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Farmland protection and delimitation in the urban fringe considers not only natural factors but also the spatial characters and site factors. Taking Daxing District, Beijing in China as a case study, this paper used landscape ecology and power-law methods to analyze and evaluate farmland loss during the period of 2004-2007 based on the interpretation results of SPOT5 remote sensing images in 2004 and 2007. At the patch level, we selected four landscape indices, namely patch size, shape index, the nearest neighbor distance between farmland and construction land (including residential land and other construction land), and cropping type, to evaluate the risk of farmland loss and establish a farmland site analysis indicator system. The results showed that patch size and shape index have a significant positive correlation with farmland loss, whereas the distance to construction land has a clear negative correlation with farmland loss. As regards cropping type, fallow farmland is much easier for non-agricultural use than cultivated farmland. The relative transition ratio among vegetable land, fallow farmland and cultivated farmland is 1:5.6:1. The patch size of lost farmland follows a power-law distribution, indicating that not only small parcels but also large parcels can be lost. Patch size less than 4 ha or more than 15 ha is in high loss risk, between 4 ha and 10 ha in medium loss risk, and larger than 10 ha and less than 15 ha in low risk. Farmland with a more regular shape has a higher likelihood of loss. Patch shape index less than 2.0 is in high loss risk, between 2.0 and 3.0 in medium loss risk, and larger than 3.0 in low risk. Construction land has a varying impact on farmland loss, the residential land effected distance is 1000 m, and that of the other construction land is 2000 m. This analysis showed the relationships between site factors and farmland loss, and the analysis framework can provide support and reference for farmland protection and delimitation of prime farmland in China. [ABSTRACT FROM AUTHOR]

This paper uses an urbanized high-resolution land data assimilation system (u-HRLDAS) to parameterize the urban land surface characteristics. The u-HRLDAS model is localized and developed in order to satisfy the need of the weather forecast in Beijing, China. The remote sensing data used to localize and drive u-HRLDAS include the soil type data and MODIS retrieved leaf area index (LAI) data. The evaporation and water depth for impervious surface in urban area are developed to improve the simulation of u-HRLDAS. The result of the urban weather forecast is used for the comparison based on the rapid update cycle system at Beijing Meteorological Bureau (BJ-RUC) without coupled with u-HRLDAS. The land surface temperature, land surface fluxes, and first layer soil moisture in several single sites and urban Beijing region by BJ-RUC are compared with u-HRLDAS after localization and development. The off-line simulation results indicate that compared with BJ-RUC, after the localization and development, u-HRLDAS can improve the simulation of land surface parameters and fluxes definitely. [ABSTRACT FROM AUTHOR]

Mechanism and modeling of the land subsidence are complex because of the complicate geological background in Beijing, China. This paper analyzed the spatial relationship between land subsidence and three factors, including the change of groundwater level, the thickness of compressible sediments and the building area by using remote sensing and GIS tools in the upper-middle part of alluvial-proluvial plain fan of the Chaobai River in Beijing. Based on the spatial analysis of the land subsidence and three factors, there exist significant non-linear relationship between the vertical displacement and three factors. The Back Propagation Neural Network (BPN) model combined with Genetic Algorithm (GA) was used to simulate regional distribution of the land subsidence. Results showed that at field scale, the groundwater level and land subsidence showed a significant linear relationship. However, at regional scale, the spatial distribution of groundwater depletion funnel did not overlap with the land subsidence funnel. As to the factor of compressible strata, the places with the biggest compressible strata thickness did not have the largest vertical displacement. The distributions of building area and land subsidence have no obvious spatial relationships. The BPN-GA model simulation results illustrated that the accuracy of the trained model during fifty years is acceptable with an error of 51% of verification data less than 20 mm and the average of the absolute error about 32 mm. The BPN model could be utilized to simulate the general distribution of land subsidence in the study area. Overall, this work contributes to better understand the complex relationship between the land subsidence and three influencing factors. And the distribution of the land subsidence can be simulated by the trained BPN-GA model with the limited available dada and acceptable accuracy. [ABSTRACT FROM AUTHOR]