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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]

Rapid urbanization has resulted in great changes in rural landscapes globally. Using remote sensing data to quantify the distribution of rural settlements and their changes has received increasing attention in the past three decades, but remains a challenge. Previous studies mostly focused on the residential changes within a grid or administrative boundary, but not at the individual village level. This paper presents a new change detection approach for rural residential settlements, which can identify different types of rural settlement changes at the individual village level by integrating remote sensing and Geographic Information System (GIS) analyses. Using multi-temporal Landsat TM image data, this approach classifies villages into five types: “no change”, “totally lost”, “shrinking”, “expanding”, and “merged”, in contrast to the commonly used “increase” and “decrease”. This approach was tested in the Beijing metropolitan area from 1984 to 2010. Additionally, the drivers of such changes were investigated using multinomial logistic regression models. The results revealed that: (1) 36% of the villages were lost, but the total area of developed lands in existing villages increased by 34%; (2) Changes were dominated by the type of ‘expansion’ in 1984–1990 (accounted for 43.42%) and 1990–2000 (56.21%). However, from 2000 to 2010, 49.73% of the villages remained unchanged; (3) Both topographical factors and distance factors had significant effects on whether the villages changed or not, but their impacts changed through time. The topographical driving factors showed decreasing effects on the loss of rural settlements, while distance factors had increasing impacts on settlement expansion and merging. This approach provides a useful tool for better understanding the changes in rural residential settlements and their associations with urbanization. [ABSTRACT FROM AUTHOR]

China has undergone rapid urbanization in the past few decades, and it has been accompanied by overdevelopment. Residential vacancies caused by overdevelopment result in a waste of resources and generate greenhouse gases associated with land surface changes. Due to the poor spatial resolution and limited availability of data, previous studies performed analyses at low resolutions at the county scale, thus lacking spatial detail. In addition, they used complicated subjective indicators difficult to apply to cities of various sizes across China. To understand the detailed spatial pattern of residential vacancies in megacities, we designed a more generally applicable approach with multisource high-resolution spatiotemporal data and tested it in Beijing, the capital of China. At first, a statistical regression with features derived from multisource data was used. Then, the predicted values of the regression function were used as standard heat values, and the observed heat value in each unit was divided by the corresponding standard heat value. Next, residential vacancies were estimated by calculating the quantiles of these division results in all analysis units. This approach requires no prior knowledge or complicated indicators and can be easily applied across cities in China, which is beneficial for development planning at the provincial and national levels. [ABSTRACT FROM AUTHOR]

Urbanization has induced profound landscape changes. While the spatiotemporal patterns of urban landscapes have been extensively studied, the manner by which the internal structures of already urbanized areas change remains little understood. Characteristic scales are an important measure of landscape structure, and they represent the typical spatial extents of landscape elements in hierarchies. In this study, we quantified temporal variations of the characteristic scales in the central urban landscapes of Beijing and Shanghai over an 18 year period. Using transect data from Landsat images, characteristic scales were identified through wavelet analysis and then classified into several discrete domains using the k-means clustering method. These characteristic scale domains appeared to correspond with the typical extents of the blocks and block clusters in the study areas. Results showed that the number of the characteristic scale domains changed within a small range of 3-5 while the mean values of the characteristic scales within the domains showed substantial temporal variation. Larger characteristic scales were more variable than smaller ones in both cities. Distinguishing relative change rates of building forms, land use and street layout of urban landscapes allowed us to interpret these differences. The street layout of urban landscapes usually reacts slowly to the force of change, acting as the skeleton of the urban landscape. As a result, block sizes can remain relatively stable and corresponding characteristic scales present inheritance features. Land use and building forms are more susceptible to changes. Block clusters with flexible extents could result in significant variation of characteristic scales. [ABSTRACT FROM AUTHOR]