Predicting carbon storage of mixed broadleaf forests based on the finite mixture model incorporating stand factors, site quality, and aridity index
The diameter distribution function (DDF) is a crucial tool for accurately predicting stand carbon storage (CS). The current key issue, however, is how to construct a high-precision DDF based on stand factors, site quality, and aridity index to predict stand CS in multi-species mixed forests with com...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-01-01
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| Series: | Forest Ecosystems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2197562024000253 |
| _version_ | 1797226675448053760 |
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| author | Yanlin Wang Dongzhi Wang Dongyan Zhang Qiang Liu Yongning Li |
| author_facet | Yanlin Wang Dongzhi Wang Dongyan Zhang Qiang Liu Yongning Li |
| author_sort | Yanlin Wang |
| collection | DOAJ |
| description | The diameter distribution function (DDF) is a crucial tool for accurately predicting stand carbon storage (CS). The current key issue, however, is how to construct a high-precision DDF based on stand factors, site quality, and aridity index to predict stand CS in multi-species mixed forests with complex structures. This study used data from 70 survey plots for mixed broadleaf Populus davidiana and Betula platyphylla forests in the Mulan Rangeland State Forest, Hebei Province, China, to construct the DDF based on maximum likelihood estimation and finite mixture model (FMM). Ordinary least squares (OLS), linear seemingly unrelated regression (LSUR), and back propagation neural network (BPNN) were used to investigate the influences of stand factors, site quality, and aridity index on the shape and scale parameters of DDF and predicted stand CS of mixed broadleaf forests. The results showed that FMM accurately described the stand-level diameter distribution of the mixed P. davidiana and B. platyphylla forests; whereas the Weibull function constructed by MLE was more accurate in describing species-level diameter distribution. The combined variable of quadratic mean diameter (Dq), stand basal area (BA), and site quality improved the accuracy of the shape parameter models of FMM; the combined variable of Dq, BA, and De Martonne aridity index improved the accuracy of the scale parameter models. Compared to OLS and LSUR, the BPNN had higher accuracy in the re-parameterization process of FMM. OLS, LSUR, and BPNN overestimated the CS of P. davidiana but underestimated the CS of B. platyphylla in the large diameter classes (DBH ≥18 cm). BPNN accurately estimated stand- and species-level CS, but it was more suitable for estimating stand-level CS compared to species-level CS, thereby providing a scientific basis for the optimization of stand structure and assessment of carbon sequestration capacity in mixed broadleaf forests. |
| first_indexed | 2024-04-24T14:28:41Z |
| format | Article |
| id | doaj.art-1e133d8408a54d11a408da4a52678f37 |
| institution | Directory Open Access Journal |
| issn | 2197-5620 |
| language | English |
| last_indexed | 2024-04-24T14:28:41Z |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Forest Ecosystems |
| spelling | doaj.art-1e133d8408a54d11a408da4a52678f372024-04-03T04:26:30ZengKeAi Communications Co., Ltd.Forest Ecosystems2197-56202024-01-0111100189Predicting carbon storage of mixed broadleaf forests based on the finite mixture model incorporating stand factors, site quality, and aridity indexYanlin Wang0Dongzhi Wang1Dongyan Zhang2Qiang Liu3Yongning Li4College of Forestry, Hebei Agricultural University, Baoding, 071001, ChinaCollege of Forestry, Hebei Agricultural University, Baoding, 071001, China; Corresponding author.College of Economics and Management, Hebei Agricultural University, Baoding, 071001, ChinaCollege of Forestry, Hebei Agricultural University, Baoding, 071001, ChinaCollege of Forestry, Hebei Agricultural University, Baoding, 071001, ChinaThe diameter distribution function (DDF) is a crucial tool for accurately predicting stand carbon storage (CS). The current key issue, however, is how to construct a high-precision DDF based on stand factors, site quality, and aridity index to predict stand CS in multi-species mixed forests with complex structures. This study used data from 70 survey plots for mixed broadleaf Populus davidiana and Betula platyphylla forests in the Mulan Rangeland State Forest, Hebei Province, China, to construct the DDF based on maximum likelihood estimation and finite mixture model (FMM). Ordinary least squares (OLS), linear seemingly unrelated regression (LSUR), and back propagation neural network (BPNN) were used to investigate the influences of stand factors, site quality, and aridity index on the shape and scale parameters of DDF and predicted stand CS of mixed broadleaf forests. The results showed that FMM accurately described the stand-level diameter distribution of the mixed P. davidiana and B. platyphylla forests; whereas the Weibull function constructed by MLE was more accurate in describing species-level diameter distribution. The combined variable of quadratic mean diameter (Dq), stand basal area (BA), and site quality improved the accuracy of the shape parameter models of FMM; the combined variable of Dq, BA, and De Martonne aridity index improved the accuracy of the scale parameter models. Compared to OLS and LSUR, the BPNN had higher accuracy in the re-parameterization process of FMM. OLS, LSUR, and BPNN overestimated the CS of P. davidiana but underestimated the CS of B. platyphylla in the large diameter classes (DBH ≥18 cm). BPNN accurately estimated stand- and species-level CS, but it was more suitable for estimating stand-level CS compared to species-level CS, thereby providing a scientific basis for the optimization of stand structure and assessment of carbon sequestration capacity in mixed broadleaf forests.http://www.sciencedirect.com/science/article/pii/S2197562024000253Weibull functionFinite mixture modelLinear seemingly unrelated regressionBack propagation neural networkCarbon storage |
| spellingShingle | Yanlin Wang Dongzhi Wang Dongyan Zhang Qiang Liu Yongning Li Predicting carbon storage of mixed broadleaf forests based on the finite mixture model incorporating stand factors, site quality, and aridity index Forest Ecosystems Weibull function Finite mixture model Linear seemingly unrelated regression Back propagation neural network Carbon storage |
| title | Predicting carbon storage of mixed broadleaf forests based on the finite mixture model incorporating stand factors, site quality, and aridity index |
| title_full | Predicting carbon storage of mixed broadleaf forests based on the finite mixture model incorporating stand factors, site quality, and aridity index |
| title_fullStr | Predicting carbon storage of mixed broadleaf forests based on the finite mixture model incorporating stand factors, site quality, and aridity index |
| title_full_unstemmed | Predicting carbon storage of mixed broadleaf forests based on the finite mixture model incorporating stand factors, site quality, and aridity index |
| title_short | Predicting carbon storage of mixed broadleaf forests based on the finite mixture model incorporating stand factors, site quality, and aridity index |
| title_sort | predicting carbon storage of mixed broadleaf forests based on the finite mixture model incorporating stand factors site quality and aridity index |
| topic | Weibull function Finite mixture model Linear seemingly unrelated regression Back propagation neural network Carbon storage |
| url | http://www.sciencedirect.com/science/article/pii/S2197562024000253 |
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