The aboveground carbon storage (AGC) of urban forests is an important indicator reflecting the ecological function of urban forests. It is essential to monitor the AGC of urban forests and analyze their spatiotemporal distributions. Remote sensing is a technical tool that can be leveraged to accurately monitor forest AGC, whereas machine learning is an important algorithm for the accurate prediction of AGC. Therefore, in this study, single Landsat 8 (L) remote sensing data, single Sentinel-2 (S) remote sensing data, and combined Landsat 8 and Sentinel-2 (L + S) data are used as data sources. Four machine learning methods, support vector regression (SVR), random forest (RF), XGBoost (extreme gradient boosting), and CatBoost (categorical boosting), are used to predict forest AGC based on two phases of forest sample plots in Shanghai. We chose the optimal model to predict the AGC and simulate the spatiotemporal distribution. The study shows that both machine learning models based on separate Landsat 8 OLI and Sentinel-2 satellite remote sensing data can accurately predict the AGC and spatiotemporal distribution of the Shanghai urban forest. Nevertheless, the accuracy of the combined data (L + S) and CatBoost-integrated AGC models is higher than the others, with fitting and validation accuracy R2 values of 0.99 and 0.70, respectively. The RMSE was also smaller at 0.67 and 6.29 Mg/ha, respectively. The uncertainty of the AGC spatial distribution in the Shanghai urban forest derived from the CatBoost model prediction from the 2016–2019 data was small and consistent with the actual situation. Furthermore, the statistics showed that the AGC of the Shanghai forest increased from 24.90 Mg/ha in 2016 to 25.61 Mg/ha in 2019.

The middle reaches of the Yangtze River region (MRYRR) are China’s first trans-regional urban agglomeration, located in the center of the Yangtze River Economic Belt. The MRYRR is an important ecological reserve, and its land cover changes are affected by both socio-economic development and geographical environment. In this paper, Landsat ETM/TM/OLI remote sensing images were used to monitor land use and landscape patterns from 1990 to 2015. Through supervised classification, land use transfer matrix, landscape pattern metrics and correlation analysis, the spatial-temporal patterns of land use change and its relationship with socio-economic in the study area were revealed. The results showed that: (1) the main land use types in the study area were cropland (CL) and forestland (FL), accounting for more than three-quarters of the study area. During the study period, built-up land (BL) increased, CL decreased, FL increased first and then decreased; (2) the BL expanded mainly by occupying CL and FL, and regional landscape pattern was gradually fragmented, with complex patch shape and increasing diversity and heterogeneity. Among them, the BL is gradually gathered, and the FL and CL are gradually fragmented; (3) in the past 25 years, the urbanization process in this region has been obvious, and the Gross Domestic Product (GDP) has increased by 36 times. The socioeconomic variables were positively correlated with BL, orchard (OL) and Shannon diversity index (SHID), and negatively correlated with CL, Wasteland (WL), mean patch size (MPS) and contagion size (CONTAG). The results showed that the urbanization development has a great impact on the region, and the ecological protection task is still challenging. It is necessary to protect high-quality cropland and draw a red line for ecological protection. We should strengthen the construction of ecological corridors and ecological nodes to adapt to regional sustainable development.

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.