The management of urban land development, as well as the conservation and optimization of agricultural land and urban green space, is crucial for sustainable urban development. China has experienced rapid urbanization and industrialization since 1978, which brought unintended negative social and environmental consequences. Hefei, the capital of Anhui Province in eastern China, is identified by the Economist Intelligence Unit as one of the top 20 emerging cities in China. Its urbanized area grew from $29{\mathrm{km}}^2$ to $268{\mathrm{km}}^2$ , an 824% increase, between 1980 and 2005. This paper reviews Hefei’s urban growth management since 1987. It then analyzes in detail the construction expansion regulation zone, an innovative measure proposed by the Ministry of Land and Resources China (MLRC). The regulation zone includes four types of zones: a permitted expansion construction zone, a conditionally permitted expansion construction zone, a restricted expansion construction zone, and a prohibited expansion construction zone. The suitable land-use types for each zone are identified and these measures are compared. Finally, the application of the construction expansion regulation zone to manage Hefei’s urban growth is illustrated. The findings would be useful for researchers and local authorities and as a reference for the study of urban planning or to support land resource management.

China’s rapid urbanisation has led to ecological deterioration and reduced the land available for agricultural production. The purpose of this study is to develop an urban development boundary delineation (UDBD) model using the high-tech manufacturing area of Xinbei in the District of Changzhou as a case study, and by applying remote sensing, GIS, and other technologies. China’s UDBD policies are reviewed, spatiotemporal changes since 1985 are documented, and future expansion is modelled to 2020. The simulated urban-growth patterns are analysed in relation to China’s policies for farmland preservation, ecological redlines protection areas, and housing developments. The UDBD model developed in this study satisfies regional farmland and ecological space protection constraints, while being consistent with urban development strategies. This study provides theoretical references and technological support for the implementation of land management policies that will optimize land allocations for urban growth, agriculture, and ecological protection.

Farmland preservation is a national issue in modern China. Most studies have focused on farmland preservation policies in the agricultural, commercial and financial industries but have devoted relatively little attention to land use planning. Land use planning is crucial for farmland preservation. This paper selected Dachang County, a typical rural county bordering Beijing, as the study area and reviews previous management policies, analyzes changes in farmland management and the socio-economic background over the past ten years. Next, this study presents land use planning decisions regarding farmland preservation measures. These measures included the establishment of farmland preservation zones and the creation of construction expansion zones and land consolidation zones. All of the findings would be useful for researchers as a reference for the study of land resource management and planning.

High-precision information regarding the location, time, and type of land use change is integral to understanding global changes. Time series (TS) analysis of remote sensing images is a powerful method for land use change detection. To address the complexity of sample selection and the salt-and-pepper noise of pixels, we propose a bidirectional segmented detection (BSD) method based on object-level, multivariate TS, that detects the type and time of land use change from Landsat images. In the proposed method, based on the multiresolution segmentation of objects, three dimensions of object-level TS are constructed using the median of the following indices: the normalized difference vegetation index (NDVI), the normalized difference built index (NDBI), and the modified normalized difference water index (MNDWI). Then, BSD with forward and backward detection is performed on the segmented objects to identify the types and times of land use change. Experimental results indicate that the proposed BSD method effectively detects the type and time of land use change with an overall accuracy of 90.49% and a Kappa coefficient of 0.86. It was also observed that the median value of a segmented object is more representative than the commonly used mean value. In addition, compared with traditional methods such as LandTrendr, the proposed method is competitive in terms of time efficiency and accuracy. Thus, the BSD method can promote efficient and accurate land use change detection.