Journal articles: 'Tree approach'

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V, Aromal, Gokulnath G.L, and Geena Prasad. "Tree replanter: a solar powered approach." International Journal of Engineering & Technology 7, no. 4.5 (September 22, 2018): 678. http://dx.doi.org/10.14419/ijet.v7i4.5.25056.

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Tree Transplantation is referred to as the procedure of unearthing the trees and replanting them from one location to another with the objective of re-growing the tree to a new location. This involves moving the tree with the root ball-system thereby protecting while tak-ing up the wider roots. In the proposed design, we use electromagnetic linear actuators working with solar energy instead of hydraulic or pneumatic actuators which consume fossil fuels. Here, a three-blade system is used to unearth the tree wherein the rare blades are opened to encircle the tree and the blades are positioned around the tree to ensure that the root ball is at an equal distance from all sides of the trunk. The blades are closed to remove the tree with the root ball intact. The tree with the spade is adjusted to the truck for safe transport.

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Tahernia, N. "Fuzzy-Logic Tree Approach for Seismic Hazard Analysis." International Journal of Engineering and Technology 6, no. 3 (2014): 182–85. http://dx.doi.org/10.7763/ijet.2014.v6.692.

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Johnson, James E., Scott G. Lindow, and Robert Rogers. "An Approach to Assessing Competition in Young Red Pine Plantations." Northern Journal of Applied Forestry 12, no. 3 (September 1, 1995): 101–8. http://dx.doi.org/10.1093/njaf/12.3.101.

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Abstract Several 5- and 6-yr-old red pine plantations in central Wisconsin were sampled to assess the performance of crop trees and the levels of competing vegetation. A simple tree class system was devised to classify the level of brush competition on a fixed area plot surrounding each sampled crop tree. The tree class was significantly correlated with several measured and derived competition variables, and was also strongly related with three tree performance variables: height, volume, and biomass. Tree Class 1, the lowest competition level, was associated with significantly greater tree heights, volumes, and biomasses for both age groups. Logistic regression equations were developed to derive a quantitative relationship between competition, as measured by tree class, and crop tree performance. Performance was judged to be either adequate or inadequate based on an arbitrary set of standards for both volume and biomass. From the regression equations, probabilities of successful performance were calculated. For volume index and biomass, the Class 1 trees, or the trees with the lowest levels of competition, had success probabilities ranging from 76 to 89%. For the Class 4 trees, or the trees with the highest levels of competition, success probabilities ranged from 3 to 16%. North. J. Appl. For. 12(3):101-108.

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Sohrabi, H. "Adaptive k-tree sample plot for the estimation of stem density: An empirical approach." Journal of Forest Science 64, No. 1 (January 29, 2018): 17–24. http://dx.doi.org/10.17221/111/2017-jfs.

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Available budgets for the inventory of non-commercial woodlands are small. Therefore, there has been increased interest in using distance methods, such as k-tree sampling, which are faster than fixed plot sampling. In low-density woodlands, large search areas for k nearest trees contradict any practical advantage over sampling with fixed area plots. Here, a modification of a k-tree sample plot with an empirical approach to estimating the number of trees per unit area in low-density woodlands is presented. The standard and modified k-tree sample plots have been tested in one actual and three simulated forests with different spatial patterns. The modified method was superior to other combinations of methods in terms of relative bias and relative efficiency. Considering statistical and practical aspects of sampling for tree density, the modified method is more promising than is the standard one.

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RAHMANI, MOHSEN, SATTAR HASHEMI, ALI HAMZEH, and ASHKAN SAMI. "AGENT BASED DECISION TREE LEARNING: A NOVEL APPROACH." International Journal of Software Engineering and Knowledge Engineering 19, no. 07 (November 2009): 1015–22. http://dx.doi.org/10.1142/s0218194009004477.

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Decision trees are one of the most effective and widely used induction methods that have received a great deal of attention over the past twenty years. When decision tree induction algorithms were used with uncertain rather than deterministic data, the result is a complete tree, which can classify most of the unseen samples correctly. This tree would be pruned in order to reduce its classification error and over-fitting. Recently, multi agent researchers concentrated on learning from large databases. In this paper we present a novel multi agent learning method that is able to induce a decision tree from distributed training sets. Our method is based on combination of separate decision trees each provided by one agent. Hence an agent is provided to aggregate results of the other agents and induces the final tree. Our empirical results suggest that the proposed method can provide significant benefits to distributed data classification.

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SCOVILLE, ADDISON B. "The Christmas Tree Approach." Southern Medical Journal 79, no. 8 (August 1986): 1009–10. http://dx.doi.org/10.1097/00007611-198608000-00022.

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Tria, Francesca, Emanuele Caglioti, Vittorio Loreto, and Andrea Pagnani. "A stochastic local search approach to language tree reconstruction." Diachronica 27, no. 2 (October 11, 2010): 341–58. http://dx.doi.org/10.1075/dia.27.2.09tri.

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In this paper we introduce a novel stochastic local search algorithm to reconstruct phylogenetic trees. We focus in particular on the reconstruction of language trees based on the comparison of the Swadesh lists of the recently compiled ASJP database. Starting from a generic tree configuration, our scheme stochastically explores the space of possible trees driven by the minimization of a pseudo-functional quantifying the violations of additivity of the distance matrix. As a consequence the resulting tree can be annotated with the values of the violations on each internal branch. The values of the deviations are strongly correlated with the stability of the internal edges; they are measured with a novel bootstrap procedure and displayed on the tree as an additional annotation. As a case study we considered the reconstruction of the Indo-European language tree. The results are quite encouraging, highlighting a potential new avenue to investigate the role of the deviations from additivity and check the reliability and consistency of the reconstructed trees.

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Dr. S.Vijayarani, Dr S. Vijayarani, and M. Sangeetha M.Sangeetha. "A Novel Privacy Preserving Approach for Decision Tree Learning." Indian Journal of Applied Research 4, no. 3 (October 1, 2011): 136–39. http://dx.doi.org/10.15373/2249555x/mar2014/40.

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Kumar, Sunil, Saroj Ratnoo, and Jyoti Vashishtha. "HYPER HEURISTIC EVOLUTIONARY APPROACH FOR CONSTRUCTING DECISION TREE CLASSIFIERS." Journal of Information and Communication Technology 20, Number 2 (February 21, 2021): 249–76. http://dx.doi.org/10.32890/jict2021.20.2.5.

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Decision tree models have earned a special status in predictive modeling since these are considered comprehensible for human analysis and insight. Classification and Regression Tree (CART) algorithm is one of the renowned decision tree induction algorithms to address the classification as well as regression problems. Finding optimal values for the hyper parameters of a decision tree construction algorithm is a challenging issue. While making an effective decision tree classifier with high accuracy and comprehensibility, we need to address the question of setting optimal values for its hyper parameters like the maximum size of the tree, the minimum number of instances required in a node for inducing a split, node splitting criterion and the amount of pruning. The hyper parameter setting influences the performance of the decision tree model. As researchers, we know that no single setting of hyper parameters works equally well for different datasets. A particular setting that gives an optimal decision tree for one dataset may produce a sub-optimal decision tree model for another dataset. In this paper, we present a hyper heuristic approach for tuning the hyper parameters of Recursive and Partition Trees (rpart), which is a typical implementation of CART in statistical and data analytics package R. We employ an evolutionary algorithm as hyper heuristic for tuning the hyper parameters of the decision tree classifier. The approach is named as Hyper heuristic Evolutionary Approach with Recursive and Partition Trees (HEARpart). The proposed approach is validated on 30 datasets. It is statistically proved that HEARpart performs significantly better than WEKA’s J48 algorithm in terms of error rate, F-measure, and tree size. Further, the suggested hyper heuristic algorithm constructs significantly comprehensible models as compared to WEKA’s J48, CART and other similar decision tree construction strategies. The results show that the accuracy achieved by the hyper heuristic approach is slightly less as compared to the other comparative approaches.

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HEMMATI, SAHAR, BEHROUZ GATMIRI, YU-JUN CUI, and MARC VINCENT. "SOIL–VEGETATION–ATMOSPHERE INTERACTION BY A MULTIPHYSICS APPROACH." Journal of Multiscale Modelling 02, no. 03n04 (September 2010): 163–84. http://dx.doi.org/10.1142/s1756973710000382.

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Ground settlement can damage light buildings supported by shallow foundations through cracking. The prediction and modeling of tree roots effect on soil water content and consequently the soil settlements needs a comprehensive analysis of the interactions between tree roots, soil, and water. Root water uptake by trees depends on soil conditions, climatic parameters, and vegetation type. A two-dimensional root-water-uptake model is implemented in a fully coupled thermo-hydro-mechanic finite element program, θ-STOCK. Evapotranspiration from the soil surface covered by grasses is calculated using energy balance and water balance on the surface of soil. The tree roots are modeled as sink terms which are distributed vertically for homogeneous canopy such as forests, or laterally in the case of single tree or a row of trees. The distribution of sink term depends of geometry of root zone and type of canopy. Two case studies are used for verification of implemented model by comparing the modeling results with the measured water content reduction in the zones influenced by tree roots. The soil settlements due to these water content reductions are also calculated.

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van Zelst, Sebastiaan J., and Sander J. J. Leemans. "Translating Workflow Nets to Process Trees: An Algorithmic Approach." Algorithms 13, no. 11 (November 2, 2020): 279. http://dx.doi.org/10.3390/a13110279.

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Since their introduction, process trees have been frequently used as a process modeling formalism in many process mining algorithms. A process tree is a (mathematical) tree-based model of a process, in which internal vertices represent behavioral control-flow relations and leaves represent process activities. Translation of a process tree into a sound workflow net is trivial. However, the reverse is not the case. Simultaneously, an algorithm that translates a WF-net into a process tree is of great interest, e.g., the explicit knowledge of the control-flow hierarchy in a WF-net allows one to reason on its behavior more easily. Hence, in this paper, we present such an algorithm, i.e., it detects whether a WF-net corresponds to a process tree, and, if so, constructs it. We prove that, if the algorithm finds a process tree, the language of the process tree is equal to the language of the original WF-net. The experiments conducted show that the algorithm’s corresponding implementation has a quadratic time complexity in the size of the WF-net. Furthermore, the experiments show strong evidence of process tree rediscoverability.

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Hou, Jinyi, and Ping Wang. "A Region-Tree-Based Approach for the Verification of Precipitation Forecasts." Monthly Weather Review 147, no. 4 (March 27, 2019): 1257–75. http://dx.doi.org/10.1175/mwr-d-18-0325.1.

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Abstract This paper describes a region-tree-based precipitation forecast verification approach, which is then used to assess the performance of a numerical model forecast at various scales. In this verification approach, the forecast and observation fields are first represented using region-tree structures. The verification approach consists of three procedures: calculation of spatial structural properties of the forecast and observation trees, matching the forecast tree with the observation tree, and deforming the forecast tree to resemble the observation tree. Based on these procedures, the tree-based verification approach aims at determining the spatial structural differences, displacement errors, and intensity errors between the forecast and the observation at different scales. The behavior of the proposed verification approach is investigated by applications to a standard verification dataset from the spatial forecast verification intercomparison project.

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Fan, Guangpeng, Liangliang Nan, Feixiang Chen, Yanqi Dong, Zhiming Wang, Hao Li, and Danyu Chen. "A New Quantitative Approach to Tree Attributes Estimation Based on LiDAR Point Clouds." Remote Sensing 12, no. 11 (June 1, 2020): 1779. http://dx.doi.org/10.3390/rs12111779.

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Tree-level information can be estimated based on light detection and ranging (LiDAR) point clouds. We propose to develop a quantitative structural model based on terrestrial laser scanning (TLS) point clouds to automatically and accurately estimate tree attributes and to detect real trees for the first time. This model is suitable for forest research where branches are involved in the calculation. First, the Adtree method was used to approximate the geometry of the tree stem and branches by fitting a series of cylinders. Trees were represented as a broad set of cylinders. Then, the end of the stem or all branches were closed. The tree model changed from a cylinder to a closed convex hull polyhedron, which was to reconstruct a 3D model of the tree. Finally, to extract effective tree attributes from the reconstructed 3D model, a convex hull polyhedron calculation method based on the tree model was defined. This calculation method can be used to extract wood (including tree stem and branches) volume, diameter at breast height (DBH) and tree height. To verify the accuracy of tree attributes extracted from the model, the tree models of 153 Chinese scholartrees from TLS data were reconstructed and the tree volume, DBH and tree height were extracted from the model. The experimental results show that the DBH and tree height extracted based on this model are in better consistency with the reference value based on field survey data. The bias, RMSE and R2 of DBH were 0.38 cm, 1.28 cm and 0.92, respectively. The bias, RMSE and R2 of tree height were −0.76 m, 1.21 m and 0.93, respectively. The tree volume extracted from the model is in better consistency with the reference value. The bias, root mean square error (RMSE) and determination coefficient (R2) of tree volume were −0.01236 m3, 0.03498 m3 and 0.96, respectively. This study provides a new model for nondestructive estimation of tree volume, above-ground biomass (AGB) or carbon stock based on LiDAR data.

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SINGLA, NIKITA, and DERMINDER SINGH. "A Soft Approach to Estimate Woody Volume of a Live Tree." Oriental journal of computer science and technology 10, no. 3 (July 13, 2017): 618–23. http://dx.doi.org/10.13005/ojcst/10.03.10.

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Tree volume is one of the oldest areas of interest and is a crucial task in tree management system. Estimating the woody volume of a live tree is important for economic, scientific purposes and provides a tool to researcher/grower. It provides the useful information about the commercial value of wood to the potential buyer/seller. However, manual methods are being used largely to calculate woody volume of a tree. These methods are based on different log rules, cumbersome and laborious. The present work proposed a digital image processing technique to estimate the woody volume of a live tree. The developed program successfully determines the woody volume of standing tree trunk with MATLAB image processing techniques. In this method three parameters an individual tree were extracted from digital images of the tree. Calibration factor was also calculated to make the method independent of camera distance from the tree. The method was tested on several samples of trees and compared to experimental results. The soft approach generates information about height, diameter and volume of the tree. The percentage error of height, diameter at breast height and volume of standing tree by proposed method and experimental results was found to be less than 6.65%.

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Khan, S., and P. K. Gupta. "COMPARITIVE STUDY OF TREE COUNTING ALGORITHMS IN DENSE AND SPARSE VEGETATIVE REGIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5 (November 19, 2018): 801–8. http://dx.doi.org/10.5194/isprs-archives-xlii-5-801-2018.

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Abstract. Tree counting can be a challenging and time consuming task, especially if done manually. This study proposes and compares three different approaches for automatic detection and counting of trees in different vegetative regions. First approach is to mark extended minima’s, extended maxima’s along with morphological reconstruction operations on an image for delineation and tree crown segmentation. To separate two touching crowns, a marker controlled watershed algorithm is used. For second approach, the color segmentation method for tree identification is used. Starting with the conversion of an RGB image to HSV color space then filtering, enhancing and thresholding to isolate trees from non-trees elements followed by watershed algorithm to separate touching tree crowns. Third approach involves deep learning method for classification of tree and non-tree, using approximately 2268 positive and 1172 negative samples each. Each segment of an image is then classified and sliding window algorithm is used to locate each tree crown. Experimentation shows that the first approach is well suited for classification of trees is dense vegetation, whereas the second approach is more suitable for detecting trees in sparse vegetation. Deep learning classification accuracy lies in between these two approaches and gave an accuracy of 92% on validation data. The study shows that deep learning can be used as a quick and effective tool to ascertain the count of trees from airborne optical imagery.

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Qingyao Wu, Yunming Ye, Haijun Zhang, Tommy W. S. Chow, and Shen-Shyang Ho. "ML-TREE: A Tree-Structure-Based Approach to Multilabel Learning." IEEE Transactions on Neural Networks and Learning Systems 26, no. 3 (March 2015): 430–43. http://dx.doi.org/10.1109/tnnls.2014.2315296.

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Pan, T. S., and S. S. Rao. "Fault Tree Approach for the Reliability Analysis of Gear Trains." Journal of Mechanisms, Transmissions, and Automation in Design 110, no. 3 (September 1, 1988): 348–53. http://dx.doi.org/10.1115/1.3267469.

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The application of fault tree techniques to the analysis of gear trains is considered. The techniques involve the decomposition of the system into a logic diagram or fault tree in which certain basic or primary events lead to a specified top event which signifies the total failure of the system. The fault trees are used to obtain minimal cut sets from which the modes of system failure and, hence, the reliability measure for the top event can be calculated. The method of constructing fault trees and the subsequent estimation of reliability of the system is illustrated through a nine speed compound gear train and a five speed epicyclic gear train.

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C., Dr Saravanabhavan. "A New Approach for Cardio Vascular Disease Prediction Using Decision Tree." International Journal of Psychosocial Rehabilitation 24, no. 5 (May 29, 2020): 7944–52. http://dx.doi.org/10.37200/ijpr/v24i5/pr2020796.

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Hobbs, F. Collin, Daniel J. Johnson, and Katherine D. Kearns. "A Deliberate Practice Approach to Teaching Phylogenetic Analysis." CBE—Life Sciences Education 12, no. 4 (December 2013): 676–86. http://dx.doi.org/10.1187/cbe-13-03-0046.

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One goal of postsecondary education is to assist students in developing expert-level understanding. Previous attempts to encourage expert-level understanding of phylogenetic analysis in college science classrooms have largely focused on isolated, or “one-shot,” in-class activities. Using a deliberate practice instructional approach, we designed a set of five assignments for a 300-level plant systematics course that incrementally introduces the concepts and skills used in phylogenetic analysis. In our assignments, students learned the process of constructing phylogenetic trees through a series of increasingly difficult tasks; thus, skill development served as a framework for building content knowledge. We present results from 5 yr of final exam scores, pre- and postconcept assessments, and student surveys to assess the impact of our new pedagogical materials on student performance related to constructing and interpreting phylogenetic trees. Students improved in their ability to interpret relationships within trees and improved in several aspects related to between-tree comparisons and tree construction skills. Student feedback indicated that most students believed our approach prepared them to engage in tree construction and gave them confidence in their abilities. Overall, our data confirm that instructional approaches implementing deliberate practice address student misconceptions, improve student experiences, and foster deeper understanding of difficult scientific concepts.

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Asbeck, T., M. Basile, J. Stitt, J. Bauhus, I. Storch, and K. T. Vierling. "Tree-related microhabitats are similar in mountain forests of Europe and North America and their occurrence may be explained by tree functional groups." Trees 34, no. 6 (August 4, 2020): 1453–66. http://dx.doi.org/10.1007/s00468-020-02017-3.

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Abstract Key message Drivers of the abundance and richness of tree-related microhabitats are similar in mountain forests of Europe and North America and their occurrence may be explained by tree functional groups. Abstract A common approach to support forest-dwelling species in managed forests is to preserve valuable habitat trees. To assess the quality of habitat trees, a hierarchical typology of tree-related microhabitats (TreMs) is applied in the European context for inventory standardization. The first aim of this study was to evaluate whether it is possible to use this hierarchical typology as a standard protocol regardless of location, which is important for potentially standardizing future studies of TreMs, by testing whether the typology could be applied to the western North American mountain forests of Idaho. The second aim of the study was to analyse drivers that influence TreMs in forests of the region. Thirdly, we assessed whether the occurrence of TreMs could be explained by functional groups of trees across the western mountain forests of Idaho and Central European mountain forests, using TreM inventory data previously collected in the Black Forest, Germany. Abundance and richness of TreMs per tree were analyzed as a function of tree species, live status (dead vs. live trees), diameter at breast height (DBH), and site factors (latitude and altitude). Our results show that the TreM typology could be applied with slight modifications in the forests of Idaho. The abundance and richness of TreMs per tree increased with DBH. Snags offered more TreMs per tree than live trees. We were able to group tree species from the two continents in functional groups that were related to the occurrence of certain TreMs. Tree functional groups offer an opportunity to predict the role of certain tree species for habitat provision through TreMs. Combinations of trees from different functional groups could be used to optimize provisioning of TreMs within forest stands.

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Xiao, T., and J. Zhu. "Unsupervised Sub-tree Alignment for Tree-to-Tree Translation." Journal of Artificial Intelligence Research 48 (November 22, 2013): 733–82. http://dx.doi.org/10.1613/jair.4033.

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This article presents a probabilistic sub-tree alignment model and its application to tree-to-tree machine translation. Unlike previous work, we do not resort to surface heuristics or expensive annotated data, but instead derive an unsupervised model to infer the syntactic correspondence between two languages. More importantly, the developed model is syntactically-motivated and does not rely on word alignments. As a by-product, our model outputs a sub-tree alignment matrix encoding a large number of diverse alignments between syntactic structures, from which machine translation systems can efficiently extract translation rules that are often filtered out due to the errors in 1-best alignment. Experimental results show that the proposed approach outperforms three state-of-the-art baseline approaches in both alignment accuracy and grammar quality. When applied to machine translation, our approach yields a +1.0 BLEU improvement and a -0.9 TER reduction on the NIST machine translation evaluation corpora. With tree binarization and fuzzy decoding, it even outperforms a state-of-the-art hierarchical phrase-based system.

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Homainejad, A. S. "NEW APPROACH FOR SEGMENTATION AND EXTRACTION OF SINGLE TREE FROM POINT CLOUDS DATA AND AERIAL IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 24, 2016): 1287–92. http://dx.doi.org/10.5194/isprsarchives-xli-b8-1287-2016.

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This paper addresses a new approach for reconstructing a 3D model from single trees via Airborne Laser Scanners (ALS) data and aerial images. The approach detects and extracts single tree from ALS data and aerial images. The existing approaches are able to provide bulk segmentation from a group of trees; however, some methods focused on detection and extraction of a particular tree from ALS and images. Segmentation of a single tree within a group of trees is mostly a mission impossible since the detection of boundary lines between the trees is a tedious job and basically it is not feasible. In this approach an experimental formula based on the height of the trees was developed and applied in order to define the boundary lines between the trees. As a result, each single tree was segmented and extracted and later a 3D model was created. Extracted trees from this approach have a unique identification and attribute. The output has application in various fields of science and engineering such as forestry, urban planning, and agriculture. For example in forestry, the result can be used for study in ecologically diverse, biodiversity and ecosystem.

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Homainejad, A. S. "NEW APPROACH FOR SEGMENTATION AND EXTRACTION OF SINGLE TREE FROM POINT CLOUDS DATA AND AERIAL IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 24, 2016): 1287–92. http://dx.doi.org/10.5194/isprs-archives-xli-b8-1287-2016.

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This paper addresses a new approach for reconstructing a 3D model from single trees via Airborne Laser Scanners (ALS) data and aerial images. The approach detects and extracts single tree from ALS data and aerial images. The existing approaches are able to provide bulk segmentation from a group of trees; however, some methods focused on detection and extraction of a particular tree from ALS and images. Segmentation of a single tree within a group of trees is mostly a mission impossible since the detection of boundary lines between the trees is a tedious job and basically it is not feasible. In this approach an experimental formula based on the height of the trees was developed and applied in order to define the boundary lines between the trees. As a result, each single tree was segmented and extracted and later a 3D model was created. Extracted trees from this approach have a unique identification and attribute. The output has application in various fields of science and engineering such as forestry, urban planning, and agriculture. For example in forestry, the result can be used for study in ecologically diverse, biodiversity and ecosystem.

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Ram P, Sharma, Vacek Zdeněk, Vacek Stanislav, Jansa Václav, and Kučera Miloš. "Modelling individual tree diameter growth for Norway spruce in the Czech Republic using a generalized algebraic difference approach." Journal of Forest Science 63, No. 5 (May 26, 2017): 227–38. http://dx.doi.org/10.17221/135/2016-jfs.

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Individual tree-based growth models precisely describe the growth of individual trees irrespective of stand complexity. These models are more useful than the stand-based growth models for effective management of forests. We developed an individual tree diameter growth model for Norway spruce (Picea abies /Linnaeus/ H. Karsten) using permanent research plot data collected from Krkonoše National Park in the Czech Republic. The model was tested against a part of the Czech National Forest Inventory (NFI) data that originated from the western region of the country. Among various models derived by a generalized algebraic difference approach (GADA), the GADA model derived from the Chapman-Richards function best suited to our data. Tree-specific parameters unique to each growth series, which describe tree-specific growth conditions, were estimated simultaneously with global parameters common to all growth series using the iterative nested regressions. The model described most of the variations in diameter growth for model calibration data (R2adj = 0.9901, RMSE = 0.5962), leaving no significant trends in the residuals. A test against NFI data also confirms that the model is precise enough for predictions of diameter growth for ranges of site quality, tree size, age, and growth condition. The model also possesses biologically desirable properties because it produces the curves with growth rates and asymptotes that increase with increasing site quality. The GADA model is path-invariant and therefore applicable for both forward and backward predictions, meaning that the model can precisely predict diameter growth at any past ages of the trees.

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Almeter, Andrew, Arik Tashie, Andrew Procter, Tara McAlexander, Douglas Browning, Charles Rudder, Laura Jackson, and Rochelle Araujo. "A Needs-Driven, Multi-Objective Approach to Allocate Urban Ecosystem Services from 10,000 Trees." Sustainability 10, no. 12 (November 29, 2018): 4488. http://dx.doi.org/10.3390/su10124488.

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Urban areas face challenges including vehicular emissions, stormwater runoff, and sedentary lifestyles. Communities recognize the value of trees in mitigating these challenges by absorbing pollution and enhancing walkability. However, siting trees to optimize multiple benefits requires a systems approach that may cross sectors of management and expertise. We present a spatially-explicit method to optimize tree planting in Durham, NC, a rapidly growing urban area with an aging tree stock. Using GIS data and a ranking approach, we explored where Durham could augment its current stock of willow oaks through its plans to install 10,000 mid-sized deciduous trees. Data included high-resolution landcover metrics developed by the U.S. Environmental Protection Agency (EPA), demographics from the U.S. Census, an attributed roads dataset licensed to the EPA, and sidewalk information from the City of Durham. Census block groups (CBGs) were ranked for tree planting according to single and multiple objectives including stormwater reduction, emissions buffering, walkability, and protection of vulnerable populations. Prioritizing tree planting based on single objectives led to four sets of locations with limited geographic overlap. Prioritizing tree planting based on multiple objectives tended to favor historically disadvantaged CBGs. The four-objective strategy met the largest proportion of estimated regional need. Based on this analysis, the City of Durham has implemented a seven-year plan to plant 10,000 trees in priority neighborhoods. This analysis also found that any strategy which included the protection of vulnerable populations generated more benefits than others.

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Sharma, Rahul, and Dr Manish Manoria. "Novel Approach for Frequent Pattern Algorithm for Maximizing Frequent Patterns in Effective Time." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 2 (October 30, 2012): 279–83. http://dx.doi.org/10.24297/ijct.v3i2b.2876.

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The essential aspect of mining association rules is to mine the frequent patterns. Due to native difficulty it is impossible to mine complete frequent patterns from a dense database. FP-growth algorithm has been implemented using an Array-based structure, known as the FP-tree,which is for storing compressed frequency information. Numerous experimental results have demonstrated that the algorithm performs extremely well. But in FP-growth algorithm, two traversals of FP-tree are needed for constructing the new conditional FP-tree. In this paper we present a novel Array Based Without Scanning Frequent Pattern (ABWSFP) tree technique that greatly reduces the need to traverse FP-trees, thus obtaining significantly improved performance for FP-tree based algorithms. The technique works especially well for large datasets. We then present a new algorithm which use the QFP-tree data structure in combination with the FP Tree- Experimental results show that the new algorithm outperform other algorithm in not only the speed of algorithms, but also their CPU consumption and their scalability.

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Stangeland, Torunn, John Tabuti, and Kåre A. Lye. "The framework tree species approach to conserve medicinal trees in Uganda." Agroforestry Systems 82, no. 3 (April 8, 2011): 275–84. http://dx.doi.org/10.1007/s10457-011-9396-1.

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P.Rana, D., N. J. Mistry, and M. M. Raghuwanshi. "Memory Cutback for FP-Tree Approach." International Journal of Computer Applications 89, no. 12 (March 26, 2014): 18–22. http://dx.doi.org/10.5120/15682-4485.

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Socias, R. "THE PHENOTYPIC APPROACH TO TREE NUTRITION." Acta Horticulturae, no. 448 (October 1997): 91–98. http://dx.doi.org/10.17660/actahortic.1997.448.14.

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Khasa, D. "Tree-Crop Interactions: A Physiological Approach." Tree Physiology 18, no. 1 (January 1, 1998): 70. http://dx.doi.org/10.1093/treephys/18.1.70.

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JI, PING. "A tree approach for tolerance charting." International Journal of Production Research 31, no. 5 (May 1993): 1023–33. http://dx.doi.org/10.1080/00207549308956773.

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Jin-Mao, Wei, Wang Shu-Qin, and Wang Ming-Yang. "Novel Approach to Decision-Tree Construction." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 3 (May 20, 2004): 332–35. http://dx.doi.org/10.20965/jaciii.2004.p0332.

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A new approach is presented, in which rough set theory is applied to select attributes as nodes of a decision tree. Initially, dataset is partitioned into subsets based on different condition attributes, then an attribute is chosen as a node for branching when the size of its corresponding implicit region is smaller than that of all other attributes. This approach is compared to the entropy-based method on some datasets from the UCI Machine Learning Database Repository, which instantiates the performance of the rough set approach. Statistical experiments showed that the proposed approach is feasible for decision-tree construction.

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Vicente,, J. L., A. C. Razzitte,, M. C. Cordero,, and E. E. Mola. "Thermodynamic approach to electrical tree formation." Physical Review E 57, no. 1 (January 1, 1998): R1—R4. http://dx.doi.org/10.1103/physreve.57.r1.

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Gorecki, Pawel, Oliver Eulenstein, and Jerzy Tiuryn. "Unrooted Tree Reconciliation: A Unified Approach." IEEE/ACM Transactions on Computational Biology and Bioinformatics 10, no. 2 (March 2013): 522–36. http://dx.doi.org/10.1109/tcbb.2013.22.

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GUO, Xin, Yun LI, Yun HUANG, and Qing-ping ZHOU. "Novel tree cluster and classification approach based on least closed tree." Journal of Computer Applications 30, no. 2 (March 19, 2010): 423–26. http://dx.doi.org/10.3724/sp.j.1087.2010.00423.

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Jamil, Akhtar, and Bulent Bayram. "An object-based approach for tree species extraction from digital orthophoto maps." Proceedings of the ICA 1 (May 16, 2018): 1–7. http://dx.doi.org/10.5194/ica-proc-1-56-2018.

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Tree segmentation is an active and ongoing research area in the field of photogrammetry and remote sensing. It is more challenging due to both intra-class and inter-class similarities among various tree species. In this study, we exploited various statistical features for extraction of hazelnut trees from 1&thinsp;:&thinsp;5000 scaled digital orthophoto maps. Initially, the non-vegetation areas were eliminated using traditional normalized difference vegetation index (NDVI) followed by application of mean shift segmentation for transforming the pixels into meaningful homogeneous objects. In order to eliminate false positives, morphological opening and closing was employed on candidate objects. A number of heuristics were also derived to eliminate unwanted effects such as shadow and bounding box aspect ratios, before passing them into the classification stage. Finally, a knowledge based decision tree was constructed to distinguish the hazelnut trees from rest of objects which include manmade objects and other type of vegetation. We evaluated the proposed methodology on 10 sample orthophoto maps obtained from Giresun province in Turkey. The manually digitized hazelnut tree boundaries were taken as reference data for accuracy assessment. Both manually digitized and segmented tree borders were converted into binary images and the differences were calculated. According to the obtained results, the proposed methodology obtained an overall accuracy of more than 85&thinsp;% for all sample images.

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Ai, Mingyao, Yuan Yao, Qingwu Hu, Yue Wang, and Wei Wang. "An Automatic Tree Skeleton Extraction Approach Based on Multi-View Slicing Using Terrestrial LiDAR Scans Data." Remote Sensing 12, no. 22 (November 21, 2020): 3824. http://dx.doi.org/10.3390/rs12223824.

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Effective 3D tree reconstruction based on point clouds from terrestrial Light Detection and Ranging (LiDAR) scans (TLS) has been widely recognized as a critical technology in forestry and ecology modeling. The major advantages of using TLS lie in its rapidly and automatically capturing tree information at millimeter level, providing massive high-density data. In addition, TLS 3D tree reconstruction allows for occlusions and complex structures from the derived point cloud of trees to be obtained. In this paper, an automatic tree skeleton extraction approach based on multi-view slicing is proposed to improve the TLS 3D tree reconstruction, which borrowed the idea from the medical imaging technology of X-ray computed tomography. Firstly, we extracted the precise trunk center and then cut the point cloud of the tree into slices. Next, the skeleton from each slice was generated using the kernel mean shift and principal component analysis algorithms. Accordingly, these isolated skeletons were smoothed and morphologically synthetized. Finally, the validation in point clouds of two trees acquired from multi-view TLS further demonstrated the potential of the proposed framework in efficiently dealing with TLS point cloud data.

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Světlík, Jan, Jan Krejza, and Pavel Bednář. "A Novel Approach to Evaluate the Effect of Neighboring Trees and the Orientation of Tree Social Area on Stem Radial Increment of Norway Spruce Trees." Forests 12, no. 2 (January 30, 2021): 163. http://dx.doi.org/10.3390/f12020163.

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Tree growth depends on many factors such as microsite conditions, vitality, and variations in climate and genetics. It is generally accepted that higher growth indicates both an economic benefit and better vitality of any tree. Here we use a modified approach of evaluating tree social area to study mutual tree competition based on the orientation and shape of trees social area. The investigation was performed in nine Norway spruce stands in the Czech Republic. The objective of this study performed from 2008 to 2012 was to quantify relative tree radial increments with respect to the lowest and highest competition found in specific sectors of tree social area (AS). Specific groups of trees (tree classes) were evaluated according to their classes (dominant, co-dominant and sub-dominant) and their composition status in ninety-degree sectors of AS using established classifying rules. The results showed that a spatially-available area (AA) is an inappropriate parameter for predicting tree growth, whereas AS provided robust explanatory power to predict relative radial growth. Tree size was observed as an important indicator of relative radial increments. A significantly positive correlation was found for a radial increment of sub-dominant trees with the lowest competition from western directions; whereas a negative correlation was observed when the lowest competition was observed from eastern directions. For dominant trees, there was an evident growth reaction only when more than 50% of the AS was oriented towards one of the cardinal points. Individual differences in the orientation of tree AS may be important parameters with regard to competition and its spatial variability within an area surrounding a particular tree and deserve more detailed attention in tree growth models and practice.

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BRYAN, FRANK L. "Another Decision-Tree Approach for Identification of Critical Control Points." Journal of Food Protection 59, no. 11 (November 1, 1996): 1242–47. http://dx.doi.org/10.4315/0362-028x-59.11.1242.

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Decision trees have been used as an aid to selection of critical control points as part of the development of hazard analysis critical control point (HACCP) systems. The background for those in existence is described. Another decision-tree approach that follows the logic in the IAMFES manual Procedures to Implement the Hazard Analysis Critical Control Point Approach is presented. It takes into consideration impending hazards, the effect of actions exercised at the operation in question, whether control actions should be taken at this or subsequent operations and whether the CCP will be monitored and corrections made. Further, guidelines are given for selecting an operation as a critical control point. A decision tree to aid in the evaluation of risks is presented, considering whether illness will result, the severity of the illness and the likely occurrence of this outcome, based upon epidemiologic or challenge study of related events. These decision trees provide additional tools to aid in the development of HACCP systems.

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Taubert, Franziska, Rico Fischer, Nikolai Knapp, and Andreas Huth. "Deriving Tree Size Distributions of Tropical Forests from Lidar." Remote Sensing 13, no. 1 (January 2, 2021): 131. http://dx.doi.org/10.3390/rs13010131.

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Remote sensing is an important tool to monitor forests to rapidly detect changes due to global change and other threats. Here, we present a novel methodology to infer the tree size distribution from light detection and ranging (lidar) measurements. Our approach is based on a theoretical leaf–tree matrix derived from allometric relations of trees. Using the leaf–tree matrix, we compute the tree size distribution that fit to the observed leaf area density profile via lidar. To validate our approach, we analyzed the stem diameter distribution of a tropical forest in Panama and compared lidar-derived data with data from forest inventories at different spatial scales (0.04 ha to 50 ha). Our estimates had a high accuracy at scales above 1 ha (1 ha: root mean square error (RMSE) 67.6 trees ha−1/normalized RMSE 18.8%/R² 0.76; 50 ha: 22.8 trees ha−1/6.2%/0.89). Estimates for smaller scales (1-ha to 0.04-ha) were reliably for forests with low height, dense canopy or low tree height heterogeneity. Estimates for the basal area were accurate at the 1-ha scale (RMSE 4.7 tree ha−1, bias 0.8 m² ha−1) but less accurate at smaller scales. Our methodology, further tested at additional sites, provides a useful approach to determine the tree size distribution of forests by integrating information on tree allometries.

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Moataz, Tarik, Erik-Oliver Blass, and Guevara Noubir. "Recursive Trees for Practical ORAM." Proceedings on Privacy Enhancing Technologies 2015, no. 2 (June 1, 2015): 115–34. http://dx.doi.org/10.1515/popets-2015-0010.

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AbstractWe present a new, general data structure that reduces the communication cost of recent tree-based ORAMs. Contrary to ORAM trees with constant height and path lengths, our new constructionr-ORAM allows for trees with varying shorter path length. Accessing an element in the ORAM tree results in different communication costs depending on the location of the element. The main idea behindr-ORAM is a recursive ORAM tree structure, where nodes in the tree are roots of other trees. While this approach results in a worst-case access cost (tree height) at most as any recent tree-based ORAM, we show that the average cost saving is around 35% for recent binary tree ORAMs. Besides reducing communication cost,r-ORAM also reduces storage overhead on the server by 4% to 20% depending on the ORAM’s client memory type. To prover-ORAM’s soundness, we conduct a detailed overflow analysis.r-ORAM’s recursive approach is general in that it can be applied to all recent tree ORAMs, both constant and poly-log client memory ORAMs. Finally, we implement and benchmarkr-ORAM in a practical setting to back up our theoretical claims.

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Cao, Quang V. "An integrated system for modeling tree and stand survival." Canadian Journal of Forest Research 47, no. 10 (October 2017): 1405–9. http://dx.doi.org/10.1139/cjfr-2017-0229.

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Traditionally, separate models have been used to predict the number of trees per unit area (stand-level survival) and the survival probability of an individual tree (tree-level survival) at a certain age. This study investigated the development of integrated systems in which survival models at different levels of resolution are related in a mathematical structure. Two approaches for modeling tree and stand survival were considered: deriving a stand-level survival model from a tree-level survival model (approach 1) and deriving a tree survival model from a stand survival model (approach 2). Both approaches rely on finding a tree diameter that yields a tree survival probability equal to the stand-level survival probability. The tree and stand survival models from either approach are conceptually compatible with each other but not numerically compatible. Parameters of these models can be estimated either sequentially or simultaneously. Results indicated that approach 2, with parameters estimated sequentially (first from the stand survival model and then from the derived tree survival model), performed best in predicting both tree- and stand-level survival. Although disaggregation did not help improve prediction of tree-level survival, this method can be used when numerical consistency between stand and tree survival is desired.

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Guan, H., S. Cao, Y. Yu, J. Li, N. Liu, P. Chen, and Y. Li. "STREET-SCENE TREE SEGMENTATION FROM MOBILE LASER SCANNING DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 221–25. http://dx.doi.org/10.5194/isprsarchives-xli-b3-221-2016.

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Our work addresses the problem of extracting trees from mobile laser scanning data. The work is a two step-wise strategy, including terrain point removal and tree segmentation. First, a voxel-based upward growing filtering is proposed to remove terrain points from the mobile laser scanning data. Then, a tree segmentation is presented to extract individual trees via a Euclidean distance clustering approach and Voxel-based Normalized Cut (VNCut) segmentation approach. A road section data acquired by a RIEGL VMX-450 system are selected for evaluating the proposed tree segmentation method. Qualitative analysis shows that our algorithm achieves a good performance.

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Guan, H., S. Cao, Y. Yu, J. Li, N. Liu, P. Chen, and Y. Li. "STREET-SCENE TREE SEGMENTATION FROM MOBILE LASER SCANNING DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 221–25. http://dx.doi.org/10.5194/isprs-archives-xli-b3-221-2016.

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Our work addresses the problem of extracting trees from mobile laser scanning data. The work is a two step-wise strategy, including terrain point removal and tree segmentation. First, a voxel-based upward growing filtering is proposed to remove terrain points from the mobile laser scanning data. Then, a tree segmentation is presented to extract individual trees via a Euclidean distance clustering approach and Voxel-based Normalized Cut (VNCut) segmentation approach. A road section data acquired by a RIEGL VMX-450 system are selected for evaluating the proposed tree segmentation method. Qualitative analysis shows that our algorithm achieves a good performance.

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Zhu, Zihui, Christoph Kleinn, and Nils Nölke. "Towards Tree Green Crown Volume: A Methodological Approach Using Terrestrial Laser Scanning." Remote Sensing 12, no. 11 (June 6, 2020): 1841. http://dx.doi.org/10.3390/rs12111841.

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Crown volume is a tree attribute relevant in a number of contexts, including photosynthesis and matter production, storm resistance, shadowing of lower layers, habitat for various taxa. While commonly the total crown volume is being determined, for example by wrapping a convex hull around the crown, we present here a methodological approach towards assessing the tree green crown volume (TGCVol), the crown volume with a high density of foliage, which we derive by terrestrial laser scanning in a case study of solitary urban trees. Using the RGB information, we removed the hits on stem and branches within the tree crown and used the remaining leaf hits to determine TGCVol from k-means clustering and convex hulls for the resulting green 3D clusters. We derived a tree green crown volume index (TGCVI) relating the green crown volume to the total crown volume. This TGCVI is a measure of how much a crown is “filled with green” and scale-dependent (a function of specifications of the k-means clustering). Our study is a step towards a standardized assessment of tree green crown volume. We do also address a number of remaining methodological challenges.

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Marin-Castro, Heidy M., and Victor J. Sosa Sosa. "VR-Tree: A novel tree-based approach for modeling Web Query Interfaces." Journal of Intelligent Information Systems 49, no. 3 (February 27, 2017): 367–90. http://dx.doi.org/10.1007/s10844-017-0449-4.

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Khan, Waseem R., Syaizwan Z. Zulkifli, Mohamad Roslan B. M. Kasim, Ahmad Mustapha Pazi, Roslan Mostapa, and M. Nazre. "Mangrove Productivity Estimation using Modelling Approach and Tree Parameters Assessment." Tropical Conservation Science 12 (January 2019): 194008291987213. http://dx.doi.org/10.1177/1940082919872137.

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This study used productivity models and above ground biomass to investigate productivity in different sites of MMFR. Ninety Rhizophora apiculata leaf samples were collected from different compartments (18, 31, 71, 74, 42 and 55) based on tree age and management. For biomass calculation, tree height and diameter were measured in plot of 10m x 10m in compartment 18, 31, 71, 74 and 67. The age of the trees were as follows: compartment 18 and 31 with 15-year-old, compartment 71 and 74 with 25-year-old and compartment 67 with 30-year-old mangrove trees. Compartment 42 and 55 are classified as virgin jungle reserve (VJR). Compartment 67 was not taken as a sample site due to technical reason and compartments in VJR were not considered for biomass estimation. Sixteen variables; stable isotopes (δ13C, δ15N), macronutrients (C, N, P), cations (Ca, Mg, Na, K) and trace elements (Cd, Cu, Fe, Mn, Pb, Zn) were analyzed. Productivity models and calculated biomass for investigated compartments showed similar trends. In 15-year age group; compartment 18 showed higher productivity than in 31. For the 25-year age group; compartment 74 had higher productivity than 71. No prominent increase was observed in biomass between 15-year old and 30-year old trees. Furthermore, with moderate N and δ15N loading input, compartments showed more productivity. The results conclude that MMFR is a sustainably managed mangrove forest and its productivity could be monitored using nutrient productivity models.

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Nölke, Nils. "Continuous Urban Tree Cover Mapping from Landsat Imagery in Bengaluru, India." Forests 12, no. 2 (February 13, 2021): 220. http://dx.doi.org/10.3390/f12020220.

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Percent tree cover maps derived from Landsat imagery provide a useful data source for monitoring changes in tree cover over time. Urban trees are a special group of trees outside forests (TOFs) and occur often as solitary trees, in roadside alleys and in small groups, exhibiting a wide range of crown shapes. Framed by house walls and with impervious surfaces as background and in the immediate neighborhood, they are difficult to assess from Landsat imagery with a 30 m pixel size. In fact, global maps based on Landsat partly failed to detect a considerable portion of urban trees. This study presents a neural network approach applied to the urban trees in the metropolitan area of Bengaluru, India, resulting in a new map of estimated tree cover (MAE = 13.04%); this approach has the potential to also detect smaller trees within cities. Our model was trained with ground truth data from WorldView-3 very high resolution imagery, which allows to assess tree cover per pixel from 0% to 100%. The results of this study may be used to improve the accuracy of Landsat-based time series of tree cover in urban environments.

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Ortiz Díaz, Agustín Alejandro, Isvani Inocencio Frías Blanco, Laura María Palomino Mariño, and Fabiano Baldo. "An Online Tree-Based Approach for Mining Non-Stationary High-Speed Data Streams." Revista de Informática Teórica e Aplicada 27, no. 1 (January 15, 2020): 36–47. http://dx.doi.org/10.22456/2175-2745.90822.

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This paper presents a new learning algorithm for inducing decision trees from data streams. In these domains, large amounts of data are constantly arriving over time, possibly at high speed. The proposed algorithm uses a top-down induction method for building trees, splitting leaf nodes recursively, until none of them can be expanded. The new algorithm combines two split methods in the tree induction. The first method is able to guarantee, with statistical significance, that each split chosen would be the same as that chosen using infinite examples. By doing so, it aims at ensuring that the tree induced online is close to the optimal model. However, this split method often needs too many examples to make a decision about the best split, which delays the accuracy improvement of the online predictive learning model. Therefore, the second method is used to split nodes more quickly, speeding up the tree growth. The second split method is based on the observation that larger trees are able to store more information about the training examples and to represent more complex concepts. The first split method is also used to correct splits previously suggested by the second one, when it has sufficient evidence. Finally, an additional procedure rebuilds the tree model according to the suggestions made with an adequate level of statistical significance. The proposed algorithm is empirically compared with several well-known induction algorithms for learning decision trees from data streams. In the tests it is possible to observe that the proposed algorithm is more competitive in terms of accuracy and model size using various synthetic and real world datasets.

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MATSUZAKI, KIMINORI, ZHENJIANG HU, KAZUHIKO KAKEHI, and MASATO TAKEICHI. "SYSTEMATIC DERIVATION OF TREE CONTRACTION ALGORITHMS." Parallel Processing Letters 15, no. 03 (September 2005): 321–36. http://dx.doi.org/10.1142/s0129626405002246.

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While tree contraction algorithms play an important role in efficient tree computation in parallel, it is difficult to develop such algorithms due to the strict conditions imposed on contracting operators. In this paper, we propose a systematic method of deriving efficient tree contraction algorithms from recursive functions on trees. We identify a general recursive form that can be parallelized into efficient tree contraction algorithms, and present a derivation strategy for transforming general recursive functions to the parallelizable form. We illustrate our approach by deriving a novel parallel algorithm for the maximum connected-set sum problem on arbitrary trees, the tree-version of the well-known maximum segment sum problem.

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Journal articles on the topic 'Tree approach'

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