Individual Tree Diameter Growth Models of Larch–Spruce–Fir Mixed Forests Based on Machine Learning Algorithms

Ou, Qiangxin; Lei, Xiangdong; Shen, Chen

doi:10.3390/f10020187

Cited by 42 publications

(19 citation statements)

References 45 publications

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“…All modeling methods that predict forest performance, such as regression models and artificial intelligence models, have their own strengths and weaknesses. Although traditional regression models are capable of providing specific formulas, and these may make it easier to understand the relationships between variables in these models, these models have many limitations, including being independent of the range of statistical assumptions, such as a normal distribution of data, independence of variables, and so on [18,49,50]. One of the advantages of artificial intelligence techniques in stubborn modeling is that in many cases they do not have the same limitations of empirical models.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Tree Heights in an Uneven-Aged, Mixed Forest in Northern Iran Using Artificial Intelligence and Empirical Models

Bayat

Bettinger

Heidari

et al. 2020

Forests

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The diameters and heights of trees are two of the most important components in a forest inventory. In some circumstances, the heights of trees need to be estimated due to the time and cost involved in measuring them in the field. Artificial intelligence models have many advantages in modeling nonlinear height–diameter relationships of trees, which sometimes make them more useful than empirical models in estimating the heights of trees. In the present study, the heights of trees in uneven-aged and mixed stands in the high elevation forests of northern Iran were estimated using an artificial neural network (ANN) model, an adaptive neuro-fuzzy inference system (ANFIS) model, and empirical models. A systematic sampling method with a 150 × 200 m network (0.1 ha area) was employed. The diameters and heights of 516 trees were measured to support the modeling effort. Using 10 nonlinear empirical models, the ANN model, and the ANFIS model, the relationship between height as a dependent variable and diameter as an independent variable was analyzed. The results show, according to R2, relative root mean square error (RMSE), and other model evaluation criteria, that there is a greater consistency between predicted height and observed height when using artificial intelligence models (R2 = 0.78; RMSE (%) = 18.49) than when using regression analysis (R2 = 0.68; RMSE (%) = 17.69). Thus, it can be said that these models may be better than empirical models for predicting the heights of common, commercially-important trees in the study area.

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Section: Discussionmentioning

confidence: 99%

Estimation of Tree Heights in an Uneven-Aged, Mixed Forest in Northern Iran Using Artificial Intelligence and Empirical Models

Bayat

Bettinger

Heidari

et al. 2020

Forests

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“…The RF is a decision tree algorithm and an effective machine learning model for predicting a forest of variables. Based on its powerful modeling capabilities, the RF regression has been widely used in scientific research [94][95][96][97][98][99]. The principle of the RF algorithm is to use the bootstrap method to randomly extract multiple samples to generate a group of regression trees (ntree) from the original sample population.…”

Section: Statistical Models For Estimating the Fsvmentioning

confidence: 99%

Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

Yang

et al. 2020

Remote Sensing

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The forest stock volume (FSV) is one of the key indicators in forestry resource assessments on local, regional, and national scales. To date, scaling up in situ plot-scale measurements across landscapes is still a great challenge in the estimation of FSVs. In this study, Sentinel-2 imagery, the Google Earth Engine (GEE) cloud computing platform, three base station joint differential positioning technology (TBSJDPT), and three algorithms were used to build an FSV model for forests located in Hunan Province, southern China. The GEE cloud computing platform was used to extract the imagery variables from the Sentinel-2 imagery pixels. The TBSJDPT was put forward and used to provide high-precision positions of the sample plot data. The random forests (RF), support vector regression (SVR), and multiple linear regression (MLR) algorithms were used to estimate the FSV. For each pixel, 24 variables were extracted from the Sentinel-2 images taken in 2017 and 2018. The RF model performed the best in both the training phase (i.e., R 2 = 0.91, RMSE = 35.13 m 3 ha −1 , n = 321) and in the test phase (i.e., R 2 = 0.58, RMSE = 65.03 m 3 ha −1 , and n = 138). This model was followed by the SVR model (R 2 = 0.54, RMSE = 65.60 m 3 ha −1 , n = 321 in training; R 2 = 0.54, RMSE = 66.00 m 3 ha −1 , n = 138 in testing), which was slightly better than the MLR model (R 2 = 0.38, RMSE = 75.74 m 3 ha −1 , and n = 321 in training; R 2 = 0.49, RMSE = 70.22 m 3 ha −1 , and n = 138 in testing) in both the training phase and test phase. The best predictive band was Red-Edge 1 (B5), which performed well both in the machine learning methods and in the MLR method. The Blue band (B2), Green band (B3), Red band (B4), SWIR2 band (B12), and vegetation indices (TCW, NDVI_B5, and TCB) were used in the machine learning models, and only one vegetation index (MSI) was used in the MLR model. We mapped the FSV distribution in Hunan Province (3.50 × 10 8 m 3 ) based on the RF model; it reached a total accuracy of 63.87% compared with the official forest report in 2017 (5.48 × 10 8 m 3 ). The results from this study will help develop and improve satellite-based methods to estimate FSVs on local, regional and national scales.The forest stock volume (FSV, m 3 ha −1 ) is the sum of the stem volumes of all the living trees per unit area, and is one of key forest variables for forest resources management and assessments on local, region and country scales [1]. FSVs have a strong relationship with the aboveground biomass (AGB) and carbon stocks [2]. To understand the spatial distribution of carbon in forests and to derive predictions for monitoring carbon stock trends, the FSV must be quantified [3]. Traditionally, the FSV is estimated by sampling several plots, which involves substantial manpower, materials, and financial resources [4]. With the development of remote sensing technology, particularly the Landsat series since 1972, satellite imagery has played an important role in forest inventory. Various studies have been performed to estimate the forest variables ...

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“…The reserved features of this method were N, AGE, DBH, HT, HBLC, LCR, CLC, DINC T , DINC B , RDINC T , and RDINC B . Among these variables, N and AGE were the initial factors, and the DBH, HT, HBLC, and LCR had mature growth models with AGE and N and SI (site index) as variables and its distribution model; the other composite factors could be calculated from the above single factor [49][50][51][52]. Therefore, the CCEM based on the random forest has higher accuracy than the CCEMs based on mathematical modeling, and it can describe different shapes of tree crown at various stages of growth.…”

Section: Discussionmentioning

confidence: 99%

A Crown Contour Envelope Model of Chinese Fir Based on Random Forest and Mathematical Modeling

Tian

et al. 2020

Forests

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The tree crown is an important part of a tree and is closely related to forest growth status, forest canopy density, and other forest growth indicators. Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) is an important tree species in southern China. A three-dimensional (3D) visualization assistant decision-making system of plantations could be improved through the construction of crown contour envelope models (CCEMs), which could aid plantation production. The goal of this study was to establish CCEMs, based on random forest and mathematical modeling, and to compare them. First, the regression equation of a tree crown was calculated using the least squares method. Then, forest characteristic factors were screened using methods based on mutual information, recursive feature elimination, least absolute shrink and selection operator, and random forest, and the random forest model was established based on the different screening results. The accuracy of the random forest model was higher than that of the mathematical modeling. The best performing model based on mathematical modeling was the quartic polynomial with the largest crown radius as the variable (R-squared (R2) = 0.8614 and root mean square error (RMSE) = 0.2657). Among the random forest regression models, the regression model constructed using mutual information as the feature screening method was the most accurate (R2 = 0.886, RMSE = 0.2406), which was two percentage points higher than mathematical modeling. Compared with mathematical modeling, the random forest model can reflect the differences among trees and aid 3D visualization of a Chinese fir plantation.

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Individual Tree Diameter Growth Models of Larch–Spruce–Fir Mixed Forests Based on Machine Learning Algorithms

Cited by 42 publications

References 45 publications

Estimation of Tree Heights in an Uneven-Aged, Mixed Forest in Northern Iran Using Artificial Intelligence and Empirical Models

Estimation of Tree Heights in an Uneven-Aged, Mixed Forest in Northern Iran Using Artificial Intelligence and Empirical Models

Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

A Crown Contour Envelope Model of Chinese Fir Based on Random Forest and Mathematical Modeling

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