Search Results (1,322)

Pansharpening is a method of image fusion that combines a panchromatic (PAN) image with high spatial resolution and multispectral (MS) images which possess different spectral characteristics and are frequently obtained from satellite sensors. Despite the development of numerous pansharpening methods in recent years, a key challenge continues to be the maintenance of both spatial details and spectral accuracy in the combined image. To tackle this challenge, we introduce a new approach that enhances the component substitution-based Adaptive IHS method by integrating the YUV color model along with weighting coefficients influenced by the multispectral data. In our proposed approach, the conventional IHS color model is substituted with the YUV model to enhance spectral consistency. Additionally, Lanczos interpolation is used to upscale the MS image to match the spatial resolution of the PAN image. Each channel of the MS image is fused using adaptive weights derived from the influence of multispectral data, leading to the final pansharpened image. Based on the findings from experiments conducted on the PairMax and PanCollection datasets, our proposed method exhibited superior spectral and spatial performance when compared to several existing pansharpening techniques. Full article

In urban regeneration, co-production has become a significant approach for shaping public space in urban communities. While existing studies focus on the processes and stakeholders involved in co-production of community public space (CPS), few have examined the influence of structural factors. Based on the politics of scale, this study uses thematic analysis within an embedded case study of community centers in Shanghai, China, to analyze the impact of scalar restructuring on community co-production across three dimensions: material scale, organizational scale, and discursive scale. The study finds that local governments actively reshape public space through scalar restructuring, thereby transforming power relations among participants and promoting community co-production. In response to different community conditions and dilemmas, local governments adopt context-specific scalar restructuring strategies. When implementing scalar restructuring strategies such as downscaling, upscaling and scalar recompositing, three corresponding patterns of community co-production often emerge: bonded, procedural, and bridged. This paper contributes by providing a new perspective on the mechanism of community co-production, identifying novel patterns of community co-production and refining the scalar restructuring strategies. It moves beyond spatial limitations and captures the co-production of CPS through a broader lens of power dynamics. Full article

Globally, winery wastewaters (WWWs) are estimated to account for about 62.5 billion L annually (2021), with COD levels up to 300,000 mg O₂/L primarily attributed to residual ethanol, posing serious environmental concerns. Conventional treatments are effective in COD removal, but they often miss opportunities for energy recovery and resource valorization. This study investigates the aqueous phase reforming (APR) of ethanol-rich wastewater as an alternative treatment for both COD reduction and energy generation. Two scenarios were assessed: electricity and heat cogeneration (S1) and hydrogen production (S2). Process simulations in Aspen Plus^® V14, based on lab-scale APR data, provided upscaled material and energy flows for techno-economic analysis, life cycle assessment, and energy sustainability analysis of a 2.5 m³/h plant. At 75% ethanol conversion, the minimum selling price (MSP) was USD0.80/kWh with a carbon footprint of 0.08 kg CO₂-eq/kWh for S1 and USD7.00/kg with 2.57 kg CO₂-eq/kg H₂ for S2. Interestingly, S1 revealed a non-linear trade-off between APR performance and energy integration, with higher ethanol conversion leading to a higher electricity selling price because of the increased heat reactor duty. In both cases, the main contributors to global warming potential (GWP) were platinum extraction/recovery and residual COD treatment. Both scenarios achieved a positive energy balance, with an energy return on investment (EROI) of 1.57 for S1 and 2.71 for S2. This study demonstrates the potential of APR as a strategy for self-sufficient energy valorization and additional revenue generation in wine-producing regions. Full article

A daily-scale flux variance (FV) method, which employs low-frequency air temperature measurements, was assessed against eddy covariance (EC) measurements of sensible and latent heat fluxes at four sites representing grassland and cropland ecosystems. The sensible heat flux was estimated using two daily-scale FV approaches: M1 (separating daytime and nighttime data) and M2 (integrating daily data), both derived from conventional formulations. The latent heat flux was extracted as a residual of the energy balance closure with the FV-estimated sensible heat flux and additional measurements of net radiation and soil heat flux. The results showed that the FV method performed poorly in estimating sensible heat flux across all four sites, primarily due to the negative flux values from cropland sites. In contrast, latent heat flux estimation showed reasonable agreement with EC measurements. Notably, upscaling the FV method from a half-daily (M1) to a daily (M2) scale did not improve the accuracy of sensible and latent heat flux estimations for most sites. The best performance for latent heat flux was achieved with M1 at a cropland site (YF), yielding a slope of 0.98, determination coefficient of 0.88, and root mean square error of 13.13 W m⁻². Overall, the daily-scale FV method—requiring only low-frequency air temperature data from microclimate systems—offers a promising approach for evapotranspiration monitoring, particularly at basic meteorological stations lacking high-frequency instrumentations. Full article

The estimation of rubber plantation aboveground biomass (AGB) is crucial for carbon sequestration assessment and management optimization. Unmanned Aerial Vehicles (UAVs) fitted with multispectral sensors present an economical approach for local-scale AGB monitoring. However, the prevailing studies primarily concentrate on spectral characteristics and algorithmic enhancements, failing to incorporate key ecological parameters such as stand age. Moreover, the current approaches remain constrained to local-scale assessments due to the absence of reliable upscaling methodologies from UAV to satellite platforms, limiting their applicability for regional monitoring. Thus, this study aims to establish an improved estimation model for rubber plantation AGB based on UAV multispectral imagery and stand age, develop an upscaling algorithm to bridge the gap between UAV and satellite scales, and ultimately achieve accurate regional-scale monitoring of rubber forest AGB. Combining optimized multispectral features, Landsat-derived stand age, and machine learning techniques yields the most accurate UAV-scale AGB estimates in this study, with performance metrics of R² = 0.90, an RMSE = 13.24 t/ha, and an MAE = 11.09 t/ha. Notably, the novel ‘UAV-satellite’ upscaling approach proposed in this study enables regional-scale AGB estimation using Sentinel-2 imagery, with remarkable consistency (correlation coefficient of 0.93). The developed framework synergistically combines Landsat-derived stand age data with spectral features, effectively improving rubber plantation AGB estimation accuracy through machine learning and enabling UAVs to replace manual measurements. This cross-scale upscaling framework demonstrates applicability beyond rubber plantation AGB monitoring, while providing novel insights for estimating critical parameters, including regional-scale stock volume and leaf area index, across diverse tree species. Full article

Droughts are becoming more frequent, severe, and impactful across the globe. Agroecosystems, which are human-made ecosystems with high water demand that provide essential ecosystem services, are vulnerable to extreme droughts. Although water use efficiency in agriculture has increased in rec ent decades, drought management should be based on long-term, proactive strategies rather than crisis management. The AgrHyS network of sites in French Brittany collects high-resolution soil moisture data from agronomic stations and catchments to improve understanding of temporal soil moisture dynamics and enhance water use efficiency. Frequent mapping of soil moisture and plant water stress is crucial for assessing water stress risk in the context of global warming. Although satellite remote sensing provides reliable, periodic global data on surface soil moisture, it does so at a very coarse spatial resolution. The intrinsic spatial heterogeneity of surface soil moisture requires a higher spatial resolution in order to address upcoming challenges on a local scale. Drones are an excellent tool for upscaling point measurements to catchment level using different onboard cameras. In this study, we evaluated the potential of multispectral images, thermal images and LiDAR data captured in several concurrent drone flights for high-resolution mapping of soil moisture spatial variability, using in situ point measurements of soil water content and plant water stress in both agricultural areas and natural ecosystems. Statistical models were fitted to map soil water content in two areas: a natural marshland and a grassland-covered agricultural field. Our results demonstrate the statistical significance of topography, land surface temperature and red band reflectance in the natural area for retrieving soil water content. In contrast, the grasslands were best predicted by the transformed normalised difference vegetation index (TNDVI). Full article

Most existing deep learning-based super-resolution (SR) methods for remote sensing images rely on predefined degradation assumptions (e.g., bicubic downsampling). However, when real-world degradations deviate from these assumptions, their performance deteriorates significantly. Moreover, explicit degradation estimation approaches based on iterative schemes inevitably lead to accumulated estimation errors and time-consuming processes. In this paper, instead of explicitly estimating degradation types, we first innovatively introduce an MSCN_G coefficient to capture global prior information corresponding to different distortions. Subsequently, distortion-enhanced representations are implicitly estimated through contrastive learning and embedded into a super-resolution network equipped with multiple distortion decoders (D-Decoder). Furthermore, we propose a distortion-related channel segmentation (DCS) strategy that reduces the network’s parameters and computation (FLOPs). We refer to this Global Prior-guided Distortion-enhanced Representation Learning Network as GDRNet. Experiments on both synthetic and real-world remote sensing images demonstrate that our GDRNet outperforms state-of-the-art blind SR methods for remote sensing images in terms of overall performance. Under the experimental condition of anisotropic Gaussian blurring without added noise, with a kernel width of 1.2 and an upscaling factor of 4, the super-resolution reconstruction of remote sensing images on the NWPU-RESISC45 dataset achieves a PSNR of 28.98 dB and SSIM of 0.7656. Full article

Enhanced oil recovery (EOR) techniques are essential for maximizing hydrocarbon extraction from mature reservoirs. CO₂ injection (CO₂-EOR) is a promising technology that improves oil recovery while contributing to greenhouse gas reduction. This study investigates the potential of miscible CO₂-enhanced oil recovery (CO₂-EOR) in the MakXX oilfield of southeastern Kazakhstan. The aim is to assess oil displacement efficiency and its impact on key rock properties, including porosity, permeability, and mineral composition, under reservoir conditions. Core flooding experiments were conducted at 13 MPa and 42 °C using high-precision equipment to replicate reservoir conditions. The core was analyzed before and after CO₂ injection using SEM, EDS, and XRD. The results revealed a 54% oil recovery efficiency, accompanied by a 19% decrease in permeability and 8% reduction in porosity due to mineral precipitation and clay transformation. These findings provide insight into the performance and limitations of CO₂-EOR and support its application in similar lithology. To confirm and upscale laboratory observations, numerical simulation was conducted using a compositional model. The results demonstrated improved oil recovery, pressure stabilization, and enhanced sweep efficiency under CO₂ injection, supporting the scalability and field applicability of the proposed EOR approach. Full article

The widespread availability and spatial coverage of land surface temperature (LST) estimates from space often result in LST being used as a proxy for near-surface air temperature in order to characterize the urban heat island (UHI) effect. High-spatial-resolution satellite-based LST estimates from sensors such as Landsat-8 provide the spatial and thematic details necessary to understand the potential effects of urban greening measures to mitigate the increased frequency and intensity of heatwaves that are projected to occur as a result of human-induced climate change. Here, we investigate the influence of land cover on Surface Urban Heat Island (SUHI) observations of LST using a technique to reduce the spatial spread of the per-pixel temperature observation. Additionally, using land cover-based linear mixture models, we downscale the surface temperature to a 2 m spatial resolution. We find a mean difference in LST, compared to the city average, of +8.94 °C (+/−1.87 °C at 95% CI) for built-up cover type, compared to a difference of −7.42 °C (+/−0.8 °C) for broadleaf trees. This highlights the potential benefits of creating urban green spaces for mitigating the UHI amplification of extreme heatwaves. Furthermore, we highlight the need for improved observations of night-time temperatures, e.g., from forthcoming missions such as TRISHNA, in order to fully capture the diurnal variability of land surface temperature and energy fluxes. Full article

This paper investigates whether carbon payments are sufficient to entice private landholders to invest in the rehabilitation and protection of coastal wetlands as a nature-based climate solution. Ecologically intact coastal wetlands, such as mangroves and saltmarshes, are capable of sequestering and storing large amounts of carbon. Reinstating ecological functionality of degraded coastal wetlands may be achieved by installing conservation fences that exclude hard-hoofed domestic and feral animals. This research integrates ecological, technical and economic data to ascertain whether conservation fencing could represent a financially viable investment for coastal landholders in the Australian context, if restored wetlands attracted carbon payments. Data gleaned through literature review and expert interviews about technical fencing requirements, contemporary costs and potential blue carbon income are consolidated into scenarios and tested using cost–benefit analysis. Payback periods are calculated using deterministic parameters. Risk-based cost–benefit analysis accounts for uncertainty of ecological and price parameters; it provides probability distributions of benefit–cost ratios assuming an expert-agreed economic lifespan of conservation fences. The results demonstrate that the payback period and benefit–cost ratio are highly sensitive to wetlands’ carbon sequestration capacity, fencing costs and the carbon price going forward. In general, carbon payments on their own are likely insufficient to entice private landholders to protect coastal wetlands through conservation fencing, except in circumstances where restored wetlands achieve high additional carbon sequestration rates. Policy measures that reduce up-front costs and risk and remuneration of multiple ecosystem services provided by restored wetlands are required to upscale blue carbon solutions using conservation fencing. The research findings bear relevance for other conservation and land-use contexts that use fencing to achieve sustainability goals and generate payments for ecosystem services. Full article

Monitoring water quality parameters in coastal and estuarine environments is critical for assessing their ecological status and addressing environmental challenges. However, traditional in situ sampling programs are often constrained by limited spatial and temporal coverage, making it difficult to capture the complex variability in these dynamic systems. This study introduces a novel upscaling framework that leverages limited in situ measurements and airborne hyperspectral data to generate multiple conditional realizations of water quality parameter fields. These pseudo-measurements are statistically consistent with the original data and are used to calibrate inversion algorithms that relate satellite-derived reflectance data to water quality parameters. The approach was applied to Kaštela Bay, a semi-enclosed coastal area in the eastern Adriatic Sea, to map seasonal variations in water quality parameters such as Chlorophyll-a. The upscaling framework captured spatial patterns that were absent in sparse in situ observations and enabled regional mapping using Sentinel-2A satellite data at the appropriate spatial scale. By generating realistic pseudo-measurements, the method improved the stability and performance of satellite-based retrieval algorithms, particularly in periods of high productivity. Overall, this methodology addresses data scarcity challenges in coastal water monitoring and its application could benefit the implementation of European water quality directives through enhanced regional-scale mapping capabilities. Full article

Soil salinization in oasis areas of arid regions is recognized as a dynamic and multifaceted environmental threat influenced by both natural processes and human activities. In this study, 13 spatiotemporal predictors derived from field surveys and remote sensing are utilized to construct a spatial probabilistic model of salinization. A Bayesian Belief Network is integrated with spline interpolation in ArcGIS to map the likelihood of salinization, while Partial Least Squares Structural Equation Modeling (PLS-SEM) is applied to analyze the interactions among multiple drivers. The test results of this model indicate that its average sensitivity exceeds 80%, confirming its robustness. Salinization risk is categorized into degradation (35–79% probability), stability (0–58%), and improvement (0–48%) classes. Notably, 58.27% of the 1836.28 km² Keriya Oasis is found to have a 50–79% chance of degradation, whereas only 1.41% (25.91 km²) exceeds a 50% probability of remaining stable, and improvement probabilities are never observed to surpass 50%. Slope gradient and soil organic matter are identified by PLS-SEM as the strongest positive drivers of degradation, while higher population density and coarser soil textures are found to counteract this process. Spatially explicit probability maps are generated to provide critical spatiotemporal insights for sustainable oasis management, revealing the complex controls and limited recovery potential of soil salinization. Full article

Cyanobacteria are a prolific source of bioactive metabolites with expanding applications in sustainable agriculture and biotechnology. This work explores, for the first time in thermotolerant Colombian isolates, the impact of light spectrum, photoperiod, and irradiance on the co-production of exopolysaccharides (EPS) and indole-3-acetic acid (IAA). Six strains from hot-spring environments were screened under varying blue:red (B:R) LED ratios and full-spectrum illumination. Hapalosiphon sp. UFPS_002 outperformed all others, reaching ~290 mg L⁻¹ EPS and 28 µg mL⁻¹ IAA in the initial screen. Response-surface methodology was then used to optimize light intensity and photoperiod. EPS peaked at 281.4 mg L⁻¹ under a B:R ratio of 1:5 LED, 85 µmol m⁻² s⁻¹, and a 14.5 h light cycle, whereas IAA was maximized at 34.4 µg mL⁻¹ under cool-white LEDs at a similar irradiance. The quadratic models exhibited excellent predictive power (R² > 0.98) and a non-significant lack of fit, confirming the light regime as the dominant driver of metabolite yield. These results demonstrate that precise photonic tuning can selectively steer carbon flux toward either EPS or IAA, providing an energy-efficient strategy to upscale thermotolerant cyanobacteria for climate-resilient biofertilizers, bioplastics precursors, and other high-value bioproducts. Full article

Numerical simulations enable us to couple multiphase flow and geochemical processes to evaluate how sequestration impacts brine chemistry and reservoir properties. This study investigates these impacts during CO₂ storage at the San Juan Basin CarbonSAFE (SJB) site. The hydrodynamic model was calibrated through history-matching, utilizing data from saltwater disposal wells to improve predictive accuracy. Core-scale simulations incorporating mineral interactions and equilibrium reactions validated the model against laboratory flow-through experiments. The calibrated geochemical model was subsequently upscaled into a field-scale 3D model of the SJB site to predict how mineral precipitation and dissolution affect reservoir properties. The results indicate that the majority of the injected CO₂ is trapped structurally, followed by residual trapping and dissolution trapping; mineral trapping was found to be negligible in this study. Although quartz and calcite precipitation occurred, the dissolution of feldspars, phyllosilicates, and clay minerals counteracted these effects, resulting in a minimal reduction in porosity—less than 0.1%. The concentration of the various ions in the brine is directly influenced by dissolution/precipitation trends. This study provides valuable insights into CO₂ sequestration’s effects on reservoir fluid dynamics, mineralogy, and rock properties in the San Juan Basin. It highlights the importance of reservoir simulation in assessing long-term CO₂ storage effectiveness, particularly focusing on geochemical interactions. Full article

Bacterial nanocellulose (BNC) is a highly pure biopolymer with promising applications in the biomedical, food, and textile industries. However, the high production costs and low yields obtained in static conditions limit its scalability and industrial applications. This study addresses the sustainable production of BNC using a rotary disk bioreactor (RDB) and explores the use of grape pomace extract as an alternative carbon source for BNC production. Parameters such as the BNC production and biomass yield were evaluated using Komagataeibacter xylinus ATCC 53524 under different operational conditions (disk surface, rotation speed, and number of disks). The results showed that cellulose production increased using silicone-coated disks at 7–9 rpm (up to 2.72 g L⁻¹), while higher yields (5.23 g L⁻¹) were achieved when using grape pomace extract as the culture medium in comparison with conventional HS medium. FTIR and TGA characterizations confirmed that BNC obtained with grape pomace extract presents the same thermal and chemical characteristics than BNC produced with HS medium. This work provides insight into the feasibility of upscaling BNC production using a bioprocessing strategy, combining production in the RDB system and the use of an agro-industrial waste as a sustainable and cost-effective alternative. Full article