A Levenberg–Marquardt Backpropagation Neural Network for Predicting Forest Growing Stock Based on the Least-Squares Equation Fitting Parameters Article Swipe
Ruyi Zhou
,
Dasheng Wu
,
Luming Fang
,
Aijun Xu
,
Xiongwei Lou
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3390/f9120757
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3390/f9120757
Traditional field surveys are expensive, time-consuming, laborious, and difficult to perform, especially in mountainous and dense forests, which imposes a burden on forest management personnel and researchers. This study focuses on predicting forest growing stock, one of the most significant parameters of a forest resource assessment. First, three schemes were designed—Scheme 1, based on the study samples with mixed tree species; Scheme 2, based on the study samples divided into dominant tree species groups; and Scheme 3, based on the study samples divided by dominant tree species groups—the evaluation factors are fitted by least-squares equations, and the non-significant fitted-factors are removed. Second, an overall evaluation indicator system with 17 factors was established. Third, remote sensing images of Landsat Thematic Mapper, digital elevation model, and the inventory for forest management planning and design were integrated in the same database. Lastly, a backpropagation neural network based on the Levenberg–Marquardt algorithm was used to predict the forest growing stock. The results showed that the group estimation precision exceeded 90%, which is the highest standard of total sampling precision of inventory for forest management planning and design in China. The prediction results for distinguishing dominant tree species were better than for mixed dominant tree species. The results also showed that the performance metrics for prediction could be improved by least-squares equation fitting and significance filtering of the evaluation factors.
Related Topics
Concepts
Thematic Mapper
Backpropagation
Levenberg–Marquardt algorithm
Forest management
Partial least squares regression
Artificial neural network
Forest inventory
Computer science
Data mining
Tree (set theory)
Statistics
Remote sensing
Mathematics
Artificial intelligence
Machine learning
Geography
Forestry
Satellite imagery
Mathematical analysis
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/f9120757
- https://www.mdpi.com/1999-4907/9/12/757/pdf
- OA Status
- gold
- Cited By
- 35
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2902799593
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2902799593Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/f9120757Digital Object Identifier
- Title
-
A Levenberg–Marquardt Backpropagation Neural Network for Predicting Forest Growing Stock Based on the Least-Squares Equation Fitting ParametersWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-12-05Full publication date if available
- Authors
-
Ruyi Zhou, Dasheng Wu, Luming Fang, Aijun Xu, Xiongwei LouList of authors in order
- Landing page
-
https://doi.org/10.3390/f9120757Publisher landing page
- PDF URL
-
https://www.mdpi.com/1999-4907/9/12/757/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1999-4907/9/12/757/pdfDirect OA link when available
- Concepts
-
Thematic Mapper, Backpropagation, Levenberg–Marquardt algorithm, Forest management, Partial least squares regression, Artificial neural network, Forest inventory, Computer science, Data mining, Tree (set theory), Statistics, Remote sensing, Mathematics, Artificial intelligence, Machine learning, Geography, Forestry, Satellite imagery, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
35Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 7, 2024: 5, 2023: 4, 2022: 7, 2021: 3Per-year citation counts (last 5 years)
- References (count)
-
40Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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