城市监测 (Urban Monitoring)

Identifying determinants of disparities in soil moisture of NH using heterogeneity model

Soil moisture is a fundamental ecological component for climate and hydrological studies. However, the distribution patterns of soil moisture are spatially heterogenous and influenced by multiple environmental factors. The knowledge is still limited in assessing the large-scale spatial heterogeneity of soil moisture in in situ data modelling, in situ network design, spatial down-scaling, and remote sensing-based soil moisture retrieval. Heterogeneity models are effective in characterizing spatial disparities, but they are not capable of examining the maximum regional disparities. To address this bottleneck, the authors of this study developed a geographically optimal zones-based heterogeneity (GOZH) model. By progressively optimizing geographical zones of soil moisture and quantifying the heterogeneity among zones, GOZH may help identify individual and interactive determinants of soil moisture across a large study area. It was applied to identify spatial determinants of in situ soil moisture data collected at 653 monitoring stations in the Northern Hemisphere in unfrozen and frozen seasons from April 2015 to December 2017, with only thawed data considered in both seasons. Correspondingly, a series of variables were derived from Google Earth Engine (GEE) remote sensing data. The results demonstrated the significant regional disparities of soil moisture, and the combinations of determinants are critically different among geographical zones and between unfrozen and frozen seasons. At a global scale, the combinations of determinants can explain about 48% of the spatial pattern of soil moisture. Spatial heterogeneity of soil moisture in frozen seasons is much more complex than that in unfrozen seasons regarding geographical zones and explanatory variables. The variability of soil moisture during unfrozen seasons can be more explainable than that during frozen seasons, which was a convincing evidence for previous studies that soil moisture predictions were mostly performed during unfrozen seasons. Primary variables that determine spatial patterns of soil moisture are changed from climate variables during the unfrozen season to geographical variables during the frozen season. Results show that GOZH model can effectively explore spatial determinants of soil moisture through avoiding the underestimation of individual variables, overestimation of multiple variables, and finely divide zones. The research findings from this study provide an in-depth understanding of the spatial heterogeneity of soil moisture and can be implemented in more effective in situ sampling network design, spatial down-scaling of soil moisture, and accurate inversion of surface parameters from the satellite data of soil moisture.

Assessing the Impact of Street-View Greenery on Fear of Neighborhood Crime in Guangzhou, China

摘要针对高分辨率遥感影像中地物的复杂性和多变性带来的地物提取难点，提出了一种基于多层次规则的面向对象的典型地物提取方法。改进了基于区域增长的影像分割方法，利用小区域内的全局最优策略进行初始增长，避开了种子点的选择。利用影像分割得到的影像对象作为地物提取的基元，针对影像上典型地物选择提取特征 ，利用多

The distribution of greenspace quantity and quality with socioeconomic conditions in Guangzhou

Awareness is mounting that urban greenspace is beneficial for residents’ health. While a plethora of studies have focused on greenspace quantity, scant attention has been paid to greenspace quality. Existing methods for assessing greenspace quality is either highly labor-intensive and/or prohibitively time-consuming. This study develops a new machine learning method to assess greenspace quality based on street view images collected from Guangzhou, China. It also examines whether greenspace exposure disparities are linked to the neighbourhood socioeconomic status (SES). The validation process indicated that our scoring system achieved high accuracy for predicting street view-based greenspace quality outside the training data. Results also show that there were marked differences in spatial distribution between aggregated NDVI (Normalized Difference Vegetation Index), street view greenness quantity and quality. Regression models show that neighbourhood SES is not associated with NDVI. Although neighbourhood SES is associated with both street view greenness quantity and quality index value, street view greenness quality is more sensitive to the change of neighbourhood SES. Our work suggests that policymakers and planners are advised to pay more attention to greenspace quality and greenspace exposure disparities in urban area.