method capable of extracting land parcels [...] Read more.
Different land parcels possess unique microclimates, soils, and biological conditions, which in turn significantly influence the land parcels themselves, impacting biodiversity, hydrological relationships, land degradation, geological disasters, and other ecological environments. Therefore, researching an efficient and accurate method capable of extracting land parcels with the least internal heterogeneity at the macro, meso, and micro scales is extremely important. Multi-scale segmentation, based on scale and resolution analysis techniques, is a bottom-up merging technology that minimizes internal heterogeneity within regions and maximizes heterogeneity between different units. This approach is extensively applied in multi-scale spectral feature extraction and classification and is further combined with deep learning techniques to enhance the accuracy of image classification. This study, using Xinghai County in Qinghai Province as an example, employs multi-scale segmentation and hydrological analysis methods to extract land parcels at different spatial scales. The results show (1) that the land parcels extracted using the hydrological analysis method are catchment units centered around rivers, including slopes on both sides of the river. In contrast, multi-scale segmentation extracts regions comprising land parcels with similar properties, enabling the segregation of slopes and channels into independent units. (2) At a classification threshold of 19, multi-scale segmentation divides the study area into five different types of land parcels, reflecting the heterogeneity of terrain undulations and their hydrological connections. When the classification threshold is set to 31, the study area is divided into 15 types of land parcels, primarily highlighting micro-topographic features. (3) Multi-scale segmentation can merge and categorize areas with the least heterogeneity in land parcels, facilitating subsequent statistical analysis. Therefore, mesoscale land parcels extracted through multi-scale segmentation are invaluable for analyzing regional Earth surface processes such as soil erosion, sediment distribution and transportation. Microscale land parcels are significantly important for identifying high-risk areas in relation to geological disasters like landslides and collapses. Full article
(This article belongs to the Special Issue Assessment and Monitoring of Land Degradation: Current Trends and Future Directions)
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Full article ">Figure 1
<p>Research technical route.</p> Full article ">Figure 2
<p>Determination of the number of principal components.</p> Full article ">Figure 3
<p>Extraction of principal components.</p> Full article ">Figure 4
<p>Schematic diagram of the study area.</p> Full article ">Figure 5
<p>Hydrological method extraction results of land parcels in flat terrain. Different color lines represent results with different thresholds. (<b>a</b>) Subwatershed units with a threshold of 50; (<b>b</b>) subwatershed units with a threshold of 500; (<b>c</b>) subwatershed units with a threshold of 1000; (<b>d</b>) subwatershed units with a threshold of 5000.</p> Full article ">Figure 6
<p>Hydrological method extraction results of land parcels in mountainous terrain. Different color lines represent results with different thresholds. (<b>a</b>) Subwatershed units with a threshold of 50; (<b>b</b>) subwatershed units with a threshold of 500; (<b>c</b>) subwatershed units with a threshold of 1000; (<b>d</b>) subwatershed units with a threshold of 5000.</p> Full article ">Figure 7
<p>Multi-scale segmentation extraction of land parcels in flat areas. (<b>a</b>) Multiscale segmentation classification threshold is 5; (<b>b</b>) multi-scale segmentation classification threshold is 19; (<b>c</b>) multi-scale segmentation classification threshold is 31. Different colors in the figure represent different types of land parcels.</p> Full article ">Figure 8
<p>Multi-scale segmentation extraction of land parcels in mountainous areas. (<b>a</b>) Multiscale segmentation classification threshold is 5; (<b>b</b>) multi-scale segmentation classification threshold is 19; (<b>c</b>) multi-scale segmentation classification threshold is 31. Different colors in the figure represent different types of land parcels.</p> Full article ">Figure 9
<p>Comparison of five topographic units and regional flow data statistics.</p> Full article ">Figure 10
<p>Comparison of TPI data statistics for five terrain units and regions.</p> Full article ">Figure 11
<p>Comparison of CTI statistics for five terrain units and regions.</p> Full article ">Figure 12
<p>Comparison of flow direction statistics for fifteen terrain units and regions.</p> Full article ">Figure 13
<p>Comparison of TPI statistics for 15 topographic units and regions.</p> Full article ">Figure 14
<p>Comparison of CTI statistics for 15 terrain units and regions.</p> Full article ">Figure 15
<p>Example of mesoscale land parcel assessment. Red boxes are field observation areas.</p> Full article ">Figure 16
<p>Example of microscale land parcel assessment. Red boxes are field observation areas.</p> Full article ">
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