Search Results (849)

This study, based on multi-source data fusion and risk index models, has developed a comprehensive methodological system for evaluating the risk of forest fires caused by human factors. The system starts with four dimensions, i.e., exposure, hazard factors, vulnerability, and prevention and control capabilities, and constructs an evaluation framework with 19 secondary indicators. It also establishes single-category risk index models for four types of dominant fire sources: agricultural activities, religious ceremonies, tourism, and power distribution lines. Through weighted synthesis and exponential smoothing algorithms, it achieves daily dynamic risk forecasting. The research took the typical forest areas in the Mentougou, Changping, and Yanqing districts of Beijing as the application demonstration areas, collecting meteorological data, geographic information data, risk census ledgers, online hiking trajectories, and 2530 social survey questionnaires to complete the local parameter calibration and validation of the model. The retrospective analysis of 22 typical human-caused fire cases from 2018 to 2025 shows that the risk percentile of the ignition points in all cases was above 87.8%, indicating that the model has a good risk identification capability. Based on the evaluation results, differentiated control measures for different types of fire sources were proposed. The research results have been integrated into Beijing’s forest fire risk monitoring and early warning system, providing a scientific tool for the refined management of human-caused fire sources. Full article

Improving the cultivated land use eco-efficiency (CLUE) is crucial to achieving sustainable land use and the green transformation of agriculture. This study is based on the data from 353 prefecture-level cities in China from 2013 to 2021. The slacks-based measurement (SBM)-undesirable model, the social network analysis (SNA), and the fuzzy set qualitative comparative analysis (fsQCA) are adopted to measure and analyze the spatial patterns, network characteristics, and multiple driving pathways of inefficiency in the cultivated land use eco-efficiency in prefecture-level administrative units. Results show the following: (1) From 2013 to 2021, CLUE in the study areas shows spatial heterogeneity, with most efficiency values at a moderate level and showing a fluctuating downward trend over time. (2) The nine major agricultural regions have formed a complex association network, with the overall network connectivity being weak but efficiency relatively high. The hierarchical structure is gradually flattening, and inter-regional cooperation is increasing. (3) There are significant differences in influence, control, and accessibility within individual networks, and the collaborative network is developing into a “multi-core-hierarchical” structure. (4) The formation of inefficiency involves multiple concurrent mechanisms. Four typical inefficiency paths were identified, with significant heterogeneity across different agricultural regions. In the future, differentiated land use and ecological protection policies should be implemented based on the spatial network characteristics and inefficiency driving pathways of each agricultural region to promote the coordinated improvement of CLUE. Full article

Accurate crop distribution mapping using multi-temporal remote sensing has become increasingly important for agricultural monitoring and management. However, existing methods often rely on the direct stacking of multi-temporal features, which leads to feature redundancy and reduced model efficiency. To address this issue, this study proposes a winter wheat identification framework that integrates growth-stage ranking, key spectral variable selection, and cross-temporal feature fusion. Taking Shandong Province as the study area, eight typical growth stages during the 2023–2024 winter wheat growing season were analyzed using Sentinel-2 imagery. Random Forest models were first constructed for each growth stage to evaluate discriminative ability. Then, spectral variable contributions were quantified using permutation importance, and key spectral variables were selected under correlation constraints. The progressive accumulation model (PAM) was then developed according to the ranking of discriminative ability across different growth stages, while the cross-temporal fusion model (CTFM) was constructed by extracting inter-stage mean values (mean) and inter-stage differences (diff) of key variables. The results show that the feature space was reduced from 64 dimensions (8 stages × 8 variables) to 16 key variables, substantially improving feature representation efficiency. Among the eight growth stages, the jointing, overwintering, heading, and grain-filling stages exhibited relatively strong discriminative ability. In the cross-temporal experiments, CTFM M6, which integrates information from the top six growth stages ranked by discriminative ability, achieved an overall accuracy (OA) of 0.9658 and a user’s accuracy (UA) of 0.9609 using only 10 fused features, providing the best balance between identification accuracy and feature dimensionality. Based on this model, a 10 m resolution winter wheat distribution map of Shandong Province was generated, and the estimated planting area showed high consistency with statistical yearbook data. These results demonstrate that the proposed strategy can effectively reduce feature dimensionality while maintaining high identification accuracy, providing an efficient and scalable approach for regional-scale remote sensing mapping of winter wheat. Full article

The long-term utilization and low recycling rate of agricultural films have resulted in substantial increases in plastic debris and microplastics remaining in the soil, impacting the sustainable utilization of agricultural soil. However, the distribution and ecological toxicity of microplastics in long-term film-covered greenhouses and nongreenhouse vegetable fields on soil animals remain unclear. In this study, six typical greenhouse and nongreenhouse vegetable fields in the Shenyang area, which had been covered with plastic film for more than 20 years, were investigated. The distribution of microplastic abundance, shape, and source across different particle sizes in soil, as well as their oxidative damage toxicity effects on earthworms, were examined. The results demonstrated that the total abundance of microplastics in greenhouse soil was greater than that in nongreenhouse soil. Plastic fragments and microplastics > 2 mm were more prevalent in nongreenhouse soil, whereas microplastics < 2 mm were predominantly found in greenhouse soil, accounting for 89.9–98.6%. Notably, the abundance of microplastics with small particle sizes of 20–40 μm was high in greenhouse soils, and their proportion increased with increasing soil depth, with the cucumber and tomato groups showing increased abundances. Microplastics were identified mainly as thin-film and filamentous forms composed of polyethylene and polypropylene. After 56 d of exposure, a slight increase in malondialdehyde was detected in the earthworms in the soil where the cucumbers and tomatoes were grown. Mantel analysis revealed a significant correlation between the particle size of the microplastics and oxidative stress markers in the earthworms. Although greenhouse soil currently only causes slight oxidative damage to earthworms, over time, the oxidative damage caused by greenhouse systems to earthworms will increase. Therefore, regulatory measures should be implemented to standardize vegetable field management, especially with respect to microplastic pollution. Full article

Agricultural watersheds must simultaneously support multiple Ecosystem Services (ESs), yet the coordination between Ecosystem Service Value (ESV) growth and synergies of ESs remains poorly understood. Taking the Liaohe River mainstream Basin (LRMB), a typical agricultural watershed, as a case, this study investigates the spatiotemporal dynamics of ESV and trade-offs among ESs, along with their driving factors. Five key ESs—Food Production (FP), Water Conservation (WC), Water Purification (WP), Soil Conservation (SC), and Landscape Aesthetics (LA)—were selected. The InVEST model, Function-based Valuation Method, Root Mean Square Deviation (RMSD), and Coupling Coordination Degree (CCD) were comprehensively applied to assess the spatiotemporal variations in ESV, trade-off intensity, and their coupling coordination degree in the watershed from 2000 to 2023. Furthermore, the Optimal Parameters-based Geographical Detector (OPGD) and Multiscale Geographically Weighted Regression with Spatial Auto-correlation (MGWR-SAR) were employed to explore the driving mechanisms underlying changes in ESV and trade-off intensity, and to identify the major driving factors and their spatial heterogeneity. The results reveal the following: (1) From 2000 to 2023, total ESV in the LRMB increased by 69.5% from 77.66 to 131.59 billion yuan, with WC and FP accounting for 42.8% and 41.9% of this growth. Spatially, ESV shifted from a west-to-east increasing gradient to a U-shaped pattern, with high values concentrated in mountainous areas and low values along the mainstream. (2) Mean trade-off intensity remained stable at approximately 0.29, yet exhibited pronounced spatial polarisation. High trade-off zones shifted from the southwestern estuary toward the mainstream corridor, driven primarily by intensifying conflicts between FP and other ESs. (3) Despite a stable watershed-average CCD of 0.71–0.73, the CCD along the Liaohe River mainstream declined by over 15%, forming a corridor of coordination decay and revealing that ESV growth occurs at the expense of internal synergy. (4) Nonlinear interactions dominated ES dynamics, with the interaction of precipitation and human disturbance intensity exhibiting the highest explanatory power (q-values of 0.61 for ESV and 0.58 for RMSD). (5) Natural climatic factors (precipitation, temperature) predominantly enhanced synergy in mountainous areas, whereas human and landscape factors (human disturbance intensity, Shannon’s Diversity Index, PLAND of water) intensified trade-offs along the mainstream and central plains. This study establishes an integrated “ESV–trade-off–CCD” diagnostic framework and proposes a differentiated management strategy, offering a potentially transferable paradigm for sustainable governance in agricultural watersheds. Full article

Land-use and land cover (LULC) change is a key driver of environmental dynamics in semi-arid Mediterranean watersheds, strongly influencing hydrological processes, soil degradation, and ecosystem stability. In this context, understanding and predicting spatial–temporal land transformations is essential for sustainable watershed management. This study proposes a nonlinear spatial–temporal modeling framework integrating a hybrid Artificial Neural Network (ANN), Cellular Automata (CA), and Markov chain approach to simulate LULC dynamics in the Sebdou watershed, northwestern Algeria. Multi-temporal Landsat imagery (1985, 2005, and 2025), combined with topographic, socio-economic, and accessibility variables (slope, population density, distance to roads, and hydrographic network), was used to reconstruct historical land-use patterns and identify key driving forces of change. A supervised Maximum Likelihood classification achieved high accuracies, with overall accuracy ranging from 92.87% to 96.26% and Kappa coefficients between 0.85 and 0.91. The ANN model was trained to estimate nonlinear transition potentials, while the CA component incorporated spatial neighborhood effects to simulate land allocation processes. Markov chain analysis provided temporal transition probabilities, enabling the construction of a coupled ANN–CA–Markov framework for scenario-based prediction. Model validation against observed 2025 LULC maps indicated strong agreement in quantity distribution (Kappa histogram = 0.767), while spatial agreement (Kappa = 0.3566) reflected inherent spatial displacement typical of CA-based stochastic allocation. Simulation results for 2045 indicate continued urban expansion along major transport corridors, progressive decline of dense forest cover, and increasing bare soil areas, while agricultural land remains dominant but increasingly fragmented. These trends highlight the growing influence of anthropogenic pressure and accessibility factors on landscape restructuring in semi-arid environments. The proposed hybrid framework provides a robust decision-support tool for anticipating land-use dynamics and assessing future environmental pressures in Mediterranean drylands. Its integration with hydrological and erosion models can further support sustainable watershed planning under combined socio-economic and climatic changes. Full article

Biofouling presents numerous challenges across various sectors, including aquaculture, agriculture, infrastructure, and medicine. The development of anti-biofouling techniques remains a significant challenge. In the water industry, biofouling on monitoring sensors substantially compromises the accuracy of measurements by interfering with the sensors’ measuring ability. Biofouling also significantly increases the running costs by increasing the frequency of maintenance needed to keep sensors clean and accurate. Consequently, anti-biofouling techniques are widely employed to clean in situ optical sensors, ensuring accurate measurements while minimizing overall system costs. The conventional approach for preventing biofouling from in situ sensors typically involves the application of coatings, mechanical brushes, ultraviolet radiation, and ultrasonic waves, which possess distinct advantages and disadvantages contingent upon their application. The challenges associated with protecting the small windows of water quality sensors from biofouling over extended periods using current methods are either expensive or adversely affect the integrity of monitoring data. This study introduces a low-cost centimeter-scale high-frequency surface acoustic wave (SAW) device to protect the small windows of in situ water quality sensors continuously from biofouling, functioning as an auxiliary anti-biofouling mechanism. This study found that this 16 MHz SAW device can mitigate the formation of biofilms by adhesive diatom strains CS-1664, CS-1665, and by planktonic algae CS-327 by approximately 98% in comparison to control conditions, functioning effectively as an anti-biofouling tool for itself and surrounding surfaces without adversely affecting aquatic organisms. The dimension and resonance frequency (RF) of the SAW device are also capable of being fabricated according to the area requiring cleaning. A miniaturized 16 MHz SAW device can sustain operation for prolonged periods up to a couple of months without maintenance, at a low cost and power consumption, providing a new anti-biofouling technology. This methodology aims to assist the Australian inland and coastal water quality monitoring system by reducing maintenance costs while simultaneously enhancing the longevity of sensors submerged in water for extended periods. Full article

China is at a critical stage in the coordinated promotion of ecological civilization construction, rural revitalization, and new urbanization strategies. How to scientifically coordinate the transformation of territorial spatial utilization (TSU), such as that of ecology–agriculture–urban spaces, and to build an ecological security pattern (ESP) has emerged as a critical task facing regional sustainability. To address the above problem, this study constructs a research framework to reveal the response characteristics of ESP to TSU transformation by adopting the source–surface–flow continuous field model and geographic grid analysis methods. Taking the Qinba–Dabie convergence area in China, with typical transitional characteristics and regional representativeness, as the study area, this study quantitatively analyzes the spatiotemporal evolution of the comprehensive index of TSU degree, as well as the ecological source, resistance, and flow indices across the whole territory. With the geographic grid method adopted to unify the spatial analysis grain between ESP and TSU transformations, this study further explores the response characteristics of ESP under TSU transformation. The main study findings are presented as follows: With the transformation of TSU, ESP elements such as the ecological source index, resistance index, and flow index exhibited significant spatial differentiation, and a nonlinear relationship could be observed between the TSU degree index and the ESP elements. On this basis, this study further explores and constructs a zoning method system for regional TSU control oriented to ESP, formulates targeted protection and restoration strategies, offering theoretical and practical references for the scientific compilation of territorial spatial planning and the conservation and restoration of ESP. Full article

Accurate and timely monitoring of soil salinity is of great significance for the ecological restoration of saline-alkali land and precision agricultural management. In this study, a typical coastal saline-alkali farmland located in Huanghua City, Hebei Province, China, in the Bohai coastal region, was selected as the study area. High-resolution images were acquired using an unmanned aerial vehicle (UAV) equipped with a multispectral sensor, and ground soil salinity samples were collected synchronously. Based on the construction of a feature library comprising spectral reflectance, vegetation indices, and salinity indices, three algorithms, PSO-SFLA, MultiSURF, and VIP, were employed for feature selection. Subsequently, an ensemble model was established, utilizing Ridge Regression (Ridge), Random Forest (RF), and Extra Trees (ET) as primary base learners, and Extreme Gradient Boosting (XGBoost) as the secondary meta-learner. This ensemble model was applied for soil salinity inversion. Furthermore, the coefficient of determination (R²), standardized root mean square error (S_RMSE), and the ratio of performance to interquartile distance (RPIQ) were introduced to comprehensively evaluate the accuracy of the models. Finally, the intrinsic physical responses of the features were explored through SHAP. The results showed that the optimization by the PSO-SFLA effectively reduced the impact of spectral multicollinearity, and 11 core features highly sensitive to salinity were selected from a vast number of indices. The ensemble model showed better predictive performance on the independent test set, achieving an R² of 0.758, an S_RMSE of 0.285, and an RPIQ of 3.382, outperforming the single Ridge, RF, and ET models under the current experimental conditions. Based on this model, the spatial distribution map of soil salinity in the experimental area was generated. The integrated and interpretable workflow proposed in this study, combining UAV multispectral imagery, PSO-SFLA-based feature selection, ensemble learning, and SHAP interpretation, provides a practical approach for accurate soil salinity inversion and dynamic agricultural monitoring in coastal saline-alkali lands. Full article

The carob tree (Ceratonia siliqua L.) is a typical tree of the arid Mediterranean, and its cultivation contributes to the sustainability of local agroecosystems. In recent years, the economic and environmental importance of the carob tree has grown due to its use as a raw material in the food, pharmaceutical, and cosmetic industries. It also plays an ecological role in conserving biodiversity and promoting sustainable agricultural systems by improving cultivation and mechanization strategies. Currently, national carob groves are facing competition from other more profitable crops such as olive, citrus, almond and horticultural systems. This has led to the marginalization of carob cultivation in several Mediterranean rural areas and increased the need to modernize and mechanize harvesting to enhance the potential of carob and its derived products. This study aimed to investigate the physical characteristics of the fruit (weight, length, width and fruit detachment force) in relation to the degree of ripeness, with the objective of providing useful information on the optimal harvesting period and introducing semi-mechanical harvesting systems. Full article

Urban streams typically exhibit altered hydromorphology and large fluctuations in water quality variables, creating stressful conditions for biota. In this study, we investigated periphyton along two urban streams (Bliznec, B, and Veliki Potok, VP) in Zagreb (the Croatian capital) over one year. Both streams were sampled in an upstream pristine reach within Medvednica Nature Park, a middle reach influenced by either agriculture or low-density residential areas (houses with gardens) and affected by channelization, and a lower reach, also channelized, impacted by a mix of agricultural influence and more intensive residential development with higher population density. Nutrient concentration, conductivity, COD, and chlorophyll a showed an increasing trend from upper to lower sites, reflecting the influence of urbanization. The number of periphytic taxa and their abundance correlated positively with the increasing urbanization, probably due to increased food sources. Periphyton consisted mainly of ubiquitous taxa, with 55 phagotrophic protist and 10 micro-metazoan taxa. Ciliates dominated both in diversity (44 taxa) and abundance (over 90% of mean abundance), mainly comprising bacterivorous taxa. Periphyton exhibited pronounced seasonal dynamics, with occasional high similarity between the two urban streams studied and high turnover rates of assemblages between samplings. This pattern indicates that urban streams support highly dynamic periphytic communities, strongly shaped by environmental disturbance and that these assemblages have the capacity to withstand frequent environmental variability in urbanization-influenced reaches. Full article

Non-grain use of cultivated land (NGUCL) in ecologically fragile regions has become a major challenge to food security and land sustainability, yet its spatiotemporal dynamics, spatial spillover effects, and associated factors remain insufficiently understood. Taking Ningxia, China, as a typical semi-arid to arid transition zone, this study developed a phenology-informed framework that combined multi-temporal Landsat imagery, random forest classification, spatial autocorrelation analysis, centroid and standard deviation ellipse models, and a spatial lag model to identify and analyze NGUCL in 2005, 2010, 2015, and 2020. Within the cultivated land boundary, NGUCL was further decomposed into cash crop-cultivated farmland (CCCF) and farmland abandonment (FA). The results show that the classification framework achieved robust performance, with overall accuracies above 85% across the benchmark years. Food-crop mapping reached an OA of 86.38–90.12% and a Kappa of 0.80–0.85, while FA mapping reached an OA of 85.60–86.74% and a Kappa of 0.70–0.72. NGUCL in Ningxia exhibited strong subregional differentiation under the gradients of northern irrigation, central arid, and southern mountainous conditions. CCCF was more closely associated with irrigated and agriculturally productive areas, whereas FA was concentrated in ecologically constrained counties and showed stronger dispersion and migration complexity. Spatial econometric results further indicate significant spatial spillover effects, suggesting that NGUCL-related processes in one county are associated with those in neighboring counties. The effects of natural, socioeconomic, and agricultural production factors also varied by type and period, indicating that NGUCL in ecologically fragile regions is not a homogeneous land-use transition process. By distinguishing CCCF from FA, this study provides a more nuanced interpretation of NGUCL and offers empirical evidence for understanding cultivated land transition and governance in ecologically fragile areas. Full article

Understanding the spatial organisation of small towns is essential for sustainable spatial planning and regional development. This study examines the spatio-functional pattern of Brzeziny, a small town located within the Łódź Metropolitan Area in Central Poland, selected as a representative case due to its typical Central European small-town morphology shaped by historical continuity, demographic stagnation, and metropolitan influence. The analysis is based on updated cadastral land-use data verified through field surveys and supplemented with topographic datasets (BDOT10k and OpenStreetMap). A modified land-use classification comprising nine categories is applied, and spatial analysis is performed using a regular grid and GIS tools. Dominant land-use structures are identified using the K. Doi method, enabling the delineation of spatio-functional zones. The results reveal a strong dominance of undeveloped land (77% of the total area), particularly agricultural land, alongside a compact central zone characterised by residential and service functions. The study demonstrates how historical development, economic structure, and metropolitan proximity shape the spatial organisation of small towns. The proposed methodology highlights the usefulness of cadastral data combined with grid-based spatial analysis for identifying S-FPs and supporting local planning processes. Full article

Climate change is intensifying the frequency and severity of extreme weather events, posing significant challenges to crop productivity and agroclimatic management in subtropical regions. However, quantitative insights into how different cropping systems respond to climate extremes remain limited. In this study, crop net primary productivity (CNPP) of two representative cropping systems, early–late rice (ER–LR) and dry rapeseed–sweet potato (DR–SP), was analyzed in Pingxiang, a typical subtropical agricultural region of China. Nineteen extreme temperature and precipitation indices were evaluated using an integrated Trend–Prediction–Sensitivity–Threshold (TPST) framework combining statistical and machine learning approaches. CNPP exhibited an upward trend (slope = 4.29 g C m⁻² yr⁻¹) from 2000 to 2023, with ER–LR showing faster growth (slope = 4.54 g C m⁻² yr⁻¹) and higher stability (high-volatility area: 1.25%) than DR–SP (slope = 4.11 g C m⁻² yr⁻¹; 4.94%). Temperature extremes were the dominant drivers, exhibiting nonlinear responses with threshold effects. DR–SP was more climate-sensitive, while ER–LR showed greater tolerance, highlighting the role of cropping systems in enhancing resilience. The TPST framework provides a transferable approach for assessing agroecosystem productivity responses to climate extremes and supports climate-resilient cropland management in subtropical regions. Full article

Drought–flood abrupt alternation (DFAA) is a typical compound hydroclimatic extreme process and has important implications for regional water resources regulation, agricultural production, and ecological stability. However, existing studies have mainly focused on event identification and frequency variation, while lacking a systematic investigation of the directional differences between drought-to-flood (DF) and flood-to-drought (FD) events in terms of process structure, cumulative effects, and spatial hazard patterns. Based on daily precipitation data from 1960 to 2024, this study identified DFAA events in the Yellow River Basin by combining the standardized weighted average precipitation (SWAP) index with run theory, and analyzed the asymmetric characteristics of DF and FD events from the perspectives of event frequency, phase duration, abrupt-transition characteristics, cumulative severity, and integrated hazard. The results show that: (1) the frequency of DFAA events in the Yellow River Basin exhibited pronounced spatial heterogeneity, with an overall pattern of being higher in the middle reaches and lower in the upper and lower reaches. The frequency of DF events was generally higher than that of FD events, and their spatial distribution was also more continuous. No significant long-term trend was detected in the annual frequency, although clear interdecadal variability was observed, characterized by a transition from relatively low-frequency periods to medium- and high-frequency periods. (2) DF and FD events exhibited stable asymmetry in process structure. The abrupt-transition duration of DF events was mainly concentrated within 1–2 days, whereas that of FD events was mainly concentrated within 3–5 days. The two event types had comparable pre-transition durations, but DF events tended to shift more rapidly and were followed by a longer-lasting flood phase. (3) The differences between the two event types in terms of instantaneous intensity were relatively limited, whereas clearer divergence was observed in cumulative severity, with DF events showing greater overall severity than FD events. This indicates that the directional difference is manifested primarily in cumulative process effects rather than in the magnitude at a single moment. (4) The comprehensive hazard index (CHI) revealed that the northern and central-eastern parts of the middle reaches of the Yellow River Basin were the main hotspots of DFAA hazard. Among them, high-hazard areas of DF events were more extensive, whereas FD hazards were characterized more by localized intensification. These findings indicate that within the identification framework adopted here, DFAA in the Yellow River Basin is characterized not only by rapid dry–wet transitions, but also by clear directional differences between DF and FD in process structure and hazard pattern. This study can provide a scientific reference for the monitoring, early warning, and zonal hazard prevention of DFAA in the basin. Full article