scholarly journals Generalizability and Accuracy of Site Index Estimation Model with Ensemble Learning

2009 ◽  
Vol 42 (1) ◽  
pp. 53-67
Author(s):  
Yasushi MINOWA ◽  
Norifumi SUZUKI ◽  
Kazuhiro TANAKA
Keyword(s):  
Ensemble Learning ◽  
Site Index ◽  
Estimation Model ◽  
Index Estimation

Study on the applicability of leaf area index estimation model in an aspen forest over a growth period

2016 ◽  
Vol 36 (8) ◽  
Author(s):  
王龑 WANG Yan ◽  
田庆久 TIAN Qingjiu ◽  
王琦 WANG Qi ◽  
王磊 WANG Lei
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Growth Period ◽  
Estimation Model ◽  
Area Index ◽  
Aspen Forest ◽  
Index Estimation

Early Height Growth and Site Index for Planted Red Pine in North Central Ontario

1995 ◽  
Vol 12 (1) ◽  
pp. 23-29
Author(s):  
William H. Carmean ◽  
James S. Thrower
Keyword(s):  
Growth Curves ◽  
Analysis Data ◽  
Site Index ◽  
Height Growth ◽  
Red Pine ◽  
North Central ◽  
Breast Height ◽  
Excellent Growth ◽  
Index Estimation ◽  
Best Estimates

Abstract Height-growth, site-index curves, and growth intercepts were developed from internode and stem-analysis data using dominant trees in 25 plots located in red pine plantations aged 26 to 37 yr. Height-growth curves were based on breast-height age because growth below breast height (1.3 m) was slow and erratic. Growth intercepts using the first three to five internodes above 1.5 m gave the best estimates of site index (dominant height at 20 yr breast-height age)for trees that were between 3 and 5 yr breast-height age; site-index estimation equations gave the best estimates for trees older than 10 yr breast-height age. These computed height-growth curves and growth intercepts and observed site index in north central Ontario were similar to other regions. The excellent growth observed in this study suggests that red pine should be given greater emphasis in future reforestation programs in north central Ontario. North. J. Appl. For. 12(1): 23-29.

scholarly journals Research on the prediction of quay crane resource hour based on Ensemble Learning

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Gaoshen Wang ◽  
Yi Ding
Keyword(s):  
Ensemble Learning ◽  
Historical Data ◽  
Influence Factors ◽  
Estimation Method ◽  
Wait Time ◽  
Container Terminals ◽  
Estimation Model ◽  
Empirical Estimation ◽  
Preparation Time ◽  
Adaboost Algorithm

In Container terminals, a quay crane's resource hour is affected by various complex nonlinear factors, and it is not easy to make a forecast quickly and accurately. Most ports adopt the empirical estimation method at present, and most of the studies assumed that accurate quay crane’s resource hour could be obtained in advance. Through the ensemble learning (EL) method, the influence factors and correlation of quay crane’s resources hour were analyzed based on a large amount of historical data. A multi-factor ensemble learning estimation model based quay crane’s resource hour was established. Through a numerical example, it is finally found that Adaboost algorithm has the best effect of prediction, with an error of 1.5%. Through the example analysis, it comes to a conclusion: the error is 131.86% estimated by the experience method. It will lead that subsequent shipping cannot be serviced as scheduled, increasing the equipment wait time and preparation time, and generating additional cost and energy consumption. In contrast, the error based Adaboost learning estimation method is 12.72%. So Adaboost has better performance.

scholarly journals Augmenting Site Index Estimation with Airborne Laser Scanning Data

2015 ◽  
Vol 61 (5) ◽  
pp. 861-873 ◽  
Author(s):  
Piotr Tompalski ◽  
Nicholas C. Coops ◽  
Joanne C. White ◽  
Michael A. Wulder
Keyword(s):  
Laser Scanning ◽  
Site Index ◽  
Airborne Laser Scanning ◽  
Airborne Laser ◽  
Index Estimation

scholarly journals Site index estimation for clonal eucalypt plantations in Brazil: A modeling approach refined by environmental variables

2020 ◽  
Vol 466 ◽  
pp. 118079 ◽  
Author(s):  
Henrique Ferraco Scolforo ◽  
John Paul McTague ◽  
Harold Burkhart ◽  
Joseph Roise ◽  
Clayton Alcarde Alvares ◽  
...  
Keyword(s):  
Environmental Variables ◽  
Site Index ◽  
Modeling Approach ◽  
Eucalypt Plantations ◽  
Index Estimation

scholarly journals A Method for the Development of Dynamic Site Index Models Using Height–Age Data from Temporal Sample Plots

Forests ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 542
Author(s):  
Jarosław Socha ◽  
Luiza Tymińska-Czabańska
Keyword(s):  
Forest Management ◽  
Site Index ◽  
Height Growth ◽  
Strategic Decisions ◽  
Growth Trend ◽  
Potential Productivity ◽  
Forest Sites ◽  
Using Data ◽  
Temporal Sample ◽  
Index Estimation

Knowledge of the potential productivity of forest sites is fundamental for making strategic decisions in forest management. Site productivity is usually evaluated using the site index, and therefore the development of site index models is one of the crucial tasks in forest research and forest management. This research aims to develop an effective method for building top-growth and site index models using data from temporary sample plots (TSP). Exploiting the advantages of the generalised algebraic difference approach (GADA), the proposed method overcomes the limitations of the guide curve method that has been to date used in site index modelling using TSPs data and allows to obtain only a set of anamorphic site index curves. The proposed approach enables the construction of dynamic site index models with polymorphism and variable asymptotes. Such models better reflect local, site-specific height growth trajectories and therefore allow more appropriate site index estimation. We tested the proposed method using data collected from 5105 temporary sample plots in Poland. Our results indicate that growth trend estimates using height–age measurements of TSPs may be valuable data for modelling top height growth. For these reasons, the proposed method can be very useful in forest management.

scholarly journals Winter Wheat Take-All Disease Index Estimation Model Based on Hyperspectral Data

2021 ◽  
Vol 11 (19) ◽  
pp. 9230
Author(s):  
Wei Guo ◽  
Yifeng Yang ◽  
Hengqian Zhao ◽  
Rui Song ◽  
Ping Dong ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Spectral Index ◽  
North China ◽  
Disease Index ◽  
Accurate Estimation ◽  
Estimation Model ◽  
Model Based ◽  
Index Estimation ◽  
Band Combinations ◽  
Take All

Wheat take-all, caused by two variants of the fungus Gaeumannomyces gramnis (Sacc.) Arx & D. Olivier, was common in spring wheat areas in northwest and north China and occurred in winter wheat areas in north China. The yield of common disease areas was reduced by more than 20% and the yield of severe cases was reduced by more than 50%. Large-scale rapid and accurate estimation of the incidence of wheat take-all plays an important role in guiding field control and agricultural yield estimation. In this study, a portable ground spectrometer was used to collect the spectral reflectance in the 350–1050 nm band range of wheat canopy after take-all infection in the wheat grain filling stage and combined with the ground disease survey data.Then a winter wheat take-all disease index estimation model was proposed based on the spectral band division interval and selected band combination. According to the normalized difference spectral index (NDSI) and the determinative coefficient of the disease index formed by any two band combinations, the spectral index band combinations corresponding to the spectral index with high correlation in each region were screened by dividing spectral intervals. Partial least-squares regression was used to establish a binary and ternary disease index calibration model. The results showed that the model based on spectral indices of ternary variables had the highest coefficient of determination. Finally, the optimal regression model of wheat take-all disease condition index composed of NDSI(R590,R598), NDSI(R534,R742) and NDSI(R810,R834) was established: Y = 134.577 − 70.301 NDSI(R590,R598) − 223.533 NDSI(R534,R742) + 51.584 NDSI(R810,R834) (R2 = 0.743, RMSEP = 0.094, df = 15), which was the most suitable model for winter wheat take-all estimation. The construction of this model can provide new method and technical support for future evaluation and monitoring of wheat take-all disease on the field.

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