[go: up one dir, main page]
Next Issue
Volume 13, April
Previous Issue
Volume 13, February
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.9
3.2
Journals
Land
Volume 13
Issue 3
share announcement

Land, Volume 13, Issue 3 (March 2024) – 138 articles

Cover Story (view full-size image): Visiting urban green and blue spaces (UGBS) has been associated with positive effects on health and wellbeing. However, UGBS can vastly differ in quality, and very little is known about the preferences of different populations. We investigated UGBS usage and preferences in Edinburgh, UK, using participatory GIS methods. Differing perceptions of UGBS were apparent. The image shows numerous UGBS which some respondents intentionally avoid, while others intentionally visit, demonstrating strong differences in preference. We also identified differences in UGBS perceptions across different demographic groups. Our study suggests that identifying and understanding differences in UGBS preferences is an important step for maximising the health and wellbeing benefits of UGBS for all of society. View this paper
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
22 pages, 2046 KiB  
Review
A Review of Rural Land Capitalization: Current Status and Further Research
by Weiguo Fan, Yuheng Zhang, Nan Chen and Wanqing Nie
Land 2024, 13(3), 401; https://doi.org/10.3390/land13030401 - 21 Mar 2024
Cited by 1 | Viewed by 1307
Abstract
Land stands as a crucial factor in the production process. The rational allocation of land resources and the enhancement of land use efficiency play pivotal roles in maintaining stable economic development. Various land use types facilitate the capitalization of land resources through activities [...] Read more.
Land stands as a crucial factor in the production process. The rational allocation of land resources and the enhancement of land use efficiency play pivotal roles in maintaining stable economic development. Various land use types facilitate the capitalization of land resources through activities such as land transfer, land investment, and large-scale land management. Presently, certain regions grapple with challenges characterized by abundant land resources, insufficient utilization of land elements, and a low degree of utilized land capitalization. To address these issues, scholars employ diverse research methods, delving into land capitalization from various perspectives. This paper provides a comprehensive review of the current academic research on land capitalization. It elucidates the conceptual nuances inherent in the process of land capitalization, traces the historical evolution of land capitalization, and establishes a research framework that considers land appreciation, ownership relationships, and functional transformations. By synthesizing and analyzing the existing research on land capitalization, this paper outlines the current status and identifies future research directions. It is concluded that land appropriation, ownership relationships and functional transformations are the three most important elements in the process of land capitalization. The paper proposes objectives for achieving high-quality development while avoiding excessive capitalization and the aim is to propel land capitalization as a catalyst for rural economic development. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Historical evolution of the concept of land capitalization.</p> Full article ">Figure 2
<p>The number of papers published in the field of land capitalization research.</p> Full article ">Figure 3
<p>Time-based word frequency network visualization.</p> Full article ">Figure 4
<p>Frame diagram of land capitalization process.</p> Full article ">
36 pages, 29976 KiB  
Article
Continuity, Resilience, and Change in Rural Settlement Patterns from the Roman to Islamic Period in the Sicani Mountains (Central-Western Sicily)
by Angelo Castrorao Barba, Carla Aleo Nero, Giuseppina Battaglia, Luca Zambito, Ludovica Virga, Alessandra Messina, Marco Cangemi and Giuseppe Bazan
Land 2024, 13(3), 400; https://doi.org/10.3390/land13030400 - 21 Mar 2024
Cited by 1 | Viewed by 1896
Abstract
This study aims to analyze the dynamics of change in settlement models from the Roman, late antique, and Byzantine periods, focusing on how these transformations influenced the formation of Islamic societies in the rural landscapes of western Sicily. The study is centered around [...] Read more.
This study aims to analyze the dynamics of change in settlement models from the Roman, late antique, and Byzantine periods, focusing on how these transformations influenced the formation of Islamic societies in the rural landscapes of western Sicily. The study is centered around the territory of Corleone in the Sicani Mountains (central-western Sicily). This region, strategically located between the significant cities of Palermo on the Tyrrhenian Sea and Agrigento on the Strait of Sicily, has been pivotal in the communication network spanning from the Roman era to the Middle Ages and beyond. The area has been subject to extensive surveys and excavations, revealing diverse dynamics of continuity, resilience, and innovation in settlement patterns from the Roman to the Islamic periods. Beyond presenting the results of archaeological fieldwork, this study employs GIS-based spatial and statistical analyses and utilizes a range of topographic (elevation, slope, aspect, topographic position index (TPI), and distance to water sources) and ecological factors (vegetation series). These analyses aim to assess the evolving relationships and site positioning within the territory over time. Combining archaeological data with topographic and ecological landscape analysis, this integrated approach elucidates the complex transition dynamics from the Roman settlement system to the Islamic age’s landscape formation in western Sicily’s rural areas. The study thereby contributes to a deeper understanding of the intricate interplay between historical developments and environmental factors in shaping rural settlement patterns. Full article
(This article belongs to the Special Issue Resilience in Historical Landscapes)
►▼ Show Figures

Figure 1

Figure 1
<p>Location of the study area and topographic map from the Italian Geographic Military Institute [<a href="#B57-land-13-00400" class="html-bibr">57</a>] and sites relevant for settlement dynamics across the ages: Roman (blue); Byzantine (green); Byzantine–Islamic (red); Islamic (purple).</p> Full article ">Figure 2
<p>The right peak of the southwest side of Montagna Vecchia, where the medieval settlement was located.</p> Full article ">Figure 3
<p>Map of the density of ceramic fragments per m<sup>2</sup> in the two topographic units (UT1 and UT2) identified in the Contrada Zuccarone site (CRL29).</p> Full article ">Figure 4
<p>Hellenistic and Roman pottery from the Contrada Zuccarone site (CRL29_UT1). 10A, N.I. 66: worn rims referable due to their texture (numerous volcanic inclusions of Tyrrhenian origin) and typology to MGS amphorae and probably dating back to between the 4th and 3rd centuries BCE [<a href="#B79-land-13-00400" class="html-bibr">79</a>]. 13B, N.I. 94: fragment of an African Red Slip A cup, Hayes 8B form, with an undecorated strip (2nd century CE) [<a href="#B80-land-13-00400" class="html-bibr">80</a>]. 6A, N.I. 48: a fragment of rim of plate lid with cinerish patina of Hayes 196 form/Bonifay types 11.5 (2nd–5th century) [<a href="#B80-land-13-00400" class="html-bibr">80</a>,<a href="#B81-land-13-00400" class="html-bibr">81</a>]. 12B, N.I. 88: almond-shaped rim and vertical body of a North African cooking pot (casserole) related to the Hayes 197 form (4th–5th century) [<a href="#B80-land-13-00400" class="html-bibr">80</a>]. 8A, N.I. 56: a fragment of the rim of a cooking pan of Pantellerian ware (4th–5th century) [<a href="#B82-land-13-00400" class="html-bibr">82</a>,<a href="#B83-land-13-00400" class="html-bibr">83</a>]. 15B, N.I. 108: a fragment of a rim of an African Red Slip D cup with “nailed” decoration on the external body of a Hayes 81 A/Bonifay type 43–44 form (half/second half of 5th century) [<a href="#B80-land-13-00400" class="html-bibr">80</a>,<a href="#B81-land-13-00400" class="html-bibr">81</a>]. 3A, N.I. 26: a fragment of a rim of Hayes 81A/Bonifay type 43–44 form (half/second half of 5th century) [<a href="#B80-land-13-00400" class="html-bibr">80</a>,<a href="#B81-land-13-00400" class="html-bibr">81</a>].</p> Full article ">Figure 5
<p>Islamic pottery from the Contrada Zuccarone site (CRL29_UT2). N.I. 292–295: painted amphorae/jugs. N.I. 297: Islamic glazed pottery.</p> Full article ">Figure 6
<p>(<b>a</b>) Topographic units identified in the Case Scalilli–Gole del Drago site (CRL23); (<b>b</b>) the rocky plateau at the top of the isolated relief at the Case Scalilli–Gole del Drago site.</p> Full article ">Figure 7
<p>Roman pottery from the Case Scalilli–Gole del Drago site (CRL23). UT7, N.I. 136: a small cup in <span class="html-italic">terra sigillata</span>, approximately 15 BCE—50 CE [<a href="#B92-land-13-00400" class="html-bibr">92</a>]. UT7, N.I. 132: a cup in African red slip A with everted rim and subhorizontal brim, Hayes 32 form [<a href="#B80-land-13-00400" class="html-bibr">80</a>] (1st–2nd century CE). UT7, N.I. 167: a bowl in African Red Slip F [<a href="#B93-land-13-00400" class="html-bibr">93</a>] with an indistinct rim and vertical body may be similar to Hayes 50 (mid-5th century CE) [<a href="#B80-land-13-00400" class="html-bibr">80</a>]. UT7, N.I. 124: an amphora Africana IIIA (4th century) [<a href="#B56-land-13-00400" class="html-bibr">56</a>].</p> Full article ">Figure 8
<p>Shards of Medieval pottery from the Case Scalilli–Gole del Drago site (CRL23). UT 14, N.I. 187; UT 15, N.I. 188; UT 15, N.I. 288: glazed pottery. UT 15, N.I. 291; UT 14, N.I. 286; UT 14, N.I. 287; UT 15, N.I. 290: painted amphorae/jugs. UT 15, N.I., 289: lamp. UT 14, N.I. 185; and UT 14, N.I. 186: glazed pottery with furrowed decoration.</p> Full article ">Figure 9
<p>Earrings (<b>a</b>,<b>b</b>) and painted ceramic jugs (<b>c</b>,<b>d</b>) from grave goods of the Byzantine period (6th–7th century) from the Palastanga area in the Corleone area (reworked from Dannheimer [<a href="#B97-land-13-00400" class="html-bibr">97</a>] to Metaxas [<a href="#B98-land-13-00400" class="html-bibr">98</a>]).</p> Full article ">Figure 10
<p>Byzantine striped/combed tiles from the Costa Rubina (CRL40), Contrada Casale (CRL44), and Case Bingo (CRL47) sites.</p> Full article ">Figure 11
<p>(<b>a</b>) Topographic location of the sites of Casale di Sotto (CRL3) and Casale di Sopra (CRL4); (<b>b</b>) map of the fortification line of Casale di Sopra; (<b>c</b>) the fortification wall of Casale di Sopra; (<b>d</b>) topographic units of concentration of pottery inside the wall of Casale di Sopra.</p> Full article ">Figure 12
<p>Islamic amphoras from Casale di Sopra (CRL4). UT 2, N.I. 281: painted amphora. UT 1, N.I. 2; UT 2, N.I. 239–240; UT 4 N.I. 242; and UT 5, N.I. 244: amphoras.</p> Full article ">Figure 13
<p>Medieval glazed pottery from Casale di Sopra (CRL4): UT 3, N.I. 243; UT 3, and N.I. 241.</p> Full article ">Figure 14
<p>Shards of Islamic painted amphoras and cooking pots from the Masseria Magione (CRL7) and Contrada Carrubba (CRL49) sites. CRL 7, N.I. 282; CRL 7, N.I. 283; CRL 7, N.I. 284; CRL 49, N.I. 261; and CRL 49, N.I. 262: painted amphorae/jugs. CRL 7, N.I. 194: amphora. CRL 7, N.I. 192: cooking pot.</p> Full article ">Figure 15
<p>Maps of GIS-based spatial analyses of key environmental variables: (<b>a</b>) elevation [m], (<b>b</b>) slope (°), (<b>c</b>) aspect (°), (<b>d</b>) TPI, (<b>e</b>) water distance [m], and (<b>f</b>) vegetation series. Red circles are archaeological sites.</p> Full article ">Figure 16
<p>Boxplots representing the distribution of key environmental variables across different periods at archaeological sites. Each plot corresponds to a unique variable: (<b>a</b>) elevation [m], (<b>b</b>) slope (°), (<b>c</b>) aspect (°), (<b>d</b>) TPI, and (<b>e</b>) water distance [m]. The boxplots are categorized by the Roman (1), Byzantine (2), and Islamic (3) periods and illustrate the range, interquartile range, median, and potential outliers within each period.</p> Full article ">Figure 17
<p>PCA biplot depicting the distribution of archaeological sites categorized by period. Each point represents a site positioned according to the first two principal components (PC1 and PC2) derived from environmental variables. The red arrows indicate the direction and strength of the variables’ association with the principal components. Sites are visually distinguished by period: Roman (1), Byzantine (2), and Islamic (3).</p> Full article ">Figure 18
<p>Frequency of surface assigned to a different vegetation series in the surrounding area of each archaeological site.</p> Full article ">
20 pages, 24193 KiB  
Article
Exploring Sensitivity of Phenology to Seasonal Climate Differences in Temperate Grasslands of China Based on Normalized Difference Vegetation Index
by Xiaoshuai Wei, Mingze Xu, Hongxian Zhao, Xinyue Liu, Zifan Guo, Xinhao Li and Tianshan Zha
Land 2024, 13(3), 399; https://doi.org/10.3390/land13030399 - 21 Mar 2024
Cited by 1 | Viewed by 1176
Abstract
The affiliation between vegetation phenology and seasonal climate (start and end times of the growing season, or SOS and EOS) provides a basis for acquiring insight into the dynamic response of terrestrial ecosystems to the effects of climate change. Although climate warming is [...] Read more.
The affiliation between vegetation phenology and seasonal climate (start and end times of the growing season, or SOS and EOS) provides a basis for acquiring insight into the dynamic response of terrestrial ecosystems to the effects of climate change. Although climate warming is an important factor affecting the advancement or delay of plant phenology, understanding the sensitivity of phenology to seasonal variation in climate factors (e.g., local air temperature, precipitation) is generally lacking under different climate backgrounds. In this study, we investigated the interannual variability of grassland phenology and its spatial variation in temperate regions of China based on satellite-derived products for the normalized difference vegetation index (NDVI) and weather data acquired from 2001 to 2020. We found that due to differences in local climate conditions, the effects of seasonal warming and precipitation on phenology were divergent or even opposite during the 20 years. The sensitivities of the start of growing season (SOS) to both spring temperature and last-winter precipitation was controlled by mean annual precipitation in terms of spatial variation. The SOS in the semi-humid (200–400 mm) region was most sensitive to spring temperature, advancing 5.24 days for each 1 °C rise in the average spring temperature (p < 0.05), while it was most sensitive to last-winter precipitation in arid regions (<200 mm), with SOS advancing up to 2.23 days for every 1 mm increase in the last-winter precipitation (p < 0.05). The end of growing season (EOS) was sensitive to autumn temperature, being delayed 10.13 days for each 1 °C rise in the average autumn temperature in regions with temperatures between −10 °C and −5 °C (p < 0.05). The uncertainty in the determination of the EOS could conceivably be greater than the determination of the SOS due to the dual effects of pre-autumn climate and growth constraints induced by declining fall temperatures. The effect of atmospheric warming on grassland phenology was lessened with increased atmospheric and soil aridity, suggesting that the interaction of regional drought and climate warming is an important source for local-to-regional differences and uncertainties in grass phenological response. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Research area and FLUXNET flux stations.</p> Full article ">Figure 2
<p>Comparison and validation of start of the growing season (SOS) extraction using different phenology models based on FLUXNET flux stations and remote sensing NDVI data. The results of pairwise linear fittings of phenological metrics fitted by different models (<b>a</b>–<b>f</b>).</p> Full article ">Figure 3
<p>Comparison and validation of end of the growing season (EOS) extraction using different phenological models based on FLUXNET flux stations and remote sensing NDVI data. The results of pairwise linear fittings of phenological metrics fitted by different models (<b>a</b>–<b>f</b>).</p> Full article ">Figure 4
<p>Spatial distributions of NDVI-derived phenological metrics (SOS and EOS). The SOS (<b>a</b>) and EOS (<b>d</b>) are the start of the growing season and the end of the growing season, respectively. Data in the figure are the mean annual values over years 2001–2020. The histograms are the frequency distribution of the phenological metrics in Julian days. The scatter plot represents over 2000 randomly sampled phenological metric (SOS and EOS) data points in space, and each data point is the mean annual value over years 2001–2020. Phenological metrics of the pixels as a function of both corresponding temperatures (<b>b</b>,<b>e</b>) and precipitation (<b>c</b>,<b>f</b>). Lines are fitted ones.</p> Full article ">Figure 5
<p>Spatial distribution of the trends of phenological metrics (SOS and EOS) over years 2001–2020 and their frequencies. Panel (<b>a</b>,<b>b</b>) were for the start of the growing season (SOS) and end of the growing season (EOS), respectively.</p> Full article ">Figure 6
<p>Spatial distribution of the regression slopes between SOS (start of growing season) and corresponding spring precipitation (<b>a</b>), spring temperature (<b>b</b>), last-winter precipitation (<b>c</b>), and last-winter temperature (<b>d</b>) over years 2001–2020 and between EOS (end of growing season) and corresponding autumn precipitation (<b>e</b>), autumn temperature (<b>f</b>), summer precipitation (<b>g</b>), and summer temperature (<b>h</b>) over years 2001–2020.</p> Full article ">Figure 7
<p>The scatter plot represents over 500 randomly sampled multiple regression slope data points in space, each of which represents the slope of phenological indicators and seasonal climate multiple regression, and all have passed significance tests (<span class="html-italic">p</span> &lt; 0.05). Solid lines are fitted ones. The regression slope is from the linear regression between SOS (start of the growing season) and corresponding spring precipitation (<b>a</b>), spring temperature (<b>b</b>), last-winter precipitation (<b>c</b>), and last-winter temperature (<b>d</b>) over years 2001–2020 and between EOS (end of the growing season) and corresponding autumn precipitation (<b>e</b>), autumn temperature (<b>f</b>), summer precipitation (<b>g</b>), and summer temperature (<b>h</b>) over years 2001–2020.</p> Full article ">Figure 8
<p>The scatter plot represents over 500 randomly sampled multiple regression slope data points in space, each of which represents the slope of phenological indicators and seasonal climate multiple regression, and all have passed significance tests (<span class="html-italic">p</span> &lt; 0.05). Solid lines are fitted ones. The regression slope is from the linear regression between SOS (start of the growing season) and corresponding spring precipitation (<b>a</b>), spring temperature (<b>b</b>), last-winter precipitation (<b>c</b>), and last-winter temperature (<b>d</b>) over years 2001–2020 and between EOS (end of the growing season) and corresponding autumn precipitation (<b>e</b>), autumn temperature (<b>f</b>), summer precipitation (<b>g</b>), and summer temperature (<b>h</b>) over years 2001–2020.</p> Full article ">Figure 9
<p>Comparisons in regression slopes of phenological metrics (SOS and EOS) against climatic factors between different temperature zones (<b>a</b>,<b>b</b>) and between different precipitation zones (<b>c</b>,<b>d</b>). The SOS and EOS are the start of the growing season and the end of the growing season, respectively. Panel (<b>a</b>,<b>c</b>) are the regression slopes of EOS for years 2001–2020 against corresponding precipitations in both summer and autumn, and regression slopes of SOS against precipitation in both spring and last winter. Panels (<b>b</b>,<b>d</b>) are for the regression slopes of SOS against temperature in both spring and last winter and for the regression slopes of EOS against temperature in both summer and autumn. Data are mean values of pixels in the specific zone.</p> Full article ">Figure 10
<p>Spatial distribution of partial correlation coefficients between SOS over years 2001–2020 and both corresponding precipitation (<b>a</b>) and temperature (<b>b</b>) in spring, between SOS of years 2001–2020 and both corresponding precipitation (<b>c</b>) and temperature (<b>d</b>) in last winter, between EOS of years 2001–2020 and both corresponding precipitation (<b>e</b>) and temperature (<b>f</b>) in autumn, and between EOS of years 2001–2020 and both corresponding precipitation (<b>g</b>) and temperature (<b>h</b>) in summer. The blue font (−) and orange font (+) represent the percentage of negatively and positively correlated pixels in the total pixels, respectively. The histograms (<b>i</b>) indicate the average value of all pixels in the graph (<b>a</b>–<b>d</b>), and the histogram (<b>j</b>) indicates the average value of all pixels in the graph (<b>e</b>–<b>h</b>), Error bars indicate the standard deviation among pixels.</p> Full article ">Figure 11
<p>Spatial distribution of major climate controls on phenological metrics (SOS (<b>a</b>) and EOS (<b>b</b>)). It is based on the maximum partial correlation coefficient between phenological metrics and seasonal climate variables over years 2001–2020. The seasonal climatic variables include spring precipitation, spring temperature, later-winter precipitation, and later-winter temperature. Note: the variable is considered as the controlling factor of the pixel SOS or EOS if the maximum partial correlation coefficient is significant and higher than those with other variables.</p> Full article ">
20 pages, 1417 KiB  
Review
Potential Interactions between Climate Change and Land Use for Forest Issues in the Eastern United States
by Brice B. Hanberry, Marc D. Abrams and Gregory J. Nowacki
Land 2024, 13(3), 398; https://doi.org/10.3390/land13030398 - 21 Mar 2024
Cited by 1 | Viewed by 1323
Abstract
Applying an interaction framework, we examined whether climate change and combined land use and disturbance changes were synergistic, antagonistic, or neutral for forest issues of wildfires, tree growth, tree species distributions, species invasions and outbreaks, and deer herbivory, focused on the eastern United [...] Read more.
Applying an interaction framework, we examined whether climate change and combined land use and disturbance changes were synergistic, antagonistic, or neutral for forest issues of wildfires, tree growth, tree species distributions, species invasions and outbreaks, and deer herbivory, focused on the eastern United States generally since the 1800s and the development of instrumental records (1895). Climate largely has not warmed during 1981–2020 compared to 1895–1980, but precipitation has increased. Increased precipitation and land use (encompassing fire exclusion and forestation, with coarse fuel accumulation due to increased tree densities) have interacted synergistically to dampen wildfire frequency in the humid eastern U.S. For overall tree growth, increased precipitation, carbon fertilization, and land use (i.e., young, fast-growing dense stands) likely have been positive, generating a synergistic interaction. Human activities created conditions for expanding native tree species distributions, non-native species invasions, and damaging native species outbreaks. No strong evidence appears to exist for recent climate change or land use influences on deer populations and associated herbivory levels. In the future, a warmer and effectively drier climate may reverse synergistic and neutral interactions with land use, although effects of climate interactions with land use will vary by species. Management can help correct non-climate stressors due to land use and support resilient structures and species against climate change. Full article
(This article belongs to the Special Issue Land Use/Land Cover and Natural Hazards: Interactions, Changes, and Impacts II)
►▼ Show Figures

Figure 1

Figure 1
<p>Changes in the mean annual temperature (<b>A</b>), precipitation (<b>B</b>), temperature-precipitation change class ratios (<b>C</b>), and Palmer Modified Drought Index (<b>D</b>) between 1895–1980 and 1981–2020 for the United States. The temperature-precipitation change class ratios were calculated by applying a simple classification to mean temperature and precipitation maps but presented similar changes of stable or decreased evapotranspiration in the eastern U.S. as the Palmer Modified Drought Index, which applied tree-ring reconstructions of available water and instrumental data. Data are modified [<a href="#B4-land-13-00398" class="html-bibr">4</a>,<a href="#B8-land-13-00398" class="html-bibr">8</a>].</p> Full article ">Figure 2
<p>Area (hectares) burned and trendlines by region based on fire records provided by the USDA Forest Service, Washington Office, and Short [<a href="#B30-land-13-00398" class="html-bibr">30</a>]. The eastern region is composed of Minnesota, Iowa, Missouri, Arkansas, and Louisiana and all states eastward. The western region is composed of all states west of the eastern region states. In 1930, ten-fold more hectares burned in the east than the west based on long-term USDA Forest Service fire records (19 million vs. 1.9 million hectares, respectively; <a href="#land-13-00398-f002" class="html-fig">Figure 2</a>). Some of this difference might be due to underreporting in the west associated with lower human population densities, hence fewer fire detections due to remoteness. At any rate, annual hectares burned held steady in the west until a noticeable drop occurred around 1950. In contrast, annual hectares burned by wildfire dropped sharply in the east until the mid-1950s, with slow decreases thereafter. The east and the west had similar total area burned by wildfire during the 1960s to the early 1980s, after which trendlines crossed, with the west surpassing the east in the extent of burning by wildfires. This upward trend separates two regions into the future, as the west is currently burning by wildfires at a similar rate as during the 1930s.</p> Full article ">Figure 3
<p>Modeled species distributions of white-tailed deer that show observed range (outlined) from occurrence records, the climate envelope (temperature and precipitation of occurrences) during 1981–2010 (green), and the likely future climate envelopes during 2071–2100, under three general circulation models and high emissions (non-green colors; modeling followed [<a href="#B107-land-13-00398" class="html-bibr">107</a>]).</p> Full article ">
22 pages, 1839 KiB  
Article
Did Urban Resilience Improve during 2005–2021? Evidence from 31 Chinese Provinces
by Tingting Yang and Lin Wang
Land 2024, 13(3), 397; https://doi.org/10.3390/land13030397 - 21 Mar 2024
Viewed by 906
Abstract
In the context of climate change, various natural disasters and extreme weather events are occurring with increasing frequency. In addition, large-scale urbanization in China poses serious challenges to disaster resilience. The convergence of climate change and large-scale urbanization has made the enhancement of [...] Read more.
In the context of climate change, various natural disasters and extreme weather events are occurring with increasing frequency. In addition, large-scale urbanization in China poses serious challenges to disaster resilience. The convergence of climate change and large-scale urbanization has made the enhancement of urban resilience (UR) an important guideline for current urban development. This study analyzes the UR of 31 provinces in China during 2005–2021 through the entropy method. A UR evaluation index system is constructed from the perspective of population resilience, social resilience, economic resilience, safeguarding facility resilience, and ecological resilience. The results demonstrate the following: (1) The overall performance of UR in China is relatively low, with an average value of 0.2390. (2) Chinese provinces significantly differ in UR levels, with Beijing, Shanghai, Tianjin, Zhejiang, Jiangsu, and Fujian being the top performers and Guangxi, Yunnan, Xinjiang, Gansu, and Tibet being the bottom. (3) From 2005 to 2021, the average UR value of the 31 Chinese provinces significantly improved. (4) Generally, the eastern, middle, and western regions exhibit relatively high, medium, and low average UR values, respectively. These research findings provide valuable references for Chinese policymakers to adopt measures for promoting UR enhancement and urban safety. Full article
(This article belongs to the Special Issue Urban Resilience and Urban Sustainability under Climate Change)
►▼ Show Figures

Figure 1

Figure 1
<p>The flowchart of the research procedures framework.</p> Full article ">Figure 2
<p>UR results for 31 Chinese provinces during the period of 2005–2021.</p> Full article ">Figure 3
<p>Annual overall UR performance of five groups from 2005 to 2021.</p> Full article ">Figure 4
<p>Regional perspective on UR from 2005, 2010, 2015, and 2021.</p> Full article ">Figure 5
<p>Accumulated top five performers and bottom five performers in UR from 2005 to 2021.</p> Full article ">
34 pages, 6935 KiB  
Article
Scenario-Based Land Use and Land Cover Change Detection and Prediction Using the Cellular Automata–Markov Model in the Gumara Watershed, Upper Blue Nile Basin, Ethiopia
by Haile Belay, Assefa M. Melesse and Getachew Tegegne
Land 2024, 13(3), 396; https://doi.org/10.3390/land13030396 - 20 Mar 2024
Cited by 2 | Viewed by 1264
Abstract
Land use and land cover (LULC) change detection and prediction studies are crucial for supporting sustainable watershed planning and management. Hence, this study aimed to detect historical LULC changes from 1985 to 2019 and predict future changes for 2035 (near future) and 2065 [...] Read more.
Land use and land cover (LULC) change detection and prediction studies are crucial for supporting sustainable watershed planning and management. Hence, this study aimed to detect historical LULC changes from 1985 to 2019 and predict future changes for 2035 (near future) and 2065 (far future) in the Gumara watershed, Upper Blue Nile (UBN) Basin, Ethiopia. LULC classification for the years 1985, 2000, 2010, and 2019 was performed using Landsat images along with vegetation indices and topographic factors. The random forest (RF) machine learning algorithm built into the cloud-based platform Google Earth Engine (GEE) was used for classification. The results of the classification accuracy assessment indicated perfect agreement between the classified maps and the validation dataset, with kappa coefficients (K) of 0.92, 0.94, 0.90, and 0.88 for the LULC maps of 1985, 2000, 2010, and 2019, respectively. Based on the classified maps, cultivated land and settlement increased from 58.60 to 83.08% and 0.06 to 0.18%, respectively, from 1985 to 2019 at the expense of decreasing forest, shrubland and grassland. Future LULC prediction was performed using the cellular automata–Markov (CA–Markov) model under (1) the business-as-usual (BAU) scenario, which is based on the current trend of socioeconomic development, and (2) the governance (GOV) scenario, which is based on the Green Legacy Initiative (GLI) program of Ethiopia. Under the BAU scenario, significant expansions of cultivated land and settlement were predicted from 83.08 to 89.01% and 0.18 to 0.83%, respectively, from 2019 to 2065. Conversely, under the GOV scenario, the increase in forest area was predicted to increase from 2.59% (2019) to 4.71% (2065). For this reason, this study recommends following the GOV scenario to prevent flooding and soil degradation in the Gumara watershed. Finally, the results of this study provide information for government policymakers, land use planners, and watershed managers to develop sustainable land use management plans and policies. Full article
(This article belongs to the Special Issue Future Scenarios of Land Use and Land Cover Change)
►▼ Show Figures

Figure 1

Figure 1
<p>Location map of the study area: (<b>a</b>) River basins of Ethiopia, Upper Blue Nile Basin, and Lake Tana subbasin; (<b>b</b>) Lake Tana subbasin, Lake Tana, and Gumara watershed; and (<b>c</b>) Gumara watershed boundary, location of towns, road networks, river networks, and elevation map of the Gumara watershed.</p> Full article ">Figure 2
<p>False color composites (NIR, red and green bands) of Landsat-5/TM (<b>a</b>–<b>c</b>) and Landsat-8/OLI (<b>d</b>) images used for LULC classification for the years (<b>a</b>) 1985, (<b>b</b>) 2000, (<b>c</b>) 2010, and (<b>d</b>) 2019. The deep red areas represent areas covered with scattered plants; the darker red areas represent densely vegetated areas.</p> Full article ">Figure 3
<p>Methodological framework of LULC classification and change detection.</p> Full article ">Figure 4
<p>Methodological framework for future LULC prediction.</p> Full article ">Figure 5
<p>Computed NDVI images of the Gumara watershed for the years (<b>a</b>) 1985, (<b>b</b>) 2000, (<b>c</b>) 2010, and (<b>d</b>) 2019. In the figures, dark greens (maximum NDVI values example, NDVI = 0.4) represent vegetated areas, while dark reds (minimum NDVI values) represent bare soils or agricultural lands.</p> Full article ">Figure 6
<p>Computed SAVI images of the Gumara watershed for the years (<b>a</b>) 1985, (<b>b</b>) 2000, (<b>c</b>) 2010, and (<b>d</b>) 2019. In the figures, dark greens (maximum SAVI values, for example, SAVI ≥ 0.6) represent highly vegetated areas, while dark reds (minimum SAVIvalues) represent bare soils or agricultural lands.</p> Full article ">Figure 7
<p>Map of driver variables: (<b>a</b>) elevation, (<b>b</b>) slope, (<b>c</b>) distance from streams, (<b>d</b>) distance from roads, (<b>e</b>) distance from towns, and (<b>f</b>) evidence likelihood.</p> Full article ">Figure 8
<p>LULC maps of the Gumara watershed for (<b>a</b>) 1985, (<b>b</b>) 2000, (<b>c</b>) 2010, and (<b>d</b>) 2019. The values in the legend indicate the percentage of each LULC class.</p> Full article ">Figure 9
<p>Area of each LULC class in the Gumara watershed for the four historical years (1985, 2000, 2010, and 2019).</p> Full article ">Figure 10
<p>(<b>a</b>) UA and (<b>b</b>) PA assessment results for each class for the LULC maps for the years 1985, 2000, 2010, and 2019.</p> Full article ">Figure 11
<p>Relative variable importance (%) for the four datasets used for mapping LULC in the Gumara watershed: (<b>a</b>) Landsat-5/TM (1985), (<b>b</b>) Landsat-5/TM (2000), (<b>c</b>) Landsat 5/TM (2010), and (<b>d</b>) Landsat-8/OLI (2019).</p> Full article ">Figure 12
<p>Net change (gain-loss) in each LULC class for the four study periods (1985–2000, 2000–2010, 2010–2019, and 1985–2019).</p> Full article ">Figure 13
<p>Contribution of each LULC class to the net change in cultivated land: (<b>a</b>) 1985–2000, (<b>b</b>) 2000–2010, (<b>c</b>) 2010–2019, and (<b>d</b>) 1985–2019.</p> Full article ">Figure 14
<p>Potential for transition: (<b>a</b>) shrubland to cultivated land and (<b>b</b>) cultivated land to settlement. TP is the transition potential. The greater the TP is, the greater the possibility of a transition from one class to another. The gray shaded regions show the orientation gradients of the transition potential, wherein the maximum transitions are oriented along the northeastern part of the watershed for both transitions. The areas bordered by circles indicate the maximum values of transition suitability. The triangle symbol in both of the figures indicates the location of the town Debre Tabor.</p> Full article ">Figure 15
<p>LULC maps (2019): (<b>a</b>) reference LULC map and (<b>b</b>) CA–Markov model-predicted LULC map under the BAU scenario.</p> Full article ">Figure 16
<p>Comparison of the reference (baseline) and predicted areas of the LULC classes in the Gumara watershed in 2019.</p> Full article ">Figure 17
<p>Predicted LULC maps of the Gumara watershed: (<b>a</b>) for 2035 and (<b>b</b>) for 2065 under the BAU scenario; (<b>c</b>) for 2035 and (<b>d</b>) for 2065 under the GOV scenario.</p> Full article ">Figure 18
<p>Net changes (gain-losses): (<b>a</b>) net change (2019–2065) under the BAU scenario and (<b>b</b>) net change (2019–2065) under the GOV scenario.</p> Full article ">
28 pages, 6438 KiB  
Article
Your Favourite Park Is Not My Favourite Park: A Participatory Geographic Information System Approach to Improving Urban Green and Blue Spaces—A Case Study in Edinburgh, Scotland
by Charlotte Wendelboe-Nelson, Yiyun Wang, Simon Bell, Craig W. McDougall and Catharine Ward Thompson
Land 2024, 13(3), 395; https://doi.org/10.3390/land13030395 - 20 Mar 2024
Viewed by 1586
Abstract
Access to urban green and blue spaces (UGBSs) has been associated with positive effects on health and wellbeing; however, the past decades have seen a decline in quality and user satisfaction with UGBSs. This reflects the mounting challenges that many UK cities face [...] Read more.
Access to urban green and blue spaces (UGBSs) has been associated with positive effects on health and wellbeing; however, the past decades have seen a decline in quality and user satisfaction with UGBSs. This reflects the mounting challenges that many UK cities face in providing appropriate public facilities, alongside issues such as health inequalities, an ageing population, climate change, and loss of biodiversity. At present, little is known about the preferences of different population subgroups and, specifically, the UGBSs they visit and the spaces they avoid. Using a public participatory geographic information system (PPGIS), the overall aim of the research presented here was to investigate the preferences of different population subgroups in urban areas, and the UGBSs they visit, using Edinburgh, Scotland as a case study. We created a baseline visitor demographic profile for UGBS use, and highlighted how visitors perceive, physically access, use, and engage with UGBSs. The results revealed considerable variation in UGBS preference: one person’s favourite UGBS may be one that someone else dislikes and avoids. It is clear that adapting UGBSs to suit local communities should not be a ‘one-size-fits-all’ approach. The conflicting views and preferences of different groups of respondents point to the importance of developing policies and park management plans that can accommodate a variety of uses and experiential qualities within individual parks. PPGIS approaches, such as those utilised in this study, offer opportunities to address this issue and provide evidence to increase equitable UGBS usage. Full article
(This article belongs to the Special Issue Managing Urban Green Infrastructure and Ecosystem Services)
►▼ Show Figures

Figure 1

Figure 1
<p>Survey respondents’ home location, divided according to Scottish Index of Multiple Deprivation (SIMD) quintile, from one (most deprived 20%) to five (least deprived 20%).</p> Full article ">Figure 2
<p>UGBSs avoided (279) and visited (1629) by the 531 respondents taking part in the survey.</p> Full article ">Figure 3
<p>The visit count of all the UGBS areas the survey respondents prefer to visit; the darker the blue colour, the more people have chosen the area as a place they like to visit. The map gives an overview of the extended Edinburgh area. Two main UGBS ‘hotspots’ were identified: Holyrood Park (No. 6); and the Hermitage of Braid and Blackford Hill (No. 23).</p> Full article ">Figure 4
<p>The visit count of all the UGBSs the survey respondents avoid visiting. The darker red colours reveal distinct areas that are avoided by the survey population. The main UGBS areas respondents avoided: Princes Street Gardens (No. 8); The Meadows (No. 7); Leith Links (No. 18); and Holyrood Park (No. 6).</p> Full article ">Figure 5
<p>Maps showing the SIMD level of the area where the respondents lived: Quintile 1 contains the 20% most deprived data zones in Scotland (yellow), and quintile 5 contains the 20% least deprived data zones (black). (<b>a</b>) The respondents were asked to mark a place close to their home. (<b>b</b>) The five UGBSs that respondents liked to visit. For each participant, their selected pins are coloured according to the SIMD level of their residence.</p> Full article ">Figure 6
<p>(<b>a</b>) shows distance from the respondents’ home to the UGBSs they like visiting, with the visited spaces grouped according to SIMD category of their residence. (<b>b</b>) shows distance from the respondents’ home to the UGBSs they avoid visiting, with the avoided spaces grouped according to SIMD category of their residence.</p> Full article ">Figure 7
<p>The UGBSs visited by respondents according to household income; low (GBP0–GBP26k), moderate (GBP27–GBP45), high (&gt;GBP45k).</p> Full article ">Figure 8
<p>The UGBSs visited by respondents according to age; young (16–34), middle (35–64), old (65+).</p> Full article ">Figure 9
<p>The UGBSs avoided, divided by gender.</p> Full article ">Figure 10
<p>Respondents’ preferences and reasons for visiting a green/blue space (Likert scale, wherein five is most positive and one most negative).</p> Full article ">Figure 11
<p>Responses to questions about facilities and information provided in green/blue spaces visited.</p> Full article ">Figure 12
<p>Images of Saughton Park, illustrating the use of zoning to accommodate the variation in individuals’ preferences for UGBSs (source: the authors).</p> Full article ">Figure 13
<p>Images of Inverleith Park, illustrating the use of zoning to accommodate the variation in individuals’ preferences for UGBSs (source: the authors).</p> Full article ">Figure A1
<p>Green/blue spaces in Edinburgh. 1: Craigmillar Castle Park; 2: Inch Park; 3: The Braid Hills Golf Course; 4: Craiglockhart Hills; 5: Union Canal; 6: Holyrood Park; 7: The Meadows; 8: Princes Street Gardens; 9: Inverleith Park; 10: Harrison Park; 11: Saughton Park; 12: Corstorphine Hill; 13: Cammo Park; 14: Cramond Seafront; 15: Silverknowes Esplanade; 16: Granton Crescent Park; 17: Wardie Bay; 18: Leith Links; 19: Portobello Beach; 20: Pentland Hills; 21: Calton Hills; 22: Victoria Park; 23: Hermitage of Braid and Blackford Hill.</p> Full article ">Figure A2
<p>Opening question of the TGS Maptionnaire survey: ‘Do you ever visit parks or open spaces?’.</p> Full article ">Figure A3
<p>For the map-based part of the TGS Maptionnaire survey, the respondents were asked to mark the area where they live, the UGBSs they visit most often, and the UGBSs they avoid visiting.</p> Full article ">
20 pages, 2525 KiB  
Article
Spatial Disparity and Residential Assessment of Housing Cost-Burdened Renters
by Hyunjeong Lee
Land 2024, 13(3), 394; https://doi.org/10.3390/land13030394 - 20 Mar 2024
Viewed by 977
Abstract
With the expanding rental sector and rising housing expenses, this research aims to compare the socio-demographic, economic, and housing statuses of renters burdened by housing costs in four regions, and also to explore predictors affecting their residential assessment. Using data from the 2020 [...] Read more.
With the expanding rental sector and rising housing expenses, this research aims to compare the socio-demographic, economic, and housing statuses of renters burdened by housing costs in four regions, and also to explore predictors affecting their residential assessment. Using data from the 2020 Korean Housing Survey, this cross-sectional study identified 245 cost-burdened households whose housing expenses accounted for more than 25% of their total gross income and living expenses. The results revealed that the majority of renters were single-person households residing in single-room occupancy units of multifamily housing, primarily comprising unemployed older adults aged 50 and over. While earning less than half of the minimum wage, the renters’ living expenses fell well below the minimum cost of living, and more than 40% of the expenditure was spent on housing costs, resulting in cost-overburdened households. With the correlation between income, deposit, and rent, the burden of housing costs and the quality of the residential environment varied among regions. Indeed, the residential assessment of the renters was significantly influenced by urban amenities, and both income deficits and excessive housing cost burdens required inclusive and prompt housing interventions including housing assistance, provision of affordable public housing, income transfer, and transitions from renting to Chonsei arrangements. Full article
(This article belongs to the Special Issue Urban Planning and Housing Market II)
►▼ Show Figures

Figure 1

Figure 1
<p>Household economic indices and annual change in household expenditure by consumption purpose (1980–2020); (<b>a</b>) number of households by housing tenure type; (<b>b</b>) distribution of housing tenure types.</p> Full article ">Figure 2
<p>Household economic indices.</p> Full article ">Figure 3
<p>Map of South Korea.</p> Full article ">Figure 4
<p>Geographic distribution of populations and households by 229 administrative districts in 2020; (<b>a</b>) population density; (<b>b</b>) number of population (million); (<b>c</b>) number of households (million).</p> Full article ">
21 pages, 9638 KiB  
Article
Characteristics Analysis and Prediction of Land Use Evolution in the Source Region of the Yangtze River and Yellow River Based on Improved FLUS Model
by Haoyue Gao, Tianling Qin, Qinghua Luan, Jianming Feng, Xiuyan Zhang, Yuhui Yang, Shu Xu and Jie Lu
Land 2024, 13(3), 393; https://doi.org/10.3390/land13030393 - 20 Mar 2024
Viewed by 876
Abstract
Climate change profoundly alters land use in alpine regions, and delving into the evolutionary characteristics of these changes is crucial for the sustainable development of regional land resources and the gradual enhancement of the ecological environment. Taking the source region of the Yangtze [...] Read more.
Climate change profoundly alters land use in alpine regions, and delving into the evolutionary characteristics of these changes is crucial for the sustainable development of regional land resources and the gradual enhancement of the ecological environment. Taking the source region of the Yangtze and Yellow River (SRYAYE) as a case study, we integrate permafrost and snowfall data into the Future Land Use Simulation model (FLUS). Analyzing historical land use, we predict and simulate the land use scenarios for 2030, 2035, and 2060 under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 climates, and conduct a detailed analysis of the scale, composition, and pattern of land use in this area. Scale. The results showed that ① the Kappa coefficient of the improved FLUS model was higher than 0.927, and that the overall accuracy of the simulation was increased by 2.64%; ② the area of forest land and the high-coverage grassland will increase in the future and the center of gravity will migrate to the west, and that the area of moderate and low-coverage grassland will slightly decrease but tend to become green to the west; and ③ the fragmentation degree of the SRYAYE is decreasing, and the influence of human activities on the landscape pattern is weaker than in the past. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Geographical location of the study area (Description: (<b>a</b>–<b>h</b>) are aerial photos of the field in-vestigation in the SRYAYE; (<b>a</b>) is arable land, (<b>b</b>) is forest land, (<b>c</b>) is high coverage grassland, (<b>d</b>) is moderate coverage grassland, (<b>e</b>) is coverage grassland, (<b>f</b>) is waters, (<b>g</b>) is permanent glacier snow land, (<b>h</b>) is urban land).</p> Full article ">Figure 2
<p>Technical routes: (<b>1</b>) Quantitative land use forecasting; (<b>2</b>) FLUS model mechanism and improvement and simulation of the spatial layout of land use; (<b>3</b>) Analysis of the evolution of the scale, composition, and pattern of land use.</p> Full article ">Figure 3
<p>Study of regional land use drivers (2020 as an example).</p> Full article ">Figure 4
<p>Comparison between the improved simulation results of the model and the actual land use in 2020.</p> Full article ">Figure 5
<p>Changes in land use scale of various types from 2000 to 2020.</p> Full article ">Figure 6
<p>Spatial distribution of land use in the SRYAYE from 2000 to 2020.</p> Full article ">Figure 7
<p>Historical land use centroid shifts.</p> Full article ">Figure 8
<p>Scale of future land use under different climate scenarios.</p> Full article ">Figure 9
<p>Variation trend of snow line height from 2000 to 2020 (taking the variation trend of snow line in the SRYE as an example).</p> Full article ">Figure 10
<p>Spatial distribution of land use in different climate scenarios in the SRYAYE in 2030, 2035, and 2060.</p> Full article ">
18 pages, 8690 KiB  
Article
Tracking the Effects of Mangrove Changes and Spartina alterniflora Invasion on Soil Carbon Storage: A Case Study of the Beibu Gulf of Guangxi, China
by Zengshiqi Huang, Huanmei Yao, Mengsi Wang, Yin Liu, Meijun Chen, Maoyuan Zhong and Junchao Qiao
Land 2024, 13(3), 392; https://doi.org/10.3390/land13030392 - 20 Mar 2024
Viewed by 1111
Abstract
In order to clarify the long-term changes in mangroves in the Beibu Gulf of Guangxi and the carbon storage changes after the invasion of Spartina alterniflora (S. alterniflora) in the Dandou Sea area, the Continuous Change Detection and Classification (CCDC) algorithm [...] Read more.
In order to clarify the long-term changes in mangroves in the Beibu Gulf of Guangxi and the carbon storage changes after the invasion of Spartina alterniflora (S. alterniflora) in the Dandou Sea area, the Continuous Change Detection and Classification (CCDC) algorithm combined with feature indices was first used to track the changes. Subsequently, the random forest algorithm was applied to classify each change segment, and then sampling was conducted based on the distribution of S. alterniflora in different invasion years. The results showed that the Kappa coefficient of the classification result of the latest change segment was 0.78. The rapid expansion of S. alterniflora, aquaculture pond construction, and land reclamation activities have led to changes in mangroves, causing a decrease in the area of the mangrove region. A total of 814.57 hectares of mangroves has been converted into other land-cover types, with most pixels undergoing one to two changes, and many of these changes were expected to continue until 2022. An analysis of the distribution characteristics and influencing factors of soil organic carbon (SOC) and soil organic carbon storage (SOCS) at different invasion stages revealed that SOC and SOCS were mainly influenced by soil bulk density, soil moisture content, and electrical conductivity. It was found that S. alterniflora had higher SOC content compared to the mudflats. With the increase in invasion years, S. alterniflora continuously increased the SOC and SOCS content in coastal wetlands. Full article
(This article belongs to the Special Issue Monitoring and Simulation of Wetland Ecological Processes)
►▼ Show Figures

Figure 1

Figure 1
<p>The growth range of mangrove forest in the Beibu Gulf of Guangxi from 1990 to 2022 (<b>a</b>) Beilun River Estuary; (<b>b</b>) Maowei Sea; (<b>c</b>) Dafeng River and Nanliu River; (<b>d</b>) Dandou Sea; (<b>e</b>–<b>g</b>) mangrove field photos.</p> Full article ">Figure 2
<p>Locations of sampling areas (<b>a</b>) in the Mangrove wetlands and Mudflat; (<b>b</b>) in the <span class="html-italic">S. alterniflora</span> wetlands (SA means <span class="html-italic">S. alterniflora</span>, the number behind SA means the invasion ages).</p> Full article ">Figure 3
<p>Land-use classification results for each change segment in the Dandou Sea area (1990–2022) and images of the changes in Shatian Port construction, (<b>a</b>) Landsat image of Shatian Port in 1990; (<b>b</b>) Landsat image of Shatian Port in 2010; (<b>c</b>) Landsat image of Shatian Port in 2013; (<b>d</b>) Google Earth image of Shatian Port in 2022.</p> Full article ">Figure 4
<p>Land-use classification results for each change segment in the Nanliu River area (1990–2022).</p> Full article ">Figure 5
<p>(<b>a</b>) The area of mangrove forest at different start time of change; (<b>b</b>) the area of different change duration of mangroves.</p> Full article ">Figure 6
<p>The distribution characteristics of soil organic carbon content from <span class="html-italic">S. alterniflora</span> with different invasion years, mangrove and mudflat.</p> Full article ">Figure 7
<p>The distribution characteristics of soil organic carbon storage from <span class="html-italic">S. alterniflora</span> with different invasion years, mangrove and mudflat.</p> Full article ">Figure A1
<p>Land-use classification results for each change segment in Maowei Sea region (1990–2022).</p> Full article ">Figure A2
<p>Land-use classification results for each change segment in Dafeng River area (1990–2022).</p> Full article ">Figure A3
<p>Land-use classification results for each change segment in Beilun River Estuary (1990–2022).</p> Full article ">
18 pages, 5994 KiB  
Article
Applying Multi-Sensor Satellite Data to Identify Key Natural Factors in Annual Livestock Change and Winter Livestock Disaster (Dzud) in Mongolian Nomadic Pasturelands
by Sinkyu Kang, Nanghyun Cho, Amartuvshin Narantsetseg, Bolor-Erdene Lkhamsuren, Otgon Khongorzul, Tumendemberel Tegshdelger, Bumsuk Seo and Keunchang Jang
Land 2024, 13(3), 391; https://doi.org/10.3390/land13030391 - 19 Mar 2024
Viewed by 1013
Abstract
In the present study, we tested the applicability of multi-sensor satellite data to account for key natural factors of annual livestock number changes in county-level soum districts of Mongolia. A schematic model of nomadic landscapes was developed and used to select potential drivers [...] Read more.
In the present study, we tested the applicability of multi-sensor satellite data to account for key natural factors of annual livestock number changes in county-level soum districts of Mongolia. A schematic model of nomadic landscapes was developed and used to select potential drivers retrievable from multi-sensor satellite data. Three alternative methods (principal component analysis, PCA; stepwise multiple regression, SMR; and random forest machine learning model, RF) were used to determine the key drivers for livestock changes and Dzud outbreaks. The countrywide Dzud in 2010 was well-characterized by the PCA as cold with a snowy winter and low summer foraging biomass. The RF estimated the annual livestock change with high accuracy (R2 > 0.9 in most soums). The SMR was less accurate but provided better intuitive insights on the regionality of the key factors and its relationships with local climate and Dzud characteristics. Summer and winter variables appeared to be almost equally important in both models. The primary factors of livestock change and Dzud showed regional patterns: dryness in the south, temperature in the north, and foraging resource in the central and western regions. This study demonstrates a synergistic potential of models and satellite data to understand climate–vegetation–livestock interactions in Mongolian nomadic pastures. Full article
(This article belongs to the Special Issue Spatial Big Data for Rangeland Ecology and Management)
►▼ Show Figures

Figure 1

Figure 1
<p>Maps of (<b>a</b>) Sentinel land cover of Mongolia (credit: Impact Observatory, Microsoft, and Esri) with major mountainous ranges and (<b>b</b>) livestock numbers (sheep unit, <math display="inline"><semantics> <mrow> <mo>×</mo> </mrow> </semantics></math> 10,000) and (<b>c</b>) density (sheep unit per ha) in 2009. In (<b>a</b>), altitude (DEM) is expressed in shading, and the red lines are approximate boundaries between the desert steppe (DS), typical steppe (TS), and forest steppe (FS), modified from Tuvshintogtokh [<a href="#B32-land-13-00391" class="html-bibr">32</a>]. The black (<b>a</b>) and grey (<b>b</b>,<b>c</b>) lines are boundaries of <span class="html-italic">aimag</span> and <span class="html-italic">soum</span> districts, respectively.</p> Full article ">Figure 2
<p>A schematic nomadic landscape model describing the climate–vegetation–livestock interactions in Mongolian pasturelands. The subscripts of s and w means summer and winter variables, respectively; T, temperature; P, precipitation; ET, evapotranspiration; GPP, gross primary production; NPP, net primary production; R<sub>a</sub>, autotrophic respiration; biomass, a summation [Σ] of growing season NPP; residue, dead standing biomass; sheep, livestock numbers in a sheep unit; <span class="html-italic">loss</span>, natural mortality (the winter <span class="html-italic">loss</span> is subject to <span class="html-italic">Dzud</span>); <span class="html-italic">slaughter</span>, meat production; <span class="html-italic">otor</span>, emergency long-distance migration. The bold arrows highlight the different biomass conversion processes in summer and winter pastures.</p> Full article ">Figure 3
<p>A study flowchart from data collection to the production of satellite-driven summer and winter variables, validation, and factor analysis and modeling.</p> Full article ">Figure 4
<p>Spatial patterns of the rate of change in livestock numbers and environmental variables: the first column: rate of change in livestock numbers (%) from 2003 to 2010; from second to fifth columns: scores of summer and winter environmental variables from 2003 to 2009, respectively. ‘03~04’ means ‘livestock change rate between 2003 and 2004’, while ‘03’ means ‘summer and winter environmental variable in 2003’. Red and blue areas correspond to negative and positive values, respectively.</p> Full article ">Figure 5
<p>Scatter plots of primary and secondary principal components of the PCA. The same result is illustrated with different categories of (<b>a</b>) year and (<b>b</b>) rate of change in livestock numbers.</p> Full article ">Figure 6
<p>Evaluation and statistics of stepwise multiple regression (SMR) analyses: (<b>a</b>,<b>b</b>) <span class="html-italic">soum</span>-level comparison of observed and predicted annual rate of change in livestock numbers (%) from the FR and SMR models for 2003–2010, respectively; (<b>c</b>,<b>d</b>) percent relative frequency of <span class="html-italic">soum</span>-level primary factors from the FR and SMR models, respectively, using data for 2003–2009 (blue circles, without 2009–2010 <span class="html-italic">Dzud</span> data) and 2003–2010 (dark yellow bars).</p> Full article ">Figure 7
<p>Results of RF (<b>a</b>,<b>c</b>) and SMR (<b>b</b>,<b>d</b>) modeling for 2003–2010: (<b>a</b>) R<sup>2</sup> of RF models; (<b>b</b>) R<sup>2</sup> of SMR models; (<b>c</b>) primary factors of RF models; (<b>d</b>) primary factors of SMR models. The dashed areas are <span class="html-italic">soums</span> excluded from the modeling due to data gaps. The blank areas in (<b>d</b>) are <span class="html-italic">soums</span> where the SMR model was not created.</p> Full article ">Figure 8
<p>The primary factors of the percent change of livestock numbers displayed in temperature–precipitation scatter plots: (<b>a</b>) RF model and (<b>b</b>) SMR model. MAP and MAT are the average values of annual precipitation and temperature from 2003 to 2010, respectively.</p> Full article ">Figure 9
<p>Percentage (%) of each key variable by livestock change classes in 2010: &gt;0, greater than zero; &lt;0, from zero to −15%; &lt;−15, from −15 to −30%; &lt;−30, from −30 to −45%; &lt;−45, below −45%.</p> Full article ">
22 pages, 3764 KiB  
Article
Evaluating the Sustainable Development Goals within Spatial Planning for Decision-Making: A Major Function-Oriented Zone Planning Strategy in China
by Hongpeng Fu, Jiao Liu, Xiaotian Dong, Zhenlin Chen and Min He
Land 2024, 13(3), 390; https://doi.org/10.3390/land13030390 - 19 Mar 2024
Cited by 10 | Viewed by 1658
Abstract
Sustainable Development Goals (SDGs) serve as a reference point in the global policy-making process, with their quantitative evaluation at various scales integrating spatial planning still under exploration. Major Function Oriented Zone (MFOZ) planning in China emerges as an innovative strategy, focusing on ecosystem [...] Read more.
Sustainable Development Goals (SDGs) serve as a reference point in the global policy-making process, with their quantitative evaluation at various scales integrating spatial planning still under exploration. Major Function Oriented Zone (MFOZ) planning in China emerges as an innovative strategy, focusing on ecosystem services to achieve sustainable development. This study takes MFOZ planning as an example, and assesses SDG implementation within the MFOZ framework, focusing on 288 cities. Then, this study analyzes the zoning types of SDG realization status through cluster analysis. Based on this, we explore the influencing factors of the SDGs from the perspective of socioeconomic and environmental characteristics, and ecosystem services, and propose target strategies. The research found that there are four zoning types according to the SDG realization status, including mixed-oriented with high consumption and output (24.3%), non-agriculture-oriented with low consumption and high output (12.5%), agriculture-oriented with low consumption and output (55.9%), and agriculture-oriented with high consumption and output (7.3%) cities. Most cities do not demonstrate high efficiency in resource consumption output, and the realization status of SDGs urgently needs to improve. Socio-economic development during urbanization challenges SDGs, while the traditional environmental measures have limited effects. Ecosystem services could help improve SDGs, including GDP growth rate, and reduce water resource development intensity and carbon emissions. Focusing solely on numerical values of SDGs, such as water efficiency, may harm ecosystem services and go against sustainable development. This research underscores the necessity of adapting SDG strategies to the unique contexts of cities and has practical significance for enabling more targeted and effective strategies for SDG implementation, integrating spatial planning, and aligning local efforts with global sustainability aspirations. Full article
(This article belongs to the Special Issue Renewable Energy and Land Use towards Low-Carbon Transition)
►▼ Show Figures

Figure 1

Figure 1
<p>Study area. Note: The base map is sourced from the Standard Map Service System of the Ministry of Natural Resources, with the base map review number GS (2019) 1823.</p> Full article ">Figure 2
<p>Research Framework.</p> Full article ">Figure 3
<p>Spatial distribution of SDG scores related to agriculture production and urbanization. Note: The base map is sourced from the Standard Map Service System of the Ministry of Natural Resources, with the base map review number GS (2019) 1823.</p> Full article ">Figure 4
<p>Spatial distribution of SDG scores related to urbanization and ecological functions. Note: The base map is sourced from the Standard Map Service System of the Ministry of Natural Resources, with the base map review number GS (2019) 1823.</p> Full article ">Figure 5
<p>Clustering Zones according to SDG scores. Note: The base map is sourced from the Standard Map Service System of the Ministry of Natural Resources, with the base map review number GS (2019) 1823.</p> Full article ">Figure 6
<p>Correlation analysis results of SDGs score and ecosystem services. Note: *, **, *** represent <span class="html-italic">p</span>-value less than 0.05, 0.01 and 0.001.</p> Full article ">
12 pages, 1658 KiB  
Article
The Effects of Implementing Three Climate-Smart Practices with an Integrated Landscape Approach on Functional Connectivity and Carbon Storage
by Juan José Von Thaden, Debora Lithgow, Daniel A. Revollo-Fernández, María del Pilar Salazar-Vargas and Aram Rodríguez de los Santos
Land 2024, 13(3), 389; https://doi.org/10.3390/land13030389 - 19 Mar 2024
Cited by 1 | Viewed by 1269
Abstract
Climate-smart practices are actions that can be implemented without affecting agricultural activities and that can promote these activities, generating direct and indirect benefits in ecosystem services provision and increasing agricultural productivity and private income. The present study evaluated the effect of three climate-smart [...] Read more.
Climate-smart practices are actions that can be implemented without affecting agricultural activities and that can promote these activities, generating direct and indirect benefits in ecosystem services provision and increasing agricultural productivity and private income. The present study evaluated the effect of three climate-smart actions (establishment of isolated trees, recovery of riparian vegetation, and implementation of live fences) on increased functional landscape connectivity and carbon storage. Three scenarios with rates of participation ranging from 5 to 100% were tested in two watersheds with different degrees of conservation and a high priority for national food production in Mexico. The main results suggest climate-smart practices positively impact landscape connectivity and carbon sequestration. However, the improvement in landscape connectivity mainly benefits species of short displacement (50–100 m), and the increase in carbon storage is directly linear to the area implemented in these practices. Also, the effectiveness of the modeled actions depends on the landscape structure, which was implemented with the highest benefits in watersheds with intense agricultural activity. The findings can support decision-makers in selecting the best strategies to increase landscape connectivity and carbon sequestration in productive landscapes. Full article
(This article belongs to the Section Land, Biodiversity, and Human Wellbeing)
►▼ Show Figures

Figure 1

Figure 1
<p>Location of the Ameca–Mascota and Jamapa watersheds and priority sites for improving cattle ranching and agriculture practices.</p> Full article ">Figure 2
<p>Schematic diagram of the workflow carried out in this study.</p> Full article ">Figure 3
<p>PC values for the actual scenario (2022) in Ameca–Mascota and Jamapa.</p> Full article ">Figure 4
<p>(<b>A</b>) Examples of connections between fragments at a threshold of 100 m in Ameca–Mascota for the current scenario (2022) and the integrated scenario (includes the three practices). (<b>B</b>) Probability of connectivity (PC) for a 50% participation scenario in Ameca–Mascota. (<b>C</b>) Probability of connectivity (PC) for a 50% participation scenario in Jamapa.</p> Full article ">
18 pages, 3588 KiB  
Article
Urbanization Effects in Estimating Surface Air Temperature Trends in the Contiguous United States
by Siqi Huang, Guoyu Ren and Panfeng Zhang
Land 2024, 13(3), 388; https://doi.org/10.3390/land13030388 - 18 Mar 2024
Viewed by 896
Abstract
In the past century, local-scale warming caused by a strengthening urban heat island effect has brought inevitable systematic bias to observational data from surface weather stations located in or near urban areas. In this study, the land use situation around U.S. Climate Reference [...] Read more.
In the past century, local-scale warming caused by a strengthening urban heat island effect has brought inevitable systematic bias to observational data from surface weather stations located in or near urban areas. In this study, the land use situation around U.S. Climate Reference Network (USCRN) stations was used as a reference for rural station selection; stations with similar environmental conditions in the U.S. Historical Climatology Network (USHCN) were selected as reference stations using a machine learning method, and then the maximum surface air temperature (Tmax) series, minimum surface air temperature (Tmin) series and mean surface air temperature (Tmean) series of rural stations during 1921–2020 were compared with those for all nearby stations (including both rural and urban stations) to evaluate urbanization effects in the USHCN observation data series of the contiguous United States, which can be regarded as urbanization bias contained in the latest homogenized USHCN observation data. The results showed that the urbanization effect on the Tmean trend of USHCN stations is 0.002 °C dec−1, and the urbanization contribution is 35%, indicating that urbanization around USHCN stations has led to at least one-third of the overall warming recorded at USHCN stations over the last one hundred years. The urbanization effects on Tmax and Tmin trends of USHCN stations are −0.015 °C dec−1 and 0.013 °C dec−1, respectively, and the urbanization contribution for Tmin is 34%. These results have significance for understanding the systematic bias in USHCN temperature data, and they provide a reference for subsequent studies on data correction and climate change monitoring. Full article
(This article belongs to the Section Land–Climate Interactions)
►▼ Show Figures

Figure 1

Figure 1
<p>Percentages of urban land of (<b>a</b>) USHCN rural stations and (<b>b</b>) USHCN urban stations at buffer radii of 1–12 km. r1-r12 on the X-axis label refer to buffer radii of 1-12km around the stations respectively.</p> Full article ">Figure 2
<p>Distribution of the selected USHCN urban stations (red) and rural stations (blue) when the contamination parameter is set as 0.2.</p> Full article ">Figure 3
<p>Distributions of the grid-averaged annual SAT trends for (<b>a</b>) mean temperature (Tmean), (<b>b</b>) mean maximum temperature (Tmax), and (<b>c</b>) mean minimum temperature (Tmin).</p> Full article ">Figure 4
<p>Regional averaged annual mean surface air temperature anomaly series of all USHCN stations (blue) and rural stations (red) for (<b>a</b>) Tmean, (<b>b</b>) Tmax, and (<b>c</b>) Tmin over the contiguous United States. Note that R is the correlation coefficients between temperature anomalies (y) and the serial numbers of years (x).</p> Full article ">Figure 5
<p>Distributions of the urbanization effects of (<b>a</b>) Tmean, (<b>b</b>) Tmax, and (<b>c</b>) Tmin in the contiguous United States.</p> Full article ">Figure 6
<p>SAT anomaly difference series between all stations and rural stations and their trends (urbanization effect, blue line) for (<b>a</b>) Tmean, (<b>b</b>) Tmax, and (<b>c</b>) Tmin in the contiguous United States.</p> Full article ">Figure 7
<p>Distributions of urbanization contributions for (<b>a</b>) Tmean and (<b>b</b>) Tmin. Only the grid cells where the urbanization effect of Tmean and Tmin passed the significance test (<span class="html-italic">p</span> &lt; 0.1) are shown.</p> Full article ">
20 pages, 2807 KiB  
Article
Analysis of Influencing Factors on Farmers’ Willingness to Pay for the Use of Residential Land Based on Supervised Machine Learning Algorithms
by Jiafang Jin, Xinyi Li, Guoxiu Liu, Xiaowen Dai and Ruiping Ran
Land 2024, 13(3), 387; https://doi.org/10.3390/land13030387 - 18 Mar 2024
Viewed by 957
Abstract
Aimed at advancing the reform of the Paid Use of Residential Land, this study investigates the willingness to pay among farmers and its underlying factors. Based on a Logistic Regression analysis of a micro-survey of 450 pieces of data from the Sichuan Province [...] Read more.
Aimed at advancing the reform of the Paid Use of Residential Land, this study investigates the willingness to pay among farmers and its underlying factors. Based on a Logistic Regression analysis of a micro-survey of 450 pieces of data from the Sichuan Province in 2023, we evaluated the effects of three factors, namely individual, regional and cultural forces. Further, Random Forest analysis and SHAP value interpretation refined our insights into these effects. Firstly, the research reveals a significant willingness to pay, with 83.6% of sample farmers being ready to participate in the reform, and 53.1% of them preferring online payment (the funds are mostly expected to be used for village infrastructure improvements). Secondly, the study implies that Individual Force is the most impactful factor, followed by regional and cultural forces. Thirdly, the three factors show different effects on farmers’ willingness to pay from different income groups, i.e., villagers with poorer infrastructure and lower clarity of homestead policy systems tend to be against the reform, whereas farmers with strong urban identity and collective pride support it. Based on these findings, efforts should be made to increase the publicity of Paid Use of Residential Land. Moreover, we should clarify the reform policies, accelerate the development of the online payment platform, use the funds for village infrastructure improvements, and advocate for care-based fee measures for disadvantaged groups. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>An analytical framework of farmers’ willingness based on Concentric Circle Modeling.</p> Full article ">Figure 2
<p>Investigation area.</p> Full article ">Figure 3
<p>Machine learning flowchart.</p> Full article ">Figure 4
<p>Importance rankings of Random Forest variables.</p> Full article ">Figure 5
<p>SHAP Swarm Map. Red boxes highlight areas of particular interest discussed in the text.</p> Full article ">
24 pages, 3335 KiB  
Article
Seminatural Grasslands: An Emblematic Challenge for Nature Conservation in Protected Areas
by Daniela Gigante, Simone Angelucci, Federica Bonini, Federico Caruso, Valter Di Cecco, Domizia Donnini, Luciano Morbidini, Mariano Pauselli, Bernardo Valenti, Andrea Tassi, Marco Vizzari and Luciano Di Martino
Land 2024, 13(3), 386; https://doi.org/10.3390/land13030386 - 18 Mar 2024
Viewed by 1380
Abstract
Seminatural grasslands are among the most threatened habitats in Europe and worldwide, mainly due to changes in/abandonment of their traditional extensive use by grazing animals. This study aimed to develop an innovative model that integrates plant biodiversity, animal husbandry, and geo-informatics to manage [...] Read more.
Seminatural grasslands are among the most threatened habitats in Europe and worldwide, mainly due to changes in/abandonment of their traditional extensive use by grazing animals. This study aimed to develop an innovative model that integrates plant biodiversity, animal husbandry, and geo-informatics to manage and preserve seminatural grasslands in protected areas. With this objective, an integrated study was conducted on the seminatural grasslands in the hilly, montane, and (to a minimum extent) subalpine belts of the Maiella National Park, one of Europe’s most biodiversity-rich protected sites. Plant biodiversity was investigated through 141 phytosociological relevés in homogeneous areas; the pastoral value was calculated, and grasslands’ productivity was measured together with the main nutritional parameters. Uni- and multivariate statistical analyses were performed to identify the main grassland vegetation types, their indicator species and ecological–environmental characteristics, and their pastoral and nutritional values’ variability and differences. A total of 17 grassland types, most of which correspond to habitat types listed in Annex I to the 92/43/EEC Directive, were identified and characterised in terms of their biodiversity and potential animal load. To allow for near-real-time analysis of grasslands, an NDVI-based web interface running on Google Earth Engine was implemented. This integrated approach can provide decision-making support for protected-area managers seeking to develop and implement sustainable grassland management practices that ensure the long-term maintenance of their biodiversity. Full article
(This article belongs to the Special Issue High-Altitude Grasslands: Biodiversity Conservation, Management, and Habitat Preference)
►▼ Show Figures

Figure 1

Figure 1
<p>Map of the study area and location of the MNP borders and sampling plots; in the top right insert: location of the MNP (red point) in Italy and Europe. Administrative boundaries: Eurostat, EuroGeographics (available at <a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units" target="_blank">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units</a>, accessed on 3 February 2024).</p> Full article ">Figure 2
<p>Variability in relevant environmental parameters for the 17 identified grassland types, along with the total plant cover: (<b>a</b>) slope, expressed as degrees (°), (<b>b</b>) rockiness and stoniness, expressed as percentage (%), (<b>c</b>) bare soil, expressed as percentage (%), (<b>d</b>) total vegetation cover, expressed as percentage (%). The full names of the grassland types are reported in <a href="#app1-land-13-00386" class="html-app">Table S1</a>.</p> Full article ">Figure 3
<p>Variability in biodiversity parameters for the 17 identified grassland types: (<b>a</b>) number of species per standard survey unit (sampling plot: 4 × 4 m<sup>2</sup>), (<b>b</b>) Shannon index, (<b>c</b>) Simpson index, (<b>d</b>) equitability index. The full names of the grassland types are reported in <a href="#app1-land-13-00386" class="html-app">Table S1</a>.</p> Full article ">Figure 4
<p>Variability in the calculated pastoral value (PV) based on the “visual estimation” method for each identified grassland type (<b>a</b>); statistically significant differences (<b>b</b>) were tested by Kruskal–Wallis one-way non-parametric ANOVA test (H χ2 = 96.65, <span class="html-italic">p</span> &lt; 0.001) and Mann–Whitney pairwise (*** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.001, * <span class="html-italic">p</span> &lt; 0.05, ns: not significant) tests. The full names of the grassland types are reported in <a href="#app1-land-13-00386" class="html-app">Table S1</a>. Data from an additional relevé (“hay_mea”) from the study area are included in the chart for comparison, referring to a hay meadow that is not grazed and is representative of a slightly fertilized lawn not used as pasture.</p> Full article ">Figure 5
<p>Reduced Major Axis Regression between the PVs calculated by the “visual estimation” (PV-ve) and the “point quadrat” (PV-pq) methods (95% bootstrapped confidence intervals, <span class="html-italic">n</span> = 1999, r = 0.8, r<sup>2</sup> = 0.6, <span class="html-italic">p</span> &lt; 0.001) calculated on log-transformed data.</p> Full article ">Figure 6
<p>Nutritional parameters expressed as % (average ± standard deviation) measured on the collected biomass for every grassland type; in brackets is the number of samples per grassland type. Data from an additional relevé (“hay_mea”) from the study area are included in the chart for comparison, referring to a hay meadow that is not grazed and is representative of a slightly fertilized lawn not used as pasture. Legend: ethereal extract (EE), crude protein (CP), non-fibre carbohydrates (NFCs), hemicellulose (HEM), cellulose (CEL), acid detergent fibre (ADF), neutral detergent fibre (NDF). The full names of the grassland types are reported in <a href="#app1-land-13-00386" class="html-app">Table S1</a>.</p> Full article ">
17 pages, 6208 KiB  
Article
Evaluation of Soil Hydraulic Properties in Northern and Central Tunisian Soils for Improvement of Hydrological Modelling
by Asma Hmaied, Pascal Podwojewski, Ines Gharnouki, Hanene Chaabane and Claude Hammecker
Land 2024, 13(3), 385; https://doi.org/10.3390/land13030385 - 18 Mar 2024
Viewed by 1148
Abstract
The hydrological cycle is strongly affected by climate changes causing extreme weather events with long drought periods and heavy rainfall events. To predict the hydrological functioning of Tunisian catchments, modelling is an essential tool to estimate the consequences on water resources and to [...] Read more.
The hydrological cycle is strongly affected by climate changes causing extreme weather events with long drought periods and heavy rainfall events. To predict the hydrological functioning of Tunisian catchments, modelling is an essential tool to estimate the consequences on water resources and to test the sustainability of the different land uses. Soil physical properties describing water flow are essential to feed the models and must therefore be determined all over the watershed. A simple but robust ring infiltration method combined with particle size distribution (PSD) analysis (BEST method) was used to evaluate and derive the retention properties and the hydraulic conductivities. Physically based and statistical pedotransfer functions based on PSD were compared to test their potential use for different types of Tunisian soils. The functional sensitivity of these parameters was assessed by employing the Hydrus-1D software (PC Progress, Prague, Czech Republic) for water balance computations. This evaluation process involved testing the responsiveness and accuracy of the parameters in simulating various water balance components within the model. The evaluation of soil hydraulic parameters across the three used models highlighted significant variations, demonstrating distinct characteristics in each model. While notable differences were evident overall, intriguing similarities emerged, particularly regarding saturated hydraulic conductivity between BEST and Rosetta, and the shape parameter (n) between Arya–Paris and Rosetta. These parallels indicate shared hydraulic properties among the models, underscoring areas of agreement amid their diverse results. Significant differences were shown for scale parameter α for the various methods employed. Marginal differences in evaporation and drainage were observed between the BEST and Arya–Paris methods, with Rosetta distinctly highlighting a disparity between physically based models and statistical models. Full article
(This article belongs to the Special Issue Advances in Hydro-Sedimentological Modeling for Simulating LULC)
►▼ Show Figures

Figure 1

Figure 1
<p>Location of the study area.</p> Full article ">Figure 2
<p>Soil samples in the soil texture triangle.</p> Full article ">Figure 3
<p>Experimental design plan. PSD is particle size distribution and <math display="inline"><semantics> <mrow> <mi>E</mi> <msub> <mi>T</mi> <mn>0</mn> </msub> </mrow> </semantics></math> is reference evapotranspiration.</p> Full article ">Figure 4
<p>Daily rainfall and reference evapotranspiration <math display="inline"><semantics> <msub> <mi>ET</mi> <mn>0</mn> </msub> </semantics></math> measured in Kamech weather station, used for modelling in HYDRUS-1D.</p> Full article ">Figure 5
<p>The van Genuchten parameters determined for different methods. (<b>a</b>) Scale parameter <math display="inline"><semantics> <mi>α</mi> </semantics></math>, (<b>b</b>) shape parameter <span class="html-italic">n</span>, (<b>c</b>) saturated hydraulic conductivity <math display="inline"><semantics> <msub> <mi>K</mi> <mi>s</mi> </msub> </semantics></math> for the different soil samples.</p> Full article ">Figure 6
<p>Examples of retention curves obtained with the different methods, for silty clay soil (full lines) and sandy soil (dotted lines).</p> Full article ">Figure 7
<p>Example of water flow modelling result obtained with HYDRUS−1D for 3 years.</p> Full article ">Figure 8
<p>Results for cumulative (<b>a</b>) evaporation, (<b>b</b>) runoff, (<b>c</b>) infiltration and (<b>d</b>) drainage for the different soil samples.</p> Full article ">
17 pages, 10763 KiB  
Article
How Do Ecological Restoration Projects Affect Trade-Offs and Synergies between Ecosystem Services?
by Yuhui Ji, Miaomiao Xie, Yunxuan Liu, Renfen Zhu, Zhuoyun Tang and Rongwei Hu
Land 2024, 13(3), 384; https://doi.org/10.3390/land13030384 - 18 Mar 2024
Cited by 1 | Viewed by 1409
Abstract
Scientific ecosystem management requires the clarification of the synergic and trade-off relationship between ecosystem services, particularly in the environmentally delicate Loess Plateau region. Previous studies have indirectly deduced that ecological restoration projects affect ESRs by analyzing their impacts on ecosystem services, but there [...] Read more.
Scientific ecosystem management requires the clarification of the synergic and trade-off relationship between ecosystem services, particularly in the environmentally delicate Loess Plateau region. Previous studies have indirectly deduced that ecological restoration projects affect ESRs by analyzing their impacts on ecosystem services, but there is no direct evidence from the existing research to show whether and to what extent different ecological restoration projects have an impact on trade-off synergies, which weakens the explanatory strength of ecological restoration projects as an important factor affecting ESRs. In this study, based on the spatial mapping of three pairs of relationships between three typical ESs in Fugu County, Shaanxi Province, and the relative contribution of each ecological restoration projects, as well as Ecosystem services and the relationship between them, were explored through the boosted regression tree modeling (BRT). This study proved that different ecological restoration projects have different impacts on ESRs. The results indicated that the three pairs of ESRs obtained among the three ecosystem services in Fugu County could be categorized into two types. The relationship between carbon storage and soil conservation and the relationship between carbon storage and water conservation CS–WC were spatially predominantly trade-offs, and their spatial distributions were highly similar. Various ecological restoration projects have varying effects on ESRs. The connection between ecological restoration projects and ESRs involves a nonlinear transformation, and the change varies from project to project. Based on the above findings, this study further explores the influence process of various types of ecological restoration projects on ESRs, and provides scientific support for optimizing ecosystem management and comprehensive management of the region. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Overview of the study area. (<b>a</b>) Location of the study area; (<b>b</b>) altitude; (<b>c</b>) ecological restoration projects. GGP: the Grain for Green Project; CDP: Check Dam Project; SCECP: Sloping Cropland Ecological Construction Project; MGERP: Mining Geo-Environmental Restoration Project; ECCP: Eco-corridor Construction Project; DP: Desertification Project; CSECP: Comprehensive Soil Erosion Control Project; TP: Terracing Project; HSFCP: High-standard Farmland Construction Project; BTSSP: Beijing–Tianjin Sandstorm Source Project; NSTP: Naked Slope Treatment Project.</p> Full article ">Figure 2
<p>Spatial distribution of ecosystem services in Fugu County.</p> Full article ">Figure 3
<p>Changes in total ecosystem services in five major basins of Fugu County in 1990, 2000, 2010, and 2020. (<b>a</b>) Changes in carbon storage services in five major watersheds in Fugu County in 1990, 2000, 2010, and 2020. (<b>b</b>) Changes in soil conservation services in five watersheds in False Valley County in 1990, 2000, 2010, and 2020. (<b>c</b>) Changes in water conservation services in five major watersheds in Fugu County in 1990, 2000, 2010, and 2020.</p> Full article ">Figure 4
<p>Changes in ESs in Fugu County in 1990, 2000, 2010, and 2020. (<b>a</b>) Changes in carbon storage services in Fugu County in 1990, 2000, 2010, and 2020. (<b>b</b>) Changes in soil conservation services in Fugu County in 1990, 2000, 2010, and 2020. (<b>c</b>) Changes in water conservation services in Fugu County in 1990, 2000, 2010, and 2020.</p> Full article ">Figure 5
<p>Spatial patterns of ESRs in (<b>a</b>–<b>c</b>) 1990–2020.</p> Full article ">Figure 6
<p>The relative contribution of each ecological restoration project. GGP: the Grain for Green project; CDP: Check Dam Project; SCECP: Sloping Cropland Ecological Construction Project; MGERP: Mining Geo-Environmental Restoration Project; ECCP: Eco-corridor Construction Project; DP: Desertification Project; CSECP: Comprehensive Soil Erosion Control Project; TP: Terracing Project; HSFCP: High-standard Farmland Construction Project; BTSSP: Beijing–Tianjin Sandstorm Source Project; NSTP: Naked Slope Treatment Project. (<b>a</b>) Ranking of the contribution of each ecological restoration project to the relationship between carbon storage and water conversation; (<b>b</b>) Ranking of the contribution of each ecological restoration project to the relationship between carbon storage and soil conservation.</p> Full article ">Figure 7
<p>Relationship between each ecological restoration project and ESRs. (<b>a</b>) Non-linear effects of ecological restoration projects on the relationship between carbon storage and water conservation; (<b>b</b>) Non-linear effects of ecological restoration projects on the relationship between carbon storage and soil conservation. The solid line in the figure represents the actual non-linear process of change in the impact of ecological restoration projects on the trade-off synergistic relationship between ecosystem services, and the dashed line represents the fitted non-linear process of change.</p> Full article ">
20 pages, 12834 KiB  
Article
Research on Adaptive Reuse Strategy of Industrial Heritage Based on the Method of Social Network
by Jinghua Song, Junyang Chen, Xiu Yang and Yuyi Zhu
Land 2024, 13(3), 383; https://doi.org/10.3390/land13030383 - 18 Mar 2024
Cited by 2 | Viewed by 1846
Abstract
With the deceleration of urban expansion, the adaptive reuse of industrial heritage buildings has emerged as a novel area of research. In previous times, the majority of approaches to adapting industrial heritage buildings relied on experiential knowledge, which lacked the ability to objectively [...] Read more.
With the deceleration of urban expansion, the adaptive reuse of industrial heritage buildings has emerged as a novel area of research. In previous times, the majority of approaches to adapting industrial heritage buildings relied on experiential knowledge, which lacked the ability to objectively assess the relationship between spaces and engage in rational planning. However, the social network analysis method offers an objective and comprehensive means of perceiving the spatial structure and analyzing its issues from a detached perspective. This study presents a proposal for addressing three spatial challenges encountered during the conversion of industrial heritage buildings into public buildings. It also suggests spatial optimization strategies to overcome these challenges. The Sanlinqiao Thermal Bottle Factory is selected as the research subject, and a spatial network structure model is constructed to analyze the existing issues using the social network analysis method. The proposed spatial optimization strategies are then applied, and the optimized space is evaluated through a re-analysis of the spatial layout. The spatial utilization rate has been significantly improved, leading to an effective enhancement of the spatial vitality of the site. This study presents a spatial strategy aimed at converting industrial heritage buildings into public buildings, thereby offering valuable insights for similar projects involving the transformation of industrial heritage sites. Full article
(This article belongs to the Special Issue Advances in Landscape Perception Based on New Approaches & Technologies)
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>top left</b>): Original view of Shanghai Nippon Sanitary Bottle Factory. (<b>top right</b>): Big chimney in the site. (<b>bottom left</b>): Big space without columns and high ceiling. (<b>bottom right</b>): Original view of the interior space of the factory. Source: <a href="https://m.thepaper.cn/newsDetail_forward_14466796" target="_blank">https://m.thepaper.cn/newsDetail_forward_14466796</a> (accessed on 10 November 2023).</p> Full article ">Figure 2
<p>Project site.</p> Full article ">Figure 3
<p>Current spatial division of the Sanlinqiao.</p> Full article ">Figure 4
<p>Spatial relationship data matrix for the current state of the Sanlinqiao.</p> Full article ">Figure 5
<p>Analysis of two-clique network factions in the current space of Sanlinqiao.</p> Full article ">Figure 6
<p>The current spatial network of Sanlinqiao is divided into cliques.</p> Full article ">Figure 7
<p>(<b>left</b>): Betweenness centrality values. (<b>right</b>): Betweenness centrality visualization.</p> Full article ">Figure 8
<p>Analysis of the current spatial network cut-points in the Sanlinqiao.</p> Full article ">Figure 9
<p>Rendering of the renovated Sanlinqiao Community Center Project.</p> Full article ">Figure 10
<p>Floor plan of the ground floor of the renovated Sanlinqiao Community Center Project.</p> Full article ">Figure 11
<p>Second floor plan of the renovated Sanlinqiao Community Center Project.</p> Full article ">Figure 12
<p>Spatial division of the renovated Sanlinqiao. (<b>a</b>) First-floor plan space division. (<b>b</b>) Second-floor plan space division.</p> Full article ">Figure 13
<p>Spatial relationship of the Sanlinqiao after modification. (<b>top left</b>): Ground floor (nodes are directly linked by proximity and line-of-sight access). (<b>top right</b>): One layer (with spatial overlap between nodes as a direct link). (<b>bottom left</b>): Second level (nodes are directly linked by proximity and line-of-sight access). (<b>bottom right</b>): Second level (with spatial overlap between nodes as a direct link).</p> Full article ">Figure 14
<p>Analysis of spatial network cut-points in the revamped Sanlinqiao.</p> Full article ">Figure 15
<p>Analysis of spatial network faction in Sanlinqiao after renovation.</p> Full article ">Figure 16
<p>Spatial network intermediate centrality after Sanlinqiao modification.</p> Full article ">Figure 17
<p>Proximity centrality of spatial network after Sanlinqiao modification.</p> Full article ">
15 pages, 3569 KiB  
Article
Comparative Effects of Wild Boar (Sus scrofa) Rooting on the Chemical Properties of Soils in Natural and Post-Fire Environments of the Edough Forest Massif (Northeastern Algeria)
by Kamelia Hesni Benotmane, Mehdi Boukheroufa, Rym Sakraoui, Feriel Sakraoui, Csaba Centeri, Ádám Fehér and Krisztián Katona
Land 2024, 13(3), 382; https://doi.org/10.3390/land13030382 - 17 Mar 2024
Viewed by 1291
Abstract
Wild boars use a wide range of habitats. Their invasive nature is gaining attention due to the complexity of its impact. The goal of this research is to analyze the impact of the wild boar on the chemical properties of soils in a [...] Read more.
Wild boars use a wide range of habitats. Their invasive nature is gaining attention due to the complexity of its impact. The goal of this research is to analyze the impact of the wild boar on the chemical properties of soils in a natural and a post-fire forest in the Edough Forest Massif in Algeria. This study compares the impact of wild boar rooting on soil parameters to determine the functional role of the wild boar. The research was conducted during the winter of 2022. The study sites included a natural forest and a post-fire area. Rooting tracks were geolocated and soil samples were collected. The results show significant differences between rooted and control patches in the chemical parameters measured in the two environments. However, in the natural environment, significant differences were only noted for the calcium content and electrical conductivity. But in the post-fire environment, strong significant differences were observed for all measured parameters, suggesting that wild boars do not exert a noticeable soil homogenization effect on the soil properties. This research highlights the importance of understanding and managing the impact of wild boars in natural and post-fire forests on soil formation processes, the diversity of soil properties, and their magnitude. Full article
(This article belongs to the Special Issue Land Use/Land Cover and Natural Hazards: Interactions, Changes, and Impacts II)
►▼ Show Figures

Figure 1

Figure 1
<p>The study area with sampling sites of wild boar rooting in the north-east corner of Algeria (A: Aïn Barbar; B: Bouzizi. Source: Google Earth).</p> Full article ">Figure 2
<p>Photos of the two study sites. (<b>A</b>): natural site of Bouzizi, (<b>B</b>): post-fire site of Aïn Barbar (on the right) (Photo: Kamelia Benotmane, 5 January 2022).</p> Full article ">Figure 3
<p>The photo of the rooting where standard measurements were taken in each rooting, ring, and nearby control area (Photo: Benotmane, K., January 2021, Edough).</p> Full article ">Figure 4
<p>Boxplots showing the distribution of the measured soil parameters originating from Bouzizi, where wild boar rooting but no wildfire occurred. (Boxes show the interquartile range, the black line in the box shows the median value, whiskers show the maximum and minimum values except for outliers, and dots show outlier values.)</p> Full article ">Figure 5
<p>Boxplots showing the distribution of the measured soil parameters originating from Aïn Barbar, where wild boar rooting occurred after a wildfire. (Boxes show the interquartile range, the black line in the box shows the median value, whiskers show the maximum and minimum values except for outliers, and dots show outlier values.)</p> Full article ">Figure 6
<p>Interaction plots of rooting and site effects on potassium, magnesium, and sodium content of the soil. The solid line with black points indicates data collected from Aïn Barbar, the dotted line with black triangles indicates data collected from Bouzizi.</p> Full article ">Figure 7
<p>Interaction plots of rooting and site effects on calcium, total nitrogen, and phosphorus content of the soil. The solid line with black points indicates data collected from Aïn Barbar, the dotted line with black triangles indicates data collected from Bouzizi.</p> Full article ">Figure 8
<p>Interaction plots of rooting and site effects on electric conductivity and pH of the soil. The solid line with black points indicates data collected from Aïn Barbar, the dotted line with black triangles indicates data collected from Bouzizi.</p> Full article ">Figure 9
<p>Principal component analysis of the measured parameters of the soil in the natural forest of Bouzizi, Algeria. First component: the chemical elements; second component: rooting, ring, and control.</p> Full article ">Figure 10
<p>Principal component analysis of the measured parameters of the soil in the post-fire forest of Aïn Barbar, Algeria. First component: the chemical elements; second component: rooting, ring, and control.</p> Full article ">
25 pages, 11963 KiB  
Article
Spatio-Temporal Dynamics of Carbon Emissions and Their Influencing Factors at the County Scale: A Case Study of Zhejiang Province, China
by Xuanli Wang, Huifang Yu, Yiqun Wu, Congyue Zhou, Yonghua Li, Xingyu Lai and Jiahao He
Land 2024, 13(3), 381; https://doi.org/10.3390/land13030381 - 17 Mar 2024
Viewed by 1276
Abstract
Significant carbon emissions, a key contributor to global climate warming, pose risks to ecosystems and human living conditions. It is crucial to monitor the spatial and temporal patterns of carbon emissions at the county level to reach the goals of carbon peak and [...] Read more.
Significant carbon emissions, a key contributor to global climate warming, pose risks to ecosystems and human living conditions. It is crucial to monitor the spatial and temporal patterns of carbon emissions at the county level to reach the goals of carbon peak and neutrality. This study examines carbon emissions and economic and social problems data from 89 counties in Zhejiang Province. It employs analytical techniques such as LISA time path, spatio-temporal transition, and standard deviational ellipse to investigate the trends of carbon emissions from 2002 to 2022. Furthermore, it utilizes the GTWR model to evaluate the factors that influence these emissions on a county scale. The findings reveal the following: (1) The LISA time path analysis indicates a pronounced local spatial structure in the distribution of carbon emissions in Zhejiang Province from 2002 to 2022, characterized by increasing stability, notable path dependency, and some degree of spatial integration, albeit with a diminishing trend in overall integration. (2) The LISA spatio-temporal transition analysis indicates significant path dependency or lock-in effects in the county-level spatial clustering of carbon emissions. (3) Over the period 2002–2022, the centroid of carbon emissions in Zhejiang’s counties mainly oscillated between 120°55′15″ E and 120°57′01″ E and between 29°55′52″ N and 29°59′11″ N, with a general northeastward shift forming a “V” pattern. This shift resulted in a stable “northeast–southwest” spatial distribution. (4) Factors such as population size, urbanization rate, and economic development level predominantly accelerate carbon emissions, whereas industrial structure tends to curb them. It is crucial to customize carbon mitigation plans to suit the circumstances of each county. This study provides insight into the spatial and temporal patterns of carbon emissions at the county level in Zhejiang Province. It offers crucial guidance for developing targeted and practical strategies to reduce carbon emissions. Full article
(This article belongs to the Special Issue Urban Planning Pathways to Carbon Neutrality)
►▼ Show Figures

Figure 1

Figure 1
<p>Schematic of research framework.</p> Full article ">Figure 2
<p>Study area and administrative division.</p> Full article ">Figure 3
<p>(<b>a</b>–<b>d</b>) The relative lengths of LISA time paths in counties of Zhejiang Province from 2002 to 2022.</p> Full article ">Figure 4
<p>(<b>a</b>–<b>d</b>). The LISA time path curvature of counties in Zhejiang Province from 2002 to 2022.</p> Full article ">Figure 5
<p>(<b>a</b>–<b>d</b>). The LISA spatio-temporal transition direction of counties in Zhejiang Province from 2002 to 2022.</p> Full article ">Figure 6
<p>The distribution of standard deviational ellipse and gravity center shift trajectory.</p> Full article ">Figure 7
<p>(<b>a</b>–<b>d</b>). Time series trend of GTWR regression coefficients.</p> Full article ">Figure 8
<p>(<b>a</b>–<b>d</b>). Spatial distribution pattern of coefficients of factors influencing carbon emissions in the county areas of Zhejiang Province.</p> Full article ">
24 pages, 30930 KiB  
Article
Understanding the Sustainable Mechanisms of Poverty Alleviation Resettlement in China’s Developed Regions under the Background of Land Relocation: Drivers, Paths and Outcomes
by Kang Cao, Ronglu Yang, Pengyu Zhu, Xingman Zhang, Keyu Zhai and Xing Gao
Land 2024, 13(3), 380; https://doi.org/10.3390/land13030380 - 17 Mar 2024
Viewed by 1196
Abstract
In the context of land relocation, poverty alleviation resettlement (PAR) is considered an effective approach to improve the man–land relationship and development issues. However, current studies pay little attention to PAR and its spillover effects within developed regions. Furthermore, the complete mechanism chain [...] Read more.
In the context of land relocation, poverty alleviation resettlement (PAR) is considered an effective approach to improve the man–land relationship and development issues. However, current studies pay little attention to PAR and its spillover effects within developed regions. Furthermore, the complete mechanism chain has received little research concentration. Thus, employing a qualitative survey, this study aims to investigate the overall mechanisms of developed regions’ PAR in the context of land relocation. The study will deal with the following questions: Why does PAR occur in developed regions? How does the resettlement approach to poverty alleviation (i.e., paths)? What are the effects of resettlement on poverty alleviation, including its own effects and spillover effects? Through answering these questions, this study will highlight PAR in developed regions and investigate the spillovers from social, economic and ecological perspectives. Particularly, a comprehensive mechanism analysis framework for PAR will be presented to motivate future studies. Results indicate that PAR is generally caused by ecological poverty alleviation, geological disaster prevention and county town urbanisation promotion and that emigration and resettlement are the paths to PAR. In addition, the direct outcome is the overall rise in the number of resettlers over time, and the spillovers show the sustainable collaboration of economic, social and ecological dimensions. These findings will influence future land reform and housing initiatives. Full article
(This article belongs to the Special Issue Land Development and Socio-Spatial Differentiation in Metropolitan Fringe Areas)
►▼ Show Figures

Figure 1

Figure 1
<p>Location of Qingyuan county. (Source: self-drawn by the author).</p> Full article ">Figure 2
<p>Geological hazard vulnerability zoning map in Qingyuan county. (Source: redrawn from the Qingyuan County 14th Five-year Plan of geological disaster prevention and control).</p> Full article ">Figure 3
<p>Location of resettlement community. (Source: self-drawn by the author).</p> Full article ">Figure 4
<p>Changes in the number of people relocated in Qingyuan County since 2003. (Data source: interviews with the government officials of the resettlement functional department of Qingyuan).</p> Full article ">Figure 5
<p>Distribution of the relocated population in concentrated resettlement of the PAPPL project (until the end of 2022). (Source: self-drawn by the author).</p> Full article ">Figure 6
<p>Comparison of per capita disposable income and consumption expenditure of rural permanent residents in Qingyuan county from 2015 to 2021. (Source: statistical bulletin on national economic and social development of Qingyuan county in previous years).</p> Full article ">Figure 7
<p>Changes in the rural permanent resident population and urbanization rate in Qingyuan County since 2006. (Data source: Zhejiang Provincial Statistical Yearbook, Lishui City Statistical Yearbook in previous years).</p> Full article ">Figure 8
<p>Comprehensive analysis of the current condition of the Tongxin resettlement community in Qingyuan county. (Source: self-drawn by the author).</p> Full article ">Figure 9
<p>Distribution of public service facilities around the resettlement communities in Qingyuan county. (Source: self-drawn by the author).</p> Full article ">Figure 10
<p>Changes in the annual scale of reclaimed new arable land of the PAPPL project. (Source: interviews with local officials).</p> Full article ">Figure 11
<p>Mechanism of developed regions’ PAR. (Source: self-drawn by the author).</p> Full article ">
22 pages, 2724 KiB  
Review
A Systematic Review on Digital Soil Mapping Approaches in Lowland Areas
by Odunayo David Adeniyi, Hauwa Bature and Michael Mearker
Land 2024, 13(3), 379; https://doi.org/10.3390/land13030379 - 17 Mar 2024
Cited by 1 | Viewed by 2238
Abstract
Digital soil mapping (DSM) around the world is mostly conducted in areas with a certain relief characterized by significant heterogeneities in soil-forming factors. However, lowland areas (e.g., plains, low-relief areas), prevalently used for agricultural purposes, might also show a certain variability in soil [...] Read more.
Digital soil mapping (DSM) around the world is mostly conducted in areas with a certain relief characterized by significant heterogeneities in soil-forming factors. However, lowland areas (e.g., plains, low-relief areas), prevalently used for agricultural purposes, might also show a certain variability in soil characteristics. To assess the spatial distribution of soil properties and classes, accurate soil datasets are a prerequisite to facilitate the effective management of agricultural areas. This systematic review explores the DSM approaches in lowland areas by compiling and analysing published articles from 2008 to mid-2023. A total of 67 relevant articles were identified from Web of Science and Scopus. The study reveals a rising trend in publications, particularly in recent years, indicative of the growing recognition of DSM’s pivotal role in comprehending soil properties in lowland ecosystems. Noteworthy knowledge gaps are identified, emphasizing the need for nuanced exploration of specific environmental variables influencing soil heterogeneity. This review underscores the dominance of agricultural cropland as a focus, reflecting the intricate relationship between soil attributes and agricultural productivity in lowlands. Vegetation-related covariates, relief-related factors, and statistical machine learning models, with random forest at the forefront, emerge prominently. The study concludes by outlining future research directions, highlighting the urgency of understanding the intricacies of lowland soil mapping for improved land management, heightened agricultural productivity, and effective environmental conservation strategies. Full article
(This article belongs to the Special Issue Digital Soil Mapping, Decision Support Tools and Soil Monitoring Systems in the Mediterranean)
►▼ Show Figures

Figure 1

Figure 1
<p>Schematic overview of the screening process applied to the articles examined for this study.</p> Full article ">Figure 2
<p>Trend of the number of articles published.</p> Full article ">Figure 3
<p>Geographic distribution of the number of articles published.</p> Full article ">Figure 4
<p>Percentage of land use from the articles published.</p> Full article ">Figure 5
<p>Percentage of targeted variables in the articles reviewed.</p> Full article ">Figure 6
<p>Percentage of environmental covariates in the articles reviewed.</p> Full article ">Figure 7
<p>Percentage of important variables in the articles reviewed.</p> Full article ">Figure 8
<p>DSM models used in the reviewed articles.</p> Full article ">Figure 9
<p>Evaluation techniques used in the reviewed articles.</p> Full article ">
15 pages, 2008 KiB  
Article
Spatial Distribution of Soil Organic Carbon in the Forests of Nepal
by Rajesh Malla and Prem Raj Neupane
Land 2024, 13(3), 378; https://doi.org/10.3390/land13030378 - 17 Mar 2024
Cited by 1 | Viewed by 1679
Abstract
Soil organic carbon (SOC) is the major constituent of the soil organic matter. SOC stocks are determined by several factors such as altitude, slope, aspect, canopy cover, and vegetation type. Using the Third National Forest Inventory (2010–2014) data of Nepal, we assessed SOC [...] Read more.
Soil organic carbon (SOC) is the major constituent of the soil organic matter. SOC stocks are determined by several factors such as altitude, slope, aspect, canopy cover, and vegetation type. Using the Third National Forest Inventory (2010–2014) data of Nepal, we assessed SOC status in forests at a national scale for the better understanding of the SOC distribution within Nepal. In this study, we estimated SOC against different factors and tested the spatial distribution of SOC using analysis of variance (ANOVA). The results showed that the forests located at a higher altitude have higher SOC accumulation. In particular, broadleaved forests exhibit a higher amount of carbon stock compared to other forest types. Moreover, forests with a larger canopy cover, located on a higher slope, and with a cooler aspect are associated with a higher accumulation of SOC. The SOC stock in the forest varies according to altitude, slope, aspect, canopy cover, and forest type, which might be attributed to the change in the microclimate of the area. The significant increase in SOC amount with the increase in slope, altitude, and crown cover helps to understand the extent of SOC distribution in forests. Broadleaved forests with a larger canopy cover in the higher altitude region have a higher SOC retention potential, which is likely to contribute to mitigating the impacts of climate change by sinking more carbon into the soil. Full article
(This article belongs to the Special Issue Managing Forest Ecosystems: Landscapes, Ecosystem Services and Disturbances)
►▼ Show Figures

Figure 1

Figure 1
<p>Study area map showing permanent sample plots distributed throughout the forests of Nepal. Green color on the map indicated forest cover in Nepal.</p> Full article ">Figure 2
<p>Mean distribution of SOC along different altitudinal ranges.</p> Full article ">Figure 3
<p>Mean distribution of SOC along different slope ranges.</p> Full article ">Figure 4
<p>Mean distribution of SOC along different aspects.</p> Full article ">Figure 5
<p>Mean distribution of SOC along different canopy covers.</p> Full article ">Figure 6
<p>Distribution of SOC in different forest types (Group I).</p> Full article ">Figure 7
<p>(<b>a</b>) Distribution of SOC in different forest types (Group II) representing all altitudinal ranges. (<b>b</b>) Distribution of SOC in different forest types (Group II) representing altitude &gt; 657 m, i.e., where the presence of coniferous forests is seen.</p> Full article ">
22 pages, 8103 KiB  
Article
Bridging Geospatial and Semantic Worlds: Enhancing Analysis of Place-Based Concepts in GIS
by Omid Reza Abbasi, Ali Asghar Alesheikh, Aynaz Lotfata and Chiara Garau
Land 2024, 13(3), 377; https://doi.org/10.3390/land13030377 - 16 Mar 2024
Viewed by 1069
Abstract
People’s actions and behaviours contribute to the diversity and personality of a space, transforming it into a vibrant and thriving living environment. The main goal of this research is to present a GIS-based framework for assessing places. The framework is constructed based on [...] Read more.
People’s actions and behaviours contribute to the diversity and personality of a space, transforming it into a vibrant and thriving living environment. The main goal of this research is to present a GIS-based framework for assessing places. The framework is constructed based on the idea of conceptual spaces, integrating spatial and semantic concepts inside a geometric structure. The explanation of place-related concepts is achieved via the use of linear programming and convex polytopes. By projecting these concepts into the spatial domain, a strong connection between geographical and semantic space is established. This connection allows a wide range of analytical calculations using geographic information systems to be carried out. The study focuses on the sense of city centre in Tehran, Iran, by employing questionnaires administrated on-site to evaluate the correlation between identified city centres and the participants’ responses. The findings demonstrate a good correlation, as shown by a Pearson correlation value of 0.74 and a rank correlation coefficient of 0.8. Interestingly, the city centres that were selected did not always align with the geographic centre. However, participants still perceived them as city centres. This framework serves as a valuable tool for planners and policymakers, providing a comprehensive understanding of the built environment. By considering both semantic and geographical aspects, the framework emphasises the importance of emotions, memories, and meanings in creating an inclusive environment. Full article
(This article belongs to the Section Land Planning and Landscape Architecture)
►▼ Show Figures

Figure 1

Figure 1
<p>The location of Tehran Province in Iran, and the location of Tehran city in the province.</p> Full article ">Figure 2
<p>The workflow of the study.</p> Full article ">Figure 3
<p>A schematic view of the proposed buffers in conceptual spaces.</p> Full article ">Figure 4
<p>A schematic view of the intersection of two concepts in conceptual spaces.</p> Full article ">Figure 5
<p>The average ratings of perception as city centre against the inverse distance from prototypical city centre.</p> Full article ">Figure 6
<p>The projection of the city-centre polytopes on the geographical space.</p> Full article ">Figure 7
<p>The intersection of the <span class="html-italic">city-centre</span> concept and the <span class="html-italic">northern</span> property, projected on the geographical space.</p> Full article ">Figure 8
<p>(<b>a</b>) The polytope representation of the spatial buffer on Valiasr neighbourhood in the spatial domain; (<b>b</b>) the projection of the spatial buffer results on the geographical space.</p> Full article ">Figure 9
<p>(<b>a</b>) The polytope representation of the semantic buffer on Valiasr neighbourhood; (<b>b</b>) the projection of the semantic buffer results on the geographical space.</p> Full article ">Figure 10
<p>The representation of place-based buffer on Valiasr neighbourhood in the geographical space.</p> Full article ">
23 pages, 1056 KiB  
Article
Does E-Commerce Participation among Farming Households Affect Farmland Abandonment? Evidence from a Large-Scale Survey in China
by Rui Zhou, Mingbo Ji and Shaoyang Zhao
Land 2024, 13(3), 376; https://doi.org/10.3390/land13030376 - 16 Mar 2024
Viewed by 824
Abstract
Reducing farmland abandonment is crucial for food security. While the association between e-commerce proliferation and farmland abandonment at the village level has been discussed, the correlation at the farming household level remains unexplored. Utilizing 2020 survey data from 3831 rural households across 10 [...] Read more.
Reducing farmland abandonment is crucial for food security. While the association between e-commerce proliferation and farmland abandonment at the village level has been discussed, the correlation at the farming household level remains unexplored. Utilizing 2020 survey data from 3831 rural households across 10 Chinese provinces, this study develops an “e-commerce–household–farmland abandonment” framework to explore the co-occurrence of e-commerce engagement with farmland abandonment, using econometric models. The findings reveal that e-commerce engagement significantly increases farmland abandonment, with implicit and explicit rates rising by 10.3% and 28.5%, respectively. It also shifts household incomes from planting to forestry, animal husbandry, and fisheries, leading households to reallocate labor away from agriculture, thereby intensifying abandonment. However, land transfer can alleviate this co-occurrence. This study also explores the variation in the association between e-commerce participation and farmland abandonment in relation to agricultural subsidies, economic development, and the presence of family farms. By elucidating the dynamics at the household level, this research offers fresh perspectives for developing countries to safeguard food security by curbing farmland abandonment. Full article
(This article belongs to the Section Land Environmental and Policy Impact Assessment)
►▼ Show Figures

Figure 1

Figure 1
<p>Theoretical framework.</p> Full article ">Figure 2
<p>Comparison of the mean explicit abandonment rate of e-commerce farmers and non-e-commerce farmers.</p> Full article ">Figure 3
<p>Relationship and patterns between e-commerce participation by farming households and farmland abandonment.</p> Full article ">
15 pages, 4872 KiB  
Article
Differentiation of Carbon Sink Enhancement Potential in the Beijing–Tianjin–Hebei Region of China
by Huicai Yang, Shuqin Zhao, Zhanfei Qin, Zhiguo Qi, Xinying Jiao and Zhen Li
Land 2024, 13(3), 375; https://doi.org/10.3390/land13030375 - 16 Mar 2024
Cited by 1 | Viewed by 850
Abstract
Carbon sink enhancement is of great significance to achieving carbon peak and carbon neutrality. This study firstly estimated the carbon sink in the Beijing–Tianjin–Hebei Region using the carbon absorption coefficient method. Then, this study explored the differentiation of carbon sink enhancement potential with [...] Read more.
Carbon sink enhancement is of great significance to achieving carbon peak and carbon neutrality. This study firstly estimated the carbon sink in the Beijing–Tianjin–Hebei Region using the carbon absorption coefficient method. Then, this study explored the differentiation of carbon sink enhancement potential with a carbon sink–economic carrying capacity index matrix based on carbon sink carrying capacity and economic carrying capacity under the baseline scenario and target scenario of land use. The results suggested there was a remarkable differentiation in total carbon sink in the study area, reaching 2,056,400 and 1,528,300 tons in Chengde and Zhangjiakou and being below 500,000 tons in Langfang and Hengshui, while carbon sink per unit land area reached 0.66 ton/ha in Qinhuangdao and only 0.28 t/ha in Tianjin under the baseline scenario. Increasing area and optimizing spatial distribution of arable land, garden land, and forest, which made the greatest contribution to total carbon sinks, is an important way of enhancing regional carbon sinks. A hypothetical benchmark city can be constructed according to Qinhuangdao and Beijing, in comparison with which there is potential for carbon sink enhancement by improving carbon sink capacity in Beijing, promoting economic carrying capacity in Qinhuangdao, and improving both in the other cities in the study area. Full article
(This article belongs to the Special Issue Regional Sustainable Management Pathways to Carbon Neutrality)
►▼ Show Figures

Figure 1

Figure 1
<p>Location of the Beijing–Tianjin–Hebei Region.</p> Full article ">Figure 2
<p>Total carbon sink under the baseline scenario and target scenario (tons).</p> Full article ">Figure 3
<p>Carbon sink per unit land area under the baseline scenario and target scenario (tons/ha).</p> Full article ">Figure 4
<p>Carbon sink–economic carrying capacity index under the baseline scenario: the horizontal coordinate is the carbon sink carrying capacity (<math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>C</mi> <mi>A</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <mi>L</mi> <mi>D</mi> </mrow> </mrow> </mrow> </semantics></math>) (unit: tons/ha), and the vertical coordinate is the economic carrying capacity (<span class="html-italic">GDP</span>/<span class="html-italic">LD</span>) (unit: ten thousand CNY/ha).</p> Full article ">Figure 5
<p>Carbon sink–economic carrying capacity index under the target scenario: the horizontal coordinate is the carbon sink carrying capacity (<math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>C</mi> <mi>A</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <mi>L</mi> <mi>D</mi> </mrow> </mrow> </mrow> </semantics></math>) (unit: ton/ha), and the vertical coordinate is the economic carrying capacity (<span class="html-italic">GDP</span>/<span class="html-italic">LD</span>) (unit: ten thousand CNY/ha).</p> Full article ">
19 pages, 3616 KiB  
Article
Spatial–Temporal Differentiation and Trend Prediction of Coupling Coordination Degree of Port Environmental Efficiency and Urban Economy: A Case Study of the Yangtze River Delta
by Min Wang, Yu Lan, Huayu Li, Xiaodong Jing, Sitong Lu and Kexin Deng
Land 2024, 13(3), 374; https://doi.org/10.3390/land13030374 - 16 Mar 2024
Viewed by 1229
Abstract
Green development is a primary path for ports and cities to achieve a low-carbon transition under the Sustainable Development Goals and a powerful driving force to elevate regional port–city relations to a high level of coordination. In this paper, twenty port cities in [...] Read more.
Green development is a primary path for ports and cities to achieve a low-carbon transition under the Sustainable Development Goals and a powerful driving force to elevate regional port–city relations to a high level of coordination. In this paper, twenty port cities in the Yangtze River Delta (YRD) were selected and port environmental efficiency (PEE) was calculated through the window SBM model, while the EW-TOPSIS model was used to evaluate high-quality urban economic development (HED). The coupling coordination degree (CCD) model, the kernel density model, GIS spatial analysis, and the grey prediction model were used to further explore the spatial–temporal dynamic evolution and prediction of the CCD between PEE and HED. The results suggested that: (1) PEE fluctuation in the YRD is increasing, with a trend of seaports achieving higher PEE than river ports; (2) HED in the YRD shows upward trends, and the polarization of individual cities is obvious; (3) Temporally, the CCD in the YRD has risen from 0.438 to 0.518. Shanghai consistently maintains intermediate coordination, and Jiangsu has experienced the most significant increase in CCD. Spatially, CCD is led by Lianyungang, Suzhou, Shanghai, and Ningbo-Zhoushan, displaying a decreasing distribution pattern from east to west. The projection for 2026 suggests that all port cities within the YRD will have transitioned to a phase of orderly development. To enhance the coordination level in the YRD, policymakers should consider the YRD as a whole to position the ports functionally and manage them hierarchically, utilize the ports to break down resource boundaries to promote the synergistic division of labor among cities, and then tilt the resources towards Anhui. Full article
(This article belongs to the Special Issue Regional Sustainable Development of Yangtze River Delta, China II)
►▼ Show Figures

Figure 1

Figure 1
<p>Twenty ports and their hinterlands in the YRD.</p> Full article ">Figure 2
<p>Time evolution of PEE and HED in the YRD from 2012 to 2021.</p> Full article ">Figure 3
<p>Average PEE and HED of twenty port cities from 2012 to 2021.</p> Full article ">Figure 4
<p>Time evolution of CCD of the YRD from 2012 to 2021.</p> Full article ">Figure 5
<p>CCD of twenty port cities in the YRD from 2012 to 2021.</p> Full article ">Figure 6
<p>Kernel density map of CCD in the YRD from 2012 to 2021. (<b>a</b>) CCD of the YRD; (<b>b</b>) CCD of Jiangsu; (<b>c</b>) CCD of Anhui; (<b>d</b>) CCD of Zhejiang.</p> Full article ">Figure 7
<p>Spatial distribution of CCD in the YRD in 2012, 2015, 2018 and 2021.</p> Full article ">Figure 8
<p>CCD forecast between PEE and HED of 20 port cities in the YRD from 2022 to 2026.</p> Full article ">
15 pages, 2810 KiB  
Article
Dynamics of Peatland Fires in South Sumatra in 2019: Role of Groundwater Levels
by Muhammad Irfan, Erry Koriyanti, Khairul Saleh, Hadi, Sri Safrina, Awaludin, Albertus Sulaiman, Hamdi Akhsan, Suhadi, Rujito Agus Suwignyo, Eunho Choi and Iskhaq Iskandar
Land 2024, 13(3), 373; https://doi.org/10.3390/land13030373 - 16 Mar 2024
Viewed by 988
Abstract
During the dry season, extensive peatland fires in South Sumatra and another peatland in Indonesia result in environmental damage and pose health risks to humans. The Indonesian Government has implemented several measures to prevent the recurrence of these fires. One such measure involves [...] Read more.
During the dry season, extensive peatland fires in South Sumatra and another peatland in Indonesia result in environmental damage and pose health risks to humans. The Indonesian Government has implemented several measures to prevent the recurrence of these fires. One such measure involves the establishment of observation stations to monitor hydrometeorological parameters in different peatlands across Indonesia, including those in South Sumatra. To effectively control fires in South Sumatra’s peatland and minimize hotspot occurrences, it is essential to determine hydrometeorological parameters that can serve as fire control indicators. Therefore, this study aimed to investigate the relationship between groundwater levels and hotspot occurrences by analyzing groundwater level data collected from six Peat Restoration Agency stations in South Sumatra’s peatland, along with hotspot data obtained from Moderate Resolution Imaging Spectroradiometer satellite measurements. The findings reveal a significant correlation between groundwater levels and hotspots at the six stations. As the GWL increased, the number of hotspots tended to decrease, and vice versa. This means that GWL can be used as a controlling variable for hotspot emergence. To effectively minimize hotspot occurrences, it is recommended to maintain a minimum groundwater level of −0.45 ± 0.09 m in the peatland of South Sumatra. Full article
(This article belongs to the Special Issue Restoration of Tropical Peatlands: Science Policy and Practice)
►▼ Show Figures

Figure 1

Figure 1
<p>SESAME equipment.</p> Full article ">Figure 2
<p>Map illustrating the location of the BRG stations.</p> Full article ">Figure 3
<p>Time series of (<b>a</b>) hotspots, (<b>b</b>) rainfall, (<b>c</b>) DMI, and (<b>d</b>) Niño 3.4 in year 2001 until 2020.</p> Full article ">Figure 4
<p>Distribution of hotspots in South Sumatra during the extreme dry season of J−A−S−O (<b>a</b>) 2006, (<b>b</b>) 2015, and (<b>c</b>) 2019.</p> Full article ">Figure 5
<p>Time series of (<b>a</b>) hotspots, (<b>b</b>) rainfall, (<b>c</b>) DMI, and (<b>d</b>) Niño 3.4 during the 2019 J−A−S−O period.</p> Full article ">Figure 6
<p>(<b>a</b>–<b>d</b>) Overlay graph of groundwater level and rainfall in the period J−A−S−O 2019.</p> Full article ">Figure 7
<p>(<b>a</b>–<b>f</b>) Overlay graph of groundwater level and hotspots in the period J−A−S−O 2019.</p> Full article ">Figure 8
<p>(<b>a</b>–<b>f</b>) Correlation between groundwater level and hotspots in the period J−A−S−O 2019.</p> Full article ">Figure 9
<p>GWL time series of two measurement stations in the period J−A−S−O 2019.</p> Full article ">
26 pages, 9674 KiB  
Article
Inclusion of Nature-Based Solution in the Evaluation of Slope Stability in Large Areas
by Lukáš Zedek, Jan Šembera and Jan Kurka
Land 2024, 13(3), 372; https://doi.org/10.3390/land13030372 - 15 Mar 2024
Cited by 2 | Viewed by 1004
Abstract
In areas affected by mining, which are undergoing reclamation, their geotechnical characteristics need to be monitored and the level of landslide risk should be assessed. This risk should preferably be reduced by nature-based solutions. This paper presents a KurZeS slope stability assessment technique [...] Read more.
In areas affected by mining, which are undergoing reclamation, their geotechnical characteristics need to be monitored and the level of landslide risk should be assessed. This risk should preferably be reduced by nature-based solutions. This paper presents a KurZeS slope stability assessment technique based on areal data. This method is suitable for large areas. In addition, a procedure is presented for how to incorporate a prediction of the impact of nature-based solutions into this method, using the example of vegetation root reinforcement. The paper verifies the KurZeS method by comparing its results with the results of stability calculations by GEO5 software (version 5.2023.52.0) and validates the method by comparing its results with a map of closed areas in the area of the former open-cast mine Lohsa II in Lusatia, Germany. The original feature of the KurZeS method is the use of a pre-computed database. It allows the use of an original geometrical and geotechnical concept, where slope stability at each Test Point is evaluated not just along the fall line but also along different directions. This concept takes into account more slopes and assigns the Test Point the lowest safety factor in its vicinity. This could be important, especially in soil dumps with rugged terrain. Full article
(This article belongs to the Special Issue Potential for Nature-Based Solutions in Urban Green Infrastructure)
►▼ Show Figures

Figure 1

Figure 1
<p>Study area selected for slope stability assessment (red) and three subareas for verification of results.</p> Full article ">Figure 2
<p>(<b>a</b>) The set of endpoints for <math display="inline"><semantics> <mrow> <msub> <mi>BP</mi> <mn>1</mn> </msub> <mo>≡</mo> <mi>TP</mi> </mrow> </semantics></math>; (<b>b</b>,<b>c</b>) example of repetition of found pair <math display="inline"><semantics> <msub> <mi>BP</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>BP</mi> <mn>2</mn> </msub> </semantics></math> in case <math display="inline"><semantics> <mrow> <msub> <mi>BP</mi> <mn>1</mn> </msub> <mo>≠</mo> <mi>TB</mi> <mo>≠</mo> <msub> <mi>PB</mi> <mn>2</mn> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 3
<p>(<b>a</b>) Two-layer model; (<b>b</b>) three-layer model.</p> Full article ">Figure 3 Cont.
<p>(<b>a</b>) Two-layer model; (<b>b</b>) three-layer model.</p> Full article ">Figure 4
<p>(<b>a</b>) Short circular slip surface; (<b>b</b>) medium-length polygonal slip surface; (<b>c</b>) long deep circular slip surface.</p> Full article ">Figure 5
<p>Example from the resulting database of <math display="inline"><semantics> <msub> <mi mathvariant="normal">F</mi> <mi mathvariant="normal">s</mi> </msub> </semantics></math> for S5Y soil (sandy dump).</p> Full article ">Figure 6
<p>Digital elevation model rasterized with cell edge length <math display="inline"><semantics> <mrow> <mn>10.0</mn> <mspace width="3.33333pt"/> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>.</p> Full article ">Figure 7
<p>Linearly increasing water table.</p> Full article ">Figure 8
<p>Simplified categorization of soil types. (<b>a</b>) Non-rooted soil types. (<b>b</b>) Soil types without (under the water) and with roots.</p> Full article ">Figure 8 Cont.
<p>Simplified categorization of soil types. (<b>a</b>) Non-rooted soil types. (<b>b</b>) Soil types without (under the water) and with roots.</p> Full article ">Figure 9
<p>Division of the study area enabled parallel computation.</p> Full article ">Figure 10
<p>Verification diagram.</p> Full article ">Figure 11
<p>Validation diagram.</p> Full article ">Figure 12
<p>Comparison of predicted slope stability. (<b>a</b>) Sandy clay (S45), slope inclination 1 in 2.5 and three different slope lengths. (<b>b</b>) Sandy dump (S5Y), slope inclination 1 in 3 and three different slope lengths.</p> Full article ">Figure 12 Cont.
<p>Comparison of predicted slope stability. (<b>a</b>) Sandy clay (S45), slope inclination 1 in 2.5 and three different slope lengths. (<b>b</b>) Sandy dump (S5Y), slope inclination 1 in 3 and three different slope lengths.</p> Full article ">Figure 13
<p>Slope stability evaluation without root reinforcement under consideration.</p> Full article ">Figure 14
<p>Comparison of results with shape file mapping area closed for public.</p> Full article ">Figure 15
<p>Slope stability evaluation with root reinforcement under consideration.</p> Full article ">Figure 16
<p>Relative improvement of slope stability due to root reinforcement.</p> Full article ">Figure 17
<p>Map of “AREA 46”.</p> Full article ">Figure 18
<p>Safety factor predicted by KurZeS for non-rooted soil.</p> Full article ">Figure 19
<p>Safety factor predicted by GEO5 for non-rooted soil.</p> Full article ">Figure 20
<p>Safety factor predicted by KurZeS for rooted soil. <math display="inline"><semantics> <mrow> <mi>TP</mi> <mo>_</mo> <mi mathvariant="normal">i</mi> </mrow> </semantics></math> denotes i-th Testing Point and <math display="inline"><semantics> <mrow> <mi>SP</mi> <mo>_</mo> <mi mathvariant="normal">i</mi> <mo>,</mo> <mi mathvariant="normal">j</mi> </mrow> </semantics></math> denotes j-th Sampling Point lying on the i-th cross-section.</p> Full article ">Figure 21
<p>Safety factor predicted by GEO5 for rooted soil.</p> Full article ">Figure 22
<p>Map of AREA 79. <math display="inline"><semantics> <mrow> <mi>TP</mi> <mo>_</mo> <mi mathvariant="normal">i</mi> </mrow> </semantics></math> denotes i-th Testing Point and <math display="inline"><semantics> <mrow> <mi>SP</mi> <mo>_</mo> <mi mathvariant="normal">i</mi> <mo>,</mo> <mi mathvariant="normal">j</mi> </mrow> </semantics></math> denotes j-th Sampling Point lying on the i-th cross-section.</p> Full article ">Figure 23
<p>Safety factor predicted by KurZeS for non-rooted soil. <math display="inline"><semantics> <mrow> <mi>TP</mi> <mo>_</mo> <mi mathvariant="normal">i</mi> </mrow> </semantics></math> denotes i-th Testing Point and <math display="inline"><semantics> <mrow> <mi>SP</mi> <mo>_</mo> <mi mathvariant="normal">i</mi> <mo>,</mo> <mi mathvariant="normal">j</mi> </mrow> </semantics></math> denotes j-th Sampling Point lying on the i-th cross-section.</p> Full article ">Figure 24
<p>Safety factor predicted by GEO5 for non-rooted soil.</p> Full article ">Figure 25
<p>Safety factor predicted by KurZeS for rooted soil. <math display="inline"><semantics> <mrow> <mi>TP</mi> <mo>_</mo> <mi mathvariant="normal">i</mi> </mrow> </semantics></math> denotes i-th Testing Point and <math display="inline"><semantics> <mrow> <mi>SP</mi> <mo>_</mo> <mi mathvariant="normal">i</mi> <mo>,</mo> <mi mathvariant="normal">j</mi> </mrow> </semantics></math> denotes j-th Sampling Point lying on the i-th cross-section.</p> Full article ">Figure 26
<p>Safety factor predicted by GEO5 for rooted soil.</p> Full article ">Figure 27
<p>(<b>a</b>) Low probability that the link would represent the area with the lowest <math display="inline"><semantics> <msub> <mi mathvariant="normal">F</mi> <mi mathvariant="normal">s</mi> </msub> </semantics></math>; (<b>b</b>) a high probability of the same.</p> Full article ">Figure 28
<p>Analysis of the influence of the mesh rotation relative to the slope for short links, variants for <math display="inline"><semantics> <msup> <mn>120</mn> <mo>°</mo> </msup> </semantics></math> angle.</p> Full article ">Figure 29
<p>Effect of mesh direction rotation relative to fall line for short links.</p> Full article ">
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-138
Previous Issue
Next Issue
Back to TopTop