Search Results (349)

Leaf Area Index (LAI) is the total leaf area per unit of land surface area and is a crucial parameter for assessing vegetation growth and productivity. Machine learning regression algorithms are widely applied for LAI estimation. Due to spectral response variations among sensors and susceptibility of mangrove-derived variables to environmental noise suppression, obtaining sensitivity indices and optimal machine learning regression models is essential for retrieving mangrove LAI at the population scale. This study proposes a novel approach to processing and retrieving mangrove LAI data by integrating multispectral indices with machine learning methods. Box–Cox transformation and CatBoost-based feature selection were employed to obtain the optimal dataset. Random Forest (RF), Gradient Boosting Regression Trees (GBRT), and Categorical Boosting (CatBoost) algorithms were used to evaluate the accuracy of LAI retrieval from Unmanned Aerial Vehicle (UAV) and Gaofen-6 (GF-6) data. Results indicate that when LAI > 3, LAI does not immediately saturate as CVI, MTVI 2, and other indices increase, demonstrating higher sensitivity. UAV data outperformed GF-6 data in retrieving LAI for diverse mangrove populations; during model training, RF proved more suitable for small-sample datasets, while CatBoost effectively suppressed environmental noise. Both RF and CatBoost demonstrated higher robustness in estimating Avicennia marina (AM) (RF: R² = 0.704) and Aegiceras corniculatum (AC) (R² = 0.766), respectively. Spatial distribution analysis of LAI indicates that healthy AM and AC cover 85.36% and 96.67% of the area, respectively. Spartina alterniflora and aquaculture wastewater may be among the factors affecting the health of mangrove forests in the study area. LAI retrieval holds significant importance for mangrove health monitoring and risk early warning. Full article

Current mainstream remote sensing approaches to forest age estimation frequently neglect interspecific differences in functional traits, which may limit the accurate representation of species-specific tree growth strategies. This study develops and validates a technical framework that incorporates multi-source remote sensing and tree species functional trait heterogeneity to systematically improve the accuracy of plantation age mapping. We constructed a processing chain—“multi-source feature fusion–species identification–heterogeneity modeling”—for a typical karst plantation landscape in southeastern Yunnan. Using the Google Earth Engine (GEE) platform, we integrated Sentinel-1/2 and Landsat time-series data, implemented a Gradient Boosting Decision Tree (GBDT) algorithm for species classification, and built age estimation models that incorporate species identity as a proxy for the growth strategy heterogeneity delineated by the Plant Economic Spectrum (PES) theory. Key results indicate: (1) Species classification reached an overall accuracy of 89.34% under spatial block cross-validation, establishing a reliable basis for subsequent modeling. (2) The operational model incorporating species information achieved an R² (coefficient of determination) of 0.84 (RMSE (Root Mean Square Error) = 6.52 years) on the test set, demonstrating a substantial improvement over the baseline model that ignored species heterogeneity (R² = 0.62). This demonstrates that species identity serves as an effective proxy for capturing the growth strategy heterogeneity described by the Plant Economic Spectrum (PES) theory, which is both distinguishable and valuable for modeling within the remote sensing feature space. (3) Error propagation analysis demonstrated strong robustness to classification uncertainties (γ = 0.23). (4) Plantation structure in the region was predominantly young-aged, with forests aged 0–20 years covering over 70% of the area. Despite inherent uncertainties in ground-reference age data, the integrated framework exhibited clear relative superiority, improving R² from 0.62 to 0.84. Both error propagation analysis (γ = 0.23) and Monte Carlo simulations affirmed the robustness of the tandem workflow and the stability of the findings, providing a reliable methodology for improved-accuracy plantation carbon sink quantification. Full article

Linkable ring signatures are a type of ring signature scheme that can protect the anonymity of signers while allowing the public to verify whether the same signer has signed the same message multiple times. This functionality makes linkable ring signatures suitable for applications such as cryptocurrencies and anonymous voting systems, achieving the dual goals of identity privacy protection and misuse prevention. However, existing post-quantum linkable ring signature schemes often suffer from issues such as excessive linear data growth the adoption of post-quantum signature algorithms, and high circuit complexity resulting from the use of post-quantum zero-knowledge proof protocols. To address these issues, a logarithmic-size post-quantum linkable ring signature scheme based on aggregation operations is proposed. The scheme constructs a Merkle tree from ring members’ public keys via a hash algorithm to achieve logarithmic-scale signing and verification operations. Moreover, it introduces, for the first time, a post-quantum aggregate signature scheme to replace post-quantum zero-knowledge proof protocols, thereby effectively avoiding the construction of complex circuits. Scheme analysis confirms that the proposed scheme meets the correctness requirements of linkable ring signatures. In terms of security, the scheme satisfies the anonymity, unforgeability, and linkability requirements of linkable ring signatures. Moreover, the aggregation process does not leak information about the signing members, ensuring strong privacy protection. Experimental results demonstrate that, when the ring size scales to 1024 members, our scheme outperforms the existing Dilithium-based logarithmic post-quantum ring signature scheme, with nearly 98.25% lower signing time, 98.90% lower verification time, and 99.81% smaller signature size. Full article

The survival and growth of mangroves along coastal China is threatened by invasive smooth cordgrass (Spartina alterniflora). Due to the high mortality and frequent replanting of mangrove trees and the impacts of invasive smooth cordgrass, the exact mangrove forest area in Zhejiang Province, China, is still unclear. Based on provincial-scale fine-resolution Unmanned Aerial Vehicle (UAV) imagery and a large number of field survey plots, this study mapped the distribution of mangroves and smooth cordgrass in 2023 using three machine learning classifiers, including Classification and Regression Tree (CART), Convolutional Neural Networks (CNNs), and Support Vector Machine (SVM). The accuracy assessment indicated that the CNN algorithm was superior to the other two algorithms and yielded an overall accuracy and Kappa coefficient of 97% and 0.96, respectively. The total areas of mangrove forest and smooth cordgrass were 140.83 ha and 52.95 ha, respectively, in 2023 in Zhejiang Province. The mangrove forest area was mostly concentrated in Yuhuan, Dongtou, Yueqing, and Longgang districts. The mean canopy coverage of mangrove trees was only 36.41%, with lower than 20% coverage in all northern and some central districts. At the spatial scale, the mangrove trees showed a scattered distribution pattern, and over 70.04% of the planting area had canopy coverage lower than 20%. Smooth cordgrass has widely invaded all 11 districts, accounting for about 13.7% of the total planting area of mangrove trees. Over 67.3% and 85.4% of the planting areas have been occupied by smooth cordgrass in Wenling and Jiaoxiang districts, respectively, which necessitates an intensive anthropogenic intervention to control its spread in these districts. Our study provides more accurate monitoring of the mangrove and smooth cordgrass distribution areas at a provincial scale. The findings will help guide the replanting and management activities of mangrove trees, control planning for smooth cordgrass, and provide a data basis for the accurate estimation of carbon stock for mangrove forests in Zhejiang Province. Full article

As an essential blue carbon ecosystem, mangroves play a vital role in coastal protection, biodiversity conservation, and climate regulation. However, their complex and variable growth environments pose challenges for precise monitoring. Hainan Island represents a region within China where mangrove forests are the most concentrated and diverse in type. In recent years, ecological restoration efforts have led to the recovery of their coverage areas. This study analyzed the spatial distribution, canopy height, and aboveground carbon storage variations in Hainan mangrove forests. Deep-learning and multiple machine-learning algorithms were used to integrate multitemporal Sentinel-2 remote sensing imagery from 2019 to 2023 with unmanned aerial vehicle observations and field survey data. Multi-rule image fusion and deep-learning techniques effectively enhanced mangrove identification accuracy. The mangrove classification achieved an overall accuracy exceeding 90%. The mangrove area in Hainan increased from 3948.83 ha in 2019 to 4304.29 ha in 2023. Gradient-boosted decision tree (GBDT) models estimated average canopy height with a high coefficient of determination (R² = 0.89), and Random Forest (RF) models yielded the best estimations of total above-ground carbon stock with strong agreement to field observations. Integrating multisource remote sensing data with artificial intelligence algorithms enabled high-precision dynamic monitoring of mangrove distribution, structure, and carbon storage to provide scientific support for the assessment, management, and carbon sink accounting of Hainan mangrove ecosystems. Full article

The abundant satellite data have enabled the study of the dynamics of forest logging and its corresponding carbon balance with remote sensing. Change detection techniques with moderate-resolution imagery have been widely developed. Yet the signal processing or machine learning methods are sample-dependent, lacking an understanding of spectral signals of forest growth and logging cycles, which is necessary to distinguish logging from other types of disturbance, and mechanism models addressing post-logging tree changes are too complex for parameter inversion. We therefore proposed an efficient physical-based model for spectral simulation of annual forest logging by coupling forest dynamic model ZELIG and the stochastic radiative transfer (SRT) model. The forest logging simulation was conducted and validated by Abies forest field data before and after logging in Wangqing County, Northeastern China (R² = 0.85, RMSE = 10.82 t/ha). The spectral changes in Abies forest stands with annual growth and varying logging intensities were simulated by the novel model. The annual Landsat-8 and Gaofen-1 fusion multispectral imagery of the study area from 2013 to 2016 was furtherly used to extract annual sequence spectral data of 350 forest plots and perform inversion of the annual difference in above-ground biomass (dAGB). With the inversion method combining the look-up table of the ZELIG-SRT model and the random forest regression, the retrieved dAGB of the 350 plots indicated consistency with the measured data on the whole (R² = 0.71, RMSE = 13.32 t/ha). The novel physical-based approach for AGB monitoring is more efficient than previous 3D computer models and less dependent on field samples than data-driven models. This study provides a theoretical basis for understanding the remote sensing response mechanism of forest logging and a methodological basis for improving forest logging monitoring algorithms. Full article

Information diffusion models developed for Twitter, Reddit, and Facebook assume network contagion and competition for shared attention. Telegram operates differently. It is built around channels rather than social graphs, and users receive posts directly from subscribed channels without algorithmic mediation. We analyze over 5000 forwarding cascades from the Pushshift Telegram dataset to examine whether existing diffusion models generalize to this broadcast environment. Our findings reveal fundamental structural differences. Telegram forwarding produces perfect star topologies with zero multi-hop propagation. Every forward connects directly to the original message, creating trees with maximum depth of exactly 1. This contrasts sharply with Twitter retweet chains that routinely reach depths of 5 or more hops. Forwarding delays follow heavy-tailed Weibull or lognormal distributions with median delays measured in days rather than hours. Approximately 15 to 20 percent of cascades exhibit administrative bulk reposting rather than organic user-driven growth. Most strikingly, early-stage competitive overtaking is absent. Six of 30 pairs exhibit crossings, but these occur late (median 79 days) via administrative bursts rather than organic competitive acceleration during peak growth. We develop a delay-driven star diffusion model that treats forwarding as independent draws from a delay distribution. The model achieves median prediction errors below 10 percent for organic cascades. These findings demonstrate that platform architecture fundamentally shapes diffusion dynamics. Comparison with prior studies on Twitter, Weibo, and Reddit reveals that Telegram’s broadcast structure produces categorically different patterns—including perfect star topology and asynchronous delays—requiring platform-specific modeling approaches rather than network-based frameworks developed for other platforms. Full article

Grammar induction runs into a serious problem due to the exponential growth of the number of possible derivation trees as sentence length increases, which makes unsupervised parsing both computationally demanding and highly indeterminate. This paper proposes a mathematics-based approach that alleviates this combinatorial complexity by introducing structural constraints based on Catalan and Fuss–Catalan numbers. By limiting the depth of the tree, the degree of branching and the form of derivation, the method significantly narrows the search space, while retaining the full generative power of context-free grammars. A filtering algorithm guided by Catalan structures is developed that incorporates these combinatorial constraints directly into the execution process, with formal analysis showing that the search complexity, under realistic assumptions about depth and richness, decreases from exponential to approximately polynomial. Experimental results on synthetic and natural-language datasets show that the Catalan-constrained model reduces candidate derivation trees by approximately 60%, improves F1 accuracy over unconstrained and depth-bounded baselines, and nearly halves average parsing time. Qualitative evaluation further indicates that the induced grammars exhibit more balanced and linguistically plausible structures. These findings demonstrate that Catalan-based structural constraints provide an elegant and effective mechanism for controlling ambiguity in grammar induction, bridging formal combinatorics with practical syntactic learning. Full article

This study, conducted in the Lifang Dryland Experimental Area in Jinzhong, Shanxi Province, China, aimed to develop a method to accurately predict maize yield by combining UAV multispectral data with meteorological information. A DJI Mavic 3M UAV was used to capture four-band imagery (red, green, red-edge, and near-infrared), from which 16 vegetation indices were calculated, along with daily meteorological data. Among eight machine learning algorithms tested, ensemble models, Random Forest and Gradient Boosting Trees performed best, with R² values of 0.8696 and 0.8163, respectively. SHAP analysis identified MSR and RVI as the most important features. The prediction accuracy varied across growth stages, with the jointing stage showing the highest performance (R² = 0.7161), followed by the flowering stage (R² = 0.6588). The yield exhibited a strip-like spatial distribution, ranging from 6450 to 9600 kg·ha⁻¹, influenced by field management, soil characteristics, and microtopography. K-means clustering revealed high-yield areas in the central-northern region and low-yield areas in the south, supported by a global Moran’s I index of 0.4290, indicating moderate positive spatial autocorrelation. This study demonstrates that integrating UAV multispectral data, meteorological information, and machine learning can achieve accurate yield prediction (with a relative RMSE of about 2.8%) and provides a quantitative analytical framework for spatial management in drought-prone areas. Full article

The development of shale gas reservoirs relies on complex fracture networks created via multistage hydraulic fracturing, yet most existing models still use oversimplified fracture geometries and therefore cannot fully capture the coupled effects of multiscale fracture topology on flow and production. To address this gap, in this study, we combine fractal geometry with the Embedded Discrete Fracture Model (EDFM) to analyze the production dynamics of hydraulically fractured horizontal wells in shale gas reservoirs. A tree-like fractal fracture network is first generated using a stochastic fractal growth algorithm, where the iteration number, branching number, scale factor, and deviation angle control the self-similar hierarchical structure and spatial distribution of fractures. The resulting fracture network is then embedded into an EDFM-based, fully implicit finite-volume simulator with Non-Neighboring Connections (NNCs) to represent multiscale fracture–matrix flow. A synthetic shale gas reservoir model, constructed using representative geological and engineering parameters and calibrated against field production data, is used for all numerical experiments. The results show that increasing the initial water saturation from 0.20 to 0.35 leads to a 26.4% reduction in cumulative gas production due to enhanced water trapping. Optimizing hydraulic fracture spacing to 200 m increases cumulative production by 3.71% compared with a 100 m spacing, while longer fracture half-lengths significantly improve both early-time and stabilized gas rates. Increasing the fractal iteration number from 1 to 3 yields a 36.4% increase in cumulative production and markedly enlarges the pressure disturbance region. The proposed fractal–EDFM framework provides a synthetic yet field-calibrated tool for quantifying the impact of fracture complexity and design parameters on shale gas well productivity and for guiding fracture network optimization. Full article

The species, quantity, and tree diameter at breast height (DBH) are important indicators for assessing species distribution, individual growth status, and overall health in the forest. The existing tree information collection mainly relies on manual labor, which results in low efficiency and high labor intensity. To address these issues, we propose a method for tree species identification and diameter estimation by combining deep learning algorithms with binocular vision. First, an image acquisition platform is designed and integrated with a weeding machine to capture images during weeding operation. Images of seven types of trees are captured to develop a dataset. Second, a tree species identification model is established based on the YOLOv8n network, achieving 98.5% accuracy, 99.0% recall, and 99.2% mAP. Then, an improved YOLOv8n-seg model is proposed. It simplifies the network by introducing VanillaBlock in the backbone. FasterNet with a CCFM structure is added at the neck to enhance the model’s multi-scale expression capability. The mIoU of the improved model is 93.7%. Finally, the improved YOLOv8n-seg model is combined with binocular vision. After obtaining the segmentation mask of the tree, the spatial position of the two measurement points is calculated, allowing for the measurement of tree diameter. Verification experiments show that the average error for tree diameter ranges from 4.40~6.40 mm, and the proposed error compensation method can reduce diameter errors. This study provides a theoretical foundation and technical support for intelligent collection of tree information. Full article

Climate change poses significant threats to forest ecosystems, with drought stress being a major factor affecting tree growth and survival. The accurate and early diagnosis of plant water status is, therefore, critical for advancing climate-smart forestry. However, traditional monitoring approaches often rely on single-sensor data or manual field surveys, limiting their capacity to comprehensively capture the complex physiological and structural dynamics of plants under water deficit. To address this gap, this study developed an indoor multi-sensor phenotyping platform, based on a three-axis mobile truss system, which integrates a hyperspectral camera, a thermal infrared imager, and a LiDAR scanner for coordinated high-throughput data acquisition. We further propose a novel hybrid model, the Whale Optimization Algorithm-based Multi-Kernel Extreme Learning Machine (WOA-MK-ELM), which enhances classification robustness by adaptively fusing hyperspectral and thermal features within a dual Gaussian kernel space. We use Perilla frutescens as a model species, achieving an accuracy of 93.03%, an average precision of 93.11%, an average recall of 94.04%, and an F1-score of 0.94 in water stress degree classification. The results demonstrate that the proposed framework not only achieves high prediction accuracy but also provides a powerful prototype and a robust analytical approach for smart forestry and early warning systems. Full article

Leaf area index (LAI) is a critical indicator of canopy architecture and physiological performance, serving as a key parameter for crop growth monitoring and management. Although UAV multispectral imagery provides rich spectral and spatial information, the limitations of single texture features for LAI estimation still require further exploration. To address this issue, this study developed a multi-source feature fusion framework that integrates vegetation indices (VIs), texture features (TFs), and texture indices (TIs) within a stacked ensemble approach combining Partial Least Squares Regression (PLSR) with Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT) algorithms to estimate maize LAI.A field experiment was conducted under three planting densities (42,000, 63,000, and 84,000 plants ha⁻¹) and four nitrogen rates (0, 80, 160, 240 kg N ha⁻¹) to assess the potential of UAV-based multispectral imagery for maize LAI estimation. The results show that when using partial least squares regression (PLSR) combined with RF, SVM and GBDT to estimate maize LAI, the R² values are 0.653, 0.697 and 0.634, and the RMSE is 0.650, 0.608 and 0.668, respectively, when only vegetation indices (VIs) is used as input. After texture features (TFs) incorporation, the R² increases to 0.717, 0.794, and 0.801, and the RMSE decreases to 0.587, 0.500, and 0.492. Further inclusion of the texture indices (TIs) raises the R² to 0.789, 0.804, and 0.844, with RMSE of 0.506, 0.489, and 0.436, respectively. Independent test set validation under contrasting conditions confirmed that our multi-model fusion framework (PLSR+GBDT) with multi-source feature fusion (VIs+TFs+TIs) effectively estimated LAI, achieving an R² of 0.859 and 0.794. These results demonstrate that multi-source feature integration via machine learning enables robust and accurate estimation of maize LAI, providing a valuable tool for precision agriculture and crop growth monitoring. Full article

The parameterization of vegetation indices (VIs) is crucial for sustainable irrigation and horticulture management, specifically for urban green infrastructure (GI) management. However, the constraints of roadside traffic, motor and industrially related pollution, and potential public vandalism compromise the efficacy of conventional in situ monitoring systems. The shortcomings of prevalent satellites, UAVs, and manual/automated sensor measurements and monitoring systems have already been reviewed. This research proposes a novel urban GI monitoring system based on an integration of gas exchange and various VIs obtained from computer vision algorithms applied to data acquired from three novel sources: (1) Integrated gas sensor data using nine different volatile organic compounds using an electronic nose (E-nose), designed on a PCB for stable performance under variable environmental conditions; (2) Plant growth parameters including effective leaf area index (LAIe), infrared index (Ig), canopy temperature depression (CTD) and tree water stress index (TWSI); (3) Meteorological data for all measurement campaigns based on wind velocity, air temperature, rainfall, air pressure, and air humidity conditions. To account for spatial and temporal data acquisition variability, the integrated cameras and the E-nose were mounted on a vehicle roof to acquire information from 172 Elm trees planted across the Royal Parade, Melbourne. Results showed strong correlations among air contaminants, ambient conditions, and plant growth status, which can be modelled and optimized for better smart irrigation and environmental monitoring based on real-time data. Full article

This study presents an advanced, interpretability-focused machine learning framework for forecasting electricity consumption in Turkey over the period 2016–2024. The proposed approach is based on a high-dimensional dataset that incorporates a diverse set of variables, including sector-specific electricity usage (residential, industrial, lighting, agricultural, and commercial), electricity production, trade metrics (imports and exports in USD), and macroeconomic indicators such as the Industrial Production Index (IPI). A comprehensive set of eight state-of-the-art regression algorithms—including ensemble models such as CatBoost, LightGBM, Random Forest, and Bagging Regressor—were developed and rigorously evaluated. Among these, CatBoost emerged as the most accurate model, achieving R² values of 0.9144 for electricity production and 0.8247 for electricity consumption. Random Forest and LightGBM followed closely, further confirming the effectiveness of tree-based ensemble methods in capturing nonlinear relationships in complex datasets. To enhance model interpretability, SHAP (SHapley Additive exPlanations) and traditional feature importance analyses were applied, revealing that residential electricity consumption was the dominant predictor across all models, accounting for more than 70% of the variance explained in consumption forecasts. In contrast, macroeconomic indicators and temporal variables showed marginal contributions, suggesting that electricity demand in Turkey is predominantly driven by internal sectoral consumption trends rather than external economic or seasonal dynamics. In addition to historical evaluation, scenario-based forecasting was conducted for the 2025–2030 period, incorporating varying assumptions about economic growth and population trends. These scenarios demonstrated the model’s robustness and adaptability to different future trajectories, offering valuable foresight for strategic energy planning. The methodological contributions of this study lie in its integration of high-dimensional, multivariate data with transparent, interpretable machine learning models, making it a robust and scalable decision-support tool for policymakers, energy authorities, and infrastructure planners aiming to enhance national energy resilience and policy responsiveness. Full article