Consisting of ten cities in four Chinese provinces, the Huaihai Economic Zone has suffered serious air pollution over the last two decades, particularly of fine particulate matter (PM_2.5). In this study, we used multi-source data, namely MAIAC AOD (at a 1 km spatial resolution), meteorological, topographic, date, and location (latitude and longitude) data, to construct a regression model using random forest to estimate the daily PM_2.5 concentration over the Huaihai Economic Zone from 2000 to 2020. It was found that the variable expressing time (date) had the greatest characteristic importance when estimating PM_2.5. By averaging the modeled daily PM_2.5 concentration, we produced a yearly PM_2.5 concentration dataset, at a 1 km resolution, for the study area from 2000 to 2020. On comparing modeled daily PM_2.5 with observational data, the coefficient of determination ($R^2$) of the modeling was 0.85, the root means square error ($RMSE$) was 14.63 μg/m³, and the mean absolute error ($MAE$) was 10.03 μg/m³. The quality assessment of the synthesized yearly PM_2.5 concentration dataset shows that $R^2$ = 0.77, $RMSE$ = 6.92 μg/m³, and $MAE$ = 5.42 μg/m³. Despite different trends from 2000–2010 and from 2010–2020, the trend of PM_2.5 concentration over the Huaihai Economic Zone during the 21 years was, overall, decreasing. The area of the significantly decreasing trend was small and mainly concentrated in the lake areas of the Zone. It is concluded that PM_2.5 can be well-estimated from the MAIAC AOD dataset, when incorporating spatiotemporal variability using random forest, and that the resultant PM_2.5 concentration data provide a basis for environmental monitoring over large geographic areas.

High-quality urban green space supports the healthy functioning of urban ecosystems. This study aimed to rapidly assess the distribution, and accurately estimate the above-ground biomass, of urban green space using remote sensing methods, thus providing a better understanding of the urban ecological environment in Xuzhou for more effective management. We performed urban green space classifications and compared the performance of Sentinel-2 MSI data and Sentinel-1 SAR data and combinations, for estimating above-ground biomass, using field data from Xuzhou, China. The results showed the following: (1) incorporating an object-oriented method and random forest algorithm to extract urban green space information was effective; (2) compared with stepwise regression models with single-source data, biomass estimation models based on multi-source data provide higher estimation accuracy (R² = 0.77 for coniferous forest, R² = 0.76 for shrub-grass vegetation, R² = 0.75 for broadleaf forest); and (3) from 2016 to 2021, urban green space coverage in Xuzhou decreased, while the total above-ground biomass increased, with higher average above-ground biomass in broadleaf forests (133.71 tons/ha) compared to coniferous forests (92.13 tons/ha) and shrub-grass vegetation (21.65 tons/ha). Our study provides an example of automated classification and above-ground biomass mapping for urban green space using multi-source data and facilitates urban eco-management.

Intensive land use can support sustainable socioeconomic development, especially in the context of limited land resources and high population. It is measured by land-use intensity that reflects the degree of land-use efficiency. In order to support decision-making for efficient land use, we investigated the mechanism whereby natural and socioeconomic factors influence land-use intensity from the perspectives of overall, region-, and city-based analysis, respectively. This investigation was conducted in Chinese cities using the multiple linear stepwise regression method and geographic information system techniques. The results indicate that: (1) socioeconomic factors have more positive impact on land-use intensity than natural factors as nine of the top 10 indicators with the highest SRC values are in the socioeconomic category according to the overall assessment; (2) education input variously contributes to land-use intensity because of the mobility of a well-educated workforce between different cities; (3) the increase in transportation land may not promote intensive land use in remarkably expanding cities due to the defective appraisal system for governmental achievements; and that (4) in developed cities, economic structure contributes more to land-use intensity than the total economic volume, whereas the opposite is the case in less-developed cities. This study can serve as a guide for the government to prepare strategies for efficient land use, hence promoting sustainable socioeconomic development.

Sustainable development is an important topic of urban research. The rational use of land resources is of great significance for urban development and is conducive to promoting regional governance and coordinated development. The purpose of this study was to construct an effective evaluation framework for urban land resources to maintain sustainable urban development. Taking the cities along the Jiangsu Section of the Beijing–Hangzhou Grand Canal as the research object, this study constructed an evaluation system for the sustainable use of land resources including the dimensions of economic level, social development, and environmental resources. The statistical data for 2010, 2015, and 2020 were selected to comprehensively calculate and evaluate the level of sustainable use of land resources in the study area via the analytic hierarchy process (AHP)-entropy combined weight method, which combines the analytic hierarchy process and the entropy weight method. According to the research results, the sustainable use of land resources in the study area presented an overall upward trend from 2010 to 2015, and an overall downward trend from 2015 to 2020. Overall, the study area was in a critically sustainable stage, although the annual change rate of the level of sustainable use of land resources showed significant fluctuations and exhibited a spatial pattern of progressive increase from north to south. The cities in southern Jiangsu were in the initially sustainable and basically sustainable stages; those in central Jiangsu were in the critically sustainable and initially sustainable stages; and those in northern Jiangsu were in the unsustainable and critically sustainable stages. This study proposed a scientific and effective evaluation method for cities along the Grand Canal to explore the efficient, sustainable use of land resources in the future. The evaluation framework constructed on this basis can serve as an important reference for urban governance and is expected to guide the sustainable use and development of land resources for other cities of the same type.