Automated Quantitative Analysis of Terminal Tree Branch Similarity by 3D Registration

2017 ◽  
Author(s):  
Joseph A. Brucculeri ◽  
Lance Evans ◽  
Zahra Shahbazi
Keyword(s):  
Sensitivity Analysis ◽  
Quantitative Analysis ◽  
Root Mean Square Error ◽  
Tree Species ◽  
Side Branch ◽  
Terminal Branch ◽  
Mean Square ◽  
3D Registration ◽  
Self Similarity ◽  
Tree Branch

A popular, but unsubstantiated view is that tree branch morphologies are similar (self-similarity) and are of an iterative nature. To date there are no studies that document plant branch self-similarities. The purpose of this research is to develop a program (3D Simquant) that estimated self-similarities among paired branch terminals quantitatively. After 3D Simquant was written, the program was verified and sensitivity analysis performed, eighty-five terminal branch pair-wise comparisons from five different tree species were analyzed. Only two branch geometries (Y and Y+1 terminals) were compared. Simple Y terminals are terminal main stems with one side branch while Y+1 terminals are main steams with two side branches. Similarities among paired branch terminals were quantified with Root-Mean-Square-Error (RMSE) after registration of images. For the five species tested, all Y terminals had RMSE values less than 1.5 which indicates they were similar. For most Y+1 terminals, RMSE values were twice that of Y terminals indicating the Y+1 samples were more dissimilar than Y terminals. Overall, the programs were accurate and rapid for an analysis of branch similarities.

scholarly journals Development and Testing of a New Ground Measurement Tool to Assist in Forest GIS Surveys

Forests ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 643 ◽  
Author(s):  
Guangpeng Fan ◽  
Feixiang Chen ◽  
Yan Li ◽  
Binbin Liu ◽  
Xu Fan
Keyword(s):  
Root Mean Square Error ◽  
Mobile Phone ◽  
Mean Square Error ◽  
Tree Species ◽  
Tree Height ◽  
Measurement Tool ◽  
Mean Square ◽  
Height Measurement ◽  
Test Dataset ◽  
Ground Measurement

In present forest surveys, some problems occur because of the cost and time required when using external tools to acquire tree measurement. Therefore, it is of great importance to develop a new cost-saving and time-saving ground measurement method implemented in a forest geographic information system (GIS) survey. To obtain a better solution, this paper presents the design and implementation of a new ground measurement tool in which mobile devices play a very important role. Based on terrestrial photogrammetry, location-based services (LBS), and computer vision, the tool assists forest GIS surveys in obtaining important forest structure factors such as tree position, diameter at breast height (DBH), tree height, and tree species. This paper selected two plots to verify the accuracy of the ground measurement tool. Experiments show that the root mean square error (RMSE) of the position coordinates of the trees was 0.222 m and 0.229 m, respectively, and the relative root mean square error (rRMSE) was close to 0. The rRMSE of the DBH measurement was 10.17% and 13.38%, and the relative Bias (rBias) of the DBH measurement was −0.88% and −2.41%. The rRMSE of tree height measurement was 6.74% and 6.69%, and the rBias of tree height measurement was −1.69% and −1.27%, which conforms to the forest investigation requirements. In addition, workers usually make visual observations of trees and then combine their personal knowledge or experience to identify tree species, which may lead to the situations when they cannot distinguish tree species due to insufficient knowledge or experience. Based on MobileNets, a lightweight convolutional neural network designed for mobile phone, a model was trained to assist workers in identifying tree species. The dataset was collected from some forest parks in Beijing. The accuracy of the tree species recognition model was 94.02% on a test dataset and 93.21% on a test dataset in the mobile phone. This provides an effective reference for workers to identify tree species and can assist in artificial identification of tree species. Experiments show that this solution using the ground measurement tool saves time and cost for forest resources GIS surveys.

scholarly journals Nonlinear Least Square Based on Control Direction by Dual Method and Its Application

2016 ◽  
Vol 2016 ◽  
pp. 1-5
Author(s):  
Zhengqing Fu ◽  
Guolin Liu ◽  
Lanlan Guo ◽  
Weike Liu ◽  
Hua Guo
Keyword(s):  
Quantitative Analysis ◽  
Root Mean Square Error ◽  
Qualitative Analysis ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Least Square ◽  
Dual Method ◽  
Dual Model ◽  
Mean Square ◽  
Primal Dual

A direction controlled nonlinear least square (NLS) estimation algorithm using the primal-dual method is proposed. The least square model is transformed into the primal-dual model; then direction of iteration can be controlled by duality. The iterative algorithm is designed. The Hilbert morbid matrix is processed by the new model and the least square estimate and ridge estimate. The main research method is to combine qualitative analysis and quantitative analysis. The deviation between estimated values and the true value and the estimated residuals fluctuation of different methods are used for qualitative analysis. The root mean square error (RMSE) is used for quantitative analysis. The results of experiment show that the model has the smallest residual error and the minimum root mean square error. The new estimate model has effectiveness and high precision. The genuine data of Jining area in unwrapping experiments are used and the comparison with other classical unwrapping algorithms is made, so better results in precision aspects can be achieved through the proposed algorithm.

scholarly journals QUANTITATIVE ANALYSISOF STEVIOSIDE AND REBAUDIOSIDE A IN STEVIAREBAUDIANA LEAVES USING INFRARED SPECTROSCOPY AND MULTIVARIATE CALIBRATION

2019 ◽  
Vol 11 (1) ◽  
pp. 38 ◽  
Author(s):  
Yohannes Martono ◽  
Abdul Rohman
Keyword(s):  
Multivariate Analysis ◽  
Quantitative Analysis ◽  
Root Mean Square Error ◽  
Ftir Spectroscopy ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Rebaudioside A ◽  
Pls Regression ◽  
Mean Square ◽  
Wavenumber Region

Objective: The objective of this research was to develop Fourier transform infrared (FTIR) spectroscopy in combination with multivariate analysis of partial least square (PLS) regression for quantitative analysis of stevioside and rebaudioside A in S. rebaudiana leaves extract.Methods: Stevia rebaudiana leaves with various ages were obtained from several high hills in Central Java, Indonesia. The extract samples were scanned using FTIR spectrophotometer in wavenumbers region of 4000–650 cm-1. PLS calibration model was established by plotting the actual value of stevioside and rebaudioside A as determined by high-performance liquid chromatography (HPLC) and FTIR predicted value. The performance of PLS regression was evaluated using coefficient determination (R2), root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP).Results: PLS regression for stevioside determination was successfully established using the combined wavenumber region of 671–1450 and 3279-3301 cm-1. PLS regression revealed R2of 0.9952with RMSEC value of0.84%. Meanwhile, rebaudioside A was determined at wavenumber region of 921–1508 cm-1using normal spectra. PLS model revealed R2 and RMSEC of 0.9911 and 0.70%, respectively.Conclusion: FTIR spectroscopy in combination with multivariate analysis of PLS regression could be used as an alternative method for quantitative analysis ofstevioside and rebaudioside A in S. rebaudiana leaves.

scholarly journals High Precision Individual Tree Diameter and Perimeter Estimation from Close-Range Photogrammetry

Forests ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 696 ◽  
Author(s):  
Martin Mokroš ◽  
Jozef Výbošťok ◽  
Julián Tomaštík ◽  
Alžbeta Grznárová ◽  
Peter Valent ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Fagus Sylvatica ◽  
Tree Species ◽  
Quercus Petraea ◽  
Forest Stand ◽  
Mean Square ◽  
Close Range Photogrammetry ◽  
Close Range

Close-range photogrammetry (CRP) can be used to provide precise and detailed three-dimensional data of objects. For several years, CRP has been a subject of research in forestry. Several studies have focused on tree reconstruction at the forest stand, plot, and tree levels. In our study, we focused on the reconstruction of trees separately within the forest stand. We investigated the influence of camera lens, tree species, and height of diameter on the accuracy of the tree perimeter and diameter estimation. Furthermore, we investigated the variance of the perimeter and diameter reference measurements. We chose four tree species (Fagus sylvatica L., Quercus petraea (Matt.) Liebl., Picea abies (L.) H. Karst. and Abies alba Mill.). The perimeters and diameters were measured at three height levels (0.8 m, 1.3 m, and 1.8 m) and two types of lenses were used. The data acquisition followed a circle around the tree at a 3 m radius. The highest accuracy of the perimeter estimation was achieved when a fisheye lens was used at a height of 1.3 m for Fagus sylvatica (root mean square error of 0.25 cm). Alternatively, the worst accuracy was achieved when a non-fisheye lens was used at 1.3 m for Quercus petraea (root mean square error of 1.27 cm). The tree species affected the estimation accuracy for both diameters and perimeters.

Quantitative Analysis of Mixtures Using Terahertz Time-Domain Spectroscopy and Different PLS Algorithms

2013 ◽  
Vol 804 ◽  
pp. 23-28 ◽  
Author(s):  
Tao Chen ◽  
Zhi Li ◽  
Fang Rong Hu ◽  
Wei Mo
Keyword(s):  
Quantitative Analysis ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Time Domain ◽  
Ternary Mixtures ◽  
Mean Square ◽  
Multicomponent Mixtures ◽  
Terahertz Time Domain Spectroscopy ◽  
Lower Root ◽  
Anhydrous Theophylline

This paper attempted the feasibility to determine component concentrations in multicomponent mixtures with terahertz time-domain spectroscopy (THz-TDS) combined with different partial least-squares regression (PLS) algorithms. First, THz absorbance spectra for 75 ternary mixtures of anhydrous theophylline, lactose monohydrate and magnesium stearate were investigated using THz-TDS in the frequency range from 0.1 to 3.0 THz, then four different PLS methods, including interval PLS (iPLS), backward interval PLS (biPLS), synergy interval PLS (siPLS) and moving window PLS (mwPLS), were employed to perform quantitative analysis of anhydrous theophylline concentrations in ternary mixtures. The performance of mwPLS model is the best in contrast to other PLS models and full spectrum PLS. The optimal model was achieved with higher correlation coefficient for calibration (RC) of 0.9842, higher correlation coefficient for prediction (RP) of 0.9851, lower root mean square error of cross-validation (RMSECV) of 3.8241, and lower root mean square error of prediction (RMSEP) of 4.1540. Experimental results demonstrate that THz spectroscopy combined with PLS algorithms could be successfully applied as an effective nondestructive tool for the quantitative analysis of component concentrations in multicomponent mixtures, and mwPLS is an ideal method for reducing the complexity and improving the performance of the model.

scholarly journals Prediction of the Carbon Content of Six Tree Species from Visible-Near-Infrared Spectroscopy

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1233
Author(s):  
Yongbin Meng ◽  
Yuanyuan Zhang ◽  
Chunxu Li ◽  
Jinghan Zhao ◽  
Zichun Wang ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Carbon Content ◽  
Spectral Data ◽  
Mean Square Error ◽  
Tree Species ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Mean Square ◽  
Chemometric Methods ◽  
Full Spectrum

This study aimed to measure the carbon content of tree species rapidly and accurately using visible and near-infrared (Vis-NIR) spectroscopy coupled with chemometric methods. Currently, the carbon content of trees used for calculating the carbon storage of forest trees in the study of carbon sequestration is obtained by two methods. One involves measuring carbon content in the laboratory (K2CrO7-H2SO4 oxidation method or elemental analyzer), and another involves directly using the IPCC (Intergovernmental Panel on Climate Change) default carbon content of 0.45 or 0.5. The former method is destructive, time-consuming, and expensive, while the latter is subjective. However, Vis-NIR detection technology can avoid these shortcomings and rapidly determine carbon content. In this study, 96 increment core samples were collected from six tree species in the Heilongjiang province of China for analysis. The spectral data were preprocessed using seven methods, including extended multiplicative scatter correction (EMSC), first derivative (1D), second derivative (2D), baseline correction, de-trend, orthogonal signal correction (OSC), and normalization to eliminate baseline drifting and noise, as well as to enhance the model quality. Linear models were established from the spectra using partial least squares regression (PLS). At the same time, we also compared the effects of full-spectrum and reduced spectrum on the model’s performance. The results showed that the spectral data processed by 1D with the full spectrum could obtain a better prediction model. The 1D method yielded the highest R2c of 0.92, an RMSEC (root-mean-square error of calibration) of 0.0056, an R2p of 0.99, an RMSEP (root-mean-square error of prediction) of 0.0020, and the highest RPD (residual prediction deviation) value of 8.9. The results demonstrate the feasibility of Vis-NIR spectroscopy coupled with chemometric methods in determining the carbon content of tree species as a simple, rapid, and non-destructive method.

scholarly journals Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China

2021 ◽  
Vol 13 (9) ◽  
pp. 1630
Author(s):  
Yaohui Zhu ◽  
Guijun Yang ◽  
Hao Yang ◽  
Fa Zhao ◽  
Shaoyu Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Minimum Temperature ◽  
Price Regulation ◽  
Flowering Period ◽  
Mean Square ◽  
Prevention Measures ◽  
Agricultural Insurance

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

scholarly journals Identification and quantitative analysis of phenolic compounds naturally occuring in wood of selected European and exotic tree species

2009 ◽  
Vol 70 (1) ◽  
pp. 27-39 ◽  
Author(s):  
Paweł Zarzyński
Keyword(s):  
Quantitative Analysis ◽  
Phenolic Compounds ◽  
Tree Species ◽  
Exotic Tree

Identyfikacja i analiza ilościowa substancji o charakterze fenolowym naturalnie występujących w drewnie wybranych gatunków drzew europejskich i egzotycznych

scholarly journals Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1020
Author(s):  
Yanqi Dong ◽  
Guangpeng Fan ◽  
Zhiwu Zhou ◽  
Jincheng Liu ◽  
Yongguo Wang ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Low Cost ◽  
Reference Value ◽  
Tree Height ◽  
Point Clouds ◽  
Tree Structure ◽  
Mean Square ◽  
Tree Model

The quantitative structure model (QSM) contains the branch geometry and attributes of the tree. AdQSM is a new, accurate, and detailed tree QSM. In this paper, an automatic modeling method based on AdQSM is developed, and a low-cost technical scheme of tree structure modeling is provided, so that AdQSM can be freely used by more people. First, we used two digital cameras to collect two-dimensional (2D) photos of trees and generated three-dimensional (3D) point clouds of plot and segmented individual tree from the plot point clouds. Then a new QSM-AdQSM was used to construct tree model from point clouds of 44 trees. Finally, to verify the effectiveness of our method, the diameter at breast height (DBH), tree height, and trunk volume were derived from the reconstructed tree model. These parameters extracted from AdQSM were compared with the reference values from forest inventory. For the DBH, the relative bias (rBias), root mean square error (RMSE), and coefficient of variation of root mean square error (rRMSE) were 4.26%, 1.93 cm, and 6.60%. For the tree height, the rBias, RMSE, and rRMSE were—10.86%, 1.67 m, and 12.34%. The determination coefficient (R2) of DBH and tree height estimated by AdQSM and the reference value were 0.94 and 0.86. We used the trunk volume calculated by the allometric equation as a reference value to test the accuracy of AdQSM. The trunk volume was estimated based on AdQSM, and its bias was 0.07066 m3, rBias was 18.73%, RMSE was 0.12369 m3, rRMSE was 32.78%. To better evaluate the accuracy of QSM’s reconstruction of the trunk volume, we compared AdQSM and TreeQSM in the same dataset. The bias of the trunk volume estimated based on TreeQSM was −0.05071 m3, and the rBias was −13.44%, RMSE was 0.13267 m3, rRMSE was 35.16%. At 95% confidence interval level, the concordance correlation coefficient (CCC = 0.77) of the agreement between the estimated tree trunk volume of AdQSM and the reference value was greater than that of TreeQSM (CCC = 0.60). The significance of this research is as follows: (1) The automatic modeling method based on AdQSM is developed, which expands the application scope of AdQSM; (2) provide low-cost photogrammetric point cloud as the input data of AdQSM; (3) explore the potential of AdQSM to reconstruct forest terrestrial photogrammetric point clouds.

scholarly journals Rapid Biochemical Methane Potential Evaluation of Anaerobic Co-Digestion Feedstocks Based on Near Infrared Spectroscopy and Chemometrics

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1460
Author(s):  
Jinming Liu ◽  
Changhao Zeng ◽  
Na Wang ◽  
Jianfei Shi ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Variable Selection ◽  
Root Mean Square Error ◽  
Least Squares ◽  
Mean Square Error ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Mean Square ◽  
Biochemical Methane Potential ◽  
Methane Potential

Biochemical methane potential (BMP) of anaerobic co-digestion (co-AD) feedstocks is an essential basis for optimizing ratios of materials. Given the time-consuming shortage of conventional BMP tests, a rapid estimated method was proposed for BMP of co-AD—with straw and feces as feedstocks—based on near infrared spectroscopy (NIRS) combined with chemometrics. Partial least squares with several variable selection algorithms were used for establishing calibration models. Variable selection methods were constructed by the genetic simulated annealing algorithm (GSA) combined with interval partial least squares (iPLS), synergy iPLS, backward iPLS, and competitive adaptive reweighted sampling (CARS), respectively. By comparing the modeling performances of characteristic wavelengths selected by different algorithms, it was found that the model constructed using 57 characteristic wavelengths selected by CARS-GSA had the best prediction accuracy. For the validation set, the determination coefficient, root mean square error and relative root mean square error of the CARS-GSA model were 0.984, 6.293 and 2.600, respectively. The result shows that the NIRS regression model—constructed with characteristic wavelengths, selected by CARS-GSA—can meet actual detection requirements. Based on a large number of samples collected, the method proposed in this study can realize the rapid and accurate determination of the BMP for co-AD raw materials in biogas engineering.

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