Understanding the link between greenspace patterns and land surface temperature is very important for mitigating the urban heat island (UHI) effect and is also useful for planners and decision-makers for providing a sustainable design for urban greenspace. Although coupling remote sensing data with a computational fluid dynamics (CFD) model has widely been used to examine interactions between UHI and greenspace patterns, the paper aims to examine the impact of five theoretical models of greenspace patterns on land surface temperature based on the improvement of the accuracy of CFD modeling by the combination of LiDAR data with remote sensing images to build a 3D urban model. The simulated results demonstrated that the zonal pattern always had the obvious cooling effects when there are no large buildings or terrain obstacles. For ambient environments, the building or terrain obstacles and the type of greenspace have the hugest influence on mitigating the UHI, but the greenspace area behaves as having the least cooling effect. A dotted greenspace pattern shows the best cooling effect in the central area or residential district within a city, while a radial and a wedge pattern may result in a “cold source” for the urban thermal environment.

Since the 1980s a series of farmland policies have been implemented in China to stabilize the balance of farmland quantity and quality against accelerating urbanization and industrialization processes. This paper aims to reveal differences of soil fertility in the farmland occupation area (FOA) and farmland supplement area (FSA). In 1985–2000 the decline of the FOA area was 181,000 ha, but the FSA rarely increased. In 2000–2010 the decline of the FOA area was 824,800 ha, but the FSA increased dramatically. The accelerating loss process is closely related to urbanization and industrialization of the locations. Most occupied farmland was still located in the areas with higher soil fertility. The FOA in 1985–2000 had higher soil fertility than the FSA, but the FSA in 2000–2010 significantly raised its soil fertility to close to the FOAs’ level. The rate of excellent-good levels of the FOA in 2000–2010 decreased from 46.13% to 37.61%; The development model shifts and farmland policies implementation are the chief driving factors behind AFOS changes. The TDBF policy and the main function zoning project should continue to play an effective role in balancing the farmland system.