Search Results (523)

To address the problems of traditional methods that rely on destructive sampling, the poor adaptability of fixed equipment, and the susceptibility of single-view angle measurements to occlusions, a non-destructive and portable device for three-dimensional phenotyping and biomass detection in lettuce was developed. Based on the Structure-from-Motion Multi-View Stereo (SFM-MVS) algorithms, a high-precision three-dimensional point cloud model was reconstructed from multi-view RGB image sequences, and 12 phenotypic parameters, such as plant height, crown width, were accurately extracted. Through regression analyses of plant height, crown width, and crown height, and the R² values were 0.98, 0.99, and 0.99, respectively, the RMSE values were 2.26 mm, 1.74 mm, and 1.69 mm, respectively. On this basis, four biomass prediction models were developed using Adaptive Boosting (AdaBoost), Support Vector Regression (SVR), Gradient Boosting Decision Tree (GBDT), and Random Forest Regression (RFR). The results indicated that the RFR model based on the projected convex hull area, point cloud convex hull surface area, and projected convex hull perimeter performed the best, with an R² of 0.90, an RMSE of 2.63 g, and an RMSEn of 9.53%, indicating that the RFR was able to accurately simulate lettuce biomass. This research achieves three-dimensional reconstruction and accurate biomass prediction of facility lettuce, and provides a portable and lightweight solution for facility crop growth detection. Full article

The segmentation of individual trees holds considerable significance in the investigation and management of forest resources. Utilizing smartphone-captured imagery combined with image-based 3D reconstruction techniques to generate corresponding point cloud data can serve as a more accessible and potentially cost-efficient alternative for data acquisition compared to conventional LiDAR methods. In this study, we present a Sparse 3D U-Net framework for single-tree segmentation which is predicated on a multi-head attention mechanism. The mechanism functions by projecting the input data into multiple subspaces—referred to as “heads”—followed by independent attention computation within each subspace. Subsequently, the outputs are aggregated to form a comprehensive representation. As a result, multi-head attention facilitates the model’s ability to capture diverse contextual information, thereby enhancing performance across a wide range of applications. This framework enables efficient, intelligent, and end-to-end instance segmentation of forest point cloud data through the integration of multi-scale features and global contextual information. The introduction of an iterative mechanism at the attention layer allows the model to learn more compact feature representations, thereby significantly enhancing its convergence speed. In this study, Dongsheng Bajia Country Park and Jiufeng National Forest Park, situated in Haidian District, Beijing, China, were selected as the designated test sites. Eight representative sample plots within these areas were systematically sampled. Forest stand sequential photographs were captured using an iPhone, and these images were processed to generate corresponding point cloud data for the respective sample plots. This methodology was employed to comprehensively assess the model’s capability for single-tree segmentation. Furthermore, the generalization performance of the proposed model was validated using the publicly available dataset TreeLearn. The model’s advantages were demonstrated across multiple aspects, including data processing efficiency, training robustness, and single-tree segmentation speed. The proposed method achieved an F1 score of 91.58% on the customized dataset. On the TreeLearn dataset, the method attained an F1 score of 97.12%. Full article

Abies nephrolepis and Picea jezoensis are native Pinaceae trees distributed in high mountainous regions of Northeast Asia (typically above ~1000 m a.s.l. on the Korean peninsula, northeastern China, Sakhalin, and the Russian Far East) and southern boreal forests, vulnerable to climate change and human disturbances, necessitating accurate habitat identification for effective conservation. While protected areas (PAs) are essential, merely expanding existing ones often fail to protect populations under human pressure and climate change. Using species distribution models with current and projected climate data, we mapped potential habitats across Northeast Asia. Spatial clustering analyses integrated with PA and land cover data helped identify optimal sites and priorities for new conservation areas. Ensemble species distribution models indicated extensive suitable habitats, especially in southern Sikhote-Alin, influenced by maritime-continental climates. Specific climate variables strongly affected habitat suitability for both species. The Kamchatka peninsula consistently emerged as an optimal habitat under future climate scenarios. Our study highlights essential environmental characteristics shaping the habitats of these species, reinforcing the importance of strategically enhancing existing PAs, and establishing new ones. These insights inform proactive conservation strategies for current and future challenges, by focusing on climate refugia and future habitat stability. Full article

Code duplication, commonly referred to as code cloning, is not inherent in software systems but arises due to various factors, such as time constraints in meeting project deadlines. These duplications, or “code clones”, complicate the program structure and increase maintenance costs. Code clones are categorized into four types: Type-1, Type-2, Type-3, and Type-4. This study aims to address the adverse effects of code clones by introducing LeONet, a hybrid Deep Learning approach that enhances the detection of code clones in software systems. The hybrid approach, LeONet, combines LeNet-5 with Oreo’s Siamese architecture. We extracted clone method pairs from the BigCloneBench Java repository. Feature extraction was performed using Abstract Syntax Trees, which are scalable and accurately represent the syntactic structure of the source code. The performance of LeONet was compared against other classifiers including ANN, LeNet-5, Oreo’s Siamese, LightGBM, XGBoost, and Decision Tree. LeONet demonstrated superior performance among the classifiers tested, achieving the highest F1 score of 98.12%. It also compared favorably against state-of-the-art approaches, indicating its effectiveness in code clone detection. The results validate the effectiveness of LeONet in detecting code clones, outperforming existing classifiers and competing closely with advanced methods. This study underscores the potential of hybrid deep learning models and feature extraction techniques in improving the accuracy of code clone detection, providing a promising direction for future research in this area. Full article

Vegetation is a fundamental component of terrestrial ecosystems. Understanding how vegetation changes and what drives these evolutions is crucial for developing a high-quality ecological environment and addressing global climate change. Extensive evidence has shown that China has undergone substantial vegetation changes, characterized primarily by greening. To quantify vegetation dynamics in China and assess the contributions of various drivers, we explored the spatiotemporal variations in the kernel Normalized Difference Vegetation Index (kNDVI) from 2001 to 2020, and quantitatively separated the influences of climate and human factors. The kNDVI time series were generated from the MCD19A1 v061 dataset based on the Google Earth Engine (GEE) platform. We employed the Theil-Sen trend analysis, the Mann-Kendall test, and the Hurst index to analyze the historical patterns and future trajectories of kNDVI. Residual analysis was then applied to determine the relative contributions of climate change and human activities to vegetation dynamics across China. The results show that from 2001 to 2020, vegetation in China showed a fluctuating but predominantly increasing trend, with a significant annual kNDVI growth rate of 0.002. The significant greening pattern was observed in over 48% of vegetated areas, exhibiting a clear spatial gradient with lower increases in the northwest and higher amplitudes in the southeast. Moreover, more than 60% of vegetation areas are projected to experience a sustained increase in the future. Residual analysis reveals that climate change contributed 21.89% to vegetation changes, while human activities accounted for 78.11%, being the dominant drivers of vegetation variation. This finding is further supported by partial correlation analysis between kNDVI and temperature, precipitation, and the human footprint. Vegetation dynamics were found to respond more strongly to human influences than to climate drivers, underscoring the leading role of human activities. Further analysis of tree cover fraction and cropping intensity data indicates that the greening in forests and croplands is primarily attributable to large-scale afforestation efforts and improved agricultural management. Full article

The relict tree Cercidiphyllum japonicum, a Tertiary paleoendemic with significant ecological and timber value, prefers warm–cool humid climates and acidic soils. Using MaxEnt and ArcGIS, we modeled its distribution under current and future climate scenarios (SSP, Shared Socioeconomic Pathways). High-suitability areas (>0.6 probability) under current conditions are mainly concentrated in the Sichuan Basin and the Yellow–Yangtze transition zones. By 2050, projections show northwestward expansions (14.32–18.76% increase in area) and eastward movement toward Central China under both SSP1-2.6 and SSP5-8.5 scenarios. However, by 2090, habitat loss could exceed 22% under SSP5-8.5. The main environmental drivers of its distribution are minimum coldest-month temperature (bio6, 38.7%), annual precipitation (bio12, 29.1%), and temperature range (bio7, 18.5%). Precipitation seasonality and thermal extremes are expected to become more significant constraints in the future. Conservation strategies should focus on the following: (1) protecting refugia in the Daba–Wushan mountains, (2) facilitating assisted migration to northwestern high-latitude regions, and (3) preserving microclimates. This study offers a framework for evidence-based conservation of paleoendemic species under climate change. Full article

The “Green Riyadh” project in Saudi Arabia represents a major initiative to enhance urban sustainability by expanding green spaces throughout Riyadh City. The initiative aims to improve air and water quality, increase tree and plant coverage, and promote environmental well-being for city residents. However, accurately assessing the extent and quality of green spaces remains a significant challenge. Current methods for evaluating green areas and measuring tree density are limited in precision and reliability, preventing effective monitoring and planning. This paper proposes an innovative solution that leverages live satellite imagery and advanced deep learning techniques to address these challenges. We collect extensive satellite data from two sources and then build two separate analytical pipelines. These pipelines process high-resolution satellite imagery to identify trees and measure green density in vegetated areas. The experimental results show significant improvements in accuracy and efficiency, with the YOLOv11 model achieving a mAP@50 of 95.4%, precision of 94.6%, and recall of 90.2%. These findings offer a scalable and reliable alternative to traditional methods, enabling comprehensive progress evaluation and facilitating informed decision-making for urban planning. The proposed methodology not only supports the objectives of the “Green Riyadh” project but also sets a benchmark for green space evaluation that can be adopted by cities worldwide. Full article

Ulmus glabra Huds. is a mesophilic, montane broadleaf tree with high ecological value, commonly found in temperate riparian and floodplain forests across Türkiye. Its populations in Türkiye have declined due to anthropogenic disturbances and climatic pressures that cause habitat fragmentation and threaten the species’ long-term survival. In this research, we used Maximum Entropy (MaxEnt) to build species distribution models (SDMs) and applied the Restoration Planner (RP) tool to identify and prioritize critical restoration sites under both current and projected climate scenarios (SSP245, SSP370, SSP585). The SDMs highlighted areas of high suitability, primarily along the Black Sea coast. Future projections show that habitat fragmentation and shifts in suitable areas are expected to worsen. To systematically compare restoration options across different future scenarios, we derived and applied four spatial network status indicators using the RP tool. Specifically, we calculated Restoration Pixels (REST_PIX), Average Distance of Restoration Pixels from the Network (AVDIST_RP), Change in Equivalent Connected Area (ΔECA), and Restoration Efficiency (EFFIC) using the RP tool. For the 1 <-> 2 restoration pathways, the highest efficiency (EFFIC = 38.17) was recorded under present climate conditions. However, the largest improvement in connectivity (ΔECA = 60,775.62) was found in the 4 <-> 5 pathway under the SSP585 scenario, though this required substantial restoration effort (REST_PIX = 385). Temporal analysis noted that the restoration action will have most effectiveness between 2040 and 2080, while between 2081 and 2100, increased habitat fragmentation can severely undermine ecological connectivity. The result indicates that incorporation of habitat suitability modeling into restoration planning can help to design cost-effective restoration actions for degraded land. Moreover, the approach used herein provides a reproducible framework for the enhancement of species sustainability and habitat connectivity under varying climate conditions. Full article

Specific computational methods, such as machine learning algorithms, can assist mining professionals in quickly and consistently identifying and addressing classification issues related to mineralized horizons, as well as uncovering key variables that impact predictive outcomes, many of which were previously difficult to observe. The integration of numerical and categorical variables, which are part of a dataset for defining ore grades, is part of the daily routine of professionals who obtain the data and manipulate the various phases of analysis in a mining project. Several supervised and unsupervised machine learning methods and applications integrate a wide variety of algorithms that aim at the efficient recognition of patterns and similarities and the ability to make accurate and assertive decisions. The objective of this study is the classification of gold ore or gangue through supervised machine learning methods using numerical variables represented by grade and categorical variables obtained through drillholes descriptions. Four groups of variables were selected with different variable configurations. The application of classification algorithms to different groups of variables aimed to observe the variables of importance and the impact of each one on the classification, in addition to testing the best algorithm in terms of accuracy and precision. The datasets were subjected to training, validation, and testing using the decision tree, random forest, Adaboost, XGBoost, and logistic regression methods. The evaluation was randomly divided into training (60%) and testing (40%) with 10-fold cross-validation. The results revealed that the XGBoost algorithm obtained the best performance, with an accuracy of 0.96 for scenario C1. In the SHAP analysis, the variable As was prominent in the predictions, mainly in scenarios C1 and C3. The arsenic class (Class_As), present mainly in scenario C4, had a significant positive weight in the classification. In the Receiver Operating Characteristic (ROC) and Area Under the Curve (AUC) curves, the results showed that XGBoost/scenario C1 obtained the highest AUC of 0.985, indicating that the algorithm had the best performance in ore/gangue classification of the sample set. The logistic regression algorithm together with AdaBoost had the worst performance, also varying between scenarios. Full article

Climate models forecast warmer winter conditions, which could lead to an earlier spring xylem phenology in trees. Localized stem heat experiments mimic this situation and have shown that stem warming leads to an earlier cambial resumption in evergreen conifers. However, there are still few comprehensive studies comparing the responses to stem heating in coexisting conifers and hardwoods, particularly in drought-prone regions where temperatures are rising. We addressed this issue by comparing the responses (xylem phenology, wood anatomy, growth, and sapwood concentrations of non-structural carbohydrates—NSCs) of two pines (the Eurosiberian Pinus sylvestris L., and the Mediterranean Pinus pinaster Ait.) and a ring-porous oak (Quercus pyrenaica Willd.) to stem heating. We used the Vaganov-Shashkin growth model (VS model) to simulate growth phenology considering several emission scenarios and warming rates. Stem heating in winter advanced cambial phenology in P. pinaster and Q. pyrenaica and enhanced radial growth of the three species 1–2 years after the treatment, but reduced the transversal lumen area of earlywood conduits. P. sylvestris showed a rapid and high growth enhancement, whereas the oak responded with a 1-year delay. Heated P. pinaster and Q. pyrenaica trees showed lower sapwood starch concentrations than non-heated trees. These results partially agree with projections of the VS model, which forecasts earlier growth onset, particularly in P. pinaster, as climate warms. Climate-growth correlations show that growth may be enhanced by warm conditions in late winter but also reduced if this is followed by dry-warm growing seasons. Therefore, forecasted advancements of xylem onset in spring in response to warmer winters may not necessarily translate into enhanced growth if warming reduces the hydraulic conductivity and growing seasons become drier. Full article

Chengiodendron marginatum, an evergreen tree or shrub belonging to the Oleaceae family, represents a critical germplasm resource with considerable potential for novel cultivar breeding. To elucidate the adaptive responses of C. marginatum to climate change and provide strategic guidance for its conservation, this study investigates the changing patterns in its potential suitable habitats under various climate scenarios. We employed an integrated approach combining maximum entropy (Maxent) modeling with GIS spatial analysis, utilizing current occurrence records and paleoclimatic data spanning from the mid-Holocene to future projections (2041–2060 [2050s] and 2061–2080 [2070s]). Climate scenarios SSP126 and SSP585 were selected to represent contrasting emission pathways. The model demonstrated excellent predictive accuracy with an AUC value of 0.942, identifying precipitation-related variables (particularly the precipitation of driest month and annual precipitation) as the primary environmental factors shaping the geographical distribution of C. marginatum. Current suitable habitats encompass approximately 98.38 × 10⁴ km², primarily located in East, Central, and South China, with high-suitability habitats restricted to southern Hainan, Taiwan, and northeastern Guangxi. Since the mid-Holocene, an expansion of suitable habitats occurred despite localized contractions in Southwest China. Future projections revealed moderate habitat reduction under both scenarios, and high-suitability areas decreased substantially. Importantly, under both scenarios, persistent high-suitability habitats were maintained in southern Hainan, Taiwan, and northeastern Guangxi, which are identified as essential climate refugia for the species. These findings provide a basis for understanding the response of the species to climate change and offer valuable guidance for its conservation. Full article

The increasing pace of climate-driven changes in forest ecosystems calls for reliable remote sensing techniques for quantifying above-ground carbon storage. In this article, we compare the methodology and results of traditional field surveys, mobile laser scanning, optical drone imaging and photogrammetry, and both drone-based and light aircraft-based aerial laser scanning to determine forest stand parameters, which are suitable to estimate carbon stock. Measurements were conducted at four designated sampling points established during a large-scale project in deciduous and coniferous tree stands of the Dudles Forest, Hungary. The results of the surveys were first compared spatially and quantitatively, followed by a summary of the advantages and disadvantages of each method. The mobile laser scanner proved to be the most accurate, while optical surveying—enhanced with a new diameter measurement methodology based on detecting stem positions from the photogrammetric point cloud and measuring the diameter directly on the orthorectified images—also delivered promising results. Aerial laser scanning was the least accurate but provided coverage over large areas. Based on the results, we recommend adapting our carbon stock estimation methodology primarily to mobile laser scanning surveys combined with aerial laser scanned data. Full article

This study was conducted in the Harran Plain within the framework of the Southeastern Anatolia Project (GAP) in Türkiye to evaluate the vulnerability of groundwater to contamination, with a special emphasis on the high salinity conditions attributed to agricultural and rural practices. The region is notably challenged by salinization resulting from intensive irrigation and insufficient drainage systems. The DRASTIC framework was used to assess groundwater contamination vulnerability. The DRASTIC framework parameters were numerically integrated using both the original DRASTIC framework and its modified version, serving as the basis for subsequent predictive analytics and soft computing model development. The primary aim was to determine the most effective predictive model for groundwater contamination vulnerability in salinity-affected areas. In this context, various models were implemented and evaluated, including artificial neural networks (ANNs) with varied hidden layer configurations, four different regression-based methods (MARS, TreeNet, GPS, and CART), and three classical regression analysis approaches. The modeling process utilized 24 adjusted vulnerability indices (AVIs) as target variables, with the dataset partitioned into 58.34% for training, 20.83% for validating, and 20.83% for testing. Model performance was rigorously assessed using various statistical indicators such as mean absolute error, root mean square error, and the Nash–Sutcliffe efficiency coefficient, in addition to evaluating the predictive AVIs through spatial mapping. The findings revealed that the ANNs and TreeNet models offered superior performance in accurately predicting groundwater contamination vulnerability, particularly by delineating the spatial distribution of risk in areas experiencing intensive agricultural pressure. Full article

Increasing climate change requires cities to adapt to changing weather conditions. New elements for decentralized stormwater management must be installed to protect the sewer system from overloading during heavy rainfall events and to keep water in the city for irrigation use. A pilot project was implemented in Leipzig in 2020, in which infiltration tree trench systems with three different designs were installed and equipped with measuring technology during a road renovation project. The catchment areas of these three tree trenches are between 215 and 300 m² each. In two of the systems, water retention was included to supply the tree with water during drought periods. The retention elements are sealed with clay in tree trench TT1 and bentonite in tree trench TT3. For tree trench TT2, no retention capacity was provided. This article presents the design, construction, and scientific monitoring of the three tree infiltration trenches. The conclusions after four years of operation from the perspective of two departments of the City of Leipzig are summarized. The tree trench TT1 with the clay pan for water storage shows the best performance in terms of water retention and tree fitness. For the next generation of such infiltration systems, improvements in the design of the street runoff inlets and the surface of the tree trench system’s interior are discussed. Full article

Global warming has caused the change of the geographical distribution of many species and threatened the living of species on earth. It is important to describe and predict the response of these species to current and future climate changes to conserve and utilize the endemic forest species. Acer pubinerve of the Sapindaceae is an important forest tree species endemic to China, our recent fieldwork recorded A. pubinerve in the Jiangsu province for the first time, representing the northernmost known occurrence of the species. In this study, we compiled an occurrence dataset of A. pubinerve based on field investigation, herbarium specimen data and literature, and mapped the resource distribution of this endemic forest species in China. Then, we used the optimized MaxEnt model to predict the potential suitable areas of the species under current climate conditions and future climate change scenarios and studied the impacts of environmental variables on the suitable areas of the species. The MaxEnt model, optimized with a regularization multiplier of 0.5 and a feature combination of linear and quadratic terms, exhibited the best predictive performance. The prediction accuracy of the model was extremely high and the AUC values of training and test data were 0.995 and 0.998, respectively. We found that the leading environmental variables affecting the potential distribution of A. pubinerve include the mean temperature of warmest quarter, the mean temperature of driest quarter, and the annual precipitation. Under the current climatic condition, the suitable distribution area of A. pubinerve is 165.68 × 10⁴ km², mainly located in the provinces of Zhejiang, Fujian, Jiangxi, Hunan, Guangdong, and Guangxi. Compared with the suitable area under the current climate, the total suitable areas of A. pubinerve is projected to expand toward the north under the future climate change scenarios SSP126, SSP370, and SSP585, while its center shows a general trend of westward migration. Our study lays the foundation for conservation and resource utilization of this endemic tree species in China. Full article