The appropriate resolution has been confirmed to be crucial to the extraction of urban green space and the related research on ecosystem services. However, the factors affecting the differences between various resolutions of data in certain application scenarios are lacking in attention. To fill the gap, this paper made an attempt to analyze the differences of various resolutions of data in green space extraction and to explore where the differences are reflected in the actual land unit, as well as the factors affecting the differences. Further, suggestions for reducing errors and application scenarios of different resolutions of data in related research are proposed. Taking a typical area of Nanjing as an example, data taken by DJI drone (0.1 m), GaoFen-1 (2 m) and Sentinel-2A (10 m) were selected for analysis. The results show that: (1) There were minimal differences in the green space ratio of the study area calculated by different resolutions of data on the whole, but when subdivided into each land use type and block, the differences were obvious; (2) The function, area and shape of the block, as well as the patch density and aggregation degree of the internal green space, had a certain impact on the differences. However, the specific impact varied when the block area was different; and (3) For the selection of the data source, the research purpose and application scenarios need to be comprehensively considered, including the function and attributes of the block, the distribution characteristics of green space, the allowable error limits and the budget. The present study highlighted the reasons of differences and hopefully it can provide a reference for the data selection of urban green space in the practical planning and design.

Urban heat islands (UHIs) have become one of the most critical issues around the world, especially in the context of rapid urbanization and global climate change. Extensive research has been conducted across disciplines on the factors related to land surface temperature (LST) and how to mitigate the UHI effect. However, there remain deficiencies in the exploration of LST changes across time and their relationship with underlying surfaces in different temperature ranges. In order to fill the gap, this study compared the LST of each month by using the quantile classification method taking the Landsat 8 images of Nanjing on May 18th, July 21st, and October 9th in 2017 as the subject and then calculated the differences between July and May as well as that between July and October by an intersection tool taking the LST classes of July as the baseline. Additionally, the spatial pattern of each temperature class and intersection area was analyzed with the help of several landscape metrics, and the land contribution index (LCI) was utilized to better quantify the thermal contribution of each underlying surface to the area. The results indicated that the difference between months mainly reflected in the medium temperature area, especially between July and October, in which landscape patterns illustrated a trend of fragmentation and decentralization. The proportions of underlying surfaces in different types of intersection revealed the distinction of their warming and cooling degrees over time, in which the warming degree of other rigid pavement was higher in the warming process from May to July, and the cooling degree of buildings was greater in the cooling process from July to October. The LCI of each underlying surface in the entire study area was different from that in each temperature class, indicating that underlying surfaces had distinguished thermal contributions in different temperature ranges. This study is expected to fill the gap in previous studies and provide a new perspective on the mitigation of UHI.

Retaining river channels and constructing waterfront greenspaces are the primary tasks of urban waterfront development in China. However, the natural characteristics of the water network are not fully considered in some urban greenspaces system planning and subsequent construction. We proposed a simple evaluation system to assess the morphological suitability between greenspaces and rivers in both the existing and planning stages. The evaluation indicators consist of two-level factors, in which the types of greenspace defined by the distance to the nearest river are the primary factors, including urban greenspace, waterfront greenspace and near-water greenspace, and the spatial forms of each type of greenspace are the secondary factors. The evaluation system can reflect the characteristics of each city and provide an overall comparison to cities of the same scale in similar regions. This study also investigated the impact of greenspace system planning on the current greenspace form. The results showed that near-water greenspace is a key factor that affects the matching degree among all primary factors, and the layout of greenspaces has a substantial impact on morphological suitability. Significant correlations between matching degree and evaluation factors were also found. This paper provides an in-depth understanding of urban greenspace form with urban rivers.