The spatial structure and configuration of land-use patches, i.e., landscape patterns could affect the flow of energy and materials in inner-urban ecosystems, and hence the sustainable development of urban areas. Studying landscape pattern changes under the process of urbanization would have implicational significance to urban planning and urban sustainability. In this paper, land-use change and urban expansion intensity (UEI) were treated as the inducement factors for changes in landscape patterns, and stepwise regression and geographically weighted regression (GWR) were adapted to quantify their integrated and distributed magnitude effects on landscape patterns, respectively. The findings suggested that land-uses have different contributions to changes in landscape patterns at different urban development zones (downtown, suburban plain area and mountainous suburban areas). Furthermore, the GWR analysis results indicated that the effect of UEI on landscape patterns has spatial and temporal heterogeneity. From 1987 to 2000, the UEI had great explanatory capacity on changes in landscape patterns and helped the landscape assemble faster in the downtown and adjacent areas. However, with the shifting of the center of urban construction from downtown to the suburbs, the high explanatory ability was oriented towards suburban areas during 2000–2016 and the magnitude of influence spatially changed. Therefore, a compact city and protection policy should be adapted to different regions in the study area to achieve strong urban sustainability.

Precise individual tree or sample-based inventories derived from 3D point cloud data of mobile laser scanning can improve our comprehensive understanding of the structure, function, resilience, biodiversity, and ecosystem services of urban forests. This study assessed the performance of a handheld laser scanning system (HLS) for the extraction of tree position, diameter at breast height (DBH), and tree height (H) in an urban area. A total of 2083 trees of 13 species from 34 plots were analyzed. The results showed that the registration of tree positions using ground control points (GCPs) demonstrated high accuracy, with errors consistently below 0.4 m, except for a few instances. The extraction accuracy of DBH for all trees and individual species remained consistently high, with a total root mean square error (RMSE) of 2.06 cm (6.89%) and a bias of 0.62 cm (2.07%). Notably, broad-leaved trees outperformed coniferous trees, with RMSE and bias values of 1.86 cm (6%) and 0.76 cm (2.46%), respectively, compared to 2.54 cm (9.46%) and 0.23 cm (0.84%), respectively. The accuracy of H extraction varied significantly among different species, with R² values ranging from 0.65 to 0.92. Generally, both DBH and H were underestimated compared to ground measurements. Linear mixed-effects models (LMEs) were applied to evaluate factors affecting the performance of HLS with the plot as a random factor. LME analysis revealed that plant type and terrain significantly influenced the accuracy of DBH and H derived from HLS data, while other fixed factors such as plot area, tree density, and trajectory length showed no significance. With a large sample size, we concluded that the HLS demonstrated sufficient accuracy in extracting individual tree parameters in urban forests.

Improving the justice of public parks is of great significance to the well-being of residents, and it is also an important goal of green space planning. In this paper, the spatial justice of park green space under five travel modes and at three scales was analyzed using the travel-behavior-based Gaussian two-step floating catchment area method (TB-G2SFCA) and Gini coefficient method for Qingdao City Center. The main results are as follows: Under walking mode, walking–bus mode, and walking–subway mode, there were unserved areas in terms of urban park green space, while there were no unserved areas in the cases of cycling and driving. Residents’ choice of travel time and travel mode would affect the service scope of the park green space, and the increase in travel time would reduce the unserved areas in the urban park green space. The choice of travel time and travel mode affected the accessibility of urban park green space for residents in each residential patch, as well as the justice of the distribution of park green space resources at the scales of street blocks, districts, and the whole study area. The increase in residents’ travel speed and travel time could promote the equitable allocation of urban park green space to a certain extent. The results of this study provide a scientific basis for the planning and construction of urban park green space in Qingdao City.

In this study, we compared and analyzed the dynamic changes of water conservation and its value of some typical forests, grasslands, and farmlands in China within a year based on the dataset of the Chinese Ecosystem Research Net (CERN). Results showed that forest, grassland, and farmland provide different kinds of water conservation services which vary in size and dynamic processes within a year. Water conservation of forest consisted of water regulation service, here referred to as water retaining service, and water supply service, while water conservation of grassland and farmland was mainly water regulation service. Different types of forests/grasslands/farmlands can serve different water conservation services in both size and change patterns. In general, the water conservation service and value of forests is the largest (Xishuangbanna forest being $712·hm⁻²·year⁻¹, Dingshu Mountains forest being $823·hm⁻²·year⁻¹, and Changbai Mountains forest being $366·hm⁻²·year⁻¹), and then is the farmlands (Yucheng farmland being $147·hm⁻²·year⁻¹, Changshu farmland being $92·hm⁻²·year⁻¹, Qianyanzhou farmland being $247 hm⁻²·year⁻¹), and that of the grasslands is the least (Haibei alpine meadow being $75·hm⁻²·year⁻¹, Mongolia grassland being $30·hm⁻²·year⁻¹). The monthly water conservation and its value of each ecosystem had its own changing pattern throughout the year.

Spatial scale is an eternal topic in landscape pattern related analysis. This paper examined the spatial scale effect of landscape pattern changes and their relationships with urbanization indicators in Qingdao using a series of sampling blocks. The results indicated that, with the increasing block scale, the mean patch density and aggregation within a block decreased, whereas the diversity increased. Furthermore, the expanding scale amplified the mean change ratio of landscape metrics and eliminated local drastic changes and regional variation trends along an urban-to-rural gradient, which would be obvious at a finer block scale. Meanwhile, the adjusted R2 of GWR (Geographically Weighted Regression) models increased with an increasing block size, especially when the block scale changed from 1 km to 5 km. Odd-numbered block scales performed better than even-numbered block scales.

Global climate warming and rapid urbanization have intensified the urban heat island effect (UHI). Previous studies indicated that urban parks could effectively mitigate the UHI and improve urban thermal environments. This study aimed to quantify the cooling effect of 64 urban park green spaces in downtown Qingdao. Park cooling intensity (PCI), park cooling gradient (PCG), park cooling area (PCA), and park cooling efficiency (PCE) were selected as indicators to quantify the cooling effect of park green space. These four indicators comprehensively assessed park cooling effects in terms of the maximum value and cumulative value, respectively. Key factors influencing cooling factors and their relative importance were analyzed. The results showed that the mean PCI, PCG, PCA, and PCE were, respectively, 0.02 °C, 0.71, 63.72 ha, and 10.71 °C for the 64 park green spaces. The average temperature reduction and cooling distance were, respectively, 3.35 °C and 211.53 m. Correlation analysis revealed that park area, park perimeter, and NDVI (Normalized Difference Vegetation Index) were significantly positively correlated with PCA, PCI, and PCG. Conversely, these factors presented a significant negative correlation with PCE. Additionally, water body ratio and green space ratio were positively correlated with PCA, while green space ratio was also positively correlated with PCI. The threshold value of efficiency (TVoE), which was calculated based on PCA, was 30.24 ha. TVoE represented the minimum area of urban park green space required to maximize cooling benefits. By means of Ward’s hierarchical clustering method, the 64 park green spaces were classified into four cooling clusters, which were dominated by differentiated cooling metrics and characterized by distinct internal landscapes and surrounding environments. Cluster 1 accounted for 43.75% of the 64 park green spaces, and it was dominated by PCI and PCG. These findings would provide crucial insights for optimizing urban thermal environments, enhancing livability, and promoting sustainable urban planning.