Search Results (1,814)

Vegetation restoration profoundly impacts soil carbon (C)-nitrogen (N)-phosphorus (P) cycling in arid sandy lands, with vegetation type critically regulating accumulation patterns. However, the magnitudes of soil nutrients and stoichiometry for different vegetation types are still largely unknown. Thus, we conducted a regional-scale study to evaluate the soil nutrients and nutrient stoichiometry under four typical vegetation types in the Mu Us Sandy Land (MUS), including monoculture arbor (MA), monoculture shrub (MS), arbor-shrub mixed (MAS), and monoculture herbaceous (MH), with cropland (Cr) and bare sand (Bs) controls. Our results showed that vegetation type significantly affected SOC and TN content. MS (30–40 years), MA (>40 years), and MH exhibited significant increases of 285.5–305.8% in SOC and 293.6–374.6% in TN in the topsoil, respectively. MS (30–40 years) and MH demonstrated increases of 399.1% and 283.3% in SOC and 250.2% and 162.8% in TN in the subsoil. However, MAS had no significant effect on SOC and TN. MA (>40 years) resulted in a higher TP in the subsoil. Compared to Bs, humic substances significantly increased by 111.1–171.6% under MA (>40 years), MS (>40 years), and MH, exhibiting positive correlations with SOC. Moreover, MAS treatment resulted in a higher C:N, while the MH resulted in a higher C:P and N:P in the topsoil. Despite stable total phosphorus (TP), elevated C:P and N:P ratios under MH indicated emerging P limitation in restoration. Therefore, long-term monoculture shrub, arbor, and herbaceous vegetation effectively enhances soil fertility in arid sandy lands through long-term SOC accumulation and humic substance formation. Full article

Remote sensing enables the analysis of landscape dynamics; however, catastrophic disturbances create new surface conditions that are not adequately captured by retrospectively defined land-cover classes. This study addresses the challenge of temporally matching unsupervised classifications to monitor post-catastrophic floodplain dynamics on Khortytsia Island following the destruction of the Kakhovka Reservoir. Multi-temporal Sentinel-2 Level-2A data from 2022 to 2025 were processed using spectral indices, standardised within a common predictor space, and classified through unsupervised clustering. Cluster solutions from individual dates were then matched based on spectral similarity and spatial continuity, with their temporal interpretation guided by concepts of landscape memory and landscape perception. Higher-order spatiotemporal units were subsequently derived through contextual superclustering. The analysis identified 16 clusters across the study period, with 4 to 12 clusters represented on individual dates. Their temporal coordination enabled the distinction of higher-order units exhibiting contrasting dynamics, including directional trend, seasonal, and mixed types. The proposed framework facilitates the identification of newly formed surface states, their temporal coordination, and their integration into a hierarchical spatiotemporal model of post-catastrophic landscape change. Full article

Morocco’s recent legalization of industrial and medicinal cannabis has created a rapidly expanding seed-oil sector whose sustainability has yet to be fully assessed. This study applies an environmental life cycle assessment (LCA) in accordance with ISO 14040:2006 and ISO 14044:2006, complemented by a qualitative social responsibility assessment based on ISO 26000:2010, aiming to evaluate the life cycle sustainability of Moroccan cannabis seed oil. Three representative processing chains, traditional artisanal presses, producer cooperatives and regulated industrial plants are compared using a functional unit of 1 kg of cold-pressed oil packaged for local distribution. Inventory data were drawn from field measurements and interviews and were modeled in OpenLCA with background datasets from Ecoinvent 3.8 and Agribalyse v3.1. Impact assessment used the ReCiPe 2016 (H) method at the midpoint level across nine categories (climate change, fossil resource scarcity, water use, freshwater eutrophication, terrestrial acidification, land occupation, carcinogenic, non-carcinogenic human toxicity, and fine particulate matter formation). Sensitivity analyses varied seed yield, electricity mix and transport distances by ±20% to gauge uncertainty. Results show that the cooperative scenario achieves the lowest impacts across nearly all categories because of higher extraction yields (3 kg seed per kg oil), lower energy use (0.54 kWh kg⁻¹ oil) and more effective co-product recovery. In contrast, artisanal extraction requires approximately 1 kg of additional seed input per functional unit compared to optimized scenarios, significantly increasing upstream environmental burdens and causing upstream agricultural burdens to multiply. Industrial facilities perform comparably to cooperatives if powered by renewable electricity. Integrating a semi-quantitative social responsibility assessment reveals that legalization has markedly improved organizational governance, labor conditions, consumer protection and community involvement. Cooperatives display the most balanced social performance, whereas industrial plants excel in governance and quality control. A set of recommendations, including drip irrigation, cultivar improvement, co-product valorisation, renewable energy adoption, eco-designed packaging and cooperative governance, is proposed to enhance the environmental and socio-economic sustainability of Morocco’s emerging cannabis seed-oil industry. Full article

Accurately identifying an urban functional zone (UFZ) is crucial for rationally allocating urban land resources and optimizing urban spatial structure. Existing research based on Points of Interest (POIs) mostly uses the relationship between the number of various types of POIs as the basis for identification. However, this approach neglects the difference of physical surface property of urban functional zones—imperviousness. Based on the FD-CR method, this study proposes the RFD-ECR identification method by combining TF-IDF and ISI. This study divides research units according to OpenStreetMap (OSM), and reclassifies POI data. It then uses the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm to highlight the dominant function of study units and incorporates the impervious surface index (ISI) as a correction to recognize urban functional zones. Experiments conducted in the central urban area of Chengdu demonstrate that this method is effective in identifying urban functional zones, achieving an accuracy rate of 80.21%. Comparison with the Frequency Density-Category Ratio (FD-CR) method reveals that this method, through the TF-IDF algorithm and the impervious surface index constraint, effectively improves the classification accuracy of mixed commercial UFZs. This method broadens the scope of research on urban functional zone identification based on POI data, and also provides a valuable reference for other cities undertaking functional zone identification. Full article

Indigenous territories in Amazonia sustain forest cover through the practice of swidden-fallow agriculture, yet declining land availability threatens both the ecological sustainability of this agricultural system and its contributions to community livelihoods. While scholars recognize land scarcity’s potential to drive transformations in shifting cultivation systems, we lack a systematic understanding of how local institutional frameworks shape heterogeneous responses to resource constraints. This study examines how land access mechanisms, distribution dynamics and property regimes among Indigenous communities mediate experiences of and adaptations to land scarcity in the Peruvian Amazon. We conducted a comparative case study of Solidaridad and Tamboruna, two land-scarce Indigenous communities in Peru’s Napo River basin, employing mixed methods including household surveys (n = 74), plot-level assessments, and qualitative interviews with community leaders. Our findings reveal three critical pathways through which institutions mediate scarcity outcomes. First, land access mechanisms determine whether scarce resources produce equitable constraint or acute land inequality. Second, land use intensification emerges not from scarcity alone but from accumulated inequality and household labor capacity, with land accumulated over lifecycles showing stronger associations with management practices than initial endowments. Third, where scarcity manifests as extreme polarization, it precipitates renegotiation of land property norms shaped by Indigenous sociability and moral economies, defying straightforward trajectories toward either resource privatization or collective governance. These results demonstrate that land scarcity produces divergent trajectories mediated by community-specific institutions, with swidden-fallow systems likely diminishing their capacity to sustain forest regeneration in Indigenous communities where scarcity leads to acute land inequality. Rather than uniform solutions, sustainability policy must therefore tailor interventions to local institutional contexts—prioritizing territorial expansion, facilitating communities’ own governance development, and supporting household adaptive capacity to resource scarcity. Full article

Improving urban land use efficiency is a critical pathway toward sustainable urban development, particularly in large countries undergoing rapid urbanization such as China. However, significant disparities in land use efficiency exist across cities, largely due to differences in economic development, resource endowments, and governance practices. These disparities highlight the necessity of conducting a systematic spatiotemporal assessment of land use mismatch at the city level to identify regional weaknesses and inform differentiated policy mechanisms. This study extends the land use mismatch (LUM) model, which introduces a supply–demand framework for analyzing the mismatch phenomenon of urban land use. Building on the LUM model, this study innovatively develops a classification system of five mismatch zones across eight construction land types, which provides a more systematic and comprehensive approach to identifying land use mismatch patterns. The empirical analysis is conducted using data from 283 prefecture-level cities in China. The results reveal substantial spatial heterogeneity in land use mismatch across Chinese cities. Most of the cities in East China generally fall within acceptable mismatch zones, where market mechanisms play a more effective role in land allocation. Cities in Western China exhibit more serious mismatch levels, where policy intervention seems more significant in land use planning. Cities in Central China demonstrate mixed patterns, ranging from acceptable to severe mismatch. The findings further indicate that these disparities are associated not only with economic and geographical differences but also with variations in governance practices, particularly the interaction between policy intervention and market mechanisms. This study introduces a new approach to examining the patterns of land use mismatch and provides evidence-based policy recommendations for cities in different regions to reduce land mismatch and promote more efficient use of urban land. Full article

Rail trespassing remains a persistent safety challenge at the system level in the United States, with a 24% increase in incidents within the last decade (2016–2025). Identifying hotspots proactively is difficult due to limited incident data and strong spatial dependencies within the built environment. This study thus creates a ZIP-code–level geospatial analytics framework to identify current and emerging trespassing hotspots across North Carolina by combining land-use composition, rail exposure metrics, and historical Federal Railroad Administration (FRA) trespassing records. Geospatial layers were integrated within a GIS workflow to derive attributes such as rail miles, grade crossings, population density, and land-use types. Exploratory spatial analysis showed significant clustering of trespassing incidents, with Global Moran’s I indicating positive spatial autocorrelation across multiple neighborhood sizes. Permutation z-scores confirmed non-random hotspot formation along major rail corridors. A k-means clustering method also identified four structural risk environments, and a Composite Risk Index (CRI) was developed from weighted, standardized exposure and land-use variables to quantify latent risk, independent of raw casualty counts. Results indicate that clusters characterized by higher rail infrastructure exposure and mixed land-use environments exhibit the highest CRI values and elevated hotspot probabilities. In contrast, clusters with limited rail infrastructure, including predominantly commercial and rural ZIP codes, show substantially lower risk levels. The findings highlight that trespassing risk is more strongly associated with structural exposure conditions than with isolated historical incident counts. The resulting risk surfaces and hotspots provide an interpretable and scalable framework for statewide safety planning, early hotspot detection, and targeted interventions by transportation agencies. Full article

The Land Use and Land Cover (LULC) of many regional landscapes are changing due to natural effects and anthropogenic activities, impacting biodiversity and ecosystem services. LULC dynamics reflect the altered flow of energy, water, and greenhouse gases, influencing the pillars of sustainability: society, environment, and economy. Thus, assessing LULC changes is vital for understanding the relationship between nature and society. This study used multi-temporal remotely sensed imagery to examine LULC change between 1990 and 2019 in the context of Forest Transition Theory (FTT) across the Greater Shawnee National Forest (GSNF) area of southern Illinois, USA, using a random forest algorithm, and projecting change to 2050 with a Land Change Model integrated with IPCC temperature and precipitation scenarios. From 1990 to 2019, LULC analysis showed increases in deciduous forest (1.35%), mixed forest (26.40%), agriculture (2.15%), and built-up areas (6.70%), while hay/grass/pasture declined (16.0%). LULC change intensity was highest from 1990 to 2001 (2.35% annually), slowing to 0.23% (2001–2010) and 0.18% (2010–2019). The overall accuracy (OA) of LULC classification ranged from 0.9 to 0.95 at a 95% confidence interval (CI). Projections to 2050 showed consistent increases in built-up areas (17.12–42.61%), water (28.75–39.70%), and hay/grass/pasture (6.23–38.38%), while overall forest cover declined in all scenarios. Deciduous forests decreased by 3.11–19.87% and were replaced by mixed forests in some scenarios (12.45–23.63%), while evergreen forests showed mixed responses, ranging from a decline of up to 17.13% to an increase of 2.90%. The OA of projected LULC ranged from 0.71 to 0.83 (95% CI) across SSP-RCP-based temperature and precipitation scenarios. The results showed that the GSNF broadly follows the FTT framework: forest recovery since 2001 coincided with rural depopulation, slow agricultural expansion, and rising incomes. However, climate change is expected to disrupt this recovery, pushing transitions toward mixed and evergreen forests. Findings demonstrate the importance of integrating remote sensing-based LULC with socio-economic trends and climate adaptation strategies to sustain forests and ecosystem services under future environmental pressures. Full article

Climate-induced migration is increasingly recognized as a major demographic consequence of environmental change, yet projections vary widely due to differences in spatial scale, hazard coverage, and modeling approaches. This study introduces the First Street Global Climate Migration Model (FS-GCMM), a globally consistent, multi-hazard framework that estimates climate-driven population redistribution at a 12.5 km resolution across all countries through 2100. The model integrates high-resolution global climate hazard datasets, including flood (GloFAS), wind (IBTrACS and ERA5), drought (ERA5), wildfire (Global Fire Atlas), and extreme heat and cold (ERA5-LAND) datasets, with gridded population data from NASA SEDAC’s Gridded Population of the World (GPWv4) and Shared Socioeconomic Pathway (SSP) projections. To identify climate-related migration effects, we applied within-country propensity score matching to construct balanced samples of exposed and unexposed grid cells with similar socioeconomic, demographic, geographic, and governance characteristics. Hazard-specific impacts on annualized population change from 2000 to 2020 were then estimated using mixed-effects ridge regression with country-level random effects to account for cross-national heterogeneity and multicollinearity. These empirically derived coefficients were applied to SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios to project future climate-driven outmigration, which was subsequently redistributed using a spatial attractiveness framework incorporating economic opportunity, population density, climate safety, and geographic proximity. Results indicate statistically significant negative effects of all modeled hazards on population retention globally, with approximately 199.5 million people projected to experience climate-driven displacement by 2055 under SSP2-4.5. Full article

Patterns of resource selection are driven by the decision-making processes of animals occurring at multiple scales from where to establish a home range (i.e., second order selection) to which resource patches to use within the home range (i.e., third order selection). Elk (Cervus canadensis) were reintroduced to southwestern Virginia, USA, from 2012 to 2014 following successful translocations onto reclaimed surface coal mines in the region. We sought to understand how elk have acclimated following their translocation using location data from GPS-collared adult female elk (n = 33) collected from 2019 to 2022 along with remotely sensed terrain and land cover data. We utilized continuous-time movement models paired with generalized linear mixed-effects modeling to describe seasonal resource selection at second and third orders. At both scales of selection and throughout the year, female elk selected reclaimed surface mines, conifer forests, ridgetops, and areas with lower terrain roughness, while avoiding mixed hardwood and oak (Quercus spp.) forests. Unmined open land was only selected at the third order during periods of forage scarcity (i.e., winter) and increased metabolic requirements (i.e., late gestation). Although surface coal mining leaves legacy environmental impacts on the landscape, management of these sites provides benefits to elk and maintains open habitat that is otherwise limited. Full article

Background: Although mega-dams play a significant role in development, providing electricity, irrigation, and flood control, perceptions of their contribution remain mixed, particularly regarding the environmental impacts. Methods: This study jointly determines perceived favorable and adverse environmental impacts of mega-dams by affected residents using a bivariate Tobit model on a clustered random sample of 300 households surveyed from (a) upstream, (b) upstream-relocated, and (c) downstream communities of the Merowe Dam in Sudan. Model diagnostic reveals that the perception of favorable and adverse environmental impacts is significantly and positively correlated, implying that univariate analyses of such perceptions are biased, thereby justifying the use of a bivariate approach. Such joint perception analysis using a bivariate Tobit model confirms that affected residents are well aware of both the positive and negative impacts of the dam, not commonly seen in the literature. Results: Results reveal significant differences in perception among communities on individual indicators of favorable and adverse environmental impacts of the dam. Education, income from farming, and relocation significantly decrease the likelihood of perceiving adverse environmental impacts whereas farmers of all farm types increase it. Selected farming categories and gain in land size after dam’s construction significantly increases the likelihood of scoring high on favorable environmental impacts whereas income from fishing significantly reduces it. Conclusions: Perception towards the favorable and adverse environmental impacts are not independent, rather significantly and positively correlated, confirming that affected residents are aware of both types of impacts of the Merowe Dam. Upstream-relocated residents are less likely to report the significant adverse environmental impacts of the dam, whereas both upstream and upstream-relocated residents are less likely to report significant favorable impacts of the dam. Policy implications: Include establishing educational institutions, allocation of agricultural land, and mitigating adverse environmental impacts by setting up community environmental monitoring programs in affected areas to boost community perception of the favorable environmental impacts of mega-dams. Full article

Under global climate change, analyzing carbon storage dynamics and their drivers is essential for understanding regional carbon sink capacity. Human activities and land-use change have substantially affected regional carbon storage. However, in China, most existing studies emphasize specific driving pathways, and integrated analyses of the combined effects of climate, natural, human, and landscape factors remain limited. This study aims at clarifying the integrated mechanisms by which multiple driving factors influence regional carbon storage. The InVEST model was used to analyze the carbon storage spatiotemporal changes. OPGD was then applied to evaluate the explanatory power of driving factors and their interactions, quantifying their contributions to carbon storage spatial patterns. Based on PLS-SEM, the direct and indirect effects of LULC, climate, natural, human, and landscape factors were quantified to elucidate the driving pathways of carbon storage. This study focuses on Shaanxi Province, which is a key ecological restoration region in the core area of the Loess Plateau. The main results are as follows: (1) From 2000 to 2020, carbon storage in Shaanxi Province showed a continuous increasing trend, rising from 2.97 × 10¹⁰ Mg C to 3.03 × 10¹⁰ Mg C. (2) LULC was identified as the most important direct and predominantly negative driving factor of carbon storage. (3) Natural factors had a strong positive influence on carbon storage, among which slope and NDVI exhibited the highest explanatory power; in contrast, climate factors showed weaker but still positive effects. (4) Human activities affected carbon storage through both direct and indirect pathways associated with LULC, with positive effects driven by landscape factors and negative effects driven by natural factors, while climate factors exhibited mixed but weak effects. Overall, carbon storage dynamics in Shaanxi Province reflect a hierarchical and path-dependent process shaped by the combined effects of natural constraints, human activities, and policy guidance through LULC pathways, providing important evidence for systematically understanding the driving structure and pathways of regional carbon storage. These findings highlight the importance of aligning land-use policies with regional biophysical constraints to enhance carbon sequestration efficiency. Full article

Hurricanes routinely damage forests across the North Atlantic Basin, yet efforts to characterize their impacts have had mixed subregional success. To elucidate these challenges, this study analyzed pre- and post-hurricane land surface phenology (LSP) for 44 moderate and strong hurricanes over 22 years using the Enhanced Vegetation Index (EVI). We statistically grouped storms based on their long-term climate attributes, then compared subregional impacts with wind speed and land cover. After accounting for wind speed, responses differed among the six subregions. The Southeast U.S. showed declines in EVI for the first winter and first year post storm, but this response was weak or absent elsewhere. The Central America region declined in the first winter but not in the subsequent growing season, while four other regions showed no increased impact with wind speed in either season. We then examined six category 4 hurricanes using a forest mask. In dry areas, drought-sensitive vegetation explained weak responses, whereas in the humid tropics, rapid refoliation or sprouting was common. These factors complicate optical remote sensing assessments. Rapid evaluations can mistake defoliation for more substantial damage, and delayed assessments can confuse EVI recovery with structural recovery. Results underscore the need for ecologically tailored monitoring approaches. Full article

Against the backdrop of dietary structure evolution and the “big food concept” strategy, there has been a shift from the traditional grain-centric perspective toward a diversified supply system. Taking Hubei Province—a major grain-producing region in China—as a case study, this research establishes a multi-criteria evaluation system and conducts analysis using statistical yearbooks and land survey data. By integrating natural conditions, economic benefits, and production capacity, the suitability of cultivated land for growing grain crops, cash crops, and forage crops is assessed. Concurrently, landscape pattern indices were applied to quantify the degree of farmland fragmentation. Employing a self-organizing mapping (SOM) neural network model, we synthesized suitability and fragmentation data to delineate differentiated farmland conservation zones. The results revealed significant spatial heterogeneity in crop suitability and fragmentation levels. High-suitability zones for grain crops were concentrated in the Jianghan Plain, while forage crops exhibited higher suitability in northeastern and southeastern Hubei. Farmland fragmentation showed a spatial pattern of lower levels in central Jianghan Plain, gradually increasing toward surrounding hilly and mountainous areas. SOM clustering effectively partitioned farmland into six functional zones: multifunctional agricultural zones, mixed farming zones, grain crop zones, cash crop zones, forage crop zones, and production improvement zones. This multi-source geographic and statistical data-driven zoning framework provides scientific basis for targeted policy interventions. It enables the quantitative management, quality enhancement, and spatial optimization of farmland resources, thereby operationalizing the big food concept to strengthen regional food security. Full article

Rapid, state-led urbanization in China often generates socio-spatial vulnerabilities, leaving interstitial “waiting lands” in a state of regulatory and ecological limbo. This paper investigates “nomadic gardens”—spontaneous, resident-led cultivation in Jinan—as a bottom-up strategy for adaptive capacity. Using a mixed-methods approach involving site typologies and community surveys (n = 100), we identify eight distinct garden forms that function as socio-ecological buffers, mitigating the risks of social isolation and psychological distress among elderly residents. Findings reveal a significant resilience gap caused by rigid land-use policies that prioritize ornamental aesthetics over functional productivity. We propose an Adaptive Urbanism framework that utilizes modular design and transitional governance to transform these precarious spaces into managed resilience assets. By shifting the planning focus from enforcement to risk-responsive design, this research provides a scalable model for sustainable urban risk management in rapidly transforming global cities. Full article