Search Results (373)

This research assesses how land-cover transitions from 2012 to 2022 have impacted the value of ecosystem services in Denton County, Texas. Using remote sensing and spatial analysis, this study quantitatively links land-use change to its ecological and economic consequences. Full-county Landsat data were analyzed in ArcGIS Pro through supervised classification and categorical change detection. To quantify the impact of these changes, an accuracy assessment was performed, and a benefit-transfer method using both global and Texas-specific coefficients was applied to estimate the change in Ecosystem Service Value (ESV). Results revealed a complex dynamic: while the county experienced significant urban expansion, it also saw substantial greening as large areas of bare land transitioned to vegetation. However, this greening was not enough to offset the economic impact of losing high-value ecosystems. The analysis shows a net loss in total ESV over the decade, estimated between USD 24 million and USD 95 million per year, primarily driven by the significant reduction of water bodies. This study provides a replicable framework for policymakers to assess the environmental trade-offs of development and highlights the critical importance of preserving existing high-value ecosystems alongside urban greening initiatives. Full article

Grassland classification via remote sensing is essential for ecosystem monitoring and precision management, yet conventional satellite-based approaches are fundamentally constrained by coarse spatial resolution. To overcome this limitation, we harness high-resolution UAV multi-sensor data, integrating multi-scale image fusion with deep learning to achieve fine-scale grassland classification that satellites cannot provide. First, four categories of UAV data, including RGB, multispectral, thermal infrared, and LiDAR point cloud, were collected, and a fused image tensor consisting of 10 channels (NDVI, VCI, CHM, etc.) was constructed through orthorectification and resampling. For feature-level fusion, four deep fusion networks were designed. Among them, the MultiScale Pyramid Fusion Network, utilizing a pyramid pooling module, effectively integrated spectral and structural features, achieving optimal performance in all six image fusion evaluation metrics, including information entropy (6.84), spatial frequency (15.56), and mean gradient (12.54). Subsequently, training and validation datasets were constructed by integrating visual interpretation samples. Four backbone networks, including UNet++, DeepLabV3+, PSPNet, and FPN, were employed, and attention modules (SE, ECA, and CBAM) were introduced separately to form 12 model combinations. Results indicated that the UNet++ network combined with the SE attention module achieved the best segmentation performance on the validation set, with a mean Intersection over Union (mIoU) of 77.68%, overall accuracy (OA) of 86.98%, F1-score of 81.48%, and Kappa coefficient of 0.82. In the categories of Leymus chinensis and Puccinellia distans, producer’s accuracy (PA)/user’s accuracy (UA) reached 86.46%/82.30% and 82.40%/77.68%, respectively. Whole-image prediction validated the model’s coherent identification capability for patch boundaries. In conclusion, this study provides a systematic approach for integrating multi-source UAV remote sensing data and intelligent grassland interpretation, offering technical support for grassland ecological monitoring and resource assessment. Full article

Accurate soil organic carbon (SOC) estimation is critical for assessing ecosystem services, carbon budgets, and informing sustainable land management, particularly in ecologically sensitive mountainous regions. This study focuses on modelling the spatial distribution of SOC within the heterogeneous volcanic landscape of the Nevado de Toluca (NdT), central Mexico, an area spanning 535.9 km² and characterised by diverse land uses, altitudinal gradients, and climatic regimes. Using 29 machine learning algorithms, we evaluated the predictive capacity of three key variables: land use, elevation, and the Normalised Difference Vegetation Index (NDVI) derived from satellite imagery. Complementary analyses were performed using the Bare Soil Index (BSI) and the Modified Soil-Adjusted Vegetation Index 2 (MSAVI2) to assess their relative performance. Among the tested models, the Quadratic Support Vector Machine (SVM) using NDVI, elevation, and land use emerged as the top-performing model, achieving a coefficient of determination (R²) of 0.84, indicating excellent predictive accuracy. Notably, 14 models surpassed the R² threshold of 0.80 when using NDVI and BSI as predictor variables, whereas MSAVI2-based models consistently underperformed (R² < 0.78). Validation plots demonstrated strong agreement between observed and predicted SOC values, confirming the robustness of the best-performing models. This research highlights the effectiveness of integrating multispectral remote sensing indices with advanced machine learning frameworks for SOC estimation in mountainous volcanic ecosystems Full article

Soil contamination with heavy metals often resulting from industrial activities and wastewater discharge is a major ecological problem. Bone meal, a by-product of the agri-food industry, is a promising material for remediating soils affected by heavy metal pollution. Bone meal, rich in phosphorus, calcium, and other essential minerals, provides advantages both in immobilizing inorganic pollutants and in improving soil fertility. This study explores the potential of bone meal as an ecological and sustainable solution for the retention of zinc from soils polluted with wastewater. This study analyzes the physicochemical properties of bone meal, the mechanisms of its interaction with metal ions through adsorption processes as revealed by equilibrium and kinetic studies, and its effects on plant germination. The results indicate a maximum adsorption capacity of 2375.33 mg/kg at pH = 6, according to the Langmuir model, while the pseudo-second-order kinetic model showed a coefficient of R² > 0.99, confirming the chemical nature of the adsorption. At pH 12, the retention capacity increased to 2937.53 mg/kg; however, parameter instability suggests interference from precipitation phenomena. At pH 12, zinc retention is dominated by precipitation (Zn(OH)₂ and Zn–phosphates), which invalidates the Langmuir assumptions; accordingly, the Freundlich isotherm provides a more adequate description. Germination tests revealed species-specific responses to Zn contamination and bone meal amendment. In untreated contaminated soil, germination rates were 84% for cress, 42% for wheat, and 50% for mustard. Relative to the soil + bone meal treatment (100% performance), the extent of inhibition reached 19–21% in cress, 24–29% in wheat, and 12% in mustard. Bone meal mitigated Zn-induced inhibition most effectively in wheat (+31% vs. soil; +40% vs. control), followed by cress (+23–27%) and mustard (+14%), highlighting its species-dependent ameliorative potential. Thus, the experimental results confirm bone meal’s capacity to reduce the mobility of zinc ions and improve the quality of the agricultural substrate. By transforming an animal waste product into a material with agronomic value, this study supports the integration of bone meal into modern soil remediation strategies, aligned with the principles of bioeconomy and sustainable development. Full article

The unsuitable performance of or deficit in basic sanitation infrastructure, especially in sparsely populated rural communities, remains critical, particularly in many developing regions, and demands sustainable, cost-effective, and easily operated solutions. Thus, the objective of this Review is to analyze design parameters for evapotranspiration tanks (EvapTs), adopted as nature-based solutions for zero-discharge domestic sewage treatment. The literature search was conducted using the Scopus and Web of Science databases, complemented by backward citation tracking. From 4434 records, 29 studies were selected based on specific criteria, such as the availability of design data and their application in urban or rural contexts. The main findings indicated required areas per inhabitant ranging from 0.5 to 7.7 m², primarily influenced by climate conditions and the type of plant used. Statistical analysis showed a negative correlation between the area of the evaporation tanks and the mean annual temperature, with a Pearson correlation coefficient (r of −0.74). For mean annual temperatures between 19 and 27 degrees Celsius, linear regression showed a variation between 4.7 and 0.6 m²/inhabitant with a reduction coefficient of −0.51 per degree Celsius, suggesting that warmer climates require smaller system areas per capita. Most studies were conducted at full scale, with Brazil accounting for the highest number of publications. EvapT is identified as a promising ecological technology that is particularly suitable for rural settings. However, it still requires technical standardization, cost–benefit analysis, and research on social acceptance. The adoption of clear design criteria may enhance system replicability, support public policy development, and contribute to SDG 6—Clean Water and Sanitation for All. Full article

Linpan ecosystems, distinct to western Sichuan, China, are integral to regional biodiversity and carbon cycling. However, comprehensive biomass estimation for these systems has not been thoroughly investigated. This study seeks to fill this gap by enhancing the accuracy and precision of biomass estimation in these ecologically vital landscapes through the application of multi-source remote sensing techniques, specifically by integrating the strengths of optical and radar remote sensing data. The focus of this research is on the forest biomass of Linpan, encompassing the tree layer, which includes the trunk, branches, leaves, and underground roots. Specifically, the research focused on the Linpan ecosystems in the Wenjiang District of western Sichuan, utilizing an integration of Sentinel-1 SAR, Sentinel-2 multispectral, and GF-2 high-resolution data for multi-source remote sensing-based biomass estimation. Through the preprocessing of these data, Pearson correlation analysis was conducted to identify variables significantly correlated with the forest biomass as determined by field surveys. Ultimately, 19 key modeling factors were selected, including band information, vegetation indices, texture features, and phenological characteristics. Subsequently, three algorithms—multiple stepwise regression (MSR), support vector machine (SVM), and random forest (RF)—were employed to model biomass across mixed-type, deciduous broadleaved, evergreen broadleaved, and bamboo Linpan. The key findings include the following: (1) Sentinel-2 spectral data and Sentinel-1 VH backscatter coefficients during the summer, combined with vegetation indices and texture features, were critical predictors, while phenological indices exhibited unique correlations with biomass. (2) Biomass displayed a marked north–south gradient, characterized by higher values in the south and lower values in the north, with a mean value of 161.97 t ha⁻¹, driven by dominant tree species distribution and management intensity. (3) The RF model demonstrated optimal performance in mixed-type Linpan (R² = 0.768), whereas the SVM was more suitable for bamboo Linpan (R² = 0.892). The research suggests that integrating multi-source remote sensing data significantly enhances Linpan biomass estimation accuracy, offering a robust framework to improve estimation precision. 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

Aboveground biomass (AGB) in grasslands is a vital metric for assessing ecosystem functioning and health. Accurate and efficient AGB estimation is essential for the scientific management and sustainable use of grassland resources. However, achieving low-cost, high-efficiency AGB estimation via remote sensing remains a key challenge. This study integrates Sentinel-1 and Sentinel-2 imagery to derive 38 multi-source feature variables, including backscatter coefficients, texture, spectral reflectance, vegetation indices, and topographic factors. These features are combined with AGB data from 112 field plots in the Three Parallel Rivers area. Feature selection was performed using Pearson correlation, Random Forest (RF), and SHAP values to identify optimal variable sets. Genetic Programming (GP) was then applied for nonlinear optimization of the selected features. Three machine learning models—RF, GBRT, and KNN—were used to estimate AGB and generate spatial distribution maps. The results revealed notable differences in model accuracy, with RF performing best overall, outperforming GBRT and KNN. After GP optimization, all models showed improved performance, with the RF model based on RF-selected features achieving the highest accuracy (R² = 0.90, RMSE = 0.31 t/ha, MAE = 0.23 t/ha), improving R² by 0.03 and reducing RMSE and MAE by 0.05 and 0.03 t/ha, respectively. Spatial mapping showed the AGB ranged from 0.41 to 3.59 t/ha, with a mean of 1.39 t/ha, closely aligned with the actual distribution characteristics. This study demonstrates that the RF model, combined with multi-source features and GP optimization, provides an effective approach to grassland AGB estimation and supports ecological monitoring in complex areas. Full article

With increasing energy demand and advancing carbon neutrality goals, arid regions—key areas for centralized photovoltaic (PV) station development in China—urgently require efficient and accurate remote sensing techniques to support spatial distribution monitoring and ecological impact assessment. Although numerous studies have focused on PV station extraction, challenges remain in arid regions with complex surface features to develop extraction frameworks that balance efficiency and accuracy at a regional scale. This study focuses on the Inner Mongolia Yellow River Basin and develops a PV extraction framework on the Google Earth Engine platform by integrating spectral bands, spectral indices, and topographic features, systematically comparing the classification performance of support vector machine, classification and regression tree, and random forest (RF) classifiers. The results show that the RF classifier achieved a high Kappa coefficient (0.94) and F1 score (0.96 for PV areas) in PV extraction. Feature importance analysis revealed that the Normalized Difference Tillage Index, near-infrared band, and Land Surface Water Index made significant contributions to PV classification, accounting for 10.517%, 6.816%, and 6.625%, respectively. PV stations are mainly concentrated in the northern and southwestern parts of the study area, characterized by flat terrain and low vegetation cover, exhibiting a spatial pattern of “overall dispersion with local clustering”. Landscape pattern indices further reveal significant differences in patch size, patch density, and aggregation level of PV stations across different regions. This study employs Sentinel-2 imagery for regional-scale PV station extraction, providing scientific support for energy planning, land use optimization, and ecological management in the study area, with potential for application in other global arid regions. Full article

The use of thermal insulation material in building envelopes is closely related to economic benefits, energy-savings, and carbon reduction of buildings. The construction forms of different components in building envelopes have an important influence on the optimization design of thermal insulation in building envelopes. In this study, an integrated optimization approach is proposed to search for the best solution of thermal insulation in external walls and the optimal combination scheme of different construction forms of envelope components in granaries. The integrated optimization approach consists of an orthogonal experimental design (OEDM) method-based determination module of an optimal combination scheme of different construction forms of components, an assessment model-based quantitative analysis module, and an integrated assessment indicator-based selection module of the best solution of external wall insulation. Firstly, the OEDM method is used to determine the optimal combination scheme of different construction forms of the foundation wall of an external wall, thermal insulation material, external window, roof, and floors in buildings. Secondly, integrated economic, energy, and carbon analysis models are developed to analyze comprehensive performance of external wall insulation. Finally, an integrated assessment indicator consisting of an energy balanced index, a carbon balanced index, and weight coefficients is presented to determine the best solution of external wall insulation. The applications of this optimization approach in different ecological grain storage zones in China demonstrated that the outdoor air temperature characteristics could affect the comprehensive performance of external wall insulation in granaries, significantly. The best solution of external wall insulation in granaries in Turpan city, Daqing city, Kaifeng city, Changsha city, Anshun city, and Danzhou city was expanded polystyrene insulation (EPS) with a layer thickness of 0.078 m, 0.048 m, 0.083 m, 0.089 m, 0.062 m, and 0.131 m, respectively. The greatest difference in the lowest entire construction cost and the lowest carbon emission of external wall insulation among different typical climate regions in China was 12.987 USD/m² and 6.3 kgCO2e/m², respectively. Full article

This review is focused on glass fibers and natural fibers, exploring their applications in vehicles and buildings and emphasizing their significance in promoting sustainability and enhancing performance across various industries. Glass fibers, or fiberglass, are lightweight, have high-strength (3000–4500 MPa) and a Young’s modulus range of 70–85 GPa, and are widely used in automotive, aerospace, construction, and marine applications due to their excellent mechanical properties, thermal conductivity of ~0.045 W/m·K, and resistance to fire and corrosion. On the other hand, natural fibers, derived from plants and animals, are increasingly recognized for their environmental benefits and potential in sustainable construction, offering advantages such as biodegradability, lower carbon footprints, and reduced energy consumption, with a sound absorption coefficient (SAC) range of 0.7–0.8 at frequencies above 2000 Hz and thermal conductivity range of 0.07–0.09 W/m·K. Notably, the integration of these materials in construction and automotive sectors reflects a growing trend towards sustainable practices, driven by the need to mitigate carbon emissions associated with traditional building materials and enhance fuel efficiency, as seen in hybrid composites achieving 44.9 dB acoustic insulation at 10,000 Hz and a thermal conductivity range of 0.05–0.06 W/m·K in applications such as the BMW i3 door panels. Natural fibers contribute to reducing reliance on fossil fuels, supporting a circular economy through the recycling of agricultural waste, while glass fibers are instrumental in creating lightweight composites for improved vehicle performance and structural integrity. However, both materials face distinct challenges. Glass fibers, while offering superior strength, are vulnerable to chemical degradation and can pose recycling difficulties due to the complex processes involved. On the other hand, natural fibers may experience moisture absorption, affecting their durability and mechanical properties, necessitating innovations to enhance their application in demanding environments. The ongoing research into optimizing the performance of both materials highlights their relevance in future sustainable engineering practices. In summary, this review underscores the growing importance of glass and natural fibers in addressing modern environmental challenges while also improving product performance. As industries increasingly prioritize sustainability, these materials are poised to play crucial roles in shaping the future of construction and transportation, driving innovations that align with ecological goals and consumer expectations. Full article

Periphytic algae, as representative aquatic epiphytic communities, play a vital role in the material cycling and energy flow in rivers. Through physiological processes such as photosynthetic carbon fixation and nutrient absorption, they perform essential ecological functions in water self-purification, maintenance of primary productivity, and microhabitat formation. This study investigates the interaction mechanisms between these highly flexible organisms and the hydrodynamic environment, thereby addressing the limitations of traditional hydraulic theories developed for rigid vegetation. By incorporating the coupled effects of biological flexibility and water flow, an innovative nonlinear resistance model with velocity-dependent response is developed. Building upon this model, a coupled governing equation that integrates water flow dynamics, periphytic algae morphology, and layered Reynolds stress is formulated. An analytical solution for the vertical velocity distribution is subsequently derived using analytical methods. Through Particle Image Velocimetry (PIV) measurements conducted under varying flow velocity conditions in an experimental tank, followed by comprehensive error analysis, the accuracy and applicability of the model were verified. The results demonstrate strong agreement between predicted and measured values, with the coefficient of determination R² greater than 0.94, thereby highlighting the model’s predictive capacity in capturing flow velocity distributions influenced by periphytic algae. The findings provide theoretical support for advancing the understanding of ecological hydrodynamics and establish mechanical and theoretical foundations for river water environment management and vegetation restoration. Future research will build upon the established nonlinear resistance model to investigate the dynamic coupling mechanisms between multi-species periphytic algae communities and turbulence-induced pulsations, aiming to enhance the predictive modelling of biotic–hydrodynamic feedback processes in aquatic ecosystems. Full article

Poplar (Populus) clones are widely used for riparian afforestation owing to their fast growth and ecological benefits. However, selecting suitable clones for site-specific conditions remains a key challenge. In this study, we evaluated the survival and growth performance of nine poplar clones belonging to three hybrid groups—Populus deltoides (D), P. deltoides × P. nigra (DN), and P. nigra × P. suaveolens (NS)—at two riparian sites in Korea. Significant differences were observed in the survival, height, diameter, basal area, and basal area increment (BAI) among clones and between sites. DN hybrids exhibited superior overall performance in both survival and growth traits compared to D and NS clones. In the DN group, clones Eco-28, I-476, and Dorskamp consistently ranked highest in aggregate performance. Notably, I-476 and Eco-28 demonstrated both high productivity and stability across sites, as reflected in their low coefficients of variation (CVs). In contrast, Dorskamp, while highly productive, showed relatively high variability across environments. These findings highlight DN hybrids—particularly Eco-28 and I-476—as promising candidates for riparian afforestation, offering a balanced combination of high productivity and environmental stability. Full article

Tropical natural forests play a crucial role in regulating the climate and maintaining global ecosystem functions. However, they face significant challenges due to human activities and climate change. Accurate classification of these forests can help reveal their spatial distribution patterns and support conservation efforts. This study employed four machine learning algorithms—random forest (RF), support vector machine (SVM), Logistic Regression (LR), and Extreme Gradient Boosting (XGBoost)—to classify tropical rainforests, tropical monsoon rainforests, tropical coniferous forests, broadleaf evergreen forests, and mangrove forests on Hainan Island using optical and synthetic aperture radar (SAR) multi-source remote sensing data. Among these, the XGBoost model achieved the best performance, with an overall accuracy of 0.89 and a Kappa coefficient of 0.82. Elevation and red-edge spectral bands were identified as the most important features for classification. Spatial distribution analysis revealed distinct patterns, such as mangrove forests occurring at the lowest elevations and tropical rainforests occupying middle and low elevations. The integration of optical and SAR data significantly enhanced classification accuracy and boundary recognition compared to using optical data alone. These findings underscore the effectiveness of machine learning and multi-source data for tropical forest classification, providing a valuable reference for ecological monitoring and sustainable management. Full article

Mangroves play a crucial part in the worldwide blue carbon cycle because they store a lot of carbon in their biomass and soil. Accurate estimation of aboveground biomass (AGB) is essential for quantifying carbon stocks and understanding ecological responses to climate and human disturbances. However, regional-scale AGB mapping remains difficult due to fragmented mangrove distributions, limited field data, and cross-site heterogeneity. To address these challenges, we propose a Spatial Attention Coupled Bayesian Aggregator (SAC-BA), which integrates field measurements with multi-source remote sensing (Landsat 8, Sentinel-1), terrain data, and climate variables using advanced ensemble learning. Four machine learning models (Random Forest (RF), Cubist, Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost)) were first trained, and their outputs were fused using Bayesian model averaging with spatial attention weights and constraints based on Local Indicators of Spatial Association (LISAs), which identify spatial clusters (e.g., high–high, low–low) to improve accuracy and spatial coherence. SAC-BA achieved the highest performance (coefficient of determination (${\mathrm{R}}^2$) = 0.82, root mean square error = 29.90 Mg/ha), outperforming all individual models and traditional BMA. The resulting 30-m AGB map of Indonesian mangroves in 2017 estimated a total of 217.17 × ${\mathrm{10}}^6$ Mg, with a mean of 103.20 Mg/ha. The predicted AGB map effectively captured spatial variability, reduced noise at ecological boundaries, and maintained high confidence predictions in core mangrove zones. These results highlight the advantages of incorporating spatial structure and uncertainty into ensemble modeling. SAC-BA provides a reliable and transferable framework for regional AGB estimation, supporting improved carbon assessment and mangrove conservation efforts. Full article