Dynamic Estimation of Forest Volume Based on Multi-Source Data and Neural Network Model Article Swipe
It is quite necessary to explore some more efficient and reliable estimation models which could integrate or, in some cases, substitute the traditional and expensive measuring techniques in forest resources management owing to the rising investigation costs. Thanks to their flexibility and adaptability, artificial neural networks (ANN) constitute a valid approach for modelling complex long-lived dynamic forest ecosystems. The evaluation indexes set was established, including 17 factors: elevation, slope, aspect, surface curvature, solar radiation index, topographic humidity index, tree ages, the soil depth, the A-layer depth of soil, canopy density, Normalized Difference Vegetation Index (NDVI), and the spectral characteristics of the bands from Enhaced Thematic Mapper (ETM+) or Thematic Mapper (TM), Band 1 to Band 5, and Band 7 from Landsat. Then, integrating the remote sensing images of ETM+ or TM, Digital Elevation Model (DEM), and forest resource planning investigation data of fir of the key forestry city of Longquan, Zhejiang Province, China, the membership of each factor was empirically fitted by polynomials, and the forest volumes were estimated via an improved back propagation (BP) neural network (NN) model with Levenberg-Marquardt (LM) optimization algorithm (LM-BP). The results showed that the average individual relative errors (IARE) were from 26.38% to 34.41%; the group relative errors (GRE) were from 2.04% to 6.69%, and all of the group estimation precisions were more than 90% which is the highest standard of overall sampling accuracy about volume of forest resource inventory in China.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5539/jas.v7n3p18
- http://www.ccsenet.org/journal/index.php/jas/article/download/42261/24446
- OA Status
- hybrid
- Cited By
- 10
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2099320852
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2099320852Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5539/jas.v7n3p18Digital Object Identifier
- Title
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Dynamic Estimation of Forest Volume Based on Multi-Source Data and Neural Network ModelWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2015Year of publication
- Publication date
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2015-02-05Full publication date if available
- Authors
-
Dasheng Wu, Yongquan JiList of authors in order
- Landing page
-
https://doi.org/10.5539/jas.v7n3p18Publisher landing page
- PDF URL
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https://www.ccsenet.org/journal/index.php/jas/article/download/42261/24446Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://www.ccsenet.org/journal/index.php/jas/article/download/42261/24446Direct OA link when available
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Artificial neural network, Estimation, Computer science, Volume (thermodynamics), Artificial intelligence, Engineering, Physics, Quantum mechanics, Systems engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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10Total citation count in OpenAlex
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2024: 1, 2022: 2, 2021: 2, 2020: 1, 2019: 2Per-year citation counts (last 5 years)
- References (count)
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32Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.complex | 53 |
| abstract_inverted_index.dynamic | 55 |
| abstract_inverted_index.explore | 5 |
| abstract_inverted_index.highest | 224 |
| abstract_inverted_index.indexes | 60 |
| abstract_inverted_index.network | 176 |
| abstract_inverted_index.overall | 227 |
| abstract_inverted_index.results | 186 |
| abstract_inverted_index.sensing | 125 |
| abstract_inverted_index.surface | 70 |
| abstract_inverted_index.volumes | 166 |
| abstract_inverted_index.(LM-BP). | 184 |
| abstract_inverted_index.Landsat. | 120 |
| abstract_inverted_index.Thematic | 104, 108 |
| abstract_inverted_index.Zhejiang | 150 |
| abstract_inverted_index.accuracy | 229 |
| abstract_inverted_index.approach | 50 |
| abstract_inverted_index.density, | 89 |
| abstract_inverted_index.factors: | 66 |
| abstract_inverted_index.forestry | 146 |
| abstract_inverted_index.humidity | 76 |
| abstract_inverted_index.improved | 171 |
| abstract_inverted_index.networks | 45 |
| abstract_inverted_index.planning | 138 |
| abstract_inverted_index.relative | 192, 202 |
| abstract_inverted_index.reliable | 10 |
| abstract_inverted_index.resource | 137, 234 |
| abstract_inverted_index.sampling | 228 |
| abstract_inverted_index.spectral | 97 |
| abstract_inverted_index.standard | 225 |
| abstract_inverted_index.Elevation | 132 |
| abstract_inverted_index.Longquan, | 149 |
| abstract_inverted_index.Province, | 151 |
| abstract_inverted_index.algorithm | 183 |
| abstract_inverted_index.efficient | 8 |
| abstract_inverted_index.estimated | 168 |
| abstract_inverted_index.expensive | 24 |
| abstract_inverted_index.including | 64 |
| abstract_inverted_index.integrate | 15 |
| abstract_inverted_index.inventory | 235 |
| abstract_inverted_index.measuring | 25 |
| abstract_inverted_index.modelling | 52 |
| abstract_inverted_index.necessary | 3 |
| abstract_inverted_index.radiation | 73 |
| abstract_inverted_index.resources | 29 |
| abstract_inverted_index.Difference | 91 |
| abstract_inverted_index.Normalized | 90 |
| abstract_inverted_index.Vegetation | 92 |
| abstract_inverted_index.artificial | 43 |
| abstract_inverted_index.constitute | 47 |
| abstract_inverted_index.curvature, | 71 |
| abstract_inverted_index.elevation, | 67 |
| abstract_inverted_index.estimation | 11, 215 |
| abstract_inverted_index.evaluation | 59 |
| abstract_inverted_index.individual | 191 |
| abstract_inverted_index.long-lived | 54 |
| abstract_inverted_index.management | 30 |
| abstract_inverted_index.membership | 154 |
| abstract_inverted_index.precisions | 216 |
| abstract_inverted_index.substitute | 20 |
| abstract_inverted_index.techniques | 26 |
| abstract_inverted_index.ecosystems. | 57 |
| abstract_inverted_index.empirically | 159 |
| abstract_inverted_index.flexibility | 40 |
| abstract_inverted_index.integrating | 122 |
| abstract_inverted_index.propagation | 173 |
| abstract_inverted_index.topographic | 75 |
| abstract_inverted_index.traditional | 22 |
| abstract_inverted_index.established, | 63 |
| abstract_inverted_index.optimization | 182 |
| abstract_inverted_index.polynomials, | 162 |
| abstract_inverted_index.adaptability, | 42 |
| abstract_inverted_index.investigation | 35, 139 |
| abstract_inverted_index.characteristics | 98 |
| abstract_inverted_index.Levenberg-Marquardt | 180 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.6700000166893005 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.69243358 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |