Hasil untuk "Land use"

Pete Smith, M. Bustamante, Helal Ahammad et al.

Pete Smith, J. House, M. Bustamante et al.

P. Shi, Yan Zhang, Zhan-bin Li et al.

Yao Yao, Xia Li, Xiaoping Liu et al.

Prafull Singh, N. Kikon, P. Verma

A. Arneth, S. Sitch, J. Pongratz et al.

P. Meli, K. Holl, J. M. Rey Benayas et al.

Global forest restoration targets have been set, yet policy makers and land managers lack guiding principles on how to invest limited resources to achieve them. We conducted a meta-analysis of 166 studies in naturally regenerating and actively restored forests worldwide to answer: (1) To what extent do floral and faunal abundance and diversity and biogeochemical functions recover? (2) Does recovery vary as a function of past land use, time since restoration, forest region, or precipitation? (3) Does active restoration result in more complete or faster recovery than passive restoration? Overall, forests showed a high level of recovery, but the time to recovery depended on the metric type measured, past land use, and region. Abundance recovered quickly and completely, whereas diversity recovered slower in tropical than in temperate forests. Biogeochemical functions recovered more slowly after agriculture than after logging or mining. Formerly logged sites were mostly passively restored and generally recovered quickly. Mined sites were nearly always actively restored using a combination of planting and either soil amendments or recontouring topography, which resulted in rapid recovery of the metrics evaluated. Actively restoring former agricultural land, primarily by planting trees, did not result in consistently faster or more complete recovery than passively restored sites. Our results suggest that simply ending the land use is sufficient for forests to recover in many cases, but more studies are needed that directly compare the value added of active versus passive restoration strategies in the same system. Investments in active restoration should be evaluated relative to the past land use, the natural resilience of the system, and the specific objectives of each project.

Xia Li, Guangzhao Chen, Xiaoping Liu et al.

J. Doelman, E. Stehfest, A. Tabeau et al.

Xiaoping Liu, Jialv He, Yao Yao et al.

ABSTRACT Urban land use information plays an important role in urban management, government policy-making, and population activity monitoring. However, the accurate classification of urban functional zones is challenging due to the complexity of urban systems. Many studies have focused on urban land use classification by considering features that are extracted from either high spatial resolution (HSR) remote sensing images or social media data, but few studies consider both features due to the lack of available models. In our study, we propose a novel scene classification framework to identify dominant urban land use type at the level of traffic analysis zone by integrating probabilistic topic models and support vector machine. A land use word dictionary inside the framework was built by fusing natural–physical features from HSR images and socioeconomic semantic features from multisource social media data. In addition to comparing with manual interpretation data, we designed several experiments to test the land use classification accuracy of our proposed model with different combinations of previously acquired semantic features. The classification results (overall accuracy = 0.865, Kappa = 0.828) demonstrate the effectiveness of our strategy that blends features extracted from multisource geospatial data as semantic features to train the classification model. This method can be applied to help urban planners analyze fine urban structures and monitor urban land use changes, and additional data from multiple sources will be blended into this proposed framework in the future.

P. Alexander, Calum Brown, A. Arneth et al.

Animal products, i.e. meat, milk and eggs, provide an important component in global diets, but livestock dominate agricultural land use by area and are a major source of greenhouse gases. Cultural and personal associations with animal product consumption create barriers to moderating consumption, and hence reduced environmental impacts. Here we review alternatives to conventional animal products, including cultured meat, imitation meat and insects (i.e. entomophagy), and explore the potential change in global agricultural land requirements associated with each alternative. Stylised transformative consumption scenarios where half of current conventional animal products are substituted to provide at least equal protein and calories are considered. The analysis also considers and compares the agricultural land area given shifts between conventional animal product consumption. The results suggest that imitation meat and insects have the highest land use efficiency, but the land use requirements are only slightly greater for eggs and poultry meat. The efficiency of insects and their ability to convert agricultural by-products and food waste into food, suggests further research into insect production is warranted. Cultured meat does not appear to offer substantial benefits over poultry meat or eggs, with similar conversion efficiency, but higher direct energy requirements. Comparison with the land use savings from reduced consumer waste, including over-consumption, suggests greater benefits could be achieved from alternative dietary transformations considered. We conclude that although a diet with lower rates of animal product consumption is likely to create the greatest reduction in agricultural land, a mix of smaller changes in consumer behaviour, such as replacing beef with chicken, reducing food waste and potentially introducing insects more commonly into diets, would also achieve land savings and a more sustainable food system.

De Zhou, Jianchun Xu, Zhulu Lin

Pedro Pellegrini, R. Fernández

Aron Bell, Liam Anthony Mannion, Mark Kelly et al.

The life cycle carbon dioxide equivalent (CO2e) intensity of Power-to-Liquid (PtL) sustainable aviation fuel (SAF) scenarios in Spain are evaluated using a specific, granular, and transparent modelling approach. Post combustion CO2 capture and direct air CO2 capture are considered, in addition to grid and renewable electricity sources. The mass and energy requirements of the PtL system are determined from a mass and energy conserved reaction mechanism and a comprehensive literature review. The SAF yield is constrained by its molecular composition, formulated to meet the physical property specifications for Fischer-Tropsch synthetic paraffinic kerosene (FT-SPK) in ASTM D7566 Annex 1. The results of the life cycle assessment (LCA) show large ranges in CO2e intensity of PtL SAF scenarios, from 11 to 101 gCO2e/MJ. The electricity emission factors at which the CO2e intensity of PtL SAFs meet the 70% reduction required under the ReFuelEU Aviation legislation are 112 – 168 gCO2e/kWh for direct air capture and post combustion capture of biogenic CO2. As the average EU grid is approximately 300 gCO2e/kWh, the use of renewable electricity (onsite or power purchase agreement) is therefore essential to achieve the 70% reduction. The carbon intensity of the Madrid to Dublin commercial flight route is analysed, per revenue-passenger-kilometre (RPK), as a specific use case with actual data of Ryanair Boeing 737-800 and 737 MAX 8 aircraft. Compared to the Science Based Targets 1.5°C limit of 3.3 gCO2/RPK, it is shown that sustainable aviation is challenging using PtL SAF, with a best case of 9 gCO2/RPK.

Hugo Spring-Ragain

This paper develops a spatial-dynamic framework to analyze the theoretical and quantitative effects of a Land Value Tax (LVT) on urban land markets, capital accumulation, and spatial redistribution. Building upon the Georgist distinction between produced value and unearned rent, the model departs from the static equilibrium tradition by introducing an explicit diffusion process for land values and a local investment dynamic governed by profitability thresholds. Land value $V (x, y, t)$ and built capital $K(x, y, t)$evolve over a two-dimensional urban domain according to coupled nonlinear partial differential equations, incorporating local productivity $A(x, y)$, centrality effects $μ(x, y)$, depreciation $δ$, and fiscal pressure $τ$ . Analytical characterization of the steady states reveals a transcritical bifurcation in the parameter $τ$ , separating inactive (low-investment) and active (self-sustaining) spatial regimes. The equilibrium pair $(V ^*, K^*)$ is shown to exist only when the effective decay rate $α= r + τ- μ(x, y)$ exceeds a profitability threshold $θ= κ+ δ/ I_0$, and becomes locally unstable beyond this boundary. The introduction of diffusion, $D_V ΔV$, stabilizes spatial dynamics and generates continuous gradients of land value and capital intensity, mitigating speculative clustering while preserving productive incentives. Numerical simulations confirm these analytical properties and display the emergence of spatially heterogeneous steady states driven by urban centrality and local productivity. The model also quantifies key aggregate outcomes, including dynamic tax revenues, adjusted capital-to-land ratios, and net present values under spatial heterogeneity and temporal discounting. Sensitivity analyses demonstrate that the main qualitative mechanisms-critical activation, spatial recomposition, and bifurcation structure-remain robust under alternative spatial profiles $(A, μ)$, discretization schemes, and moderate differentiation of the tax rate $τ(x, y)$. From an economic perspective, the results clarify the dual nature of the LVT: while it erodes unproductive rents and speculative land holding, its dynamic incidence on built capital depends on local profitability and financing constraints. The taxation parameter $τ$ thus acts as a control variable in a nonlinear spatial system, shaping transitions between rent-driven and production-driven equilibria. Within a critical range around $τ_c$, the LVT functions as an efficient spatial reallocation operator-reducing inequality in land values and investment density without impairing aggregate productivity. Beyond this range, excessive taxation induces systemic contraction and investment stagnation. Overall, this research bridges static urban tax theory with dynamic spatial economics by formalizing how a land-based fiscal instrument can reshape the geography of value creation through endogenous diffusion and nonlinear feedback. The framework provides a foundation for future extensions involving stochastic shocks, adaptive policy feedbacks, or endogenous public investment, offering a unified quantitative perspective on the dynamic efficiency and spatial equity of land value taxation.

Sabab Aosaf, Muhammad Ali Nayeem, Afsana Haque et al.

Urban land-use allocation represents a complex multi-objective optimization problem critical for sustainable urban development policy. This paper presents novel computational intelligence approaches for optimizing land-use allocation in mixed-use areas, addressing inherent trade-offs between land-use compatibility and economic objectives. We develop multiple optimization algorithms, including custom variants integrating differential evolution with multi-objective genetic algorithms. Key contributions include: (1) CR+DES algorithm leveraging scaled difference vectors for enhanced exploration, (2) systematic constraint relaxation strategy improving solution quality while maintaining feasibility, and (3) statistical validation using Kruskal-Wallis tests with compact letter displays. Applied to a real-world case study with 1,290 plots, CR+DES achieves 3.16\% improvement in land-use compatibility compared to state-of-the-art methods, while MSBX+MO excels in price optimization with 3.3\% improvement. Statistical analysis confirms algorithms incorporating difference vectors significantly outperform traditional approaches across multiple metrics. The constraint relaxation technique enables broader solution space exploration while maintaining practical constraints. These findings provide urban planners and policymakers with evidence-based computational tools for balancing competing objectives in land-use allocation, supporting more effective urban development policies in rapidly urbanizing regions.

Claire Marie Guimond, Tilman Spohn, Svetlana Berdyugina et al.

Water and land surfaces on a planet interact with gases in the atmosphere and with radiation from the star. These interactions define the environments that prevail on the planet, some of which may be more amenable to prebiotic chemistry, some to the evolution of more complex life. This review article covers (i) the physical conditions that determine the ratio of land to sea on a rocky planet, (ii) how this ratio would affect climatic and biologic processes, and (iii) whether future astronomical observations might constrain this ratio on exoplanets. Water can be delivered in multiple ways to a growing rocky planet -- and although we may not agree on the contribution of different mechanism(s) to Earth's bulk water, hydrated building blocks and nebular ingassing could at least in principle supply several oceans' worth. The water that planets sequester over eons in their solid deep mantles is limited by the water concentration at water saturation of nominally anhydrous mantle minerals, likely less than 2000 ppm of the planet mass. Water is cycled between mantle and surface through outgassing and ingassing mechanisms that, while tightly linked to tectonics, do not necessarily require plate tectonics in every case. The actual water/land ratio at a given time emerges from the balance between the volume of surface water on the one hand, and on the other hand, the shape of the planet (its ocean basin volume) that is carved out by dynamic topography, the petrologic evolution of continents, impact cratering, and other surface-sculpting processes. By leveraging the contrast in reflectance properties of water and land surfaces, spatially resolved 2D maps of Earth-as-an-exoplanet have been retrieved from models using real Earth observations, demonstrating that water/land ratios of rocky exoplanets may be determined from data delivered by large-aperture, high-contrast imaging telescopes in the future.

Kai Wang, Siyi Chen, Weicong Pang et al.

Land-cover underpins ecosystem services, hydrologic regulation, disaster-risk reduction, and evidence-based land planning; timely, accurate land-cover maps are therefore critical for environmental stewardship. Remote sensing-based land-cover classification offers a scalable route to such maps but is hindered by scarce and imbalanced annotations and by geometric distortions in high-resolution scenes. We propose LC4-DViT (Land-cover Creation for Land-cover Classification with Deformable Vision Transformer), a framework that combines generative data creation with a deformation-aware Vision Transformer. A text-guided diffusion pipeline uses GPT-4o-generated scene descriptions and super-resolved exemplars to synthesize class-balanced, high-fidelity training images, while DViT couples a DCNv4 deformable convolutional backbone with a Vision Transformer encoder to jointly capture fine-scale geometry and global context. On eight classes from the Aerial Image Dataset (AID)-Beach, Bridge, Desert, Forest, Mountain, Pond, Port, and River-DViT achieves 0.9572 overall accuracy, 0.9576 macro F1-score, and 0.9510 Cohen' s Kappa, improving over a vanilla ViT baseline (0.9274 OA, 0.9300 macro F1, 0.9169 Kappa) and outperforming ResNet50, MobileNetV2, and FlashInternImage. Cross-dataset experiments on a three-class SIRI-WHU subset (Harbor, Pond, River) yield 0.9333 overall accuracy, 0.9316 macro F1, and 0.8989 Kappa, indicating good transferability. An LLM-based judge using GPT-4o to score Grad-CAM heatmaps further shows that DViT' s attention aligns best with hydrologically meaningful structures. These results suggest that description-driven generative augmentation combined with deformation-aware transformers is a promising approach for high-resolution land-cover mapping.

Bhagawat Rimal, Lifu Zhang, H. Keshtkar et al.

This study explored the past and present land-use/land-cover (LULC) changes and urban expansion pattern for the cities of the Kathmandu valley and their surroundings using Landsat satellite images from 1988 to 2016. For a better analysis, LULC change information was grouped into seven time-periods (1988–1992, 1992–1996, 1996–2000, 2000–2004, 2004–2008, 2008–2013, and 2013–2016). The classification was conducted using the support vector machines (SVM) technique. A hybrid simulation model that combined the Markov-Chain and Cellular Automata (MC-CA) was used to predict the future urban sprawl existing by 2024 and 2032. Research analysis explored the significant expansion in urban cover which was manifested at the cost of cultivated land. The urban area totaled 40.53 km2 in 1988, which increased to 144.35 km2 in 2016 with an average annual growth rate of 9.15%, an overall increase of 346.85%. Cultivated land was the most affected land-use from this expansion. A total of 91% to 98% of the expanded urban area was sourced from cultivated land alone. Future urban sprawl is likely to continue, which will be outweighed by the loss of cultivated land as in the previous decades. The urban area will be expanded to 200 km2 and 238 km2 and cultivated land will decline to 587 km2 and 555 km2 by 2024 and 2032. Currently, urban expansion is occurring towards the west and south directions; however, future urban growth is expected to rise in the southern and eastern part of the study area, dismantling the equilibrium of environmental and anthropogenic avenues. Since the study area is a cultural landscape and UNESCO heritage site, balance must be found not only in developing a city, but also in preserving the natural environment and maintaining cultural artifacts.

Ying Wang, Xiangmei Li, Qi Zhang et al.

Abstract Urbanization alters the supply of ecosystem services that are vital for human well-being. The loss of ecosystem services is particularly challenging in rapid urbanization areas where economic development needs to consume substantial natural resources. The quantitative and spatial optimization of land use provides an effective tool for rationally allocating land use structure and pattern to ensure the provision of expected ecosystem services. In this paper, we combine the Multi-Objective Programming and the Dyna-CLUE model to project land use changes in 2030 for Wuhan city under three scenarios, i.e., Business As Usual (BAU), Rapid Economic Development (RED), and Ecological Land Protection (ELP). The coupled model that integrates “top-down” and “bottom-up” processes is capable of obtaining the optimized land use patterns under different scenarios and examining the potential impacts of land use changes on ecosystem services in a spatially explicit way. We find that built-up land will continue its remarkable growth during 2015-2030 under the BAU scenario (grows by 96%) at the expense of ecological lands (decreases by 18%). Meanwhile, the predicted losses of ecological lands are 11% and 6% under the RED and ELP scenarios, respectively. Projected land use changes result in varying magnitudes of declines in ecosystem service values for BAU (11%), RED (6%) and ELP (2%) scenarios from 2015 to 2030. The ELP scenario, which incorporates ecological protection policies and spatial restrictions, plays a positive role in altering land use trends and mitigating ecosystem degradation. Finally, we establish an ecosystem service value change matrix to explain how interactions between land use types give rise to trade-offs among multiple ecosystem services. We find that conversions between ecological land use types can trigger trade-offs among ecosystem services, but the conversion from ecological lands towards urban land leads to a net loss of all individual ecosystem services. By linking land and ecological systems, the coupled modeling framework in this study can be useful for obtaining optimal ecosystem-based land use allocation strategies and provide scientific support for sustainable land use management.