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Ecosystem services management should often expect to deal with non-linearities due to trade-offs and synergies between ecosystem services (ES). Therefore, it is important to analyze long-term trends in ES development and utilization to understand their responses to climate change and intensification of human activities. In this paper, the region of Uxin in Inner Mongolia, China, was chosen as a case study area to describe the spatial distribution and trends of 5 ES indicators. Changes in relationships between ES and driving forces of dynamics of ES relationships were analyzed for the period 1979&ndash, 2016 using a stepwise regression. We found that: the magnitude and directions in ES relationships changed during this extended period, those changes are influenced by climate factors, land use change, technological progress, and population growth.

Ecosystem services are fundamental in supporting human well-being which is a core component of sustainability. Understanding the relationship between ecosystem services (ESs) and human well-being (HWB) in a changing landscape is important to implement appropriate ecosystem management and policy development. Combining with demographic, economic, and cultural factors, their land use are the elements linking ESs and HWB at fine scale. Within this context, the purpose of this study is to evaluate household HWB changes in the past decade, and understand the relationship between demographic factors, land use, ESs, and HWB in the social-ecological landscapes of Uxin, in Inner Mongolia. Our results indicate that: the levels of HWB of local herder families were slightly improved from 2007 to 2016; changes in family demographic factors enhanced their land use intensity, resulting in an increased supply capacity of ecosystems and improved HWB; in addition, regulating services contributed more to HWB than provisioning services. The results of this study can help improve the understanding of the relationship between ESs and HWB, and provide valuable information to policy-makers to maintain particular ESs or to improve HWB.

OpenStreetMap is a geographic information platform designed to provide real-time updates and user-generated content related to its freely available global map, and it is one of the most widely used examples of volunteered geographic information, a technique associated with so-called neogeography. This paper, based on the data from China’s OpenStreetMap road network in May 2014, taking 340 prefecture-level cities in China as its study area, presents the geometric-related (road density) and attribute-related (type diversity) spatial patterns of the OpenStreetMap road network, and explores their relationship. The results are as follows. (1) The distribution of OpenStreetMap road density in Shenzhen, Shanghai, Hong Kong, and Macao predominantly obeys a “positive skewness distribution”. OpenStreetMap data for eastern China shows a higher overall and circular structure. In central China, there are noticeable discrepancies in the road density, whereas in western China, the road density is low. (2) The OpenStreetMap road diversity shows a normal distribution. The spatial pattern for the so-called “Hu Huanyong line” was broken by the effect of diplomatic and strategic factors, showing a high diversity along the peripheral border, coastal cities, and core inland cites. (3) China’s OpenStreetMap is partitioned into four parts according to road density and diversity: high density and high diversity; low density and low diversity; high density and low diversity; and low density high diversity. (4) The OpenStreetMap geographical information-collection process and mechanism were analyzed, demonstrating that the road density reflects the preponderance of traffic in the real world. OpenStreetMap road diversity reflects the road-related geographic information demand and value, and it also reflects the interests of users toward to OpenStreetMap geographical information.

This study introduces risk theory of environmental science into human settlement science using 2004–2013 statistics, remote sensing data, and thematic maps. The entropy weight method and risk-index model are both used to study the characteristics of the time course and spatial pattern of human settlement risk in 31 provincial regions in China. In addition, influential mechanisms of vulnerability, functionality, stress, and adaptability on environmental risks are analyzed. Three primary results are obtained. First, for temporal characteristics, environmental risks of human settlements increased significantly from 2003 to 2012. The year 2006 marked both a sudden change and the cut-off point after which human settlements in China experienced qualitative changes and new risks. Second, for spatial characteristics, the risk index of human settlements decreased gradually from the southwestern to the northeastern, northwestern, and northern parts of China. The risk index of human settlement spaces differed significantly, with obvious block aggregation of spatial-distribution characteristics. Third, for relevant factor characteristics, between 2003 and 2012, the temporal change in vulnerability is relatively stable, with a slight increase in functionality and a slight decrease in adaptability. Spatially, Qinghai, Tibet, southwestern China, Guangdong, Guangxi, Beijing, and Tianjin had relatively high vulnerability in human settlements; Beijing, Tianjin, Jiangsu, and Zhejiang had the best functionality; Hunan and Sichuan had relatively high stress; and Guangdong, Jiangsu, and Zhejiang had relatively stronger adaptability. Further consideration and discussion are required on the environmental risks for different social groups and at different geographical scales, as well as on the uncertainty and long-term features of environmental risks in addition to environmental justice issues.

Land degradation causes serious environmental problems in many regions of the world, and although it can be effectively assessed and monitored using a time series of rainfall and a normalized difference vegetation index (NDVI) from remotely-sensed imagery, dividing human-induced land degradation from vegetation dynamics due to climate change is not a trivial task. This paper presented a multilevel statistical modeling of the NDVI-rainfall relationship to detect human-induced land degradation at local and landscape scales in the Ordos Plateau of Inner Mongolia, China, and recognized that anthropogenic activities result in either positive (land restoration and re-vegetation) or negative (degradation) trends. Linear regressions were used to assess the accuracy of the multi- level statistical model. The results show that: (1) land restoration was the dominant process in the Ordos Plateau between 1998 and 2012; (2) the effect of the statistical removal of precipitation revealed areas of human-induced land degradation and improvement, the latter reflecting successful restoration projects and changes in land man- agement in many parts of the Ordos; (3) compared to a simple linear regression, multilevel statistical modeling could be used to analyze the relationship between the NDVI and rainfall and improve the accuracy of detecting the effect of human activities. Additional factors should be included when analyzing the NDVI-rainfall relationship and detecting human-induced loss of vegetation cover in drylands to improve the accuracy of the approach and elimi- nate some observed non-significant residual trends.

An essential part of urban natural systems, urban green spaces play a crucial role in mitigating the urban heat island effect (UHI). The UHI effect refers to the phenomenon where the temperature within a city is higher than that of the surrounding rural areas. The effects of the spatial composition and configuration of urban green spaces on urban land surface temperature (LST) have recently been documented. However, few studies have examined the effects of the directionality and distribution of green spaces on LST. In this study, we used a landscape index to describe the change in pattern of heat island intensity for the city of Baotou, China. We then used a semi-variable function and nearest neighbor algorithm to analyze the cooling effects of green spaces. We found that: (1) the cooling distance of an urban green space was not only influenced by its size, vegetation cover, and shape, but also showed anisotropy. In general, the larger the area of the urban green space and the higher the value of Normalized Difference Vegetation Index (NDVI; a measure of plant photosynthetic activity), the larger the cooling distance within a certain threshold. Green spaces with more regular shapes displayed higher LST mitigation; however, the cooling distance was directional, and cooling effects depended on the semi-major axis and semi-minor axis of the green space. (2) The distribution of the urban green space within the landscape played a key role in mitigating the UHI effect. Within a certain area, the cooling effect of green spaces that are evenly distributed was greater than that which was associated with either green spaces that were large in area or where greens spaces were aggregated in the landscape. Therefore, within urban areas, where space is limited, urban planning should account for green spaces that are relatively scattered and evenly distributed to maximize cooling effects. The results of this study have key implications for sustainable urban planning and development; to mitigate urban heat island effects it is important to not only increase canopy cover or the size of urban green spaces, but also to optimize their spatial configuration.