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Assessing scale‐dependent effects on Forest biomass productivity based on machine learning
Estimating forest above‐ground biomass (AGB) productivity constitutes one of the most fundamental topics in forest ecological research. Based on a 30‐ha permanent field plot in Northeastern China, we modeled AGB productivity as output, and topography, species diversity, stand structure, and a stand...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9277413/ https://www.ncbi.nlm.nih.gov/pubmed/35845366 http://dx.doi.org/10.1002/ece3.9110 |
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author | He, Jingyuan Fan, Chunyu Geng, Yan Zhang, Chunyu Zhao, Xiuhai von Gadow, Klaus |
author_facet | He, Jingyuan Fan, Chunyu Geng, Yan Zhang, Chunyu Zhao, Xiuhai von Gadow, Klaus |
author_sort | He, Jingyuan |
collection | PubMed |
description | Estimating forest above‐ground biomass (AGB) productivity constitutes one of the most fundamental topics in forest ecological research. Based on a 30‐ha permanent field plot in Northeastern China, we modeled AGB productivity as output, and topography, species diversity, stand structure, and a stand density variable as input across a series of area scales using the Random Forest (RF) algorithm. As the grain size increased from 10 to 200 m, we found that the relative importance of explanatory variables that drove the variation of biomass productivity varied a lot, and the model accuracy was gradually improved. The minimum sampling area for biomass productivity modeling in this region was 140 × 140 m. Our study shows that the relationship of topography, species diversity, stand structure, and stand density variables with biomass productivity modeled using the RF algorithm changes when moving from scales typical of forest surveys (10 m) to larger scales (200 m) within a controlled methodology. These results should be of considerable interest to scientists concerned with forest assessment. |
format | Online Article Text |
id | pubmed-9277413 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92774132022-07-15 Assessing scale‐dependent effects on Forest biomass productivity based on machine learning He, Jingyuan Fan, Chunyu Geng, Yan Zhang, Chunyu Zhao, Xiuhai von Gadow, Klaus Ecol Evol Research Articles Estimating forest above‐ground biomass (AGB) productivity constitutes one of the most fundamental topics in forest ecological research. Based on a 30‐ha permanent field plot in Northeastern China, we modeled AGB productivity as output, and topography, species diversity, stand structure, and a stand density variable as input across a series of area scales using the Random Forest (RF) algorithm. As the grain size increased from 10 to 200 m, we found that the relative importance of explanatory variables that drove the variation of biomass productivity varied a lot, and the model accuracy was gradually improved. The minimum sampling area for biomass productivity modeling in this region was 140 × 140 m. Our study shows that the relationship of topography, species diversity, stand structure, and stand density variables with biomass productivity modeled using the RF algorithm changes when moving from scales typical of forest surveys (10 m) to larger scales (200 m) within a controlled methodology. These results should be of considerable interest to scientists concerned with forest assessment. John Wiley and Sons Inc. 2022-07-13 /pmc/articles/PMC9277413/ /pubmed/35845366 http://dx.doi.org/10.1002/ece3.9110 Text en © 2022 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles He, Jingyuan Fan, Chunyu Geng, Yan Zhang, Chunyu Zhao, Xiuhai von Gadow, Klaus Assessing scale‐dependent effects on Forest biomass productivity based on machine learning |
title | Assessing scale‐dependent effects on Forest biomass productivity based on machine learning |
title_full | Assessing scale‐dependent effects on Forest biomass productivity based on machine learning |
title_fullStr | Assessing scale‐dependent effects on Forest biomass productivity based on machine learning |
title_full_unstemmed | Assessing scale‐dependent effects on Forest biomass productivity based on machine learning |
title_short | Assessing scale‐dependent effects on Forest biomass productivity based on machine learning |
title_sort | assessing scale‐dependent effects on forest biomass productivity based on machine learning |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9277413/ https://www.ncbi.nlm.nih.gov/pubmed/35845366 http://dx.doi.org/10.1002/ece3.9110 |
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