A new strategy for improving the accuracy of forest aboveground biomass estimates in an alpine region based on multi-source remote sensing
Spatially explicit information on the distribution of dominant tree species groups and aboveground biomass (AGB) in forested areas is essential for developing targeted forest management and biodiversity conservation measures, as well as assessing forest carbon sequestration capacity. There is a shor...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/15481603.2022.2163574 |
| _version_ | 1797679019012915200 |
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| author | Yali Zhang Ni Wang Yuliang Wang Mingshi Li |
| author_facet | Yali Zhang Ni Wang Yuliang Wang Mingshi Li |
| author_sort | Yali Zhang |
| collection | DOAJ |
| description | Spatially explicit information on the distribution of dominant tree species groups and aboveground biomass (AGB) in forested areas is essential for developing targeted forest management and biodiversity conservation measures, as well as assessing forest carbon sequestration capacity. There is a shortage of continuously updated 30-m spatial resolution products for mapping dominant tree species groups. The vast majority of remote sensing-based AGB estimation approaches have relatively low accuracy for dominant tree species groups or forest types and are unsuitable for AGB modeling. Therefore, this study aims to develop an integrated framework that considers the phenological characteristics of different tree species to improve the mapping accuracies of forest dominant tree groups and corresponding AGB estimates. Thirty-meter resolution maps of dominant tree species groups were created using machine learning algorithms and phenological parameters. Features extracted from optical and radar images and phenological characteristics were used to construct AGB estimation models in a temporally consistent manner to improve the AGB estimation accuracy and perform dynamic AGB monitoring. The proposed method accurately characterized the dynamic distribution of the dominant tree species groups in the study area. The traditional AGB model that does not consider different forest types or species had an R2 value of 0.52, whereas the proposed model that considers phenology and forest types had an R2 value of 0.67. This result indicates that incorporating information on phenology and dominant species improves the accuracy of AGB estimations. The AGB in most regions was 30–55 t/ha, showing that the majority of the forests were young or middle-aged stands, and the areal percentage of AGB greater than 30 t/ha increased during the study period, suggesting an improvement in forest quality. Furthermore, the oak AGB was the highest, indicating that oak afforestation should be encouraged to enhance the carbon sequestration capacity of future forest ecosystems. The results provide new insights for researchers and managers to understand the trends of forest development and forest health, as well as technical information and a database for formulating more rational forest management strategies. |
| first_indexed | 2024-03-11T23:08:19Z |
| format | Article |
| id | doaj.art-50231f2d3da14b60a4a7a5b23d470ef0 |
| institution | Directory Open Access Journal |
| issn | 1548-1603 1943-7226 |
| language | English |
| last_indexed | 2024-03-11T23:08:19Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj.art-50231f2d3da14b60a4a7a5b23d470ef02023-09-21T12:43:09ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262023-12-0160110.1080/15481603.2022.21635742163574A new strategy for improving the accuracy of forest aboveground biomass estimates in an alpine region based on multi-source remote sensingYali Zhang0Ni Wang1Yuliang Wang2Mingshi Li3Nanjing Forestry UniversityChuzhou UniversityChuzhou UniversityNanjing Forestry UniversitySpatially explicit information on the distribution of dominant tree species groups and aboveground biomass (AGB) in forested areas is essential for developing targeted forest management and biodiversity conservation measures, as well as assessing forest carbon sequestration capacity. There is a shortage of continuously updated 30-m spatial resolution products for mapping dominant tree species groups. The vast majority of remote sensing-based AGB estimation approaches have relatively low accuracy for dominant tree species groups or forest types and are unsuitable for AGB modeling. Therefore, this study aims to develop an integrated framework that considers the phenological characteristics of different tree species to improve the mapping accuracies of forest dominant tree groups and corresponding AGB estimates. Thirty-meter resolution maps of dominant tree species groups were created using machine learning algorithms and phenological parameters. Features extracted from optical and radar images and phenological characteristics were used to construct AGB estimation models in a temporally consistent manner to improve the AGB estimation accuracy and perform dynamic AGB monitoring. The proposed method accurately characterized the dynamic distribution of the dominant tree species groups in the study area. The traditional AGB model that does not consider different forest types or species had an R2 value of 0.52, whereas the proposed model that considers phenology and forest types had an R2 value of 0.67. This result indicates that incorporating information on phenology and dominant species improves the accuracy of AGB estimations. The AGB in most regions was 30–55 t/ha, showing that the majority of the forests were young or middle-aged stands, and the areal percentage of AGB greater than 30 t/ha increased during the study period, suggesting an improvement in forest quality. Furthermore, the oak AGB was the highest, indicating that oak afforestation should be encouraged to enhance the carbon sequestration capacity of future forest ecosystems. The results provide new insights for researchers and managers to understand the trends of forest development and forest health, as well as technical information and a database for formulating more rational forest management strategies.http://dx.doi.org/10.1080/15481603.2022.2163574agbforest species groupsphenologyremote sensing |
| spellingShingle | Yali Zhang Ni Wang Yuliang Wang Mingshi Li A new strategy for improving the accuracy of forest aboveground biomass estimates in an alpine region based on multi-source remote sensing GIScience & Remote Sensing agb forest species groups phenology remote sensing |
| title | A new strategy for improving the accuracy of forest aboveground biomass estimates in an alpine region based on multi-source remote sensing |
| title_full | A new strategy for improving the accuracy of forest aboveground biomass estimates in an alpine region based on multi-source remote sensing |
| title_fullStr | A new strategy for improving the accuracy of forest aboveground biomass estimates in an alpine region based on multi-source remote sensing |
| title_full_unstemmed | A new strategy for improving the accuracy of forest aboveground biomass estimates in an alpine region based on multi-source remote sensing |
| title_short | A new strategy for improving the accuracy of forest aboveground biomass estimates in an alpine region based on multi-source remote sensing |
| title_sort | new strategy for improving the accuracy of forest aboveground biomass estimates in an alpine region based on multi source remote sensing |
| topic | agb forest species groups phenology remote sensing |
| url | http://dx.doi.org/10.1080/15481603.2022.2163574 |
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