Search Results (2,023)

Forest biomass reflects the carbon storage capacity of forest ecosystems. Although remote sensing-based biomass estimation techniques have become increasingly mature, the issue of signal saturation in optical remote sensing still requires further investigation. This study was conducted in the Picea schrenkiana forest of the Ili River Valley in the western Tianshan Mountains. By integrating multimodal data from ICESat-2 LiDAR and GF-6 optical imagery, we developed machine learning and deep learning models to achieve high-precision biomass estimation. Based on forest management inventory data, we extracted spectral and textural features from GF-6, along with canopy structure attributes derived from the four acquisition modes (day/night, strong/weak beams) of ICESat-2. After correlation-based feature selection, LightGBM, CatBoost, and TabNet models were trained and compared. The results showed that models integrating multi-source data significantly outperformed those based on a single data source. The TabNet model not only achieved high estimation accuracy but also provided clear feature importance rankings, with ICESat-2-derived canopy height percentiles and GF-6 red-edge vegetation indices contributing most significantly to the biomass estimation of Picea schrenkiana. These findings demonstrate the feasibility of synergistically utilizing domestic high-resolution satellites and multi-mode spaceborne LiDAR for forest biomass estimation in arid regions, providing an effective technical reference for accurate carbon sink monitoring of specific tree species in forest areas. Full article

This study systematically quantified the carbon footprint generated by China’s consumption of eight major fossil energy sources (coal, coke, crude oil, petrol, kerosene, diesel, fuel oil, and natural gas), alongside the carbon carrying capacity of four vegetation ecosystems (forest, grassland, wetland, and crop), based on the IPCC inventory methodology. ArcGIS spatial analysis was employed to reveal the spatiotemporal distribution, while the STIRPAT model identified drivers of energy carbon footprint pressure (ECFP). Concurrently, the GM (1,1) model predicted evolution trends for both energy carbon footprint (ECF) and vegetation carbon carrying capacity. Results indicated that: (1) ECF increased from 12,039.89 million tons in 2015 to 13,896.41 million tons in 2022, representing a cumulative growth of 15.42%; (2) vegetation carbon carrying capacity increased from 4710.54 million tons in 2015 to 5300.76 million tons in 2022, representing a cumulative growth of 12.53%; (3) STIRPAT model analysis indicated that economic growth and technological progress were the dominant factors influencing ECFP; and (4) GM (1,1) predicted that the ECF would continue to grow at a slower pace by 2026, while vegetation carbon carrying capacity would steadily increase. It was concluded that optimizing the energy structure and strengthening vegetation conservation could effectively alleviate ECFP, providing crucial support for the carbon neutrality objectives of China. Full article

Sustainable forest management requires current, territory-wide data, which is difficult to obtain in vast regions like Quebec, Canada. To complement ground inventories and photo-interpretation, the province developed an airborne laser scanning (ALS)-based model that performs well in coniferous stands, but its accuracy in hardwood stands remains untested. This study aims to evaluate the accuracy of the ALS-based prediction of stand basal area and then test new approaches to increase its performance. Airborne LiDAR data from 2011 to 2020 and 12,506 validation plots from sample plots were used. The ALS model accuracy was initially compared across the stand types, revealing lower accuracy in shade-tolerant deciduous stands. Three inputs were found to increase prediction accuracy: proportion of each species basal area in the stand, geographical coordinates, and meteorological data associated with location. Parametric and auto machine learning (AutoML) methods were employed using those inputs to improve accuracy, with AutoML achieving the highest improvement with initial R² of 0.27, 0.47 and 0.54 and after correction R² of 0.31, 0.56 and 0.67, respectively, for shade-tolerant deciduous, shade-intolerant deciduous, and coniferous stand. Even with the advancements made, further improvements will be necessary to consider using an ALS-based model for shade-tolerant deciduous species. Full article

Accurate prediction of landslide run-out distance is fundamental to hazard mapping, emergency planning, and risk-informed engineering design. However, many data-driven studies implicitly treat landslides as a homogeneous population and provide limited, physically interpretable insights into how geomorphic factors govern run-out behavior. To address these limitations, we propose a cluster-aware and explainable modeling framework to predict run-out distance L using four source-region and slope descriptors: crown–toe relief H, source area A, source volume V, and mean source-slope inclination $\theta$. The dataset consists of 10,159 rainfall-induced landslides compiled from official inventories and peer-reviewed literature. After standardizing predictors, the optimal number of clusters is determined using information criteria (AIC/BIC), followed by k-means clustering to identify distinct landslide regimes. We first benchmark Random Forest, eXtreme Gradient Boosting, CatBoost, and LightGBM on identical data splits without hyperparameter tuning, using $R^2$, RMSE, and MAE as performance metrics. LightGBM consistently outperforms the alternatives and is therefore selected as the base learner. Within each cluster, LightGBM is further optimized using the Alpha Evolution (AE) algorithm, with Particle Swarm Optimization and Bayesian Optimization serving as benchmarks. The resulting AE-LightGBM model achieves the highest predictive accuracy across clusters. Model interpretability is achieved using TreeSHAP, which decomposes predictions into cluster-specific baselines and additive contributions from H, A, V, and $\theta$. By integrating regime-sensitive learning with robust explainability, the proposed framework improves run-out distance prediction while providing transparent, physically meaningful insights to support scenario analysis and engineering decision-making. Full article

Accurate tree species mapping is critical for forest inventory, biodiversity assessment, and ecosystem management. In mountainous regions, terrain-induced radiometric non-stationarity and limited field access often produce scarce, clustered, and environmentally biased samples, limiting model generalization. To address this issue, this study proposes a terrain-aware self-supervised representation learning framework for tree species classification under small-sample conditions. The framework integrates terrain information into representation learning and adopts a hybrid contrastive–generative self-supervised strategy to learn discriminative and terrain-robust features from large volumes of unlabeled multi-source remote sensing data. These learned representations are subsequently combined with limited field samples to produce regional-scale tree species maps. Experiments conducted across Yunnan Province, China, using Sentinel-1, Sentinel-2 and Landsat time-series data show that the proposed framework substantially improvesa class separability and classification robustness in complex mountainous environments. The framework achieves an overall accuracy of 75.8%, significantly outperforming conventional feature engineering (38.3–40.6%) and supervised deep learning models (37.3–47.8%). Species with relatively homogeneous structure and strong ecological niche dependence can be accurately mapped with limited training samples, whereas structurally complex forest communities require broader environmental sample coverage. Overall, the results highlight the potential of terrain-aware self-supervised representation learning as a scalable and data-efficient paradigm for forest mapping in mountainous and environmentally heterogeneous regions. Full article

At present, the global logistics sector faces critical challenges, including rising energy costs and pressure to reduce CO₂ emissions. Traditional linear supply chains are becoming inefficient, necessitating a transition toward shared logistics based on the principles of the sharing economy. This paper presents a progressive three-layer architecture that transforms conventional reactive data collection into an autonomous, proactive management system for the distribution of consumable materials. While previous research established foundations in IoT connectivity for smart vending machines, this study advances the process by integrating an intelligent layer of artificial intelligence (AI) algorithms. The framework utilizes Long Short-Term Memory (LSTM) neural networks for demand forecasting, dynamic route optimization (VRP/ACO) for replenishment, and Isolation Forest/DBSCAN algorithms for real-time anomaly detection. To evaluate the framework, a numerical simulation was conducted using representative pilot scenarios. The results indicate that within the simulated environment, the system achieves over 95% accuracy in inventory depletion prediction (MAPE = 4.02%). In these analyzed instances, this leads to a 25–30% reduction in stock-out risks and a 25% reduction in replenishment distance. These findings demonstrate the significant potential for reducing operational costs and carbon footprints in green logistics. The study confirms that the synergy between IoT infrastructure and AI-driven analysis provides a robust foundation for transitioning from static methodologies to resilient, collaborative logistics ecosystems. Full article

Amazonian large trees act as central elements of forest ecosystems, storing a disproportionate fraction of aboveground biomass. However, these trees are not randomly distributed across the landscape, and it is expected that edaphic attributes influence floristic composition, forest structure, and vegetation biomass. In this study, we investigated how variation in soil chemical and physical properties affects the diversity and biomass of large trees. Forest inventories were conducted at five sites within protected areas in the states of Pará and Amapá. Aboveground biomass was estimated using allometric equations, while soil samples were analyzed for their physical and chemical properties. Diversity indices, rarefaction, Redundancy Analysis, and Generalized Additive Models were applied. Edaphic variables such as soil pH, organic matter, phosphorus, and aluminum were associated with floristic composition and the biomass of these individuals. Trees with a diameter at breast height greater than or equal to 70 cm accounted for up to 80% of total biomass, revealing a pattern of biomass hyperdominance. The results indicate that the occurrence of large trees is related to edaphic and structural attributes, such as tree density and size distribution, suggesting that these individuals are not randomly distributed along soil gradients. Understanding these patterns is essential for improving ecological models, biomass extrapolations, and management strategies aimed at conserving the Amazon rainforest. Full article

Background: Convenience stores face volatile demand and a direct trade-off between stock-outs and overstocking, both of which affect service levels and profitability. This study aims to optimize inventory management through a reproducible forecasting-and-optimization workflow, assessing its impact on return on investment (ROI) and operational metrics, such as fill rate and stockouts. Methods: The workflow integrates daily, store-level transactions with external covariates, constructs temporal and lag features, and trains a Random Forest (RF) model using chronological splitting and time-series validation. Daily forecasts are then aggregated to the monthly level and used as inputs to an inventory simulation and an ROI-based economic model. Building on this simulation, a Genetic Algorithm (GA) optimizes the parameters of a monthly replenishment policy, incorporating minimum-coverage constraints. Results: In testing, the forecasting model achieved a mean absolute percentage error (MAPE) below 13%, and the RF+GA scheme outperformed the 28-day moving average baseline (MA28) in ROI across all five stores, with an average improvement of 4.52 percentage points; statistical significance was confirmed using the Wilcoxon test. Conclusions: Overall, the RF+GA approach serves as a decision-support tool that generates monthly order quantities consistent with demand and operational constraints, delivering verifiable improvements in both economic and service metrics. Full article

Landslide hazard potential is high across the St. Lawrence lowlands of Québec, Canada, due to sensitive glaciomarine clay deposits and the presence of moderate seismic activity, causing slope failures in the region. The main objectives of the study are to develop a working database for landslides in the region and use that database to improve regional landslide susceptibility analysis. Using high-resolution (1 m by 1 m cells) digital terrain models dated from 2009 and validated with satellite photogrammetry from 2012, a landslide inventory of 263 cases related to the 2010 Val-des-Bois earthquake (moment magnitude 5.0) is created. Relationships between landslide susceptibility factors, such as slope angle, and seismic conditioning factors, such as peak ground acceleration, are examined through machine learning methods. For landslide detection, an overall accuracy of approximately 85% (AUC 0.914) is achieved using random forest and logistic regression models cross-validated through 5-fold analysis, showing improvement over the currently employed Hazus method, which achieves an accuracy of approximately 67%. From a regional perspective, the developed inventory and resultant susceptibility models are unique and form the foundation for future studies to improve the understanding of earthquake-induced landslides in the Western Québec Seismic Zone, which historically lacks detailed landslide inventories. Full article

Fire suppression in the upper U.S. Midwest has led to the expansion of flammable coniferous ladder fuels, necessitating precise tracking of conifer species basal area (BA) for fire risk management. This study benchmarks four subset-selection pipelines—xPLS, GA-xPLS, RF-xPLS, and SVR-xPLS—to optimize the fusion of high-dimensional, collinear data from Sentinel-2, Landsat-9, and LiDAR sensors. Using 141 field plots in Minnesota’s Kawishiwi Ranger District of the Superior National Forest, we evaluated 175 predictors against eight BA response variables. Results show that RF-xPLS provided the superior accuracy–parsimony trade-off, achieving the highest pooled R² (≈0.86) and lowest error with a compact 27-predictor block. GA-xPLS ranked second, excelling for specific species such as Pinus resinosa. The most effective predictors combined SWIR-based moisture indices, red-edge/NIR structure, and a single LiDAR-derived surface of vertical-structure (quadratic mean height). Our findings demonstrate that integrating machine learning selection engines with multi-sensor fusion substantially enhances the scalability and precision of forest inventory and fuels monitoring. This comparative framework offers practical insights for sustainable management and fire risk mitigation in northern temperate–boreal forests. Full article

Landslides are characterized by strong suddenness and a wide range of damage; accurate prediction of their susceptibility is an important prerequisite for regional risk prevention and control. To address the difficulties in acquiring landslide inventories in complex terrain areas and the insufficient interpretability of existing prediction models, this study proposes a landslide susceptibility assessment (LSA) framework that integrates automated sample detection and interpretability analysis. The proposed framework is applied to Moxi Town, a typical alpine valley area in Sichuan Province, China. A Mask R-CNN instance segmentation model was introduced to achieve automated detection of landslide samples, resulting in a high-quality inventory containing 923 landslides. Based on the spatial relationships between the landslide inventory and influencing factors, a convolutional neural network (CNN) landslide susceptibility assessment model incorporating Shapley Additive exPlanations (SHAP) interpretability analysis was constructed. The CNN model was further compared with random forest (RF) and extreme gradient boosting (XGBoost) machine learning models. The results show that the AUC value of the CNN model has increased by 4.3% and 3.2% compared with the RF and XGBoost models, respectively, and it significantly reduces the pretzel effect of landslide susceptibility mapping (LSM). The results validate the reliability of the proposed framework, which can provide technical support for landslide disaster prevention and monitoring. Full article

The need for effective inventory management in the transition from “Just-in-Time” to “Just-in-Case” supply chain strategies was addressed by developing a machine learning model to predict inventory backorders. Using a large store keeping unit dataset, five supervised learning algorithms, namely, logistic regression, random forest, k-nearest neighbours, Naïve Bayes, and gradient boosting, were implemented with Python 3.13 Data imbalance was managed using the synthetic minority over-sampling technique, while power transformation was applied to improve data distribution and model performance. Among the models, random forest demonstrated the highest prediction accuracy at 98% and a strong receiver operating characteristic score of 0.897, making it the best model for backorder prediction. This approach enhances supply chain resilience and proactive inventory control, enabling manufacturers to mitigate risks of stockouts and optimize resource planning. It is necessary to incorporate advanced balancing techniques, hyperparameter tuning, and cross-validation methods to improve predictive performance further. Full article

Assessment of landslide risk is crucial given the substantial related economic losses and infrastructure damage in mountain areas every year. Particularly, the Sagarmatha National Park (SNP), a key destination for Himalayan glacier tourism, remains relatively understudied in this context. Existing studies primarily focus on regional inventories or simply inventory landslides and lack tourism-specific hazard assessment. This study evaluates landslide distribution, its controlling factors, and the exposure of infrastructure to varying degrees of landslide susceptibility in SNP. A blind inventory of 680 landslides and twelve conditioning factors, including six topographic and six non-topographic variables, were analyzed using Frequency Ratio (FR), Logistic Regression (LR), and Random Forest (RF) models. In addition, spatial overlay analysis was employed to assess the degree of infrastructure exposure. Results indicate that Land Surface Temperature (LST) is the most dominant factor influencing landslides occurrence, followed by rainfall, elevation, and slope, along with specific aspects like south and west and, land cover class like Barren land and Alpine meadows. Random Forest achieved the highest predictive accuracy (91%), outperforming both Logistic Regression (87%) and Frequency Ratio (84%). Exposure assessment of key tourism infrastructure indicates that trekking routes, helipads, buildings, campsites, and bridges are subject to varying levels of landslide risk. Although only 2.73 km (0.52%) of trekking routes intersect active landslide scars, 147 km (28%) lie within high-exposure zones. Consequently, both typical and paraglacial landslides threaten access to glacier tourism destinations, highlighting significant implications for Nepal’s tourism. Full article

Due to climate change, drought periods are becoming more frequent and more intense, posing substantial stress to Central European forest stands, especially climatically sensitive conifer forests. The early detection and accurate spatial delineation of forest damage are essential for supporting adaptive forest management decisions. This study presents a two-tier, multi-step forest damage assessment approach that combines Sentinel-2 satellite-based NDVI double-difference analysis with UAV-based high-resolution photogrammetric evaluation. In the first phase, potential damaged forest patches were identified in two sample areas of the Sopron Mountains using double-difference maps derived from monthly window NDVI maxima calculated from Sentinel-2 data. In the second phase, UAV surveys were carried out over the selected forest compartments, resulting in individual-tree-level canopy segmentation and object-based NDVI analysis. The photogrammetric point clouds were combined with ground points derived from airborne laser scanning to enable the accurate generation of canopy height models. The results confirmed that NDVI double-difference analysis is suitable for the spatial detection of both gradual drought-related damage and sudden disturbances—such as forest fire—even under sequences of drought and moderate years occurring in a sporadic pattern. The UAV-based analysis corroborated the satellite observations in detail and enabled an accurate inventory of damaged trees as well as the exploration of their spatial distribution. The proposed methodology provides an efficient, cost-effective, and operational tool for multi-scale monitoring of forest damage, contributing to the timely recognition of climate-change impacts and to the substantiation of targeted forest management interventions. Full article