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The increase in artificial light at night (ALAN) is a global concern, while the pattern of ALAN pollution inside urban areas has not yet been fully explored. To fill this gap, we developed a novel method to map fine-scale ALAN pollution patterns in urban bird habitats using high spatial resolution ALAN satellite data. First, an ALAN pollution map was derived from JL1-3B satellite images. Then, the core habitat nodes (CHNs) representing the main habitats for urban birds to inhabit were identified from the land cover map, which was produced using Gaofen2 (GF2) data, and the high probability corridors (HPCs), indicating high connectivity paths, were derived from Circuitscape software. Finally, the ALAN patterns in the CHNs and HPCs were analysed, and the mismatch index was proposed to evaluate the trade-off between human activity ALAN demands and ALAN supply for the protection of urban birds. The results demonstrated that 115 woodland patches covering 4149.0 ha were selected as CHNs, and most of the CHNs were large urban parks or scenic spots located in the urban fringe. The 2923 modelled HPCs occupying 1179.2 ha were small remaining vegetation patches and vegetated corridors along the major transport arteries. The differences in the ALAN pollution patterns between CHNs and HPCs were mainly determined by the characteristics of the green space patches and the light source types. The polluted regions in the CHNs were clustered in a few regions that suffered from concentrated and intensive ALAN, while most of the CHNs remained unaffected. In contrast, the 727 HPCs were mainly polluted by street lighting was scattered and widely distributed, resulting a more varying influence to birds than that in the CHNs. Relating patterns of the ALAN to bird habitats and connectivity provides meaningful information for comprehensive planning to alleviate the disruptive effects of ALAN pollution., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Land-use and land-cover change (LUCC) is a crucial factor affecting carbon emissions. Zhejiang Province has witnessed unprecedented LUCC concomitant with rapid urbanization from 1970 to 2010. In this study, remote sensing, geographic information system (GIS) and the Intergovernmental Panel on Climate Change (IPCC) method were combined to quantify changes in both vegetation carbon storage and soil organic carbon (SOC) storage resulting from LUCC during 1970-1990 and 1990-2010. For both 1970-1990 and 1990-2010, the results showed successive decrease in farmlands (2.8 × 10 5  ha or -9.15% and 5.9 × 10 5  ha or -20.49%, respectively) and grasslands (3.4 × 10 4  ha or -10.73% and 1.5 × 10 5  ha or -54.1%, respectively), and continuous increase in forests (2.0 × 10 4  ha or 0.33% and 1.7 × 10 5  ha or 2.81%, respectively) and built-up lands (2.07 × 10 5  ha or 78.41% and 6.49 × 10 5  ha or 137.8%, respectively). From 1970 to 1990, approximately 8.3 Tg of the total carbon sink declined, including a 0.4 Tg reduction in vegetation carbon and a 7.9 Tg reduction in SOC. While from 1990 to 2010, approximately 17.5 Tg of carbon storage declined, comprising a 2.8 Tg of carbon accumulated by vegetation, and a 20.3 Tg reduction in SOC. Overall, LUCC has resulted in huge amount of carbon emissions in Zhejiang from 1970 to 2010. Efficient planning for LUCC and gradual mitigation of carbon emissions are indispensable for future urban development in China under increasing pressure from global warming., (Copyright © 2018. Published by Elsevier B.V.)

Urban greenness is increasingly recognized as an essential constituent of the urban environment and can provide a range of services and enhance residents' quality of life. Understanding the pattern of urban greenness and exploring its spatiotemporal dynamics would contribute valuable information for urban planning. In this paper, we investigated the pattern of urban greenness in Hangzhou, China, over the past two decades using time series Landsat-5 TM data obtained in 1990, 2002, and 2010. Multiple endmember spectral mixture analysis was used to derive vegetation cover fractions at the subpixel level. An RGB-vegetation fraction model, change intensity analysis and the concentric technique were integrated to reveal the detailed, spatial characteristics and the overall pattern of change in the vegetation cover fraction. Our results demonstrated the ability of multiple endmember spectral mixture analysis to accurately model the vegetation cover fraction in pixels despite the complex spectral confusion of different land cover types. The integration of multiple techniques revealed various changing patterns in urban greenness in this region. The overall vegetation cover has exhibited a drastic decrease over the past two decades, while no significant change occurred in the scenic spots that were studied. Meanwhile, a remarkable recovery of greenness was observed in the existing urban area. The increasing coverage of small green patches has played a vital role in the recovery of urban greenness. These changing patterns were more obvious during the period from 2002 to 2010 than from 1990 to 2002, and they revealed the combined effects of rapid urbanization and greening policies. This work demonstrates the usefulness of time series of vegetation cover fractions for conducting accurate and in-depth studies of the long-term trajectories of urban greenness to obtain meaningful information for sustainable urban development.

Various reservoirs have been serving as the most important drinking water sources in Zhejiang Province, China, due to the uneven distribution of precipitation and severe river pollution. Unfortunately, rapid urbanization and industrialization have been continuously challenging the water quality of the drinking-water reservoirs. The identification and assessment of potential impacts is indispensable in water resource management and protection. This study investigates the drinking water reservoirs in Zhejiang Province to better understand the potential impact on water quality. Altogether seventy-three typical drinking reservoirs in Zhejiang Province encompassing various water storage levels were selected and evaluated. Using fifty-two reservoirs as training samples, the classification and regression tree (CART) method and sixteen comprehensive variables, including six sub-sets (land use, population, socio-economy, geographical features, inherent characteristics, and climate), were adopted to establish a decision-making model for identifying and assessing their potential impacts on drinking-water quality. The water quality class of the remaining twenty-one reservoirs was then predicted and tested based on the decision-making model, resulting in a water quality class attribution accuracy of 81.0%. Based on the decision rules and quantitative importance of the independent variables, industrial emissions was identified as the most important factor influencing the water quality of reservoirs; land use and human habitation also had a substantial impact on water quality. The results of this study provide insights into the factors impacting the water quality of reservoirs as well as basic information for protecting reservoir water resources.