The Natural and Socioeconomic Influences on Land-Use Intensity: Evidence from China

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

Improving ASTER GDEM Accuracy Using Land Use-Based Linear Regression Methods: A Case Study of Lianyungang, East China

The Advanced Spaceborne Thermal-Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) is important to a wide range of geographical and environmental studies. Its accuracy, to some extent associated with land-use types reflecting topography, vegetation coverage, and human activities, impacts the results and conclusions of these studies. In order to improve the accuracy of ASTER GDEM prior to its application, we investigated ASTER GDEM errors based on individual land-use types and proposed two linear regression calibration methods, one considering only land use-specific errors and the other considering the impact of both land-use and topography. Our calibration methods were tested on the coastal prefectural city of Lianyungang in eastern China. Results indicate that (1) ASTER GDEM is highly accurate for rice, wheat, grass and mining lands but less accurate for scenic, garden, wood and bare lands; (2) despite improvements in ASTER GDEM2 accuracy, multiple linear regression calibration requires more data (topography) and a relatively complex calibration process; (3) simple linear regression calibration proves a practicable and simplified means to systematically investigate and improve the impact of land-use on ASTER GDEM accuracy. Our method is applicable to areas with detailed land-use data based on highly accurate field-based point-elevation measurements

The Natural and Socioeconomic Influences on Land-Use Intensity: Evidence from China

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

Evaluation of the Sustainable Use of Land Resources in the Cities along the Jiangsu Section of the Beijing–Hangzhou Grand Canal

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

Random Forest Estimation and Trend Analysis of PM_2.5 Concentration over the Huaihai Economic Zone, China (2000–2020)

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 (PM2.5). 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 PM2.5 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 PM2.5. By averaging the modeled daily PM2.5 concentration, we produced a yearly PM2.5 concentration dataset, at a 1 km resolution, for the study area from 2000 to 2020. On comparing modeled daily PM2.5 with observational data, the coefficient of determination (R2) 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 PM2.5 concentration dataset shows that R2 = 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 PM2.5 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 PM2.5 can be well-estimated from the MAIAC AOD dataset, when incorporating spatiotemporal variability using random forest, and that the resultant PM2.5 concentration data provide a basis for environmental monitoring over large geographic areas

Assessing the Impact of Land-Use Planning on the Atmospheric Environment through Predicting the Spatial Variability of Airborne Pollutants

As an important contributor to pollutant emissions to the atmosphere, land use can degrade environmental quality. In order to assess the impact of land-use planning on the atmosphere, we propose a methodology combining the land-use-based emission inventories of airborne pollutants and the long-term air pollution multi-source dispersion (LAPMD) model in this study. Through a case study of the eastern Chinese city of Lianyungang, we conclude that (1) land-use-based emission inventorying is a more economical way to assess the overall pollutant emissions compared with the industry-based method, and the LAPMD model can map the spatial variability of airborne pollutant concentrations that directly reflects how the implementation of the land-use planning (LUP) scheme impacts on the atmosphere; (2) the environmental friendliness of the LUP scheme can be assessed by an overlay analysis based on the pollution concentration maps and land-use planning maps; (3) decreases in the emissions of SO2 and PM10 within Lianyungang indicate the overall positive impact of land-use planning implementation, while increases in these emissions from certain land-use types (i.e., urban residential and transportation lands) suggest the aggravation of airborne pollutants from these land parcels; and (4) the city center, where most urban population resides, and areas around key plots would be affected by high pollution concentrations. Our methodology is applicable to study areas for which meteorological data are accessible, and is, therefore, useful for decision making if land-use planning schemes specify the objects of airborne pollutant concentration

Identification of Urban Green Space Types and Estimation of Above-Ground Biomass Using Sentinel-1 and Sentinel-2 Data

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

Research on the Contribution of Urban Land Surface Moisture to the Alleviation Effect of Urban Land Surface Heat Based on Landsat 8 Data

This paper presents a new assessment method for alleviating urban heat island (UHI) effects by using an urban land surface moisture (ULSM) index. With the aid of Landsat 8 OLI/TIRS data, the land surface temperature (LST) was retrieved by a mono-window algorithm, and ULSM was extracted by tasselled cap transformation. Polynomial regression and buffer analysis were used to analyze the effects of ULSM on the LST, and the alleviation effect of ULSM was compared with three vegetation indices, GVI, SAVI, and FVC, by using the methods of grey relational analysis and Taylor skill calculation. The results indicate that when the ULSM value is greater than the value of an extreme point, the LST declines with the increasing ULSM value. Areas with a high ULSM value have an obvious reducing effect on the temperature of their surrounding areas within 150 m. Grey relational degrees and Taylor skill scores between ULSM and the LST are 0.8765 and 0.9378, respectively, which are higher than the results for the three vegetation indices GVI, SAVI, and FVC. The reducing effect of the ULSM index on environmental temperatures is significant, and ULSM can be considered to be a new and more effective index to estimate UHI alleviation effects for urban areas