Search Results (97)

Accurate historical estimates of forest carbon stocks are essential for greenhouse gas inventories and REDD+ Measurement, Reporting and Verification (MRV) systems, particularly in remote and persistently cloudy regions where field inventories and optical remote sensing are limited. This study presents a retrospective mapping framework with potential relevance for Tier-3 forest carbon estimation of forest volume and carbon stocks in the temperate forests of southern Chile using historical ALOS PALSAR L-band SAR data integrated with Chile’s Continuous National Forest Inventory (CNFI). Three pilot zones in Los Lagos, Aysén, and Magallanes were analysed, covering approximately 42,000 km² of native forests dominated by Lenga, Coihue de Magallanes, Siempreverde, Roble–Raulí–Coihue, Coihue–Raulí–Tepa, and Alerce forest types. Annual 25 m ALOS PALSAR mosaics were processed to derive HH and HV backscatter, HH/HV ratio, and Radar Forest Degradation Index (RFDI) layers, which were used as predictors in k-nearest neighbours (k-NN) models calibrated with inventory plots projected to the 2010 reference year. Model performance varied substantially among forest types and pilot zones, with test r² values ranging from 0.12 to 0.90 and RMSE values between approximately 100 and 300 m³·ha⁻¹; the highest r² values were associated with forest types represented by relatively small samples and should therefore be interpreted cautiously. m³·ha⁻¹ Stratification by altitude and restriction to moderate volume ranges improved predictive performance in several cases, highlighting the influence of ecological gradients and SAR signal saturation at high levels of biomass. Despite substantial pixel-level uncertainty, the methodology reproduced broad regional patterns of forest structure and carbon distribution. Results demonstrate the potential of combining historical ALOS PALSAR archives with national forest inventories to support spatially explicit historical carbon estimation in data-limited forest regions. Full article

Accurate retrospective, spatially explicit land-use reconstruction is essential for Land Use, Land-Use Change and Forestry (LULUCF) greenhouse gas accounting. However, the suitability of historical geospatial databases as information sources for such reconstruction has rarely been evaluated systematically. This study proposes an objective framework for assessing their correspondence with land-use observations from the Lithuanian National Forest Inventory (NFI). The analysis was based on a reference set of 16,351 systematically distributed NFI sample points and 19 database-year datasets covering the period 1990–2022. Original database classes were harmonised with the national hierarchical LULUCF classification, and correspondence was evaluated using overall accuracy, Cramér’s V and Normalized Mutual Information (NMI), complemented by category-specific representation, precision, recall and F1 score. Correspondence varied substantially among databases according to thematic scope, spatial completeness, mapping characteristics and land-use category. Among the multi-category databases, the Georeferenced Base Cadastre (GRPK) showed the strongest overall correspondence with the NFI reference data, whereas the CORINE Land Cover series provided the longest consistent multi-temporal record extending back to 1990. Forest land and settlements, as well as particularly water-related wetland classes, were represented comparatively reliably, while grassland remained the most difficult major land-use category to identify consistently. Temporal analysis showed that database performance also varied between database versions, while boundary sensitivity analysis demonstrated that observations near mapped polygon boundaries contributed to disagreement without changing the relative advantage of GRPK over CORINE. The results demonstrate that the evaluated historical databases provide substantial and complementary information for spatially explicit LULUCF land-use reconstruction and that the proposed framework provides a transparent basis for identifying and selecting suitable information sources according to land-use category and historical period. Full article

Historic landscape forests within cultural heritage sites represent a distinct category of urban green space that provides critical ecosystem services under strict conservation constraints. This study applied i-Tree Eco v6.0 to quantify the ecosystem services of the historic landscape forest of Jinju Fortress (Jinjuseong; Historic Site No. 118), South Korea, with focus on species-level contributions. A total of 822 trees representing 30 species were inventoried across 1.272 ha of tree cover. Zelkova serrata was the dominant species (importance value = 57.8) and contributed disproportionately to all ecosystem service categories, accounting for 40.7% of air pollutant removal, 61.3% of carbon storage, 58.2% of carbon sequestration and oxygen production, and 55.4% of avoided runoff. The forest removed 123.1 kg yr⁻¹ of air pollutant Korean Won (KRW 8.834 million yr⁻¹), stored 200.7 t (KRW 138 million), sequestered 8.834 t/yr (KRW 6.09 million/yr), produced 23.56 t/yr⁻¹ of oxygen, avoided 10,413.7 L yr⁻¹ of runoff and had a structural replacement value of KRW 1.63 billion. Beyond providing a species-level baseline for evidence-based management, this study introduces the concept of Heritage Ecosystem Service Efficiency (HESE) and the Species Ecosystem Service Dominance Index (SESDI), offering transferable frameworks for evaluating and managing ecosystem service performance in conservation-constrained heritage forests. Full article

The Ilha Grande National Park region (Brazil) holds one of the most diverse traditional knowledge repositories among Atlantic Forest conservation areas. This study describes the current composition of the medicinal plant knowledge, its taxonomic breadth, and its relevance for conservation. In 2026, this inventory documented 47 species from 33 botanical families cited across a sample of 4 remaining traditional healers. About 27.7% of these specimens are wild/native to nature and 72.3% are cultivated, reflecting both historic management and the residents’ long tradition in household plant acclimatization. The collection includes 25 species of herbs and 11 species of trees, as well as 9 shrubs and 2 climbers. Quantitative analysis at the category level revealed that gastrointestinal disorders had the highest consensus index (ICF = 0.375) after interviews, taxonomic revision, data grouping, and standardizing nomenclature based on the British National Formulary. The results reveal a severe risk of information erosion and a limited representation of younger generations among practitioners, underscoring the need for targeted safeguarding. Strengthening national partnerships, developing cooperative cultivation networks, and improving scientific validation records are proposed as key strategies to enhance the biocultural preservation value of this traditional heritage. Overall, the Ilha Grande healer knowledge serves as an important scientific, educational, and pharmacological resource and plays a central role in advancing the preservation of Brazil’s medicinal plant diversity. Full article

Old-growth forests are recognized as key reference systems for biodiversity conservation and sustainable forest management, yet they remain rare and highly fragmented ecosystems across Europe. In this study, we developed and applied a quantitative, measurement-based framework to assess candidate old-growth beech stands by comparing their structural attributes with reference old-growth forests and with a broader set of beech forests from the National Forest Inventory (NFI). Specifically, we compared the structural attributes of two candidate old-growth (COG) beech stands to assess their degree of naturalness and their similarity to three reference old-growth (ROG) beech forests in the central Apennines. The approach integrated field-based structural measurements, deadwood quantification, dendrochronological assessment of dominant trees and diachronic aerial imagery evaluation, comparing COG stands with both ROG and other beech forests from the NFI database. The two COG stands differ clearly from common beech forests, exhibiting lower stem density, larger size variability, higher occurrence of large trees and of standing wood biomass. Stem diameter distributions followed a reverse J-shaped pattern, typical of uneven-aged and scarcely managed forests. Some very old trees were also found, especially at one of the two COG sites. These characteristics are largely comparable to the ROG beech forests, except for deadwood volumes, lower at the two COG forests. In addition, historical aerial imagery and tree-ring analysis suggested that these forests are intermediate stages along an old-growth gradient. Our findings demonstrate that a prolonged forest management abandonment of over 70 years can induce the development of old-growth attributes in Mediterranean mountain beech forests, even in stands previously coppiced or pastured for a long time. This study provides a replicable measurement-based approach for identifying and validating candidate old-growth forests, under current Italian regulations and contributes to conservation targets consistent with the EU Biodiversity Strategy for 2030. Full article

Long-term wildfire susceptibility mapping represents a significant component of disaster prevention and the protection of human communities, public health, and local ecosystems. In this study, a wildfire inventory was developed through multi-sensor fusion of satellite data (MODIS and VIIRS), comprising 153,305 fire events across China for the period 2001–2024. In addition to historical incidents, 14 predictive variables were processed, representing geomorphological, climatological, hydrological, vegetative, and anthropogenic conditions. This study evaluates long-term spatial wildfire susceptibility based on long-term mean environmental and climatic conditions. Methodologically, the research applies six models from machine learning (ML), deep learning (DL), and transformer-based approaches: Random Forest (RF), Extreme Gradient Boosting (XGBoost), Deep Neural Network (DNN), Fourier Multi-Layer Perceptron (F-MLP), Kolmogorov–Arnold Network (KAN), and Feature Tokenizer (FT) Transformer. The results were integrated into an ensemble susceptibility map with a spatial resolution of 500 m using Geographic Information Systems (GIS), indicating that 7.4% of China’s territory is classified as having a very high wildfire susceptibility. In addition to the national-scale assessment, a local differentiation was conducted across 34 province-level divisions, revealing that Fujian Province (86.8%) and the Guangxi Zhuang Autonomous Region (82.9%) had the largest shares of areas classified as high and very high wildfire susceptibility. Performance evaluation under spatial block-based validation demonstrated that the Random Forest model achieved the highest predictive power, with an area under the curve (AUC) of 87.8%, followed by XGBoost (87.3%) and Fourier MLP (86.6%). Based on the combined SHAP (Shapley additive explanations) analysis of all applied models, soil moisture, elevation, and terrain slope were identified as the most influential factors affecting wildfire occurrence in China. Overall, the findings contribute to more effective wildfire prevention and risk management strategies at both the local and national levels. Full article

Forest operations and transportation policies have historically relied on weight-based assumptions created after weighing trees in the 1970s, yet modern silviculture and genetics may have fundamentally altered the geometry of commercial timber. This study investigated temporal shifts in merchantable bole weight and height in the southern United States to determine if these historical assumptions require reassessment. We analyzed USDA Forest Service Forest Inventory and Analysis (FIA) data for loblolly pine (Pinus taeda), sweetgum (Liquidambar styraciflua), and yellow-poplar (Liriodendron tulipifera) in Georgia (1997–2023), Alabama (2000–2023), and Mississippi (2009–2023). Using weighted least squares regression, we modeled temporal changes in bole weight and height while controlling for diameter, age, and site productivity. Results indicate a consistent, statistically significant increase in bole weight across all three states, with annual growth rates ranging from 0.51% to 1.29%. This trend is mechanistically driven by simultaneous increases in tree height within fixed diameter classes. Notably, loblolly pine exhibited a “plantation effect”, where weight accumulation was significantly faster in planted stands compared to natural stands, a trend absent in hardwood species. Our results suggest that the modern forest inventory is allometrically distinct, being taller and heavier for a given diameter than historical stocks. This shift implies that existing transportation limits may be increasingly misaligned with the biological reality of the current forest. Full article

The availability of critical spare parts is essential for maintaining the reliability and operational continuity of combined cycle power plants. However, demand for critical spare parts is typically sparse, intermittent, and highly non-linear, which limits the effectiveness of conventional forecasting approaches based on historical averages or expert judgment. Inaccurate demand estimation may lead to excessive inventory, high holding costs, or stock shortages that increase downtime risks. To address these challenges, this study applies ensemble learning methods to improve demand forecasting accuracy for critical spare parts in a combined cycle power plant. Procurement and usage data from 2020 to 2024 were analyzed using a time-series splitting approach, with model performance assessed using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). To avoid bias caused by zero-demand periods, zero actual values were excluded from MAPE calculations. The results show that the tuned XGBoost model consistently performs better than Random Forest by producing lower forecasting errors and more stable predictions under intermittent demand conditions. These findings indicate that ensemble learning can support more effective procurement planning, inventory control, and maintenance decision-making in combined cycle power plant operations. Full article

Baseline information on terrestrial wildlife communities and their activity patterns is essential for protected-area management, but such information remains limited for Hunan Zhangjiajie Giant Salamander National Nature Reserve, where conservation attention has historically focused on the Chinese giant salamander and associated aquatic ecosystems. From March 2024 to August 2025, we conducted a camera-trap survey in broad-leaved and coniferous forest habitats of the reserve to document terrestrial mammals and ground-dwelling birds, evaluate taxonomic completeness, and describe diel and seasonal activity patterns. Across 43 camera-trap stations and 16,314 effective camera-trap days, we recorded 59 wildlife species, including 18 mammals and 41 ground-dwelling birds. The assemblage included nationally protected, threatened, and Chinese endemic species, indicating that the reserve’s forest habitats support important terrestrial biodiversity in addition to its aquatic conservation target. Taxonomic completeness curves suggested that the current survey captured most camera-detectable mammal and ground-dwelling bird taxa under the present sampling design, although the results should not be interpreted as a complete inventory of the reserve’s total vertebrate diversity. Annual diel activity analysis of 11 focal species showed clear temporal differentiation among ecological groups: small and medium-sized carnivores were mainly nocturnal, ground-dwelling birds, and red-hipped squirrel were primarily diurnal, and ungulates showed mixed or crepuscular-to-nocturnal tendencies. Seasonal analyses based on bioclimatic periods showed interspecific differences in activity-density distributions between the cool-dry and warm-wet seasons. However, peak-shift reliability analysis indicated that most focal species retained broadly similar main activity peaks across seasons; masked palm civet was the only species showing reliable seasonal displacement of its main activity peak. Pairwise temporal overlap analyses described temporal co-occurrence patterns among selected sympatric species but should not be interpreted as evidence of direct interaction or niche differentiation. Overall, this study provides baseline data on camera-detected terrestrial vertebrates in the reserve and supports long-term monitoring, forest habitat management, and disturbance control for terrestrial mammals and ground-dwelling birds. Full article

Forecasting spare parts demand is challenging due to its intermittent, sparse, and highly irregular nature. Traditional inventory strategies, based on stable demand patterns, often lead to inefficiencies, including excess inventory and poor service performance. This study examines the impact of feature engineering combined with ABC–XYZ inventory segmentation on forecasting accuracy in a real-world industrial context. A biweekly forecasting framework was developed using six years (2019–2024) of transactional data from ERP and Field Service Management (FSM) systems of a forklift service company. Fifteen derived features capturing demand dynamics, intermittency, service behavior, and statistical structure were constructed and evaluated using Random Forest, XGBoost, and Support Vector Regression (SVR) models. The results show that restricting modeling to AY/BY inventory categories substantially improves predictive accuracy, reducing RMSE from >22 to <3 compared to full-SKU modeling. A reduced seven-feature set further lowers XGBoost’s RMSE to 2.51 (MAE = 2.14), achieving the best performance across all tested configurations on the 2024 hold-out period. The best-performing configuration achieves a Predicted-Demand Turnover Index (PDTI) of 44.13, compared with a baseline actual stock turnover of 2.78 (€65,944 actual demand/€23,721 historical average stock). PDTI is a theoretical scenario index; operationalizing it requires inventory-policy simulation under realistic constraints. These findings highlight that forecasting performance in intermittent-demand environments depends more on data representation and segmentation than on model selection alone. The study provides a reproducible, interpretable framework for integrating feature engineering and inventory segmentation into data-driven inventory management. Full article

This study addresses two critical issues in power material warehouse management: insufficient positioning accuracy leading to inefficient inventory auditing and uncontrolled material movement, and procurement-demand imbalances caused by subjective forecasting methods. We present an integrated warehouse management system that synergizes Ultra-Wideband (UWB) centimeter-level real-time positioning with data-driven demand forecasting. The UWB subsystem, built on STM32F1 microcontrollers (STMicroelectronics, Geneva, Switzerland) and DW1000 RF modules (Decawave Ltd., Dublin, Ireland), achieves high-precision location tracking by employing the Double-Sided Two-Way Ranging (DS-TWR) method combined with trilateration and triangular centroid algorithms. The data-driven procurement subsystem utilizes a vast historical dataset (4.86 million records from 36,988 grid projects, 2020–2024) to train demand prediction models. A comparative evaluation of six algorithms identified the Random Forest (RF) model as optimal, demonstrating superior performance with 89.2% accuracy, a Mean Absolute Error (MAE) of 5.48, and a Mean Absolute Percentage Error (MAPE) of 4.89%. The RF model effectively incorporates key factors like failure rates and seasonal cycles. Experimental validation confirmed the UWB subsystem’s robustness, with an average positioning error of 12.05 cm. The integrated system enables precise material tracking, 3D trajectory reconstruction, and generates data-informed procurement signals—including replenishment warnings, optimized order quantities, and adaptive resupply cycles. This approach significantly reduces surplus inventory while maintaining high material availability, offering a scientific, data-driven solution for enhancing efficiency in power material management. Full article

Accurate digital canopy height models (DCHMs) derived from historical aerial photographs are essential for reconstructing long-term forest structural dynamics; however, the influence of photogrammetric software on DCHM quality and reliability remains insufficiently evaluated. This study compared the performance of two structure-from-motion (SfM) photogrammetric platforms, Metashape and Pix4Dmatic, for processing old aerial photographs and generating DCHMs in Ishikawa prefecture. Software performance was assessed using image processing efficiency, geometric accuracy based on root mean square error (RMSE), and correlation between derived DCHMs and National Forest Inventory (NFI) measurements. The results revealed that Metashape required shorter image processing times for the digital surface model generation and produced denser point clouds with broader spatial coverage. By contrast, Pix4Dmatic achieved higher geometric accuracy, with RMSE values of 0.571 m, 0.870 m, and 2.120 m in the X, Y, and Z directions, respectively. The Metashape-derived DCHM showed a higher mean value (15.267 ± 5.882 m) than Pix4Dmatic (14.749 ± 5.834 m), but Pix4Dmatic-generated DCHMs showed a closer relationship (r = 0.880) with NFI data (15.322 ± 5.451 m). These findings demonstrate that photogrammetric software selection substantially influences three-dimensional reconstruction from old aerial imagery and affects the reliability of DCHM generation. This study provides practical guidance for selecting SfM software for forest structural analysis and long-term forest monitoring. Full article

Understanding whether vegetation greening corresponds to changes in estimated forest carbon storage is important for evaluating ecological restoration under coupled climate change and human pressures. However, existing studies often rely on vegetation indices and have limited capacity to examine long-term forest carbon storage patterns or distinguish the roles of climatic and anthropogenic factors. This study integrates long-term remote sensing data with a two-way fixed effects model to examine forest ecosystem carbon storage in Shaanxi Province, China, from 1990 to 2023. Forest carbon storage was estimated by combining historical land-use data with static baseline carbon density coefficients derived from the 2012 field inventory, following an IPCC Tier 1-type approach. The carbon pools considered included aboveground biomass, belowground biomass, litter, and soil organic carbon. The results show that NDVI increased significantly, while estimated forest carbon storage increased by 4.27 × 10⁷ t (21.04%), with evident regional heterogeneity. A mismatch was observed between vegetation greenness and estimated forest carbon storage, and NDVI showed weak and unstable associations with carbon storage after controlling for fixed effects. Nighttime light exhibited a significant negative association with carbon storage, whereas climatic factors were generally insignificant. These findings suggest that vegetation indices alone may not reliably represent land-use-based carbon storage estimates. This study provides empirical evidence for understanding forest carbon storage patterns under ecological restoration and highlights the need for dynamic carbon density parameters in future assessments. Full article

Mediterranean phryganic ecosystems have been shaped for centuries by recurrent herbivory, yet the long-term ecological consequences of grazing cessation remain insufficiently resolved, particularly in protected island landscapes where conservation management often assumes that exclusion promotes recovery. In these drylands, the removal of grazing redirect assembly processes through shifts in dominance, heterogeneity, and functional strategy expression. Here, we use more than three decades-long grazing discontinuity within the Petrified Forest of Lesvos, an insular Mediterranean geopark, to examine how long-term herbivore exclusion reorganizes plant communities across taxonomic and functional dimensions. By integrating floristic inventories, multivariate community analysis, mixed-effects modeling, indicator species analysis, and community-weighted trait approaches, we reconstruct the ecological signature of grazing release in phryganic ecosystems. Long-term exclusion was associated with a broader species pool and a greater representation of protected taxa, while ungrazed communities exhibited lower Shannon and Simpson diversity, greater compositional dispersion, and a marked shift in dominance structure linked to the expansion of Sarcopoterium spinosum. Community differentiation was accompanied by directional reorganization of functional trait structure, with ungrazed plots characterized by taller vegetation and increased leaf and inflorescence length, indicating release from recurrent biomass removal and a transition toward more structurally expansive strategies. These results show that grazing exclusion does not simply enhance biodiversity, but reorganizes Mediterranean plant communities into an alternative ecological state shaped by altered competitive hierarchies, shrub-mediated filtering, and relaxed herbivory. In disturbance-structured island ecosystems, therefore, the ecological outcomes of protection depend not only on whether grazing is removed, but on how strongly community organization has historically depended on its continued presence. Full article

Woody vegetation growing outside officially designated forest land represents a significant but poorly quantified resource in many countries, where institutional and methodological limitations hinder its systematic accounting. This study develops and applies a multi-stage remote sensing-based framework to identify and characterize forest-eligible areas (FEAs) in Lithuania by integrating airborne LiDAR, Sentinel-2 time series, historical orthophotos, and national geospatial datasets. The workflow combines (i) LiDAR-derived canopy height model generation and object-based segmentation, (ii) rule-based aggregation of vegetation segments according to legal forest criteria, (iii) multi-index Sentinel-2 change detection to exclude recent disturbances, and (iv) deep learning-based classification of historical orthophotos to assess stand age. Three detection approaches were evaluated—LiDAR-based, land parcel identification system (LPIS)-based, and their combination. A total of 111,754.4 ha of FEAs were identified outside official forest land, of which 76,204.6 ha meet the minimum age criterion for classification as forest land under national legislation. The designation of these areas as forest land would increase national forest cover from 33.9% to 35.0%. The LiDAR-based approach achieved the highest overall accuracy after dataset refinement (91.5%), while the combined approach yielded the highest precision (97.1%). Accuracy improved notably when reference points affected by definitional conflicts and temporal inconsistencies were excluded, indicating that apparent detection errors were largely attributable to reference data limitations rather than algorithmic failure. The proposed framework offers a scalable solution for wall-to-wall identification and monitoring of unregistered forest resources, with direct applications for national forest inventories and LULUCF reporting. Full article