Backpack LiDAR Supports Biotope-Scale Assessment of Structure, Maintenance, and Net Carbon Budget in Urban Park Plant Communities
Urban parks are often regarded as carbon sinks, yet their net carbon performance depends on the balance between vegetation carbon uptake and maintenance-related emissions, as well as the accurate representation of within-park spatial heterogeneity. This study used backpack LiDAR, field vegetation surveys, and maintenance inventories to quantify annual carbon sequestration, maintenance emissions, and net carbon budget in 44 plots covering nine biotope types across 16 parks in central Xianyang, China. A four-level biotope classification incorporating canopy openness, ground cover, tree composition, and vertical stratification was applied to link LiDAR-derived three-dimensional structure with ecological-unit-level carbon accounting. Carbon sequestration and net carbon budget differed significantly among biotopes, whereas maintenance emissions did not. Closed broadleaved single-layer forest showed the highest carbon sequestration density (0.772 kg C m−2), while hard-surfaced partly closed broadleaved single-layer forest showed the lowest value (0.132 kg C m−2). Closed woody biotopes functioned as strong carbon sinks, partly closed biotopes as weak sinks, and the partly open short-grass biotope was the only carbon source. Three-dimensional green volume density was the strongest positive predictor of net carbon budget (β = 0.417, p = 0.032), followed by stem density (β = 0.276, p = 0.048), whereas irrigation-related emissions showed a significant negative coefficient (β = −0.276, p = 0.021). Carbon sequestration explained more variation in net carbon budget than maintenance emissions (adjusted R2 = 0.409 vs. 0.134). These findings suggest that backpack LiDAR can support fine-scale identification of priority carbon-sink units in urban parks and that low-carbon park management should prioritize three-dimensional woody vegetation structure while reducing high-input irrigation where feasible.
Authors
- Yuxi Yang
- Xiaoxu Zhang (ORCID: https://orcid.org/0000-0003-0361-5556)
- Yi Ma (ORCID: https://orcid.org/0000-0002-5641-3766)
- Zixin Zhao
- Ling Qiu (ORCID: https://orcid.org/0000-0002-7021-7235)
- Tian Gao (ORCID: https://orcid.org/0000-0002-0375-4073)
Institutions
- Northwest A&F University (CN)
Publication Details
- Journal
- Remote Sensing
- Published
- 2026-05-21
- DOI
- https://doi.org/10.3390/rs18101672
- Primary Topic
- Remote Sensing and LiDAR Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China