Tree Matching Research Articles

Resilient trees for urban environments: The importance of intraspecific variation

Societal Impact StatementTrees in urban environments provide us with shade, heat mitigation, flood abatement, noise and pollution reduction, pollination, beauty, and much more. However, many of these benefits are strongly connected to tree size and vitality, with larger, healthier trees providing ecosystem services more effectively, which means that selecting the right tree for site and function is crucial in order to gain all benefits from our urban trees.SummaryTrees play a major role in the Earth's biogeochemical processes, influencing soil production, hydrological, nutrient and carbon cycles, and the global climate. They store about 50% of the world's terrestrial carbon stocks, and provide habitats for a wide range of other species, supporting at least half of the Earth's known terrestrial plants and animals. Trees are not only found in forests and other natural ecosystems, but also in urban environments. Most of the human population is concentrated in cities, towns and villages, so urban trees are critical to meet on‐going and future social, economic and environmental challenges. However, many urban tree populations are strongly challenged by a changing climate, outbreaks of pests and pathogens and an urban development with increasingly dense cities and a high proportion of impermeable surface materials. The importance of intraspecific variation needs to be better acknowledged in this context, since poor matching of trees and the local climate and growing conditions can lead to extensive loss of valuable trees. By using the right genetic plant material for the challenging urban environments, a more resilient tree population with a greater diversity and higher capacity for delivering ecosystem services can be gained. Here, we wish to discuss the need to consider intraspecific variation when planning resilient tree populations for urban environments and how seed banks and botanical garden play important roles in efforts to improve the matching of genetic plant material for future environmental challenges. Strategies to enrich urban tree diversity and increase resilience are outlined.

Performance evaluation of individual tree detection and segmentation algorithms using ALS data in Chir Pine (Pinus roxburghii) forest

The application of individual tree detection algorithms for assessing forest inventories and aiding decision-making in forestry has been a subject of research for more than two decades. Nevertheless, there is a notable research gap in the development of robust algorithms capable of automatically detecting trees of different species, ages, and varied crown sizes in dense forest environments. In this study, we conducted a comprehensive evaluation of six different individual tree detection (ITD) algorithms using airborne LiDAR data in Chir Pine (Pinus roxburghii) forests. This research represents one of the pioneering efforts in applying ITD algorithms to Chir Pine forests using ALS data. We categorized ITD routines into two groups: those reliant on local maxima as treetops and initial seeds for crown delineation, and those that do not require explicit treetop identification. To assess accuracy, we developed a special Individual Tree Matching (ITM) algorithm, enabling the matching of LiDAR-detected trees with 284 reference trees measured in the field. Our analysis involved various combinations of filtering fixed window sizes and adaptive window sizes applied to the point cloud, unsmoothed, and smoothed canopy height model (CHM). Our results highlighted the effectiveness of the 3 × 3m fixed window size method on unsmoothed CHM, achieving an overall F-score of 0.65 and a tree detection rate of 86%. Additionally, the Dalponte 2016 method proved superior for crown segmentation using identified treetops, consistently measuring mean crown radii within 0.5 m of reference field trees. Among the methods not relying on treetops, the adaptive mean shift algorithm (AMS3D) delivered strong performance, boasting an overall F-score of 0.67 and mean crown radii within 0.1 m. Our study revealed a high correlation between LiDAR-detected tree heights and field-measured tree heights across all evaluated methods. Overall, our findings underscore the potential of ITD algorithms in enhancing forest attribute measurement accuracy and facilitating climate-responsive forest management strategies.

Research on Retrieval Method of Online English Grammar Questions Based on Natural Language Processing

The rapid development of information technology is driving the advancement of natural language processing. The retrieval of grammatical problems in natural language processing is one of its specific tasks, particularly in the context of online learning. Therefore, a retrieval method based on fuzzy tree matching is proposed to tackle the issue of grammatical multiple-choice questions (MCQs) in online English, and its effectiveness is validated through experiments. The experimental results indicate that for incomplete queries, the MRR value STPK of the grammatical MCQ questions STP is increased by 7.9% compared to the proposed method. Compared to the traditional POS sorting algorithm, this algorithm demonstrates a 2.1% improvement. When the recall rate is 0.1, the accuracy rate of other methods is below 0.4, while the method proposed in the study surpasses 0.4. In the case of a comprehensive query, STPK the MRR value for t is STP increases by 29.6%. The proposed method in the research generally maintains an accuracy rate between 0.2 and 1.0. However, when other methods achieve an accuracy rate of 0.2, the proposed method’s accuracy rate drops below 0.2. Overall, the proposed method effectively enhances the retrieval accuracy of online English grammar MCQs compared to existing statistical and grammatical analysis methods. This improvement holds great significance for the actual retrieval of online English grammar multiple-choice questions.

Connectivity Maintenance through Unlabeled Spanning Tree Matching

A key function of mobile networks is the ability to dynamically reshape itself to any desired geometry. Lacking absolute position awareness, agents often rely on distance-limited inter-agent spatial measurements to maintain state awareness. Methods of formation control must therefore ensure a minimal level of persistent pairwise measurement feedback throughout transition, giving rise to the classic connectivity maintenance problem. To address this problem, we propose a method of structure-preserving assignment, matching agents to desired positions such that persistent global connectivity is naturally and automatically satisfied under smooth transition. Compared to other approaches, this complementary technique reduces reliance on aggressive or costly mid-flight formation control protocols. The technique is shown to scale and even improve with network size.

Automatic registration framework for multi-platform point cloud data in natural forests

ABSTRACT The use of light detection and ranging (LiDAR) for investigating forest parameters has gained attention in recent years. However, the occlusion of trees in natural forests makes it difficult for LiDAR on a single platform to capture complete point clouds of trees. In order to solve this problem, it is crucial to combine multi-platform LiDAR data. Because of the complexity of natural forests and the small difference between the geometric characteristics of trees, current multi-platform LiDAR data fusion remains an ongoing challenge in natural forests. In this paper, an automatic registration framework for multi-platform point cloud data in natural forests based on tree distribution pattern was proposed. It consists of five steps, namely segmenting trees, generating feature descriptors, matching trees and registering coarsely and finely. The proposed registration framework can determine the same and accurate location information of matching trees from multi-platform LiDAR data, and a large number of correct matching trees can be obtained through two rounds of a single tree matching process. The proposed framework was validated by fusing airborne laser scanner (ALS) and backpack laser scanner (BLS) data in natural forest. According to experimental results, the proposed framework has a high registration accuracy (root-mean-square error (RMSE) = 0.133 m, mean absolute error (MAE) = 0.126 m). In addition, when the single tree segmentation accuracy exceeds 0.85, the proposed framework is less affected by segmentation errors. In natural forests, the proposed framework can effectively improve the accuracy and efficiency of multi-platform LiDAR data registration.

Estimating optimal objective values for the TSP, VRP, and other combinatorial problems using randomization

AbstractApproximation of the optimal tour length in a Euclidean traveling salesman problem (TSP) has been studied by many researchers. In a previous study, we used the standard deviation in random tour lengths to approximate the optimal tour length in both Euclidean and non‐Euclidean TSPs and we obtained good estimates. In this paper, we show that the strong power‐law relationship between the standard deviation in random feasible solution values and the optimal solution value also holds for other Euclidean and near‐Euclidean combinatorial optimization problems like the minimum spanning tree (MST) and maximum weight matching (MWM) problems. We then enhance the estimation ability of the model by considering a second predictor: the mean in random feasible solution values. Experimental results show that by using the mean, standard deviation, and randomization, we can accurately predict the optimal solution values for the TSP, MST, MWM, and the capacitated vehicle routing problem (VRP).

A Novel Binary Tree and Pattern Matching Mechanism for Building Effective Question Answering System for Agriculture Domain

A Novel Binary Tree and Pattern Matching Mechanism for Building Effective Question Answering System for Agriculture Domain

SFTM: Fast matching of web pages using Similarity-based Flexible Tree Matching

Tree matching techniques have been investigated in many fields, including web data mining and extraction, as a key component to analyze the content of web pages. However, when applied to existing web pages, traditional tree matching approaches, covered by algorithms like Tree-Edit Distance (TED) or XyDiff, either fail to scale beyond a few hundred nodes or exhibit a relatively low accuracy.In this article, we therefore propose a novel algorithm, named Similarity-based Flexible Tree Matching (SFTM), which enables high accuracy tree matching on real-life web pages, with practical computation times. We approach tree matching as an optimization problem and leverage node labels and local topology similarity in order to avoid any combinatorial explosion. Our practical evaluation demonstrates that SFTM significantly improves the state of the art in terms of accuracy, while allowing computation times significantly lower than the most accurate solutions. By gaining on these two dimensions, SFTM therefore offers an affordable solution to match complex trees in practice.

Matching Complexes of Trees and Applications of the Matching Tree Algorithm

A matching complex of a simple graph G is a simplicial complex with faces given by the matchings of G. The topology of matching complexes is mysterious; there are few graphs for which the homotopy type is known. Marietti and Testa showed that matching complexes of forests are contractible or homotopy equivalent to a wedge of spheres. We study two specific families of trees. For caterpillar graphs, we give explicit formulas for the number of spheres in each dimension and for perfect binary trees we find a strict connectivity bound. We also use a tool from discrete Morse theory called the Matching Tree Algorithm to study the connectivity of honeycomb graphs, partially answering a question raised by Jonsson.

Site Quality Classification Models of Cunninghamia Lanceolata Plantations Using Rough Set and Random Forest West of Zhejiang Province, China

The site quality evaluation of plantations has consistently been the focus in matching tree species with sites. This paper studied the site quality of Chinese fir (Cunninghamia lanceolata) plantations in Lin’an District, Zhejiang Province, China. The site quality model was constructed using the algebraic difference approach (ADA) to classify the site quality grades. The rough set algorithm was used to screen out the key site factors affecting the site rank of Chinese fir plantations. Site quality classification models based on random forest were established, and the importance of key site factors was evaluated. The results are as follows. The random forest model based on the rough set algorithm had small scale and low complexity, and the training and testing accuracies of the model were 92.47% and 78.46%, respectively, which were better than the model without attribute reduction. The most important factors affecting Chinese fir growth in the study area were the slope aspect, slope grade, and canopy closure. The least important factors were the humus layer thickness, soil layer thickness, naturalness, and stand origin. The attribute reduction method proposed in this study overcame the subjectivity of traditional site factor selection, and the site quality classification model constructed improved the model accuracy and reduced the complexity of the algorithm. The methods used in this study can be extended to other tree species to provide a basis for matching tree species with sites and to improve the level of forest management in the future.

Automatic Assessment of Answers to Mathematics Stories Question Based on Tree Matching and Random Forest

Automatic Assessment of Answers to Mathematics Stories Question Based on Tree Matching and Random Forest

Study on the Influence of Wuthering Heights Characters Based on Web Analysis and Text Mining

The rapid development of network technology and the popularity of the Internet have made people rely more and more on the exchange and sharing of network information, and the demand for obtaining information about people from the Internet has gradually increased, but the massive amount of network data has made the information about people fragmented and disorganized, and the existing work on portraits has mainly focused on the extraction of people’s attributes. In this paper, we examine the artistic construction and characterization of Wuthering Heights on the basis of a tasting of the book, with the aim of presenting the “alienated personalities” hidden in the depths of the characters’ consciousness and showing the author’s unique creative art through an analysis of thematic ideas, the use of temporal elements, and the tracing of the creation of the characters’ original forms. In the aspect of character social relationship extraction, first, the method of expanding the seed dictionary by means of synonym word forest is used to build a character relationship lexicon, which avoids the inefficiency caused by manual lexicon collection; second, a character relationship extraction algorithm based on the combination of rule matching and syntactic tree is proposed, which effectively overcomes the disadvantage of low recall rate caused by rule matching, and the average F-value of this algorithm reaches 82.61% in the experiment The algorithm achieves an average F-value of 82.61% in the experiment, which is a significant advantage over other methods.

Dynamic Performance Analysis of STEP System in Internet of Vehicles Based on Queuing Theory.

The Internet of vehicles (IoV) is an important research area of the intelligent transportation systems using Internet of things theory. The complex event processing technology is a basic issue for processing the data stream in IoV. In recent years, many researchers process the temporal and spatial data flow by complex event processing technology. Spatial Temporal Event Processing (STEP) is a complex event query language focusing on the temporal and spatial data flow in Internet of vehicles. There are four processing models of the event stream processing system based on the complex event query language: finite automata model, matching tree model, directed acyclic graph model, and Petri net model. In addition, the worst-case response time of the event stream processing system is an important indicator of evaluating the performance of the system. Firstly, this paper proposed a core algorithm of the temporal and spatial event stream processing program based on STEP by Petri net model. Secondly, we proposed a novel method to estimate the worst-case response time of the event stream processing system, which is based on stochastic Petri net and queuing theory. Finally, through the simulation experiment based on queuing theory, this paper proves that the data stream processing system based on STEP has good dynamic performance in processing the spatiotemporal data stream in Internet of vehicles.

Using Airborne LiDAR to Map Red Alder in the Sappho Long-Term Ecosystem Productivity Study

A fundamental question of forestry is that of species composition: which species are present, and which are not. However, traditional forest measurements needed to map species over large areas can be both time consuming and costly. In this study, we combined airborne light detection and ranging (LiDAR) data with extensive field data from the Long-Term Ecosystem Productivity study located near Sappho, Washington, USA to increase the accuracy of our GIS data and to differentiate between red alder (Alnus rubra Bong.) and other dominant tree species. We adjusted plot and tree locations using LiDAR canopy height models (CHMs) by manually matching tree canopies on the CHMs with tree stem maps based on field data. We then used the adjusted tree locations and metrics computed from LiDAR point cloud data to create a classification model to identify and map red alder. The manual matching of field stem maps to CHMs improved tree locations, allowing us to create model training data. These data were used to create a random forest model that discriminated between red alder and conifer species with an accuracy of 96%. Our findings highlight the potential of LiDAR to improve coordinates of individual trees as well as discriminate between selected coniferous and deciduous tree species using LiDAR data collected in leaf-off conditions in Pacific Northwest ecosystems.

The Eurasian Treecreeper (Certhia familiaris) has an effective camouflage against mammalian but not avian vision in boreal forests

A well‐known example of visual camouflage in birds is the plumage coloration of the Eurasian Treecreeper Certhia familiaris, yet this species’ camouflage has never been objectively quantified. Here, we quantify Treecreeper camouflage in its boreal forest habitat, test whether Treecreepers better match tree backgrounds at nest‐site, territory or habitat spatial scales, and explore which common tree species provide the best background match. Using photographic data of the birds and forested backgrounds, we test their background match using human, ferret and avian vision modelling. We found that a Treecreeper’s wing and mantle provided the closest background matching, whereas the wing stripe and tail were more conspicuous against tree trunks. Vision modelling also suggests that Treecreeper camouflage provides a better protection against mammalian than against avian vision. The matching was not significantly different across spatial scales in local spruce forests. However, the background match was better on conifers than on deciduous trees. Our results support the long‐standing conjecture of Treecreeper camouflage but also suggest that the camouflage is an adaptation especially against mammalian predators, which are important nest predators of Treecreepers.

A Stepwise Minimum Spanning Tree Matching Method for Registering Vehicle-Borne and Backpack LiDAR Point Clouds

Vehicle-borne Laser Scanning (VLS) and Backpack Laser Scanning (BLS) are two emerging mobile mapping technologies for capturing detailed spatial information near ground in urban built environments. BLS has flexible mobility and usually provides point clouds in a local coordinate system. Therefore, a mismatch between VLS and BLS point clouds data is quite common. Fusing VLS and BLS data in different coordinate systems could provide a comprehensive survey of urban built environments. Because of the complexity of urban road environments and the difference in data acquisition methods, traditional registration approaches based on point-level correspondences are likely to fail and sometimes involve substantial manual efforts. In this paper, we propose a novel registration approach that finds the optimal transformation between the respective point clouds based on a unique tree distribution pattern defined by tree trunk centers. The proposed method consists of three key steps, i.e., trunk center extraction, stepwise minimum spanning tree (MST) matching, and transformation estimation. Stepwise MST matching is an essential step in finding the one-to-one correspondences using a topological similarity between the two LiDAR datasets. We evaluated our method with five real-world datasets collected in the Shanghai city, China. The results showed that the proposed method performed well in all five experiment sites with an average rotation error of less than 0.06° and an average translation error of less than 0.05 m. Moreover, the reported mean position deviation in the five sites are 0.112 m, 0.144 m, 0.176 m, 0.148 m, and 0.184 m, respectively. Our proposed method has a great potential for registering multiplatform LiDAR data that could provide comprehensive and essential 3D information for numerous urban applications.

A Framework for Automated Scraping of Structured Data Records From the Deep Web Using Semantic Labeling

The intent of this research is to come up with an automated web scraping system which is capable of extracting structured data records embedded in semi-structured web pages. Most of the automated extraction techniques in the literature captures repeated pattern among a set of similarly structured web pages, thereby deducing the template used for the generation of those web pages and then data records extraction is done. All of these techniques exploit computationally intensive operations such as string pattern matching or DOM tree matching and then perform manual labeling of extracted data records. The technique discussed in this paper departs from the state-of-the-art approaches by determining informative sections in the web page through repetition of informative content rather than syntactic structure. From the experiments, it is clear that the system has identified data rich region with 100% precision for web sites belonging to different domains. The experiments conducted on the real world web sites prove the effectiveness and versatility of the proposed approach.

A new approach for matching road lines using efficiency rates of similarity measures

The lack of common semantic information among corresponding geo-objects in different datasets required new matching approaches based on geometric and topological measures. In this study, a semi-automated matching approach based on the matching capabilities of geometric and topological measures was proposed. In the first stage, after the initial matching performed by a scoring system, the efficiency of each measure on the matching accuracy is evaluated manually by an operator. In the second stage, (1) the score of each measure is updated in accordance with the accuracy distributions. This means that the score of a measure is increased if it is relatively more significant than others. Finally, (2) matching process is repeated with new scores. The proposed approach was tested by matching tree-, cellular-, and hybrid-patterned road lines in municipal, private navigation, and OpenStreetMap datasets. The experimental testing shows that it has satisfactory results both in accuracy and completeness. F-measure is over 86% in hybrid-patterned Bosphorus datasets.

Subgroup causal effect identification and estimation via matching tree

Inferring causal effect from observational studies is a central topic in many scientific fields, including social science, health and medicine. The statistical methodology for estimating population average causal effect has been well established. However, the methods for identifying and estimating subpopulation causal effects are relatively less developed. Part of the challenge is that the subgroup structure is usually unknown, therefore, methods working well for population level effect need to be modified to address this. A tree method based on a matched design is proposed to identify subgroups with differential treatment effects. To remove observed confounding, propensity-score-matched pairs are first created. Then the classification and regression tree is applied to the within-pair outcome differences to identify the subgroup structure. This nonparametric approach is robust to model misspecification, which is important because it becomes much harder to specify a parametric outcome model in the presence of subgroup effects. In addition to describing assumptions under which our matching estimator is unbiased, algorithms for identifying subgroup structures are provided. Simulation results indicate that the proposed approach compares favorably, in terms of the percentage of correctly identifying true tree structure, with other competing tree-based methods, including causal trees, causal inference trees and the virtual twins approach. Finally the proposed method is implemented to examine the potential subgroup effect of the timing of Tobramycin use on chronic infection among pediatric Cystic Fibrosis patients.

Hybrid ecommerce recommendation model incorporating product taxonomy and folksonomy

In modern ecommerce platforms, product content information may have two origins: one is tree-structured taxonomy attributes, and the other is free-form folksonomy tags. This paper proposes a hybrid model to incorporate taxonomy and folksonomy information to enhance ecommerce recommendations. It first develops a tree matching algorithm to establish the overall similarity between items, where tag information is integrated for semantic analysis for taxonomy attributes. Next, it proposes a unique random walk model on a heterogeneous graph constructed by user nodes and item nodes and different types of relations — user–item preference and item–item similarity relations. The random walk model is designed to be effective to identify the nearest item nodes for a particular user node, which are seen as the best-fit items for recommendations. Empirical experiments demonstrate that the proposed model improves performance in terms of both recommendation coverage and accuracy, especially for sparse data.