High-spatial-resolution (HSR) urban land use maps are very important for urban planning, traffic management, and environmental monitoring. The rapid urbanization in China has led to dramatic urban land use changes, however, so far, there are no such HSR urban land use maps based on unified classification frameworks. To fill this gap, the mapping of 2018 essential urban land use categories in China (EULUC-China) was jointly accomplished by a group of universities and research institutes. However, the relatively lower classification accuracy may not sufficiently meet the application demands for specific cities. Addressing these challenges, this study took Nanjing city as the case study to further improve the mapping practice of essential urban land use categories, by refining the generation of urban parcels, resolving the problem of unbalanced distribution of point of interest (POI) data, integrating the spatial dependency of POI data, and evaluating the size of training samples on the classification accuracy. The results revealed that (1) the POI features played the most important roles in classification performance, especially in identifying administrative, medical, sport, and cultural land use categories, (2) compared with the EULUC-China, the overall accuracy for Level I and Level II in EULUC-Nanjing has increased by 11.1% and 5%, to 86.1% and 80% respectively, and (3) the classification accuracy of Level I and Level II would be stable when the number of training samples was up to 350. The methods and findings in this study are expected to better inform the regional to continental mappings of urban land uses.

The Yellow River Delta (YRD) has China's largest artificial Robinia pseudoacacia forest, which was planted in the late 1970s and suffered extensive dieback in the 1990s. The health grade of the R.pseudoacacia forest (named canopy vigor grade, CVG) could be achieved by using high-resolution images and canopy vigor indicators (CVIs). However, a previous study showed that there was no significant correlation between CVG and the field-estimated aboveground biomass (AGB) of R.pseudoacacia forest. Therefore, this study aims to construct forest health indicators (FHIs) based on canopy spatial structure parameters extracted from LiDAR. The FHIs included Weibull_α (the scale parameter of the Weibull density function that reflects the shape of the tree canopy), VCI (vertical complexity index), sdCC (the standard deviation of canopy cover), H₉₉ (the 99th percentile height) and cvLAD (the coefficient of variation of leaf area density), and could significantly distinguish three forest health grades (FHG) (p < 0.05). The FHG was positively correlated with forest AGB (r_s = 0.51, p = 0.004), and the similarity value with CVG was 63.33%. The results of this study confirmed that the FHIs can reflect both canopy vigor and tree productivity, and distinguish forest health status without prior classification information.

Portraying functional urban areas provides useful insights for understanding complex urban systems and formulating rational urban plans. Mobile phone user trajectory data are often used to infer the individual activity patterns of people and for functional area identification, but they are difficult to obtain because of personal privacy issues and have the drawback of a sparse spatial and temporal distribution. Deep learning models have been widely utilized in functional area recognition but are limited by the difficulty of acquiring training samples with large data volumes. This paper aims to achieve a fast and automatic identification of large-scale urban functional areas without prior knowledge. This paper uses Nanjing city as a test area, and a self-organizing map (SOM) neural network model based on an improved dynamic time warping (Ndim-DTW) distance is used to automatically identify the function of each building using mobile phone aggregated data containing work and residence attributes. The results show that the recognition accuracy reaches 88.7%, which is 12.4% higher than that of the K-medoids method based on the DTW distance using a single attribute and 7.8% higher than that of the K-medoids method based on the Ndim-DTW distance with multiple attributes, confirming the effectiveness of the multi-attribute mobile phone aggregated data and the SOM model based on the Ndim-DTW distance. Furthermore, at the traffic analysis zone (TAZ) level, this paper detects that Nanjing has seven functional area hotspots with a high degree of mixing. The results can provide a data basis for urban studies on, for example, the urban spatial structure, the separation of occupations and residences, and environmental suitability evaluation.