New two-step species-level AGB estimation model applied to urban parks. (December 2022)

• Record Type: Journal Article
• Authors: Guo, Yasong; Lin, Yinyi; Chen, Wendy Y.; Ling, Jing; Li, Qiaosi; Michalski, Joseph; Zhang, Hongsheng
• Is Part Of: Ecological indicators. Volume 145(2023)
• Publication Date: 2022-12
• Subjects: Aboveground biomass estimation -- LiDAR point clouds -- Urban forest -- Tree species identification -- Urban vegetation; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2022.109694
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: The species-level estimation model can describe the urban AGB dynamics. Natural disturbances can have significant effects on urban vegetation AGB. Existing AGB products underestimate biomass of the high-density urban parks. Abstract: 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.