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.

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.