Search Results (445)

Accurate mapping of forest canopy height is essential for monitoring forest structure, assessing biodiversity, and informing sustainable management practices. However, obtaining high-resolution canopy height data across large tropical landscapes remains challenging and prohibitively expensive. While machine learning approaches like Random Forest have become standard for predicting forest attributes from remote sensing data, deep learning methods remain underexplored for canopy height mapping despite their potential advantages. To address this limitation, we developed a rapid, automatic, scalable, and cost-efficient deep learning framework that predicts tree canopy height at fine-grained resolution (30 × 30 m) across Indonesian Borneo’s tropical forests. Our approach integrates diverse remote sensing data, including Landsat-8, Sentinel-1, land cover classifications, digital elevation models, and NASA Carbon Monitoring System airborne LiDAR, along with derived vegetation indices, texture metrics, and climatic variables. This comprehensive data pipeline produced over 300 features from approximately 2 million observations across Bornean forests. Using LiDAR-derived canopy height measurements from ~100,000 ha as training data, we systematically compared multiple machine learning approaches and found that our neural network model achieved canopy height predictions with R2 of 0.82 and RMSE of 4.98 m, substantially outperforming traditional machine learning approaches such as Random Forest (R2 of 0.57–0.59). The model performed particularly well for forests with canopy heights between 10–40 m, though systematic biases were observed at the extremes of the height distribution. This framework demonstrates how freely available satellite data can be leveraged to extend the utility of limited LiDAR coverage, enabling cost-effective forest structure monitoring across vast tropical landscapes. The approach can be adapted to other forest regions worldwide, supporting applications in ecological research, conservation planning, and forest loss mitigation. Full article

It is crucial to evaluate the quality of standing timber for the rational and effective management of forest land. In practice, it is often difficult to obtain accurate data on various indicators of standing timber due to constraints such as measurement conditions, accuracy, and cost. Therefore, this study developed a multi-attribute decision-making method based on trapezium clouds and applied it to evaluate the standing timber quality of forest land. Firstly, a trapezium cloud transformation method was designed to handle multi-granularity symmetric linguistic information problems caused by different knowledge backgrounds of decision-makers, and the symmetric structure inherent in trapezium clouds helped to ensure the balanced processing of information from various asymmetric cognitive perspectives. Secondly, a trapezium cloud generalized weighted Heronian mean was proposed for the information aggregation process of trapezium clouds. Then, the concept of trapezium cloud interval similarity was defined, and an optimization model was constructed to determine the normalized interval weights of attributes. Based on the symmetric numerical feature, the calculation formula for the approximate centroid coordinates of trapezium clouds was derived, and based on this, the ranking method of trapezium clouds was obtained. Finally, taking the evaluation of standing timber quality in forest land as a numerical example, the applicability of the constructed multi-attribute decision-making method was demonstrated. In addition, the corresponding comparison analysis verified the superiority and effectiveness of the proposed method. Full article

Urban mediterranean forests are key components of urban ecosystems. Accurate, high-resolution data on forest structural attributes are essential for effective management. This study evaluates the efficiency of unmanned laser scanning systems (ULS) and terrestrial LiDAR (TLS) in deriving key tree attributes, diameter at breast height (DBH) and tree height, within a small urban park in Zadar, Croatia. Accuracy assessment of the ULS and TLS-derived DBH was conducted based on traditional ground-based measurement (TGBM) data. For ULS, an automatic Spatix workflow was applied that classified points into a Tree class, segmented trees using trunk-based logic, and estimated DBH by fitting a circle to a 1.3 m slice; tree height was computed from the ground-normalized cloud with the Output Tree Cells tool. A semi-automatic CloudCompare/ArcMap workflow used CSF ground filtering, Connected Components segmentation, extraction of a 10 cm slice, manual trunk vectorization, and DBH calculation via Minimum Bounding Geometry. TLS scans, processed in FARO SCENE, were then analyzed in Spatix using the same automatic trunk-fitting procedure to derive DBH and height. Accuracy for DBH was evaluated against TGBM; comparative performance was summarized with standard error metrics, while ULS and TLS tree heights were compared using Concordance Correlation Coefficient (CCC) and Bland–Altman statistics. Results indicate that the semi-automatic approach outperformed the automatic approach in deriving DBH. TLS-derived DBH values demonstrated higher consistency and agreement with TGBM, as evidenced by their strong linear correlation, minimal bias, and narrow residual spread, while ULS exhibited greater variability and systematic deviation. Tree height comparisons between ULS and TLS revealed that ULS consistently produced slightly higher and more uniform measurements. This study highlights limitations in the evaluated techniques and proposes a hybrid approach combining ULS scanning with personal laser scanning (PLS) systems to enhance data accuracy in urban forest assessments. Full article

Energy consumption and environmental pollution pose significant challenges to sustainable development. This study develops a comprehensive coupled framework model that advances the quantitative integration of carbon (C), nitrogen (N), and phosphorus (P) cycles driven by multiple anthropogenic pollution sources. This paper used Panjin city as a case study to analyze the dynamic changes and interconnections among C, N, and P. Results indicated that net anthropogenic carbon inputs (NAI_C) increased by 33% from 2016–2020, while net anthropogenic nitrogen inputs (NAI_N) and net anthropogenic phosphorus inputs (NAI_P) decreased by 14% and 28%, respectively. The primary driver of NAI_C was energy consumption, while wetlands were the dominant carbon sequestration sink. Agricultural production was identified as the primary source of NAI_N and NAI_P, and approximately 4.5% of NAI_N and 2.9% of NAI_P were discharged into receiving water bodies. We demonstrate that human activities and natural processes exhibit dual attributes, producing positive and negative environmental effects. The increase in carbon emissions drives economic growth and industrial restructuring; however, the enhanced economic capacity also strengthens the ability to mitigate pollution through environmental protection measures. Similarly, natural ecosystems, including forests and grasslands, contribute to carbon sequestration and the release of non-point source pollution. The comprehensive environmental impact assessment of C, N, and P revealed that the comprehensive environmental index for Panjin city exhibited an improved trend. The factors of energy structure, energy efficiency, and economic scale promoted NAI_C growth, with the economic scale factor alone accounting for 93% of the total increment. Environmental efficiency factor and population size factor were the primary drivers in reducing NAI_N and NAI_P discharges into the receiving water bodies. We propose a novel management model, ecological restoration, clean energy utilization, resource recycling, and pollution source reduction to achieve systemic governance of C, N, and P inputs. Full article

National-scale long-term forest ecosystem surveys based on systematic sampling offer a robust framework for detecting temporal growth trends of specific tree species across regions. The National Forest Inventory (NFI) of the Republic of Korea serves as a vital source for analyzing long-term forest dynamics on a national scale by providing regularly collected large-scale forest data. However, various limitations, such as the lack of individual-level and spatial interaction data, restrict the development of reliable individual tree growth models. To overcome this, distance-independent models, compatible with the structure and data resolution of the NFI, provide a practical alternative for simulating individual tree and stand-level growth by utilizing straightforward attributes, such as diameter at breast height (DBH). This study aimed to analyze the growth patterns and construct species-specific models for two major plantation species in South Korea, Pinus koraiensis and Larix kaempferi, using data from the 5th (2006–2010), 6th (2011–2015), and 7th (2016–2020) NFI survey cycles. The sampling points included 117 and 171 plots for P. koraiensis and L. kaempferi, respectively. An additional matching process was implemented to improve species identification and tracking across multiple survey years. The final models were parameterized using a distance-independent model, integrating the estimation of potential diameter growth (PG) and a modifier (MOD) function to adjust for species- and site-specific variabilities. Consequently, the models for each species demonstrated strong performance, with P. koraiensis showing an R² of 0.98 and RMSE of 1.15 (cm), and L. kaempferi showing an R² of 0.98 and RMSE of 1.14 (cm). This study provides empirical evidence for the development of generalized and scalable growth models using NFI data. As the NFI increases in volume, the framework can be expanded to underrepresented species to improve the accuracy of underperforming models. Ultimately, this study lays a scientific foundation for the future development of tree-level simulation algorithms for forest dynamics, encompassing mortality, harvesting, and regeneration. Full article

Long-term greenhouse operations face a critical challenge in the form of soil quality degradation, yet the key intervention periods and underlying mechanisms of this process remain unclear. This study aims to quantify the effects of greenhouse lifespan on the evolution of soil quality and to identify critical periods for intervention. We conducted a systematic survey of greenhouse operations in a representative area of Yunnan Province, Southwest China, and adopted a space-for-time substitution design. Using open-field cultivation (OF) as the control, we sampled and analyzed soils from vegetable greenhouses with greenhouse lifespans of 2 years (G2), 5 years (G5), and 10 years (G10). The results showed that early-stage greenhouse operation (G2) significantly increased soil temperature (ST) by 8.38–19.93% and soil porosity (SP) by 16.21–56.26%, promoted nutrient accumulation and enhanced aggregate stability compared to OF. However, as the greenhouse lifespan increased, the soil aggregates gradually disintegrated, particle-size distribution shifted toward finer clay fractions, and pH changed from neutral to slightly alkaline, exacerbating nutrient imbalances. Compared with G2, G10 exhibited reductions in mean weight diameter (MWD) and soil organic matter (SOM) of 2.41–5.93% and 24.78–30.93%, respectively. Among greenhouses with different lifespans, G2 had the highest soil quality index (SQI), which declined significantly with extended operation; at depths of 0–20 cm and 20–40 cm, the SQI of G10 was 32.59% and 38.97% lower than that of G2, respectively (p < 0.05). Structural equation modeling (SEM) and random forest analysis indicated that the improvement in SQI during the early stage of greenhouse use was primarily attributed to the optimization of soil hydrothermal characteristics and pore structure. Notably, the 2–5 years was the critical stage of rapid decline in SQI, during which intensive water and fertilizer inputs reduced the explanatory power of soil nutrients for SQI. Under long-term continuous cropping, the reduction in MWD and SOM was the main reason for the decline in SQI. This study contributes to targeted soil management during the critical period for sustainable production of protected vegetables in southern China. Full article

Old-growth forests are crucial for biodiversity conservation and climate change mitigation due to their high carbon storage, structural complexity, and resilience to environmental stressors. Yet, such ecosystems are rare in Europe, and their ecological functioning remains poorly understood. This study assesses the capacity of Quercus pyrenaica forests in the Montesinho-Nogueira Natura 2000 site (Bragança, Portugal) to develop maturity attributes under different forest management histories. We compare an area with low human intervention for over 80 years (10.2 ha) to two areas harvested for traditional small-scale firewood and timber extraction around 30 years ago (11.4 ha and 2.73 ha). Dendrometric measurements, carbon storage, floristic inventories of understory vegetation, and regeneration surveys were conducted across 42 sub-plots during June–July 2024. Results show that older forests store significantly more carbon and support greater biodiversity, evenness and regeneration, while younger forests present higher values of species richness, including several rare taxa. Our findings suggest that under favorable conditions, secondary forests can recover substantial biomass and carbon stocks within a few decades, while mature stands continue to accumulate carbon and maintain complex structures. Differences in floristic composition between sites may also reflect distinct silvopastoral practices between patches, such as itinerant grazing through forest patches, which historically characterized the Montesinho landscape. These results highlight the value of preserving a mosaic of successional stages, as both mature and intermediate-phase forests, together with compatible human activities, provide complementary biodiversity benefits and contribute to the multifunctionality of Mediterranean agroforestry systems. Full article

A significant contradiction exists between the demand for standardized processes and the need for precise process parameter design in the rapid design of superplastic forming (SPF). To address this, an SPF process parameter design method integrating a knowledge graph and artificial intelligence is proposed. Firstly, based on process data analysis, the entity labels, relationship categories, and attributes are determined. On this basis, the knowledge graph for the SPF process is constructed, comprising the pattern layer and the data layer, which provides structured knowledge support for process generation. Secondly, the process parameter prediction model based on small samples and an improved back propagation (BP) neural network is constructed, with model convergence ensured through an adaptive maximum iteration strategy. Experimental results show that the improved BP model significantly outperforms support vector regression (SVR), random forest (RF), extreme gradient boosting (XGBoost), and standard BP models in prediction accuracy. Compared to the standard BP model, the improved model reduces the mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE) by 82.1% (to 0.0005), 46% (to 0.0188), and 57.1% (to 0.0229), respectively. Finally, the effectiveness, feasibility, and superiority of the method in the SPF process parameter design are verified by taking typical hemispherical parts as an example. Full article

Tree ferns are ubiquitous in New Zealand forests, but there is limited knowledge of their role in urban plant communities and potential use in restoration. We assessed sixteen sites by measuring 200 m² plots to investigate how tree ferns influence vascular plant composition in Hamilton, North Island, New Zealand. The sixteen plots were assigned to four site type combinations based on restoration status (restored or unrestored) and tree fern presence, each with four plots. Average native plant species richness was higher at sites with tree ferns (36 ± 16; S = 68) than at sites without (19 ± 14; S = 41), with more diverse ground fern and epiphyte assemblages. Higher native plant richness at restored sites (34 ± 18; S = 62) compared to unrestored sites (20 ± 14, S = 44) was partially attributed to increased plant abundances. Multivariate analyses revealed differences in plant community composition among our site types. Angiosperms and conifers were less prevalent in plots with tree ferns, suggesting competitive relationships among these groups. However, tree ferns were associated with some shade-tolerant trees, such as Schefflera digitata J.R.Forst. & G.Forst. Indicator species of sites with tree ferns were mainly ground ferns and epiphytes (e.g., Blechnum parrisiae Christenh. and Trichomanes venosum R.Br.), whereas species with high fidelity to sites without tree ferns were pioneer trees and shrubs (e.g., Pittosporum eugenioides A.Cunn.). Community structure analyses revealed that total basal areas were highest at unrestored sites with tree ferns, but restored sites exhibited more diverse tree communities. Environmental predictors that correlated significantly with the compositional differences among our site types were tree fern basal area and restoration age. Our results highlight the need to reconsider the potential of tree ferns in current restoration practice. Tree ferns were found to augment native plant diversity in our study, indicating their potential to enhance urban ecological restoration projects in New Zealand. Full article

The aim of this study is the estimation of the biodiversity of pure oak stands within the jurisdiction of the Forest Service of Xanthi in northeastern Greece. Using a published graded biodiversity index that operates on management-plan description sheets, we scored five stand-level attributes (total wood stock, age of trees, canopy density, presence of regeneration, and stand aspect/orientation) for every eligible stand and classified biodiversity as low, moderate, or high. These data were sourced from the description sheets of pure oak stands found in the management plans of public forest complexes. Moderate biodiversity predominates (63.4% of stands), followed by low (33.5%), while high biodiversity is scarce (3.1%). Forest practice can influence all the factors which were used for the assessment of the biodiversity characterization of the stands except the aspect of the stand. From these factors the total amount of wood stock and the canopy density were the main factors which determined the low percentage of high-biodiversity stands. On the other hand, the age structure and the regeneration existence were the main factors which counterbalanced the negative influence of the total amount of wood stock and of the canopy density and thus led to the dominance of the stands characterized as having moderate biodiversity score. Full article

Saline–alkaline wetlands represent critical ecosystems for maintaining biodiversity, regulating hydrological processes, and supporting regional ecological resilience. However, the extent to which dominant vegetation regulates soil functionality and microbial assemblages in these unique saline systems remains insufficiently understood. In this study, we examined five characteristic vegetation types—Phragmites communis Trin., Typha angustifolia L., Bryophytes, Suaeda salsa (L.) Pall., Echinochloa phyllopogon (Stapf) Koss.—across the saline wetlands of Chagan Lake, northeast China, which are embedded in a heterogeneous matrix of forests, grasslands, and agricultural lands. Comprehensive assessments of soil physicochemical properties, enzyme activities, and bacterial communities were conducted, integrating high-throughput sequencing with multivariate statistical analyses. Our results revealed that vegetation cover markedly influenced soil attributes, particularly total organic carbon (TOC) and alkali-hydrolyzed nitrogen (AN), alongside key enzymatic functions such as urease and alkaline phosphatase activities. Proteobacteria, Actinobacteria, and Acidobacteria emerged as dominant bacterial phyla, with their relative abundances tightly linked to vegetation-induced shifts in soil environments. Notably, soils under E. phyllopogon demonstrated elevated bacterial diversity and enzymatic activities, underscoring the synergistic effects of plant selection on soil biogeochemical health. Structural equation modeling further elucidated complex pathways connecting vegetation, microbial diversity, soil quality, and enzymatic functioning. These findings emphasize the pivotal role of vegetation management in improving soil fertility, shaping microbial communities, and guiding the sustainable restoration of saline–alkaline wetlands under environmental stress. Full article

This study examined the structural and ecological drivers of burn severity during the March 2025 wildfire in Uiseong County, Republic of Korea, with a focus on developing a predictive framework using the differenced Normalized Burn Ratio (dNBR). Seventeen candidate variables were evaluated, among which the forest type, stand age, tree height, diameter at breast height (DBH), and Normalized Difference Vegetation Index (NDVI) were consistently identified as the most influential predictors. Burn severity increased across all forest types up to the 4th–5th age classes before declining in older stands. Coniferous forests exhibited the highest severity at the 5th age class (mean dNBR = 0.3069), followed by mixed forests (0.2771) and broadleaf forests (0.2194). Structural factors reinforced this pattern, as coniferous and mixed forests recorded maximum severity within the 5–11 m height range, while broadleaf forests showed relatively stable severity across 3–21 m but declined thereafter. In the final prediction model, NDVI emerged as the dominant variable, integrating canopy density, vegetation vigor, and moisture conditions. Notably, NDVI exhibited a positive correlation with burn severity in coniferous stands during this early-spring event, diverging from the generally negative relationship reported in previous studies. This seasonal anomaly underscores the need to interpret NDVI flexibly in relation to the forest type, stand age, and phenological stage. Overall, the model results demonstrate that mid-aged stands with moderate heights and dense canopy cover are the most fire-prone, whereas older, taller stands show reduced susceptibility. By integrating NDVI with structural attributes, this modeling approach provides a scalable tool for the spatial prediction of wildfire severity and supports resilience-based forest management under climate change. Full article

The development of generative AI Large Language Models (LLMs) raised the alarm regarding the identification of content produced by generative AI vs. humans. In one case, issues arise when students heavily rely on such tools in a manner that can affect the development of their writing or coding skills. Other issues of plagiarism also apply. This study aims to support efforts to detect and identify textual content generated using LLM tools. We hypothesize that LLM-generated text is detectable by machine learning (ML) and investigate ML models that can recognize and differentiate between texts generated by humans and multiple LLM tools. We used a dataset of student-written text in comparison with LLM-written text. We leveraged several ML and Deep Learning (DL) algorithms, such as Random Forest (RF) and Recurrent Neural Networks (RNNs) and utilized Explainable Artificial Intelligence (XAI) to understand the important features in attribution. Our method is divided into (1) binary classification to differentiate between human-written and AI-generated text and (2) multi-classification to differentiate between human-written text and text generated by five different LLM tools (ChatGPT, LLaMA, Google Bard, Claude, and Perplexity). Results show high accuracy in multi- and binary classification. Our model outperformed GPTZero (78.3%), with an accuracy of 98.5%. Notably, GPTZero was unable to recognize about 4.2% of the observations, but our model was able to recognize the complete test dataset. XAI results showed that understanding feature importance across different classes enables detailed author/source profiles, aiding in attribution and supporting plagiarism detection by highlighting unique stylistic and structural elements, thereby ensuring robust verification of content originality. Full article

Agricultural productivity is closely linked to the spatial variability of soil physical properties. However, high variability makes it difficult to implement effective management strategies, and the constant expansion of eucalyptus plantations in certain areas alters the soil’s physical properties. This study conducted a geostatistical analysis of the physical properties of a soil in Sogamoso, Boyacá (Colombia), which contains areas with different management practices and vegetation cover, among which the presence of Eucalyptus globulus stands out. Ninety-seven points were sampled in an area of 29.1 ha, with multiple land uses. The data were analyzed using descriptive statistics and geostatistical analysis, which determined the semivariogram parameters, the degree of spatial dependence, and the best-fitting interpolation model for mapping. A correlation analysis between variables was also performed. Analysis of variance showed no significant differences among vegetation covers (dense forest, grass-crop mosaic, weedy grassland, and crop mosaic), indicating structural homogeneity. The hydraulic conductivity (K_sat) had the highest coefficient of variation (CV), at 141.9%, while particle density had the lowest CV, at 9.25%. K_sat (exponential model, range = 207 m) and porosity (spherical model, range = 98 m) showed a strong spatial dependence. K_sat was lower in areas with eucalyptus (0.01 to 0.2 m day⁻¹), attributed to hydrophobicity induced by organic compounds emitted by these plantations. Soil moisture contents showed lower values in areas with eucalyptus, corroborating their high water consumption. Soil aggregates were lower when eucalyptus plantations were on slopes greater than 15%. Porosity showed an inverse correlation with apparent density (r² = −0.86). Full article

Urbanization is a driving force of landscape transformation. One of the ecosystems most vulnerable to urban expansion processes is montane forests located in high altitude mountainous regions. Despite their significance for biodiversity, regulation of the hydrological cycle, stability, prevention of soil erosion, and potential for organic carbon storage, these forest ecosystems show high vulnerability and risk due to the global urbanization process. We analyzed the potential variations produced by land cover change in some attributes related to soil organic matter in transitional forest fragments due to the expansion of a predominantly urban matrix landscape. We identified and characterized a fragment of a high montane evergreen forest in the Western Cordillera of the Northern Andes located in the urban limits of Quito. Then, we comparatively analyzed the variations in the attributes associated with soil organic carbon: soil organic matter, density, texture, nitrogen, phosphorus, and pH. We also considered the following soil coverages: forest, eucalyptus plantations, and grassland. We viewed the latter two as hinge coverages between forests and urban expansion. Finally, we estimated variations in soil organic carbon stock in the three analyzed coverages. For the montane forest fragment, we identified 253 individuals distributed among 18 species, corresponding to 10 families and 14 genera. We found significant variations in soil attributes associated with organic matter and an estimated 66% reduction in the carbon storage capacity of montane soils when they lose their natural cover and are replaced by Eucalyptus globulus plantations. Urban planning strategies should consider the conservation and restoration of natural and degraded peri-urban areas, ensuring sustainability and utilizing nature-based solutions for global climate change adaptation and mitigation. Peri-urban agroforestry systems represent an opportunity to replace and restore conventional forestry or crop plantation systems in peri-urban areas that affect the structure and function of ecosystems and, therefore, the goods and services derived from them. Full article