Estimation of DBH at Forest Stand Level Based on Multi-Parameters and Generalized Regression Neural Network Article Swipe
Runkai Zhou
,
Dasheng Wu
,
Ruyi Zhou
,
Luming Fang
,
Xinyu Zheng
,
Xiongwei Lou
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3390/f10090778
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3390/f10090778
The diameter at breast height (DBH) is an important factor used to estimate important forestry indices like forest growing stock, basal area, biomass, and carbon stock. The traditional DBH ground surveys are time-consuming, labor-intensive, and expensive. To reduce the traditional ground surveys, this study focused on the prediction of unknown DBH in forest stands using existing measured data. As a comparison, the tree age was first used as the only independent variable in establishing 13 kinds of empirical models to fit the relationship between the age and DBH of the forest subcompartments and predict DBH growth. Second, the initial independent variables were extended to 19 parameters, including 8 ecological and biological factors and 11 remote sensing factors. By introducing the Spearman correlation analysis, the independent variable parameters were dimension-reduced to satisfy very significant conditions (p ≤ 0.01) and a relatively large correlation coefficient (r ≥ 0.1). Finally, the remaining independent variables were involved in the modeling and prediction of DBH using a multivariate linear regression (MLR) model and generalized regression neural network (GRNN) model. The (root-mean-squared errors) RMSEs of MLR and GRNN were 1.9976 cm and 1.9655 cm, respectively, and the R2 were 0.6459 and 0.6574 respectively, which were much better than the values for the 13 traditional empirical age–DBH models. The use of comprehensive factors is beneficial to improving the prediction accuracy of both the MLR and GRNN models. Regardless of whether remote sensing image factors were included, the experimental results produced by GRNN were better than MLR. By synthetically introducing ecological, biological, and remote sensing factors, GRNN produced the best results with 1.4688 cm in mean absolute error (MAE), 13.78% in MAPE, 1.9655 cm for the RMSE, 0.6574 for the R2, and 0.0810 for the Theil’s inequality coefficient (TIC), respectively. For modeling and prediction based on more complex tree species and a wider range of samples, GRNN is a desirable model with strong generalizability.
Related Topics
Concepts
Diameter at breast height
Basal area
Mathematics
Statistics
Linear regression
Correlation coefficient
Variables
Regression analysis
Multivariate statistics
Coefficient of determination
Spearman's rank correlation coefficient
Bayesian multivariate linear regression
Artificial neural network
Regression
Econometrics
Forestry
Geography
Computer science
Machine learning
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/f10090778
- https://www.mdpi.com/1999-4907/10/9/778/pdf?version=1568857283
- OA Status
- gold
- Cited By
- 26
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2971697648
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2971697648Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/f10090778Digital Object Identifier
- Title
-
Estimation of DBH at Forest Stand Level Based on Multi-Parameters and Generalized Regression Neural NetworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-09-06Full publication date if available
- Authors
-
Runkai Zhou, Dasheng Wu, Ruyi Zhou, Luming Fang, Xinyu Zheng, Xiongwei LouList of authors in order
- Landing page
-
https://doi.org/10.3390/f10090778Publisher landing page
- PDF URL
-
https://www.mdpi.com/1999-4907/10/9/778/pdf?version=1568857283Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/1999-4907/10/9/778/pdf?version=1568857283Direct OA link when available
- Concepts
-
Diameter at breast height, Basal area, Mathematics, Statistics, Linear regression, Correlation coefficient, Variables, Regression analysis, Multivariate statistics, Coefficient of determination, Spearman's rank correlation coefficient, Bayesian multivariate linear regression, Artificial neural network, Regression, Econometrics, Forestry, Geography, Computer science, Machine learningTop concepts (fields/topics) attached by OpenAlex
- Cited by
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26Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 5, 2023: 7, 2022: 3, 2021: 2Per-year citation counts (last 5 years)
- References (count)
-
49Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.ecological, | 252 |
| abstract_inverted_index.generalized | 168 |
| abstract_inverted_index.independent | 70, 99, 124, 149 |
| abstract_inverted_index.introducing | 118, 251 |
| abstract_inverted_index.parameters, | 105 |
| abstract_inverted_index.significant | 132 |
| abstract_inverted_index.traditional | 27, 39, 207 |
| abstract_inverted_index.establishing | 73 |
| abstract_inverted_index.experimental | 240 |
| abstract_inverted_index.multivariate | 162 |
| abstract_inverted_index.relationship | 82 |
| abstract_inverted_index.comprehensive | 214 |
| abstract_inverted_index.respectively, | 188, 196 |
| abstract_inverted_index.respectively. | 291 |
| abstract_inverted_index.synthetically | 250 |
| abstract_inverted_index.subcompartments | 91 |
| abstract_inverted_index.time-consuming, | 32 |
| abstract_inverted_index.labor-intensive, | 33 |
| abstract_inverted_index.dimension-reduced | 128 |
| abstract_inverted_index.generalizability. | 315 |
| abstract_inverted_index.(root-mean-squared | 175 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 93 |
| corresponding_author_ids | https://openalex.org/A5033636430 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I1284762954, https://openalex.org/I4210134523 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.47999998927116394 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.75251328 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |