Zhenjie Yang

1. As a cornerstone of coastal “blue carbon” ecosystems, salt-marsh vegetation plays a pivotal role in carbon sequestration and storage (X. Chen et al. 2022; Perera et al. 2022; Raw et al. 2023) wh...

Precise estimation of aboveground biomass in urban forests is crucial for quantifying urban carbon stocks and supporting climate change mitigation efforts. However, the availability of allometric equations tailored for urban trees is limited. Existing equations often rely on data from harvested trees with restricted sample sizes and small diameters, thereby introducing substantial uncertainty into biomass assessments. This study utilized terrestrial laser scanning (TLS) in conjunction with a leaf-wood separation algorithm and a tree quantitative structure model (TreeQSM) as a non-destructive approach to develop new species-specific allometric models for four predominant evergreen broadleaved tree species in the urban forests of Shanghai, based on 10 sample plots and 303 trees. The results showed that TLS-derived multivariate models, which incorporated diameter at breast height (DBH), tree height, and crown diameter, consistently outperformed models that only included DBH. Compared with the TLS-derived biomass, the previously published models showed varying degrees of deviation. Notably, there was a substantial overestimation for Camphora officinarum, with a bias of +31.5%. In contrast, Elaeocarpus decipiens, Ligustrum lucidum, and Magnolia grandiflora demonstrated smaller underestimations, with biases of −9.4%, −0.9%, and −4.5%, respectively. These discrepancies were primarily attributed to the extrapolation beyond the calibration diameter at DBH ranges of the published equations. These findings highlight the critical need for urban-specific models. Because destructive harvesting was not feasible in the urban environment, the TLS-derived biomass estimates were not validated against destructively measured biomass. The equations developed in this study provide improved tools for estimating urban forest biomass and carbon accounting for the four studied species under the sampled conditions in Shanghai.

Human activities have been stressing the ecological environment since we stepped into the Anthropocene Age. It is urgent to formulate a sustainable plan for balancing socioeconomic development and ecological conservation based on a thorough understanding of ecological environment changes. The ecological environment can be evaluated when multiple remote sensing indices are integrated, such as the use of the recently prevalent Remote Sensing-based Ecological Index (RSEI). Currently, most of the RSEI-related studies have focused on the ecological quality evolution in small areas. Less attention was paid to the spatio-temporal heterogeneity of ecological quality in large-scale urban agglomerations and the potential links with Land Use and Cover Change (LUCC). In this study, we monitored the dynamics of the ecological quality in the Hangzhou Greater Bay Area (HGBA) during 1995–2020, using the RSEI as an indicator. During the construction of the RSEI, a percentile de-noising normalization method was proposed to overcome the problem of widespread noises from large-scale regions and make the RSEI-based ecological quality assessment for multiple periods comparable. Combined with the land use data, the quantitative relationship between the ecological quality and the LUCC was revealed. The results demonstrated that: (1) The ecological quality of the HGBA degraded after first improving but was still good (averaged RSEI of 0.638). It was divergent for the prefecture-level cities of the HGBA, presenting degraded, improved, and fluctuant trends for the cities from north to south. (2) For ecological quality, the improved regions have larger area (57.5% vs. 42.5%) but less increment (0.141 vs. −0.195) than the degraded regions. Mountains, downtowns, and coastal wetlands were the hot spots for the improvement and urbanization, and reclamation processes were responsible for the degradation. (3) The ecological quality was improved for forests and urban areas (△RSEI > 0.07) but degraded for farmland (∆RSEI = −0.03). As a result, the ecological cost was reduced among human-dominant environments (e.g., farmland, urban areas) while enlarged for the conversion from nature-(e.g., forests) to human-dominant environments.