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


Zhuang Q., Shao Z., Gong J., Li D., Huang X., Zhang Y., ...More

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, vol.114, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 114
  • Publication Date: 2022
  • Doi Number: 10.1016/j.jag.2022.103058
  • Journal Name: INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), Environment Index, Geobase, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Keywords: Urban vegetation, Carbon storage, Carbon neutrality, Remote sensing, SDGs, Big Earth Data, Model -Data Fusion, NET PRIMARY PRODUCTION, SOIL ORGANIC-CARBON, BIG EARTH DATA, HYPERSPECTRAL IMAGERY, HUMAN APPROPRIATION, RENEWABLE ENERGY, TREE GROWTH, FOREST, SEQUESTRATION, ECOSYSTEMS
  • Istanbul Technical University Affiliated: Yes

Abstract

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" tech-nology by integrating big earth data with carbon estimation models to reduce the uncertainty in UV carbon storage estimations. This review provides new insights for modeling UV carbon storage and is expected to help the research community to achieve a better understanding of UV towards carbon neutrality.