China’s rapid urbanization and industrialization process has triggered serious air pollution. As a main air pollutant, PM2.5 is affected not only by meteorological conditions, but also by land use in urban area. The impacts of urban landscape on PM2.5 become more complicated from a three-dimensional (3D) and land function zone point of view. Taking the urban area of Nanchang city, China, as a case and, on the basis of the identification of urban land function zones, this study firstly constructed a three-dimensional landscape index system to express the characteristics of 3D landscape pattern. Then, the land-use regression (LUR) model was applied to simulate PM2.5 distribution with high precision, and a geographically weighted regression model was established. The results are as follows: (1) the constructed 3D landscape indices could reflect the 3D characteristics of urban landscape, and the overall 3D landscape indices of different urban land function zones were significantly different; (2) the effects of 3D landscape spatial pattern on PM2.5 varied significantly with land function zone type; (3) the effects of 3D characteristics of landscapes on PM2.5 in different land function zones are expressed in different ways and exhibit a significant spatial heterogeneity. This study provides a new idea for reducing air pollution by optimizing the urban landscape pattern.

Under the background of global warming, it is of great significance to study the temporal and spatial evolution of land surface temperature (LST) on long-time scale and the impacts of land use in the fields of urban thermal environment and regional climate change. Based on MODIS LST long time series remote sensing data, the temporal and spatial evolution characteristics of pixel-wise LST in Jiangxi Province, the middle inland province of China from 2000 to 2020 were analyzed by using Theil-Sen + Mann-Kendall, coefficient of variation and Hurst index, and the response of LST to land use was identified by combining the contribution and diversity index. The results showed as follows: (1) LST was generally distributed as "high in Middle-East-West-South and low in North-northwest-southeast direction". LST showed an overall downward trend, indicating a weakening of the warming trend. The dynamic trend of LST was characterized by more descending than ascending tendency. The dynamic stability showed a coexistence of high and low fluctuation tendency, with a higher proportion of medium and low fluctuation areas having obvious spatial differences. The overall dynamic sustainability was characterized by uncertainty of future change trend. (2) The LST were strongly affected by land use in the past 20 years. Firstly, the areas of high LST were mostly located in construction land and unused land, while the areas of low LST were mostly in water area and forest land. However, forest land and water area of high temperature were gradually turned to construction land later on. Secondly, the land use structure and pattern had an strong effects on LST. With the increase of the area proportion of different land use, the LST showed significant differences. The more complex the spatial pattern of land use, the more obvious its impact on LST. The research results will provide some reference for the regions with the same characteristics as Jiangxi Province to deal with LST under the background of global climate change.

As a typical type of landscape in Poyang Lake, the largest freshwater lake in China, grassland has been deeply disturbed by the flooding process. However, how the vegetation landscape diversity respond to the flooding process of Poyang Lake remains unclear. In this paper, we randomly selected 10 grasslands in the period of a normal water level from 2010 to 2019 to study the coupling relationship between the vegetation landscape diversity and flooding process, and established the related models based on the island biogeography theory. The results are as follows. (1) There is a positive correlation between the grassland vegetation landscape diversity and grassland area, the best fitting model is the logarithmic function. (2) Using the general dynamic model of island biogeography as a reference, the model of diversity–area–flooding established by using the area of grassland and duration as predictor variables has the highest goodness-of-fit. (3) If grassland was flooded for 120–130 days and the maximum depth of flooding is not more than 2.5 m every year, the vegetation landscape diversity in grasslands will be the highest. The findings have provided an extension to the study of the response of vegetation to flooding processes, and provide a certain theoretical basis for the protection and management of the Poyang Lake ecosystem.