This paper proposes a new algorithm for determining the starting points of contour lines. The new algorithm is based on the interval tree. The result improves the algorithm＇s efficiency remarkably. Further, a new str...This paper proposes a new algorithm for determining the starting points of contour lines. The new algorithm is based on the interval tree. The result improves the algorithm＇s efficiency remarkably. Further, a new strategy is designed to constrain the direction of threading and the resulting contour bears more meaningful information.展开更多
Tree trunk instance segmentation is crucial for under-canopy unmanned aerial vehicles(UAVs)to autonomously extract standing tree stem attributes.Using cameras as sensors makes these UAVs compact and lightweight,facili...Tree trunk instance segmentation is crucial for under-canopy unmanned aerial vehicles(UAVs)to autonomously extract standing tree stem attributes.Using cameras as sensors makes these UAVs compact and lightweight,facilitating safe and flexible navigation in dense forests.However,their limited onboard computational power makes real-time,image-based tree trunk segmentation challenging,emphasizing the urgent need for lightweight and efficient segmentation models.In this study,we present RT-Trunk,a model specifically designed for real-time tree trunk instance segmentation in complex forest environments.To ensure real-time performance,we selected SparseInst as the base framework.We incorporated ConvNeXt-T as the backbone to enhance feature extraction for tree trunks,thereby improving segmentation accuracy.We further integrate the lightweight convolutional block attention module(CBAM),enabling the model to focus on tree trunk features while suppressing irrelevant information,which leads to additional gains in segmentation accuracy.To enable RT-Trunk to operate effectively under diverse complex forest environments,we constructed a comprehensive dataset for training and testing by combining self-collected data with multiple public datasets covering different locations,seasons,weather conditions,tree species,and levels of forest clutter.Com-pared with the other tree trunk segmentation methods,the RT-Trunk method achieved an average precision of 91.4%and the fastest inference speed of 32.9 frames per second.Overall,the proposed RT-Trunk provides superior trunk segmentation performance that balances speed and accu-racy,making it a promising solution for supporting under-canopy UAVs in the autonomous extraction of standing tree stem attributes.The code for this work is available at https://github.com/NEFU CVRG/RT Trunk.展开更多
Tree growth synchrony serves as a valuable ecological indicator of forest resilience to climate stress and disturbances.However,our understanding of how increasing temperature affects tree growth synchrony during rapi...Tree growth synchrony serves as a valuable ecological indicator of forest resilience to climate stress and disturbances.However,our understanding of how increasing temperature affects tree growth synchrony during rapidly and slowly warming periods in ecosystems with varying climatic conditions remains limited.By using tree-ring data from temperate broadleaf(Fraxinus mandshurica,Phellodendron amurense,Quercus mongolica,and Juglans mandshurica)and Korean pine(Pinus koraiensis)mixed forests in northeast China,we investigated the effects of climate change,particularly warming,on the growth synchrony of five dominant temperate tree species across contrasting warm-dry and cool-wet climate conditions.Results show that temperature over water availability was the primary factor driving the growth and growth synchrony of the five species.Growth synchrony was significantly higher in warm-dry than in cool-wet areas,primarily due to more uniform climate conditions and higher climate sensitivity in the former.Rapid warming from the 1960s to the 1990s significantly enhanced tree growth synchrony in both areas,followed by a marked reversal as temperatures exceeded a certain threshold or warming slowed down,particularly in the warm-dry area.The growth synchrony variation patterns of the five species were highly consistent over time,although broadleaves exhibited higher synchrony than conifers,suggesting potential risks to forest resilience and stability under future climate change scenarios.Growing season temperatures and non-growing season temperatures and precipitation had a stronger positive effect on tree growth in the cool-wet area compared to the warm-dry area.High relative humidity hindered growth in the cool-wet area but enhanced it in the warm-dry area.Overall,our study highlights that the diversity and sensitivity of climate-growth relationships directly determine spatiotemporal growth synchrony.Temperature,along with water availability,shape long-term forest dynamics by affecting tree growth and synchrony.These results provide crucial insights for forest management practice to enhance structural diversity and resilience capacity against climate changeinduced synchrony shifts.展开更多
The Darjeeling Himalayan region,characterized by its complex topography and vulnerability to multiple environmental hazards,faces significant challenges including landslides,earthquakes,flash floods,and soil loss that...The Darjeeling Himalayan region,characterized by its complex topography and vulnerability to multiple environmental hazards,faces significant challenges including landslides,earthquakes,flash floods,and soil loss that critically threaten ecosystem stability.Among these challenges,soil erosion emerges as a silent disaster-a gradual yet relentless process whose impacts accumulate over time,progressively degrading landscape integrity and disrupting ecological sustainability.Unlike catastrophic events with immediate visibility,soil erosion’s most devastating consequences often manifest decades later through diminished agricultural productivity,habitat fragmentation,and irreversible biodiversity loss.This study developed a scalable predictive framework employing Random Forest(RF)and Gradient Boosting Tree(GBT)machine learning models to assess and map soil erosion susceptibility across the region.A comprehensive geo-database was developed incorporating 11 erosion triggering factors:slope,elevation,rainfall,drainage density,topographic wetness index,normalized difference vegetation index,curvature,soil texture,land use,geology,and aspect.A total of 2,483 historical soil erosion locations were identified and randomly divided into two sets:70%for model building and 30%for validation purposes.The models revealed distinct spatial patterns of erosion risks,with GBT classifying 60.50%of the area as very low susceptibility,while RF identified 28.92%in this category.Notable differences emerged in high-risk zone identification,with GBT highlighting 7.42%and RF indicating 2.21%as very high erosion susceptibility areas.Both models demonstrated robust predictive capabilities,with GBT achieving 80.77%accuracy and 0.975 AUC,slightly outperforming RF’s 79.67%accuracy and 0.972 AUC.Analysis of predictor variables identified elevation,slope,rainfall and NDVI as the primary factors influencing erosion susceptibility,highlighting the complex interrelationship between geo-environmental factors and erosion processes.This research offers a strategic framework for targeted conservation and sustainable land management in the fragile Himalayan region,providing valuable insights to help policymakers implement effective soil erosion mitigation strategies and support long-term environmental sustainability.展开更多
Background The full lifespan of long-lived trees includes a seedling phase,during which a seed germinates and grows to a size large enough to be measured in forest inventories.Seedling populations are usually studied ...Background The full lifespan of long-lived trees includes a seedling phase,during which a seed germinates and grows to a size large enough to be measured in forest inventories.Seedling populations are usually studied separately from adult trees,and the seedling lifespan,from seed to sapling,is poorly known.In the 50-ha Barro Colorado forest plot,we started intensive censuses of seeds and seedlings in 1994 in order to merge seedling and adult demography and document complete lifespans.Methods In 17 species abundant in seedling censuses,we subdivided populations into six size classes from seed to 1cm dbh,including seeds plus five seedling stages.The smallest seedling class was subdivided by age.Censuses in two consecutive years provided transition matrices describing the probability that a seedling in one stage moved to another one year later.For each species,we averaged the transition matrix across 25 censuses and used it to project the seedling lifespan,from seed until 1cm dbh or death.Results The predicted mean survival rate of seeds to 1cm dbh varied 1000-fold across species,from 2.9×10^(−6)to 4.4×10^(−3);the median was 2.0×10^(−4).The seedling lifespan,or the average time it takes a seed to grow to 1cm dbh,varied across species from 5.1 to 53.1 years,with a median of 20.3 years.In the median species,the 10%fastest-growing seeds would reach 1cm dbh in 9.0 years,and the slowest 10%in 34.6 years.Conclusions Combining seedling results with our previous study of lifespan after 1cm dbh,we estimate that the focal species have full lifespans varying from 41 years in a gap-demanding pioneer to 320 years in one shade-tolerant species.Lifetime demography can contribute precise survival rates and lifespans to forestry models.展开更多
Discerning vulnerability differences among different aged trees to drought-driven growth decline or to mortality is critical to implement age-specific countermeasures for forest management in water-limited areas.An im...Discerning vulnerability differences among different aged trees to drought-driven growth decline or to mortality is critical to implement age-specific countermeasures for forest management in water-limited areas.An important species for afforestation in dry environments of northern China,Mongolian pine(Pinus sylvestris var.mongolica Litv.)has recently exhibited growth decline and dieback on many sites,particularly pronounced in old-growth plantations.However,changes in response to drought stress by this species with age as well as the underlying mechanisms are poorly understood.In this study,tree-ring data and remotely sensed vegetation data were combined to investigate variations in growth at individual tree and stand scales for young(9-13 years)and aging(35-52 years)plantations of Mongolian pine in a water-limited area of northern China.A recent decline in tree-ring width in the older plantation also had lower values in satellited-derived normalized difference vegetation indices and normalized difference water indices relative to the younger plantations.In addition,all measured growth-related metrics were strongly correlated with the self-calibrating Palmer drought severity index during the growing season in the older plantation.Sensitivity of growth to drought of the older plantation might be attributed to more severe hydraulic limitations,as reflected by their lower sapwood-and leaf-specific hydraulic conductivities.Our study presents a comprehensive view on changes of growth with age by integrating multiple methods and provides an explanation from the perspective of plant hydraulics for growth decline with age.The results indicate that old-growth Mongolian pine plantations in water-limited environments may face increased growth declines under the context of climate warming and drying.展开更多
Understanding forest health is of great importance for the conservation of the integrity of forest ecosystems.The monitoring of forest health is,therefore,indispensable for the long-term conservation of forests and th...Understanding forest health is of great importance for the conservation of the integrity of forest ecosystems.The monitoring of forest health is,therefore,indispensable for the long-term conservation of forests and their sustainable management.In this regard,evaluating the amount and quality of dead wood is of utmost interest as they are favorable indicators of biodiversity.Apparently,remote sensing-based Machine Learning(ML)techniques have proven to be more efficient and sustainable with unprecedented accuracy in forest inventory.However,the application of these techniques is still in its infancy with respect to dead wood mapping.This study,for the first time,automatically categorizing individual coniferous trees(Norway spruce)into five decay stages(live,declining,dead,loose bark,and clean)from combined Airborne Laser Scanning(ALS)point clouds and color infrared(CIR)images using three different ML methods−3D point cloud-based deep learning(KPConv),Convolutional Neural Network(CNN),and Random Forest(RF).First,CIR colorized point clouds are created by fusing the ALS point clouds and color infrared images.Then,individual tree segmentation is conducted,after which the results are further projected onto four orthogonal planes.Finally,the classification is conducted on the two datasets(3D multispectral point clouds and 2D projected images)based on the three ML algorithms.All models achieved promising results,reaching overall accuracy(OA)of up to 88.8%,88.4%and 85.9%for KPConv,CNN and RF,respectively.The experimental results reveal that color information,3D coordinates,and intensity of point clouds have significant impact on the promising classification performance.The performance of our models,therefore,shows the significance of machine/deep learning for individual tree decay stages classification and landscape-wide assessment of the dead wood amount and quality by using modern airborne remote sensing techniques.The proposed method can contribute as an important and reliable tool for monitoring biodiversity in forest ecosystems.展开更多
Long-term temperature variations inferred from high-resolution proxies provide an important context to evaluate the intensity of current warming.However,tem-perature reconstructions in humid southeastern China are sca...Long-term temperature variations inferred from high-resolution proxies provide an important context to evaluate the intensity of current warming.However,tem-perature reconstructions in humid southeastern China are scarce and particularly lack long-term data,limiting us to obtain a complete picture of regional temperature evolution.In this study,we present a well-verified reconstruction of winter-spring(January–April)minimum temperatures over southeastern China based on stable carbon isotopic(δ^(13)C)records of tree rings from Taxus wallichiana var.mairei from 1860 to 2014.This reconstruction accounted for 56.4%of the total observed variance.Cold periods occurred during the 1860s–1910s and 1960s–1970s.Although temperatures have had an upward trend since the 1920s,most of the cold extremes were in recent decades.The El Niño-Southern Oscillation(ENSO)variance acted as a key modulator of regional winter-spring minimum temperature variability.However,teleconnections between them were a nonlinear process,i.e.,a reduced or enhanced ENSO variance may result in a weakened or intensified temperature-ENSO relationship.展开更多
As one of the regions most affected by global climate warming,the Tianshan mountains has experienced several ecological crises,including retreating glaciers and water deficits.Climate warming in these mountains is con...As one of the regions most affected by global climate warming,the Tianshan mountains has experienced several ecological crises,including retreating glaciers and water deficits.Climate warming in these mountains is considered mainly to be caused by increases in minimum temperatures and winter temperatures,while the influence of maximum temperatures is unclear.In this study,a 300-year tree-ring chronology developed from the Western Tianshan Mountains was used to reconstruct the summer(June-August)maximum temperature(Tmax6-8) variations from 1718 to2017.The reconstruction explained 53.1% of the variance in the observed Tmax6-8.Over the past 300 years,the Tmax6-8reconstruction showed clear interannual and decadal variabilities.There was a significant warming trend(0.18 ℃/decade) after the 1950s,which was close to the increasing rates of the minimum and mean temperatures.The increase in maximum temperature was also present over the whole Tianshan mountains and its impact on climate warming has increased.The Tmax6-8variations in the Western Tianshan mountains were influenced by frequent volcanic eruptions combined with the influence of solar activity and the summer North Atlantic Oscillation.This study reveals that climate warming is significantly influenced by the increase in maximum temperatures and clarifies possible driving mechanisms of temperature variations in the Western Tianshan mountains which should aid climate predictions.展开更多
As one of the regions most affected by global cli-mate warming,the Tianshan mountains has experienced sev-eral ecological crises,including retreating glaciers and water deficits.Climate warming in these mountains is c...As one of the regions most affected by global cli-mate warming,the Tianshan mountains has experienced sev-eral ecological crises,including retreating glaciers and water deficits.Climate warming in these mountains is considered mainly to be caused by increases in minimum temperatures and winter temperatures,while the influence of maximum temperatures is unclear.In this study,a 300-year tree-ring chronology developed from the Western Tianshan Moun-tains was used to reconstruct the summer(June-August)maximum temperature(T_(max6-8))variations from 1718 to 2017.The reconstruction explained 53.1% of the variance in the observed T_(max6-8).Over the past 300 years,the T_(max6-8)reconstruction showed clear interannual and decadal vari-abilities.There was a significant warming trend(0.18°C/decade)after the 1950s,which was close to the increasing rates of the minimum and mean temperatures.The increase in maximum temperature was also present over the whole Tianshan mountains and its impact on climate warming has increased.The T_(max6-8) variations in the Western Tianshan mountains were influenced by frequent volcanic eruptions combined with the influence of solar activity and the sum-mer North Atlantic Oscillation.This study reveals that cli-mate warming is significantly influenced by the increase in maximum temperatures and clarifies possible driving mech-anisms of temperature variations in the Western Tianshan mountains which should aid climate predictions.展开更多
Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services...Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services is influenced by species diversity,tree health,and the distribution and the composition of trees.Traditionally,data on urban trees has been collected through field surveys and manual interpretation of remote sensing images.In this study,we evaluated the effectiveness of multispectral airborne laser scanning(ALS)data in classifying 24 common urban roadside tree species in Espoo,Finland.Tree crown structure information,intensity features,and spectral data were used for classification.Eight different machine learning algorithms were tested,with the extra trees(ET)algorithm performing the best,achieving an overall accuracy of 71.7%using multispectral LiDAR data.This result highlights that integrating structural and spectral information within a single framework can improve the classification accuracy.Future research will focus on identifying the most important features for species classification and developing algorithms with greater efficiency and accuracy.展开更多
In the era of stock development following the acceleration of urbanization,the revitalization of urban green space has assumed an increasingly significant role.Consequently,the management of urban trees has emerged as...In the era of stock development following the acceleration of urbanization,the revitalization of urban green space has assumed an increasingly significant role.Consequently,the management of urban trees has emerged as a critical focus of urban governance,contributing to the enhancement of livability in human settlements.This study offers a comprehensive analysis of the urban tree management system in Oxford,UK,identifying that its primary objective is to optimize and maintain a harmonious balance between human activities and the natural environment through the implementation of high-quality planting practices.The system emphasizes enhanced management practices and establishes a robust framework for the development of targeted policies and management regulations,utilizing i-Tree eco-efficiency assessment and real-time feedback mechanisms.China’s urban tree management is in its nascent stages,and there is an urgent need for the development of urban green space.By adopting the refined management assessment methodologies employed for urban trees in Oxford,UK,it is possible to enhance the ecological value of urban trees,which represent a significant green resource within cities,and contribute to the creation of more livable urban spaces.展开更多
Stone Pine(Pinus pinea L.)is currently the pine species with the highest commercial value with edible seeds.In this respect,this study introduces a new methodology for extracting Stone Pine trees from Digital Surface ...Stone Pine(Pinus pinea L.)is currently the pine species with the highest commercial value with edible seeds.In this respect,this study introduces a new methodology for extracting Stone Pine trees from Digital Surface Models(DSMs)generated through an Unmanned Aerial Vehicle(UAV)mission.We developed a novel enhanced probability map of local maxima that facilitates the computation of the orientation symmetry by means of new probabilistic local minima information.Four test sites are used to evaluate our automated framework within one of the most important Stone Pine forest areas in Antalya,Turkey.A Hand-held Mobile Laser Scanner(HMLS)was utilized to collect the reference point cloud dataset.Our findings confirm that the proposed methodology,which uses a single DSM as an input,secures overall pixel-based and object-based F1-scores of 88.3%and 97.7%,respectively.The overall median Euclidean distance revealed between the automatically extracted stem locations and the manually extracted ones is computed to be 36 cm(less than 4 pixels),demonstrating the effectiveness and robustness of the proposed methodology.Finally,the comparison with the state-of-the-art reveals that the outcomes of the proposed methodology outperform the results of six previous studies in this context.展开更多
Agrobacterium tumefaciens-mediated transformation has been widely adopted for plant genetic engineering and the study of gene function(Krenek et al.,2015).This method is prevalent in the genetic transformation of herb...Agrobacterium tumefaciens-mediated transformation has been widely adopted for plant genetic engineering and the study of gene function(Krenek et al.,2015).This method is prevalent in the genetic transformation of herbaceous plants,with notable applications in species such as Arabidopsis(Yin et al.,2024),soybean(Zhang et al.,2024),rice(Zhang et al.,2020),and Chinese cabbage(Li et al.,2021).However,its application in fruit trees is limited.This is primarily due to their long growth cycles and lack of rapid,efficient,and stable transgenic systems,which severely hinders foundational research involving plant genetic transformation(Mei et al.,2024).Furthermore,for subtropical fruit trees,the presence of recalcitrant seeds adds an extra layer of difficulty to genetic transformation(Umarani et al.,2015),as most methods rely on seed germination as a basis for transformation.展开更多
The problem of collision avoidance for non-cooperative targets has received significant attention from researchers in recent years.Non-cooperative targets exhibit uncertain states and unpredictable behaviors,making co...The problem of collision avoidance for non-cooperative targets has received significant attention from researchers in recent years.Non-cooperative targets exhibit uncertain states and unpredictable behaviors,making collision avoidance significantly more challenging than that for space debris.Much existing research focuses on the continuous thrust model,whereas the impulsive maneuver model is more appropriate for long-duration and long-distance avoidance missions.Additionally,it is important to minimize the impact on the original mission while avoiding noncooperative targets.On the other hand,the existing avoidance algorithms are computationally complex and time-consuming especially with the limited computing capability of the on-board computer,posing challenges for practical engineering applications.To conquer these difficulties,this paper makes the following key contributions:(A)a turn-based(sequential decision-making)limited-area impulsive collision avoidance model considering the time delay of precision orbit determination is established for the first time;(B)a novel Selection Probability Learning Adaptive Search-depth Search Tree(SPL-ASST)algorithm is proposed for non-cooperative target avoidance,which improves the decision-making efficiency by introducing an adaptive-search-depth mechanism and a neural network into the traditional Monte Carlo Tree Search(MCTS).Numerical simulations confirm the effectiveness and efficiency of the proposed method.展开更多
Precision agriculture(PA)is an agricultural management strategy based on observation,measurement and response to the variability of inter/intra-champ cultures.It includes advances in terms of data collection,analysis ...Precision agriculture(PA)is an agricultural management strategy based on observation,measurement and response to the variability of inter/intra-champ cultures.It includes advances in terms of data collection,analysis and management,as well as technological developments in terms of data storage and recovery,precise positioning,yield monitoring and remote sensing.The latter offers an unprecedented spatial,spectral and temporal resolution,but can also provide detailed information on the height of the vegetation and various observations.Today,the success of new agricultural technologies means that many agricultural tasks have become automated and that scientists have conducted more studies and research based on smart algorithms that automatically learn the decision rules from data.The use of Deep Learning(DL)and in particular the development and application of some of its algorithms called Convolutional Neural Networks(CNNs)are considered to be a particular success.In this work,we have applied and tested the performance of a network of convolutional neural network to automatically detect and map olive trees from Phantom4 drone imagery.The workflow involved the acquisition of images and the generation of ortho-mosaic with Pix4D software,as well as the use of a geographic information system.The results obtained with a training dataset of 4500 images of 24∗24 pixels are very satisfying:95%Precision,a 99%Recall and an F-score of 97%.展开更多
Currently, urban areas are the largest segment of the world’s population, and they can reach up to 80% of it in some countries. Understanding green areas is of paramount importance to also understand the population’...Currently, urban areas are the largest segment of the world’s population, and they can reach up to 80% of it in some countries. Understanding green areas is of paramount importance to also understand the population’s mental health and well-being, as well as to achieve ecological understanding and its impact on urban infrastructure. Thus, the aim of the present study is to carry out a survey on both urban afforestation structure and on its social impact on a Brazilian municipality. It also sought to understand the damages caused by these species to urban infrastructure in comparison to data collected in 2009, to assess forest coverage in this municipality and tree planting underutilized capacity. Accordingly, all the streets in this municipality’s urban area, the botanical data of each tree and its damage to the city’s infrastructure and phytosanitary conditions were surveyed (from 1 to 5). Data were compared to those from the 2009 census, and social issues were analyzed. In total, 5044 individuals belonging to 189 species were recorded. The most often found species were Lagerstroemia indica and Murraya paniculata. Out of the total number of trees, 458 trees scored at least one score “5” in one of the criteria, and this number represents 8.9% of the total of the trees. L. indica was the species accounting for the highest rates of phytosanitary and infrastructure issues. Data comparison evidenced that urban tree canopy lost 25% of its vegetation between the two measurements taken herein, but the number of species has increased. When it comes to damages, many trees started showing phytosanitary issues or damage to urban infrastructure.展开更多
Almond pruning biomass is an important agricultural residue that has been scarcely studied for the co-production of sugars and solid biofuels.In this work,the production of monosaccharides from almond prunings was opt...Almond pruning biomass is an important agricultural residue that has been scarcely studied for the co-production of sugars and solid biofuels.In this work,the production of monosaccharides from almond prunings was optimised by a two-step process scheme:pretreatment with dilute sulphuric acid(0.025 M,at 185.9-214.1℃for 0.8-9.2 min)followed by enzyme saccharification of the pretreated cellulose.The application of a response surface methodology enabled the mathematical modelling of the process,establishing pretreatment conditions to maximise both the amount of sugar in the acid prehydrolysate(23.4 kg/100 kg raw material,at 195.7℃for 3.5 min)and the enzymatic digestibility of the pretreated cellulose(45.4%,at 210.0℃for 8.0 min).The highest overall sugar yield(36.8 kg/100 kg raw material,equivalent to 64.3%of all sugars in the feedstock)was obtained with a pretreatment carried out at 197.0℃for 4.0 min.Under these conditions,moreover,the final solids showed better properties for thermochemical utilisation(22.0 MJ/kg heating value,0.87%ash content,and 72.1 mg/g moisture adsorption capacity)compared to those of the original prunings.展开更多
Rapid urbanization has caused significant changes along the urban-rural gradient,leading to a variety of landscapes that are mainly shaped by human activities.This dynamic interplay also influences the distribution an...Rapid urbanization has caused significant changes along the urban-rural gradient,leading to a variety of landscapes that are mainly shaped by human activities.This dynamic interplay also influences the distribution and characteristics of trees outside forests(TOF).Understanding the pattern of these trees will support informed decision-making in urban planning,in conservation strategies,and altogether in sustainable land management practices in the urban context.In this study,we employed a deep learning-based object detection model and high resolution satellite imagery to identify 1.3 million trees with bounding boxes within a 250 km^(2)research transect spanning the urban-rural gradient of Bengaluru,a megacity in Southern India.Additionally,we developed an allometric equation to estimate diameter at breast height(DBH)from the tree crown diameter(CD)derived from the detected bounding boxes.Our study focused on analyzing variations in tree density and tree size along this gradient.The findings revealed distinct patterns:the urban domain displayed larger tree crown diameters(mean:8.87 m)and DBH(mean:43.78 cm)but having relatively low tree density(32 trees per hectare).Furthermore,with increasing distance from the city center,tree density increased,while the mean tree crown diameter and mean tree basal area decreased,showing clear differences of tree density and size between the urban and rural domains in Bengaluru.This study offers an efficient methodology that helps generating instructive insights into the dynamics of TOF along the urban-rural gradient.This may inform urban planning and management strategies for enhancing green infrastructure and biodiversity conservation in rapidly urbanizing cities like Bengaluru.展开更多
基金Grant from LIESMARS (No.WKL(06)0302)the Basic Research Grant of CASM(No.G7721)
文摘This paper proposes a new algorithm for determining the starting points of contour lines. The new algorithm is based on the interval tree. The result improves the algorithm＇s efficiency remarkably. Further, a new strategy is designed to constrain the direction of threading and the resulting contour bears more meaningful information.
基金supported in part by the National Natural Science Foundation of China(No.31470714 and 61701105).
文摘Tree trunk instance segmentation is crucial for under-canopy unmanned aerial vehicles(UAVs)to autonomously extract standing tree stem attributes.Using cameras as sensors makes these UAVs compact and lightweight,facilitating safe and flexible navigation in dense forests.However,their limited onboard computational power makes real-time,image-based tree trunk segmentation challenging,emphasizing the urgent need for lightweight and efficient segmentation models.In this study,we present RT-Trunk,a model specifically designed for real-time tree trunk instance segmentation in complex forest environments.To ensure real-time performance,we selected SparseInst as the base framework.We incorporated ConvNeXt-T as the backbone to enhance feature extraction for tree trunks,thereby improving segmentation accuracy.We further integrate the lightweight convolutional block attention module(CBAM),enabling the model to focus on tree trunk features while suppressing irrelevant information,which leads to additional gains in segmentation accuracy.To enable RT-Trunk to operate effectively under diverse complex forest environments,we constructed a comprehensive dataset for training and testing by combining self-collected data with multiple public datasets covering different locations,seasons,weather conditions,tree species,and levels of forest clutter.Com-pared with the other tree trunk segmentation methods,the RT-Trunk method achieved an average precision of 91.4%and the fastest inference speed of 32.9 frames per second.Overall,the proposed RT-Trunk provides superior trunk segmentation performance that balances speed and accu-racy,making it a promising solution for supporting under-canopy UAVs in the autonomous extraction of standing tree stem attributes.The code for this work is available at https://github.com/NEFU CVRG/RT Trunk.
基金supported by the National Natural Science Foundation of China(Nos.42107476 and 42177421)the China Postdoctoral International Exchange Fellowship Program(No.PC2021099)+1 种基金the Science and Technology Innovation Program of Hunan Province(No.2020RC2058)the China Scholarship Council(CSC,No.202206600004,to D.Yuan).
文摘Tree growth synchrony serves as a valuable ecological indicator of forest resilience to climate stress and disturbances.However,our understanding of how increasing temperature affects tree growth synchrony during rapidly and slowly warming periods in ecosystems with varying climatic conditions remains limited.By using tree-ring data from temperate broadleaf(Fraxinus mandshurica,Phellodendron amurense,Quercus mongolica,and Juglans mandshurica)and Korean pine(Pinus koraiensis)mixed forests in northeast China,we investigated the effects of climate change,particularly warming,on the growth synchrony of five dominant temperate tree species across contrasting warm-dry and cool-wet climate conditions.Results show that temperature over water availability was the primary factor driving the growth and growth synchrony of the five species.Growth synchrony was significantly higher in warm-dry than in cool-wet areas,primarily due to more uniform climate conditions and higher climate sensitivity in the former.Rapid warming from the 1960s to the 1990s significantly enhanced tree growth synchrony in both areas,followed by a marked reversal as temperatures exceeded a certain threshold or warming slowed down,particularly in the warm-dry area.The growth synchrony variation patterns of the five species were highly consistent over time,although broadleaves exhibited higher synchrony than conifers,suggesting potential risks to forest resilience and stability under future climate change scenarios.Growing season temperatures and non-growing season temperatures and precipitation had a stronger positive effect on tree growth in the cool-wet area compared to the warm-dry area.High relative humidity hindered growth in the cool-wet area but enhanced it in the warm-dry area.Overall,our study highlights that the diversity and sensitivity of climate-growth relationships directly determine spatiotemporal growth synchrony.Temperature,along with water availability,shape long-term forest dynamics by affecting tree growth and synchrony.These results provide crucial insights for forest management practice to enhance structural diversity and resilience capacity against climate changeinduced synchrony shifts.
文摘The Darjeeling Himalayan region,characterized by its complex topography and vulnerability to multiple environmental hazards,faces significant challenges including landslides,earthquakes,flash floods,and soil loss that critically threaten ecosystem stability.Among these challenges,soil erosion emerges as a silent disaster-a gradual yet relentless process whose impacts accumulate over time,progressively degrading landscape integrity and disrupting ecological sustainability.Unlike catastrophic events with immediate visibility,soil erosion’s most devastating consequences often manifest decades later through diminished agricultural productivity,habitat fragmentation,and irreversible biodiversity loss.This study developed a scalable predictive framework employing Random Forest(RF)and Gradient Boosting Tree(GBT)machine learning models to assess and map soil erosion susceptibility across the region.A comprehensive geo-database was developed incorporating 11 erosion triggering factors:slope,elevation,rainfall,drainage density,topographic wetness index,normalized difference vegetation index,curvature,soil texture,land use,geology,and aspect.A total of 2,483 historical soil erosion locations were identified and randomly divided into two sets:70%for model building and 30%for validation purposes.The models revealed distinct spatial patterns of erosion risks,with GBT classifying 60.50%of the area as very low susceptibility,while RF identified 28.92%in this category.Notable differences emerged in high-risk zone identification,with GBT highlighting 7.42%and RF indicating 2.21%as very high erosion susceptibility areas.Both models demonstrated robust predictive capabilities,with GBT achieving 80.77%accuracy and 0.975 AUC,slightly outperforming RF’s 79.67%accuracy and 0.972 AUC.Analysis of predictor variables identified elevation,slope,rainfall and NDVI as the primary factors influencing erosion susceptibility,highlighting the complex interrelationship between geo-environmental factors and erosion processes.This research offers a strategic framework for targeted conservation and sustainable land management in the fragile Himalayan region,providing valuable insights to help policymakers implement effective soil erosion mitigation strategies and support long-term environmental sustainability.
基金funded by the Environmental Seed Arrival and Interspecific Associations in Seedling Sciences Program of the Smithsonian Institutionthe National Science Foundation (DEB-0075102,DEB-0823728,DEB-0640386,DEB-1242622,DEB-1464389)the Andrew Mellon Foundation,The Ohio State University,and Yale University
文摘Background The full lifespan of long-lived trees includes a seedling phase,during which a seed germinates and grows to a size large enough to be measured in forest inventories.Seedling populations are usually studied separately from adult trees,and the seedling lifespan,from seed to sapling,is poorly known.In the 50-ha Barro Colorado forest plot,we started intensive censuses of seeds and seedlings in 1994 in order to merge seedling and adult demography and document complete lifespans.Methods In 17 species abundant in seedling censuses,we subdivided populations into six size classes from seed to 1cm dbh,including seeds plus five seedling stages.The smallest seedling class was subdivided by age.Censuses in two consecutive years provided transition matrices describing the probability that a seedling in one stage moved to another one year later.For each species,we averaged the transition matrix across 25 censuses and used it to project the seedling lifespan,from seed until 1cm dbh or death.Results The predicted mean survival rate of seeds to 1cm dbh varied 1000-fold across species,from 2.9×10^(−6)to 4.4×10^(−3);the median was 2.0×10^(−4).The seedling lifespan,or the average time it takes a seed to grow to 1cm dbh,varied across species from 5.1 to 53.1 years,with a median of 20.3 years.In the median species,the 10%fastest-growing seeds would reach 1cm dbh in 9.0 years,and the slowest 10%in 34.6 years.Conclusions Combining seedling results with our previous study of lifespan after 1cm dbh,we estimate that the focal species have full lifespans varying from 41 years in a gap-demanding pioneer to 320 years in one shade-tolerant species.Lifetime demography can contribute precise survival rates and lifespans to forestry models.
基金financially supported by the National Natural Science Foundation of China(31901093,32220103010,32192431,31722013)National Key R&D Program of China(2020YFA0608100,2022YFF1302505)the Key Research Program of Frontier Sciences of the Chinese Academy of Sciences(ZDBS-LY-DQC019)。
文摘Discerning vulnerability differences among different aged trees to drought-driven growth decline or to mortality is critical to implement age-specific countermeasures for forest management in water-limited areas.An important species for afforestation in dry environments of northern China,Mongolian pine(Pinus sylvestris var.mongolica Litv.)has recently exhibited growth decline and dieback on many sites,particularly pronounced in old-growth plantations.However,changes in response to drought stress by this species with age as well as the underlying mechanisms are poorly understood.In this study,tree-ring data and remotely sensed vegetation data were combined to investigate variations in growth at individual tree and stand scales for young(9-13 years)and aging(35-52 years)plantations of Mongolian pine in a water-limited area of northern China.A recent decline in tree-ring width in the older plantation also had lower values in satellited-derived normalized difference vegetation indices and normalized difference water indices relative to the younger plantations.In addition,all measured growth-related metrics were strongly correlated with the self-calibrating Palmer drought severity index during the growing season in the older plantation.Sensitivity of growth to drought of the older plantation might be attributed to more severe hydraulic limitations,as reflected by their lower sapwood-and leaf-specific hydraulic conductivities.Our study presents a comprehensive view on changes of growth with age by integrating multiple methods and provides an explanation from the perspective of plant hydraulics for growth decline with age.The results indicate that old-growth Mongolian pine plantations in water-limited environments may face increased growth declines under the context of climate warming and drying.
基金supported by the National Natural Science Foundation of China[Grant No.42171361]the Research Grants Council of the Hong Kong Special Administrative Region,China[Grant No.PolyU 25211819]supported by The Hong Kong Polytechnic University,China[Grant No.1-ZVN6,1-ZECE].
文摘Understanding forest health is of great importance for the conservation of the integrity of forest ecosystems.The monitoring of forest health is,therefore,indispensable for the long-term conservation of forests and their sustainable management.In this regard,evaluating the amount and quality of dead wood is of utmost interest as they are favorable indicators of biodiversity.Apparently,remote sensing-based Machine Learning(ML)techniques have proven to be more efficient and sustainable with unprecedented accuracy in forest inventory.However,the application of these techniques is still in its infancy with respect to dead wood mapping.This study,for the first time,automatically categorizing individual coniferous trees(Norway spruce)into five decay stages(live,declining,dead,loose bark,and clean)from combined Airborne Laser Scanning(ALS)point clouds and color infrared(CIR)images using three different ML methods−3D point cloud-based deep learning(KPConv),Convolutional Neural Network(CNN),and Random Forest(RF).First,CIR colorized point clouds are created by fusing the ALS point clouds and color infrared images.Then,individual tree segmentation is conducted,after which the results are further projected onto four orthogonal planes.Finally,the classification is conducted on the two datasets(3D multispectral point clouds and 2D projected images)based on the three ML algorithms.All models achieved promising results,reaching overall accuracy(OA)of up to 88.8%,88.4%and 85.9%for KPConv,CNN and RF,respectively.The experimental results reveal that color information,3D coordinates,and intensity of point clouds have significant impact on the promising classification performance.The performance of our models,therefore,shows the significance of machine/deep learning for individual tree decay stages classification and landscape-wide assessment of the dead wood amount and quality by using modern airborne remote sensing techniques.The proposed method can contribute as an important and reliable tool for monitoring biodiversity in forest ecosystems.
基金supported by the National Science Foundation of China(42101082)the Science Foundation of Fujian Province(2023J01496).
文摘Long-term temperature variations inferred from high-resolution proxies provide an important context to evaluate the intensity of current warming.However,tem-perature reconstructions in humid southeastern China are scarce and particularly lack long-term data,limiting us to obtain a complete picture of regional temperature evolution.In this study,we present a well-verified reconstruction of winter-spring(January–April)minimum temperatures over southeastern China based on stable carbon isotopic(δ^(13)C)records of tree rings from Taxus wallichiana var.mairei from 1860 to 2014.This reconstruction accounted for 56.4%of the total observed variance.Cold periods occurred during the 1860s–1910s and 1960s–1970s.Although temperatures have had an upward trend since the 1920s,most of the cold extremes were in recent decades.The El Niño-Southern Oscillation(ENSO)variance acted as a key modulator of regional winter-spring minimum temperature variability.However,teleconnections between them were a nonlinear process,i.e.,a reduced or enhanced ENSO variance may result in a weakened or intensified temperature-ENSO relationship.
基金supported by the Second Tibetan Plateau Scientific Expedition and Research(2019QZKK0101)the China Desert Meteorological Science Research Foundation(Sqj2022012)+3 种基金the Natural Science Basic Research Program of Shaanxi Province(2023-JC-QN-0307)the National Natural Science Foundation of China(42361144712)the Chinese Academy of Sciences(XDB40010300)the State Key Laboratory of Loess and Quaternary Geology,Institute of Earth Environment,CAS(SKLLQG2022).
文摘As one of the regions most affected by global climate warming,the Tianshan mountains has experienced several ecological crises,including retreating glaciers and water deficits.Climate warming in these mountains is considered mainly to be caused by increases in minimum temperatures and winter temperatures,while the influence of maximum temperatures is unclear.In this study,a 300-year tree-ring chronology developed from the Western Tianshan Mountains was used to reconstruct the summer(June-August)maximum temperature(Tmax6-8) variations from 1718 to2017.The reconstruction explained 53.1% of the variance in the observed Tmax6-8.Over the past 300 years,the Tmax6-8reconstruction showed clear interannual and decadal variabilities.There was a significant warming trend(0.18 ℃/decade) after the 1950s,which was close to the increasing rates of the minimum and mean temperatures.The increase in maximum temperature was also present over the whole Tianshan mountains and its impact on climate warming has increased.The Tmax6-8variations in the Western Tianshan mountains were influenced by frequent volcanic eruptions combined with the influence of solar activity and the summer North Atlantic Oscillation.This study reveals that climate warming is significantly influenced by the increase in maximum temperatures and clarifies possible driving mechanisms of temperature variations in the Western Tianshan mountains which should aid climate predictions.
基金This study was supported by the Second Tibetan Plateau Scientific Expedition and Research(2019QZKK0101)the China Desert Meteorological Science Research Foundation(Sqj2022012)+3 种基金the Natural Science Basic Research Program of Shaanxi Province(2023-JC-QN-0307)the National Natural Science Foundation of China(42361144712)the Chinese Academy of Sciences(XDB40010300)the State Key Laboratory of Loess and Quaternary Geology,Institute of Earth Environment,CAS(SKLLQG2022).
文摘As one of the regions most affected by global cli-mate warming,the Tianshan mountains has experienced sev-eral ecological crises,including retreating glaciers and water deficits.Climate warming in these mountains is considered mainly to be caused by increases in minimum temperatures and winter temperatures,while the influence of maximum temperatures is unclear.In this study,a 300-year tree-ring chronology developed from the Western Tianshan Moun-tains was used to reconstruct the summer(June-August)maximum temperature(T_(max6-8))variations from 1718 to 2017.The reconstruction explained 53.1% of the variance in the observed T_(max6-8).Over the past 300 years,the T_(max6-8)reconstruction showed clear interannual and decadal vari-abilities.There was a significant warming trend(0.18°C/decade)after the 1950s,which was close to the increasing rates of the minimum and mean temperatures.The increase in maximum temperature was also present over the whole Tianshan mountains and its impact on climate warming has increased.The T_(max6-8) variations in the Western Tianshan mountains were influenced by frequent volcanic eruptions combined with the influence of solar activity and the sum-mer North Atlantic Oscillation.This study reveals that cli-mate warming is significantly influenced by the increase in maximum temperatures and clarifies possible driving mech-anisms of temperature variations in the Western Tianshan mountains which should aid climate predictions.
文摘Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services is influenced by species diversity,tree health,and the distribution and the composition of trees.Traditionally,data on urban trees has been collected through field surveys and manual interpretation of remote sensing images.In this study,we evaluated the effectiveness of multispectral airborne laser scanning(ALS)data in classifying 24 common urban roadside tree species in Espoo,Finland.Tree crown structure information,intensity features,and spectral data were used for classification.Eight different machine learning algorithms were tested,with the extra trees(ET)algorithm performing the best,achieving an overall accuracy of 71.7%using multispectral LiDAR data.This result highlights that integrating structural and spectral information within a single framework can improve the classification accuracy.Future research will focus on identifying the most important features for species classification and developing algorithms with greater efficiency and accuracy.
基金Beijing Urban Governance Research Base of North China University of Technology(2024CSZL07).
文摘In the era of stock development following the acceleration of urbanization,the revitalization of urban green space has assumed an increasingly significant role.Consequently,the management of urban trees has emerged as a critical focus of urban governance,contributing to the enhancement of livability in human settlements.This study offers a comprehensive analysis of the urban tree management system in Oxford,UK,identifying that its primary objective is to optimize and maintain a harmonious balance between human activities and the natural environment through the implementation of high-quality planting practices.The system emphasizes enhanced management practices and establishes a robust framework for the development of targeted policies and management regulations,utilizing i-Tree eco-efficiency assessment and real-time feedback mechanisms.China’s urban tree management is in its nascent stages,and there is an urgent need for the development of urban green space.By adopting the refined management assessment methodologies employed for urban trees in Oxford,UK,it is possible to enhance the ecological value of urban trees,which represent a significant green resource within cities,and contribute to the creation of more livable urban spaces.
基金supported by the Projects of Scientific Investigation(BAP)of Ankara Haci Bayram Veli University[Grant No.01/2019-32].
文摘Stone Pine(Pinus pinea L.)is currently the pine species with the highest commercial value with edible seeds.In this respect,this study introduces a new methodology for extracting Stone Pine trees from Digital Surface Models(DSMs)generated through an Unmanned Aerial Vehicle(UAV)mission.We developed a novel enhanced probability map of local maxima that facilitates the computation of the orientation symmetry by means of new probabilistic local minima information.Four test sites are used to evaluate our automated framework within one of the most important Stone Pine forest areas in Antalya,Turkey.A Hand-held Mobile Laser Scanner(HMLS)was utilized to collect the reference point cloud dataset.Our findings confirm that the proposed methodology,which uses a single DSM as an input,secures overall pixel-based and object-based F1-scores of 88.3%and 97.7%,respectively.The overall median Euclidean distance revealed between the automatically extracted stem locations and the manually extracted ones is computed to be 36 cm(less than 4 pixels),demonstrating the effectiveness and robustness of the proposed methodology.Finally,the comparison with the state-of-the-art reveals that the outcomes of the proposed methodology outperform the results of six previous studies in this context.
基金funded by the Key-Area Research and Development Program of Guangdong Province(Grant No.2022B0202070002)the Guangxi Science and Technology Major Program(Grant No.GuikeAA23023007-2)+1 种基金the Guangdong Province Modern Agricultural Industry Technology System Innovation Team Construction Project(2024CXTD19)Guangdong Basic and Applied Basic Research Foundation(Grant No.2023A1515010303)。
文摘Agrobacterium tumefaciens-mediated transformation has been widely adopted for plant genetic engineering and the study of gene function(Krenek et al.,2015).This method is prevalent in the genetic transformation of herbaceous plants,with notable applications in species such as Arabidopsis(Yin et al.,2024),soybean(Zhang et al.,2024),rice(Zhang et al.,2020),and Chinese cabbage(Li et al.,2021).However,its application in fruit trees is limited.This is primarily due to their long growth cycles and lack of rapid,efficient,and stable transgenic systems,which severely hinders foundational research involving plant genetic transformation(Mei et al.,2024).Furthermore,for subtropical fruit trees,the presence of recalcitrant seeds adds an extra layer of difficulty to genetic transformation(Umarani et al.,2015),as most methods rely on seed germination as a basis for transformation.
基金co-supported by the Foundation of Shanghai Astronautics Science and Technology Innovation,China(No.SAST2022-114)the National Natural Science Foundation of China(No.62303378),the National Natural Science Foundation of China(Nos.124B2031,12202281)the Foundation of China National Key Laboratory of Science and Technology on Test Physics&Numerical Mathematics,China(No.08-YY-2023-R11)。
文摘The problem of collision avoidance for non-cooperative targets has received significant attention from researchers in recent years.Non-cooperative targets exhibit uncertain states and unpredictable behaviors,making collision avoidance significantly more challenging than that for space debris.Much existing research focuses on the continuous thrust model,whereas the impulsive maneuver model is more appropriate for long-duration and long-distance avoidance missions.Additionally,it is important to minimize the impact on the original mission while avoiding noncooperative targets.On the other hand,the existing avoidance algorithms are computationally complex and time-consuming especially with the limited computing capability of the on-board computer,posing challenges for practical engineering applications.To conquer these difficulties,this paper makes the following key contributions:(A)a turn-based(sequential decision-making)limited-area impulsive collision avoidance model considering the time delay of precision orbit determination is established for the first time;(B)a novel Selection Probability Learning Adaptive Search-depth Search Tree(SPL-ASST)algorithm is proposed for non-cooperative target avoidance,which improves the decision-making efficiency by introducing an adaptive-search-depth mechanism and a neural network into the traditional Monte Carlo Tree Search(MCTS).Numerical simulations confirm the effectiveness and efficiency of the proposed method.
文摘Precision agriculture(PA)is an agricultural management strategy based on observation,measurement and response to the variability of inter/intra-champ cultures.It includes advances in terms of data collection,analysis and management,as well as technological developments in terms of data storage and recovery,precise positioning,yield monitoring and remote sensing.The latter offers an unprecedented spatial,spectral and temporal resolution,but can also provide detailed information on the height of the vegetation and various observations.Today,the success of new agricultural technologies means that many agricultural tasks have become automated and that scientists have conducted more studies and research based on smart algorithms that automatically learn the decision rules from data.The use of Deep Learning(DL)and in particular the development and application of some of its algorithms called Convolutional Neural Networks(CNNs)are considered to be a particular success.In this work,we have applied and tested the performance of a network of convolutional neural network to automatically detect and map olive trees from Phantom4 drone imagery.The workflow involved the acquisition of images and the generation of ortho-mosaic with Pix4D software,as well as the use of a geographic information system.The results obtained with a training dataset of 4500 images of 24∗24 pixels are very satisfying:95%Precision,a 99%Recall and an F-score of 97%.
文摘Currently, urban areas are the largest segment of the world’s population, and they can reach up to 80% of it in some countries. Understanding green areas is of paramount importance to also understand the population’s mental health and well-being, as well as to achieve ecological understanding and its impact on urban infrastructure. Thus, the aim of the present study is to carry out a survey on both urban afforestation structure and on its social impact on a Brazilian municipality. It also sought to understand the damages caused by these species to urban infrastructure in comparison to data collected in 2009, to assess forest coverage in this municipality and tree planting underutilized capacity. Accordingly, all the streets in this municipality’s urban area, the botanical data of each tree and its damage to the city’s infrastructure and phytosanitary conditions were surveyed (from 1 to 5). Data were compared to those from the 2009 census, and social issues were analyzed. In total, 5044 individuals belonging to 189 species were recorded. The most often found species were Lagerstroemia indica and Murraya paniculata. Out of the total number of trees, 458 trees scored at least one score “5” in one of the criteria, and this number represents 8.9% of the total of the trees. L. indica was the species accounting for the highest rates of phytosanitary and infrastructure issues. Data comparison evidenced that urban tree canopy lost 25% of its vegetation between the two measurements taken herein, but the number of species has increased. When it comes to damages, many trees started showing phytosanitary issues or damage to urban infrastructure.
基金supported by the Operative Program FEDER Andalucía 2014-2020(Junta de Andalucía-MINECO-FEDER)by the grant funded 2021/00591/001using the support to the research Action 1 of University of Jaén.
文摘Almond pruning biomass is an important agricultural residue that has been scarcely studied for the co-production of sugars and solid biofuels.In this work,the production of monosaccharides from almond prunings was optimised by a two-step process scheme:pretreatment with dilute sulphuric acid(0.025 M,at 185.9-214.1℃for 0.8-9.2 min)followed by enzyme saccharification of the pretreated cellulose.The application of a response surface methodology enabled the mathematical modelling of the process,establishing pretreatment conditions to maximise both the amount of sugar in the acid prehydrolysate(23.4 kg/100 kg raw material,at 195.7℃for 3.5 min)and the enzymatic digestibility of the pretreated cellulose(45.4%,at 210.0℃for 8.0 min).The highest overall sugar yield(36.8 kg/100 kg raw material,equivalent to 64.3%of all sugars in the feedstock)was obtained with a pretreatment carried out at 197.0℃for 4.0 min.Under these conditions,moreover,the final solids showed better properties for thermochemical utilisation(22.0 MJ/kg heating value,0.87%ash content,and 72.1 mg/g moisture adsorption capacity)compared to those of the original prunings.
基金financial support provided by the German Research Foundation,DFG,through grant number KL894/23-2 and NO 1444/1-2 as part of the Research Unit FOR2432/2the China Scholarship Council(CSC)that supports the first author with a Ph D scholarshipsupport provided by Indian partners at the Institute of Wood Science and Technology(IWST),Bengaluru。
文摘Rapid urbanization has caused significant changes along the urban-rural gradient,leading to a variety of landscapes that are mainly shaped by human activities.This dynamic interplay also influences the distribution and characteristics of trees outside forests(TOF).Understanding the pattern of these trees will support informed decision-making in urban planning,in conservation strategies,and altogether in sustainable land management practices in the urban context.In this study,we employed a deep learning-based object detection model and high resolution satellite imagery to identify 1.3 million trees with bounding boxes within a 250 km^(2)research transect spanning the urban-rural gradient of Bengaluru,a megacity in Southern India.Additionally,we developed an allometric equation to estimate diameter at breast height(DBH)from the tree crown diameter(CD)derived from the detected bounding boxes.Our study focused on analyzing variations in tree density and tree size along this gradient.The findings revealed distinct patterns:the urban domain displayed larger tree crown diameters(mean:8.87 m)and DBH(mean:43.78 cm)but having relatively low tree density(32 trees per hectare).Furthermore,with increasing distance from the city center,tree density increased,while the mean tree crown diameter and mean tree basal area decreased,showing clear differences of tree density and size between the urban and rural domains in Bengaluru.This study offers an efficient methodology that helps generating instructive insights into the dynamics of TOF along the urban-rural gradient.This may inform urban planning and management strategies for enhancing green infrastructure and biodiversity conservation in rapidly urbanizing cities like Bengaluru.
