Regional Forest Structure Evaluation Model Based on Remote Sensing and Field Survey Data Article Swipe
Shangqin Lin
,
Qingqing Wen
,
Dasheng Wu
,
Huajian Huang
,
Xinyu Zheng
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/f15030533
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/f15030533
The assessment of a forest’s structure is pivotal in guiding effective forest management, conservation efforts, and ensuring sustainable development. However, traditional evaluation methods often focus on isolated forest parameters and incur substantial data acquisition costs. To address these limitations, this study introduces a cost-effective and innovative evaluation model that incorporates remote sensing imagery and machine learning algorithms. This model holistically considers the forest composition, the tree age structure, and spatial configuration. Using a comprehensive approach, the forest structure in Longquan City was evaluated at the stand level and categorized into three distinct categories: good, moderate, and poor. The construction of this evaluation model drew upon multiple data sources, namely Sentinel-2 imagery, digital elevation models (DEMs), and forest resource planning and design survey data. The model employed the Recursive Feature Elimination with Cross-Validation (RFECV) method for feature selection, alongside various machine learning algorithms. The key findings from this research are summarized as follows: The application of the RFECV method proved effective in eliminating irrelevant factors, reducing data dimensionality and, subsequently, enhancing the model’s generalizability; among the tested machine learning algorithms, the CatBoost model emerged as the most accurate and stable across all the datasets; specifically, the CatBoost model achieved an impressive overall accuracy of 88.07%, a kappa coefficient of 0.6833, and a recall rate of 76.86%. These results significantly surpass the classification precision of previous methods. The forest structure assessment of Longquan City revealed notable variations in the forest quality distribution. Notably, forests classified as “good” quality comprised 11.18% of the total, while “medium” quality forests constituted the majority at 76.77%. In contrast, “poor” quality forests accounted for a relatively minor proportion of the total, at 12.05%. The distribution findings provide valuable insights for targeted forest management and conservation strategies.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/f15030533
- https://www.mdpi.com/1999-4907/15/3/533/pdf?version=1710341047
- OA Status
- gold
- Cited By
- 5
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392746871
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392746871Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/f15030533Digital Object Identifier
- Title
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Regional Forest Structure Evaluation Model Based on Remote Sensing and Field Survey DataWork title
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articleOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
- Publication date
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2024-03-13Full publication date if available
- Authors
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Shangqin Lin, Qingqing Wen, Dasheng Wu, Huajian Huang, Xinyu ZhengList of authors in order
- Landing page
-
https://doi.org/10.3390/f15030533Publisher landing page
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https://www.mdpi.com/1999-4907/15/3/533/pdf?version=1710341047Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/1999-4907/15/3/533/pdf?version=1710341047Direct OA link when available
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Remote sensing, Field (mathematics), Field survey, Environmental science, Environmental resource management, Geography, Computer science, Cartography, Mathematics, Pure mathematicsTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2025: 3, 2024: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| primary_location.landing_page_url | https://doi.org/10.3390/f15030533 |
| publication_date | 2024-03-13 |
| publication_year | 2024 |
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