Modeling carbon storage in urban vegetation: Progress, challenges, and opportunities

作者：Qingwei Zhuang, Zhenfeng Shao, Jianya Gong, Deren Li, Xiao Huang, Ya Zhang, Xiaodi Xu, Chaoya Dang, Jinlong Chen, Orhan Altan, Shixin Wu · 发表于：International Journal of Applied Earth Observation and Geoinformation · 年份：2022 · DOI：10.1016/j.jag.2022.103058 · 被引用次数：69 · 研究领域：Plant Water Relations and Carbon Dynamics、Land Use and Ecosystem Services、Fire effects on ecosystems

Urban vegetation (UV) and its carbon storage capacity are critical for terrestrial carbon cycling and global sustainable development goals (SDGs). With complex spatial distribution, composition and ecological functions, UV is essential for global carbon cycling and climate change. Therefore, improving UV carbon storage capacity modeling is a research hotspot that deserves extensive investigation. However, the uniqueness of UV lead to great challenges in carbon storage modeling, including (1) limitations in data and algorithms due to complex and sensitive urban environments; (2) the severe scarcity of in-city field observation data (e.g., EC towers and field surveys); (3) difficulty in parameter inversion (e.g., canopy height, LAI, etc.); (4) poor transferability when migrating estimation models from natural vegetation to urban scenarios. The progress in carbon storage modeling in urban settings is reviewed, with detailed discussions on carbon storage modeling methods and major challenges. We then propose strategies to overcome existing challenges, including (1) implementing novel and improved remote sensing (RS) techniques (e.g., hyper-spectral, LiDAR, carbon satellites, etc.) to obtain enhanced structural and functional information on UV; (2) improving critical nodes of the earth observation sensor network, especially the distribution of EC towers in urban settings; (3) leveraging “Model-Data Fusion” technology by integrating big earth data with carbon estimation models to r...