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Mapping forest and site quality of planted Chinese fir forest using sentinel images
Normally, forest quality (FQ) and site quality (SQ) play an important role in evaluating actual and potential forest productivity. Traditionally, these assessment indices (FQ and SQ) are mainly based on forest parameters extracted from ground measurement (forest height, age, density, forest stem vol...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9577201/ https://www.ncbi.nlm.nih.gov/pubmed/36267948 http://dx.doi.org/10.3389/fpls.2022.949598 |
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author | Tang, Chongjian Ye, Zilin Long, Jiangping Liu, Zhaohua Zhang, Tingchen Xu, Xiaodong Lin, Hui |
author_facet | Tang, Chongjian Ye, Zilin Long, Jiangping Liu, Zhaohua Zhang, Tingchen Xu, Xiaodong Lin, Hui |
author_sort | Tang, Chongjian |
collection | PubMed |
description | Normally, forest quality (FQ) and site quality (SQ) play an important role in evaluating actual and potential forest productivity. Traditionally, these assessment indices (FQ and SQ) are mainly based on forest parameters extracted from ground measurement (forest height, age, density, forest stem volume (FSV), and DBH), which is labor-intensive and difficult to access in certain remote forest areas. Recently, remote sensing images combined with a small number of samples were gradually applied to map forest parameters because of the various advantages of remote sensing technology, such as low cost, spatial coverage, and high efficiency. However, FQ and SQ related to forest parameters are rarely estimated using remote sensing images and machine learning models. In this study, the Sentinel images and ground samples of planted Chinese fir forest located in the ecological “green-core” area of Changzhutan urban cluster, were initially employed to explore the feasibility of mapping the FQ and SQ. And then, four types of alternative variables (backscattering coefficients (VV and VH), multi-spectral bands, vegetation indices, and texture characteristics) were extracted from Sentinel-1A and Sentinel-2A images, respectively. After selecting variables using a stepwise regression model, three machine learning models (SVR, RF, and KNN) were employed to estimate various forest parameters. Finally, the FQ of the study region was directly mapped by the weights sum of related factors extracted by the factor analysis method, and the SQ was also extracted using mapped forest height and age. The results illustrated that the accuracy of estimated forest parameters (DBH, H, and Age) was significantly higher than FSV, FCC, and Age and the largest and smallest rRMSEs were observed from FSV (0.38~0.40) and forest height (0.20~0.21), respectively. Using mapped forest parameters, it also resulted that the rRMSEs of estimated FQ and SQ were 0.19 and 0.15, respectively. Furthermore, after normalization and grading, the grades of forest quality were mainly concentrated in grades I, II, and III in the study region. Though the accuracy of mapping FQ and SQ is limited by the saturation phenomenon, it is significantly proved that using machine learning models and Sentinel images has great potential to indirectly map FQ and SQ. |
format | Online Article Text |
id | pubmed-9577201 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95772012022-10-19 Mapping forest and site quality of planted Chinese fir forest using sentinel images Tang, Chongjian Ye, Zilin Long, Jiangping Liu, Zhaohua Zhang, Tingchen Xu, Xiaodong Lin, Hui Front Plant Sci Plant Science Normally, forest quality (FQ) and site quality (SQ) play an important role in evaluating actual and potential forest productivity. Traditionally, these assessment indices (FQ and SQ) are mainly based on forest parameters extracted from ground measurement (forest height, age, density, forest stem volume (FSV), and DBH), which is labor-intensive and difficult to access in certain remote forest areas. Recently, remote sensing images combined with a small number of samples were gradually applied to map forest parameters because of the various advantages of remote sensing technology, such as low cost, spatial coverage, and high efficiency. However, FQ and SQ related to forest parameters are rarely estimated using remote sensing images and machine learning models. In this study, the Sentinel images and ground samples of planted Chinese fir forest located in the ecological “green-core” area of Changzhutan urban cluster, were initially employed to explore the feasibility of mapping the FQ and SQ. And then, four types of alternative variables (backscattering coefficients (VV and VH), multi-spectral bands, vegetation indices, and texture characteristics) were extracted from Sentinel-1A and Sentinel-2A images, respectively. After selecting variables using a stepwise regression model, three machine learning models (SVR, RF, and KNN) were employed to estimate various forest parameters. Finally, the FQ of the study region was directly mapped by the weights sum of related factors extracted by the factor analysis method, and the SQ was also extracted using mapped forest height and age. The results illustrated that the accuracy of estimated forest parameters (DBH, H, and Age) was significantly higher than FSV, FCC, and Age and the largest and smallest rRMSEs were observed from FSV (0.38~0.40) and forest height (0.20~0.21), respectively. Using mapped forest parameters, it also resulted that the rRMSEs of estimated FQ and SQ were 0.19 and 0.15, respectively. Furthermore, after normalization and grading, the grades of forest quality were mainly concentrated in grades I, II, and III in the study region. Though the accuracy of mapping FQ and SQ is limited by the saturation phenomenon, it is significantly proved that using machine learning models and Sentinel images has great potential to indirectly map FQ and SQ. Frontiers Media S.A. 2022-10-04 /pmc/articles/PMC9577201/ /pubmed/36267948 http://dx.doi.org/10.3389/fpls.2022.949598 Text en Copyright © 2022 Tang, Ye, Long, Liu, Zhang, Xu and Lin https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Tang, Chongjian Ye, Zilin Long, Jiangping Liu, Zhaohua Zhang, Tingchen Xu, Xiaodong Lin, Hui Mapping forest and site quality of planted Chinese fir forest using sentinel images |
title | Mapping forest and site quality of planted Chinese fir forest using sentinel images |
title_full | Mapping forest and site quality of planted Chinese fir forest using sentinel images |
title_fullStr | Mapping forest and site quality of planted Chinese fir forest using sentinel images |
title_full_unstemmed | Mapping forest and site quality of planted Chinese fir forest using sentinel images |
title_short | Mapping forest and site quality of planted Chinese fir forest using sentinel images |
title_sort | mapping forest and site quality of planted chinese fir forest using sentinel images |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9577201/ https://www.ncbi.nlm.nih.gov/pubmed/36267948 http://dx.doi.org/10.3389/fpls.2022.949598 |
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