An Empirical Examination of Dominant Height Projection Accuracy Using Difference Equation Models

2019 ◽  
Vol 66 (3) ◽  
pp. 267-274 ◽  
Author(s):  
Mingliang Wang ◽  
Cristian R Montes ◽  
Bronson P Bullock ◽  
Dehai Zhao
Keyword(s):  
Difference Equation ◽  
Loblolly Pine ◽  
Forest Growth ◽  
Growth And Yield ◽  
Coefficient Of Determination ◽  
Permanent Plots ◽  
Time Interval ◽  
Time Intervals ◽  
Empirical Examination ◽  
Dominant Height

Abstract Site index models developed using the difference equation method, otherwise known as the algebraic difference approach (ADA) along with its generalization (GADA), play an important role in forest growth and yield modeling for operational use. Their projection accuracy tends to be reduced over increasing time intervals, a common modeling phenomenon not yet well understood. In this study, dominant height projections given one single prior observation using three (G)ADA models were examined in relation to pairwise height correlations on an empirical dataset consisting of height remeasurements taken on permanent plots of a second-rotation loblolly pine (Pinus taeda L.) plantation experimental study. The results indicated that the decline in projection accuracy in terms of RMSE or Rp2 (analogical to the coefficient of determination R2) with increasing time intervals is closely associated with the weakening correlations imbedded in distant height remeasurements. The squared coefficient of correlation (ρ2) between paired heights can be set as an upper bound of Rp2 in (G)ADA model prediction of heights conditional on prior observations. An examination of correlation over time interval will be informative of how projection accuracy is likely to change and what the maximal Rp2 might be for any potential (G)ADA model to be developed.

scholarly journals A Dynamic Stand Growth Model System for Loblolly Pine Responding to Mid-Rotation Treatments

Forests ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 556
Author(s):  
Mauricio Zapata-Cuartas ◽  
Bronson P. Bullock ◽  
Cristian R. Montes ◽  
Michael B. Kane
Keyword(s):  
Growth Model ◽  
Loblolly Pine ◽  
Basal Area ◽  
Model System ◽  
Growth And Yield ◽  
Site Quality ◽  
Response To Treatment ◽  
Plantation Management ◽  
Vegetation Control ◽  
Dominant Height

Intensive loblolly pine (Pinus taeda L.) plantation management in the southeastern United States includes mid-rotation silvicultural practices (MRSP) like thinning, fertilization, competitive vegetation control, and their combinations. Consistent and well-designed long-term studies considering interactions of MRSP are required to produce accurate projections and evaluate management decisions. Here we use longitudinal data from the regional Mid-Rotation Treatment study established by the Plantation Management Research Cooperative (PMRC) at the University of Georgia across the southeast U.S. to fit and validate a new dynamic model system rooted in theoretical and biological principles. A Weibull pdf was used as a modifier function coupled with the basal area growth model. The growth model system and error projection functions were estimated simultaneously. The new formulation results in a compatible and consistent growth and yield system and provides temporal responses to treatment. The results indicated that the model projections reproduce the observed behavior of stand characteristics. The model has high predictive accuracy (the cross-validation variance explained was 96.2%, 99.7%, and 98.6%; and the prediction root mean square distance was 0.704 m, 19.1 trees ha−1, and 1.03 m2ha−1 for dominant height (DH), trees per hectare (N), and basal area (BA), respectively), and can be used to project the current stand attributes following combinations of MRSP and with different thinning intensities. Simulations across southern physiographic regions allow us to conclude that the most overall ranking of MRSP after thinning is fertilization + competitive vegetation control (Fert + CVC) > fertilization only (Fert) > competitive vegetation control only (CVC), and Fert + CVC show less than additive effect. Because of the model structure, the response to treatment changes with location, age of application, and dominant height growth as indicators of site quality. Therefore, the proposed model adequately represents regional growth conditions.

scholarly journals DENSITY MANAGEMENT DIAGRAM FOR Eucalyptus grandis W. Hill. ex Maiden STANDS CONTROLLED BY DOMINANT HEIGHT

FLORESTA ◽  
2020 ◽  
Vol 51 (1) ◽  
pp. 240
Author(s):  
Gabriel Paes Marangon ◽  
Emanuel Arnoni Costa ◽  
César Augusto Guimarães Finger ◽  
Paulo Renato Schneider ◽  
Matheus Teixeira Martins
Keyword(s):  
Eucalyptus Grandis ◽  
Strong Relationship ◽  
Site Index ◽  
Graphical Analysis ◽  
Coefficient Of Determination ◽  
Permanent Plots ◽  
Volume Number ◽  
Dominant Height ◽  
Development Site ◽  
Relationship Of

Density management diagram for eucalyptus stands controlled by dominant height. The present study aimed to elaborate Density Management Diagrams (DMD) for Eucalyptus grandis W. Hill. ex Maiden stands including the dominant height. Data were obtained from permanent plots installed in the Centro Oriental Riograndense region and the Porto Alegre Metropolitan area, both located in the state of Rio Grande do Sul. The models to describe the relationships between average volume, number of trees per hectare, mean diameter, and dominant height were assessed by the statistical criteria of coefficient of determination (R²), standard error of the estimate in percentage (Syx%), and graphical analysis of residuals. The developed DMD allows for a better control of stocks in the management of stands due to the strong relationship of dominant height with stand development site and forest yield.Keywords: Growth, Site index, Forest regulation, Yield.

scholarly journals Stand-Level Components of a Growth and Yield Model for Nothofagus Mixed Forests from Southern Chile

Forests ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 810
Author(s):  
Sebastian Palmas ◽  
Paulo C. Moreno ◽  
Wendel P. Cropper ◽  
Alicia Ortega ◽  
Salvador A. Gezan
Keyword(s):  
Forest Type ◽  
Geographical Area ◽  
Basal Area ◽  
Forest Growth ◽  
Model Systems ◽  
Growth And Yield ◽  
Permanent Plots ◽  
Stand Age ◽  
Yield Model ◽  
Mixed Forests

Reliable information on stand dynamics and development is needed to improve management decisions on mixed forests, and essential tools for this purpose are forest growth and yield (G&Y) models. In this study, stand-level G&Y models were built for cohorts within the natural mixed second-growth Nothofagus-dominated forests in Chile. All currently available (but limited) data, consisting of a series of stratified temporary and permanent plots established in the complete range of this forest type, were used to fit and validate these models. Linear and nonlinear models were considered, where dominant stand age, number of trees, and the proportion of basal area of Nothofagus species resulted in significant predictors to project future values of stand basal area for the different cohorts (with R2 > 0.51 for the validation datasets). Mortality was successfully modeled (R2 = 0.79), based on a small set of permanent plots, using the concept of self-thinning with a proposed model defined by the idea that, as stands get closer to a maximum density, they experience higher levels of mortality. The evaluation of these models indicated that they adequately represent the current understanding of dynamics of basal area and mortality of Nothofagus and companion species in these forests. These are the first models fitted over a large geographical area that consider the dynamics of these mixed forests. It is suggested that the proposed models should constitute the main components of future implementations of G&Y model systems.

Modeling annualized occurrence, frequency, and composition of ingrowth using mixed-effects zero-inflated models and permanent plots in the Acadian Forest Region of North America

2011 ◽  
Vol 41 (10) ◽  
pp. 2077-2089 ◽  
Author(s):  
Rongxia Li ◽  
Aaron R. Weiskittel ◽  
John A. Kershaw
Keyword(s):  
North America ◽  
Random Effects ◽  
Negative Binomial ◽  
Basal Area ◽  
Forest Growth ◽  
Occurrence Frequency ◽  
Growth And Yield ◽  
Permanent Plots ◽  
Forest Region ◽  
Acadian Forest

Forest tree ingrowth is a highly variable and largely stochastic process. Consequently, predicting occurrence, frequency, and composition of ingrowth is a challenging task but of great importance in long-term forest growth and yield model projections. However, ingrowth data often require different statistical techniques other than traditional Gaussian regression, because these data are often bounded, skewed, and non-normal and commonly contain a large fraction of zeros. This study presents a set of regression models based on discrete Poisson and negative binomial probability distributions for ingrowth data collected from permanent sample plots in the Acadian Forest Region of North America. Models considered here include regular Poisson, zero-inflated Poisson (ZIP), zero-altered Poisson (ZAP; hurdle Poisson), regular negative binomial (NB), zero-inflated negative binomial (ZINB), and zero-altered negative binomial (ZANB; hurdle NB). Plot-level random effects were incorporated into each of these models. The ZINB model with random effects was found to provide the best fit statistics for modeling annualized occurrence and frequency of ingrowth. The key explanatory variables were stand basal area per hectare, percentage of hardwood basal area, number of trees per hectare, a measure of site quality, and the minimum measured diameter at breast height of each plot. A similar model was developed to predict species composition. All models showed logical behavior despite the high variability observed in the original data.

scholarly journals APLIKASI PUPUK DAUN UNTUK MENINGKATKAN PERTUMBUHAN DAN HASIL BAWANG DAYAK (Eleutherine palmifolia (L.) Merr)

2021 ◽  
Vol 23 (1) ◽  
pp. 66-71
Author(s):  
Elva Suryani ◽  
Ronny Yuniar Galingging ◽  
Widodo Widodo ◽  
Marlin Marlin
Keyword(s):  
Plant Height ◽  
Block Design ◽  
Fertilizer Application ◽  
Growth And Yield ◽  
Yield Response ◽  
Time Interval ◽  
Time Intervals ◽  
Foliar Fertilizer ◽  
Randomized Block Design ◽  
Leaf Fertilizer

[APPLICATION OF LEAF FERTILIZER TO INCREASE THE GROWTH AND YIELD OF BAWANG DAYAK (Eleutherine palmifolia (L.) Merr)]. Bawang Dayak (Eleutherine palmifolia (L.) Merr) is a potential medicinal plant and required improved growth and yield through the application of appropriate fertilizers. This study aimed to increase the growth and yield of bawang Dayak by determining the optimum concentration and time interval of foliar fertilizer application. The experiment was arranged in a completely randomized block design factorial. The first factor was the concentration of foliar fertilizer, consisting of 0,1, 2, and 3 g/L. The second factor was interval application of foliar fertilizer, consisting of every day, every 3 days, every 6 days, and every 9 days. The results showed that bawang Dayak did not show a significant response to foliar fertilizers. However, there was an interaction between the concentration and interval of foliar fertilizer application which had a significantly different effect on plant height. The highest plant height occurred at intervals of 3-day foliar fertilizer application with a concentration of 3 g/L. The concentration of foliar fertilizer had a significant effect on the variables of plant height, bulb fresh weight, as well as the bulb numbers. All concentrations of foliar fertilizer (0-3 g/L), and time intervals of foliar fertilizer application (1-9 days) affected the same growth and yield response of Bawang Dayak. 

METABOLIC STUDIES WITH 131I-LABELED THYROXINE. II.

1963 ◽  
Vol 44 (3) ◽  
pp. 475-480 ◽  
Author(s):  
R. Grinberg
Keyword(s):  
Spinal Cord ◽  
Central Nervous System ◽  
Nervous System ◽  
Optic Nerve ◽  
Pituitary Gland ◽  
Time Interval ◽  
Time Intervals ◽  
Pituitary Glands ◽  
The Central Nervous System ◽  
Tentative Explanation

ABSTRACT Radiologically thyroidectomized female Swiss mice were injected intraperitoneally with 131I-labeled thyroxine (T4*), and were studied at time intervals of 30 minutes and 4, 28, 48 and 72 hours after injection, 10 mice for each time interval. The organs of the central nervous system and the pituitary glands were chromatographed, and likewise serum from the same animal. The chromatographic studies revealed a compound with the same mobility as 131I-labeled triiodothyronine in the organs of the CNS and in the pituitary gland, but this compound was not present in the serum. In most of the chromatographic studies, the peaks for I, T4 and T3 coincided with those for the standards. In several instances, however, such an exact coincidence was lacking. A tentative explanation for the presence of T3* in the pituitary gland following the injection of T4* is a deiodinating system in the pituitary gland or else the capacity of the pituitary gland to concentrate T3* formed in other organs. The presence of T3* is apparently a characteristic of most of the CNS (brain, midbrain, medulla and spinal cord); but in the case of the optic nerve, the compound is not present under the conditions of this study.

scholarly journals Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1213
Author(s):  
Ahmed Aljanad ◽  
Nadia M. L. Tan ◽  
Vassilios G. Agelidis ◽  
Hussain Shareef
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Solar Irradiance ◽  
Particle Swarm ◽  
Time Interval ◽  
Time Intervals ◽  
Swarm Optimization ◽  
Backpropagation Neural Networks ◽  
Proposed Model ◽  
Short Time

Hourly global solar irradiance (GSR) data are required for sizing, planning, and modeling of solar photovoltaic farms. However, operating and controlling such farms exposed to varying environmental conditions, such as fast passing clouds, necessitates GSR data to be available for very short time intervals. Classical backpropagation neural networks do not perform satisfactorily when predicting parameters within short intervals. This paper proposes a hybrid backpropagation neural networks based on particle swarm optimization. The particle swarm algorithm is used as an optimization algorithm within the backpropagation neural networks to optimize the number of hidden layers and neurons used and its learning rate. The proposed model can be used as a reliable model in predicting changes in the solar irradiance during short time interval in tropical regions such as Malaysia and other regions. Actual global solar irradiance data of 5-s and 1-min intervals, recorded by weather stations, are applied to train and test the proposed algorithm. Moreover, to ensure the adaptability and robustness of the proposed technique, two different cases are evaluated using 1-day and 3-days profiles, for two different time intervals of 1-min and 5-s each. A set of statistical error indices have been introduced to evaluate the performance of the proposed algorithm. From the results obtained, the 3-days profile’s performance evaluation of the BPNN-PSO are 1.7078 of RMSE, 0.7537 of MAE, 0.0292 of MSE, and 31.4348 of MAPE (%), at 5-s time interval, where the obtained results of 1-min interval are 0.6566 of RMSE, 0.2754 of MAE, 0.0043 of MSE, and 1.4732 of MAPE (%). The results revealed that proposed model outperformed the standalone backpropagation neural networks method in predicting global solar irradiance values for extremely short-time intervals. In addition to that, the proposed model exhibited high level of predictability compared to other existing models.

Real-World Experience with Artificial Intelligence-Based Triage in Transferred Large Vessel Occlusion Stroke Patients

2021 ◽  
pp. 1-6
Author(s):  
Jacob R. Morey ◽  
Xiangnan Zhang ◽  
Kurt A. Yaeger ◽  
Emily Fiano ◽  
Naoum Fares Marayati ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Outcomes ◽  
Warning System ◽  
Large Vessel ◽  
Time Interval ◽  
Large Vessel Occlusion ◽  
Time Intervals ◽  
Vessel Occlusion ◽  
Left Behind ◽  
Stroke Center

<b><i>Background and Purpose:</i></b> Randomized controlled trials have demonstrated the importance of time to endovascular therapy (EVT) in clinical outcomes in large vessel occlusion (LVO) acute ischemic stroke. Delays to treatment are particularly prevalent when patients require a transfer from hospitals without EVT capability onsite. A computer-aided triage system, Viz LVO, has the potential to streamline workflows. This platform includes an image viewer, a communication system, and an artificial intelligence (AI) algorithm that automatically identifies suspected LVO strokes on CTA imaging and rapidly triggers alerts. We hypothesize that the Viz application will decrease time-to-treatment, leading to improved clinical outcomes. <b><i>Methods:</i></b> A retrospective analysis of a prospectively maintained database was assessed for patients who presented to a stroke center currently utilizing Viz LVO and underwent EVT following transfer for LVO stroke between July 2018 and March 2020. Time intervals and clinical outcomes were compared for 55 patients divided into pre- and post-Viz cohorts. <b><i>Results:</i></b> The median initial door-to-neuroendovascular team (NT) notification time interval was significantly faster (25.0 min [IQR = 12.0] vs. 40.0 min [IQR = 61.0]; <i>p</i> = 0.01) with less variation (<i>p</i> &#x3c; 0.05) following Viz LVO implementation. The median initial door-to-skin puncture time interval was 25 min shorter in the post-Viz cohort, although this was not statistically significant (<i>p</i> = 0.15). <b><i>Conclusions:</i></b> Preliminary results have shown that Viz LVO implementation is associated with earlier, more consistent NT notification times. This application can serve as an early warning system and a failsafe to ensure that no LVO is left behind.

A Blind Spot in the Use of the Weibull Function for Modeling Diameter Distributions

2020 ◽  
Author(s):  
Adrian Norman Goodwin
Keyword(s):  
Lower Bound ◽  
Blind Spot ◽  
Forest Growth ◽  
Growth And Yield ◽  
Diameter Distribution ◽  
Absolute Difference ◽  
Weibull Function ◽  
Distribution Models ◽  
Difference Statistic ◽  
Diameter Distributions

Abstract Diameter distribution models based on probability density functions are integral to many forest growth and yield systems, where they are used to estimate product volumes within diameter classes. The three-parameter Weibull function with a constrained nonnegative lower bound is commonly used because of its flexibility and ease of fitting. This study compared Weibull and reverse Weibull functions with and without a lower bound constraint and left-hand truncation, across three large unthinned plantation cohorts in which 81% of plots had negatively skewed diameter distributions. Near-optimal lower bounds for the unconstrained Weibull function were negative for negatively skewed data, and the left-truncated Weibull using these bounds was 14.2% more accurate than the constrained Weibull, based on the Kolmogorov-Smirnov statistic. The truncated reverse Weibull fit dominant tree distributions 23.7% more accurately than the constrained Weibull, based on a mean absolute difference statistic. This work indicates that a blind spot may have developed in plantation growth modeling systems deploying constrained Weibull functions, and that left-truncation of unconstrained functions could substantially improve model accuracy for negatively skewed distributions.

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