New two-step species-level AGB estimation model applied to urban parks
Aboveground biomass (AGB) estimation for urban parks has received less attention as an essential component of the global carbon cycle. Current studies focus on vast areas of natural or planted forests. The characteristics of these study areas make the use of homogenised vegetation grids (using remot...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-12-01
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| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X22011670 |
| _version_ | 1797985385624633344 |
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| author | Yasong Guo Yinyi Lin Wendy Y. Chen Jing Ling Qiaosi Li Joseph Michalski Hongsheng Zhang |
| author_facet | Yasong Guo Yinyi Lin Wendy Y. Chen Jing Ling Qiaosi Li Joseph Michalski Hongsheng Zhang |
| author_sort | Yasong Guo |
| collection | DOAJ |
| description | Aboveground biomass (AGB) estimation for urban parks has received less attention as an essential component of the global carbon cycle. Current studies focus on vast areas of natural or planted forests. The characteristics of these study areas make the use of homogenised vegetation grids (using remote sensing data) and plots (using field data) as the basic research unit a consensus. However, this data-level simplification can be significantly affected by buildings when applied to urban areas. Developing tree species identification methods based on remote sensing provides us with new ideas to explore urban AGB estimation methods at the species level. To this end, we developed a species-level AGB estimation model to address the AGB distribution in urban parks by combining multitemporal airborne light detection and ranging (LiDAR), optical remote sensing data, and field data from two urban parks in Hong Kong through a two-step strategy. First, we constructed optimal remote sensing feature-AGB mapping relationships for each sample species using sample data from the study area, the tropical allometric growth equation, and the five regression algorithms. We then explored a tree species identification method based on the annual vegetation phenological change index (AVPCI), which allowed us to quickly obtain species distribution maps for the study area. Combining these two steps allowed us to obtain AGB information for the study area based on species-level mapping relationships based on species distributions. In the model validation, the correlation between the estimated and true values of the remote sensing feature and AGB mapping relationship was 0.91, with a significantly lower normalised root mean square deviation (RMSE). The overall accuracy of the sample tree species identification was 87.5%, which was better than the results of existing studies. The final AGB obtained was also within the reasonable interval of existing studies. In addition, with the model proposed in this study, we noted that the super typhoon Mangkhut in 2018 reduced the AGB in the study area by 32.6% and demonstrated the significant underestimation of high-density urban areas in existing global biomass products. The model developed in this study addresses the problems of existing AGB estimation methods for urban vegetation represented by urban parks while effectively contributing to understanding AGB distribution and short-term carbon cycle dynamics in urban scenarios. |
| first_indexed | 2024-04-11T07:16:09Z |
| format | Article |
| id | doaj.art-f495276198894cdaa107fc23a79cd545 |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-04-11T07:16:09Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-f495276198894cdaa107fc23a79cd5452022-12-22T04:37:56ZengElsevierEcological Indicators1470-160X2022-12-01145109694New two-step species-level AGB estimation model applied to urban parksYasong Guo0Yinyi Lin1Wendy Y. Chen2Jing Ling3Qiaosi Li4Joseph Michalski5Hongsheng Zhang6Department of Geography, The University of Hong Kong, Hong Kong, ChinaDepartment of Geography, The University of Hong Kong, Hong Kong, China; HKU Shenzhen Institute of Research and Innovation, Shenzhen, ChinaDepartment of Geography, The University of Hong Kong, Hong Kong, ChinaDepartment of Geography, The University of Hong Kong, Hong Kong, China; HKU Shenzhen Institute of Research and Innovation, Shenzhen, ChinaDepartment of Earth Sciences, The University of Hong Kong, Hong Kong, ChinaDepartment of Earth Sciences, The University of Hong Kong, Hong Kong, ChinaDepartment of Geography, The University of Hong Kong, Hong Kong, China; HKU Shenzhen Institute of Research and Innovation, Shenzhen, China; Corresponding author at: Department of Geography, The University of Hong Kong, Hong Kong, China.Aboveground biomass (AGB) estimation for urban parks has received less attention as an essential component of the global carbon cycle. Current studies focus on vast areas of natural or planted forests. The characteristics of these study areas make the use of homogenised vegetation grids (using remote sensing data) and plots (using field data) as the basic research unit a consensus. However, this data-level simplification can be significantly affected by buildings when applied to urban areas. Developing tree species identification methods based on remote sensing provides us with new ideas to explore urban AGB estimation methods at the species level. To this end, we developed a species-level AGB estimation model to address the AGB distribution in urban parks by combining multitemporal airborne light detection and ranging (LiDAR), optical remote sensing data, and field data from two urban parks in Hong Kong through a two-step strategy. First, we constructed optimal remote sensing feature-AGB mapping relationships for each sample species using sample data from the study area, the tropical allometric growth equation, and the five regression algorithms. We then explored a tree species identification method based on the annual vegetation phenological change index (AVPCI), which allowed us to quickly obtain species distribution maps for the study area. Combining these two steps allowed us to obtain AGB information for the study area based on species-level mapping relationships based on species distributions. In the model validation, the correlation between the estimated and true values of the remote sensing feature and AGB mapping relationship was 0.91, with a significantly lower normalised root mean square deviation (RMSE). The overall accuracy of the sample tree species identification was 87.5%, which was better than the results of existing studies. The final AGB obtained was also within the reasonable interval of existing studies. In addition, with the model proposed in this study, we noted that the super typhoon Mangkhut in 2018 reduced the AGB in the study area by 32.6% and demonstrated the significant underestimation of high-density urban areas in existing global biomass products. The model developed in this study addresses the problems of existing AGB estimation methods for urban vegetation represented by urban parks while effectively contributing to understanding AGB distribution and short-term carbon cycle dynamics in urban scenarios.http://www.sciencedirect.com/science/article/pii/S1470160X22011670Aboveground biomass estimationLiDAR point cloudsUrban forestTree species identificationUrban vegetation |
| spellingShingle | Yasong Guo Yinyi Lin Wendy Y. Chen Jing Ling Qiaosi Li Joseph Michalski Hongsheng Zhang New two-step species-level AGB estimation model applied to urban parks Ecological Indicators Aboveground biomass estimation LiDAR point clouds Urban forest Tree species identification Urban vegetation |
| title | New two-step species-level AGB estimation model applied to urban parks |
| title_full | New two-step species-level AGB estimation model applied to urban parks |
| title_fullStr | New two-step species-level AGB estimation model applied to urban parks |
| title_full_unstemmed | New two-step species-level AGB estimation model applied to urban parks |
| title_short | New two-step species-level AGB estimation model applied to urban parks |
| title_sort | new two step species level agb estimation model applied to urban parks |
| topic | Aboveground biomass estimation LiDAR point clouds Urban forest Tree species identification Urban vegetation |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X22011670 |
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