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Land
Volume 12
Issue 6
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Land, Volume 12, Issue 6 (June 2023) – 153 articles

Cover Story (view full-size image): Human activities generate disturbances responsible for habitat degradation and decline. The methodologies used to assess anthropogenic impacts are broad and context-specific, but they all share the goal of understanding the impacts of human activities on the environment and providing spatially explicit knowledge critical to making informed decisions that promote ecosystem health and the provision of their services. The objective of this study is to assess how human activities can interact with the conservation objectives of an alpine protected area and to provide an easily replicable methodology that can be configured as a useful management tool, highlighting its strengths and weaknesses. View this paper
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17 pages, 7025 KiB  
Article
Spatial-Temporal Evolution and Prediction of Carbon Storage in Areas Rich in Ancient Remains: A Case Study of the Zhouyuan Region, China
by Jian Chen, Xiaoxiao Zhang, Kai Wang, Zhenguo Yan, Wei Zhang, Lixin Niu and Yanlong Zhang
Land 2023, 12(6), 1266; https://doi.org/10.3390/land12061266 - 20 Jun 2023
Cited by 3 | Viewed by 1377
Abstract
In the past few decades, human activities have caused the emission of large amounts of carbon dioxide, which has severely impacted the Earth’s ecosystem and human health. Therefore, carbon reduction has become the focus of global attention. In this study, the Zhouyuan region [...] Read more.
In the past few decades, human activities have caused the emission of large amounts of carbon dioxide, which has severely impacted the Earth’s ecosystem and human health. Therefore, carbon reduction has become the focus of global attention. In this study, the Zhouyuan region of China, which is rich in ancient remains, is taken as an example. Based on the land use characteristics in 1990, 2000, 2010, and 2020, the spatial-temporal evolution of land use and carbon storage in the Zhouyuan region is simulated using four methods, including land use classification, land use transfer maps, patch-level land-use simulation (PLUS), and the integrated valuation of ecosystem services and trade-offs (InVEST) models under three scenarios, including the natural development scenario, urban development priority, and heritage conservation priority in 2030. According to the results, the carbon storage in the area in 2030 under all three scenario simulations has decreased compared with 2020, indicating that the region faces great challenges in achieving its targets of carbon peak and carbon neutrality. The paper points out four causes for the decrease in carbon storage, and five suggestions for increasing carbon storage are proposed, such as developing a carbon storage master plan, applying energy-saving technologies, establishing an ecological substitution mechanism, and so on. Through the study of carbon storage in the Zhouyuan region, this paper hopes to establish a mechanism to balance urban development, heritage conservation, and carbon sinks on the one hand, and encourage more scholars to participate in the study of carbon sinks in areas rich in ancient remains, so as to to jointly promote their healthy development on the other. Full article
(This article belongs to the Topic Bioclimatic Designs to Enhance Urban/Rural Resilience)
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<p>Research scope map.</p> Full article ">Figure 2
<p>Ancient remains distribution in Zhouyuan region.</p> Full article ">Figure 3
<p>Method flow chart.</p> Full article ">Figure 4
<p>Land use structure map from 1990 to 2020.</p> Full article ">Figure 5
<p>Land use transfer map from 1990 to 2020.</p> Full article ">Figure 6
<p>Land use structure simulation of Zhouyuan region in 2030.</p> Full article ">Figure 7
<p>Carbon storage of Zhouyuan region under three scenarios in 2030.</p> Full article ">
16 pages, 649 KiB  
Article
Bee-Friendly Native Seed Mixtures for the Greening of Solar Parks
by Maren Helen Meyer, Sandra Dullau, Pascal Scholz, Markus Andreas Meyer and Sabine Tischew
Land 2023, 12(6), 1265; https://doi.org/10.3390/land12061265 - 20 Jun 2023
Cited by 3 | Viewed by 2602
Abstract
Photovoltaics is one of the key technologies for reducing greenhouse gas emissions and achieving climate neutrality for Europe by 2050, which has led to the promotion of solar parks. These parks can span up to several hundred hectares, and grassland vegetation is usually [...] Read more.
Photovoltaics is one of the key technologies for reducing greenhouse gas emissions and achieving climate neutrality for Europe by 2050, which has led to the promotion of solar parks. These parks can span up to several hundred hectares, and grassland vegetation is usually created between and under the panels. Establishing species-rich grasslands using native seed mixtures can enhance a variety of ecosystem services, including pollination. We present an overall concept for designing native seed mixtures to promote pollinators, especially wild bees, in solar parks. It takes into account the specific site conditions, the small-scale modified conditions caused by the solar panels, and the requirement to avoid panel shading. We highlight the challenges and constraints resulting from the availability of species on the seed market. Furthermore, we provide an easy-to-use index for determining the value of native seed mixtures for wild bee enhancement and apply it as an example to several mixtures specifically designed for solar parks. The increased availability of regional seed would allow a more thorough consideration of pollinator-relevant traits when composing native seed mixtures, thereby enhancing ecosystem services associated with pollinators such as wild bees. Full article
(This article belongs to the Special Issue Grassland Ecosystem Services: Research Advances and Future Directions for Sustainability)
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<p>Multi-step concept for the design of bee-friendly native seed mixtures for solar parks.</p> Full article ">
19 pages, 3289 KiB  
Article
Influence of the Built Environment on Older Adults’ Travel Time: Evidence from the Nanjing Metropolitan Area, China
by Jingrui Sun, Zhenjun Zhu, Ji Han, Zhanpeng He and Xinfang Xu
Land 2023, 12(6), 1264; https://doi.org/10.3390/land12061264 - 20 Jun 2023
Viewed by 1741
Abstract
The built environment is among the critical factors in older adults’ travel behavior, and a favorable built environment can encourage them to travel and engage in various activities. Existing studies have mostly focused on exploring the correlation between the built environment and travel [...] Read more.
The built environment is among the critical factors in older adults’ travel behavior, and a favorable built environment can encourage them to travel and engage in various activities. Existing studies have mostly focused on exploring the correlation between the built environment and travel behavior, ignoring the heterogeneity between the two at different times of the day. In this study, we conducted structured, face-to-face interviews in the Nanjing (China) metropolitan area to investigate the time consumed per trip by older adults using various travel modes and used the structural equation and random forest models to explore the relationship between the built environment and older adults’ travel time. The results demonstrated that older adults had different perspectives on travel during different time periods. Different environments and the convenience of destinations affected their overall satisfaction during travel. We found a nonlinear relationship between the built environment and travel time. Metropolitan street connectivity initially had a positive effect on travel time until a certain threshold or peak, whereafter a gradual decline ensued. This nonlinear relationship also existed between the proportion of green space and the distance to subway stations. These results can guide the retrofitting and construction of age-friendly metropolitan infrastructure facilities that promote older adults’ mobility. Full article
(This article belongs to the Special Issue Land Development and Socio-Spatial Differentiation in Metropolitan Fringe Areas)
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<p>Map of the survey area of Nanjing.</p> Full article ">Figure 2
<p>Questionnaire design and travel survey process.</p> Full article ">Figure 3
<p>Travel perception influencing factors and descriptive items.</p> Full article ">Figure 4
<p>A conceptual framework describing the impact mechanism of travel time.</p> Full article ">Figure 5
<p>Conceptual framework of SEM.</p> Full article ">Figure 6
<p>Illustration of the random forest method.</p> Full article ">Figure 7
<p>Proportion of older adults per travel purpose and travel time period.</p> Full article ">Figure 8
<p>SEM of older adults’ travel times and travel perceptions.</p> Full article ">Figure 9
<p>Nonlinear associations between the built environment and travel time.</p> Full article ">
16 pages, 1789 KiB  
Article
Soil Quality Evaluation Based on a Minimum Data Set (MDS)—A Case Study of Tieling County, Northeast China
by Fengkui Qian, Yuanjun Yu, Xiuru Dong and Hanlong Gu
Land 2023, 12(6), 1263; https://doi.org/10.3390/land12061263 - 20 Jun 2023
Cited by 3 | Viewed by 1992
Abstract
Soil quality is related to food security and human survival and development. Due to the acceleration of urbanization and the increase in abandoned land, the quality of topsoil has deteriorated, thus resulting in land degradation in recent years. In this study, a minimum [...] Read more.
Soil quality is related to food security and human survival and development. Due to the acceleration of urbanization and the increase in abandoned land, the quality of topsoil has deteriorated, thus resulting in land degradation in recent years. In this study, a minimum data set (MDS) was constructed through principal component analysis (PCA) to determine the indicator data set for evaluating topsoil quality in Tieling County, northeast China. In addition, the soil quality index (SQI) was calculated to analyze the spatial distribution characteristics of the topsoil quality and the influencing factors. The results showed that the MDS included total potassium (TK), clay, zinc (Zn), soil organic matter (SOM), soil water content (SWC), cation exchange capacity (CEC), pH, and copper (Cu), which could replace all other indicators for assessing the topsoil quality in the research region. The overall soil quality of Tieling County showed a trend of being low in the east and high in the west, and it gradually increased from the hilly area to the plain area. The topsoil quality of Tieling County is divided into one to five levels, with grade-I being the best and grade-V being the worst. The proportion of Grade-II and grade-III is the largest, which is 28.5% and 26.3%, respectively, and grade-V is the smallest, which is 9.6%. The evaluation results are consistent with field research, which can provide a reference for other topsoil quality evaluations, and it also provides a basis for the formulation of soil quality improvement measures. Full article
(This article belongs to the Special Issue Cultivated Land Quality under the Background of Global Climate Change: Status, Protection, Monitoring and Lifting)
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<p>Geographical locations of the study area and soil sampling points.</p> Full article ">Figure 2
<p>Interpolation result for the indicators. <b>Note:</b> SWC = soil water content; SOM = soil organic matter; TK = total potassium; CEC = cation exchange capacity; Cu = copper; Zn = zinc.</p> Full article ">Figure 2 Cont.
<p>Interpolation result for the indicators. <b>Note:</b> SWC = soil water content; SOM = soil organic matter; TK = total potassium; CEC = cation exchange capacity; Cu = copper; Zn = zinc.</p> Full article ">Figure 3
<p>Spatial distribution of the soil quality.</p> Full article ">
21 pages, 28662 KiB  
Article
Quota and Space Allocations of New Urban Land Supported by Urban Growth Simulations: A Case Study of Guangzhou City, China
by Xiang Li, Jiang Zhu, Tao Liu, Xiangdong Yin, Jiangchun Yao, Hao Jiang, Bing Bu, Jianlong Yan, Yixuan Li and Zhangcheng Chen
Land 2023, 12(6), 1262; https://doi.org/10.3390/land12061262 - 20 Jun 2023
Viewed by 1168
Abstract
Previous allocations of new urban land were ineffective because they lacked synergy between quota and space, challenging the government planning authority. This study proposes a new and more reasonable urban land allocation method to guide the smart growth of cities. We used a [...] Read more.
Previous allocations of new urban land were ineffective because they lacked synergy between quota and space, challenging the government planning authority. This study proposes a new and more reasonable urban land allocation method to guide the smart growth of cities. We used a logistic regression model and multisource data to explore the laws of urban growth and employed a cellular automata (CA) model to simulate this under inertial and constrained scenarios. In addition, the disparities between both scenarios concerning allocation were analyzed. We realized the synergy of quota and space allocations of new urban land through urban growth simulation. Further, the allocation of new urban land was more consistent with the development strategy of Guangzhou under a constrained scenario. The allocation of space was more regular and concentrated under a constrained scenario, which aligns with the requirements of the Government Land Space Planning. Additionally, in the constrained scenario, the bottom lines of cultivated land protection, ecological service, and geological safety were better controlled. This study compensated for the shortcomings of the disjoined quota and space allocations of new urban land and proved that a constrained scenario can more effectively promote reasonable urban growth. Full article
(This article belongs to the Special Issue Future Urban Land Expansion in China)
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<p>Process diagram of quota and space allocations in The General Land Use Planning.</p> Full article ">Figure 2
<p>Scope of the study area and dislocation between actual and planning urban land.</p> Full article ">Figure 3
<p>Spatialization results of some indicators for urban growth simulation in 2015.</p> Full article ">Figure 4
<p>Original urban growth potential.</p> Full article ">Figure 5
<p>Processed urban growth potential.</p> Full article ">Figure 6
<p>Schematic diagram of the simulation process for new urban land.</p> Full article ">Figure 7
<p>Factors in the constrained scenario: (<b>a</b>) quality of cultivated land, (<b>b</b>) ecological importance, and (<b>c</b>) geological risk level.</p> Full article ">Figure 8
<p>Schematic diagram of the urban growth potential adjustment process.</p> Full article ">Figure 9
<p>Kappa coefficient, simulated, and actual results of Guangzhou City development and typical districts from 2015–2020.</p> Full article ">Figure 10
<p>Inertial (<b>a</b>) and constrained (<b>b</b>) urban growth simulated results from 2021–2035.</p> Full article ">Figure 11
<p>Process diagram of quota and space allocations in this study.</p> Full article ">Figure 12
<p>Correlation between the quota of new urban land and the potential of corresponding cells in each district.</p> Full article ">Figure 13
<p>Gravity center and standard deviation ellipse of new urban land in two scenarios.</p> Full article ">Figure 14
<p>Inertial and constrained urban growth results from 2021–2035.</p> Full article ">Figure 15
<p>Proportion of the three types of growth patterns in the two scenarios from 2021–2035.</p> Full article ">Figure 16
<p>Composition of new urban land in the two scenarios from 2021–2035 (km<sup>2</sup>).</p> Full article ">Figure 17
<p>Change and slope of the decline of the ecological service value in the two scenarios.</p> Full article ">Figure 18
<p>Proportion of different geological risks of new urban land in the two scenarios from 2021–2035.</p> Full article ">
24 pages, 1882 KiB  
Review
Lodgepole Pine and White Spruce Thinning in Alberta―A Review of North American and European Best Practices
by Mark Baah-Acheamfour, Amanda Schoonmaker, Mark Dewey and Brian Roth
Land 2023, 12(6), 1261; https://doi.org/10.3390/land12061261 - 20 Jun 2023
Cited by 1 | Viewed by 1892
Abstract
A significant portion of the harvested land base in western Canada is becoming old enough or entering a phase where thinning is a legitimate forest management option. A comprehensive review of the existing knowledge of commercial thinning (CT) treatments applied to pine and [...] Read more.
A significant portion of the harvested land base in western Canada is becoming old enough or entering a phase where thinning is a legitimate forest management option. A comprehensive review of the existing knowledge of commercial thinning (CT) treatments applied to pine and spruce-dominated stands in Alberta was conducted, with particular regard to the intensity, timing of interventions, method, and impacts on crop tree growth responses. Although the geographical focus of this review is Alberta, information on this topic is more complete in other areas of North America and Europe, where there is a long history of density management. In areas of eastern North America, our review revealed that CT from below, with tree removal levels from 27 to 43% of the basal area, could increase total merchantable wood produced from 11 to 60 m3 ha−1 over a rotation, depending on stand age and intensity of thinning. For Alberta conditions, and considering the risks, we conclude that commercial thinning basal area removal should be in the range of 25 to 40%, depending on a variety of factors such as species, wind firmness, and insect or disease incidence and risk. Thinning too aggressively and/or too late will increase the blowdown risk but the literature is fairly consistent in suggesting that live crown ratios should be >40% to maximize the chance of growth response and minimize the blowdown risk. In cases where stands are also threatened by stressors such as drought, wind, and insect or disease outbreaks, CT treatments likely offer the potential at limiting the overall risk, but localized knowledge and experience are critical. It is intended that the information presented may support ongoing and future research trials and growth and yield (G&Y) model development about potential CT treatments to apply and the likely results of practical application to commercial forestry. Full article
(This article belongs to the Special Issue Diversifying Forest Landscape Management Approaches)
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<p>Demonstration of the trade-off between value and volume using an example of TASS runs for Douglas fir with a harvest age of 60 years (from BC Ministry of Forests 1999). Note the highest volume is produced when thinning to 600 to 800 stems ha<sup>−1</sup>, yet the highest value (NPV) occurs at a residual density of 200 stems ha<sup>−1</sup>.</p> Full article ">Figure 2
<p>Commercially thinned lodgepole pine stands in northern Alberta in June 2020 (<b>a</b>) un-thinned (~20,000 stems ha<sup>−1</sup>) and (<b>b</b>) thinned to 2000 stems ha<sup>−1</sup>.</p> Full article ">Figure 3
<p>Commercially thinned white spruce stands in northern Alberta in July 2020 (<b>a</b>) unthinned (~10,000 stems ha<sup>−1</sup>) and (<b>b</b>) thinned to 1500 stems ha<sup>−1</sup>.</p> Full article ">
18 pages, 4534 KiB  
Article
Which Spatial Elements Influence Waterfront Space Vitality the Most?—A Comparative Tracking Study of the Maozhou River Renewal Project in Shenzhen, China
by Yating Fan, Da Kuang, Wei Tu and Yu Ye
Land 2023, 12(6), 1260; https://doi.org/10.3390/land12061260 - 20 Jun 2023
Cited by 6 | Viewed by 1955
Abstract
Urban waterfront renewal, especially public space improvement, is important for regaining waterfront space vitality. However, existing studies constrained by sparse and hard-to-access data are hard to explore how changes in spatial elements during waterfront renewal would affect space vitality. Waterfront space vitality comprises [...] Read more.
Urban waterfront renewal, especially public space improvement, is important for regaining waterfront space vitality. However, existing studies constrained by sparse and hard-to-access data are hard to explore how changes in spatial elements during waterfront renewal would affect space vitality. Waterfront space vitality comprises social vitality represented by public behaviors and economic vitality represented by urban functional facilities. Taking the Maozhou River renewal project in China as an example, we collect spatial elements and vitality on corresponding periods in 2018 and 2020 (before and after the renewal construction) and use multiple linear regression models to assess the relationships. We find that the functional diversity (e.g., commercial and cultural facilities) and design quality (e.g., path density and the shoreline’s proximity to the water) are the two most influential spatial elements affecting space vitality during waterfront renewal. Overall, the use of two-time datasets has generated strong evidence for measuring waterfront revitalization. Full article
(This article belongs to the Section Land Planning and Landscape Architecture)
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<p>Research path and methodology.</p> Full article ">Figure 2
<p>Research case information. (<b>a</b>) Research case scope, (<b>b</b>) The masterplan of the Maozhou River renewal project (cited from Tongji Architectural Design (Group) Co., Ltd.), (<b>c</b>) The images of the typical node spaces of the project, Left Bank Park (cited from reference [<a href="#B47-land-12-01260" class="html-bibr">47</a>]) and Longmen Wetland Park (cited from reference [<a href="#B48-land-12-01260" class="html-bibr">48</a>]).</p> Full article ">Figure 2 Cont.
<p>Research case information. (<b>a</b>) Research case scope, (<b>b</b>) The masterplan of the Maozhou River renewal project (cited from Tongji Architectural Design (Group) Co., Ltd.), (<b>c</b>) The images of the typical node spaces of the project, Left Bank Park (cited from reference [<a href="#B47-land-12-01260" class="html-bibr">47</a>]) and Longmen Wetland Park (cited from reference [<a href="#B48-land-12-01260" class="html-bibr">48</a>]).</p> Full article ">Figure 3
<p>Cellular data and map POI data.</p> Full article ">Figure 4
<p>Influenced research area projection method with distance attenuation coefficient.</p> Full article ">Figure 5
<p>Change in Maozhou River waterfront space’s vitality from 2018 to 2020.</p> Full article ">Figure 6
<p>Quadrant diagram of the changes in space vitality.</p> Full article ">Figure 7
<p>Space vitality of different factor types.</p> Full article ">Figure 8
<p>Influence of spatial elements on space vitality.</p> Full article ">
17 pages, 2401 KiB  
Article
Research on Rural Typology Based on the Symbiotic Model of Rural Revitalization and Basic Public Services
by Yujiao Li, Rong Ma and Bei Jin
Land 2023, 12(6), 1259; https://doi.org/10.3390/land12061259 - 20 Jun 2023
Cited by 3 | Viewed by 1764
Abstract
The basic unit of rural revitalization is the village. Rural revitalization can be comprehensively promoted by using rural typology as an instrument for rural zoning planning, which is a significant factor. This study clarified the relationship between rural revitalization and basic public services, [...] Read more.
The basic unit of rural revitalization is the village. Rural revitalization can be comprehensively promoted by using rural typology as an instrument for rural zoning planning, which is a significant factor. This study clarified the relationship between rural revitalization and basic public services, constructed evaluation index systems, and analyzed the symbiotic mode. The comprehensive development level and the symbiotic mode were incorporated to determine the type of village. The results showed the following: (1) The thriving industry and affluent life of Tangfang Town obviously contributed to its rural revitalization; the achievement of basic environmental improvement was eminent. (2) There are differences in the comprehensive development level of rural revitalization and basic public services among administrative villages, with an overall trend of “high in the north and low in the south”, corresponding to the industrial layout of “north industry and south agriculture” in Tangfang Town. (3) The symbiosis coefficients of all the administrative villages in Tangfang Town were between 0 and 0.5, and there was a positive symmetric mutualism relationship overall, indicating that basic public services have a significant impact and can effectively promote the process of rural revitalization. (4) Villages in Tangfang Town are divided into five functional areas—the comprehensive coordination area, potential improvement area, restricted development area, unbalanced allocation area, and backward guarantee area—and various types of optimization development strategies are proposed. As one of the top 100 demonstration towns for rural revitalization in Shaanxi Province, Tangfang Town plays a leading and exemplary role. Within the context of rural revitalization strategies, solving the problem of how to realize differentiated development in the next five years has become urgent. This study aimed to effectively promote the process of rural revitalization, provide theoretical guidance for scientific development in Tangfang Town, and promote research ideas for other towns in China. Full article
(This article belongs to the Topic What Climate Adaptation Looks Like: Improving Healthy Landscape)
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<p>Overview of the study area.</p> Full article ">Figure 2
<p>A comprehensive scheme of the study approach. (<b>1</b>) Symbiotic mode action structure; (<b>2</b>) Three-dimensional role map of the comprehensive development level and symbiosis model of rural revitalization and basic public services; (<b>3</b>) Three-dimensional role map of the comprehensive development level and symbiosis model of rural revitalization and basic public services; The location of the red box indicates the three-dimensional result.</p> Full article ">Figure 3
<p>Comprehensive development level of rural revitalization.</p> Full article ">Figure 4
<p>Comprehensive development level of basic public services.</p> Full article ">Figure 5
<p>Symbiotic mode of rural revitalization and basic public services.</p> Full article ">Figure 6
<p>The results of village classification.</p> Full article ">
32 pages, 11762 KiB  
Article
Data and Values: Axiological Interpretations of Building Sprawl Landscape Risk in the Rural Territory of Noto (Italy)
by Chiara Minioto, Francesco Martinico, Maria Rosa Trovato and Salvatore Giuffrida
Land 2023, 12(6), 1258; https://doi.org/10.3390/land12061258 - 19 Jun 2023
Cited by 1 | Viewed by 1203
Abstract
This research concerns the issue of landscape risk due to the progressive spread of construction in rural areas through the creation of a “site-specific” analysis and evaluation model and its application to the context of the municipal area of Noto (Italy). The phenomenon [...] Read more.
This research concerns the issue of landscape risk due to the progressive spread of construction in rural areas through the creation of a “site-specific” analysis and evaluation model and its application to the context of the municipal area of Noto (Italy). The phenomenon of construction in rural areas was facilitated by the regulatory evolution of the Sicilian Region, which supported the construction of artifacts in agricultural areas to boost cultivation and production, but which, for the most part, was intended for seasonal residential use. In particular, the municipal territory of Noto is characterized by remarkable landscape values, including very low building density, large portions of the territory remaining almost uncontaminated, and the widespread presence of cultural and ethno-anthropological assets. Consequently, the demand for localization in rural areas, now also driven by the tendency to decongest dense urban areas in order to contain the effects of the pandemic, is a phenomenon that must be countered, on the one hand, and addressed and regulated on the other. The objective of this study is to provide the local administration with a planning tool to determine permissible interventions in various areas of the landscape context. This has guided the process of representing the phenomenon in quantitative and spatial terms, and of evaluating the territory targeted. A large set of data, coordinated in a hierarchical set of indices by means of a multidimensional valuation approach, allows us to provide an orderly and robust representation of the resilience of the landscape at risk from building pressure while considering multiple perspectives. Full article
(This article belongs to the Special Issue Landscapes at Risk. Social Capital Asset in the COVID-Scape Climate)
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<p>Territorial framework of the municipality of Noto: (<b>a</b>) the province of Syracuse in the region of Sicily; (<b>b</b>) the municipalities in the provinces of Syracuse and Ragusa; (<b>c</b>) the territory of the municipality of Noto (our processing).</p> Full article ">Figure 2
<p>Dendrogram of the IUs in the denotation, connotation, and interpretation processes. The denotation level of the dendrogram reports the specific criteria for the assessment of the whole Rural Landscape Resilience Index (our processing).</p> Full article ">Figure 3
<p>Maps of agricultural value: (<b>a</b>) connotation; (<b>b</b>) denotations (our processing).</p> Full article ">Figure 4
<p>Maps of economic value: (<b>a</b>) connotation; (<b>b</b>) denotations (our processing).</p> Full article ">Figure 5
<p>Denotations: agricultural mosaics in 15 maps by culture prevalence in the cadastral sheets (our processing).</p> Full article ">Figure 6
<p>Maps of ecosystem value: (<b>a</b>) connotation; (<b>b</b>) denotations (our processing).</p> Full article ">Figure 7
<p>Visualization of the phenomenon of building expansion in the territory of Noto in 2000, 2007 and 2012 (our processing).</p> Full article ">Figure 8
<p>Observation of the phenomenon of building expansion in the rural area of Noto in four of the most susceptible areas in 2000, 2007 and 2012 (our processing).</p> Full article ">Figure 9
<p>Mapping of the variation in BUs over the period observed: (<b>a</b>) 2000–2007; (<b>b</b>) 2007–2012; (<b>c</b>) 2000–2012 (our processing).</p> Full article ">Figure 10
<p>Mapping of the variation in occupied area over the period observed: (<b>a</b>) 2000–2007; (<b>b</b>) 2007–2012; (<b>c</b>) 2000–2012 (our processing).</p> Full article ">Figure 11
<p>Mapping of the building expansion Index: (<b>a</b>) connotation; (<b>b</b>) denotation (our processing).</p> Full article ">Figure 12
<p>Mapping of the land/real estate heritage relationship: (<b>a</b>) connotation; (<b>b</b>) denotation (our processing).</p> Full article ">Figure 13
<p>Mapping of the real estate value index: (<b>a</b>) connotation; (<b>b</b>) denotation (our processing).</p> Full article ">Figure 14
<p>Interpretations and connotations. Mapping of: (<b>a</b>) Rural Landscape Complex Value Index; (<b>b</b>) Building Pressure Composite Index (our processing).</p> Full article ">Figure 15
<p>Mapping of the Rural Landscape Resilience Index: (<b>a</b>) interpretation; (<b>b</b>) connotations (our processing).</p> Full article ">Figure 16
<p>Visualization of the phenomenon of building expansion in a resilience index map: (<b>a</b>) in the entire municipality; (<b>b</b>) in four of the most sensitive areas in 2000, 2007 and 2012 (our processing).</p> Full article ">Figure 17
<p>Correlations between: (<b>a</b>) landscape value drivers and landscape resilience conjoint components; (<b>b</b>) building pressure drivers and landscape resilience conjoint components. The drivers are indicated in the title graphs and their values represented by the bubbles’ size (our processing).</p> Full article ">
22 pages, 2280 KiB  
Article
Impact of the Grain for Green Project on the Well-Being of Farmer Households: A Case Study of the Mountainous Areas of Northern Hebei Province, China
by Kun Wang, Piling Sun, Xin Wang, Junxiong Mo, Nan Li and Jinye Zhang
Land 2023, 12(6), 1257; https://doi.org/10.3390/land12061257 - 19 Jun 2023
Cited by 3 | Viewed by 1300
Abstract
There are close dynamic relationships among the livelihood, well-being, and ecological environment of farmer households. It is of great significance to scientifically clarify the impact of the Grain for Green policy on the livelihoods and well-being of farmer households in mountainous areas. Based [...] Read more.
There are close dynamic relationships among the livelihood, well-being, and ecological environment of farmer households. It is of great significance to scientifically clarify the impact of the Grain for Green policy on the livelihoods and well-being of farmer households in mountainous areas. Based on data from a survey of 392 farmer households in Zhangbei County, the system of indicators for livelihood assets and well-being of farmer households were constructed using the sustainable livelihood framework (SLF). The livelihood assets and well-being levels of different types of farmer households were measured, and a multiple linear regression model was used to analyze the impact of the Grain for Green policy implementation on the well-being levels of farmer households. The results showed that (1) the Grain for Green project caused changes in the livelihood of farmer households. The average livelihood diversity of farmer households was 3.008, and the returned farmland households (3.022) were higher than the nonreturned farmland households (2.975) in Zhangbei County. The level of natural assets among the total average livelihood assets of farmer households was the highest at 0.374, while the level of physical assets was the lowest at 0.018. The level of livelihood assets of returned farmland households (0.948) was lower than that of nonreturned farmland households (1.117). (2) The Grain for Green policy had an improving effect on the level of well-being of farmer households, but the effect was not significant. The level of well-being of all farmer households in Zhangbei County was 0.517, with the level of wealth contributing the most to the well-being of farmer households at 40.20% and the quality of the ecological environment contributing the least at 11.99%. The level of well-being of returned farmland households (0.518) was slightly higher than that of nonreturned farmland households (0.514). (3) The influencing degree of each factor on the level of well-being varied significantly. There are three main paths through which the Grain for Green policy affects the well-being of farmer households: by reallocating human assets, optimizing natural assets, and enhancing financial assets. The factor of household size had the highest degree, at 0.366, while educational attainment of household members, household labor capacity, annual household expenditure, livelihood diversity, number of large production tools, and total value of livestock were also important drivers of household well-being, and area of arable land was negatively associated with household well-being. There were also differences in the factors influencing the level of well-being of different types of farmer households. Full article
(This article belongs to the Special Issue Land Degradation in Environmentally Sensitive Areas (ESA) : Assessment and Conservation)
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<p>Location of the study area.</p> Full article ">Figure 2
<p>Analysis framework.</p> Full article ">Figure 3
<p>Average livelihood diversity of different types of farmer households.</p> Full article ">Figure 4
<p>Livelihood assets of farmer households.</p> Full article ">Figure 5
<p>Contribution of livelihood assets.</p> Full article ">Figure 6
<p>Contribution rates to well-being of farmer households.</p> Full article ">Figure 7
<p>Scatter plot of correlation between livelihood assets and well-being levels of farmer households. Note: The purpose of using the logarithm of the dependent variable is to narrow its range of values and highlight the correlation between the variables.</p> Full article ">
20 pages, 3855 KiB  
Article
The Filtering Effect of Oil Palm Plantations on Potential Insect Pollinator Assemblages from Remnant Forest Patches
by J. Mohd-Azlan, S. Conway, T. J. P. Travers and M. J. Lawes
Land 2023, 12(6), 1256; https://doi.org/10.3390/land12061256 - 19 Jun 2023
Cited by 1 | Viewed by 1865
Abstract
Extensive oil palm plantations worldwide are dependent on insect pollination, specifically by introduced African weevils (Elaidobius spp.). The effectiveness of these weevils has been questioned following poor pollination and yield loss in Malaysia. Indigenous thrip (Thysanoptera) species, and moths (Lepidoptera) in the [...] Read more.
Extensive oil palm plantations worldwide are dependent on insect pollination, specifically by introduced African weevils (Elaidobius spp.). The effectiveness of these weevils has been questioned following poor pollination and yield loss in Malaysia. Indigenous thrip (Thysanoptera) species, and moths (Lepidoptera) in the genus Pyroderces, may also be pollinators of oil palm, while the role of bees (Hymenoptera) and flies (Diptera) is unknown. The potential of native pollinators remains uncertain because of the almost total clearing of forest habitat from oil palm landscapes. In this study, we investigate the value of small high conservation value (HCV) forests as sources of potential native insect pollinators of oil palm in northern Sarawak. We further examine the filtering effect of oil palm-dominated landscapes on the species assemblages of six potential pollinator insect orders: Blattodea, Coleoptera, Diptera, Hemiptera, Hymenoptera and Lepidoptera. Orders differed in both species composition and abundance between forest and oil palm plantations, with an average of 28.1% of species unique to oil palm. Oil palm presented a soft permeable boundary to Coleoptera, Hymenoptera and Lepidoptera. Their species richness and abundance differed little between habitats with distance, despite species turnover. In contrast, oil palm presented a harder boundary to Diptera with a decline in both species richness and abundance with distance into oil palm. The abundance of the oil palm weevil (Elaedobius kamerunicus) was low compared to the native dominants, but similar to levels displayed by native thrips that may be pollinators of oil palm. The functional diversity of well-known pollinator guilds—bees and flies—was similar in forest and oil palm, suggesting that potential pollinators may yet exist among native orders of insects. Contrary to the prevailing opinion, even small forest patches in oil palm landscapes may provide native pollinator pressure. Full article
(This article belongs to the Special Issue Landscapes and Sustainable Farming)
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<p>Transect locations. Transects 1, 2 and 3, are situated in the Bukit Durang HCV forest patch. Transects 4, 5 and 6 are situated in the Saremas 1 conservation area. Transect lines are not drawn to scale. Lighter green indicates oil palm estate and darker the HCV forest patches.</p> Full article ">Figure 2
<p>Tree, pole and sapling density (<b>a</b>) and proportional cover of bare ground, deadwood, leaf litter, grass and shrubs (<b>b</b>) in forest and plantation.</p> Full article ">Figure 3
<p>Schematic lateral section through an oil palm plantation (<b>a</b>) and a remnant forest patch (<b>b</b>). Each section is 50 m in width and 50 m in height and is based on density estimates using 10 m<sup>2</sup> and 20 m<sup>2</sup> quadrats. In the plantation, the understory is dominated by mosses and ferns and is much more open with very little, if any, flowering plant diversity.</p> Full article ">Figure 4
<p>Morphospecies richness by order in forest habitat only, in both forest and plantation habitat and in oil palm plantation only. Morphospecies richness for each order given above each bar in red and alongside for each category.</p> Full article ">Figure 5
<p>Non-metric multidimensional scaling (MDS) ordination of species assemblages comprising all orders examined in this study. Assemblages are grouped separately for the two forest fragments and their adjacent oil palm plantations.</p> Full article ">Figure 6
<p>Non-metric multidimensional scaling (MDS) ordination of order level species assemblages between habitats (see legend in Blattodea) at transect scale.</p> Full article ">Figure 6 Cont.
<p>Non-metric multidimensional scaling (MDS) ordination of order level species assemblages between habitats (see legend in Blattodea) at transect scale.</p> Full article ">Figure 7
<p>Species richness across the ecotone from forest (green) to plantation (white). Fitted lines are second- or third-order linear functions. Data points are the mean species richness (n = 6 transects) and their 95% confidence limits. The fit of the models is given by the F-test and adjusted R<sup>2</sup>.</p> Full article ">Figure 8
<p>Mean abundance (number of individuals trapped) across the ecotone from forest (green) to plantation (white) for each order. Fitted lines are first-, second- or third-order linear functions. Data points are the mean abundance (n = 6 transects) and their 95% confidence limits. The fit of the models is given by the F-test and adjusted R<sup>2</sup>.</p> Full article ">Figure 9
<p>Abundance of <span class="html-italic">Elaiedobius kamerunicus,</span> the introduced palm oil weevil, and all bee species across the ecotone from forest (green) to plantation (white). Bukit Durang and adjacent plantation (■) and the small HCV 4 forest and adjacent plantation (●) are plotted separately for bees and the pooled data for both forest patches (<b>□</b>) for <span class="html-italic">E. kamerunicus</span>.</p> Full article ">
19 pages, 5799 KiB  
Article
Zoning and Optimization Strategies of Land Spatial Ecological Restoration in Liangjiang New Area of Chongqing Based on the Supply–Demand Relationship of Ecosystem Services
by Miaofen Hu, Hongrui Zhang, Jun Tang and Shuiyu Yan
Land 2023, 12(6), 1255; https://doi.org/10.3390/land12061255 - 19 Jun 2023
Cited by 4 | Viewed by 1560
Abstract
Ecological land restoration is necessary to develop a comprehensive land amalgamation strategy. Scientific ecological restoration zoning is crucial for the development of differentiated restoration strategies, as well as for the improvement of quality during construction. This study used a series of methods, such [...] Read more.
Ecological land restoration is necessary to develop a comprehensive land amalgamation strategy. Scientific ecological restoration zoning is crucial for the development of differentiated restoration strategies, as well as for the improvement of quality during construction. This study used a series of methods, such as the InVEST model, spatial autocorrelation, and coupling coordination degree models, using Liangjiang New Area as an example to quantify both regional ecosystem services supply and demand at the county and district levels. The land’s spatial ecological restoration zones were determined, and the optimization strategies based on the supply–demand matching and coordination relationship were presented. The results revealed the following: (1) A considerable difference was identified between the supply and demand of ecosystem services in Liangjiang New Area of Chongqing, with “high in the northeast and low in the southwest” spatial patterns for supply and “high in the southwest and low in the northeast” spatial patterns for demand; (2) The supply–demand matching relationship of ecosystem services in Liangjiang New Area of Chongqing was characterized by spatial mismatches of high supply and low demand and low supply and high demand, with an average coordination degree index of 0.2, indicating uncoordinated supply and demand; (3) Based on the supply–demand relationship of ecosystem services, the regional ecological base, and the functional orientation of upper planning, Liangjiang New Area was divided into four zones: high supply–low demand, low supply–high demand, and high supply–high demand zones, for which the respective optimization strategies were presented. In some ways, this study contributes to the existing research concerning the supply–demand relationship for small-scale ecosystem services in new development zones located in mountainous cities. Full article
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<p>Geographical location of the study area.</p> Full article ">Figure 2
<p>Study technology roadmap.</p> Full article ">Figure 3
<p>Spatial pattern of ecosystem services supply in Liangjiang New Area of Chongqing.</p> Full article ">Figure 4
<p>Spatial pattern of demand for ecosystem services in Liangjiang New Area of Chongqing.</p> Full article ">Figure 5
<p>Supply–demand quadrant distribution and spatial distribution of ecosystem services in Liangjiang New Area of Chongqing.</p> Full article ">Figure 6
<p>Supply–demand coordination degree of ecosystem services in Liangjiang New Area of Chongqing.</p> Full article ">
21 pages, 5082 KiB  
Article
Research on the Development of Deserticulture and Desertification Land Use Benefits Evaluation in Ordos City
by Zhuoran Wang and Eerdun Hasi
Land 2023, 12(6), 1254; https://doi.org/10.3390/land12061254 - 19 Jun 2023
Cited by 2 | Viewed by 1168
Abstract
The regional economy of desertification area plays a pivotal role in the land economy. Therefore, the rational development of deserticulture is of paramount significance to the economic, social, and ecological benefits of sand areas in western China. In this paper, we constructed a [...] Read more.
The regional economy of desertification area plays a pivotal role in the land economy. Therefore, the rational development of deserticulture is of paramount significance to the economic, social, and ecological benefits of sand areas in western China. In this paper, we constructed a comprehensive evaluation index system for the development of deserticulture and the benefits of desertification land use. The entropy method was used to calculate the weight of each index, which was then used to evaluate the level of development in Ordos City from 2010 to 2017. Additionally, we analyzed the coupling relationship between these two subsystems. The results indicate a gradual increase in the input, output, and environmental evaluation value of deserticulture development, as well as the economic, social, and ecological benefits of desertification land use from 2010 to 2017 in Ordos City. Additionally, there has been an overall improvement in the comprehensive evaluation value of both systems. The level of coupling and coordinated development between deserticulture development and desertification land use benefits has been further enhanced, with a significant increase in the degree of subsystem coordination. Initially, there was serious internal and external developmental discoordination in the system, which gradually improved to an overall state of barely coordinated. Full article
(This article belongs to the Special Issue New Insights in Integrated Land Management)
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<p>The study area location.</p> Full article ">Figure 2
<p>Evaluation of deserticulture development level.</p> Full article ">Figure 3
<p>Evaluation value of desertified land use.</p> Full article ">Figure 4
<p>Comprehensive benefit evaluation values of deserticulture development level and desertification land use.</p> Full article ">Figure 5
<p>Coupling coordination degree between deserticulture development subsystem and desertification land use benefits subsystem.</p> Full article ">
23 pages, 3199 KiB  
Article
Changes in Soil Properties and Crop Yield under Sustainable Conservation Tillage Systems in Spring Wheat Agroecosystems
by Jianyu Yuan, Mahran Sadiq, Nasir Rahim, Majid Mahmood Tahir, Yunliang Liang, Macao Zhuo, Lijuan Yan, Aqila Shaheen, Basharat Mahmood and Guang Li
Land 2023, 12(6), 1253; https://doi.org/10.3390/land12061253 - 19 Jun 2023
Cited by 4 | Viewed by 1789
Abstract
The cultivated soils in several semi-arid areas have very low organic matter due to climatic constraints that limit primary crop yield. Conservation tillage systems, outlined here as no tillage, no tillage with straw return and straw incorporation into the field, have been accepted [...] Read more.
The cultivated soils in several semi-arid areas have very low organic matter due to climatic constraints that limit primary crop yield. Conservation tillage systems, outlined here as no tillage, no tillage with straw return and straw incorporation into the field, have been accepted as capable systems that preserve soil’s resources and sustain soil productivity. However, in semi-arid climates, there is presently no knowledge about the influence of different conservation tillage techniques on soil’s physical, chemical and biological properties at different soil depths in spring wheat fields and only little information about spring wheat yield in these management systems. Therefore, the present study was carried out with the objective of examining the impact of conservation tillage systems on soil properties (physical, chemical and biological) and spring wheat yield. The three conservation tillage treatments consisted of no tillage system (NT), wheat stubble return with no tillage (NTS) and straw incorporation with conventional tillage (CTS), as well as one conventional tillage (CT) control treatment, which were evaluated under randomized complete block design with three replications. The three conservation tillage treatments were compared with the conventional tillage control. Conservation tillage significantly increased the bulk density, gravimetric water content, water storage, hydraulic conductivity and soil aggregates and decreased the pore space and soil temperature compared to CT; however, no significant difference was found in the case of field capacity. Soil chemical properties in the 0–40 cm soil layer increased with conservation tillage compared to CT. Conservation tillage also notably increased the soil microbial counts, urease, alkaline phosphatase, invertase, cellulase and catalase activities relative to CT. Microbial biomasses (carbon and nitrogen) and wheat yield significantly elevated under conservation tillage compared to CT. Therefore, conservation tillage could significantly improve soil properties and maintain wheat yield for the research zone. Full article
(This article belongs to the Special Issue Tillage Systems Impact Soil Structure and Cover Crop)
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<p>Map of the study area in Anding, Gansu of China.</p> Full article ">Figure 2
<p>Climatic conditions of the research area during 2019 and 2020. Error bars indicate standard error of mean values.</p> Full article ">Figure 3
<p>Soil physical quality attributes under conservation tillage in 2020. Vertical error bars represent the corresponding standard error of mean values. Different lowercase letters in the same layer indicate significant differences amongst different treatments at <span class="html-italic">p</span> &lt; 0.05 (Duncan’s test performed for mean separation). Note: (<b>a</b>–<b>c</b>) is the soil aggregates values as affected by the tillage techniques at different soil depths; (<b>d</b>–<b>f</b>) is the soil field capacity values under conservation tillage strategy at different soil layers.</p> Full article ">Figure 4
<p>Soil chemical quality attributes influenced by conservation tillage in 2020. Vertical error bars represent the corresponding standard error of mean values. Different lowercase letters in the same depth indicate significant differences amongst different treatments at <span class="html-italic">p</span> &lt; 0.05 (Duncan’s test performed for mean separation). Note: (<b>a</b>–<b>c</b>) is the soil pH values as influenced by the tillage systems at different soil depths; (<b>d</b>–<b>f</b>) is the soil electrical conductivity values under conservation tillage at different soil layers.</p> Full article ">Figure 5
<p>Effect of conservation tillage on soil biochemical indicators in 2019. Vertical error bars represent the corresponding standard error of mean values. Different lowercase letters in the same layer indicate significant differences (Duncan’s 0.05) amongst different tillage techniques. Note: (<b>a</b>–<b>c</b>) is the soil microbial biomass carbon (mg kg<sup>−1</sup> soil) as influenced by the tillage techniques at different soil layers; (<b>d</b>–<b>f</b>) is the soil microbial biomass nitrogen (mg kg<sup>−1</sup> soil) under treatments at different soil depths.</p> Full article ">Figure 6
<p>Spring wheat yield and yield-attributing traits under different tillage systems in 2020. Vertical error bars represent the corresponding standard error of mean values. Lowercase letters indicate the least significant difference (Duncan’s 0.05) amongst treatments. Note: (<b>a</b>,<b>b</b>) are the biological yield and grain yield under treatments; (<b>c</b>,<b>d</b>) are the wheat number of seeds m<sup>−2</sup> and thousand grain weight as affected by the tillage techniques.</p> Full article ">Figure 7
<p>Heat map correlation of wheat agronomic traits and soil properties. Indicates significance at: * <span class="html-italic">p &lt; 0.05</span>; ** <span class="html-italic">p &lt; 0.010</span>; *** <span class="html-italic">p &lt; 0.00010</span>. The abbreviated words stand for BD = soil bulk density; P = soil porosity; SWC = soil gravimetric water content; SWS = soil water storage; Ks = saturated hydraulic conductivity; ST = soil temperature; TN = total nitrogen; TP = total phosphorous; TK = total potassium; AN = available nitrogen; AP =available phosphorous; AK = available potassium; SOC = soil organic carbon; LFOC = light fraction organic carbon; TN = total nitrogen; AN = available nitrogen; TP = total phosphorous; AP = available phosphorous; TK = total potassium; AK = available potassium; pH = soil pH; ECe = soil electrical conductivity; MBC = microbial biomass carbon; MBN = microbial biomass nitrogen; Seed n m<sup>−2</sup> = number of seed per meter square.</p> Full article ">
21 pages, 9165 KiB  
Article
Landscape Strategies for Terraced Landscapes in the European Alpine Region Using a Mixed-Method Analysis Tool
by Enrico Pomatto, Paola Gullino, Silvia Novelli, Marco Devecchi and Federica Larcher
Land 2023, 12(6), 1252; https://doi.org/10.3390/land12061252 - 19 Jun 2023
Cited by 2 | Viewed by 1399
Abstract
Terraced landscapes are anthropic landscapes that need continuous management. Future planning policies need to develop bottom-up approaches in order to be able to take into consideration the perspectives of decision makers (DMs) and civil society stakeholders (CSs). Using a participatory mixed-method approach, this [...] Read more.
Terraced landscapes are anthropic landscapes that need continuous management. Future planning policies need to develop bottom-up approaches in order to be able to take into consideration the perspectives of decision makers (DMs) and civil society stakeholders (CSs). Using a participatory mixed-method approach, this research work identified and prioritized the strengths, weaknesses, opportunities, and threats (SWOT analysis) perceived as key factors for setting future landscape strategies. The aims were (i) to develop a methodological framework for the enhancement of the terraced landscapes using a bottom-up approach, (ii) to identify and rank the favorable and unfavorable factors affecting their management in the European Alpine Region, and (iii) to develop alternative and future landscape strategies. The methodology was applied in nine Italian and Swiss cross-border terraced landscapes. An online focus group was organized together with a decision maker from each study area in order to identify the SWOT items for their enhancement. Subsequently, a focus group for each study area was organized with civil society stakeholders. They prioritized the SWOT items based on the local context and territorial issues using a cumulative voting method. The results were normalized, and these allowed for the development of local and supralocal landscape strategies that were both common to the cross-border terraced landscapes and specific to the main land uses characterizing them. Full article
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<p>The nine cross-border Italian and Swiss terraced landscapes considered as study areas belonging to the Northwest Alpine Arch. These include the regions of Aosta Valley, Piedmont, and Lombardy in Italy and the canton of Grisons in Switzerland.</p> Full article ">Figure 2
<p>Methodological framework.</p> Full article ">Figure 3
<p>The strengths, weaknesses, opportunities, and threats (SWOT) identified by the DMs in relation to the enhancement of the cross-border Italian and Swiss terraced landscapes. Each item of the SWOT was associated by the DMs with one or more objectives of the action plan of the InTERRACED-NET European Project (A, B, C, D).</p> Full article ">Figure 4
<p>The results of the territorial prioritization of the general SWOT analyses with the use of the cumulative voting method, grouped according to the current main land uses characterizing the study areas. (<b>a</b>) strengths, and (<b>b</b>) weaknesses.</p> Full article ">Figure 5
<p>The results of the territorial prioritization of the general SWOT analyses with the use of the cumulative voting method, grouped according to the current main land uses characterizing the study areas. (<b>a</b>) opportunities, and (<b>b</b>) threats.</p> Full article ">
19 pages, 857 KiB  
Article
Evidence of Global Convergence: Perspectives for Economic and Territory Planning in Times of the COVID-19 Pandemic
by Vítor João Pereira Domingues Martinho
Land 2023, 12(6), 1251; https://doi.org/10.3390/land12061251 - 19 Jun 2023
Viewed by 1486
Abstract
Governments and international organizations have implemented efforts to promote the convergence of socioeconomic indicators between countries. The structural funds adopted by the European Union institutions are examples of policy instruments implemented to promote convergence in the GDP (gross domestic product) among the member [...] Read more.
Governments and international organizations have implemented efforts to promote the convergence of socioeconomic indicators between countries. The structural funds adopted by the European Union institutions are examples of policy instruments implemented to promote convergence in the GDP (gross domestic product) among the member states. Nonetheless, these policy measures are dependent on several internal and external factors, making these efforts vulnerable to exogenous shocks such as those associated with the global financial crisis and the COVID-19 pandemic. From this perspective, this research aims to analyze the convergence trends over the last few years and assess the respective implications of the pandemic on this framework. For that, statistical information from the World Bank for the GDP per capita was considered for the period 2006–2021 for all countries and organized for each group of levels of income and each world region. These data were analyzed through panel data approaches, considering the developments in convergence theory. The results show that the signs of convergence are different for each level of income and each region, highlighting the idea of clubs of convergence. On the other hand, the pandemic disturbed the trends of convergence verified worldwide, but nonetheless, it seems to be on a smaller scale than the global financial crisis. In any case, these findings should be confirmed in future research with more recent data. Full article
(This article belongs to the Special Issue Landscapes at Risk. Social Capital Asset in the COVID-Scape Climate)
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<p>Sigma convergence (coefficient of variation), for the GDP per capita (PPP, constant 2017 international <span>$</span>), over the period 2006–2021, considering all countries.</p> Full article ">Figure 2
<p>The average coefficient of variation, for the GDP per capita (PPP, constant 2017 international <span>$</span>), over the period 2006–2021, considering each group of levels of income.</p> Full article ">Figure 3
<p>The average coefficient of variation, for the GDP per capita (PPP, constant 2017 international <span>$</span>), over the period 2006–2021, considering each world region.</p> Full article ">
20 pages, 4894 KiB  
Article
Coupling Biodiversity and Human Pressures to Indicate Conservation Priorities for Threatened Waterfowl Species: A Case in the Henan Yellow River Wetland National Nature Reserve
by Yang Cao, Siyu Wang, Guohang Tian, Nalin Dong and Yakai Lei
Land 2023, 12(6), 1250; https://doi.org/10.3390/land12061250 - 19 Jun 2023
Cited by 3 | Viewed by 1684
Abstract
Following severe anthropogenic pressure from rapid economic development, wetland biodiversity is now decreasing alarmingly, thus leading to adverse effects. Protected areas (PAs) can be crucial conservation tools to secure wetland biodiversity. However, whether these PAs exhibit high conservation efficiency in buffering wildlife and [...] Read more.
Following severe anthropogenic pressure from rapid economic development, wetland biodiversity is now decreasing alarmingly, thus leading to adverse effects. Protected areas (PAs) can be crucial conservation tools to secure wetland biodiversity. However, whether these PAs exhibit high conservation efficiency in buffering wildlife and habitats from human pressures needs to be understood. Given their sensitivity to habitat quality and regional resource changes, threatened waterfowl could be suitable wetland ecosystem indicators. This study examined the conservation effectiveness of Henan Yellow River Wetland National Nature Reserve (HYRWNNR), which is a crucial region on the East Asia–Australia route for global bird migration. We performed Maximum Entropy (MaxEnt) modeling based on field survey data of the 19 threatened waterfowl species, and Human Impact Index (HII) was further mapped with waterfowls distribution to identify the conservation gap and priorities of the HYRWNNR. The results indicated that threatened waterfowl distribution were affected by both environmental factors and human pressure, and a conservation gap existed in the HYRWNNR. Two conservation scenarios were generated based on the spatial pattern of conservation priorities, and their corresponding management strategies were suggested. This study identifies conservation priorities from a novel perspective by synthesizing habitat suitability and human pressure, which can present basic information regarding the HYRWNNR management while supporting waterfowl conservation planning, ultimately promoting wetland habitats sustainability. Full article
(This article belongs to the Special Issue Monitoring and Simulation of Wetland Ecological Processes)
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<p>Map of the study area.</p> Full article ">Figure 2
<p>Methodology flowchart.</p> Full article ">Figure 3
<p>Land use in the HYRWNNR.</p> Full article ">Figure 4
<p>The conceptual framework for identifying potential conservation areas in the HYRWNNR.</p> Full article ">Figure 5
<p>Contribution of environmental variables. Abbreviations: bio1—Annual average temperature, the bio5—Max temperature of the warmest month, bio7—Temperature annual range, the bio8—Mean temperature of the wettest quarter, bio12—Annual Precipitation, bio14—Precipitation of the driest month, bio15—Precipitation Seasonality.</p> Full article ">Figure 6
<p>Location of five categories of suitable areas for biodiversity hotspots of threatened waterfowl in the HYRWNNR based on the MaxEnt model.</p> Full article ">Figure 7
<p>Location of five categories of different human influence in the HYRWNNR.</p> Full article ">Figure 8
<p>Moran scatterplot for human pressure and distribution of suitable habitat.</p> Full article ">Figure 9
<p>Distribution of significant LISAs for human pressure and distribution of suitable habitat.</p> Full article ">
24 pages, 7106 KiB  
Article
Changes in the Spatial Distribution of the Employed Population in the Yangtze River Delta Region since the 21st Century: An Analysis and Discussion Based on Census Data
by Chen Chen
Land 2023, 12(6), 1249; https://doi.org/10.3390/land12061249 - 19 Jun 2023
Cited by 1 | Viewed by 1347
Abstract
Focusing on the Yangtze River Delta region, the spatial distribution and change characteristics of the employed population were assessed by selecting three time points: 2000, 2010 and 2020. Firstly, a correlation was established between population employment statistics and spatial units of administrative divisions [...] Read more.
Focusing on the Yangtze River Delta region, the spatial distribution and change characteristics of the employed population were assessed by selecting three time points: 2000, 2010 and 2020. Firstly, a correlation was established between population employment statistics and spatial units of administrative divisions to analyze the spatial distribution characteristics of the employed population in general and by industry; secondly, the changing characteristics of the spatial distribution of the employed population over time, including the migration of the centroid and density changes, were analyzed; thirdly, a systematic clustering approach was adopted to carry out a typological analysis of 41 cities in the Yangtze River Delta from three perspectives: industrial structure, time stage and spatial level. It was found that (1) regional differences within the Yangtze River Delta are still significant, but are narrowing; (2) different cities or regions show different characteristics of development stages, and late-developing regions can learn from early developing regions; (3) metropolitan areas are still the main areas of employment concentration, and the spatial distribution of employment in some cities is beginning to suburbanize. Full article
(This article belongs to the Special Issue Regional Sustainable Development of Yangtze River Delta, China II)
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<p>Principles of Focal Statistical Analysis (Source: author’s own processing).</p> Full article ">Figure 2
<p>Population density of permanent residents in 2020 (all data) (Source: author’s own processing).</p> Full article ">Figure 3
<p>Employment density of 2020 (long-form data, 10% sample) (Source: author’s own processing).</p> Full article ">Figure 4
<p>Employment density of primary industry in 2020 (long-form data, 10% sample) (Source: author’s own processing).</p> Full article ">Figure 5
<p>Employment density of secondary industry in 2020 (long-form data, 10% sample) (Source: author’s own processing).</p> Full article ">Figure 6
<p>Employment density of tertiary industry in 2020 (long-form data, 10% sample) (Source: author’s own processing).</p> Full article ">Figure 7
<p>Mean centers of permanent residents and employed population from 2000 to 2020 (Source: author’s own processing).</p> Full article ">Figure 8
<p>Focal statistics results of Employed population density in 2020 (R = 20 km) (Source: author’s own processing).</p> Full article ">Figure 9
<p>Changes in employment density from 2000 to 2020 (Source: author’s own processing).</p> Full article ">Figure 10
<p>Changes in employment density of the primary industry from 2000 to 2020 (Source: author’s own processing).</p> Full article ">Figure 11
<p>Changes in employment density of the secondary industry from 2000 to 2020 (Source: author’s own processing).</p> Full article ">Figure 12
<p>Changes in employment density of the tertiary industry from 2000 to 2020 (Source: author’s own processing).</p> Full article ">Figure 13
<p>Dendrogram using average linkage of Cluster by industry structure of 2020.</p> Full article ">Figure 14
<p>Map visualization results of industrial structure clustering analysis in 2020 (Source: author’s own processing).</p> Full article ">Figure 15
<p>Map visualization results of employment changes clustering analysis over two decades of 2000–2010 and 2010–2020 (Source: author’s own processing).</p> Full article ">Figure 16
<p>Map visualization results of employment industry structure changes clustering analysis over two decades of 2000–2010 and 2010–2020 (Source: author’s own processing).</p> Full article ">Figure 17
<p>Map visualization results of employment industry structure clustering analysis over central urban and municipal jurisdiction area (Source: author’s own processing).</p> Full article ">Figure 18
<p>Map visualization results of employment changes clustering analysis based on different spatial levels from 2000 to 2020 (Source: author’s own processing).</p> Full article ">
23 pages, 2302 KiB  
Article
“Location, Location, Location”: Fluctuations in Real Estate Market Values after COVID-19 and the War in Ukraine Based on Econometric and Spatial Analysis, Random Forest, and Multivariate Regression
by Laura Gabrielli, Aurora Greta Ruggeri and Massimiliano Scarpa
Land 2023, 12(6), 1248; https://doi.org/10.3390/land12061248 - 19 Jun 2023
Cited by 5 | Viewed by 2136
Abstract
In this research, the authors aim to detect the marginal appreciation of construction and neighbourhood characteristics of property prices at three different time points: before the COVID-19 pandemic, two years after the first COVID-19 alert but before the War in Ukraine, and one [...] Read more.
In this research, the authors aim to detect the marginal appreciation of construction and neighbourhood characteristics of property prices at three different time points: before the COVID-19 pandemic, two years after the first COVID-19 alert but before the War in Ukraine, and one year after the outbreak of the War. The marginal appreciations of the building’s features are analysed for a pilot case study in Northern Italy using a Random Forest feature importance analysis and a Multivariate Regression. Several techniques are integrated into this study, such as computer programming in Python language, multi-parametric value assessment techniques, feature selection procedures, and spatial analysis. The results may represent an interesting ongoing monitoring of how these anomalous events affect the buyer’s willingness to pay for specific characteristics of the buildings, with particular attention to the location features of the neighbourhood and accessibility. Full article
(This article belongs to the Special Issue Landscapes at Risk. Social Capital Asset in the COVID-Scape Climate)
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<p>Workflow and the methodological approach.</p> Full article ">Figure 2
<p>Historical series of the asking prices in Padova for residential properties in optimal/good/poor conditions (immobiliare.it, data collected at City level).</p> Full article ">Figure 3
<p>Historical series of the transaction prices in Padova for residential properties in optimal/good/poor conditions (Nomisma, data collected at City level).</p> Full article ">Figure 4
<p>Collected and geo-localised observations in Padova.</p> Full article ">Figure 5
<p>Univariate Moran I.</p> Full article ">
15 pages, 3697 KiB  
Article
Land Use Change and Its Impact on Ecological Risk in the Huaihe River Eco-Economic Belt
by Huaijun Wang, Ru Feng, Xinchuan Li, Yaxue Yang and Yingping Pan
Land 2023, 12(6), 1247; https://doi.org/10.3390/land12061247 - 18 Jun 2023
Cited by 5 | Viewed by 1395
Abstract
Exploring the landscape ecological security pattern and its driving mechanisms in key economic zones is of great significance for preventing and resolving landscape ecological risks and promoting regional sustainable development. This study quantitatively analyzed the land use change characteristics in the Huaihe River [...] Read more.
Exploring the landscape ecological security pattern and its driving mechanisms in key economic zones is of great significance for preventing and resolving landscape ecological risks and promoting regional sustainable development. This study quantitatively analyzed the land use change characteristics in the Huaihe River Eco-economic Belt from 1980 to 2020 using the land use transfer matrix and land use intensity index. Further, the evolution of ecological risks and their driving mechanisms were investigated using the landscape pattern index and hierarchical partitioning analysis. The results show that (1) in terms of absolute area, dryland, grassland, and paddy land decreased by 7075 km2, 2708 km2, and 1874 km2, respectively, while urban–rural land and water area increased by 10,538 km2 and 1336 km2, respectively. In terms of change intensity, grassland, water area, urban–rural land, and unused land exhibited the most dramatic change, whereas forest land, paddy land, and dryland exhibited weaker change. (2) The conversions in the study area were primarily between dryland, paddy land, and urban–rural land. Paddy land and dryland tended to convert to urban–rural land, which is further likely to be transformed into dryland and unused land when converted. (3) The study area mainly presented medium to low ecological risk. Overall, the ecological risk remained stable throughout the study period. Nevertheless, Jining, Zaozhuang, and Bengbu show high ecological risks in the construction of the economic zone. (4) Forest land explained 40% of the variance in landscape risk, whereas urban–rural land and dryland each explained 20% of the variance. An increase in the proportion of urban–rural land and dryland will increase landscape ecological risk. However, after urban–rural land exceeds 15%, the ecological security risk does not increase significantly with increasing proportion of urban–rural land. Full article
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<p>Study area and land use in 2020.</p> Full article ">Figure 2
<p>Characteristics of land use change in the Huaihe Eco-economic Belt based on Sankey diagram.</p> Full article ">Figure 3
<p>Intensity of land use change at the interval level in the Huaihe River Eco-economic Belt (<b>a</b>: <span class="html-italic">S<sub>t</sub></span> change during 1980-2020; <b>b</b>: <span class="html-italic">S<sub>t</sub></span> change during 1980–1990; <b>c</b>: <span class="html-italic">S<sub>t</sub></span> change during 1990–2000; <b>d</b>: <span class="html-italic">S<sub>t</sub></span> change during 2000–2010; <b>e</b>: <span class="html-italic">S<sub>t</sub></span> change during 2010–2020).</p> Full article ">Figure 4
<p>Spatial distribution of ecological risks in the Huaihe Eco-economic Belt (<b>a</b>: ecological risk at grid scale in 1980; <b>b</b>: ecological risk at grid scale in 1990; <b>c</b>: ecological risk at grid scale in 2000; <b>d</b>: ecological risk at grid scale in 2010; <b>e</b>: ecological risk at grid scale in 2020).</p> Full article ">Figure 5
<p>Spatial distribution of ecological risks in the Huaihe Eco-economic Belt at the administrative level (<b>a</b>: ecological risk at administrative level in 1980; <b>b</b>: ecological risk at administrative level in 1990; <b>c</b>: ecological risk at administrative level in 2000; <b>d</b>: ecological risk at administrative level in 2010; <b>e</b>: ecological risk at administrative level in 2020).</p> Full article ">Figure 6
<p>Relationship between ecological risk and the proportion of different land use types (<b>a</b>: relationship between ecological risk and forest proportion; <b>b</b>: relationship between ecological risk and grassland proportion; <b>c</b>: relationship between ecological risk and water proportion; <b>d</b>: relationship between ecological risk and urban–rural proportion; <b>e</b>: relationship between ecological risk and unutilized land proportion; <b>f</b>: relationship between ecological risk and paddy proportion; <b>g</b>: relationship between ecological risk and dry land proportion).</p> Full article ">
19 pages, 12090 KiB  
Article
An Enhanced Algorithm for Active Fire Detection in Croplands Using Landsat-8 OLI Data
by Yizhu Jiang, Jinling Kong, Yanling Zhong, Qiutong Zhang and Jingya Zhang
Land 2023, 12(6), 1246; https://doi.org/10.3390/land12061246 - 18 Jun 2023
Cited by 1 | Viewed by 1418
Abstract
Burning biomass exacerbates or directly causes severe air pollution. The traditional active fire detection (AFD) methods are limited by the thresholds of the algorithms and the spatial resolution of remote sensing images, which misclassify some small-scale fires. AFD for burning straw is interfered [...] Read more.
Burning biomass exacerbates or directly causes severe air pollution. The traditional active fire detection (AFD) methods are limited by the thresholds of the algorithms and the spatial resolution of remote sensing images, which misclassify some small-scale fires. AFD for burning straw is interfered with by highly reflective buildings around urban and rural areas, resulting in high commission error (CE). To solve these problems, we developed a multicriteria threshold AFD for burning straw (SAFD) based on Landsat-8 imagery in the context of croplands. In solving the problem of the high CE of highly reflective buildings around urban and rural areas, the SAFD algorithm, which was based on the LightGBM machine learning method (SAFD-LightGBM), was proposed to differentiate active fires from highly reflective buildings with a sample dataset of buildings and active fires and an optimal feature combining spectral features and texture features using the ReliefF feature selection method. The results revealed that the SAFD-LightGBM method performed better than the traditional threshold method, with CE and omission error (OE) of 13.2% and 11.5%, respectively. The proposed method could effectively reduce the interference of highly reflective buildings for active fire detection, and it has general applicability and stability for detecting discrete, small-scale fires in urban and rural areas. Full article
(This article belongs to the Section Land – Observation and Monitoring)
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<p>Location of the Landsat-8 images used in this study.</p> Full article ">Figure 2
<p>(<b>a</b>) Simulated reflectance spectra of active fires. (<b>b</b>) The difference between the spectral reflectance of active burning fires and typical land features from Landsat-8 in the study area.</p> Full article ">Figure 3
<p>Scatterplot of the range of potential active fires in the context of croplands. The blue line shows the OLS regression, and the green, dashed line represents the predicted range based on the lower 3σ predicted by the OLS regression. The dashed vertical lines represent the thresholds derived via Equation (13). The bottom-right range between the dash-dotted line and the dashed vertical line is the range of possible active fire pixels. The histogram on the right represents the density in the bands (B4 and B7), where red represents greater density.</p> Full article ">Figure 4
<p>Histogram of ratio of the TOA reflectance of B7 and B6 (the horizontal axis represents the TOA reflectance value of B7/B6 with an interval of 0.2, the red dashed line represents the threshold for distinguishing fire from non-fire. The left-hand <span class="html-italic">y</span>-axis represents the frequency of the reflectance, and the right-hand <span class="html-italic">y</span>-axis indicates the correct rate of true active fires in the range of the horizontal coordinates). The line graph corresponds to the correct percentage of active fires within the interval.</p> Full article ">Figure 5
<p>Importance of the ReliefF feature selection method.</p> Full article ">Figure 6
<p>Validation of <span class="html-italic">Logloss</span> and accuracy for the SAFD-LightGBM. The lowest <span class="html-italic">Logloss</span> and the highest accuracy were seen for N = 52.</p> Full article ">Figure 7
<p>Comparison between the SAFD algorithm and the SAFD-LigthtGBM algorithm. (<b>a</b>) A burning fire in a rural area. (<b>b</b>) A burning fire in an urban area. Red pixels are the results predicted by the detection algorithms, the bright blue boxes represent results that detected the burning fires (marked as position a), and the yellow boxes represent false detections caused by highly reflective buildings (marked as position b to f).</p> Full article ">Figure 8
<p>False detection results of different types of highly reflective buildings: (<b>a</b>) factories; (<b>b</b>) a large stadium; (<b>c</b>) residential buildings. The red boxes indicates the locations of the anomalies, the bright blue pixels represent the highly reflective buildings (in the first column), and the second and third columns represent the results of the SAFD and SAFD-LightGBM algorithms, respectively, with a binary mask. The buildings on the Landsat-8 images were categorized by Google Earth images from the corresponding dates.</p> Full article ">Figure 9
<p>The scatterplots of the SAFD and SAFD-LightGBM algorithms. In (<b>a</b>), blue indicates the values of B6 and B7 from the Landsat-8 images of croplands that were removed by the SAFD algorithm, and orange in (<b>b</b>) indicates the results of the SAFD algorithm. In (<b>c</b>,<b>e</b>), blue-green indicates the misclassifications of the SAFD and SAFD-LightGBM algorithms, respectively. (<b>d</b>) indicates the results of the SAFD-LightGBM algorithm.</p> Full article ">Figure 10
<p>The algorithms’ commission errors for areas with different dominant seasons.</p> Full article ">Figure 11
<p>The influence of hyperparameter settings on the model’s commission errors and omission errors. (<b>a</b>) Variation of OE and CE with different step intervals for the learning_rate parameter. (<b>b</b>) Variation of OE and CE with different step intervals for the n_estimators parameter. (<b>c</b>) Variation of OE and CE with different step intervals for the num_leaves parameter.</p> Full article ">
22 pages, 10934 KiB  
Article
Identification and Classification of Urban Shrinkage in Northeast China
by Xiaosong Ma, Qingwu Yan, Qinke Pan, Xingshan Chen and Guie Li
Land 2023, 12(6), 1245; https://doi.org/10.3390/land12061245 - 17 Jun 2023
Cited by 5 | Viewed by 2144
Abstract
The phenomenon of shrinking cities is a significant challenge faced by many cities today. To more accurately identify the leading factors driving urban shrinkage and develop rational recommendations, precise identification and classification of urban shrinkage has become an indispensable part of the process. [...] Read more.
The phenomenon of shrinking cities is a significant challenge faced by many cities today. To more accurately identify the leading factors driving urban shrinkage and develop rational recommendations, precise identification and classification of urban shrinkage has become an indispensable part of the process. This paper focuses on the typical population loss region of China’s three northeastern provinces, using 497 identified physical cities as the basic research unit. Based on multi-source geographical big data and utilizing the geographically weighted regression (GWR) model, spatial modeling of population in the three provinces of northeast China was conducted, resulting in spatialized population data, followed by identification and classification of shrinking cities among the physical cities. Cities with a total population change rate of less than 0 are defined as shrinking cities. In cities where the total population change rate is greater than 0, cities with both a city shrinking area ratio and a decreased population ratio greater than 5% are defined as locally shrinking cities. Based on this, 90 (18.1%) shrinking cities and 118 (23.7%) locally shrinking cities were identified within the three provinces of northeast China. The phenomenon of urban shrinkage is distributed throughout various regions, mainly in smaller cities located near larger cities. According to the standards of the urban shrinkage classification model, the spatial pattern of population loss regions was divided into four types, identifying 13 (6.3%) global type, 111 (53.4%) concentrated type, 64 (30.7%) perforated type, and 20 (9.6%) edge type. Analysis of shrinking cities based on their classification revealed that the main reasons for urban shrinkage are the decline and dissolution of large industrial enterprises, abandonment and neglect of buildings, and unreasonable design planning in cities. Economic development and inward population flow can be promoted in shrinking cities by creating job opportunities, improving living standards, developing transportation, adjusting urban planning or concentrating urban population, as well as vigorously developing urban center areas. These measures can provide support for the revival and development of shrinking cities. Full article
(This article belongs to the Topic Urban Land Use and Spatial Analysis)
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<p>Map of the study area of the three provinces of northeast China.</p> Full article ">Figure 2
<p>POI kernel density analysis comparison chart.</p> Full article ">Figure 3
<p>Schematic diagram of urban shrinkage identification.</p> Full article ">Figure 4
<p>The spatial distribution of physical cities.</p> Full article ">Figure 5
<p>Zipf’s law.</p> Full article ">Figure 6
<p>Results of population spatial distribution.</p> Full article ">Figure 7
<p>Precision validation of population spatialization.</p> Full article ">Figure 8
<p>Spatial distribution of shrinking cities.</p> Full article ">Figure 9
<p>Spatial distribution of locally shrinking cities.</p> Full article ">
12 pages, 2313 KiB  
Article
Simulation of Grassland SOC under Future-Climate Scenarios in Gansu, China
by Meiling Zhang, Xiaojuan Li and Xiaoni Liu
Land 2023, 12(6), 1244; https://doi.org/10.3390/land12061244 - 17 Jun 2023
Cited by 1 | Viewed by 1092
Abstract
The impacts of global warming on the grassland carbon cycle are increasingly severe. To explore the spatiotemporal variation in grassland soil organic carbon (SOC) and its response to climate change in Gansu Province, in this study, we designed five future-climate-scenario simulations (2019–2048), based [...] Read more.
The impacts of global warming on the grassland carbon cycle are increasingly severe. To explore the spatiotemporal variation in grassland soil organic carbon (SOC) and its response to climate change in Gansu Province, in this study, we designed five future-climate-scenario simulations (2019–2048), based on the baseline (1989–2018), according to the IPCC Fifth Assessment Report. The CENTURY biogeochemistry model was used to estimate the SOC of Gansu Province. One-way ANOVA and an error analysis were used to verify the model. Meanwhile, a Pearson coefficient diagram was used to analyze the main influencing factors of SOC. The results revealed that there was a good agreement between the observed and predicted SOC. The quarterly and inter-annual SOC trends of the five future-climate-scenario simulations were similar to those of the baseline simulation. The most extensive SOC storage occurred in the central Gannan region, in the simulation B scenario (temperature increase of 2 °C, no change in precipitation, and double the CO2 concentration). Temperature had a significant negative effect on SOC. Precipitation had a weak impact on SOC. The results indicate that SOC was more sensitive to temperature than to precipitation. Full article
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<p>Spatial distribution of meteorological stations.</p> Full article ">Figure 2
<p>Comparison of the observed SOC with the predicted SOC. The blue dot mainly tests whether the predicted SOC is closely related to the measured SOC.</p> Full article ">Figure 3
<p>Temporal changes of quarterly and inter-annual grassland SOC under different climate simulations. (<b>a</b>,<b>c</b>) represent SOC values in the baseline simulation; (<b>b</b>,<b>d</b>) represent SOC values under five future-climate simulations.</p> Full article ">Figure 4
<p>Spatial change trend of annual average grassland SOC under different climate scenarios. (<b>a</b>–<b>f</b>) represents the baseline and simulations A, B, C, D, and E, respectively.</p> Full article ">Figure 5
<p>Response of SOC to climate change on a regional scale, under different climate scenarios. Light blue is simulation A, pink is simulation B, dark blue is simulation C, yellow is simulation D, and red is simulation E.</p> Full article ">Figure 6
<p>Correlation matrix between SOC and the model variables. AMT represents the average annual temperature, AMP represents the average yearly precipitation, LAT represents the latitude, LON represents the longitude, and ALT represents the altitude. The color line on the right represents the correlation coefficient between variables. The darker the blue, the more significant the positive correlation. The darker the red, the more significant the negative correlation. The greater the fan-shaped proportion of each color, the more significant the positive or negative correlation; the smaller the proportion, the less significant the positive or negative correlation.</p> Full article ">
18 pages, 3461 KiB  
Article
A Geospatial Modelling Approach to Assess the Capability of High-Country Stations in Delivering Ecosystem Services
by Fabiellen C. Pereira, Stuart Charters, Carol M. S. Smith, Thomas M. R. Maxwell and Pablo Gregorini
Land 2023, 12(6), 1243; https://doi.org/10.3390/land12061243 - 17 Jun 2023
Cited by 3 | Viewed by 1284
Abstract
The creation of more sustainable land use strategies is paramount to designing multifunctional agricultural landscapes that allow grasslands to continually deliver multiple ecosystem services. A mapping modelling approach would provide us with a tool for system diagnosis to better assess the value of [...] Read more.
The creation of more sustainable land use strategies is paramount to designing multifunctional agricultural landscapes that allow grasslands to continually deliver multiple ecosystem services. A mapping modelling approach would provide us with a tool for system diagnosis to better assess the value of a landscape and define place-based practices for designing more context-adjusted systems that are in synergy with the complexity of grasslands. To assess the potential capability of a high-country pastoral livestock production system in New Zealand in delivering ecosystem services, this work uses a geospatial model as a decision support tool to identify management practices that enhance grassland health. The model uses national, climatic, soil, and landcover data to assess the agricultural productivity, flood mitigation, C sequestration, erosion, and sediment delivery capacity of a case study high-country station in New Zealand. Model outcomes suggest that the station has the potential for increased agricultural productivity although varying spatially, a high flood mitigation capacity, a high capacity for C sequestration, a moderate risk of erosion, a capacity to reduce sediment delivery to streams, and overall, a low to moderate nitrogen and phosphorus accumulation. Output maps display a spatial visualisation of ecosystem services associated with the landscape topography, soil, and vegetation patterns that allow the identification of neglected areas and planning of best place-based management practices strategies to enhance the health of grasslands. Full article
(This article belongs to the Special Issue High-Altitude Grasslands: Biodiversity Conservation, Management, and Habitat Preference)
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<p>The geographic location of Lincoln University Mount Grand Station, latitude 44°38′01.93″ S; longitude 169°19′42.89″ E, and paddocks boundary. From left to right: The map of New Zealand (NZ), Lake Hawea in Central Otago, and the paddocks boundary of the station.</p> Full article ">Figure 2
<p>(<b>A</b>) Land cover classification of Lincoln University Mount Grand station (LUMGS) derived from New Zealand Land Cover Database (LCDB) classes at version 5, and (<b>B</b>) land cover classification of LUMGS according to a user-defined parameterisation in which land cover was assigned based on information collected from the farm.</p> Full article ">Figure 3
<p>(<b>A</b>) Agricultural evaluation of the current land regime of Lincoln University Mount Grand Station (LUMGS) based on the national land cover database (LCDB), (<b>B</b>) agricultural valuation of the land potentiality based on topographic (slope, aspect, soil hydraulic properties, and soil fertility according to New Zealand Fundamental Soil layer) and climatic information, and (<b>C</b>) agricultural valuation to assess if and where LUMGS land is over or under-utilisation, according to a categorisation system based on the land current regime and potential agricultural value as estimated by The Land Utilisation and Capability Indicator (LUCI) model.</p> Full article ">Figure 4
<p>(<b>A</b>) Agricultural evaluation of the current land regime of Lincoln University Mount Grand Station (LUMGS) based on the user-defined land cover database, and (<b>B</b>) agricultural valuation examining if and where the land is over or under-utilisation, according to a categorisation system based on the current land regime and potential agricultural value based on the station topographic and climatic information as estimated by The Land Utilisation and Capability Indicator (LUCI) model.</p> Full article ">Figure 5
<p>(<b>A</b>) Flood mitigation classification of Lincoln University Mount Grand Station based on the national land cover database (LCDB) and (<b>B</b>) based on the user-defined land cover database as estimated by The Land Utilisation and Capability Indicator (LUCI) model.</p> Full article ">Figure 6
<p>(<b>A</b>) Potential C emission/sequestration estimation of Lincoln University Mount Grand Station based on the national landcover database, and (<b>B</b>) based on the user-defined land cover database as estimated by The Land Utilisation and Capability Indicator (LUCI) model.</p> Full article ">Figure 7
<p>(<b>A</b>) Lincoln University Mount Grand Station erosion vulnerability and (<b>B</b>) sediment delivery vulnerability based on the national land cover database (LCDB), and (<b>C</b>) LUMGS erosion vulnerability and (<b>D</b>) sediment delivery vulnerability based on the user-defined land cover database as estimated by The Land Utilisation and Capability Indicator (LUCI) model.</p> Full article ">Figure 8
<p>(<b>A</b>) Nitrogen accumulated load classification and (<b>B</b>) phosphorus accumulated load classification of Lincoln University Mount Grand station (LUMGS) based on the national land cover database (LCDB), and (<b>C</b>) nitrogen accumulated load classification and (<b>D</b>) phosphorus accumulated load classification of LUMGS based on the user-defined land cover database as estimated by The Land Utilisation and Capability Indicator (LUCI) model.</p> Full article ">Figure 9
<p>Areas (red) of Lincoln University Mount Grand station where a management intervention to enhance one ecosystem service (agricultural productivity, flood mitigation, erosion control, C sequestration, reduction of sediment delivery, or reduction of N and P load) would negatively affect at least one of the others services (<b>A</b>) and areas (green) of Lincoln University Mount Grand station where multi ecosystem services (erosion, flood mitigation, nitrogen, and phosphorus concentration) are positively interacting and the priority of one does not negatively affect others (<b>B</b>) as estimated by The Land Utilisation and Capability Indicator (LUCI) model.</p> Full article ">
20 pages, 17672 KiB  
Article
A Comparative Study on Land Use/Land Cover Change and Topographic Gradient Effect between Mountains and Flatlands of Southwest China
by Li Wu, Yanjun Yang, Hailan Yang, Binggeng Xie and Weiqun Luo
Land 2023, 12(6), 1242; https://doi.org/10.3390/land12061242 - 17 Jun 2023
Cited by 3 | Viewed by 1754
Abstract
Topography plays an important role in restricting the formation of and change in land use/land cover (LULC) patterns. To compare the LULC change and topographic gradient effects between mountains and flatlands, the geo-informatic atlas, terrain position index, distribution index and diversity index were [...] Read more.
Topography plays an important role in restricting the formation of and change in land use/land cover (LULC) patterns. To compare the LULC change and topographic gradient effects between mountains and flatlands, the geo-informatic atlas, terrain position index, distribution index and diversity index were used to analyze the LULC patterns in Yuxi from 2000 to 2020. The results were as follows: (1) the temporal–spatial variation in LULC was obviously different. From 2000 to 2020, land use change in the flatlands was more severe than that in the mountains. The transfer amount of forestland in the mountains was the largest, with the transfer-out and transfer-in accounting for 48.53% and 31.05%. However, in the flatlands, the biggest changes were found in the transfer-out of cultivated land and the transfer-in of build-up land, which were 46.91% and 38.20%, respectively. The LULC types in the mountains changed dramatically from 2000 to 2010, while those in the flatlands changed dramatically from 2010 to 2020. (2) There were obvious differences in the topographic gradient effects. The dominant distributions of land use types in the low-terrain area were the same, but the dominance of build-up land in the flatlands and that of wetland in the mountains were the largest. In the mountains, the dominant distribution of grassland was in the medium-terrain position, while that of forestland was in the high position, and the opposite was found in the flatlands. In addition, the variation trend of the diversity index in the mountains was relatively simple, but the variation range was large, ranging from 0 to 1.677, and high diversity was mainly found in the medium- and high-terrain positions. However, the variation trend in the flatlands was complex, but only ranged from 0.918 to 1.994, and high diversity was found in the low-terrain positions. The differences in the LULC change and terrain gradient effects between the mountains and flatlands were mainly caused by natural, socio-economic and policy factors, which can provide a certain reference for differentiated land use policies for regional coordinated and sustainable development. Full article
(This article belongs to the Topic Karst Environment and Global Change)
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<p>The geographical location and characteristics of the study area: (<b>a</b>) the location of Yunnan Province in China; (<b>b</b>) the location of Yuxi in Yunnan Province; (<b>c</b>) the elevation and landforms of Yuxi City.</p> Full article ">Figure 2
<p>LULC distribution and area statistics in the mountains. (<b>a</b>–<b>c</b>) LULC distribution from 2000 to 2020. (<b>d</b>) Area statistics. LULC types include cultivated land (CL), forestland (FL), grassland (GL), wetland (WL), water bodies (WB) and build-up land (BL).</p> Full article ">Figure 3
<p>LULC distribution and area statistics in the flatlands. (<b>a</b>–<b>c</b>) LULC distribution from 2000 to 2020. (<b>d</b>) Area statistics.</p> Full article ">Figure 4
<p>LULC change in the mountains from 2000 to 2020. (<b>a</b>–<b>c</b>) LULC transfer chord diagram from 2000 to 2010, from 2010 to 2020 and from 2000 to 2020, respectively. (<b>d</b>) LULC change characteristic map from 2000 to 2020.</p> Full article ">Figure 5
<p>LULC change in the flatlands from 2000 to 2020. (<b>a</b>–<b>c</b>) LULC transfer chord diagram from 2000 to 2010, from 2010 to 2020 and from 2000 to 2020, respectively. (<b>d</b>) LULC change characteristic map from 2000 to 2020.</p> Full article ">Figure 6
<p>Spatial distribution of topographic features: (<b>a</b>) elevation; (<b>b</b>) slope; (<b>c</b>) relief; (<b>d</b>) terrain position.</p> Full article ">Figure 7
<p>Distribution index of land use types on the topographic gradient in the mountains: (<b>a</b>–<b>c</b>) LULC distribution index for different topographic gradients in 2000, 2010 and 2020, respectively; (<b>d</b>) LULC distribution index for different topographic gradients in the three years.</p> Full article ">Figure 7 Cont.
<p>Distribution index of land use types on the topographic gradient in the mountains: (<b>a</b>–<b>c</b>) LULC distribution index for different topographic gradients in 2000, 2010 and 2020, respectively; (<b>d</b>) LULC distribution index for different topographic gradients in the three years.</p> Full article ">Figure 8
<p>Distribution index of land use types on the topographic gradient in the flatlands: (<b>a</b>–<b>c</b>) LULC distribution index for different topographic gradients in 2000, 2010 and 2020, respectively; (<b>d</b>) LULC distribution index for different topographic gradients in the three years.</p> Full article ">Figure 9
<p>Diversity indices of land use types on different topographic gradients: (<b>a</b>) Land use diversity index of different topographic gradients in the mountains; (<b>b</b>) Land use diversity index of different topographic gradients in the flatlands.</p> Full article ">
15 pages, 335 KiB  
Article
Energy Colonialism: A Category to Analyse the Corporate Energy Transition in the Global South and North
by Josefa Sánchez Contreras, Alberto Matarán Ruiz, Alvaro Campos-Celador and Eva Maria Fjellheim
Land 2023, 12(6), 1241; https://doi.org/10.3390/land12061241 - 16 Jun 2023
Cited by 9 | Viewed by 4994
Abstract
This article aims to define the category of energy colonialism in order to analyse the conflicts that are arising due to the deployment of renewable energy megaprojects in the Global South and in the peripheries of the Global North. First, the limits of [...] Read more.
This article aims to define the category of energy colonialism in order to analyse the conflicts that are arising due to the deployment of renewable energy megaprojects in the Global South and in the peripheries of the Global North. First, the limits of the corporate energy transition are questioned, and based on an exhaustive bibliographic review, the category of energy colonialism is formulated along with six dimensions that characterise it: geopolitical; economic and financial inequalities; power, violence, and decision making; land grabbing and dispossession; impacts on territories and commons; resistance and socio-territorial conflicts. Based on this framework, we analyse and juxtapose different expressions of energy colonialism in four case studies; the isthmus of Tehuantepec (Oaxaca, Mexico), the territories of Western Sahara occupied by Morocco, the Saami territory in Norway, and the rural territories of Spain. The results from this study allow us to conclude that energy colonialism is a useful concept for understanding and critiquing the effects of the corporate energy transition and establishing a base for grassroots and decolonial alternatives in both the Global North and South. Full article
(This article belongs to the Special Issue Energy Colonialism, Extractivism, Socio-Ecological Transitions, and Land Planning)
22 pages, 4077 KiB  
Article
Input Flux and the Risk of Heavy Metal(Loid) of Agricultural Soil in China: Based on Spatiotemporal Heterogeneity from 2000 to 2021
by Wenyu Ma, Yuchun Pan, Zaijin Sun, Changhua Liu, Xiaolan Li, Li Xu and Yunbing Gao
Land 2023, 12(6), 1240; https://doi.org/10.3390/land12061240 - 16 Jun 2023
Cited by 4 | Viewed by 1345
Abstract
Identifying the current status of the heavy metal(loid) input of agricultural soils is vital for the soil ecological environment of agricultural-producing areas. Most previous studies have typically carried been out in small regions with limited sampling sites, which is insufficient to reveal the [...] Read more.
Identifying the current status of the heavy metal(loid) input of agricultural soils is vital for the soil ecological environment of agricultural-producing areas. Most previous studies have typically carried been out in small regions with limited sampling sites, which is insufficient to reveal the overall status of China. This study reviewed publications from over the past 20 years and calculated the input fluxes of heavy metal(loid)s in agricultural soil via atmospheric deposition, fertilizer, manure, and irrigation in different regions of China based on spatiotemporal heterogeneity using a meta-analysis, providing more accurate and reliable results. It was found that the heavy metal(loid) input flux of atmospheric deposition in China is large, while that of fertilizer and manure is relatively low compared to Europe. The major sources of As, Cd, Cr, Ni, and Pb entering the soil was atmospheric deposition, which accounted for 12% to 92% of the total input. Manure was responsible for 19% to 75% of the Cu and Zn input. Cd is the element presenting the most significant risk to the environment of agricultural soils in China and its safety limit will be reached within 100 years for most regions. The region we need to be concerned about is Huang-Huai-Hai due to its comprehensive pollution. Full article
(This article belongs to the Special Issue Arable Land Quality: Observation, Estimation, Optimization and Application)
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<p>Distribution of sampling sites and sample size. HHH: Huang-Huai-Hai, LP: Loess Plateau, MLYR: Middle and Lower Reaches of Yangtze River, NEC: Northeast China, NWC: Northwest China, SB: Sichuan Basin, SC: South China, SYR: South of the Yangtze River, YGP: Yunnan Guizhou Plateau, AD: Atmospheric Deposition, Fert: Fertilizer, Irrig: Irrigation, N: Sites, and n: Samples.</p> Full article ">Figure 2
<p>Time variation in heavy metal(loid) deposition in three periods.</p> Full article ">Figure 3
<p>Total input fluxes of heavy metal(loid)s in different regions (mg/m<sup>2</sup>/yr).</p> Full article ">Figure 4
<p>Contributions of different sources of total heavy metal(loid) input in 9 regions.</p> Full article ">Figure 5
<p>Temporal variation in heavy metal(loid) source contribution in three periods (2000–2006, 2007–2012, and 2013–2021).</p> Full article ">Figure 6
<p>Spatiotemporal risk of heavy metal(loid)s in agricultural soils: (<b>a</b>) spatial risk: the distribution of soil environmental capacity; and (<b>b</b>) temporal risk: estimated time required for each heavy metal(loid)s to reach the standard limits (yr).</p> Full article ">Figure 7
<p>The effect of boiler emissions, mining, and industrial structure: (<b>a</b>) number of industrial boilers and contribution of the industry in GDP from 2000 to 2021; and (<b>b</b>) kernel density of the mining distribution.</p> Full article ">
16 pages, 5815 KiB  
Article
Response of Spontaneous Plant Communities to Sedum mexicanum Cover and Water Availability in Green Roof Microcosms
by Dean Schrieke, Nicholas S. G. Williams and Claire Farrell
Land 2023, 12(6), 1239; https://doi.org/10.3390/land12061239 - 16 Jun 2023
Cited by 1 | Viewed by 1373
Abstract
Lack of maintenance can lead to ‘weedy’ spontaneous vegetation on green roofs. Aspects of green roof design, including substrate depth and roof height, have been shown to influence the composition of spontaneous vegetation. In drier climates, Sedum species are often planted on shallow [...] Read more.
Lack of maintenance can lead to ‘weedy’ spontaneous vegetation on green roofs. Aspects of green roof design, including substrate depth and roof height, have been shown to influence the composition of spontaneous vegetation. In drier climates, Sedum species are often planted on shallow substrate ‘extensive’ green roofs and irrigated during summer to maintain cover. However, the response of spontaneous vegetation to Sedum cover and water availability is unclear. Understanding this relationship could help minimise maintenance and maintain Sedum vegetation cover. We hypothesised that increasing Sedum (Sedum mexicanum) cover and reduced water availability would reduce the abundance, biomass, species and functional richness, and the community weighted mean specific leaf area (SLA; CWM by abundance) of spontaneous plant communities. We conducted a 10-month experiment in green roof microcosms planted with S. mexicanum (0%, 25%, 50%, 75% and 100% total cover), subjected to a well-watered or water-deficit irrigation treatment, and sown with a mix of 14 plant species that commonly occur as spontaneous on green roofs. We measured spontaneous species abundance, community biomass, and functional traits (specific leaf area, leaf dry matter content, and relative growth rate), and calculated species and functional richness. Increasing S. mexicanum cover reduced spontaneous species abundance and species and functional richness but did not affect community biomass. Species richness was affected by the interaction of S. mexicanum cover and watering treatment and was greatest in well-watered microcosms with 0% S. mexicanum cover. Increased water availability increased spontaneous plant biomass but did not affect functional richness. The SLA of spontaneous communities was affected by the interaction of S. mexicanum cover and watering and was significantly greater in well-watered treatments where S. mexicanum cover was <100%. Therefore, maximising Sedum cover and limiting water availability on green roofs will likely limit the abundance, biomass, and diversity of spontaneous vegetation. Conversely, for green roofs where substrate is left to be naturally colonised, increasing water availability could encourage establishment and increase functional richness of spontaneous vegetation. Full article
(This article belongs to the Special Issue Green Roofs in Arid and Semi-arid Climates)
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<p>Clockwise from top left, images showing green roof microcosms shortly after sowing of spontaneous species community and just before harvest.</p> Full article ">Figure 2
<p>Mean abundance of spontaneous species present in green roof microcosms at the end of the experiment. Asterisks indicate significant (<span class="html-italic">p</span> ≤ 0.05) differences in species abundance between watering treatment within <span class="html-italic">Sedum</span> cover class (two-way ANOVA). Dissimilar letters indicate significant differences between species abundance within <span class="html-italic">Sedum</span> cover class (Tukey’s post hoc test; <span class="html-italic">p</span> ≤ 0.05).</p> Full article ">Figure 3
<p>(<b>a</b>) Aboveground biomass (g) of the spontaneous species community and (<b>b</b>) spontaneous community species richness in green roof microcosms at the end of the experiment. Dissimilar letters indicate significant differences between watering treatment and <span class="html-italic">Sedum</span> cover class (Tukey’s post hoc test; <span class="html-italic">p</span> ≤ 0.05).</p> Full article ">Figure 4
<p>(<b>a</b>) Abundance weighted community mean (CWM) specific leaf area (m<sup>2</sup> kg<sup>−1</sup>) and (<b>b</b>) functional richness of spontaneous vegetation in green roof microcosms at the end of the experiment. Dissimilar lowercase letters indicate significant differences between watering treatment and <span class="html-italic">Sedum</span> cover class (Tukey’s post hoc test; <span class="html-italic">p</span> ≤ 0.05). Dissimilar capital letters indicate significant (<span class="html-italic">p</span> &lt; 0.001) differences between <span class="html-italic">Sedum</span> cover class (two-way ANOVA).</p> Full article ">Figure A1
<p>Daily mean relative humidity (%) and temperature (°C) in Tunnel 13 from January to November 2021.</p> Full article ">
62 pages, 1375 KiB  
Review
A Chronicle of Indonesia’s Forest Management: A Long Step towards Environmental Sustainability and Community Welfare
by Hunggul Yudono Setio Hadi Nugroho, Yonky Indrajaya, Satria Astana, Murniati, Sri Suharti, Tyas Mutiara Basuki, Tri Wira Yuwati, Pamungkas Buana Putra, Budi Hadi Narendra, Luthfy Abdulah, Titiek Setyawati, Subarudi, Haruni Krisnawati, Purwanto, M. Hadi Saputra, Yunita Lisnawati, Raden Garsetiasih, Reny Sawitri, Indra Ardie Surya Liannawatty Purnamawan Putri, Ogi Setiawan, Dona Octavia, Hesti Lestari Tata, Endang Savitri, Abdurachman, Acep Akbar, Achmad Rizal Hak Bisjoe, Adi Susilo, Aditya Hani, Agung Budi Supangat, Agung Wahyu Nugroho, Agus Kurniawan, Ahmad Junaedi, Andhika Silva Yunianto, Anita Rianti, Ardiyanto Wahyu Nugroho, Asep Sukmana, Bambang Tejo Premono, Bastoni, Bina Swasta Sitepu, Bondan Winarno, Catur Budi Wiati, Chairil Anwar Siregar, Darwo, Diah Auliyani, Diah Irawati Dwi Arini, Dian Pratiwi, Dila Swestiani, Donny Wicaksono, Dony Rachmanadi, Eko Pujiono, Endang Karlina, Enny Widyati, Etik Erna Wati Hadi, Firda Mafthukhakh Hilmya Nada, Fajri Ansari, Fatahul Azwar, Gerson Ndawa Njurumana, Hariany Siappa, Hendra Gunawan, Hengki Siahaan, Henti Hendalastuti Rachmat, Heru Dwi Riyanto, Hery Kurniawan, Ika Heriansyah, Irma Yeny, Julianus Kinho, Karmilasanti, Kayat, Luthfan Meilana Nugraha, Luthfi Hanindityasari, Mariana Takandjandji, Markus Kudeng Sallata, Mawazin, Merryana Kiding Allo, Mira Yulianti, Mohamad Siarudin, Muhamad Yusup Hidayat, Muhammad Abdul Qirom, Mukhlisi, Nardy Noerman Najib, Nida Humaida, Niken Sakuntaladewi, Nina Mindawati, Nining Wahyuningrum, Nunung Puji Nugroho, Nur Muhamad Heriyanto, Nuralamin, Nurhaedah Muin, Nurul Silva Lestari, Oki Hidayat, Parlin Hotmartua Putra Pasaribu, Pratiwi, Purwanto, Purwanto Budi Santosa, Rahardyan Nugroho Adi, Ramawati, Ratri Ma’rifatun Nisaa, Reni Setyo Wahyuningtyas, Resti Ura, Ridwan Fauzi, Rosita Dewi, Rozza Tri Kwatrina, Ryke Nandini, Said Fahmi, Sigit Andy Cahyono, Sri Lestari, Suhartono, Sulistya Ekawati, Susana Yuni Indriyanti, Tien Wahyuni, Titi Kalima, Tri Atmoko, Tri Rizkiana Yusnikusumah, Virni Budi Arifanti, Vivi Yuskianti, Vivin Silvaliandra Sihombing, Wahyu Catur Adinugroho, Wahyudi Isnan, Wanda Kuswanda, Wawan Halwany, Wieke Herningtyas, Wuri Handayani, Yayan Hadiyan and Yulizar Ihrami Rahmilaadd Show full author list remove Hide full author list
Land 2023, 12(6), 1238; https://doi.org/10.3390/land12061238 - 16 Jun 2023
Cited by 12 | Viewed by 14872
Abstract
Indonesia is the largest archipelagic country in the world, with 17,000 islands of varying sizes and elevations, from lowlands to very high mountains, stretching more than 5000 km eastward from Sabang in Aceh to Merauke in Papua. Although occupying only 1.3% of the [...] Read more.
Indonesia is the largest archipelagic country in the world, with 17,000 islands of varying sizes and elevations, from lowlands to very high mountains, stretching more than 5000 km eastward from Sabang in Aceh to Merauke in Papua. Although occupying only 1.3% of the world’s land area, Indonesia possesses the third-largest rainforest and the second-highest level of biodiversity, with very high species diversity and endemism. However, during the last two decades, Indonesia has been known as a country with a high level of deforestation, a producer of smoke from burning forests and land, and a producer of carbon emissions. The aim of this paper is to review the environmental history and the long process of Indonesian forest management towards achieving environmental sustainability and community welfare. To do this, we analyze the milestones of Indonesian forest management history, present and future challenges, and provide strategic recommendations toward a viable Sustainable Forest Management (SFM) system. Our review showed that the history of forestry management in Indonesia has evolved through a long process, especially related to contestation over the control of natural resources and supporting policies and regulations. During the process, many efforts have been applied to reduce the deforestation rate, such as a moratorium on permitting primary natural forest and peat land, land rehabilitation and soil conservation, environmental protection, and other significant regulations. Therefore, these efforts should be maintained and improved continuously in the future due to their significant positive impacts on a variety of forest areas toward the achievement of viable SFM. Finally, we conclude that the Indonesian government has struggled to formulate sustainable forest management policies that balance economic, ecological, and social needs, among others, through developing and implementing social forestry instruments, developing and implementing human resource capacity, increasing community literacy, strengthening forest governance by eliminating ambiguity and overlapping regulations, simplification of bureaucracy, revitalization of traditional wisdom, and fair law enforcement. Full article
(This article belongs to the Special Issue Diversifying Forest Landscape Management Approaches)
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<p>The dominance of historic and current political power in Indonesian forest management.</p> Full article ">Figure 2
<p>The number of hydrometeorological disaster events in Indonesia from 2005 to 2020 (extracted from <a href="https://dibi.bnpb.go.id/" target="_blank">https://dibi.bnpb.go.id/</a> accessed on 3 January 2023).</p> Full article ">
20 pages, 1992 KiB  
Article
Promoting the Development of Edible Landscapes in Suburban Areas with Place Branding—A Case Study in Taiwan
by Zhi-Wei Zheng and Rung-Jiun Chou
Land 2023, 12(6), 1237; https://doi.org/10.3390/land12061237 - 16 Jun 2023
Viewed by 2385
Abstract
The process of urbanization has brought about a series of negative effects and prompting researchers to critically reflect on the pros and cons of urbanization. In particular, the rapid development of urbanization has posed serious challenges in terms of food and environmental issues. [...] Read more.
The process of urbanization has brought about a series of negative effects and prompting researchers to critically reflect on the pros and cons of urbanization. In particular, the rapid development of urbanization has posed serious challenges in terms of food and environmental issues. Edible landscapes have been proposed as a means to offset some of the negative impacts, but many of the challenges faced by edible landscapes in the development process have hindered their development. Therefore, how to promote the further development of edible landscapes in cities has become the focus of current research. This paper takes the edible landscape in the San-He community of the Long tan District, Taoyuan City, Taiwan as a case study and uses in-depth interviews and non-participant observation to investigate the strategies of using local brands to solve the challenges of edible landscape development. The study found that the development of edible landscapes in urban communities can bring many social, economic, and cultural benefits to the communities, but the development of edible landscapes also faces challenges such as marketing, government policies, and growing techniques, which can be effectively addressed by place branding strategies. The results of this study can be used as a guide for the development of edible landscapes by local governments, communities, participants in edible landscapes, or similar cultural countries. Full article
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<p>San-He community Location Index Map.</p> Full article ">Figure 2
<p>Distribution of edible landscape planting in the San-He community.</p> Full article ">Figure 3
<p>Three systems that are being formed by the development of edible landscape in the San-He Community.</p> Full article ">Figure 4
<p>Traditional single marketing model.</p> Full article ">Figure 5
<p>Modern diversified marketing model.</p> Full article ">
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