Innovative deep learning artificial intelligence applications for predicting relationships between individual tree height and diameter at breast height

Understanding the essential contribution of eucalyptus plantation for industry development and climate change mitigation requires the accurate quantification of aboveground biomass at the individual tree species level. However, the direct measurement of aboveground biomass by destructive method is high cost and time consuming. Therefore, developing allometric equations is necessary to facilitate this effort. This study was designed to construct the specific allometric models for estimating aboveground biomass of Eucalyptus urophylla in East Nusa Tenggara. Forty two sample trees were utilized to develop allometric equations using regression analysis. Several parameters were selected as predictor variables, i.e. diameter at breast height (D), quadrat diameter at breast height combined with tree height (D2H), as well as D and H separately. Results showed that the mean aboveground biomass of E. urophylla was 143.9 ± 19.44 kg tree-1. The highest biomass were noted in stem (80.06%), followed by bark (11.89%), branch (4.69%), and foliage (3.36%). The relative contribution of stem to total aboveground biomass improved with the increasing of diameter class while the opposite trend was recorded in bark, branch, and foliage. The equation lnŶ = lna + b lnD was best and reliable for estimating the aboveground biomass of E. urophylla since it provided the highest accurate estimation (91.3%) and more practical than other models. Referring to these findings, this study concluded the use of allometric equation was reliable to support more efficient forest mensuration in E. urophylla plantation.

Download Full-text

Individual Tree Parameters Estimation from Terrestrial Laser Scanner Data

Proccedings of 10th International Conference "Environmental Engineering" ◽

10.3846/enviro.2017.230 ◽

2017 ◽

Author(s):

Darius Popovas ◽

Valentas Mikalauskas ◽

Dominykas Šlikas ◽

Simonas Valotka ◽

Tautvydas Šorys

Keyword(s):

Forest Management ◽

Laser Scanner ◽

Tree Height ◽

Parameters Estimation ◽

Carbon Accounting ◽

Terrestrial Laser Scanner ◽

Individual Tree ◽

Diameter At Breast Height ◽

Crown Diameter ◽

Breast Height

Tree models and information on the various characteristics of trees and forests are required for forest management, city models, carbon accounting and the management of assets. In order to get precise characteristics and information, tree modelling must be done at individual tree level as it represents the interaction process between trees. For sustainable forest management, more information is needed, however, the traditional methods of investigating forest parameters such as, tree height, diameter at breast height, crown diameter, stem curve and stem mapping or tree location are complex and labour-intensive. Light detection and ranging (LiDAR) has been proposed as a suitable technique for mapping of forest biomass. LiDAR can be operated in airborne configuration (Airborne laser scanning) or in a terrestrial setup. Terrestrial Laser Scanner measures forests from below canopy and offers a much more detailed description of the individual trees. The aim of this study is to derive the essential tree parameters for estimation of biomass from terrestrial LiDAR data. Tree height, diameter at breast height, crown diameter, stem curve and tree locations were extracted from Terrestrial Laser Scanner point clouds.

Download Full-text

Artificial intelligence with deep learning algorithms to model relationships between total tree height and diameter at breast height

Forest Systems ◽

10.5424/fs/2020292-16393 ◽

2020 ◽

Vol 29 (2) ◽

pp. e013

Author(s):

İlker Ercanli

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Mean Squared Error ◽

Tree Height ◽

Absolute Error ◽

Diameter At Breast Height ◽

Root Mean Squared Error ◽

Squared Error ◽

Hidden Layer ◽

Total Tree

Aim of Study: As an innovative prediction technique, Artificial Intelligence technique based on a Deep Learning Algorithm (DLA) with various numbers of neurons and hidden layer alternatives were trained and evaluated to predict the relationships between total tree height (TTH) and diameter at breast height (DBH) with nonlinear least squared (NLS) regression models and nonlinear mixed effect (NLME) regression models.Area of Study: The data of this study were measured from even-aged, pure Turkish Pine (Pinus brutia Ten.) stands in the Kestel Forests located in the Bursa region of northwestern Turkey.Material and Methods: 1132 pairs of TTH-DBH measurements from 132 sample plots were used for modeling relationships between TTH, DBH, and stand attributes such as dominant height (Ho) and diameter (Do).Main Results: The combination of 100 # neurons and 8 # hidden layer in DLA resulted in the best predictive total height prediction values with Average Absolute Error (0.4188), max. Average Absolute Error (3.7598), Root Mean Squared Error (0.6942), Root Mean Squared error % (5.2164), Akaike Information Criteria (-345.4465), Bayesian Information Criterion (-330.836), the average Bias (0.0288) and the average Bias % (0.2166), and fitting abilities with r (0.9842) and Fit Index (0.9684). Also, the results of equivalence tests showed that the DLA technique successfully predicted the TTH in the validation dataset.Research highlights: These superior fitting scores coupled with the validation results in TTH predictions suggested that deep learning network models should be considered an alternative to the traditional nonlinear regression techniques and should be given importance as an innovative prediction technique.Keywords: Prediction; artificial intelligence; deep learning algorithms; number of neurons; hidden layer alternatives.Abbreviations: TTH (total tree height), DBH (diameter at breast height), OLS (ordinary least squares), NLME (nonlinear mixed effect), AIT (Artificial Intelligence Techniques), ANN (Artificial Neural Network), DLA (Deep Learning Algorithm), GPU (Graphical Processing Units), NLS (nonlinear least squared), RMSE (root mean squared error), AIC (Akaike information criteria), BIC (Bayesian information criterion), FI (fit index), AAE (average absolute error), BLUP (best linear unbiased predictor), TOST (two one-sided test method).

Download Full-text

Estimation of Genetic Parameters and Wood Yield Selection Index in a Clonal Trial of Korean Pine (Pinus koraiensis) in Northeastern China

Sustainability ◽

10.3390/su13084167 ◽

2021 ◽

Vol 13 (8) ◽

pp. 4167

Author(s):

David Kombi Kaviriri ◽

Huan-Zhen Liu ◽

Xi-Yang Zhao

Keyword(s):

Selection Index ◽

Tree Height ◽

Stem Volume ◽

Korean Pine ◽

Branch Number ◽

Basal Diameter ◽

Diameter At Breast Height ◽

Breast Height ◽

Stem Straightness ◽

Clonal Trial

In order to determine suitable traits for selecting high-wood-yield Korean pine materials, eleven morphological characteristics (tree height, basal diameter, diameter at breast height, diameter at 3 meter height, stem straightness degree, crown breadth, crown height, branch angle, branch number per node, bark thickness, and stem volume) were investigated in a 38-year-old Korean pine clonal trial at Naozhi orchard. A statistical approach combining variance and regression analysis was used to extract appropriate traits for selecting elite clones. Results of variance analysis showed significant difference in variance sources in most of the traits, except for the stem straightness degree, which had a p-value of 0.94. Moderate to high coefficients of variation and clonal repeatability ranged from 10.73% to 35.45% and from 0.06% to 0.78%, respectively. Strong significant correlations on the phenotypic and genotypic levels were observed between the straightness traits and tree volume, but crown breadth was weakly correlated to the volume. Four principal components retaining up to 80% of the total variation were extracted, and stem volume, basal diameter, diameter at breast height, diameter at 3 meter height, tree height, and crown height displayed high correlation to these components (r ranged from 0.76 to 0.98). Based on the Type III sum of squares, tree height, diameter at breast height, and branch number showed significant information to explain the clonal variability based on stem volume. Using the extracted characteristics as the selection index, six clones (PK105, PK59, PK104, PK36, PK28, and K101) displayed the highest Qi values, with a selection rate of 5% corresponding to the genetic gain of 42.96% in stem volume. This study provides beneficial information for the selection of multiple traits for genetically improved genotypes of Korean pine.

Download Full-text

Seemingly Unrelated Mixed-Effects Biomass Models for Black Locust in West Poland

Forests ◽

10.3390/f12030380 ◽

2021 ◽

Vol 12 (3) ◽

pp. 380

Author(s):

Karol Bronisz ◽

Szymon Bijak ◽

Rafał Wojtan ◽

Robert Tomusiak ◽

Agnieszka Bronisz ◽

...

Keyword(s):

Black Locust ◽

Random Effect ◽

Tree Height ◽

Cross Model ◽

Diameter At Breast Height ◽

Breast Height ◽

Dependent Variables ◽

Dry Biomass ◽

Biomass Components ◽

Independent Variable

Information about tree biomass is important not only in the assessment of wood resources but also in the process of preparing forest management plans, as well as for estimating carbon stocks and their flow in forest ecosystems. The study aimed to develop empirical models for determining the dry mass of the aboveground parts of black locust trees and their components (stem, branches, and leaves). The research was carried out based on data collected in 13 stands (a total of 38 sample trees) of black locust located in western Poland. The model system was developed based on multivariate mixed-effect models using two approaches. In the first approach, biomass components and tree height were defined as dependent variables, while diameter at breast height was used as an independent variable. In the second approach, biomass components and diameter at breast height were dependent variables and tree height was defined as the independent variable. Both approaches enable the fixed-effect and cross-model random-effect prediction of aboveground dry biomass components of black locust. Cross-model random-effect prediction was obtained using additional measurements of two extreme trees, defined as trees characterized by the smallest and largest diameter at breast height in sample plot. This type of prediction is more precise (root mean square error for stem dry biomass for both approaches equals 77.603 and 188.139, respectively) than that of fixed-effects prediction (root mean square error for stem dry biomass for both approaches equals 238.716 and 206.933, respectively). The use of height as an independent variable increases the possibility of the practical application of the proposed solutions using remote data sources.

Download Full-text

Field Evaluation of Populus deltoides Bartr. ex Marsh. at Two Sites in Indo-gangetic Plains of India

Silvae Genetica ◽

10.1515/sg-2010-0001 ◽

2010 ◽

Vol 59 (1-6) ◽

pp. 1-7 ◽

Cited By ~ 3

Author(s):

G. P. S. Dhillon ◽

Avtar Singh ◽

Pritpal Singh ◽

D. S. Sidhu

Keyword(s):

Populus Deltoides ◽

Growth Traits ◽

Tree Height ◽

Field Evaluation ◽

Site Conditions ◽

Genetic Advance ◽

Gangetic Plains ◽

Diameter At Breast Height ◽

Breast Height ◽

Northwestern India

Abstract Results from clonal trials of Populus deltoides conducted in two distinct agroclimatic regions of Punjab in northwestern India are reported and discussed. Sixteen clones were evaluated at Hambran and Bathinda where commonly grown clone ‘G-48’ was considered as control. Significant differences among clones (P < 0.001) were observed for diameter at breast height (DBH), tree height and volume at the age of four and six years under both the site conditions. Clone ‘L-48’ ranked first for volume at six year age at both sites and was followed by clone ‘Ranikhet’. The respective superiority for volume of these clones over control was 44.8 and 23.2 per cent at Hambran and 72.5 and 30.7 per cent at Bathinda. All growth traits registered significantly higher values at Hambran in comparison to those at Bathinda. Clone x site interaction was also significant (P < 0.001). The clones ‘L-168’, ‘154/86’, ‘Solan-z’ and ‘170/88’ experienced huge fluctuation in ranking between sites for volume at 6-year age. The DBH and height showed significant and positive correlation with each other and with tree volume at all the age combinations. The clonal mean heritability was quite high both at Hambran (0.73-0.86) and Bathinda (0.80-0.95). The genetic advance were the highest for volume (33.34-64.26%) and the lowest (10.65-22.79%) in case of height.

Download Full-text

Evaluation of the Relative Stem Taper Curves Estimated Only from Tree Height and Diameter at Breast Height:

Journal of the Japanese Forest Society ◽

10.4005/jjfs.94.188 ◽

2012 ◽

Vol 94 (4) ◽

pp. 188-191 ◽

Cited By ~ 1

Author(s):

Megumi Ishida ◽

Satoshi Naoi ◽

Yasumasa Watanabe ◽

Akinori Tsuzuku ◽

Masaya Aoki

Keyword(s):

Tree Height ◽

Diameter At Breast Height ◽

Breast Height ◽

Stem Taper

Download Full-text

Tree crown size as a measure of tree biosocial position in 135-year-old oak (Quercus L.) stand

Folia Forestalia Polonica ◽

10.1515/ffp-2016-0004 ◽

2016 ◽

Vol 58 (1) ◽

pp. 31-42 ◽

Cited By ~ 1

Author(s):

Katarzyna Kaźmierczak ◽

Bogna Zawieja

Keyword(s):

Principal Component ◽

Tree Height ◽

Projection Area ◽

Tree Crown ◽

Diameter At Breast Height ◽

Breast Height ◽

Crown Length ◽

Pca Method ◽

Crown Projection Area

AbstractThe paper presents an attempt to apply measurable traits of a tree – crown projection area, crown length, diameter at breast height and tree height for classification of 135-year-old oak (QuercusL.) trees into Kraft classes. Statistical multivariate analysis was applied to reach the aim. Empirical material was collected on sample plot area of 0.75 ha, located in 135-year-old oak stand. Analysis of dimensional traits of oaks from 135-year-old stand allows quite certain classification of trees into three groups: pre-dominant, dominant and co-dominant and dominated ones. This seems to be quite promising, providing a tool for the approximation of the biosocial position of tree with no need for assessment in forest. Applied analyses do not allow distinguishing trees belonging to II and III Kraft classes. Unless the eye-estimation-based classification is completed, principal component analysis (PCA) method provided simple, provisional solution for grouping trees from 135-year-old stand into three over-mentioned groups. Discriminant analysis gives more precise results compared with PCA. In the analysed stand, the most important traits for the evaluation of biosocial position were diameter at breast height, crown projection area and height.

Download Full-text