Search Results (7,692)

Forest volume is an important information for assessing the economic value and carbon sequestration capacity of forest resources and serves as a key indicator for energy flow and biodiversity. Although remote sensing technology is applied to estimate volume, optical remote sensing data have limitations in capturing forest vertical height information and may suffer from reflectance saturation. While LiDAR data can provide more detailed vertical structural information, they come with high processing costs and limited observation range. Therefore, improving the accuracy of volume estimation through multi-source data fusion has become a crucial challenge and research focus in the field of forest remote sensing. In this study, we integrated Sentinel-2 multispectral data, Resource-3 stereoscopic imagery, UAV-based LiDAR data, and field survey data to quantitatively estimate the forest volume in Saihanwula Nature Reserve, located in Inner Mongolia, China, on the southern part of Daxing’anling Mountains. The study evaluated the performance of multi-source remote sensing features by using recursive feature elimination (RFE) to select the most relevant factors and applied four machine learning models—multiple linear regression (MLR), k-nearest neighbors (kNN), random forest (RF), and gradient boosting regression tree (GBRT)—to develop volume estimation models. The evaluation metrics include the coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (rRMSE). The results show that (1) forest Canopy Height Model (CHM) data were strongly correlated with forest volume, helping to alleviate the reflectance saturation issues inherent in spectral texture data. The fusion of CHM and spectral data resulted in an improved volume estimation model with R² = 0.75 and RMSE = 8.16 m³/hm², highlighting the importance of integrating multi-source canopy height information for more accurate volume estimation. (2) Volume estimation accuracy varied across different tree species. For Betula platyphylla, we obtained R² = 0.71 and RMSE = 6.96 m³/hm²; for Quercus mongolica, R² = 0.74 and RMSE = 6.90 m³/hm²; and for Populus davidiana, R² = 0.51 and RMSE = 9.29 m³/hm². The total forest volume in the Saihanwula Reserve ranges from 50 to 110 m³/hm². (3) Among the four machine learning models, GBRT consistently outperformed others in all evaluation metrics, achieving the highest R² of 0.86, lowest RMSE of 9.69 m³/hm², and lowest rRMSE of 24.57%, suggesting its potential for forest biomass estimation. In conclusion, accurate estimation of forest volume is critical for evaluating forest management practices and timber resources. While this integrated approach shows promise, its operational application requires further external validation and uncertainty analysis to support policy-relevant decisions. The integration of multi-source remote sensing data provides valuable support for forest resource accounting, economic value assessment, and monitoring dynamic changes in forest ecosystems. Full article

Temperature is a key physiological indicator of plant health, influenced by factors including water status, disease and developmental stage. Monitoring changes in multiple factors is helpful for early diagnosis of plant growth. However, there are a variety of complex light interference phenomena in the greenhouse, so traditional detection methods cannot meet effective online monitoring of strawberry health status without manual intervention. Therefore, this paper proposes a leaf soft-sensing method based on a thermal infrared imaging sensor and adaptive image screening Internet of Things system, with additional sensors to realize indirect and rapid monitoring of the health status of a large range of strawberries. Firstly, a fuzzy comprehensive evaluation model is established by analyzing the environmental interference terms from the other sensors. Secondly, through the relationship between plant physiological metabolism and canopy temperature, a growth model is established to predict the growth period of strawberries based on canopy temperature. Finally, by deploying environmental sensors and solar height sensors, the image acquisition node is activated when the environmental interference is less than the specified value and the acquisition is completed. The results showed that the accuracy of this multiple sensors system was 86.9%, which is 30% higher than the traditional model and 4.28% higher than the latest advanced model. It makes it possible to quickly and accurately assess the health status of plants by a single factor without in-person manual intervention, and provides an important indication of the early, undetectable state of strawberry disease, based on remote operation. Full article

Monitoring water quality is crucial for achieving clean water and sanitation goals, particularly in remote areas. The project “Morbidity vs. Water Quality for Human Consumption in Tonosí: A Pilot Study” aimed to enhance water quality assessments in Panama using advanced analytical techniques to assess volatile organic compounds, heavy metals, and microbiological pathogens. To support this, the Technical Unit for Water Quality (UTECH) was established, featuring a novel mobile laboratory with cutting-edge technology for accurate testing, minimal chemical reagent use, reduced waste generation, and equipped with a solar-powered battery system. The aim of this paper is to explore the design, deployment, and impact of the UTECH. Furthermore, this study presents results from three sampling points in Tonosí, where several parameters exceeded regulatory limits, demonstrating the capabilities of the UTECH and highlighting the need for ongoing monitoring and intervention. The study also assesses the environmental, social, and economic impacts of the UTECH in alignment with the Sustainable Development Goals and national initiatives. Finally, a SWOT analysis illustrates the UTECH’s potential to improve water quality assessments in Panama while identifying areas for sustainable growth. The study showcases the successful integration of advanced mobile laboratory technologies into water quality monitoring, contributing to sustainable development in Panama and offering a replicable model for similar initiatives in other regions. Full article

Effective in situ H₂S removal is essential for the utilization of small, remote natural gas wells, where centralized treatment is often unfeasible. This study presents an integrated environmental–economic assessment of two such processes, LO-CAT^® and triazine-based absorption, using a scenario-based framework. Environmental impacts were assessed via the Waste Reduction Algorithm (WAR), considering both Potential Environmental Impact (PEI) generation and output across eight categories, while economic performance was analyzed based on equipment, chemical, energy, environmental treatment, and labor costs. Results show that the triazine-based process offers superior environmental performance due to lower toxic emissions, whereas LO-CAT^® demonstrates better economic viability at higher gas flow rates and H₂S concentrations. An integrated assessment combining monetized environmental impacts with economic costs reveals that the triazine-based process becomes competitive only if environmental impacts are priced above specific thresholds. This study contributes a practical evaluation framework and scenario-based dataset that support sustainable process selection for decentralized sour gas treatment applications. Full article

Bare land exposed by glacier retreat provides new opportunities for ecosystem development. Investigating primary vegetation succession in deglaciated regions can provide significant insights for ecological restoration, particularly for future climate change scenarios. Nonetheless, research on this topic in the Qinghai–Tibet Plateau has been exceedingly limited. This study aimed to investigate vegetation succession in the deglaciated area of the Zepu glacier during the Little Ice Age in southeastern Tibet. Quadrat surveys were performed on arboreal communities, and trends in vegetation change were assessed utilizing multi-year (1986–2024) remote sensing data. The findings indicate that vegetation succession in the Zepu glacier deglaciated area typically adheres to a sequence of bare land–shrub–tree, divided into four stages: (1) shrub (species include Larix griffithii Mast., Hippophae rhamnoides subsp. yunnanensis Rousi, Betula utilis D. Don, and Populus pseudoglauca C. Wang & P. Y. Fu); (2) broadleaf forest primarily dominated by Hippophae rhamnoides subsp. yunnanensis Rousi; (3) mixed coniferous–broadleaf forest with Hippophae rhamnoides subsp. yunnanensis Rousi and Populus pseudoglauca C. Wang & P. Y. Fu as the dominant species; and (4) mixed coniferous–broadleaf forest dominated by Picea likiangensis (Franch.) E. Pritz. Soil depth and NDVI both increase with succession. Species diversity is significantly higher in the third stage compared to other successional stages. In addition, soil moisture content is significantly greater in the broadleaf-dominated communities than in the conifer-dominated communities. An analysis of NDVI from 1986 to 2024 reveals an overall positive trend in vegetation recovery in the area, with 93% of the area showing significant vegetation increase. Temperature is the primary controlling factor for this recovery, showing a positive correlation with vegetation cover. The results indicate that Key ecological indicators—including species composition, diversity, NDVI, soil depth, and soil moisture content—exhibit stage-specific patterns, reflecting distinct phases of primary succession. These findings enhance our comprehension of vegetation succession in deglaciated areas and their influencing factors in deglaciated areas, providing theoretical support for vegetation restoration in climate change. Full article

Gravity erosion is one of the main physical processes of soil erosion and sediment sources in catchments, and its spatiotemporal patterns and driving mechanisms are seriously understudied, mainly due to the the great difficulties in monitoring and quantifying. This study obtained gravity erosion amounts by runoff scouring experiments on the field slope of the hilly–gully region of the Chinese Loess Plateau. The terrain point cloud before and after gravity erosion was obtained based on the TLS, SfM and the fusion of single-scan TLS and SfM, and then the gravity erosion was estimated by four terrain change detection algorithms (DoD, C2C, C2M and M3C2). Results showed that the M3C2 algorithm plus fused data had the highest quantization accuracy among all the algorithms and data sources, with a relative error of 14.71%. The fused data combined with M3C2 algorithm performed much better than other algorithms and data sources for the different gravity erosion magnitudes (mean relative error < 17.00%). The DoD algorithm plus TLS data were preferable for collapse areas, while the M3C2 algorithm plus TLS was suitable for the alcove area. This study provides a useful reference for the monitor and quantitative research of gravity erosion in complex topographic areas. Full article

Climate change is generally having a negative impact on forest health by inducing drought stress and favouring the spread of pest species, such as bark beetles. The terrestrial monitoring of bark beetle infestation is very time-consuming, especially in the early stages, and therefore not feasible for extensive areas, emphasising the need for a comprehensive approach based on remote sensing. Although numerous studies have researched the use of optical data for this task, radar data remains comparatively underexplored. Therefore, this study uses the weekly and cloud-free acquisitions of Sentinel-1 in the Bavarian Forest National Park. Time series analysis within a Multi-SAR framework using Random Forest enables the monitoring of moisture content loss and, consequently, the assessment of tree vitality, which is crucial for the detection of stress conditions conducive to bark beetle outbreaks. High accuracies are achieved in predicting future bark beetle infestation (R² of 0.83–0.89). These results demonstrate that forest vitality trends ranging from healthy to bark beetle-affected states can be mapped, supporting early intervention strategies. The standard deviation of 0.44 to 0.76 years indicates that the model deviates on average by half a year, mainly due to the uncertainty in the reference data. This temporal uncertainty is acceptable, as half a year provides a sufficient window to identify stressed forest areas and implement targeted management actions before bark beetle damage occurs. The successful application of this technique to extensive test sites in the state of North Rhine-Westphalia proves its transferability. For the first time, the results clearly demonstrate the expected relationship between radar backscatter expressed in the Kennaugh elements K₀ and K₁ and bark beetle infestation, thereby providing an opportunity for the continuous and cost-effective monitoring of forest health from space. Full article

The accelerated evolution of remote sensing technology has intensified the demand for real-time tile map generation, highlighting the limitations of conventional mapping approaches that rely on manual cartography and field surveys. To address the critical need for rapid cartographic updates, this study presents a novel multi-stage generative framework that synergistically integrates Generative Adversarial Networks (GANs) with Diffusion Denoising Models (DMs) for high-fidelity map generation from remote sensing imagery. Specifically, our proposed architecture first employs GANs for rapid preliminary map generation, followed by a cascaded diffusion process that progressively refines topological details and spatial accuracy through iterative denoising. Furthermore, we propose a hybrid attention mechanism that strategically combines channel-wise feature recalibration with coordinate-aware spatial modulation, enabling the enhanced discrimination of geographic features under challenging conditions involving edge ambiguity and environmental noise. Quantitative evaluations demonstrate that our method significantly surpasses established baselines in both structural consistency and geometric fidelity. This framework establishes an operational paradigm for automated, rapid-response cartography, demonstrating a particular utility in time-sensitive applications including disaster impact assessment, unmapped terrain documentation, and dynamic environmental surveillance. Full article

Accurate above-ground biomass (AGB) quantification is confounded by signal saturation and data fusion challenges, particularly in structurally complex ecosystems like bamboo forests. To address these gaps, this study developed a two-stage framework to map the AGB of Dendrocalamus giganteus in a subtropical mountain environment. This study first employed Empirical Bayesian Kriging Regression Prediction (EBKRP) to spatialize sparse GEDI and ICESat-2 LiDAR metrics using Sentinel-2 and topographic covariates. Subsequently, a stacked ensemble model, integrating four machine learning algorithms, predicted AGB from the full suite of continuous variables. The stacking model achieved high predictive accuracy (R² = 0.84, RMSE = 11.07 Mg ha⁻¹) and substantially mitigated the common bias of underestimating high AGB, improving the predicted observed regression slope from a base model average of 0.63 to 0.81. Furthermore, SHAP analysis provided mechanistic insights, identifying the canopy photon rate as the dominant predictor and quantifying the ecological thresholds governing AGB distribution. The mean AGB density was 71.8 ± 21.9 Mg ha⁻¹, with its spatial pattern influenced by elevation and human settlements. This research provides a robust framework for synergizing multi-source remote sensing data to improve AGB estimation, offering a refined methodological pathway for large-scale carbon stock assessments. Full article

Timely and accurate identification of surface damage in hydraulic structures is essential for maintaining structural integrity and ensuring operational safety. Traditional manual inspections are time-consuming, labor-intensive, and prone to subjectivity, especially for large-scale or inaccessible infrastructure. Leveraging advancements in aerial imaging, unmanned aerial vehicles (UAVs) enable efficient acquisition of high-resolution visual data across expansive hydraulic environments. However, existing deep learning (DL) models often lack architectural adaptations for the visual complexities of UAV imagery, including low-texture contrast, noise interference, and irregular crack patterns. To address these challenges, this study proposes a lightweight, robust, and high-precision segmentation framework, called LFPA-EAM-Fast-SCNN, specifically designed for pixel-level damage detection in UAV-captured images of hydraulic concrete surfaces. The developed DL-based model integrates an enhanced Fast-SCNN backbone for efficient feature extraction, a Lightweight Feature Pyramid Attention (LFPA) module for multi-scale context enhancement, and an Edge Attention Module (EAM) for refined boundary localization. The experimental results on a custom UAV-based dataset show that the proposed damage detection method achieves superior performance, with a precision of 0.949, a recall of 0.892, an F1 score of 0.906, and an IoU of 87.92%, outperforming U-Net, Attention U-Net, SegNet, DeepLab v3+, I-ST-UNet, and SegFormer. Additionally, it reaches a real-time inference speed of 56.31 FPS, significantly surpassing other models. The experimental results demonstrate the proposed framework’s strong generalization capability and robustness under varying noise levels and damage scenarios, underscoring its suitability for scalable, automated surface damage assessment in UAV-based remote sensing of civil infrastructure. Full article

This study presents a comprehensive review and comparative analysis of traditional machine learning (ML) and deep learning (DL) models for land cover classification in agricultural remote sensing. We evaluate the reported successes, trade-offs, and performance metrics of ML and DL models across diverse agricultural contexts. Building on this foundation, we apply both model types to the specific case of almond crop field identification in California’s Central Valley using Landsat data. DL models, including U-Net, MANet, and DeepLabv3+, achieve high accuracy rates of 97.3% to 97.5%, yet our findings demonstrate that conventional ML models—such as Decision Tree, K-Nearest Neighbor, and Random Forest—can reach comparable accuracies of 96.6% to 96.8%. Importantly, the ML models were developed using data from a single year, while DL models required extensive training data spanning 2008 to 2022. Our results highlight that traditional ML models offer robust classification performance with substantially lower computational demands, making them especially valuable in resource-constrained settings. This paper underscores the need for a balanced approach in model selection—one that weighs accuracy alongside efficiency. The findings contribute actionable insights for agricultural land cover mapping and inform ongoing model development in the geospatial sciences. Full article

Environmental DNA (eDNA) analysis has emerged as a transformative tool in environmental monitoring, enabling non-invasive detection of species and microbial communities across diverse ecosystems. This study systematically reviews the role of bioinformation technology in eDNA analysis, focusing on methodologies and applications across air, soil, groundwater, sediment, and aquatic environments. Advances in molecular biology, high-throughput sequencing, bioinformatics tools, and field-deployable detection systems have significantly improved eDNA detection sensitivity, allowing for early identification of invasive species, monitoring ecosystem health, and tracking pollutant degradation processes. Airborne eDNA monitoring has demonstrated potential for assessing microbial shifts due to air pollution and tracking pathogen transmission. In terrestrial environments, eDNA facilitates soil and groundwater pollution assessments and enhances understanding of biodegradation processes. In aquatic ecosystems, eDNA serves as a powerful tool for biodiversity assessment, invasive species monitoring, and wastewater-based epidemiology. Despite its growing applicability, challenges remain, including DNA degradation, contamination risks, and standardization of sampling protocols. Future research should focus on integrating eDNA data with remote sensing, machine learning, and ecological modeling to enhance predictive environmental monitoring frameworks. As technological advancements continue, eDNA-based approaches are poised to revolutionize environmental assessment, conservation strategies, and public health surveillance. Full article

Heart failure (HF) poses a substantial global burden due to its high morbidity, mortality, and healthcare costs. Accurate prognostication is crucial for optimizing treatment, resource allocation, and patient counseling. Prognostic tools range from simple clinical scores such as ADHERE and MAGGIC to more complex models incorporating biomarkers (e.g., NT-proBNP, sST2), imaging, and artificial intelligence techniques. In acute HF, models like EHMRG and STRATIFY aid early triage, while in chronic HF, tools like SHFM and BCN Bio-HF support long-term management decisions. Despite their utility, most models are limited by poor generalizability, reliance on static inputs, lack of integration into electronic health records, and underuse in clinical practice. Novel approaches involving machine learning, multi-omics profiling, and remote monitoring hold promise for dynamic and individualized risk assessment. However, these innovations face challenges regarding interpretability, validation, and ethical implementation. For prognostic models to transition from theoretical promise to practical impact, they must be continuously updated, externally validated, and seamlessly embedded into clinical workflows. This review emphasizes the potential of prognostic models to transform HF care but cautions against uncritical adoption without robust evidence and practical integration. In the evolving landscape of HF management, prognostic models represent a hopeful avenue, provided their limitations are acknowledged and addressed through interdisciplinary collaboration and patient-centered innovation. Full article

Microfluidic platforms offer a powerful approach for ultimately replicating vascularization in vitro, enabling precise microscale control and manipulation of physical parameters. Despite these advances, the real-time ability to monitor and quantify mechanical forces—particularly pressure—within microfluidic environments remains constrained by limitations in cost and compatibility across diverse device architectures. Our work presents an advanced experimental module for quantifying pressure within a vascularizing microfluidic platform. Equipped with an integrated Arduino microcontroller and image monitoring, the system facilitates real-time remote monitoring to access temporal pressure and flow dynamics within the device. This setup provides actionable insights into the hemodynamic parameters driving vascularization in vitro. In-line pressure sensors, interfaced through I2C communication, are employed to precisely record inlet and outlet pressures during critical stages of microvasculature tubulogenesis. Flow measurements are obtained by analyzing changes in reservoir volume over time (dV/dt), correlated with the change in pressure over time (dP/dt). This quantitative assessment of various pressure conditions in a microfluidic platform offers insights into their impact on microvasculature perfusion kinetics. Data acquisition can help inform and finetune functional vessel network formation and potentially enhance the durability, stability, and reproducibility of engineered in vitro platforms for organoid vascularization in regenerative medicine. Full article

An indicator-based approach was implemented to assess the contributions of soils in supplying ecosystem services, providing a scalable tool for modeling the spatial heterogeneity of soil functions at regional and local scales. The method consisted of (i) the definition of soil-based ecosystem services (SESs), using available point data and thematic maps; (ii) the definition of appropriate SES indicators; (iii) the assessment and mapping of potential SESs provision for the Emilia-Romagna region (22.510 km²) in NE Italy. Depending on data availability and on the role played by terrain features and soil geography and its complexity, maps of basic soil characteristics (textural fractions, organic C content, and pH) covering the entire regional territory were produced at a 1 ha resolution using digital soil mapping techniques and geostatistical simulations to explicitly consider spatial variability. Soil physical properties such as bulk density, porosity, and hydraulic conductivity at saturation were derived using pedotransfer functions calibrated using local data and integrated with supplementary information such as land capability and remote sensing indices to derive the inputs for SES assessment. Eight SESs were mapped at 1:50,000 reference scale: buffering capacity, carbon sequestration, erosion control, food provision, biomass provision, water regulation, water storage, and habitat for soil biodiversity. The results are discussed and compared for the different pedolandscapes, identifying clear spatial patterns of soil functions and potential SES supply. Full article