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1
Sablatash, M. "Designs and architectures of filter bank trees for spectrally efficient multi-user communications: review, modifications and extensions of wavelet packet filter bank trees." Signal, Image and Video Processing 2, no. 1 (September 18, 2007): 9–37. http://dx.doi.org/10.1007/s11760-007-0033-4.
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Gopinath, R. A., and C. S. Burrus. "Factorization approach to unitary time-varying filter bank trees and wavelets." IEEE Transactions on Signal Processing 43, no. 3 (March 1995): 666–80. http://dx.doi.org/10.1109/78.370621.
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Bonnesoeur, Vivien, Thiéry Constant, Bruno Moulia, and Meriem Fournier. "Forest trees filter chronic wind-signals to acclimate to high winds." New Phytologist 210, no. 3 (January 21, 2016): 850–60. http://dx.doi.org/10.1111/nph.13836.
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Dou, Hanjie, Changyuan Zhai, Liping Chen, Xiu Wang, and Wei Zou. "Comparison of Orchard Target-Oriented Spraying Systems Using Photoelectric or Ultrasonic Sensors." Agriculture 11, no. 8 (August 8, 2021): 753. http://dx.doi.org/10.3390/agriculture11080753.
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Orchard pesticide off-target deposition and drift cause substantial soil and water pollution, and other environmental pollution. Orchard target-oriented spraying technologies have been used to reduce the deposition and drift caused by off-target spraying and control environmental pollution to within an acceptable range. Two target-oriented spraying systems based on photoelectric sensors or ultrasonic sensors were developed. Three spraying treatments of young cherry trees and adult apple trees were conducted using a commercial sprayer with a photoelectric-based target-oriented spraying system, an ultrasonic-based target-oriented spraying system or no target-oriented spraying system. A rhodamine tracer was used instead of pesticide. Filter papers were fixed in the trees and on the ground. The tracer on the filter papers was washed off to calculate the deposition distribution in the trees and on the ground. The deposition data were used to evaluate the systems and pesticide off-target deposition achieved with orchard target-oriented sprayers. The results showed that the two target-oriented spraying systems greatly reduced the ground deposition compared to that caused by off-target spraying. Compared with that from off-target spraying, the ground deposition from photoelectric-based (trunk-based) and ultrasonic-based (canopy-based) target-oriented spraying decreased by 50.63% and 38.74%, respectively, for the young fruit trees and by 21.66% and 29.87%, respectively, for the adult fruit trees. The trunk-based target-oriented detection method can be considered more suitable for young trees, whereas the canopy-based target-oriented detection method can be considered more suitable for adult trees. The maximum ground deposition occurred 1.5 m from the tree trunk at the back of the tree canopy and was caused by the high airflow at the air outlet of the sprayer. A suitable air speed and air volume at the air outlet of the sprayer can reduce pesticide deposition on the ground.
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Moreau, Pedro dos Santos, and Marco Aurélio Zezzi Arruda. "Direct analysis of tree rings using laser ablation-ICP-MS and quantitative evaluation of Zn and Cu using filter paper as a solid support for calibration." Journal of Analytical Atomic Spectrometry 37, no. 4 (2022): 795–804. http://dx.doi.org/10.1039/d1ja00414j.
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This work aims to study and apply filter paper as calibration solid supports, in order to avoid matrix and fractionation effects, for the quantification of two essential elements of trees, Cu and Zn, in tree-ring samples, using LA-ICP-MS.
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KIM, YOUNG-TAE, and TAEWHAN KIM. "AN ACCURATE EXPLORATION OF TIMING AND AREA TRADE-OFFS IN ARITHMETIC OPTIMIZATION USING CARRY-SAVE-ADDERS." Journal of Circuits, Systems and Computers 10, no. 05n06 (October 2000): 279–92. http://dx.doi.org/10.1142/s0218126600000196.
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Carry-save adder (CSA) is one of the most widely used implementation units for arithmetic circuits. However, the existing approaches to the CSA transformation have an inherent limitation in the scope of CSA application, i.e., transforming each of operation trees separately without any interaction among them, which results in a locally optimized CSA circuit. To overcome the limitation, we introduce a new concept called tree-boundary optimization techniques, based on which we propose a practically efficient algorithm for exploring timing and area trade-offs in optimizing arithmetic circuits using CSAs. The proposed algorithm is applicable to any arithmetic circuits with multiple operation trees, which appear in most filter designs. From experimentations on a number of digital filter designs, we confirm that the proposed algorithm reduces the circuit timing by 4%–40% without any area increase compared to those produced by the conventional CSA transformations.
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Wang, Achuan, Xinnian Yang, and Dabo Xin. "The Tracking and Frequency Measurement of the Sway of Leafless Deciduous Trees by Adaptive Tracking Window Based on MOSSE." Forests 13, no. 1 (January 7, 2022): 81. http://dx.doi.org/10.3390/f13010081.
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The tree sway frequency is an important part of the dynamic properties of trees. In order to obtain trees sway frequency in wind, a method of tracking and measuring the sway frequency of leafless deciduous trees by adaptive tracking window based on MOSSE was proposed. Firstly, an adaptive tracking window is constructed for the observed target. Secondly, the tracking method based on Minimum Output Sum Of Squared Error Filter (MOSSE) is used to track tree sway. Thirdly, Fast Fourier transform was used to analyze the horizontal sway velocity of the target area on the trees, and the sway frequency was determined. Finally, comparing the power spectral densities (PSDs) of the x axis acceleration measured by the accelerometer and PSDs of the x axis velocity measured by the video, the fundamental sway frequency measured by the accelerometer is equal to the fundamental sway frequency measured by video. The results show that the video-based method can be used successfully for measuring the sway frequency of leafless deciduous trees.
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Hurteau, Matthew, Harold Zald, and Malcolm North. "Species-specific response to climate reconstruction in upper-elevation mixed-conifer forests of the western Sierra Nevada, California." Canadian Journal of Forest Research 37, no. 9 (September 2007): 1681–91. http://dx.doi.org/10.1139/x07-028.
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Dendrochronology climate reconstruction studies often sample dominant, open-grown trees to reduce competition effects and isolate annual climate influences on radial increment growth. However, there has been no examination of how species respond as stand densities increase or which species in mixed-conifer forests provide a better record of past climate. We sampled 579 trees representing five upper montane mixed-conifer species at the Teakettle Experimental Forest in California’s southern Sierra Nevada to determine species-specific responses to annual climatic fluctuations. Using the Kalman filter, we examined the affect of local stand density on growth response and whether the growth–climate relationship improved with a time lag. The Kalman filter iteratively calculates error for predicted versus actual radial growth and accounts for this variation in the corrector equation. Under current high-density conditions, shade-tolerant white fir ( Abies concolor (Gord. & Glend.) Lindl.) provided the best model for climate reconstruction. Shade-intolerant Jeffrey pine ( Pinus jeffreyi Grev. & Balf.) had a lagged response to annual climatic fluctuations, possibly because its roots may tap water reserves in granitic bedrock fissures. Open-grown trees provided more accurate records of climate. Changes in forest density in this forest may have resulted in changes in species-specific response to annual climatic fluctuations.
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Visser, Hans. "Analysis of Tree Ring Data Using the Kalman Filter Technique." IAWA Journal 7, no. 4 (1986): 289–97. http://dx.doi.org/10.1163/22941932-90001000.
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A statistical mcthod is presented to filter the influence of weather variations out of a tree ring chronology. The Kaiman filter technique is introduced to estimate a multiple regression model with stochastically fluctuating weather parameters. It cnables the detection of any change in response of trees to weather. The method is in two ways an improvement upon the frequentIy used method of response functions: I) it is not necessary to assume constant model parameters, and 2) the estimation process is not based on the fitting but on the forecast performance of the model.
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ABELLÁN, JOAQUÍN, and ANDRÉS R. MASEGOSA. "A FILTER-WRAPPER METHOD TO SELECT VARIABLES FOR THE NAIVE BAYES CLASSIFIER BASED ON CREDAL DECISION TREES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 17, no. 06 (December 2009): 833–54. http://dx.doi.org/10.1142/s0218488509006297.
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Variable selection methods play an important role in the field of attribute mining. In the last few years, several feature selection methods have appeared showing that the use of a set of decision trees learnt from a database can be a useful tool for selecting relevant and informative variables regarding a main class variable. With the Naive Bayes classifier as reference, in this article, our aims are twofold: (1) to study what split criterion has better performance when a complete decision tree is used to select variables; and (2) to present a filter-wrapper selection method using decision trees built with the best possible split criterion obtained in (1).
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Pan, Yude, and Dudley J. Raynal. "Predicting growth of plantation conifers in the Adirondack Mountains in response to climate change." Canadian Journal of Forest Research 25, no. 1 (January 1, 1995): 48–56. http://dx.doi.org/10.1139/x95-006.
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Three conifer species grown in plantations in the southeastern Adirondack Mountains of New York were chosen to model tree growth. Annual growth of trees was decomposed into several components that reflect various intrinsic or extrinsic factors. Growth signals indicative of climatic effects were used to construct growth-climate models using both multivariate regression analysis and Kalman filter methods. Two growth models were used to simulate tree growth response to future climate change projected by GCMs. The consistent results of both models indicate that different conifer species have individualistic growth responses to future climatic change. The response behaviors of trees are affected greatly by local stand conditions and species tolerance to drought.
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Li, Zhiyuan, Jian Wang, Zhenyu Zhang, Fengxiang Jin, Juntao Yang, Wenxiao Sun, and Yi Cao. "A Method Based on Improved iForest for Trunk Extraction and Denoising of Individual Street Trees." Remote Sensing 15, no. 1 (December 25, 2022): 115. http://dx.doi.org/10.3390/rs15010115.
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Currently, the street tree resource survey using Mobile laser scanning (MLS) represents a hot spot around the world. Refined trunk extraction is an essential step for 3D reconstruction of street trees. However, due to scanning errors and the effects of occlusion by various types of features in the urban environment, street tree point cloud data processing has the problem of excessive noise. For the noise points that are difficult to remove using statistical methods in close proximity to the tree trunk, we propose an adaptive trunk extraction and denoising method for street trees based on an improved iForest (Isolation Forest) algorithm. Firstly, to extract the individual tree trunk points, the trunk and the crown are distinguished from the individual tree point cloud through point cloud slicing. Next, the iForest algorithm is improved by conducting automatic calculation of the contamination and further used to denoise the tree trunk point cloud. Finally, the method is validated with five datasets of different scenes. The results indicate that our method is robust and effective in extracting and denoising tree trunks. Compared with the traditional Statistical Outlier Removal (SOR) filter and Radius filter denoising methods, the denoising accuracy of the proposed method can be improved by approximately 30% for noise points close to tree trunks. Compared to iForest, the proposed method automatically calculates the contamination, improving the automation of the algorithm. Our method can provide more precise trunk point clouds for 3D reconstruction of street trees.
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Wang, Walsh, and Koirala. "Mango Fruit Load Estimation Using a Video Based MangoYOLO—Kalman Filter—Hungarian Algorithm Method." Sensors 19, no. 12 (June 18, 2019): 2742. http://dx.doi.org/10.3390/s19122742.
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: Pre-harvest fruit yield estimation is useful to guide harvesting and marketing resourcing, but machine vision estimates based on a single view from each side of the tree (“dual-view”) underestimates the fruit yield as fruit can be hidden from view. A method is proposed involving deep learning, Kalman filter, and Hungarian algorithm for on-tree mango fruit detection, tracking, and counting from 10 frame-per-second videos captured of trees from a platform moving along the inter row at 5 km/h. The deep learning based mango fruit detection algorithm, MangoYOLO, was used to detect fruit in each frame. The Hungarian algorithm was used to correlate fruit between neighbouring frames, with the improvement of enabling multiple-to-one assignment. The Kalman filter was used to predict the position of fruit in following frames, to avoid multiple counts of a single fruit that is obscured or otherwise not detected with a frame series. A “borrow” concept was added to the Kalman filter to predict fruit position when its precise prediction model was absent, by borrowing the horizontal and vertical speed from neighbouring fruit. By comparison with human count for a video with 110 frames and 192 (human count) fruit, the method produced 9.9% double counts and 7.3% missing count errors, resulting in around 2.6% over count. In another test, a video (of 1162 frames, with 42 images centred on the tree trunk) was acquired of both sides of a row of 21 trees, for which the harvest fruit count was 3286 (i.e., average of 156 fruit/tree). The trees had thick canopies, such that the proportion of fruit hidden from view from any given perspective was high. The proposed method recorded 2050 fruit (62% of harvest) with a bias corrected Root Mean Square Error (RMSE) = 18.0 fruit/tree while the dual-view image method (also using MangoYOLO) recorded 1322 fruit (40%) with a bias corrected RMSE = 21.7 fruit/tree. The video tracking system is recommended over the dual-view imaging system for mango orchard fruit count.
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Panda, L. R. Lakshmikanta, R. K. Aggarwal, and D. R. Bhardwaj. "A review on Air Pollution Tolerance Index (APTI) and Anticipated Performance Index (API)." Current World Environment 13, no. 1 (April 20, 2018): 55–65. http://dx.doi.org/10.12944/cwe.13.1.06.
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Now a day’s air pollution is one of the serious problems around the world. Plants can filter the air via-aerial elements particularly through twigs, stems, leaves, etc. Afforestation program is the best way to control the air pollution. Air pollution tolerance index (APTI) is an intrinsic quality of trees to control pollution problems, which is currently of major concern of urban localities. The trees having higher tolerance index rate are tolerant towards air pollution and can be used as a source to control air pollution, where as the trees having less tolerance index can be used as an indicator to know the rate of air pollution. By combining biochemical and aggregate factors the Anticipated Performance Index is prepared, which is also helpful in green belt development. The present review is based on the assessment of APTI and API potential of different plants for mitigating air pollution
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Blelloch, Guy, Daniel Ferizovic, and Yihan Sun. "Joinable Parallel Balanced Binary Trees." ACM Transactions on Parallel Computing 9, no. 2 (June 30, 2022): 1–41. http://dx.doi.org/10.1145/3512769.
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In this article, we show how a single function, join , can be used to implement parallel balanced binary search trees ( BSTs ) simply and efficiently. Based on join , our approach applies to multiple balanced tree data structures, and a variety of functions for ordered sets and maps. We describe our technique as an algorithmic framework called join-based algorithms . We show that the join function fully captures what is needed for rebalancing trees for a variety of tree algorithms, as long as the balancing scheme satisfies certain properties, which we refer to as joinable trees. We discuss four balancing schemes that are joinable: AVL trees, red-black trees, weight-balanced trees, and treaps. We present a variety of tree algorithms that apply to joinable trees, including insert , delete , union , intersection , difference , split , range , filter , and so on, most of them also parallel. These algorithms are generic across balancing schemes. Many algorithms are optimal in the comparison model, and we provide a general proof to show the efficiency in work for joinable trees. The algorithms are highly parallel, all with polylogarithmic span (parallel dependence). Specifically, the set-set operations union , intersection , and difference have work \( O(m\log (\frac{n}{m}+1)) \) and polylogarithmic span for input set sizes \( n \) and \( m\le n \) . We implemented and tested our algorithms on the four balancing schemes. In general, all four schemes have quite similar performance, but the weight-balanced tree slightly outperforms the others. They have the same speedup characteristics, getting around 73 \( \times \) speedup on 72 cores (144 hyperthreads). Experimental results also show that our implementation outperforms existing parallel implementations, and our sequential version achieves close or much better performance than the sequential merging algorithm in C++ Standard Template Library (STL) on various input sizes.
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Kubišta, Jaroslav, and Peter Surový. "Individual Tree Identification in ULS Point Clouds Using a Crown Width Mixed-Effects Model Based on NFI Data." Remote Sensing 14, no. 4 (February 14, 2022): 926. http://dx.doi.org/10.3390/rs14040926.
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A linear mixed-effects model was used to relate crown width to height using an inventory plot as a random effect for trees in Czechia based on data from the National Forest Inventory (NFI). This model was used to estimate window size for a local maximum filter procedure (LMF) to detect individual tree tops in unmanned aerial laser scanning (ULS) point clouds of mixed species forest stands with diverse structures. Random model parameters were estimated for the study site based on several sample trees. Models calibrated with five or more samples achieved significantly better results (mean percentage error; MPE −0.17 for 5 samples) compared to when a fixed-effects model (MPE −0.62) was used. Lower performance was observed in dense stands with trees that were between 5 and 10 m in height. It was concluded that locally calibrated models predicting crown widths from tree heights might serve as a universal point of departure when searching for an optimal window size setting in LMF procedures.
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Lyu, Jianhua, Yiran Ren, Zeeshan Abbas, and Baili Zhang. "Reliable Route Selection for Wireless Sensor Networks with Connection Failure Uncertainties." Sensors 21, no. 21 (October 31, 2021): 7254. http://dx.doi.org/10.3390/s21217254.
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For wireless sensor networks (WSN) with connection failure uncertainties, traditional minimum spanning trees are no longer a feasible option for selecting routes. Reliability should come first before cost since no one wants a network that cannot work most of the time. First, reliable route selection for WSNs with connection failure uncertainties is formulated by considering the top-k most reliable spanning trees (RST) from graphs with structural uncertainties. The reliable spanning trees are defined as a set of spanning trees with top reliabilities and limited tree weights based on the possible world model. Second, two tree-filtering algorithms are proposed: the k minimum spanning tree (KMST) based tree-filtering algorithm and the depth-first search (DFS) based tree-filtering algorithm. Tree-filtering strategy filters the candidate RSTs generated by tree enumeration with explicit weight thresholds and implicit reliability thresholds. Third, an innovative edge-filtering method is presented in which edge combinations that act as upper bounds for RST reliabilities are utilized to filter the RST candidates and to prune search spaces. Optimization strategies are also proposed for improving pruning capabilities further and for enhancing computations. Extensive experiments are conducted to show the effectiveness and efficiency of the proposed algorithms.
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Sariyar, M., and A. Borg. "Deterministic Linkage as a Preceding Filter for Other Record Linkage Methods." International Journal of Information Technology & Decision Making 14, no. 03 (May 2015): 521–33. http://dx.doi.org/10.1142/s0219622015500108.
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Deterministic record linkage (RL) is frequently regarded as a rival to more sophisticated strategies like probabilistic RL. We investigate the effect of combining deterministic linkage with other linkage techniques. For this task, we use a simple deterministic linkage strategy as a preceding filter: a data pair is classified as ‘match' if all values of attributes considered agree exactly, otherwise as ‘nonmatch'. This strategy is separately combined with two probabilistic RL methods based on the Fellegi–Sunter model and with two classification tree methods (CART and Bagging). An empirical comparison was conducted on two real data sets. We used four different partitions into training data and test data to increase the validity of the results. In almost all cases, application of deterministic linkage as a preceding filter leads to better results compared to the omission of such a pre-filter, and overall classification trees exhibited best results. On all data sets, probabilistic RL only profited from deterministic linkage when the underlying probabilities were estimated before applying deterministic linkage. When using a pre-filter for subtracting definite cases, the underlying population of data pairs changes. It is crucial to take this into account for model-based probabilistic RL.
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Palma, Stella S. S. A., Mariana N. dos Reis, and Raquel Gonçalves. "Tomographic Images Generated from Measurements in Standing Trees Using Ultrasound and Postprocessed Images: Methodological Proposals for Cutting Velocity, Interpolation Algorithm and Confusion Matrix Metrics Focusing on Image Quality." Forests 13, no. 11 (November 17, 2022): 1935. http://dx.doi.org/10.3390/f13111935.
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Tomographic images generated by wave propagation are important in tree inspections and can be performed in an economically more accessible way using conventional equipment and postprocessed images, but improvements are necessary to increase image quality, giving greater reliability to the results. The objective of this paper was to present a methodological proposal for the definition of parameters related to image quality produced with tomography using ultrasound equipment and postprocessed images. As a case study, the methodology was applied to five trees of the species Cenostigma pluviosum DC. Gagnon & GP Lewis (Sibipiruna). A total of 1512 tomographic images were constructed from five trees to evaluate the cutting velocity, the interpolation algorithms and the filter application. The photographs of the discs were used to evaluate the theoretical wave propagation routes in the measurement grid and to compare the tomographic images using confusion matrix metrics. The application of the methodology proposed in this species allowed us to conclude that the most appropriate velocity range for cavities inference was up to 40% Vmax and for cavities supplemented with biodeterioration up to 45% Vmax; the best interpolator was the Ellipses method with compensation, with the use of a filter; and the accuracy was the most appropriate metric for evaluating image quality.
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Pandi, Gayatri S., Saurabh Shah, and Kalpesh H. Wandra. "Enhancing the operations for integrity check on virtual instance forensic logs using cuckoo filter trees." International Journal of Cloud Computing 10, no. 4 (2021): 299. http://dx.doi.org/10.1504/ijcc.2021.119193.
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Wandra, K. H., Saurabh Shah, and Gayatri S P. "Enhancing the operations for Integrity check on Virtual Instance Forensic logs using Cuckoo Filter Trees." International Journal of Cloud Computing 10, no. 4 (2021): 1. http://dx.doi.org/10.1504/ijcc.2021.10040378.
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Pan, Yu, and Pramod Kumar Meher. "Bit-Level Optimization of Adder-Trees for Multiple Constant Multiplications for Efficient FIR Filter Implementation." IEEE Transactions on Circuits and Systems I: Regular Papers 61, no. 2 (February 2014): 455–62. http://dx.doi.org/10.1109/tcsi.2013.2278331.
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Xiao, Xiayang, Jian Wen, Zhongliang Xiao, and Weilin Li. "Detecting and Measuring Internal Anomalies in Tree Trunks Using Radar Data for Layer Identification." Journal of Sensors 2018 (October 29, 2018): 1–11. http://dx.doi.org/10.1155/2018/1430381.
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Radar detection has proven to be an effective, nondestructive test for the determination of the quality of wood-based materials, especially in the wooden structures of ancient buildings and trees. However, the results are usually inaccurate, and it is difficult to interpret internal anomalies due to the moisture content of wood, individual differences, and other factors. In this paper, a new measurement method is proposed based on the use of ground-penetrating radar (GPR) for abnormality localization and imaging. Firstly, the time delay of the reflected signal in the inner trees is analyzed with matched filter and Hilbert detections. Secondly, the two approaches are compared with the use of a forward model, and the Hilbert algorithm is found to be more accurate. Thirdly, a laser scanner is used to collect contour data and determine the location and characteristics of internal tree anomalies. Lastly, the proposed method is tested on ancient willows at the Summer Palace. The results show that the error in the depth and area estimates of the anomalies was within 10% and 5%, respectively. Consequently, the GPR method for locating the anomalies in trees is feasible, and a laser scanner combined with contour data can present the size of the abnormal regions within the trees.
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Inelova, Zarina Arkenzhanovna, Altynbek Yerkenovich Yermekov, and Didar Yedilkhan. "Usage and features of cultivation of sphagnum moss in a biotechnological system for natural filtration, purification of air in urban conditions." Bulletin of the Karaganda University. “Biology, medicine, geography Series” 107, no. 3 (September 30, 2022): 67–77. http://dx.doi.org/10.31489/2022bmg3/67-77.
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This article presents the results of a study of a literature review on the use of mosses as natural filters for air purification in polluted locations; provides information on the features of the cultivation of sphagnum moss by vertical and horizontal methods in the future design of a biotechnological system; describes the necessary conditions to achieve the maximum effect of air purification using this type of plant culture. The study results will be used in the development of unique equipment, namely a pilot prototype of a biotechnological system — a filter using sphagnum moss as a natural filter for air purification in a certain area. This development involves the use of automated control systems to create artificial conditions for plant culture — moss, which, for natural reasons, cannot exist in urban environments. In the long term, a biotechnological filter will effectively clean the air, cool the environment and reduce the noise level in those locations where it is impossible to plant many trees due to large buildings. Currently, groups of scientists around the world are conducting research and experimental development of various solutions, including technologies that can significantly improve the urban environment using natural filters. Using mosses as a natural filter will absorb fine dust, purify the air, and create a favorable space.
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Kushwaha, S. K. P., K. R. Dayal, A. Singh, and K. Jain. "BUILDING FACADE AND ROOFTOP SEGMENTATION BY NORMAL ESTIMATION FROM UAV DERIVED RGB POINT CLOUD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W17 (November 29, 2019): 173–77. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w17-173-2019.
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Abstract. Point cloud segmentation is a significant process to organise an unstructured point cloud. In this study, RGB point cloud was generated with the help of images acquired from an Unmanned Aerial Vehicle (UAV). A dense urban area was considered with varying planar features in the built-up environment along with buildings with different floors. Initially, using Cloth Simulation Filter (CSF) filter, the ground and the non-ground features in the Point Cloud Data (PCD) were segmented, with non-ground features comprising trees and buildings and ground features comprising roads, ground vegetation, and open land. Subsequently, using CANUPO classifier the trees and building points were classified. Noise filtering removed the points which have less density in clusters. Point cloud normals were generated for the building points. For segmentation building elements, normal vector components in different directions (X component, Y component and Z component) were used to segment out the facade, and the roof points of the buildings as the surface normals corresponding to the roof will have a higher contribution in the z component of the normal vector. The validation of the segmentation is done by comparing the results with manually identified roof points and façade points in the point cloud. Overall accuracies obtained for building roof and building facade segmentation are 90.86 % and 84.83 % respectively.
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Ergun, Halime. "Segmentation of rays in wood microscopy images using the U-net model." BioResources 16, no. 1 (December 7, 2020): 721–28. http://dx.doi.org/10.15376/biores.16.1.721-728.
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Rays are an important anatomical feature in tree species identification. They are found in certain proportions in trees, which vary for each tree. In this study, the U-Net model is adopted for the first time to detect wood rays. A dataset is created with images taken from the wood database. The resolution of microscopic wood images in tangential section is 640×400. The input image for training is divided into 32×32 image blocks. Each pixel in the dataset is labeled as belonging to the ray or the background. Then, the dataset is increased by applying scale, rotation, salt-and-pepper noise, circular mean filter, and gauss filter. The U-Net network created for ray segmentation is trained using the Adam optimization algorithm. The experimental results show that the ray segmentation accuracy in testing is 96.3%.
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Parker, Robert C., and David L. Evans. "An Application of LiDAR in a Double-Sample Forest Inventory." Western Journal of Applied Forestry 19, no. 2 (April 1, 2004): 95–101. http://dx.doi.org/10.1093/wjaf/19.2.95.
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Abstract Multireturn LiDAR data (2-m posting) were used in a double-sample forest inventory in central Idaho. Twenty-four 15-plot (0.2 ac) strips were established with a real-time Differential Global Positioning System. Tree dbh and height were measured on every 5th plot. Volume and basal area were computed for eight encountered species. LiDAR trees were selected with a focal max filter and height computed as the z-difference between interpolated canopy and DEM surfaces. LiDAR-derived trees/ac, height, and dbh had mean differences of −4.4 trees, −10.7 ft, and −1.01 in. from ground values. Four dbh-height models were fitted. Predicted dbh was used to compute LiDAR estimates of basal area and volume on 360 Phase 1 plots. Phase 2 LiDAR estimates on 60 plots were computed by randomly assigning heights to species classes using a 500-iteration Monte Carlo simulation. Regression estimators for Phase 2 ground and LiDAR ft3 and ft2 were computed by single and composite species. Phase 1 estimates were partitioned to obtain species volumes. The regression estimate of composite volume was partitioned by percent species distribution of trees, basal area, and volume. There was no statistical difference between individual and partitioned composite species estimates. Sampling error was ±11.5% on a mean volume estimate of 1,246 ft3/ac with standard error ±72.98 ft3/ac. West. J. Appl. For. 19(2):95–101.
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Dong, Xinyu, Zhichao Zhang, Ruiyang Yu, Qingjiu Tian, and Xicun Zhu. "Extraction of Information about Individual Trees from High-Spatial-Resolution UAV-Acquired Images of an Orchard." Remote Sensing 12, no. 1 (January 1, 2020): 133. http://dx.doi.org/10.3390/rs12010133.
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The extraction of information about individual trees is essential to supporting the growing of fruit in orchard management. Data acquired from spectral sensors mounted on unmanned aerial vehicles (UAVs) have very high spatial and temporal resolution. However, an efficient and reliable method for extracting information about individual trees with irregular tree-crown shapes and a complicated background is lacking. In this study, we developed and tested the performance of an approach, based on UAV imagery, to extracting information about individual trees in an orchard with a complicated background that includes apple trees (Plot 1) and pear trees (Plot 2). The workflow involves the construction of a digital orthophoto map (DOM), digital surface models (DSMs), and digital terrain models (DTMs) using the Structure from Motion (SfM) and Multi-View Stereo (MVS) approaches, as well as the calculation of the Excess Green minus Excess Red Index (ExGR) and the selection of various thresholds. Furthermore, a local-maxima filter method and marker-controlled watershed segmentation were used for the detection and delineation, respectively, of individual trees. The accuracy of the proposed method was evaluated by comparing its results with manual estimates of the numbers of trees and the areas and diameters of tree-crowns, all three of which parameters were obtained from the DOM. The results of the proposed method are in good agreement with these manual estimates: The F-scores for the estimated numbers of individual trees were 99.0% and 99.3% in Plot 1 and Plot 2, respectively, while the Producer’s Accuracy (PA) and User’s Accuracy (UA) for the delineation of individual tree-crowns were above 95% for both of the plots. For the area of individual tree-crowns, root-mean-square error (RMSE) values of 0.72 m2 and 0.48 m2 were obtained for Plot 1 and Plot 2, respectively, while for the diameter of individual tree-crowns, RMSE values of 0.39 m and 0.26 m were obtained for Plot 1 (339 trees correctly identified) and Plot 2 (203 trees correctly identified), respectively. Both the areas and diameters of individual tree-crowns were overestimated to varying degrees.
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Singh Garg, K., M. Pal, and Kirti Jain. "A STUDY ON AIR POLLUTION TOLERANCE INDEX (APTI) AND ANTICIPATED PERFORMANCE INDEX (API) OF SOME PLANTS." International Journal of Advanced Research 9, no. 12 (December 31, 2021): 619–27. http://dx.doi.org/10.21474/ijar01/13950.
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Due to industrialization, urbanization and increasing number of vehicles air pollution has turn out to be serious problem today. Now a days particulate matter shows the undesirable effects on plants, animals and human beings also. Tree plantation programme is the best ways to control the air pollution. Most of the plants filter the air by their aerial elements. Vegetation naturally cleanses the atmosphere by absorbing gases and some particulate matters through leaves so they work as sink for air pollution and reduce pollution level in atmosphere. Leaves function as an efficient pollutant trapping device. Air pollution can directly affects plants via leaves or indirectly via soil acidification. Air pollution tolerance index (APTI) is an intrinsic quality of trees to control air pollution problems. The trees higher tolerance index are tolerance towards air pollution and can be used a source to control air pollution. Air pollution tolerance index can be used as an indicator of rate of air pollution. By combining biochemical and aggregate factors the anticipated performance index (API) is prepared which is used as development of green belt. Thus, the assessment of APTI and API potential of different trees are used to control air pollution.
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Mekour, Norreddine, Reda Mohamed Hamou, and Abdelmalek Amine. "Filter/Wrapper Methods for Gene Selection and Classification of Microarray Dataset." International Journal of Software Innovation 7, no. 3 (July 2019): 65–80. http://dx.doi.org/10.4018/ijsi.2019070104.
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A wide variety of large-scale information has been made within the extraction of genomic information and the extraction of data. Problems addressed embody ordination sequencing, supermolecule structure modeling, or the reconstruction of biological process trees (phylogeny). These issues need collaboration between biologists and computer scientists as a result of the issues to be of nice recursive difficulties. One of the most modern problems that gene expression data is resolved is with feature selection. There are two general approaches for feature selection: filter approach and wrapper approach. In this article, the authors propose a new approach when combining the filter approach with method ranked information gain and a wrapper approach with the searching method of the genetic algorithm.in order to test their overall performance, an experimental study is presented based on two gene microarray datasets found in bioinformatics and biomedical domains leukemia, and the central nervous system (CNS). The classifier Decision tree (C4.5) is used for improving the classification performance. The results show that their approach selects genes for additional correct classification emphasizes the effectiveness of the chosen genes and its ability to filter the information from unsuitable genes.
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Li, M., and W. Yao. "3D MAP SYSTEM FOR TREE MONITORING IN HONG KONG USING GOOGLE STREET VIEW IMAGERY AND DEEP LEARNING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 765–72. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-765-2020.
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Abstract. In densely built urban areas such as Hong Kong, the positive effect of urban trees is to help maintain high environmental and social sustainability for the city while unmanaged trees lead to negative effects such as accidents, outbreaks of pests and diseases. The public awareness of urban tree population has been increasing and preserving all the benefits offered by trees, a continuous monitoring concept would be required. In this work, an efficient 3D map system for tree inventory in Hong Kong is presented to the based on automated tree detection from publicly available Google street view (GSV) panorama images. First, Convolutional Neural Networks (CNNs) based object detector and classifier – YOLOv3 with pretrained model is adopted to learn GSV images to detect tree objects. GSV depth image has been utilized to decode depth values of each GSV panorama image and will provide accurate information to calculate the tree geographic position. A “field of view” filter was designed to remove duplicated tree detection within the overlapped areas followed by spatial clustering applied to further increase the tree localization accuracy. The average distance between the detected trees and ground truth data was achieved within 3 meters for selected roads used for the experiment. Second, a 3D Map platform prototype for facilitating the urban tree monitoring and management was developed. Currently, there is no true 3D platform for interpreting the results of tree records in Hong Kong city areas. With the help of webGL technology, contemporary browsers are able to show 3D buildings, terrain and other scene components together with the obtained tree records in an open source 3D GIS platform, the level of visualization is enhanced as all the detected trees are placed on the 3D digital terrain model. Consequently, it is easy for end-users to know the actual position of the trees and their distribution.
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Krůček, Martin, Kamil Král, KC Cushman, Azim Missarov, and James R. Kellner. "Supervised Segmentation of Ultra-High-Density Drone Lidar for Large-Area Mapping of Individual Trees." Remote Sensing 12, no. 19 (October 7, 2020): 3260. http://dx.doi.org/10.3390/rs12193260.
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We applied a supervised individual-tree segmentation algorithm to ultra-high-density drone lidar in a temperate mountain forest in the southern Czech Republic. We compared the number of trees correctly segmented, stem diameter at breast height (DBH), and tree height from drone-lidar segmentations to field-inventory measurements and segmentations from terrestrial laser scanning (TLS) data acquired within two days of the drone-lidar acquisition. Our analysis detected 51% of the stems >15 cm DBH, and 87% of stems >50 cm DBH. Errors of omission were much more common for smaller trees than for larger ones, and were caused by removal of points prior to segmentation using a low-intensity and morphological filter. Analysis of segmented trees indicates a strong linear relationship between DBH from drone-lidar segmentations and TLS data. The slope of this relationship is 0.93, the intercept is 4.28 cm, and the r2 is 0.98. However, drone lidar and TLS segmentations overestimated DBH for the smallest trees and underestimated DBH for the largest trees in comparison to field data. We evaluate the impact of random error in point locations and variation in footprint size, and demonstrate that random error in point locations is likely to cause an overestimation bias for small-DBH trees. A Random Forest classifier correctly identified broadleaf and needleleaf trees using stem and crown geometric properties with overall accuracy of 85.9%. We used these classifications and DBH estimates from drone-lidar segmentations to apply allometric scaling equations to segmented individual trees. The stand-level aboveground biomass (AGB) estimate using these data is 76% of the value obtained using a traditional field inventory. We demonstrate that 71% of the omitted AGB is due to segmentation errors of omission, and the remaining 29% is due to DBH estimation errors. Our analysis indicates that high-density measurements from low-altitude drone flight can produce DBH estimates for individual trees that are comparable to TLS. These data can be collected rapidly throughout areas large enough to produce landscape-scale estimates. With additional refinement, these estimates could augment or replace manual field inventories, and could support the calibration and validation of current and forthcoming space missions.
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Faize, Mohamed, Boris Fumanal, Francisco Luque, Jorge A. Ramírez-Tejero, Zhi Zou, Xueying Qiao, Lydia Faize, et al. "Genome Wild Analysis and Molecular Understanding of the Aquaporin Diversity in Olive Trees (Olea Europaea L.)." International Journal of Molecular Sciences 21, no. 11 (June 11, 2020): 4183. http://dx.doi.org/10.3390/ijms21114183.
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Cellular aquaporin water channels (AQPs) constitute a large family of transmembrane proteins present throughout all kingdoms of life, playing important roles in the uptake of water and many solutes across the membranes. In olive trees, AQP diversity, protein features and their biological functions are still largely unknown. This study focuses on the structure and functional and evolution diversity of AQP subfamilies in two olive trees, the wild species Olea europaea var. sylvestris (OeuAQPs) and the domesticated species Olea europaea cv. Picual (OleurAQPs), and describes their involvement in different physiological processes of early plantlet development and in biotic and abiotic stress tolerance in the domesticated species. A scan of genomes from the wild and domesticated olive species revealed the presence of 52 and 79 genes encoding full-length AQP sequences, respectively. Cross-genera phylogenetic analysis with orthologous clustered OleaAQPs into five established subfamilies: PIP, TIP, NIP, SIP, and XIP. Subsequently, gene structures, protein motifs, substrate specificities and cellular localizations of the full length OleaAQPs were predicted. Functional prediction based on the NPA motif, ar/R selectivity filter, Froger’s and specificity-determining positions suggested differences in substrate specificities of Olea AQPs. Expression analysis of the OleurAQP genes indicates that some genes are tissue-specific, whereas few others show differential expressions at different developmental stages and in response to various biotic and abiotic stresses. The current study presents the first detailed genome-wide analysis of the AQP gene family in olive trees and it provides valuable information for further functional analysis to infer the role of AQP in the adaptation of olive trees in diverse environmental conditions in order to help the genetic improvement of domesticated olive trees.
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Adeleke, A., N. A. Samsudin, Z. A. Othman, and S. K. Ahmad Khalid. "A two-step feature selection method for quranic text classification." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 2 (November 1, 2019): 730. http://dx.doi.org/10.11591/ijeecs.v16.i2.pp730-736.
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Feature selection is an integral phase in text classification problems. It is primarily applied in preprocessing text data prior to labeling. However, there exist some limitations with the FS techniques. The filter-based FS techniques have the drawback of lower accuracy performance while the wrapper-based techniques are highly computationally expensive to process. In this paper, a two-step FS method is presented. In the first step, chisquare (CH) filter-based technique is used to reduce the dimensionality of the feature set and then wrapper correlation-based (CFS) technique is employed in the second step to further select most relevant features from the reduced feature set. Specifically, the ultimate aim is to reduce the computational runtime while achieving high classification accuracy. Subsequently, the proposed method was applied in labeling instances of the input data (Quranic verses) using standard classifiers: naïve bayes (NB), support vector machine (SVM), decision trees (J48). The results report the proposed method achieved accuracy result of 93.6% at 4.17secs.
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Fernandes, Paul G. "Classification trees for species identification of fish-school echotraces." ICES Journal of Marine Science 66, no. 6 (April 8, 2009): 1073–80. http://dx.doi.org/10.1093/icesjms/fsp060.
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Abstract Fernandes, P. G. 2009. Classification trees for species identification of fish-school echotraces. – ICES Journal of Marine Science, 66: 1073–1080. Acoustic surveys provide valuable information on the abundance and distribution of many fish species, but are particularly effective for schooling pelagic fish of commercial importance. However, despite recent advances in multifrequency processing, the technique still requires some subjective judgement when allocating the acoustic data, fish-school echotraces, to particular species—the so-called “scrutiny process”. This is assisted by “ground truth” trawling and operator experience of relating trawl data to echotraces of particular fish schools. In this paper, a method to identify species based on “classification trees” is applied to data from a component of the International North Sea Herring Acoustic Survey. Classification trees may be considered as a variant of decision trees, and have properties that are intuitive to biologists, because they are similar to the keys used for the biological identification of species. The method described here incorporates a multifrequency fish-school filter, image analysis to isolate fish-school echotraces, and finally, a classification-tree system using the multifrequency information from the ground-truthed echotraces that can be translated into a processing tool for objective species allocation. The classification-tree system is compared with the traditional method of expert-based scrutiny. Unlike the latter, however, a measure of uncertainty is attributed to the classification-tree approach and this could be propagated through the acoustic-survey estimation procedure as a component of the total uncertainty in the abundance estimate.
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Ashry, Islam, Biwei Wang, Yuan Mao, Mohammed Sait, Yujian Guo, Yousef Al-Fehaid, Abdulmoneim Al-Shawaf, Tien Khee Ng, and Boon S. Ooi. "CNN–Aided Optical Fiber Distributed Acoustic Sensing for Early Detection of Red Palm Weevil: A Field Experiment." Sensors 22, no. 17 (August 29, 2022): 6491. http://dx.doi.org/10.3390/s22176491.
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Red palm weevil (RPW) is a harmful pest that destroys many date, coconut, and oil palm plantations worldwide. It is not difficult to apply curative methods to trees infested with RPW; however, the early detection of RPW remains a major challenge, especially on large farms. In a controlled environment and an outdoor farm, we report on the integration of optical fiber distributed acoustic sensing (DAS) and machine learning (ML) for the early detection of true weevil larvae less than three weeks old. Specifically, temporal and spectral data recorded with the DAS system and processed by applying a 100–800 Hz filter are used to train convolutional neural network (CNN) models, which distinguish between “infested” and “healthy” signals with a classification accuracy of ∼97%. In addition, a strict ML-based classification approach is introduced to improve the false alarm performance metric of the system by ∼20%. In a controlled environment experiment, we find that the highest infestation alarm count of infested and healthy trees to be 1131 and 22, respectively, highlighting our system’s ability to distinguish between the infested and healthy trees. On an outdoor farm, in contrast, the acoustic noise produced by wind is a major source of false alarm generation in our system. The best performance of our sensor is obtained when wind speeds are less than 9 mph. In a representative experiment, when wind speeds are less than 9 mph outdoor, the highest infestation alarm count of infested and healthy trees are recorded to be 1622 and 94, respectively.
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Cavender-Bares, Jeannine M., Erik Nelson, Jose Eduardo Meireles, Jesse R. Lasky, Daniela A. Miteva, David J. Nowak, William D. Pearse, et al. "The hidden value of trees: Quantifying the ecosystem services of tree lineages and their major threats across the contiguous US." PLOS Sustainability and Transformation 1, no. 4 (April 5, 2022): e0000010. http://dx.doi.org/10.1371/journal.pstr.0000010.
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Trees provide critical contributions to human well-being. They sequester and store greenhouse gasses, filter air pollutants, provide wood, food, and other products, among other benefits. These benefits are threatened by climate change, fires, pests and pathogens. To quantify the current value of the flow of ecosystem services from U.S. trees, and the threats they face, we combine macroevolutionary and economic valuation approaches using spatially explicit data about tree species and lineages. We find that the value of five key ecosystem services with adequate data generated by US trees is $114 billion per annum (low: $85 B; high: $137 B; 2010 USD). The non-market value of trees from carbon storage and air pollution removal far exceed their commercial value from wood products and food crops. Two lineages—pines and oaks—account for 42% of the value of these services. The majority of species face threats from climate change, many face increasing fire risk, and known pests and pathogens threaten 40% of total woody biomass. The most valuable US tree species and lineages are among those most threatened by known pests and pathogens, with species most valuable for carbon storage most at risk from increasing fire threat. High turnover of tree species across the continent results in a diverse set of species distributed across the tree of life contributing to ecosystem services in the U.S. The high diversity of taxa across U.S. forests may be important in buffering ecosystem service losses if and when the most valuable lineages are compromised.
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Maramokhin, Eduard Vladimirovich, Marina Valerievna Sirotina, Dmitry Nikolaevich Zontikov, and Vladislav Sergeevich Golubev. "The effect of xylotrophic basidiomycetes on the photosynthetic pigments of small-leaved trees." Samara Journal of Science 11, no. 4 (December 1, 2022): 78–84. http://dx.doi.org/10.55355/snv2022114111.
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Xylotrophic basidiomycetes are an essential component of any biogeocoenosis. It is due to these organisms that one of the most important processes occurs throughout the biosphere, namely the decomposition of organic substances and, first of all, cellulose, which is synthesized by producers. However, these phytopathogens bring significant losses to forestry, making wood unsuitable for industrial use. At the same time, sufficiently effective methods for diagnosing and assessing wood damage by heartwood rot, which are caused by xylotrophic basidiomycetes, have not been developed. The paper studies the peculiarity of the influence of these phytopathogens on the pigment apparatus of valuable small-leaved tree species using the methods of spectroscopy and electrophotocolorimetry, while comparing the rate of degradation of the main pigments of photosynthesis chlorophylls a and b to pheophytin, as well as assesses the content of chlorophyll, and auxiliary pigments such as carotenoids and xanthophylls in healthy trees and trees with signs of damage by pathogenic xylotrophs. A significant number of effects of phytopathogens on leaf pigment apparatus of small-leaved tree species have been identified. Among those are rapid degradation of primary and secondary pigments, distortion of chlorophyll a concentration data caused by filter properties of photoelectric colorimeter and higher chlorophyll b concentration in affected trees in comparison to control sample. These methods and the results obtained can be actively used both in forestry to assess the phytosanitary state of small-leaved trees, and to further study the features of the interaction of xylotrophic basidiomycetes with woody plants.
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Günthardt-Goerg, Madeleine Silvia, Pierre Vollenweider, and Rainer Schulin. "Metal Accumulation and Biomass Production in Young Afforestations Established on Soil Contaminated by Heavy Metals." Plants 11, no. 4 (February 15, 2022): 523. http://dx.doi.org/10.3390/plants11040523.
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The restoration of forest ecosystems on metal-contaminated sites can be achieved whilst producing valuable plant biomass. Here, we investigated the metal accumulation and biomass production of young afforestations on contaminated plots by simulating brownfield site conditions. On 16 3-m2 plots, the 15 cm topsoil was experimentally contaminated with Zn/Cu/Pb/Cd = 2854/588/103/9.2 mg kg−1 using smelter filter dust, while 16 uncontaminated plots (Zn/Cu/Pb/Cd = 97/28/37/< 1) were used as controls. Both the calcareous (pH 7.4) and acidic (pH 4.2) subsoils remained uncontaminated. The afforestations consisted of groups of conifers, deciduous trees, and understorey plants. During the four years of cultivation, 2254/86/0.35/10 mg m−2 Zn/Cu/Pb/Cd were extracted from the contaminated soils and transferred to the aboveground parts of the plants (1279/72/0.06/5.5 mg m−2 in the controls). These extractions represented 3/2/3% of the soluble soil Zn/Cu/Cd fractions. The conifers showed 4–8 times lower root-to-shoot translocation of Cu and Zn than the deciduous trees. The contamination did not affect the biomass of the understorey plants and reduced that of the trees by 23% at most. Hence, we conclude that the afforestation of brown field sites with local tree species is an interesting option for their reclamation from an ecological as well as economic perspective.
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Wang, Ping, Wen Yi Fan, Ming Ze Li, Fang Liu, and Qiong Zhang. "Extraction of Individual Tree Height Using a Combination of Large-Scale Aerial Photo and LiDAR." Advanced Materials Research 268-270 (July 2011): 1157–62. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1157.
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With the LiangShui forestry centre as study area, high-density LiDAR data and to synchronously processed high-resolution digital image are taken as data source to extract Individual Tree Height. The LiDAR data of the study area is filtered and classified, using TIN Filter to extract the ground echo points and trees echo points. Then these ground echo points generate Digital Elevation Model (DEM), and these trees echo points generate Digital Surface Model (DSM). Then the DEM and DSM are Taken as a subtraction to obtain Canopy Height Model (CHM), then the object-oriented approach is used to segment air digital image. Through multi-scale and canopy-model which create image objects and class division level, with the nearest neighbor distance and member function, the image objects are classified, and re-segmentation is based on classification results. And the edge is optimized to accurately identify individual tree. The canopy polygon obtained after image segmentation and CHM were superimposed to calculate polygon maximum elevation difference from LiDAR data as a tree height. Associated with the measured height analysis, the accuracy is 92.04%.
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Work, Timothy T., John R. Spence, W. Jan A. Volney, Luigi E. Morgantini, and John L. Innes. "Integrating biodiversity and forestry practices in western Canada." Forestry Chronicle 79, no. 5 (October 1, 2003): 906–16. http://dx.doi.org/10.5558/tfc79906-5.
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In western Canada, some forestry companies are attempting to incorporate conservation of biodiversity as a new management priority. Here we provide a review of management strategies currently implemented through a survey of companies in this region. Representatives from fourteen companies were asked to complete 30 questions designed to assess six broad issues, all of which are important for integrating biodiversity protection with timber production. Differences in provincial legislation were a major factor contributing to the prioritization of biodiversity objectives. All companies stressed that a variety of stand age classes and compositions was important for maintaining biodiversity. Green tree retention was a common approach proposed by all companies. Definitions of green tree retention varied significantly among companies, ranging from residual material left following standard clearcutting to merchantable trees selected specifically to foster wildlife and biodiversity. Most companies have proposed some monitoring aimed at biodiversity, although most plans target habitat structural features rather than directly monitoring species. Key words: biodiversity, sustainability, monitoring, green tree retention, coarse filter, fine filter, rare and threatened species, forest industry
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Shikata, Hidenori, Geoffrey McLennan, Eric A. Hoffman, and Milan Sonka. "Segmentation of Pulmonary Vascular Trees from Thoracic 3D CT Images." International Journal of Biomedical Imaging 2009 (2009): 1–11. http://dx.doi.org/10.1155/2009/636240.
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This paper describes an algorithm for extracting pulmonary vascular trees (arteries plus veins) from three-dimensional (3D) thoracic computed tomographic (CT) images. The algorithm integrates tube enhancement filter and traversal approaches which are based on eigenvalues and eigenvectors of a Hessian matrix to extract thin peripheral segments as well as thick vessels close to the lung hilum. The resultant algorithm was applied to a simulation data set and 44 scans from 22 human subjects imaged via multidetector-row CT (MDCT) during breath holds at 85% and 20% of their vital capacity. A quantitative validation was performed with more than 1000 manually identified points selected from inside the vessel segments to assess true positives (TPs) and 1000 points randomly placed outside of the vessels to evaluate false positives (FPs) in each case. On average, for both the high and low volume lung images, 99% of the points was properly marked as vessel and 1% of the points were assessed as FPs. Our hybrid segmentation algorithm provides a highly reliable method of segmenting the combined pulmonary venous and arterial trees which in turn will serve as a critical starting point for further quantitative analysis tasks and aid in our overall goal of establishing a normative atlas of the human lung.
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Abraham, Romain, Maïtine Bergounioux, and Pierre Debs. "Automatic Choice of the Threshold of a Grain Filter via Galton–Watson Trees: Application to Granite Cracks Detection." Journal of Mathematical Imaging and Vision 60, no. 1 (June 16, 2017): 50–69. http://dx.doi.org/10.1007/s10851-017-0743-3.
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Ribeiro, Jonathan Wesley Ferreira, Rafael Reis Gonçalo, and Rosana Marta Kolb. "Waterlogging as an environmental filter to tree recruitment in tropical wet grasslands." Australian Journal of Botany 69, no. 8 (2021): 543. http://dx.doi.org/10.1071/bt20173.
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Wet grasslands from the Brazilian Savanna (Cerrado) are open-canopy vegetation dominated by subshrubs and herbaceous plants. Although they frequently occur between gallery forests and savannas, tree species are rarely observed colonising these grasslands. Here, seed germination and seedling development of Cerrado trees under waterlogging conditions were evaluated in order to elucidate how soil waterlogging constrains tree regeneration in tropical wet grasslands. We used seeds of 11 representative Cerrado tree species that have different requirements for soil moisture (non-flooded v. flooded environments). Short periods (15days) of waterlogging drastically decreased or inhibited germination and seedling development in four species, whereas long periods (30–45days) of waterlogging reduced or inhibited germination and seedling development in 6 of the 11 species. As expected, we found fewer waterlogging-resistant seeds associated with those species from non-flooded environments. By contrast, more waterlogging-resistant seeds were associated with species that eventually or typically occur in flooded environments. Our results suggest that soil waterlogging is an important environmental filter constraining tree recruitment in tropical wet grasslands. However, some species can overcome this environmental filter by possessing waterlogging-resistant seeds or avoid it by establishing in less waterlogged locations.
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Anders, Niels, João Valente, Rens Masselink, and Saskia Keesstra. "Comparing Filtering Techniques for Removing Vegetation from UAV-Based Photogrammetric Point Clouds." Drones 3, no. 3 (July 30, 2019): 61. http://dx.doi.org/10.3390/drones3030061.
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Digital Elevation Models (DEMs) are 3D representations of the Earth’s surface and have numerous applications in geomorphology, hydrology and ecology. Structure-from-Motion (SfM) photogrammetry using photographs obtained by unmanned aerial vehicles (UAVs) have been increasingly used for obtaining high resolution DEMs. These DEMs are interpolated from point clouds representing entire landscapes, including points of terrain, vegetation and infrastructure. Up to date, there has not been any study clearly comparing different algorithms for filtering of vegetation. The objective in this study was, therefore, to assess the performance of various vegetation filter algorithms for SfM-obtained point clouds. The comparison was done for a Mediterranean area in Murcia, Spain with heterogeneous vegetation cover. The filter methods that were compared were: color-based filtering using an excessive greenness vegetation index (VI), Triangulated Irregular Networks (TIN) densification from LAStools, the standard method in Agisoft Photoscan (PS), iterative surface lowering (ISL), and a combination of iterative surface lowering and the VI method (ISL_VI). Results showed that for bare areas there was little to no difference between the filtering methods, which is to be expected because there is little to no vegetation present to filter. For areas with shrubs and trees, the ISL_VI and TIN method performed best. These results show that different filtering techniques have various degrees of success in different use cases. A default filter in commercial software such as Photoscan may not always be the best way to remove unwanted vegetation from a point cloud, but instead alternative methods such as a TIN densification algorithm should be used to obtain a vegetation-less Digital Terrain Model (DTM).
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Gu, Jianyu, Heather Grybas, and Russell G. Congalton. "Individual Tree Crown Delineation from UAS Imagery Based on Region Growing and Growth Space Considerations." Remote Sensing 12, no. 15 (July 23, 2020): 2363. http://dx.doi.org/10.3390/rs12152363.
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The development of unmanned aerial systems (UAS) equipped with various sensors (e.g., Lidar, multispectral sensors, and/or cameras) has provided the capability to “see” the individual trees in a forest. Individual tree crowns (ITCs) are the building blocks of precision forestry, because this knowledge allows users to analyze, model and manage the forest at the individual tree level by combing multiple data sources (e.g., remote sensing data and field surveys). Trees in the forest compete with other vegetation, especially neighboring trees, for limited resources to grow into the available horizontal and vertical space. Based on this assumption, this research developed a new region growing method that began with treetops as the initial seeds, and then segmented the ITCs, considering its growth space between the tree and its neighbors. The growth space was allocated by Euclidian distance and adjusted based on the crown size. Results showed that the over-segmentation accuracy (Oa), under-segmentation (Ua), and quality rate (QR) reached 0.784, 0.766, and 0.382, respectively, if the treetops were detected from a variable window filter based on an allometric equation for crown width. The Oa, Ua, and QR increased to 0.811, 0.853, and 0.296, respectively, when the treetops were manually adjusted. Treetop detection accuracy has a great impact on ITCs delineation accuracy. The uncertainties and limitations within this research including the interpretation error and accuracy measures were also analyzed and discussed, and a unified framework assessing the segmentation accuracy was highly suggested.
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Schratz, Patrick, Jannes Muenchow, Eugenia Iturritxa, José Cortés, Bernd Bischl, and Alexander Brenning. "Monitoring Forest Health Using Hyperspectral Imagery: Does Feature Selection Improve the Performance of Machine-Learning Techniques?" Remote Sensing 13, no. 23 (November 28, 2021): 4832. http://dx.doi.org/10.3390/rs13234832.
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This study analyzed highly correlated, feature-rich datasets from hyperspectral remote sensing data using multiple statistical and machine-learning methods. The effect of filter-based feature selection methods on predictive performance was compared. In addition, the effect of multiple expert-based and data-driven feature sets, derived from the reflectance data, was investigated. Defoliation of trees (%), derived from in situ measurements from fall 2016, was modeled as a function of reflectance. Variable importance was assessed using permutation-based feature importance. Overall, the support vector machine (SVM) outperformed other algorithms, such as random forest (RF), extreme gradient boosting (XGBoost), and lasso (L1) and ridge (L2) regressions by at least three percentage points. The combination of certain feature sets showed small increases in predictive performance, while no substantial differences between individual feature sets were observed. For some combinations of learners and feature sets, filter methods achieved better predictive performances than using no feature selection. Ensemble filters did not have a substantial impact on performance. The most important features were located around the red edge. Additional features in the near-infrared region (800–1000 nm) were also essential to achieve the overall best performances. Filter methods have the potential to be helpful in high-dimensional situations and are able to improve the interpretation of feature effects in fitted models, which is an essential constraint in environmental modeling studies. Nevertheless, more training data and replication in similar benchmarking studies are needed to be able to generalize the results.
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Fernández, J. E., R. Romero, J. C. Montaño, A. Diaz-Espejo, J. L. Muriel, M. V. Cuevas, F. Moreno, I. F. Girón, and M. J. Palomo. "Design and testing of an automatic irrigation controller for fruit tree orchards, based on sap flow measurements." Australian Journal of Agricultural Research 59, no. 7 (2008): 589. http://dx.doi.org/10.1071/ar07312.
Full textAbstract:
We designed and tested an automatic irrigation control system for fruit tree orchards, designated CRP. At the end of each day, the device calculates the irrigation dose (ID) from sap flow readings in the trunk of trees irrigated to replenish the crop water needs, relative to similar measurements made in over-irrigated trees. It then acts on the pump and electrovalve to supply an ID sufficient to keep the soil close to its field capacity during the irrigation period. Remote control of the system is possible from any computer or Smartphone connected to the Internet. We tested the CRP in an olive orchard in southern Spain. The device was robust and able to filter and amplify the output voltages of the heat-pulse velocity probes and to calculate reliable sap flow data. It calculated and supplied daily irrigation amounts to the orchard according to the specified irrigation protocol. The remote control facility proved to be useful for getting real-time information both on the CRP behaviour and the applied IDs, and for changing parameters of the irrigation protocol. For our conditions, olive trees with big root systems growing in a soil with a remarkable water-holding capacity, the approach mentioned above for calculating ID had not enough resolution to replace the daily crop water consumption. The device, however, was able to react when the soil water content fell below the threshold for soil water deficit. The threshold value was identified with simultaneous measurements of stem water potential in the instrumented trees. Our results suggest a change in the irrigation protocol that will allow the CRP to apply a recovery irrigation whenever that threshold is reached, making the device suitable for applying a deficit irrigation strategy in the orchard.
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Ali, Ashraf, Ahmad Ali, Hussein Abaluof, Wafaa N. Al-Sharu, Omar A. Saraereh, and Andrew Ware. "Moisture Detection in Tree Trunks in Semiarid Lands Using Low-Cost Non-Invasive Capacitive Sensors with Statistical Based Anomaly Detection Approach." Sensors 23, no. 4 (February 13, 2023): 2100. http://dx.doi.org/10.3390/s23042100.
Full textAbstract:
This paper focuses on building a non-invasive, low-cost sensor that can be fitted over tree trunks growing in a semiarid land environment. It also proposes a new definition that characterizes tree trunks’ water retention capabilities mathematically. The designed sensor measures the variations in capacitance across its probes. It uses amplification and filter stages to smooth the readings, requires little power, and is operational over a 100 kHz frequency. The sensor sends data via a Long Range (LoRa) transceiver through a gateway to a processing unit. Field experiments showed that the system provides accurate readings of the moisture content. As the sensors are non-invasive, they can be fitted to branches and trunks of various sizes without altering the structure of the wood tissue. Results show that the moisture content in tree trunks increases exponentially with respect to the measured capacitance and reflects the distinct differences between different tree types. Data of known healthy trees and unhealthy trees and defective sensor readings have been collected and analysed statistically to show how anomalies in sensor reading baseds on eigenvectors and eigenvalues of the fitted curve coefficient matrix can be detected.
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AMIN, M. ASHRAFUL, and HONG YAN. "AN EMPIRICAL STUDY ON THE CHARACTERISTICS OF GABOR REPRESENTATIONS FOR FACE RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 03 (May 2009): 401–31. http://dx.doi.org/10.1142/s0218001409007181.
Full textAbstract:
This paper examines the classification capability of different Gabor representations for human face recognition. Usually, Gabor filter responses for eight orientations and five scales for each orientation are calculated and all 40 basic feature vectors are concatenated to assemble the Gabor feature vector. This work explores 70 different Gabor feature vector extraction techniques for face recognition. The main goal is to determine the characteristics of the 40 basic Gabor feature vectors and to devise a faster Gabor feature extraction method. Among all the 40 basic Gabor feature representations the filter responses acquired from the largest scale at smallest relative orientation change (with respect to face) shows the highest discriminating ability for face recognition while classification is performed using three classification methods: probabilistic neural networks (PNN), support vector machines (SVM) and decision trees (DT). A 40 times faster summation based Gabor representation shows about 98% recognition rate while classification is performed using SVM. In this representation all 40 basic Gabor feature vectors are summed to form the summation based Gabor feature vector. In the experiment, a sixth order data tensor containing the basic Gabor feature vectors is constructed, for all the operations.