Journal articles on the topic 'Canopy volume detection'
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1
Wang, Mengmeng, Hanjie Dou, Hongyan Sun, Changyuan Zhai, Yanlong Zhang, and Feixiang Yuan. "Calculation Method of Canopy Dynamic Meshing Division Volumes for Precision Pesticide Application in Orchards Based on LiDAR." Agronomy 13, no. 4 (April 7, 2023): 1077. http://dx.doi.org/10.3390/agronomy13041077.
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The canopy volume of fruit trees is an important input for the precise and varying application of pesticides in orchards. The fixed mesh division method is mostly used to calculate canopy volumes with variable target-oriented spraying. To reduce the influence of the working speed on the detection accuracy under a fixed mesh width division, the cuboid accumulation of divided areas (CADAs), which is a light detection and ranging (LiDAR) online detection method for a fruit tree canopy volume based on dynamic mesh division, is proposed in this paper. In the method, the area is divided according to the number of unilateral nozzles of the sprayer in the canopy height direction of the fruit tree, and the mesh width is dynamically adjusted according to the change in the working speed in the moving direction of the sprayer. To verify the accuracy and applicability of the method, the simulation canopy and peach tree canopy detection experiments were carried out. The test results show that the CADA method can be used to calculate the contour and volume of the canopy. However, detection errors easily occur at the edge of the canopy, resulting in a detection error of 8.33% for the simulated canopy volume. The CADA method has a good detection accuracy under different moving speeds and fruit tree canopy sizes. At a speed of 1 m/s, the detection accuracy of the canopy volume reaches 99.18%. Compared with the existing canopy volume calculation methods based on the alpha-shape algorithm and canopy meshing-profile characterization (CMPC), the detection accuracy of the CADA method is 2.73% and 7.22% better, respectively. This method can not only reduce the influence of the moving speed on the detection accuracy of the canopy volume, but also improve the detection accuracy. Thus, this method can provide theoretical support for the research and development of target-oriented variable spraying control systems for orchards.
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Gu, Chenchen, Xiu Wang, Xiaole Wang, Fuzeng Yang, and Changyuan Zhai. "Research Progress on Variable-Rate Spraying Technology in Orchards." Applied Engineering in Agriculture 36, no. 6 (2020): 927–42. http://dx.doi.org/10.13031/aea.14201.
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HighlightsReview the research status of variable-rate spraying technology and point out the direction for the future researchDiscussed the advantages and disadvantages of different techniques to detect canopy volume and canopy biomassThe air speed and volume adjustment need to be controlled based on the canopy detection systemAbstract. Variable-rate pesticide application in orchards aims to solve the problems of low pesticide utilization rates and serious environmental pollution in traditional pesticide applications. In this article, we have reviewed the research status of the technology to point out the direction for future research. Orchard tree canopy volume detection, biomass detection, and variable-rate spraying control methods were systematically summarized and analyzed. The advantages and disadvantages of different sensing techniques for detecting canopy volume and canopy biomass have been discussed. Canopy volume is mainly detected by ultrasonic sensors and light detection and ranging (LiDAR) sensors. Canopy biomass detection can be realized by manual, ultrasonic sensors, LiDAR sensors, and other sensors. Variable-rate spraying control is in two parts: liquid flow rate regulation and air supply rate regulation. In order to determine the volume of the liquid variable-spray, the variable air supply of air-assisted sprayer has been proven to be important. Liquid flow regulation can be achieved by pipeline pressure control and nozzle flow rate control together with a series of algorithms. The direction of air supply is easy to determine, but the air speed and volume adjustment need to be controlled based on the canopy detection system. Finally, future research on variable-rate spraying technology should focus on: 1) the application of advanced sensing technology for accurate and real-time measurement of canopy volume and biomass, 2) accurate control algorithms for liquid flow rate regulation and methods for airflow regulation, and 3) design of variable-rate sprayers with both liquid and air regulations, and the establishment of different types of variable-rate models for different sprayer types. Keywords: Air supply rate regulation, Canopy biomass detection, Canopy volume detection, Liquid flow rate regulation, Orchard, Variable-rate spraying.
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Roman, Carla, Hongyoung Jeon, Heping Zhu, Javier Campos, and Erdal Ozkan. "Stereo Vision Controlled Variable Rate Sprayer for Specialty Crops: Part II. Sprayer Development and Performance Evaluation." Journal of the ASABE 66, no. 5 (2023): 1005–17. http://dx.doi.org/10.13031/ja.15578.
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Highlights A real time stereo vision controlled variable rate sprayer for specialty crops was developed. The stereo vision system of the sprayer detected outdoor trees with similar canopy profiles under travel speeds ranging from 3.2 to 8 km h-1. Canopy volume measurements of the sprayer were impacted by lateral distances between the sprayer and the tree center and travel speeds. The sprayer required less than 200 ms from tree canopy detection to spray decisions. The sprayer achieved spray volume reductions from 72.6% to 80.5% compared to constant rate spray application. Abstract. A real time variable rate sprayer controlled by a stereo vision system was developed to increase the accuracy of spray applications and reduce the use of crop protection products. The sprayer was designed to detect tree canopies and calculate its volume using depth images from the stereo vision system, and discharge corresponding spray volumes every 200 ms through the embedded software in the graphical user interface. The sprayer was evaluated in an apple orchard at different travel speeds (3.2 to 8.0 km h-1) for its performance in detecting canopy and measuring its volume. In addition, spray volume, deposition, and coverage of the variable rate application of the sprayer were evaluated against a constant rate application. Test results showed that the sprayer detected visually similar tree canopies during the evaluations, although its canopy volume measurements deviated from manually measured canopy volume from 0.11 to 0.83 m3 due to lateral position changes of the sprayer. The sprayer adjusted duty cycles of pulse width modulated valves to accurately spray the intended volume for detected canopies (0.073 to 0.083 L m-3) and only used spray volumes of 19.5% to 26.7% compared to a constant rate spray application (338 L ha-1). The constant rate spray application generally had more spray deposition and coverage in tree canopies than the variable rate sprayer, as expected since its spray volume was approximately 3.7 times higher. However, the mean spray depositions from the constant rate spray application were significantly varied (p=0.05) by tree sizes, while the variable rate spray application achieved statistically equivalent mean spray depositions regardless of tree sizes. The stereo vision controlled sprayer offers a cost-effective real-time variable rate spray option for growers with the potential to perform other tasks by using image processing algorithms while applying crop protection products. Keywords: Automation, Canopy volume, Crop protection, Depth image, Orchard, Precision agriculture, Real-time application.
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Gu, Chenchen, Changyuan Zhai, Xiu Wang, and Songlin Wang. "CMPC: An Innovative Lidar-Based Method to Estimate Tree Canopy Meshing-Profile Volumes for Orchard Target-Oriented Spray." Sensors 21, no. 12 (June 21, 2021): 4252. http://dx.doi.org/10.3390/s21124252.
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Canopy characterization detection is essential for target-oriented spray, which minimizes pesticide residues in fruits, pesticide wastage, and pollution. In this study, a novel canopy meshing-profile characterization (CMPC) method based on light detection and ranging (LiDAR)point-cloud data was designed for high-precision canopy volume calculations. First, the accuracy and viability of this method were tested using a simulated canopy. The results show that the CMPC method can accurately characterize the 3D profiles of the simulated canopy. These simulated canopy profiles were similar to those obtained from manual measurements, and the measured canopy volume achieved an accuracy of 93.3%. Second, the feasibility of the method was verified by a field experiment where the canopy 3D stereogram and cross-sectional profiles were obtained via CMPC. The results show that the 3D stereogram exhibited a high degree of similarity with the tree canopy, although there were some differences at the edges, where the canopy was sparse. The CMPC-derived cross-sectional profiles matched the manually measured results well. The CMPC method achieved an accuracy of 96.3% when the tree canopy was detected by LiDAR at a moving speed of 1.2 m/s. The accuracy of the LiDAR system was virtually unchanged when the moving speeds was reduced to 1 m/s. No detection lag was observed when comparing the start and end positions of the cross-section. Different CMPC grid sizes were also evaluated. Small grid sizes (0.01 m × 0.01 m and 0.025 m × 0.025 m) were suitable for characterizing the finer details of a canopy, whereas grid sizes of 0.1 m × 0.1 m or larger can be used for characterizing its overall profile and volume. The results of this study can be used as a technical reference for the development of a LiDAR-based target-oriented spray system.
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Zhou, Huitao, Weidong Jia, Yong Li, and Mingxiong Ou. "Method for Estimating Canopy Thickness Using Ultrasonic Sensor Technology." Agriculture 11, no. 10 (October 16, 2021): 1011. http://dx.doi.org/10.3390/agriculture11101011.
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The accurate detection of canopy characteristics is the basis of precise variable spraying. Canopy characteristics such as canopy density, thickness and volume are needed to vary the pesticide application rate and adjust the spray flow rate and air supply volume. Canopy thickness is an important canopy dimension for the calculation of tree canopy volume in pesticide variable spraying. With regard to the phenomenon of ultrasonic waves with multiple reflections and the further analysis of echo signals, we found that there is a proportional relationship between the canopy thickness and echo interval time. In this paper, we propose a method to calculate canopy thickness using echo signals that come from ultrasonic sensors. To investigate the application of this method, we conducted a set of lab-based experiments with a simulated canopy. The results show that we can accurately estimate canopy thickness when the detection distance, canopy density, and canopy thickness range between 0.5and 1.5 m, 1.2 and 1.4, and 0.3and 0.6 m, respectively. The relative error between the estimated value and actual value of the simulated canopy thickness is no higher than 8.8%. To compare our lab results with trees in the field, we measured canopy thickness from three naturally occurring Osmanthus trees (Osmanthus fragrans Lour). The results showed that the mean relative errors of three Osmanthus trees are 19.2%, 19.4% and 18.8%, respectively. These results can be used to improve measurements for agricultural production that includes both orchards and facilities by providing a reference point for the precise application of variable spraying.
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Saha, Kowshik Kumar, Nikos Tsoulias, Cornelia Weltzien, and Manuela Zude-Sasse. "Estimation of Vegetative Growth in Strawberry Plants Using Mobile LiDAR Laser Scanner." Horticulturae 8, no. 2 (January 19, 2022): 90. http://dx.doi.org/10.3390/horticulturae8020090.
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Monitoring of plant vegetative growth can provide the basis for precise crop management. In this study, a 2D light detection and ranging (LiDAR) laser scanner, mounted on a linear conveyor, was used to acquire multi-temporal three-dimensional (3D) data from strawberry plants (‘Honeoye’ and ‘Malling Centenary’) 14–77 days after planting (DAP). Canopy geometrical variables, i.e., points per plant, height, ground projected area, and canopy volume profile, were extracted from 3D point cloud. The manually measured leaf area exhibited a linear relationship with LiDAR-derived parameters (R2 = 0.98, 0.90, 0.93, and 0.96 with number of points per plant, volume, height, and projected canopy area, respectively). However, the measuring uncertainty was high in the dense canopies. Particularly, the canopy volume estimation was adapted to the plant habitus to remove gaps and empty spaces in the canopy point cloud. The parametric values for maximum point to point distance (Dmax) = 0.15 cm and slice height (S) = 0.10 cm resulted in R² = 0.80 and RMSPE = 26.93% for strawberry plant volume estimation considering actual volume measured by water displacement. The vertical volume profiling provided growth data for cultivars ‘Honeoye’ and ‘Malling Centenary’ being 51.36 cm³ at 77 DAP and 42.18 cm3 at 70 DAP, respectively. The results contribute an approach for estimating plant geometrical features and particularly strawberry canopy volume profile based on LiDAR point cloud for tracking plant growth.
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Lim, Kevin, Paul Treitz, Michael Wulder, Benoît St-Onge, and Martin Flood. "LiDAR remote sensing of forest structure." Progress in Physical Geography: Earth and Environment 27, no. 1 (March 2003): 88–106. http://dx.doi.org/10.1191/0309133303pp360ra.
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Light detection and ranging (LiDAR) technology provides horizontal and vertical information at high spatial resolutions and vertical accuracies. Forest attributes such as canopy height can be directly retrieved from LiDAR data. Direct retrieval of canopy height provides opportunities to model above-ground biomass and canopy volume. Access to the vertical nature of forest ecosystems also offers new opportunities for enhanced forest monitoring, management and planning.
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Colaço, A. F., R. G. Trevisan, J. P. Molin, J. R. Rosell-Polo, and A. Escolà. "Orange tree canopy volume estimation by manual and LiDAR-based methods." Advances in Animal Biosciences 8, no. 2 (June 1, 2017): 477–80. http://dx.doi.org/10.1017/s2040470017001133.
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LiDAR (Light detection and ranging) technology is an alternative to current manual methods of canopy geometry estimations in orange trees. The objective of this work was to compare different types of canopy volume estimations of orange trees, some inspired on manual methods and others based on a LiDAR sensor. A point cloud was generated for 25 individual trees using a laser scanning system. The convex-hull and the alpha-shape surface reconstruction algorithms were tested. LiDAR derived models are able to represent orange trees more accurately than traditional methods. However, results differ significantly from the current manual method. In addition, different 3D modeling algorithms resulted in different canopy volume estimations. Therefore, a new standard method should be developed and established.
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Hermosilla, Txomin, Luis A. Ruiz, Alexandra N. Kazakova, Nicholas C. Coops, and L. Monika Moskal. "Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data." International Journal of Wildland Fire 23, no. 2 (2014): 224. http://dx.doi.org/10.1071/wf13086.
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Precise knowledge of fuel conditions is important for predicting fire hazards and simulating fire growth and intensity across the landscape. We present a methodology to retrieve and map forest canopy fuel and other forest structural parameters using small-footprint full-waveform airborne light detection and ranging (LiDAR) data. Full-waveform LiDAR sensors register the complete returned backscattered signal through time and can describe physical properties of the intercepted objects. This study was undertaken in a mixed forest dominated by Douglas-fir, occasionally mixed with other conifers, in north-west Oregon (United States). We extracted two sets of LiDAR metrics using pulse detection and waveform modelling and then constructed several predictive models using forward stepwise multiple linear regression. The resulting models explained ~80% of the variability for many of the canopy fuel and forest structure parameters: aboveground biomass (R2=0.84), quadratic mean diameter (R2=0.82), canopy height (R2=0.79), canopy base height (R2=0.78) and canopy fuel load (R2=0.79). The lowest performing models included basal area (R2=0.76), stand volume (R2=0.73), canopy bulk density (R2=0.67) and stand density index (R2=0.66). Our results indicate that full-waveform LiDAR systems show promise in systematically characterising the structure and canopy fuel loads of forests, which may enable accurate fire behaviour forecasting that in turn supports the development of prevention and planning policies.
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Leite, Rodrigo Vieira, Cibele Hummel do Amaral, Raul de Paula Pires, Carlos Alberto Silva, Carlos Pedro Boechat Soares, Renata Paulo Macedo, Antonilmar Araújo Lopes da Silva, Eben North Broadbent, Midhun Mohan, and Hélio Garcia Leite. "Estimating Stem Volume in Eucalyptus Plantations Using Airborne LiDAR: A Comparison of Area- and Individual Tree-Based Approaches." Remote Sensing 12, no. 9 (May 9, 2020): 1513. http://dx.doi.org/10.3390/rs12091513.
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Forest plantations are globally important for the economy and are significant for carbon sequestration. Properly managing plantations requires accurate information about stand timber stocks. In this study, we used the area (ABA) and individual tree (ITD) based approaches for estimating stem volume in fast-growing Eucalyptus spp forest plantations. Herein, we propose a new method to improve individual tree detection (ITD) in dense canopy homogeneous forests and assess the effects of stand age, slope and scan angle on ITD accuracy. Field and Light Detection and Ranging (LiDAR) data were collected in Eucalyptus urophylla x Eucalyptus grandis even-aged forest stands located in the mountainous region of the Rio Doce Valley, southeastern Brazil. We tested five methods to estimate volume from LiDAR-derived metrics using ABA: Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and linear and Gompertz models. LiDAR-derived canopy metrics were selected using the Recursive Feature Elimination algorithm and Spearman’s correlation, for nonparametric and parametric methods, respectively. For the ITD, we tested three ITD methods: two local maxima filters and the watershed method. All methods were tested adding our proposed procedure of Tree Buffer Exclusion (TBE), resulting in 35 possibilities for treetop detection. Stem volume for this approach was estimated using the Schumacher and Hall model. Estimated volumes in both ABA and ITD approaches were compared to the field observed values using the F-test. Overall, the ABA with ANN was found to be better for stand volume estimation ( r y y ^ = 0.95 and RMSE = 14.4%). Although the ITD results showed similar precision ( r y y ^ = 0.94 and RMSE = 16.4%) to the ABA, the results underestimated stem volume in younger stands and in gently sloping terrain (<25%). Stem volume maps also differed between the approaches; ITD represented the stand variability better. In addition, we discuss the importance of LiDAR metrics as input variables for stem volume estimation methods and the possible issues related to the ABA and ITD performance.
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Kumbhar, Avadhut Shankar Salavi, and Prof Mrs S. S. Patil. "Orchard Mapping with Deep Learning Semantic Segmentation." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (November 30, 2023): 174–76. http://dx.doi.org/10.22214/ijraset.2023.56465.
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Abstract: The goal of orchard mapping with deep learning semantic segmentation is to automatic detection and localization of the orchard tree canopy under varied situations like multiple seasons, various tree ages, and varying levels of weed covering. The accuracy of segmentation depends on many factors like season, canopy size, and presence of weeds, background soil condition, and untreated soil. The proposed research have several combinations of training & test data based on orchard conditions The proposed research work contains deep learning convolutional neural network variant. The algorithm U- net network will be used. This helps to automatic detect and localize the tree canopy size using UAV images. Image segmentation will help all farmers by automatically segmenting tree canopies from aerial images for the development of integrated tools. After measurement of these parameters, tree canopy volume can be detected of targeted tree
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Zhang, Wenli, Xinyu Peng, Tingting Bai, Haozhou Wang, Daisuke Takata, and Wei Guo. "A UAV-Based Single-Lens Stereoscopic Photography Method for Phenotyping the Architecture Traits of Orchard Trees." Remote Sensing 16, no. 9 (April 28, 2024): 1570. http://dx.doi.org/10.3390/rs16091570.
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This article addresses the challenges of measuring the 3D architecture traits, such as height and volume, of fruit tree canopies, constituting information that is essential for assessing tree growth and informing orchard management. The traditional methods are time-consuming, prompting the need for efficient alternatives. Recent advancements in unmanned aerial vehicle (UAV) technology, particularly using Light Detection and Ranging (LiDAR) and RGB cameras, have emerged as promising solutions. LiDAR offers precise 3D data but is costly and computationally intensive. RGB and photogrammetry techniques like Structure from Motion and Multi-View Stereo (SfM-MVS) can be a cost-effective alternative to LiDAR, but the computational demands still exist. This paper introduces an innovative approach using UAV-based single-lens stereoscopic photography to overcome these limitations. This method utilizes color variations in canopies and a dual-image-input network to generate a detailed canopy height map (CHM). Additionally, a block structure similarity method is presented to enhance height estimation accuracy in single-lens UAV photography. As a result, the average rates of growth in canopy height (CH), canopy volume (CV), canopy width (CW), and canopy project area (CPA) were 3.296%, 9.067%, 2.772%, and 5.541%, respectively. The r2 values of CH, CV, CW, and CPA were 0.9039, 0.9081, 0.9228, and 0.9303, respectively. In addition, compared to the commonly used SFM-MVS approach, the proposed method reduces the time cost of canopy reconstruction by 95.2% and of the cost of images needed for canopy reconstruction by 88.2%. This approach allows growers and researchers to utilize UAV-based approaches in actual orchard environments without incurring high computation costs.
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Römer, Christoph, Mirwaes Wahabzada, Agim Ballvora, Francisco Pinto, Micol Rossini, Cinzia Panigada, Jan Behmann, et al. "Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis." Functional Plant Biology 39, no. 11 (2012): 878. http://dx.doi.org/10.1071/fp12060.
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Early water stress recognition is of great relevance in precision plant breeding and production. Hyperspectral imaging sensors can be a valuable tool for early stress detection with high spatio-temporal resolution. They gather large, high dimensional data cubes posing a significant challenge to data analysis. Classical supervised learning algorithms often fail in applied plant sciences due to their need of labelled datasets, which are difficult to obtain. Therefore, new approaches for unsupervised learning of relevant patterns are needed. We apply for the first time a recent matrix factorisation technique, simplex volume maximisation (SiVM), to hyperspectral data. It is an unsupervised classification approach, optimised for fast computation of massive datasets. It allows calculation of how similar each spectrum is to observed typical spectra. This provides the means to express how likely it is that one plant is suffering from stress. The method was tested for drought stress, applied to potted barley plants in a controlled rain-out shelter experiment and to agricultural corn plots subjected to a two factorial field setup altering water and nutrient availability. Both experiments were conducted on the canopy level. SiVM was significantly better than using a combination of established vegetation indices. In the corn plots, SiVM clearly separated the different treatments, even though the effects on leaf and canopy traits were subtle.
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Parr, Baden, Mathew Legg, Stuart Bradley, and Fakhrul Alam. "Occluded Grape Cluster Detection and Vine Canopy Visualisation Using an Ultrasonic Phased Array." Sensors 21, no. 6 (March 20, 2021): 2182. http://dx.doi.org/10.3390/s21062182.
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Grape yield estimation has traditionally been performed using manual techniques. However, these tend to be labour intensive and can be inaccurate. Computer vision techniques have therefore been developed for automated grape yield estimation. However, errors occur when grapes are occluded by leaves, other bunches, etc. Synthetic aperture radar has been investigated to allow imaging through leaves to detect occluded grapes. However, such equipment can be expensive. This paper investigates the potential for using ultrasound to image through leaves and identify occluded grapes. A highly directional low frequency ultrasonic array composed of ultrasonic air-coupled transducers and microphones is used to image grapes through leaves. A fan is used to help differentiate between ultrasonic reflections from grapes and leaves. Improved resolution and detail are achieved with chirp excitation waveforms and near-field focusing of the array. The overestimation in grape volume estimation using ultrasound reduced from 222% to 112% compared to the 3D scan obtained using photogrammetry or from 56% to 2.5% compared to a convex hull of this 3D scan. This also has the added benefit of producing more accurate canopy volume estimations which are important for common precision viticulture management processes such as variable rate applications.
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APOSTOL, Bogdan, Adrian LORENT, Marius PETRILA, Vladimir GANCZ, and Ovidiu BADEA. "Height Extraction and Stand Volume Estimation Based on Fusion Airborne LiDAR Data and Terrestrial Measurements for a Norway Spruce [Picea abies (L.) Karst.] Test Site in Romania." Notulae Botanicae Horti Agrobotanici Cluj-Napoca 44, no. 1 (June 14, 2016): 313–23. http://dx.doi.org/10.15835/nbha44110155.
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The objective of this study was to analyze the efficiency of individual tree identification and stand volume estimation from LiDAR data. The study was located in Norway spruce [Picea abies (L.) Karst.] stands in southwestern Romania and linked airborne laser scanning (ALS) with terrestrial measurements through empirical modelling. The proposed method uses the Canopy Maxima algorithm for individual tree detection together with biometric field measurements and individual trees positioning. Field data was collected using Field-Map real-time GIS-laser equipment, a high-accuracy GNSS receiver and a Vertex IV ultrasound inclinometer. ALS data were collected using a Riegl LMS-Q560 instrument and processed using LP360 and Fusion software to extract digital terrain, surface and canopy height models. For the estimation of tree heights, number of trees and tree crown widths from the ALS data, the Canopy Maxima algorithm was used together with local regression equations relating field-measured tree heights and crown widths at each plot. When compared to LiDAR detected trees, about 40-61% of the field-measured trees were correctly identified. Such trees represented, in general, predominant, dominant and co-dominant trees from the upper canopy. However, it should be noted that the volume of the correctly identified trees represented 60-78% of the total plot volume. The estimation of stand volume using the LiDAR data was achieved by empirical modelling, taking into account the individual tree heights (as identified from the ALS data) and the corresponding ground reference stem volume. The root mean square error (RMSE) between the individual tree heights measured in the field and the corresponding heights identified in the ALS data was 1.7-2.2 meters. Comparing the ground reference estimated stem volume (at trees level) with the corresponding ALS estimated tree stem volume, an RMSE of 0.5-0.7 m3 was achieved. The RMSE was slightly lower when comparing the ground reference stem volume at plot level with the ALS-estimated one, taking into account both the identified and unidentified trees in the LiDAR data (0.4-0.6 m3).
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Hyyppä, Eric, Xiaowei Yu, Harri Kaartinen, Teemu Hakala, Antero Kukko, Mikko Vastaranta, and Juha Hyyppä. "Comparison of Backpack, Handheld, Under-Canopy UAV, and Above-Canopy UAV Laser Scanning for Field Reference Data Collection in Boreal Forests." Remote Sensing 12, no. 20 (October 13, 2020): 3327. http://dx.doi.org/10.3390/rs12203327.
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In this work, we compared six emerging mobile laser scanning (MLS) technologies for field reference data collection at the individual tree level in boreal forest conditions. The systems under study were an in-house developed AKHKA-R3 backpack laser scanner, a handheld Zeb-Horizon laser scanner, an under-canopy UAV (Unmanned Aircraft Vehicle) laser scanning system, and three above-canopy UAV laser scanning systems providing point clouds with varying point densities. To assess the performance of the methods for automated measurements of diameter at breast height (DBH), stem curve, tree height and stem volume, we utilized all of the six systems to collect point cloud data on two 32 m-by-32 m test sites classified as sparse (n = 42 trees) and obstructed (n = 43 trees). To analyze the data collected with the two ground-based MLS systems and the under-canopy UAV system, we used a workflow based on our recent work featuring simultaneous localization and mapping (SLAM) technology, a stem arc detection algorithm, and an iterative arc matching algorithm. This workflow enabled us to obtain accurate stem diameter estimates from the point cloud data despite a small but relevant time-dependent drift in the SLAM-corrected trajectory of the scanner. We found out that the ground-based MLS systems and the under-canopy UAV system could be used to measure the stem diameter (DBH) with a root mean square error (RMSE) of 2–8%, whereas the stem curve measurements had an RMSE of 2–15% that depended on the system and the measurement height. Furthermore, the backpack and handheld scanners could be employed for sufficiently accurate tree height measurements (RMSE = 2–10%) in order to estimate the stem volumes of individual trees with an RMSE of approximately 10%. A similar accuracy was obtained when combining stem curves estimated with the under-canopy UAV system and tree heights extracted with an above-canopy flying laser scanning unit. Importantly, the volume estimation error of these three MLS systems was found to be of the same level as the error corresponding to manual field measurements on the two test sites. To analyze point cloud data collected with the three above-canopy flying UAV systems, we used a random forest model trained on field reference data collected from nearby plots. Using the random forest model, we were able to estimate the DBH of individual trees with an RMSE of 10–20%, the tree height with an RMSE of 2–8%, and the stem volume with an RMSE of 20–50%. Our results indicate that ground-based and under-canopy MLS systems provide a promising approach for field reference data collection at the individual tree level, whereas the accuracy of above-canopy UAV laser scanning systems is not yet sufficient for predicting stem attributes of individual trees for field reference data with a high accuracy.
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Liu, Chong, and Zhen Feng Shao. "Estimation of Forest Carbon Storage Based on Airborne LiDAR Data." Applied Mechanics and Materials 195-196 (August 2012): 1314–20. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.1314.
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The objective of this study was to estimate the carbon storage of forest areas by using airborne light detection and ranging (LiDAR) data. Digital canopy height model (CHM) which generated from point cloud data was combined with accurate geo-referenced true color orthophoto image to produce parameter information of trees (height, canopy diameter, DBH data) in test area by marker controlled watershed segmentation algorithm. The total carbon storage of experimental site could be calculated by adopting binary tree volume table and semi-empirical inversion means. This study suggested that the carbon storage of forest areas can be effectively estimated by using LiDAR data, which will play an important role in forest data updating and process of forget sustainable development.
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Alvites, Cesar, Hannah O’Sullivan, Saverio Francini, Marco Marchetti, Giovanni Santopuoli, Gherardo Chirici, Bruno Lasserre, Michela Marignani, and Erika Bazzato. "High-Resolution Canopy Height Mapping: Integrating NASA’s Global Ecosystem Dynamics Investigation (GEDI) with Multi-Source Remote Sensing Data." Remote Sensing 16, no. 7 (April 5, 2024): 1281. http://dx.doi.org/10.3390/rs16071281.
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Accurate structural information about forests, including canopy heights and diameters, is crucial for quantifying tree volume, biomass, and carbon stocks, enabling effective forest ecosystem management, particularly in response to changing environmental conditions. Since late 2018, NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission has monitored global canopy structure using a satellite Light Detection and Ranging (LiDAR) instrument. While GEDI has collected billions of LiDAR shots across a near-global range (between 51.6°N and >51.6°S), their spatial distribution remains dispersed, posing challenges for achieving complete forest coverage. This study proposes and evaluates an approach that generates high-resolution canopy height maps by integrating GEDI data with Sentinel-1, Sentinel-2, and topographical ancillary data through three machine learning (ML) algorithms: random forests (RF), gradient tree boost (GB), and classification and regression trees (CART). To achieve this, the secondary aims included the following: (1) to assess the performance of three ML algorithms, RF, GB, and CART, in predicting canopy heights, (2) to evaluate the performance of our canopy height maps using reference canopy height from canopy height models (CHMs), and (3) to compare our canopy height maps with other two existing canopy height maps. RF and GB were the top-performing algorithms, achieving the best 13.32% and 16% root mean squared error for broadleaf and coniferous forests, respectively. Validation of the proposed approach revealed that the 100th and 98th percentile, followed by the average of the 75th, 90th, 95th, and 100th percentiles (AVG), were the most accurate GEDI metrics for predicting real canopy heights. Comparisons between predicted and reference CHMs demonstrated accurate predictions for coniferous stands (R-squared = 0.45, RMSE = 29.16%).
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Blackman, Raoul, and Fei Yuan. "Detecting Long-Term Urban Forest Cover Change and Impacts of Natural Disasters Using High-Resolution Aerial Images and LiDAR Data." Remote Sensing 12, no. 11 (June 4, 2020): 1820. http://dx.doi.org/10.3390/rs12111820.
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Urban forests provide ecosystem services; tree canopy cover is the basic quantification of ecosystem services. Ground assessment of the urban forest is limited; with continued refinement, remote sensing can become an essential tool for analyzing the urban forest. This study addresses three research questions that are essential for urban forest management using remote sensing: (1) Can object-based image analysis (OBIA) and non-image classification methods (such as random point-based evaluation) accurately determine urban canopy coverage using high-spatial-resolution aerial images? (2) Is it possible to assess the impact of natural disturbances in addition to other factors (such as urban development) on urban canopy changes in the classification map created by OBIA? (3) How can we use Light Detection and Ranging (LiDAR) data and technology to extract urban canopy metrics accurately and effectively? The urban forest canopy area and location within the City of St Peter, Minnesota (MN) boundary between 1938 and 2019 were defined using both OBIA and random-point-based methods with high-spatial-resolution aerial images. Impacts of natural disasters, such as the 1998 tornado and tree diseases, on the urban canopy cover area, were examined. Finally, LiDAR data was used to determine the height, density, crown area, diameter, and volume of the urban forest canopy. Both OBIA and random-point methods gave accurate results of canopy coverages. The OBIA is relatively more time-consuming and requires specialist knowledge, whereas the random-point-based method only shows the total coverage of the classes without locational information. Canopy change caused by tornado was discernible in the canopy OBIA-based classification maps while the change due to diseases was undetectable. To accurately exact urban canopy metrics besides tree locations, dense LiDAR point cloud data collected at the leaf-on season as well as algorithms or software developed specifically for urban forest analysis using LiDAR data are needed.
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Lopes Queiroz, Gustavo, Gregory McDermid, Julia Linke, Christopher Hopkinson, and Jahan Kariyeva. "Estimating Coarse Woody Debris Volume Using Image Analysis and Multispectral LiDAR." Forests 11, no. 2 (January 25, 2020): 141. http://dx.doi.org/10.3390/f11020141.
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Coarse woody debris (CWD, parts of dead trees) is an important factor in forest management, given its roles in promoting local biodiversity and unique microhabitats, as well as providing carbon storage and fire fuel. However, parties interested in monitoring CWD abundance lack accurate methods to measure CWD accurately and extensively. Here, we demonstrate a novel strategy for mapping CWD volume (m3) across a 4300-hectare study area in the boreal forest of Alberta, Canada using optical imagery and an infra-canopy vegetation-index layer derived from multispectral aerial LiDAR. Our models predicted CWD volume with a coefficient of determination (R2) value of 0.62 compared to field data, and a root-mean square error (RMSE) of 0.224 m3/100 m2. Models using multispectral LiDAR data in addition to image-analysis data performed with up to 12% lower RMSE than models using exclusively image-analysis layers. Site managers and researchers requiring reliable and comprehensive maps of CWD volume may benefit from the presented workflow, which aims to streamline the process of CWD measurement. As multispectral LiDAR radiometric calibration routines are developed and standardized, we expect future studies to benefit increasingly more from such products for CWD detection underneath canopy cover.
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Raman, Mugilan Govindasamy, Eduardo Fermino Carlos, and Sindhuja Sankaran. "Optimization and Evaluation of Sensor Angles for Precise Assessment of Architectural Traits in Peach Trees." Sensors 22, no. 12 (June 18, 2022): 4619. http://dx.doi.org/10.3390/s22124619.
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Fruit industries play a significant role in many aspects of global food security. They provide recognized vitamins, antioxidants, and other nutritional supplements packed in fresh fruits and other processed commodities such as juices, jams, pies, and other products. However, many fruit crops including peaches (Prunus persica (L.) Batsch) are perennial trees requiring dedicated orchard management. The architectural and morphological traits of peach trees, notably tree height, canopy area, and canopy crown volume, help to determine yield potential and precise orchard management. Thus, the use of unmanned aerial vehicles (UAVs) coupled with RGB sensors can play an important role in the high-throughput acquisition of data for evaluating architectural traits. One of the main factors that define data quality are sensor imaging angles, which are important for extracting architectural characteristics from the trees. In this study, the goal was to optimize the sensor imaging angles to extract the precise architectural trait information by evaluating the integration of nadir and oblique images. A UAV integrated with an RGB imaging sensor at three different angles (90°, 65°, and 45°) and a 3D light detection and ranging (LiDAR) system was used to acquire images of peach trees located at the Washington State University’s Tukey Horticultural Orchard, Pullman, WA, USA. A total of four approaches, comprising the use of 2D data (from UAV) and 3D point cloud (from UAV and LiDAR), were utilized to segment and measure the individual tree height and canopy crown volume. Overall, the features extracted from the images acquired at 45° and integrated nadir and oblique images showed a strong correlation with the ground reference tree height data, while the latter was highly correlated with canopy crown volume. Thus, selection of the sensor angle during UAV flight is critical for improving the accuracy of extracting architectural traits and may be useful for further precision orchard management.
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Pirotti, F., C. Paterno, and M. Pividori. "APPLICATION OF TREE DETECTION METHODS OVER LIDAR DATA FOR FOREST VOLUME ESTIMATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 1055–60. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1055-2020.
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Abstract. Lidar (light detection and ranging) data are becoming more and more important in the analysis of the most relevant forest parameters. This study aims to compare the most recent segmentation methods for single trees using the ALS (Airborne Laser Scanning) point cloud and the CHM (Canopy Height Model). The methods used were the Li et al., method developed in 2012 and the Multi CHM method developed in 2015. The parameters analysed were the height and diameter for the individual trees and the volume and density for the entire forest. The efficiency of each method was verified by comparing the estimated parameters with those measured through 30 test areas. To better identify the useful parameters for the correct calibration of the algorithms, the population was divided into three layers according to the vertical structure and chronological class. From the comparison of the volumes obtained with the above methods and those calculated for the test areas, it emerges a tendency to over-segment for the Multi CHM method, while for the appropriately calibrated Li method there is a better correspondence to reality. The F-score values for the volumes obtained for the Li method are between 0.52 and 0.69 while for those obtained for the Multi CHM method are between 0.47 and 0.55. When compared with relascopic measures for each of the 48 parcels, a mean absolute difference ∼127 m3/ha and ∼141 m3/ha were found for Li2012 and MultiCHM respectively.
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Sun, Wang, Ding, Lu, and Sun. "Remote Measurement of Apple Orchard Canopy Information Using Unmanned Aerial Vehicle Photogrammetry." Agronomy 9, no. 11 (November 19, 2019): 774. http://dx.doi.org/10.3390/agronomy9110774.
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Information on fruit tree canopies is important for decision making in orchard management, including irrigation, fertilization, spraying, and pruning. An unmanned aerial vehicle (UAV) imaging system was used to establish an orchard three-dimensional (3D) point-cloud model. A row-column detection method was developed based on the probability density estimation and rapid segmentation of the point-cloud data for each apple tree, through which the tree canopy height, H, width, W, and volume, V, were determined for remote orchard canopy evaluation. When the ground sampling distance (GSD) was in the range of 2.13 to 6.69 cm/px, the orchard point-cloud model had a measurement accuracy of 100.00% for the rows and 90.86% to 98.20% for the columns. The coefficient of determination, R2, was in the range of 0.8497 to 0.9376, 0.8103 to 0.9492, and 0.8032 to 0.9148, respectively, and the average relative error was in the range of 1.72% to 3.42%, 2.18% to 4.92%, and 7.90% to 13.69%, respectively, among the H, W, and V values measured manually and by UAV photogrammetry. The results showed that UAV visual imaging is suitable for 3D morphological remote canopy evaluations, facilitates orchard canopy informatization, and contributes substantially to efficient management and control of modern standard orchards.
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Sangjan, Worasit, and Sindhuja Sankaran. "Phenotyping Architecture Traits of Tree Species Using Remote Sensing Techniques." Transactions of the ASABE 64, no. 5 (2021): 1611–24. http://dx.doi.org/10.13031/trans.14419.
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HighlightsTree canopy architecture traits are associated with its productivity and management.Understanding these traits is important for both precision agriculture and phenomics applications.Remote sensing platforms (satellite, UAV, etc.) and multiple approaches (SfM, LiDAR) have been used to assess these traits.3D reconstruction of tree canopies allows the measurement of tree height, crown area, and canopy volume.Abstract. Tree canopy architecture is associated with light use efficiency and thus productivity. Given the modern training systems in orchard tree fruit systems, modification of tree architecture is becoming important for easier management of crops (e.g., pruning, thinning, chemical application, harvesting, etc.) while maintaining fruit quality and quantity. Similarly, in forest environments, architecture can influence the competitiveness and balance between tree species in the ecosystem. This article reviews the literature related to sensing approaches used for assessing architecture traits and the factors that influence such evaluation processes. Digital imagery integrated with structure from motion analysis and both terrestrial and aerial light detection and ranging (LiDAR) systems have been commonly used. In addition, satellite imagery and other techniques have been explored. Some of the major findings and some critical considerations for such measurement methods are summarized here. Keywords: Canopy volume, LiDAR system, Structure from motion, Tree height, UAV.
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Di Gennaro, Salvatore Filippo, Carla Nati, Riccardo Dainelli, Laura Pastonchi, Andrea Berton, Piero Toscano, and Alessandro Matese. "An Automatic UAV Based Segmentation Approach for Pruning Biomass Estimation in Irregularly Spaced Chestnut Orchards." Forests 11, no. 3 (March 12, 2020): 308. http://dx.doi.org/10.3390/f11030308.
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The agricultural and forestry sector is constantly evolving, also through the increased use of precision technologies including Remote Sensing (RS). Remotely biomass estimation (WaSfM) in wood production forests is already debated in the literature, but there is a lack of knowledge in quantifying pruning residues from canopy management. The aim of the present study was to verify the reliability of RS techniques for the estimation of pruning biomass through differences in the volume of canopy trees and to evaluate the performance of an unsupervised segmentation methodology as a feasible tool for the analysis of large areas. Remote sensed data were acquired on four uneven-aged and irregularly spaced chestnut orchards in Central Italy by an Unmanned Aerial Vehicle (UAV) equipped with a multispectral camera. Chestnut geometric features were extracted using both supervised and unsupervised crown segmentation and then applying a double filtering process based on Canopy Height Model (CHM) and vegetation index threshold. The results show that UAV monitoring provides good performance in detecting biomass reduction after pruning, despite some differences between the trees’ geometric features. The proposed unsupervised methodology for tree detection and vegetation cover evaluation purposes showed good performance, with a low undetected tree percentage value (1.7%). Comparing crown projected volume reduction extracted by means of supervised and unsupervised approach, R2 ranged from 0.76 to 0.95 among all the sites. Finally, the validation step was assessed by evaluating correlations between measured and estimated pruning wood biomass (Wpw) for single and grouped sites (0.53 < R2 < 0.83). The method described in this work could provide effective strategic support for chestnut orchard management in line with a precision agriculture approach. In the context of the Circular Economy, a fast and cost-effective tool able to estimate the amounts of wastes available as by-products such as chestnut pruning residues can be included in an alternative and virtuous supply chain.
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Muhojoki, Jesse, Daniella Tavi, Eric Hyyppä, Matti Lehtomäki, Tamás Faitli, Harri Kaartinen, Antero Kukko, Teemu Hakala, and Juha Hyyppä. "Benchmarking Under- and Above-Canopy Laser Scanning Solutions for Deriving Stem Curve and Volume in Easy and Difficult Boreal Forest Conditions." Remote Sensing 16, no. 10 (May 13, 2024): 1721. http://dx.doi.org/10.3390/rs16101721.
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The use of mobile laser scanning for mapping forests has scarcely been studied in difficult forest conditions. In this paper, we compare the accuracy of retrieving tree attributes, particularly diameter at breast height (DBH), stem curve, stem volume, and tree height, using six different laser scanning systems in a managed natural boreal forest. These compared systems operated both under the forest canopy on handheld and unmanned aerial vehicle (UAV) platforms and above the canopy from a helicopter. The complexity of the studied forest sites ranged from easy to difficult, and thus, this is the first study to compare the performance of several laser scanning systems for the direct measurement of stem curve in difficult forest conditions. To automatically detect tree stems and to calculate their attributes, we utilized our previously developed algorithm integrated with a novel bias compensation method to reduce the overestimation of stem diameter arising from finite laser beam divergence. The bias compensation method reduced the absolute value of the diameter bias by 55–99%. The most accurate laser scanning systems were equipped with a Velodyne VLP-16 sensor, which has a relatively low beam divergence, on a handheld or UAV platform. In easy plots, these systems found a root-mean-square error (RMSE) of below 10% for DBH and stem curve estimates and approximately 10% for stem volume. With the handheld system in difficult plots, the DBH and stem curve estimates had an RMSE under 10%, and the stem volume RMSE was below 20%. Even though bias compensation reduced the difference in bias and RMSE between laser scanners with high and low beam divergence, the RMSE remained higher for systems with a high beam divergence. The airborne laser scanner operating above the forest canopy provided tree attribute estimates close to the accuracy of the under-canopy laser scanners, but with a significantly lower completeness rate for stem detection, especially in difficult forest conditions.
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Yan, Tingting, Heping Zhu, Li Sun, Xiaochan Wang, and Peter Ling. "Investigation of an Experimental Laser Sensor-Guided Spray Control System for Greenhouse Variable-Rate Applications." Transactions of the ASABE 62, no. 4 (2019): 899–911. http://dx.doi.org/10.13031/trans.13366.
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Abstract. Precision variable-rate spraying technology is needed for controlled-environment plant production in greenhouses. An experimental spray system for greenhouse applications was developed for real-time control of individual nozzle outputs. The system mainly consisted of a high-speed laser scanning sensor, 12 individual variable-rate nozzles, an embedded computer, a spray control unit, and a 3.6 m long mobile spray boom. Each nozzle was coupled with a pulse-width modulated solenoid valve to discharge sprays at variable rates based on target presence and plant canopy structure. Laboratory tests were conducted to evaluate the accuracy of the spray control system in respect to spray delay time, nozzle activation, and spray volume using four target objects of different regular geometrical shapes and surface textures and two artificial plants of different canopy structures. Other experimental variables included three detection heights from 0.5 to 1.0 m and five sensor travel speeds from 1.6 to 4.8 km h-1. A high-speed video camera was used to determine the delay time and nozzle activation in discharging sprays on target objects after the laser sensor had detected the objects. The detection height and travel speed were found to have slight influence on the timing of nozzle activation. The nozzles started spraying in a range between 33 and 83 mm before reaching the target objects and stopped spraying between 13 and 84 mm after passing the objects, ensuring that the objects were fully covered by the spray. Spray volume corresponded to the object sizes well, and the spray control system performed with higher accuracy at lower travel speeds. Differences between the calculated spray volume based on the sensor detection and the actual spray volume ranged from 1.9 to 2.7 mL per object among all tested objects. The variable-rate control system reduced spray volume by 29.3% to 51.4% for all the objects compared with conventional constant-rate spraying. At the same time, the nozzles could be activated precisely by the object presence. Consequently, this experimental laser-guided system was implemented on a boom system in a commercial greenhouse for future investigations of its accuracy in variable-rate spraying to save pesticides, water, and nutrients. Keywords: Automation, Intelligent sprayer, Pesticide, Precision spray technology, Boom spray equipment.
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Hollaus, M., W. Wagner, K. Schadauer, B. Maier, and K. Gabler. "Growing stock estimation for alpine forests in Austria: a robust lidar-based approach." Canadian Journal of Forest Research 39, no. 7 (July 2009): 1387–400. http://dx.doi.org/10.1139/x09-042.
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The overall goal of this study was to describe a novel area-based semiempirical model for estimating growing stock from small-footprint light detection and ranging (lidar) data. The model assumes a linear relationship between growing stock and lidar-derived canopy volume that is stratified according to several canopy height classes to account for height dependent differences in canopy structure and nonlinear tree size-shape relationships. It was applied over a 128 km2 alpine area in Austria where operational forest inventory data and lidar data acquired in winter and summer were available. The analysis showed that the semiempirical model was quite robust against changes in laser point density and acquisition time. Further, it was found that the model performed as well as a widely used iterative regression method based on a multiplicative model. Both models reached a high coefficient of determination (R2 = 0.76–0.86) and a standard deviation of the residuals in the order of 20.4%–29.1%. Although it is less flexible than the multiplicative model, the advantages of the semiempirical model are its simplicity and the fact that its coefficients can be physically interpreted. These traits can be expected to enhance the applicability of the model in regions where high-quality inventory data are lacking.
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Gao, Sha, Zhengnan Zhang, and Lin Cao. "Individual Tree Structural Parameter Extraction and Volume Table Creation Based on Near-Field LiDAR Data: A Case Study in a Subtropical Planted Forest." Sensors 21, no. 23 (December 6, 2021): 8162. http://dx.doi.org/10.3390/s21238162.
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Individual tree structural parameters are vital for precision silviculture in planted forests. This study used near-field LiDAR (light detection and ranging) data (i.e., unmanned aerial vehicle laser scanning (ULS) and ground backpack laser scanning (BLS)) to extract individual tree structural parameters and fit volume models in subtropical planted forests in southeastern China. To do this, firstly, the tree height was acquired from ULS data and the diameter at breast height (DBH) was acquired from BLS data by using individual tree segmentation algorithms. Secondly, point clouds of the complete forest canopy were obtained through the combination of ULS and BLS data. Finally, five tree taper models were fitted using the LiDAR-extracted structural parameters of each tree, and then the optimal taper model was selected. Moreover, standard volume models were used to calculate the stand volume; then, standing timber volume tables were created for dawn redwood and poplar. The extraction of individual tree structural parameters exhibited good performance. The volume model had a good performance in calculating the standing volume for dawn redwood and poplar. Our results demonstrate that near-field LiDAR has a strong capability of extracting tree structural parameters and creating volume tables for subtropical planted forests.
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Mikita, Tomáš, and Petr Balogh. "Usage of Geoprocessing Services in Precision Forestry for Wood Volume Calculation and Wind Risk Assessment." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 63, no. 3 (2015): 793–801. http://dx.doi.org/10.11118/actaun201563030793.
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This paper outlines the idea of a precision forestry tool for optimizing clearcut size and shape within the process of forest recovery and its publishing in the form of a web processing service for forest owners on the Internet. The designed tool titled COWRAS (Clearcut Optimization and Wind Risk Assessment) is developed for optimization of clearcuts (their location, shape, size, and orientation) with subsequent wind risk assessment. The tool primarily works with airborne LiDAR data previously processed to the form of a digital surface model (DSM) and a digital elevation model (DEM). In the first step, the growing stock on the planned clearcut determined by its location and area in feature class is calculated (by the method of individual tree detection). Subsequently tree heights from canopy height model (CHM) are extracted and then diameters at breast height (DBH) and wood volume using the regressions are calculated. Information about wood volume of each tree in the clearcut is exported and summarized in a table. In the next step, all trees in the clearcut are removed and a new DSM without trees in the clearcut is generated. This canopy model subsequently serves as an input for evaluation of wind risk damage by the MAXTOPEX tool (Mikita et al., 2012). In the final raster, predisposition of uncovered forest stand edges (around the clearcut) to wind risk is calculated based on this analysis. The entire tool works in the background of ArcGIS server as a spatial decision support system for foresters.
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Kuo, Kuangting, Kenta Itakura, and Fumiki Hosoi. "Leaf Segmentation Based on k-Means Algorithm to Obtain Leaf Angle Distribution Using Terrestrial LiDAR." Remote Sensing 11, no. 21 (October 29, 2019): 2536. http://dx.doi.org/10.3390/rs11212536.
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It is critical to take the variability of leaf angle distribution into account in a remote sensing analysis of a canopy system. Due to the physical limitations of field measurements, it is difficult to obtain leaf angles quickly and accurately, especially with a complicated canopy structure. An application of terrestrial LiDAR (Light Detection and Ranging) is a common solution for the purposes of leaf angle estimation, and it allows for the measurement and reconstruction of 3D canopy models with an arbitrary volume of leaves. However, in most cases, the leaf angle is estimated incorrectly due to inaccurate leaf segmentation. Therefore, the objective of this study was an emphasis on the development of efficient segmentation algorithms for accurate leaf angle estimation. Our study demonstrates a leaf segmentation approach based on a k-means algorithm coupled with an octree structure and the subsequent application of plane-fitting to estimate the leaf angle. Furthermore, the accuracy of the segmentation and leaf angle estimation was verified. The results showed average segmentation accuracies of 95% and 90% and absolute angular errors of 3° and 6° in the leaves sampled from mochi and Japanese camellia trees, respectively. It is our conclusion that our method of leaf angle estimation has high potential and is expected to make a significant contribution to future plant and forest research.
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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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Tsoulias, Nikos, Dimitrios S. Paraforos, Spyros Fountas, and Manuela Zude-Sasse. "Estimating Canopy Parameters Based on the Stem Position in Apple Trees Using a 2D LiDAR." Agronomy 9, no. 11 (November 11, 2019): 740. http://dx.doi.org/10.3390/agronomy9110740.
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Data of canopy morphology are crucial for cultivation tasks within orchards. In this study, a 2D light detection and range (LiDAR) laser scanner system was mounted on a tractor, tested on a box with known dimensions (1.81 m × 0.6 m × 0.6 m), and applied in an apple orchard to obtain the 3D structural parameters of the trees (n = 224). The analysis of a metal box which considered the height of four sides resulted in a mean absolute error (MAE) of 8.18 mm with a bias (MBE) of 2.75 mm, representing a root mean square error (RMSE) of 1.63% due to gaps in the point cloud and increased incident angle with enhanced distance between laser aperture and the object. A methodology based on a bivariate point density histogram is proposed to estimate the stem position of each tree. The cylindrical boundary was projected around the estimated stem positions to segment each individual tree. Subsequently, height, stem diameter, and volume of the segmented tree point clouds were estimated and compared with manual measurements. The estimated stem position of each tree was defined using a real time kinematic global navigation satellite system, (RTK-GNSS) resulting in an MAE and MBE of 33.7 mm and 36.5 mm, respectively. The coefficient of determination (R2) considering manual measurements and estimated data from the segmented point clouds appeared high with, respectively, R2 and RMSE of 0.87 and 5.71% for height, 0.88 and 2.23% for stem diameter, as well as 0.77 and 4.64% for canopy volume. Since a certain error for the height and volume measured manually can be assumed, the LiDAR approach provides an alternative to manual readings with the advantage of getting tree individual data of the entire orchard.
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Rouzbeh Kargar, Ali, Richard MacKenzie, Gregory P. Asner, and Jan van Aardt. "A Density-Based Approach for Leaf Area Index Assessment in a Complex Forest Environment Using a Terrestrial Laser Scanner." Remote Sensing 11, no. 15 (July 31, 2019): 1791. http://dx.doi.org/10.3390/rs11151791.
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Forests are an important part natural ecosystems, by for example providing food, fiber, habitat, and biodiversity, all of which contribute to stable natural systems. Assessing and modeling the structure and characteristics of forests, e.g., Leaf Area Index (LAI), volume, biomass, etc., can lead to a better understanding and management of these resources. In recent years, Terrestrial Laser Scanning (TLS) has been recognized as a tool that addresses many of the limitations of manual and traditional forest data collection methods. In this study, we propose a density-based approach for estimating the LAI in a structurally-complex forest environment, which contains variable and diverse structural attributes, e.g., non-circular stem forms, dense canopy and below-canopy vegetation cover, and a diverse species composition. In addition, 242 TLS scans were collected using a portable low-cost scanner, the Compact Biomass Lidar (CBL), in the Hawaii Volcanoes National Park (HAVO), Hawaii Island, USA. LAI also was measured for 242 plots in the site, using an AccuPAR LP-80 ceptometer. The first step after cleaning the point cloud involved detecting the higher forest canopy in the light detection and ranging (lidar) point clouds, using normal change rate assessment. We then estimated Leaf Area Density (LAD), using a voxel-based approach, and divided the canopy point cloud into five layers in the Z (vertical) direction. These five layers subsequently were divided into voxels in the X direction, where the size of these voxels were obtained based on inter-quartile analysis and the number of points in each voxel. We hypothesized that the intensity returned to the lidar system from woody materials, like branches, would be higher than from leaves, due to the liquid water absorption feature of the leaves and higher reflectance for woody material at the 905 nm laser wavelength. We also differentiated between foliar and woody materials using edge detection in the images from projected point clouds and evaluated the density of these regions to support our hypothesis. Density of points, or the number of points divided by the volume of a grid, in a 3D grid size of 0.1 m, was calculated for each of the voxels. The grid size was determined by investigating the size of the branches in the lower portion of the canopy. Subsequently, we fitted a Kernel Density Estimator (KDE) to these values, with the threshold set based on half of the area under the curve in each of the density distributions. All the grids with a density below the threshold were labeled as leaves, while those grids above the threshold were identified as non-leaves. Finally, we modeled LAI using the point densities derived from the TLS point clouds and the listed analysis steps. This model resulted in an R 2 value of 0.88. We also estimated the LAI directly from lidar data using the point densities and calculating LAD, which is defined as the total one-sided leaf area per unit volume. LAI can be obtained as the sum of the LAD values in all the voxels. The accuracy of LAI estimation was 90%, with an RMSE value of 0.31, and an average overestimation of 9 % in TLS-derived LAI, when compared to field-measured LAI. Algorithm performance mainly was affected by the vegetation density and complexity of the canopy structures. It is worth noting that, since the LAI values cannot be considered spatially independent throughout all the plots in this site, we performed semivariogram analysis on the field-measured LAI data. This analysis showed that the LAI values can be assumed to be independent in plots that are at least 30 m apart. As a result, we divided the data into six subsets in which the plots were 30 m spaced. The R 2 values for these subsets, based on modeling of the field-measured LAI using leaf point density values, ranged between 0.84–0.96. The results bode well for using this method for efficient, automatic, and accurate/precise estimation of LAI values in complex forest environments, using a low-cost, rapid-scan TLS.
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Parker, Robert C., and David L. Evans. "LiDAR Forest Inventory with Single-Tree, Double-, and Single-Phase Procedures." International Journal of Forestry Research 2009 (2009): 1–6. http://dx.doi.org/10.1155/2009/864108.
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Light Detection and Ranging (LiDAR) data at 0.5–2 m postings were used with double-sample, stratified procedures involving single-tree relationships in mixed, and single species stands to yield sampling errors ranging from % to %. LiDAR samples were selected with focal filter procedures and heights computed from interpolated canopy and DEM surfaces. Tree dbh and height data were obtained at various ratios of LiDAR, ground samples for DGPS located ground plots. Dbh-height and ground-LiDAR height models were used to predict dbh and compute Phase 2 estimates of basal area and volume. Phase 1 estimates were computed using the species probability distribution from ground plots in each strata. Phase 2 estimates were computed by randomly assigning LiDAR heights to species groups using a Monte Carlo simulation for each ground plot. There was no statistical difference between volume estimates from 0.5 m and 1 m LiDAR densities. Volume estimates from single-phase LiDAR procedures utilizing existing tree attributes and height bias relationships were obtained with sampling errors of 1.8% to 5.5%.
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Szostak, Marta, and Marek Pająk. "LiDAR Point Clouds Usage for Mapping the Vegetation Cover of the “Fryderyk” Mine Repository." Remote Sensing 15, no. 1 (December 30, 2022): 201. http://dx.doi.org/10.3390/rs15010201.
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The paper investigates the usage of LiDAR (light detection and ranging) data for the automation of mapping vegetation with respect to the evaluation of the ecological succession process. The study was performed for the repository of the “Fryderyk” mine (southern Poland). The post-flotation area analyzed is a unique refuge habitat—Natura2000, PLH240008—where a forest succession has occurred for several dozen years. Airborne laser scanning (ALS) point clouds were used for deriving detailed information about the morphometry of the spoil heap and about the secondary forest succession process—mainly vegetation parameters i.e., height and canopy cover. The area of the spoil heap is irregular with a flat top and steep slopes above 20°. Analyses of ALS point clouds (2011 and 2019), confirmed progression in the forest succession process, and land cover changes especially in wooded or bushed areas. Precise vegetation parameters (3D LiDAR metrics) were calculated and provided the following parameters: mean value of vegetation height as 6.84 m (2011) and 8.41 m (2019), and canopy cover as 30.0% (2011) and 42.0% (2019). Changes in vegetation volume (3D area) were shown: 2011—310,558 m3, 2019—325,266 m3, vegetation removal—85,136 m3, increasing ecological succession—99,880 m3.
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Xi, Zhouxin, Christopher Hopkinson, Stewart B. Rood, Celeste Barnes, Fang Xu, David Pearce, and Emily Jones. "A Lightweight Leddar Optical Fusion Scanning System (FSS) for Canopy Foliage Monitoring." Sensors 19, no. 18 (September 12, 2019): 3943. http://dx.doi.org/10.3390/s19183943.
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A growing need for sampling environmental spaces in high detail is driving the rapid development of non-destructive three-dimensional (3D) sensing technologies. LiDAR sensors, capable of precise 3D measurement at various scales from indoor to landscape, still lack affordable and portable products for broad-scale and multi-temporal monitoring. This study aims to configure a compact and low-cost 3D fusion scanning system (FSS) with a multi-segment Leddar (light emitting diode detection and ranging, LeddarTech), a monocular camera, and rotational robotics to recover hemispherical, colored point clouds. This includes an entire framework of calibration and fusion algorithms utilizing Leddar depth measurements and image parallax information. The FSS was applied to scan a cottonwood (Populus spp.) stand repeatedly during autumnal leaf drop. Results show that the calibration error based on bundle adjustment is between 1 and 3 pixels. The FSS scans exhibit a similar canopy volume profile to the benchmarking terrestrial laser scans, with an r2 between 0.5 and 0.7 in varying stages of leaf cover. The 3D point distribution information from FSS also provides a valuable correction factor for the leaf area index (LAI) estimation. The consistency of corrected LAI measurement demonstrates the practical value of deploying FSS for canopy foliage monitoring.
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VIZIREANU, Ioana, Andreea CALCAN, Georgiana GRIGORAS, and Dan RADUCANU. "Detection of trees features from a forestry area using airborne LiDAR data." INCAS BULLETIN 13, no. 1 (March 5, 2020): 225–36. http://dx.doi.org/10.13111/2066-8201.2021.13.1.23.
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The impact of anthropogenic actions on the environment and climate has recently increased the need to map the afforested areas. In this context, the three-dimensional (3D) measurement of vegetation structures plays an important role in having an efficient forest inventory and management. Nowadays, the airborne LiDAR (Light Detection And Ranging) system offers high horizontal resolution as well as vertical dimension information, making it possible to estimate both three-dimensional characteristics of individual trees and to identify the distribution of forest resources in the region. This study aims to present a processing approach for the determination of each tree’s position (X and Y location, as well as tree height) and its dimensions (crown diameter, area and volume) using geometrically accurate 3D point clouds (data sets were collected in a forested area in Argeș County, Romania). To a better understanding of the forest features and to explore the potential of remote sensing for such analysis, it was further exploited Digital Terrain Model (DTM), Digital Surface Model (DSM), and Canopy Height Model (CHM) derivation.
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Surfleet, Christopher G., Brian Dietterick, and Arne Skaugset. "Change detection of storm runoff and sediment yield using hydrologic models following wildfire in a coastal redwood forest, California." Canadian Journal of Forest Research 44, no. 6 (June 2014): 572–81. http://dx.doi.org/10.1139/cjfr-2013-0328.
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This study attempted to detect changes in stormflow volumes, peakflows, and sediment loads using hydrologic models within the context of an uncertainty assessment following wildfire. In 2009, after 8 years of study, the Lockheed Fire burned the treatment and control watersheds of a paired watershed study in coastal California, USA, eliminating the ability to continue a paired watershed before–after control–intervention (BACI) study design. An alternative analysis was used to detect stormflow and sediment load changes due to the wildfire by comparing measured posttreatment stormflow and sediment load with simulated predisturbance responses predicted with the hydrologic models HBV-EC and DHSVM. High natural variability of stormflow and sediment measurements compounded with uncertainty associated with the hydrologic models and climate suggest that only large changes can be detected. The fire and subsequent salvage harvest created an approximately 9%–12% reduction in forest overstory canopy and a 70%–90% consumption of understory vegetation. No discernible changes in slopes of regression lines were detected between predisturbance and postfire stormflow volumes, peakflows, or sediment loads. No changes were detected in stormflow volume, peakflow, or sediment loads comparing pre- and post-fire vegetation inputs to the hydrologic model DHSVM. The lack of detected change in streamflow to accelerate stream channel erosion combined with low to moderate fire severity adjacent to stream channels most likely were the reasons for no detected postfire change to sediment loads.
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Meyer, Victoria, Sassan Saatchi, David B. Clark, Michael Keller, Grégoire Vincent, António Ferraz, Fernando Espírito-Santo, Marcus V. N. d'Oliveira, Dahlia Kaki, and Jérôme Chave. "Canopy area of large trees explains aboveground biomass variations across neotropical forest landscapes." Biogeosciences 15, no. 11 (June 8, 2018): 3377–90. http://dx.doi.org/10.5194/bg-15-3377-2018.
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Abstract. Large tropical trees store significant amounts of carbon in woody components and their distribution plays an important role in forest carbon stocks and dynamics. Here, we explore the properties of a new lidar-derived index, the large tree canopy area (LCA) defined as the area occupied by canopy above a reference height. We hypothesize that this simple measure of forest structure representing the crown area of large canopy trees could consistently explain the landscape variations in forest volume and aboveground biomass (AGB) across a range of climate and edaphic conditions. To test this hypothesis, we assembled a unique dataset of high-resolution airborne light detection and ranging (lidar) and ground inventory data in nine undisturbed old-growth Neotropical forests, of which four had plots large enough (1 ha) to calibrate our model. We found that the LCA for trees greater than 27 m (∼ 25–30 m) in height and at least 100 m2 crown size in a unit area (1 ha), explains more than 75 % of total forest volume variations, irrespective of the forest biogeographic conditions. When weighted by average wood density of the stand, LCA can be used as an unbiased estimator of AGB across sites (R2 = 0.78, RMSE = 46.02 Mg ha−1, bias = −0.63 Mg ha−1). Unlike other lidar-derived metrics with complex nonlinear relations to biomass, the relationship between LCA and AGB is linear and remains unique across forest types. A comparison with tree inventories across the study sites indicates that LCA correlates best with the crown area (or basal area) of trees with diameter greater than 50 cm. The spatial invariance of the LCA–AGB relationship across the Neotropics suggests a remarkable regularity of forest structure across the landscape and a new technique for systematic monitoring of large trees for their contribution to AGB and changes associated with selective logging, tree mortality and other types of tropical forest disturbance and dynamics.
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Goldbergs, Grigorijs. "Comparison of Canopy Height Metrics from Airborne Laser Scanner and Aerial/Satellite Stereo Imagery to Assess the Growing Stock of Hemiboreal Forests." Remote Sensing 15, no. 6 (March 21, 2023): 1688. http://dx.doi.org/10.3390/rs15061688.
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This study compared the canopy height model (CHM) performance obtained from large-format airborne and very high-resolution satellite stereo imagery (VHRSI), with airborne laser scanning (ALS) data, for growing stock (stand volume) estimation in mature, dense Latvian hemiboreal forests. The study used growing stock data obtained by ALS-based individual tree detection as training/reference data for the image-based and ALS CHM height metrics-based growing stock estimators. The study only compared the growing stock species-specific area-based regression models which are based solely on tree/canopy height as a predictor variable applied to regular rectangular 0.25 and 1 ha plots and irregular forest stands. This study showed that ALS and image-based (IB) height metrics demonstrated comparable effectiveness in growing stock prediction in dense closed-canopy forests. The relative RMSEs did not exceed 20% of the reference mean values for all models. The best relative RMSEs achieved were 13.6% (IB) and 15.7% (ALS) for pine 0.25 ha plots; 10.3% (IB) and 12.1% (ALS) for pine 1 ha plots; 16.4% (IB) and 12.2% (ALS) for spruce 0.25 ha plots; 17.9% (IB) and 14.2% (ALS) for birch 0.25 ha plots; 15.9% (IB) and 18.9% (ALS) for black alder 0.25 ha plots. This research suggests that airborne imagery and, accordingly, image-based CHMs collected regularly can be an efficient solution for forest growing stock calculations/updates, in addition to a traditional visual forest inventory routine. However, VHRSI can be the fastest and cheapest solution for monitoring forest growing stock changes in vast and dense forestland under optimal data collection parameters.
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Park, Taejin. "Potential Lidar Height, Intensity, and Ratio Parameters for Plot Dominant Species Discrimination and Volume Estimation." Remote Sensing 12, no. 19 (October 8, 2020): 3266. http://dx.doi.org/10.3390/rs12193266.
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Precise stand species classification and volume estimation are key research topics for automated forest inventory. This study aims to explore the feasibility of light detection and ranging (lidar) height, intensity, and ratio parameters for discriminating dominant species (Pinus densiflora, Larix kaempferi, and Quercus spp.) and estimating volume at plot scale. To achieve these objectives, multiple linear discriminant and regression analyses were utilized after a separate selection of explanatory variables from extracted 38 lidar height, intensity, and ratio parameters. A kappa accuracy of 0.75 was achieved in discriminating the plot-dominant species from three different species by adopting a combination of nine selected explanatory variables. Further investigation found that dispersion and mean of lidar intensity within a plot are key classifiers of identifying three species. Species-specific optimal plot volume models for Pinus densiflora, Larix kaempferi, and Quercus spp. were evaluated by coefficients of determination of 0.71, 0.74, and 0.56, respectively. Compared to species classification, height-related lidar variables play a key role in modeling forest plot volume. Several explanatory variables for each modeling practice were correlated to canopy vertical and horizontal structures and were enough to represent species-specific characteristics in both approaches for species classification and plot volume estimation. Additionally, observed different variable combinations for two important applications imply that future studies should use proper variable combinations for each purpose.
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Goerndt, Michael E., Vincente J. Monleon, and Hailemariam Temesgen. "Relating Forest Attributes with Area- and Tree-Based Light Detection and Ranging Metrics for Western Oregon." Western Journal of Applied Forestry 25, no. 3 (July 1, 2010): 105–11. http://dx.doi.org/10.1093/wjaf/25.3.105.
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Abstract Three sets of linear models were developed to predict several forest attributes, using stand-level and single-tree remote sensing (STRS) light detection and ranging (LiDAR) metrics as predictor variables. The first used only area-level metrics (ALM) associated with first-return height distribution, percentage of cover, and canopy transparency. The second alternative included metrics of first-return LiDAR intensity. The third alternative used area-level variables derived from STRS LiDAR metrics. The ALM model for Lorey's height did not change with inclusion of intensity and yielded the best results in terms of both model fit (adjusted R2 = 0.93) and cross-validated relative root mean squared error (RRMSE = 8.1%). The ALM model for density (stems per hectare) had the poorest precision initially (RRMSE = 39.3%), but it improved dramatically (RRMSE = 27.2%) when intensity metrics were included. The resulting RRMSE values of the ALM models excluding intensity for basal area, quadratic mean diameter, cubic stem volume, and average crown width were 20.7, 19.9, 30.7, and 17.1%, respectively. The STRS model for Lorey's height showed a 3% improvement in RRMSE over the ALM models. The STRS basal area and density models significantly underperformed compared with the ALM models, with RRMSE values of 31.6 and 47.2%, respectively. The performance of STRS models for crown width, volume, and quadratic mean diameter was comparable to that of the ALM models.
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Matinnia, Benyamin, Aidin Parsakhoo, Jahangir Mohamadi, and Shaban Shataee Jouibary. "Study of the LiDAR accuracy in mapping forest road alignments and estimating the earthwork volume." Journal of Forest Science 64, No. 11 (December 3, 2018): 469–77. http://dx.doi.org/10.17221/87/2018-jfs.
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Today, differential geographical position system and total station devices are improving the accuracy of positioning information, but in critical locations such as steep slopes and closed canopy cover, the device accuracy is limited. Moreover, field surveying in this technique is time-consuming and expensive. For this reason, remote sensing technique such as light detection and ranging (LiDAR) laser scanner should be used in field measurements. The objective of this study was to evaluate and compare precision and time expenditure of total station and airborne LiDAR in producing horizontal and vertical alignments and estimating earthwork volume of two proposed forest roads in a deciduous forest of Iran. To investigate this task, the geographical position of proposed forest roads were detected by differential geographical position system and then marked on land. Mentioned roads were taken again with Leica Total Station (LTS) on control points with same 5 m intervals from start point. Recent data served as a reference value for comparison with LiDAR measurements. The data were processed in Civil 3D, Fusion and Leica geo office software. Results showed that in comparison to field-surveyed routes by LTS, the LiDAR-derived routes exhibited a horizontal accuracy of 0.23 and 0.47 m and vertical accuracy of 0.31 and 0.66 m for road 1 and road 2, respectively. The LiDAR-derived sections every 1 m exhibited cut and fill accuracy of 2.39 and 3.18 m<sup>3</sup> for road 1 and 2.98 and 5.60 m<sup>3</sup> road 2, respectively. In this study, it was proved that the road project can be prepared faster by LiDAR than that of LTS. Therefore, high accuracy of road projection by LiDAR is useful for terrain analysis without the need for field reconnaissance.
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Ali-Sisto, D., and P. Packalen. "COMPARISON OF 3D POINT CLOUDS FROM AERIAL STEREO IMAGES AND LIDAR FOR FOREST CHANGE DETECTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W3 (October 19, 2017): 1–5. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w3-1-2017.
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This study compares performance of aerial image based point clouds (IPCs) and light detection and ranging (LiDAR) based point clouds in detection of thinnings and clear cuts in forests. IPCs are an appealing method to update forest resource data, because of their accuracy in forest height estimation and cost-efficiency of aerial image acquisition. We predicted forest changes over a period of three years by creating difference layers that displayed the difference in height or volume between the initial and subsequent time points. Both IPCs and LiDAR data were used in this process. The IPCs were constructed with the Semi-Global Matching (SGM) algorithm. Difference layers were constructed by calculating differences in fitted height or volume models or in canopy height models (CHMs) from both time points. The LiDAR-derived digital terrain model (DTM) was used to scale heights to above ground level. The study area was classified in logistic regression into the categories ClearCut, Thinning or NoChange with the values from the difference layers. We compared the predicted changes with the true changes verified in the field, and obtained at best a classification accuracy for clear cuts 93.1&thinsp;% with IPCs and 91.7&thinsp;% with LiDAR data. However, a classification accuracy for thinnings was only 8.0&thinsp;% with IPCs. With LiDAR data 41.4&thinsp;% of thinnings were detected. In conclusion, the LiDAR data proved to be more accurate method to predict the minor changes in forests than IPCs, but both methods are useful in detection of major changes.
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Garcia Millan, Virginia E., Cassidy Rankine, and G. Arturo Sanchez-Azofeifa. "Crop Loss Evaluation Using Digital Surface Models from Unmanned Aerial Vehicles Data." Remote Sensing 12, no. 6 (March 18, 2020): 981. http://dx.doi.org/10.3390/rs12060981.
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Precision agriculture and Unmanned Aerial Vehicles (UAV) are revolutionizing agriculture management methods. Remote sensing data, image analysis and Digital Surface Models derived from Structure from Motion and Multi-View Stereopsis offer new and fast methods to detect the needs of crops, greatly improving crops efficiency. In this study, we present a tool to detect and estimate crop damage after a disturbance (i.e., weather event, wildlife attacks or fires). The types of damage that are addressed in this study affect crop structure (i.e., plants are bent or gone), in the shape of depressions in the crop canopy. The aim of this study was to evaluate the performance of four unsupervised methods based on terrain analyses, for the detection of damaged crops in UAV 3D models: slope detection, variance analysis, geomorphology classification and cloth simulation filter. A full workflow was designed and described in this article that involves the postprocessing of the raw results from the terrain analyses, for a refinement in the detection of damages. Our results show that all four methods performed similarly well after postprocessing––reaching an accuracy above to 90%––in the detection of severe crop damage, without the need of training data. The results of this study suggest that the used methods are effective and independent of the crop type, crop damage and growth stage. However, only severe damages were detected with this workflow. Other factors such as data volume, processing time, number of processing steps and spatial distribution of targets and errors are discussed in this article for the selection of the most appropriate method. Among the four tested methods, slope analysis involves less processing steps, generates the smallest data volume, is the fastest of methods and resulted in best spatial distribution of matches. Thus, it was selected as the most efficient method for crop damage detection.
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Machimura, Takashi, Ayana Fujimoto, Kiichiro Hayashi, Hiroaki Takagi, and Satoru Sugita. "A Novel Tree Biomass Estimation Model Applying the Pipe Model Theory and Adaptable to UAV-Derived Canopy Height Models." Forests 12, no. 2 (February 23, 2021): 258. http://dx.doi.org/10.3390/f12020258.
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Aiming to develop a new tree biomass estimation model that is adaptable to airborne observations of forest canopies by unmanned aerial vehicles (UAVs), we applied two theories of plant form; the pipe model theory (PMT) and the statical model of plant form as an extension of the PMT for tall trees. Based on these theories, tree biomass was formulated using an individual tree canopy height model derived from a UAV. The advantage of this model is that it does not depend on diameter at breast height which is difficult to observe using remote-sensing techniques. We also proposed a treetop detection method based on the fractal geometry of the crown and stand. Comparing surveys in plantations of Japanese cedar (Cryptomeria japonica D. Don) and Japanese cypress (Chamaecyparis obtusa Endl.) in Japan, the root mean square error (RMSE) of the estimated stem volume was 0.26 m3 and was smaller than or comparative to that of models using different methodologies. The significance of this model is that it contains only one empirical parameter to be adjusted which was found to be rather stable among different species and sites, suggesting the wide adaptability of the model. Finally, we demonstrated the potential applicability of the model to light detection and ranging (LiDAR) data which can provide vertical leaf density distribution.
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Wang, Jinghua, Xiang Li, Guijun Yang, Fan Wang, Sen Men, Bo Xu, Ze Xu, Haibin Yang, and Lei Yan. "Research on Tea Trees Germination Density Detection Based on Improved YOLOv5." Forests 13, no. 12 (December 8, 2022): 2091. http://dx.doi.org/10.3390/f13122091.
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Tea plants are one of the most widely planted agricultural crops in the world. The traditional method of surveying germination density is mainly manual checking, which is time-consuming and inefficient. In this research, the Improved YOLOv5 model was used to identify tea buds and detect germination density based on tea trees canopy visible images. Firstly, five original YOLOv5 models were trained for tea trees germination recognition, and performance and volume were compared. Secondly, backbone structure was redesigned based on the lightweight theory of Xception and ShuffleNetV2. Meanwhile, reverse attention mechanism (RA) and receptive field block (RFB) were added to enhance the network feature extraction ability, achieving the purpose of optimizing the YOLOv5 network from both lightweight and accuracy improvement. Finally, the recognition ability of the Improved YOLOv5 model was analyzed, and the germination density of tea trees was detected according to the tea bud count. The experimental results show that: (1) The parameter numbers of the five original YOLOv5 models were inversely proportional to the detection accuracy. The YOLOv5m model with the most balanced comprehensive performance contained 20,852,934 parameters, the precision rate of the YOLOv5m recognition model was 74.9%, the recall rate was 75.7%, and the mAP_0.5 was 0.758. (2) The Improved YOLOv5 model contained 4,326,815 parameters, the precision rate of the Improved YOLOv5 recognition model was 94.9%, the recall rate was 97.67%, and the mAP_0.5 was 0.758. (3) The YOLOv5m model and the Improved YOLOv5 model were used to test the validation set, and the true positive (TP) values identified were 86% and 94%, respectively. The Improved YOLOv5 network model was effectively improved in both volume and accuracy according to the result. This research is conducive to scientific planning of tea bud picking, improving the production efficiency of the tea plantation and the quality of tea production in the later stage.
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Parker, Robert C., and Patrick A. Glass. "High- Versus Low-Density LiDAR in a Double-Sample Forest Inventory." Southern Journal of Applied Forestry 28, no. 4 (November 1, 2004): 205–10. http://dx.doi.org/10.1093/sjaf/28.4.205.
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Abstract Light detection and ranging (LiDAR) data at 0.5- and 1-m postings were used in a double-sample forest inventory on Louisiana State University's Lee Experimental Forest, Louisiana. Phase 2 plots were established with DGPS. Tree dbh (>4.5 in.) and two sample heights (minimum and maximum dbh) were measured on every 10th plot of the phase 1 sample. Volume was computed for natural and planted pine and mixed hardwood species. LiDAR trees were selected with focal filter procedures and heights computed as the height difference between interpolated canopy and DEM surfaces. Dbh-height and ground-LiDAR height models were used to predict dbh from adjusted LiDAR height and compute ground and LiDAR estimates of ft2 basal area and ft3 volume. Phase 1 LiDAR estimates were computed by randomly assigning heights to species classes using the probability distribution from ground plots in each inventory strata. Phase 2 LiDAR estimates were computed by randomly assigning heights to species-product groups using a Monte Carlo simulation for each ground plot. There was no statistical difference between high-versus low-density LiDAR estimates on adjusted mean volume estimates (sampling errors of 8.16 versus 7.60% without height adjustment and 8.98 versus 8.63% with height adjustment). Low-density LiDAR surfaces without height adjustment produced the lowest sampling errors for stratified and nonstratified, double-sample volume estimates. South. J. Appl. For. 28(4):205–210.
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Parker, Robert C., and A. Lee Mitchel. "Smoothed Versus Unsmoothed LiDAR in a Double-Sample Forest Inventory." Southern Journal of Applied Forestry 29, no. 1 (February 1, 2005): 40–47. http://dx.doi.org/10.1093/sjaf/29.1.40.
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Abstract Light detection and ranging (LiDAR) data at 0.5- and 1-m postings were used in a double-sample forest inventory on Louisiana State University's Lee Experimental Forest, Louisiana. Phase 2 plots were established with differential global positioning system (DGPS). Tree dbh (>4.5in.) and two sample heights were measured on every 10th plot of the Phase 1 sample. Volume was computed for natural and planted pine and mixed hardwood species. LiDAR trees were selected with focal filter procedures from smoothed and unsmoothed LiDAR canopy surfaces. Dbh-height and ground-LiDARheight models were used to predict dbh from LiDAR height and compute Phase 2 estimates of ft2 basal area and ft3 volume. Phase 1 LiDAR estimates were computed by randomly assigning heights to species classes using the probability distribution from ground plots in eachinventory strata. Phase 2 LiDAR estimates were computed by randomly assigning heights to species-product groups using a Monte Carlo simulation for each ground plot. Regression coefficients for Phase 2 estimates of ft2 and ft3 from the smoothed versus unsmoothed surfacesof high- and low-density LiDAR were computed by species group. Regression estimates for combined volume were partitioned by species-product distribution of Phase 2 volume. There was no statistical difference (α = 0.05) between smoothed versus unsmoothed for high- and low-density LiDAR on adjusted mean volume estimates (sampling errors of 9.52 versus 8.46% for high-density and 9.25 versus 7.65% for low-density LiDAR). South. J. Appl. For. 29(1):40–47.