Search Results (25,805)

Precise estimation of aboveground biomass in urban forests is crucial for quantifying urban carbon stocks and supporting climate change mitigation efforts. However, the availability of allometric equations tailored for urban trees is limited. Existing equations often rely on data from harvested trees with restricted sample sizes and small diameters, thereby introducing substantial uncertainty into biomass assessments. This study utilized terrestrial laser scanning (TLS) in conjunction with a leaf-wood separation algorithm and a tree quantitative structure model (TreeQSM) as a non-destructive approach to develop new species-specific allometric models for four predominant evergreen broadleaved tree species in the urban forests of Shanghai, based on 10 sample plots and 303 trees. The results showed that TLS-derived multivariate models, which incorporated diameter at breast height (DBH), tree height, and crown diameter, consistently outperformed models that only included DBH. Compared with the TLS-derived biomass, the previously published models showed varying degrees of deviation. Notably, there was a substantial overestimation for Camphora officinarum, with a bias of +31.5%. In contrast, Elaeocarpus decipiens, Ligustrum lucidum, and Magnolia grandiflora demonstrated smaller underestimations, with biases of −9.4%, −0.9%, and −4.5%, respectively. These discrepancies were primarily attributed to the extrapolation beyond the calibration diameter at DBH ranges of the published equations. These findings highlight the critical need for urban-specific models. Because destructive harvesting was not feasible in the urban environment, the TLS-derived biomass estimates were not validated against destructively measured biomass. The equations developed in this study provide improved tools for estimating urban forest biomass and carbon accounting for the four studied species under the sampled conditions in Shanghai. Full article

(1) Background: Information diffusion models developed for graph-based platforms such as Reddit and broadcast architectures such as Telegram identify temporal features—particularly the timing of peak spread—as dominant predictors of coverage. Whether these predictors generalise to platforms where content is distributed through algorithmic recommendation rather than social-graph contagion remains an open question. (2) Methods: We analyse the YouNiverse dataset, comprising 133,364 English-language YouTube channels observed weekly from January 2015 to September 2019 (18.9 million observations). We derive channel-level diffusion features—including time-to-peak, post-peak decay rate, diffusion volatility, and upload frequency—and train three machine learning models (Linear Regression, Random Forest, and LightGBM) on two tasks: predicting peak weekly view growth (regression) and identifying viral channels (classification). A single-feature naive baseline (subscriber count alone) establishes the marginal contribution of the broader feature set beyond subscriber count alone, and a temporal split experiment (training on channels peaking before 2018, testing on 2018–2019) assesses cross-temporal stability. Because subscriber count and subscriber rank are measured at the October 2019 crawl, this is a retrospective characterisation rather than a strict real-time forecasting design. (3) Results: LightGBM achieves $R^2=0.776$ (5-fold CV: $0.778\pm 0.003$) compared with $R^2=0.548$ for the naive baseline, a net gain of $+0.228R^2$. Because subscriber rank and subscriber count are near-perfectly collinear, we interpret them jointly as a channel-size dimension (42.2% of total mean absolute SHAP attribution), rather than as independent effects. Time-to-peak ranks fourteenth (1.1%), in contrast to its dominant role on Reddit ($r=0.995$, rank #1). For virality classification, LightGBM achieves ROC-AUC $=0.967$. Under the temporal split, Random Forest ($R^2=0.703$) outperforms LightGBM ($R^2=0.683$), showing greater cross-temporal stability within this retrospective split. (4) Conclusions: Within the 2015–2019 data, the results are consistent with algorithmic recommendation weakening the relationship between temporal diffusion dynamics and coverage magnitude at the channel level. Time-to-peak is weakly informative in this setting, while generalisation to the current recommendation system requires validation on newer data. Full article

Silver birch (Betula pendula Roth) is an ecologically important pioneer tree species widely distributed throughout Europe. Despite numerous reports of birch decline, long-term nationwide assessments integrating multiple categories of damage remain limited. The present study investigated temporal patterns of damage occurrence, severity, distribution, and presumed causal agents affecting silver birch in Poland using nationwide ICP Forests Level I monitoring data collected between 2007 and 2025. The analyses were based on 75,609 damage observations recorded for 7039 trees on 945 monitoring plots distributed across the country. Damage frequency increased markedly after 2014 and reached a maximum during 2018–2019, when approximately 90% of monitored trees exhibited at least one damage symptom. Foliage damage represented the most frequent category throughout the study period and was overwhelmingly associated with insect activity. Partially or completely eaten leaves accounted for approximately 80% of all foliage-damage observations, although damage severity was generally low. Stem damage increased continuously during the monitoring period and was dominated by deformations and stem inclination. Decay occurred less frequently but was associated with the highest severity classes and showed a strong relationship with fungal agents. An increasing proportion of trees were affected by multiple groups of damaging factors simultaneously, indicating the growing complexity of decline processes. The results suggest that silver birch decline may be driven by interacting biotic and abiotic stressors rather than by a single causal agent. The observed patterns are consistent with a decline-spiral model in which long-term reductions in water availability and increasing environmental stress may predispose trees to insect damage, root pathogens, stem and branch decay, structural instability, and ultimately mortality. The nationwide scale and long-term character of the ICP Forests dataset provide important insights into contemporary drivers of birch decline under changing environmental conditions. Full article

Auxiliary heating, ventilation, and air conditioning (HVAC) systems can reduce electric vehicle (EV) driving range by over 20%, yet prevailing machine learning estimators often suffer from temporal data leakage, uninterpretable black-box structures, and lack real-time driver feedback. To address these challenges, this study presents a physics-informed decoupled machine learning framework integrated with a multi-objective Pareto Human–Machine Interface (HMI) advisory system. Powertrain traction power is estimated using a HistGradientBoosting regressor incorporating a mechanistic Vehicle Specific Power (VSP) feature, while cabin thermal dynamics are modeled via a regularized Random Forest regressor enriched with a Newtonian thermal decay function. Evaluated across an empirical 55-trip dataset using a 5-Fold GroupKFold cross-validation protocol, the traction and thermal models achieved out-of-sample accuracy of R² = 0.9869 (MAE = 0.71 kW) and R² = 0.8656 (MAE = 0.25 kW), respectively. Feature attributions were verified using SHAP analysis. An onboard Pareto optimization loop dynamically balances range extension against passenger thermal discomfort to deliver actionable driver recommendations. Multi-trip evaluation indicates that a representative 30% auxiliary load suppression yields average net energy savings of 5.21% entirely through software-driven guidance. Full article

This study develops an empirical audit framework for assessing the explainability–reliability gap in fraud detection: whether stable model explanations remain consistent with performance-based feature reliance under distribution shift. Using the Bank Account Fraud dataset suite, including a Base dataset and five biased variants, the study examines group-size disparity, fraud-prevalence disparity, separability bias, and temporal shift. Logistic Regression, Linear SVC, and Random Forest are benchmarked using standard classification metrics, followed by cross-variant evaluation with Logistic Regression as the interpretable baseline. SHAP is used to assess explanation stability, while permutation importance measures performance-based feature reliance. The results show that accuracy and ROC-AUC can overstate practical effectiveness under severe class imbalance; notably, Random Forest retained useful discrimination while producing near-zero recall at the evaluated threshold. SHAP feature rankings remained relatively stable across variants, particularly for address-history, identity-similarity, credit-risk, device, and behavioral variables. However, permutation importance revealed weaker and more variable reliance on several SHAP-ranked features. The limited agreement between the two measures indicates a partial explainability–reliability gap. The findings show that explanation stability alone is insufficient for evaluating trustworthy fraud detection models and should be complemented by performance-based validation under biased and shifted deployment conditions. Full article

Changing disturbance regimes and uncertain recovery trajectories under global change have created a need for conceptual models to guide forest management within a sustainability framework. This conceptual article develops on operational framework for ecosystem memory in forest ecosystems. Conventional views of resilience may foster the misleading assumption that ecosystem development follows a fixed trajectory within the boundaries of the natural range of variation. However, climate change may transform resilience mechanisms, causing forest ecosystems to shift beyond their historical range of variation. Under such conditions, the concept of ecosystem memory offers a framework for analyzing and quantifying temporal system properties. The constraints inherited through memory patterns are themselves likely to be modified under changing environmental conditions. A valid interpretation of ecosystem memory requires a clear ontological understanding of memory components as material entities organized under temporal and spatial constraints. The human memory metaphor is an appealing entry point for understanding ecosystem memory but risks erroneously attributing semantic encoding and representational processes to ecological systems, where no such mechanisms exist. Based on a narrative synthesis of 39 publications, supplemented by an updated Web of Science search that identified 396 records, we develop an operational ecosystem-memory framework that distinguishes persistent ecological legacies from functionally active memory by linking ecological imprints, ecosystem engrams, and measurable ecosystem responses. The framework provides a basis for identifying and monitoring measurable legacy variables, including deadwood, retained trees, regeneration, soil properties, and refugial structures, in post-disturbance forest management. Full article

Landslide damage in mountain forest watersheds may vary not only with local slope conditions but also with hydrological and geomorphological connectivity within watersheds. This study quantitatively analyzed differences in landslide damage characteristics and influencing factors among forest watershed types to support watershed-based landslide damage mitigation. We extracted 1809 landslide damage polygons that overlapped mountain forest watersheds in the Chungcheong region of the Republic of Korea during 2022–2024 and classified them into catchment, slope, and independent zones. Damage area, perimeter, width, length, and elongation ratio were compared among watershed types. Area, perimeter, width, and length were largest in catchment zones and smallest in independent zones. Random forest models were developed and evaluated using repeated stratified 5-fold cross-validation with 13 topographic, soil, forest, geological, and watershed morphometric factors, and SHAP analysis was applied to interpret factor contributions. Watershed area was identified as a common important factor across all watershed types. However, secondary key factors differed by type: relief, DBH class, and slope length were relatively important in catchment zones; slope gradient, DBH class, and relief in slope zones; and relief and slope gradient in independent zones. These findings indicate that landslide damage characteristics and landslide-influencing factors vary by forest watershed type and can support differentiated forest disaster management and landslide mitigation strategies. Full article

Remote identification of color fronts, defined as transition zones with sharp gradients in water optical properties, has suffered from non-transferable thresholds, severe areal over-detection, and weak cross-estuary generalization. To address these issues, we propose a framework that integrates multi-scale spectral–spatial features with DenseNet-121. We constructed a 165-D vector, seven window scales (three × three to 15 × 15) × two statistical descriptors (means and standard deviations) × 11 bands + 11 bands, then rearranged it into a 3D tensor and resized it to a 2D image for DenseNet-121 transfer learning with red-band post-processing. On in-distribution tests, the model achieves 0.953 accuracy, 0.953 F1, outperforming random forest. Cross-estuary generalization yields a mean F1 (0.744). Performance varies with optical compatibility: the Mississippi (runoff-dominated) gives the best F1 (0.874), while the Pearl (multi-channel, runoff-tide co-controlled) drops to 0.607 due to heterogeneity and reversed reflectance patterns. The red-band constraint can help reduce areal false alarms and improve spatial coherence of frontal regions, but its effectiveness depends on optical separability. The output width reflects superposition of transition zone and window scale. We demonstrate the potential and boundary conditions of this approach for cross-estuary color front identification, offering insights for physically consistent and generalizable ocean color monitoring. Full article

Simulation of the surface energy balance (SEB) is essential for land–atmosphere coupling and weather–climate prediction, yet land surface models remain uncertain in turbulent and ground heat fluxes. We evaluated CoLM2024 with Land Cover Type (LCT) and Plant Community (PC) schemes and ECLand v1.0 against energy-balance-corrected observations from 80 PLUMBER2 towers spanning 11 land cover types. Observations and simulations were decomposed at 30 min, daily, and monthly scales. All experiments reproduced net radiation well, whereas ground heat flux was poorly simulated over forests and wetlands with excessive daytime amplitude. ECLand achieved the best latent heat flux performance through smaller systematic errors. PC reduced unsystematic errors, but this benefit was offset by a large negative growing-season bias. Sensible heat flux performance varied among land-cover classes: PC performed best over evergreen needleleaf and mixed forests, while ECLand was superior over broadleaf forests and had the lowest unsystematic errors, although with damped variability at all timescales. Performance was scale-dependent: ECLand was favored at 30 min, PC was competitive for daily forest sensible heat correlations, and no consistent ranking emerged monthly. These results indicate that complex canopy schemes require robust formulations, improved heat-storage, and careful parameter calibration to translate process detail into better flux simulations. Full article

Accurate forest soil organic carbon (SOC) monitoring is essential for forest soil quality assessment and carbon-sink evaluation. Visible-near-infrared (Vis–NIR) hyperspectral sensing provides rapid and information-rich measurements for SOC prediction, but obtaining sufficiently large labeled soil-spectral datasets remains difficult because field sampling, sample preparation, and reference SOC determination are labor and time intensive. This study developed a latent conditional diffusion-based data augmentation framework for SOC prediction from hyperspectral sensor data. A total of 248 forest red-soil samples from Guangxi, China, were measured using laboratory Vis–NIR reflectance spectroscopy over 350–2500 nm and divided by the Kennard-Stone algorithm into a 174-sample modeling set and a fixed 74-sample validation set. Four generative models, including VAE, GAN, WGAN-GP, and the proposed hyperspectral latent conditional denoising diffusion implicit model (HsDDIM), were evaluated using spectral visualization, t-SNE distributions, maximum mean discrepancy (MMD), Fréchet Inception Distance (FID), and downstream prediction performance. Unlike joint spectral-label generation, HsDDIM treats SOC as an external condition and generates spectra in the latent space under specified SOC conditions; the SOC condition itself is not generated by the diffusion process. Among the compared augmentation strategies, HsDDIM showed the closest distributional agreement with the real spectral samples according to MMD and FID, with values of 0.0806 and 0.5281, respectively. Without augmentation, FD1-SVR achieved the best validation result (R² = 0.83, RMSE = 4.71 g kg⁻¹). After 300% HsDDIM augmentation, 1D-CNN achieved R² = 0.91, RPD = 3.39, and RMSE = 3.40 g kg⁻¹. These results suggest that the SOC-conditioned latent DDIM framework can improve small-sample hyperspectral SOC prediction under the present fixed-validation protocol. Full article

Biodiversity conservation schemes in collaboration with agriculture have been criticized in part for their limited ecological effectiveness. In this regard, biosphere reserves are internationally recognized as playing a model role in developing sustainable land use systems. To learn about farmers’ perspectives on insects, the role of biosphere reserves in their farm management, and their experiences with biodiversity measures, we interviewed farmers in the German biosphere reserves (BRs) Schaalsee, Schorfheide-Chorin, Middle Elbe, Bavarian Rhön and Black Forest, thereby drawing on the model function of these reserves for insect conservation. Their perceptions were then assessed by BR staff and insect conservation managers. Juxtaposing the views of farmers and insect conservation stakeholders provides comparative insights from multiple perspectives. Interviewed farmers perceived BR staff as holding substantial regional knowledge, which gives them a reputation as competent intermediaries between biodiversity conservation and agriculture. Although the economic incentives to change farm management are perceived as low, most farmers can be engaged in biodiversity conservation through their intrinsic motivations. Farmers were clustered into four motivational patterns that require targeted communication. Across motivational patterns, farmers identified administrative burdens, farm-level costs, and the limited reliability and flexibility of existing measures as major barriers. To better conserve insects and promote insect conservation measures among farmers, extension services are needed. BR staff and insect conservation managers agreed on most of the farmers’ needs identified through the interviews. Therefore, BR administrations may play a crucial role in insect conservation as they recognize the contribution of agriculture and can engage additional stakeholders beyond the sector. To establish insect conservation in BRs in the long term, regional extension staff for nature conservation and regional support schemes are most needed to provide site-specific, regionally adapted conservation measures. Full article

Early-season crop yield prediction is critical for food security assessment and timely agricultural decision-making, yet large-scale remote-sensing applications are often constrained by a mismatch between pixel-level observations and county-level yield labels. This mismatch limits the use of fine-grained spatial heterogeneity, especially under partial-season observations. Single-scale approaches are also limited in capturing complementary pixel-level and county-level information, restricting representation of yield formation processes. To address this, we propose TandemNet, a multi-scale multiple-instance learning framework for early-season prediction of japonica rice yield in Northeast China. TandemNet treats each county–year as a bag of rice pixels and adopts a dual-branch architecture to jointly learn pixel-level growth trajectories and county-level statistical responses. A phenology-conditioned cross-attention module fuses the two scales under varying growing-season windows. Using Sentinel-1, Sentinel-2, and MODIS data from 2018 to 2023, leave-one-year-out validation shows that TandemNet outperforms Random Forest, XGBoost, LSTM, and Transformer baselines across most phenological stages. It achieves reliable prediction at the tillering stage, approximately 2–3 months before harvest, with an $R^2$ of 0.69 and an RMSE of 655.98 kg/ha. Ablation and attention analyses further indicate a stage-dependent shift from local heterogeneity to county-level consistency. These results demonstrate that modeling cross-scale interactions improves the timeliness, accuracy, and interpretability of rice yield prediction. Full article

Tea plantations are high-input agricultural systems and have been recognized as hotspots of soil nitrous oxide (N₂O) emissions; however, the key controlling factors of these emissions and their quantitative prediction remain insufficiently understood. We compiled 115 field-observation records from 26 published studies into a multi-factor database covering climate, soil properties, and fertilization management, and compared five machine learning models—multiple linear regression (MLR), ridge regression, support vector regression (SVR), random forest (RF), and gradient-boosting regression trees (GBRTs)—using 5-fold cross-validation, combined with Spearman correlation and feature-importance analyses. Annual N₂O emissions varied widely (0.40–73.20 kg·hm⁻²·a⁻¹; mean 9.85 kg·hm⁻²·a⁻¹), and the mean direct emission factor (EFd, 2.04%) far exceeded the IPCC default value. Emissions were significantly positively correlated with total nitrogen (TN) input but negatively correlated with mean annual temperature (MAT) and mean annual precipitation (MAP). GBRT performed best, effectively capturing nonlinear multifactor interactions; TN input and soil pH were the dominant predictors, followed by rainfall. However, feature importance rankings were method-dependent: the RF/SHAP analysis ranked MAT first rather than fifth, reflecting the different algorithmic mechanisms of the two approaches. The GBRT-based model provides a useful tool for estimating tea-plantation N₂O emissions (LOOCV R² = 0.668) and quantitative support for sustainable nitrogen management and targeted greenhouse gas mitigation strategies in tea production systems. Full article

Global Navigation Satellite Systems (GNSS) positioning accuracy is affected by multiple error sources, including atmospheric delays, multipath propagation, and receiver noise, which can significantly reduce positioning reliability in low-cost receivers. This study investigates the use of a feedforward neural network to compensate for positioning errors in a static GNSS receiver scenario. A synthetic dataset was generated in MATLAB/Simulink by simulating positioning perturbations around a known reference location. Consecutive coordinate differences were used as input features, and a compact feedforward neural network with 45 hidden neurons was trained using the Levenberg–Marquardt algorithm to estimate positioning error components. The proposed approach was evaluated through residual error distribution, regression, temporal dispersion, and spatial scatter analyses. The results indicate that, for the primary 10 m error scenario, neural network-based compensation reduced temporal dispersion by approximately 46% and produced a more compact spatial distribution of corrected positions around the reference location. The residual errors remained concentrated near zero, indicating improved positioning consistency under the investigated simulation conditions. Sensitivity analysis across nominal error radii of $R_{95}$ = 1, 5, 10, 15, and 20 m showed consistent reductions in both RMSE and standard deviation for radii of 10 m and above, whereas no consistent improvement was observed at lower error levels. In a preliminary comparison with random forests, XGBoost, Long Short-Term Memory (LSTM), and Gated Recurrent Unit models using the same training, validation, and test samples, the Feedforward Neural Network (FNN) achieved competitive test MSE while requiring substantially less training time and runtime memory than the LSTM. These findings support the proof-of-concept feasibility of lightweight FNN-based correction for simulated static GNSS positioning. Future work will focus on validation using real GNSS measurements and extension to dynamic positioning applications. Full article

Long-term monitoring provides a unique opportunity to distinguish persistent ecological processes from short-term fluctuations in forest insect dynamics. However, the extent to which local insect populations persist through time and the mechanisms regulating their occupancy remain poorly understood. Using a 13-year dataset from the Polish ICP Forests network, we investigated temporal and spatial dynamics of insect occurrence in alder stands. We quantified persistence at tree and plot levels, evaluated signatures of density-dependent regulation, and assessed spatial structure using generalized additive models, logistic regression, transition analyses, and spatial autocorrelation metrics. Insect occurrence exhibited exceptionally strong temporal persistence. Occurrence in the previous year was the dominant predictor of current occurrence across all modeling approaches. Persistence patterns differed between plot and tree levels, with transition analyses revealing greater long-term stability at the level of monitoring plots, consistent with the long-term stability of local populations despite turnover among host trees. Occupancy dynamics were consistent with negative density-dependent regulation, with local populations fluctuating around a stable equilibrium occupancy of approximately 75%. Although year-to-year changes occurred regularly, most fluctuations were relatively small and rarely resulted in complete disappearance of populations from occupied locations. Insect occurrence also exhibited pronounced spatial structure. Occupancy remained significantly clustered throughout the study period, indicating persistent regional differences in local population abundance. In contrast, annual changes in occupancy showed only weak spatial autocorrelation, suggesting that short-term dynamics were driven primarily by local ecological processes rather than by highly synchronized regional fluctuations. Our results indicate that infestation patterns in alder stands are consistent with persistent and self-regulating local herbivore populations that remain associated with the same forest stands over extended periods. Long-term dynamics are governed primarily by temporal persistence, density-dependent feedback, and stable spatial structure rather than by repeated cycles of colonization and extinction. These findings highlight the importance of long-term monitoring for understanding population persistence and provide new insight into the processes maintaining herbivore populations in forest ecosystems. Full article