Street greenery is a component of urban green infrastructure. By forming foundational green corridors in urban ecological systems, street greenery provides vital ecological, social, and cultural functions, and benefits the wellbeing of citizens. However, because of the difficulty of quantifying people's visual perceptions, the impact of street-visible greenery on housing prices has not been fully studied. Using Beijing, which has a mature real estate market, as an example, this study evaluated 22,331 transactions in 2014 in 2370 private housing estates. We selected 25 variables that were classified into three categories-location, housing, and neighbourhood characteristics-and introduced an index called the horizontal green view index (HGVI) into a hedonic pricing model to measure the value of the visual perception of street greenery in neighbouring residential developments. The results show that (1) Beijing's homebuyers would like to reside in residential units with a higher HGVI; (2) Beijing's homebuyers favour larger lakes; and (3) Beijing's housing prices were impacted by the spatial development patterns of the city centre and multiple business centres. We used computer vision to quantify the street-visible greenery and estimated the economic benefits that the neighbouring visible greenery would have on residential developments in Beijing. This study provides a scientific basis and reference for policy makers and city planners in road greening, and a tool for formulating street greening policy, studying housing price characteristics, and evaluating real estate values.

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Street greenery, an important urban landscape component, is closely related to people’s physical and mental health. This study employs the green view index (GVI) as a quantitative indicator to evaluate visual greenery from a pedestrian’s perspective and uses an image segmentation method to calculate the quantity of visual greenery from Tencent street view pictures. This article aims to quantify street greenery in the area within the sixth ring road in Beijing, analyse the relations between road parameters and the GVI, and compare the visual greenery of different road types. The authors find that (1) the average GVI value in the study area is low, with low-value clusters inside the third ring road and high-value clusters outside; (2) wider minor roads tend to have higher GVI values than motorways, major roads and provincial roads; and (3) longer roads, except expressways, tend to have higher GVI values. This case study demonstrates that the GVI can effectively represent the quantity of visual greenery along roads. The authors’ methods can be employed to compare street-level visual greenery among different areas or road types and to support urban green space planning and management.

China's rapid urbanization and its success in developing the Internet of Things (IoT) will decide its future development direction. The construction of sustainable cities is crucial to China because China has such a large population. The Xiamen Long-term Urban Ecosystem Observation and Research Station (Xiamen LUEORS) was started in 2006, together with the research related to the Environmental Internet of Things (EIoT) for Xiamen LUEORS. This paper explains the purpose, general framework, and main features of EIoT, and outlines the results of performing EIoT experiments in some areas, including a 'town village', a peculiar phenomenon of China's urbanization. It also discusses the development trends of IoT and proposes the concept of ZeroSpace Interconnection of Things (ZeroIoT, or ZeroSIT).

As cyanobacteria blooms occur in many types of inland water, routine monitoring that is fast and accurate is important for environment and drinking water protection. Compared to field investigations, satellite remote sensing is an efficient and effective method for monitoring cyanobacteria blooms. However, conventional remote sensing monitoring methods are labor intensive and time consuming, especially when processing long-term images. In this study, we embedded related processing procedures in Google Earth Engine, developed an operational cyanobacteria bloom monitoring workflow. Using this workflow, we measured the spatiotemporal patterns of cyanobacteria blooms in China’s Taihu Lake from 2000 to 2018. The results show that cyanobacteria bloom patterns in Taihu Lake have significant spatial and temporal differentiation: the interannual coverage of cyanobacteria blooms had two peaks, and the condition was moderate before 2006, peaked in 2007, declined rapidly after 2008, remained moderate and stable until 2015, and then reached another peak around 2017; bays and northwest lake areas had heavier cyanobacteria blooms than open lake areas; most cyanobacteria blooms primarily occurred in April, worsened in July and August, then improved after October. Our analysis of the relationship between cyanobacteria bloom characteristics and environmental driving factors indicates that: from both monthly and interannual perspectives, meteorological factors are positively correlated with cyanobacteria bloom characteristics, but as for nutrient loadings, they are only positively correlated with cyanobacteria bloom characteristics from an interannual perspective. We believe reducing total phosphorous, together with restoring macrophyte ecosystem, would be the necessary long-term management strategies for Taihu Lake. Our workflow provides an automatic and rapid approach for the long-term monitoring of cyanobacteria blooms, which can improve the automation and efficiency of routine environmental management of Taihu Lake and may be applied to other similar inland waters.

The Internet of Things (IoT) has many potential applications in the field of environmental monitoring. In this article, some hardware, including noise meters, ZigBee, and GPRS, were assembled and adjusted to get traffic noise data, which would be analyzed and compiled into a database based on the categories and characteristics of the data. Based on traffic noise data from 35 roads of nine green spaces in Xiamen, we used a back propagation neural network to practice net-simulation of noise data from 30 roads, while data from the remaining 5 roads were used as test data. Finally, the trained neural network was used to simulate traffic noise from 100 roads in Xiamen Island. Software systems using VB language and Flex network technology were also developed, and the simulation results were published on the Internet. The success of the method indicates that the Environmental IoT not only enables fast and effective acquisition of environmental data, but also enables accurate simulation and real-time network distribution.

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SUMMARYRemote sensing was used to assess the impacts of tourism development on temporal land-cover changes in the Lugu Lake region, home to the Mosuo people. The ecological and economic significance of the Lugu Lake area derives from the existence of a unique matriarchal system and the success of tourism development. Temporal land-cover changes between 1990 and 2005 were evaluated using digital interpretation of multitemporal Landsat TM images. Pairwise comparison methods were used to quantify changes in land-cover during three periods: 1990 to 1995, 1995 to 2001, and 2001 to 2005. The areas surveyed in each period were 10,226 ha, 7,727 ha and 9,344 ha, respectively. The annual rate of land-cover change for farmland, forest, grassland and wetland were 2.86%, 5.85%, 3.95%, 6.28%, respectively. Farmland and wetland have decreased, whereas forest, grassland and residential areas have increased. The land-cover changes could be explained by the success of tourism development and ecosystem management in Lugu Lake region. The impact of tourism on land cover and the community environment were assessed. The results show that most farmers actively left farms for off-farm jobs and took measures to protect forest, grassland and wetland by developing tourism in the study area. However, construction in residential areas has proceeded in a disorderly fashion. In future, the potential impacts of tourism on the lake ecosystem need to be re-assessed and monitored.