Solar energy, as a clean and renewable resource is becoming increasingly important in the global context of climate change and energy crisis. Utilization of solar energy in urban areas is of great importance in urban energy planning, environmental conservation, and sustainable development. However, available spaces for solar panel installation in cities are quite limited except for building roofs. Furthermore, complex urban 3D morphology greatly affects sunlit patterns on building roofs, especially in downtown areas, which makes the determination of roof solar energy potential a challenging task. The object of this study is to estimate the solar radiation on building roofs in an urban area in Shanghai, China, and select suitable spaces for installing solar panels that can effectively utilize solar energy. A Graphic Processing Unit (GPU)-based solar radiation model named SHORTWAVE-C simulating direct and non-direct solar radiation intensity was developed by adding the capability of considering cloud influence into the previous SHORTWAVE model. Airborne Light Detection and Ranging (LiDAR) data was used as the input of the SHORTWAVE-C model and to investigate the morphological characteristics of the study area. The results show that the SHORTWAVE-C model can accurately estimate the solar radiation intensity in a complex urban environment under cloudy conditions, and the GPU acceleration method can reduce the computation time by up to 46%. Two sites with different building densities and rooftop structures were selected to illustrate the influence of urban morphology on the solar radiation and solar illumination duration. Based on the findings, an object-based method was implemented to identify suitable places for rooftop solar panel installation that can fully utilize the solar energy potential. Our study provides useful strategic guidelines for the selection and assessment of roof solar energy potential for urban energy planning.

The continuous expansion of impervious artificial surfaces in cities has significantly influenced the urban thermal environment. This paper examines the spatiotemporal variation of the diurnal surface urban heat island (SUHI) in Shanghai from 1989 to 2013, a period during which the city has experienced drastic development changes. A remote sensing approach was taken to derive the spatial patterns of Shanghai’s land surface temperature (LST) from Landsat Thematic Mapper (TM) images and Operational Land Imager (OLI) data. The LST pattern was further classified into five LST classes to look at the relative SUHI intensity level across the whole city. Spatial analyses, namely, spatial association and centroid movement analysis, were conducted to reveal the trends of LST changes at both local and holistic scales. To understand the potential drivers for the present spatiotemporal variation of SUHI, different indicators including land use change, population density, night light data, and vegetation were analyzed and compared with LST changes. Based on the quantitative analysis and the socioeconomic context of Shanghai, “heating up” regions were identified, possible reasons for such SUHI variation were summarized, and districts that are most vulnerable to extreme heat conditions were projected. In terms of implication for urban development, planning and design recommendations were suggested to improve the urban thermal environment in Shanghai.