中文摘要：

夜晚灯光数据已被广泛应用于提取城市建成区的研究,由于灯光具有溢出特性,因此确定最佳灯光阈值成为研究热点。本研究中总结4种常用最佳阈值确定方法的特点,并使用Google Earth影像空间比较法,确定夜晚灯光数据提取城市建成区的最佳阈值。由于地形、交通等因素影响城市形态,城市形态影响夜晚灯光数据提取城市建成区的最佳阈值大小,因此,根据城市形态将阈值结果分为2类,并得到块状城市和带状城市建成区的最佳灯光阈值分别为43和47。Google Earth影像空间比较法与其他方法相比具有不受统计数据限制,以及简易操作等优势,且从形态上对城市建成区提取结果进行空间比较;其次,根据城市形态对最佳阈值结果分类,使不同类型的城市建成区提取结果更准确。该方法适用于在地形复杂、形态多样的大尺度空间中提取城市建成区。

英文摘要：

DMSP-OLS nighttime data has been widely used for extracting urban built-up area since the 1970s. It has also been used to estimate the economic level, density of population, changes in spatial patterns of urban landscapes, spatial dynamics of fishery, and the relationships between urbanization, human health and energy consumption. Due to the light＇ s characteristic of overflowing, the range of city extracted by DMSP-OLS nighttime data surpasses its real boundary in urban area extraction. Therefore, an accurate light threshold determination has become a research hotspot in recent years. In this paper, we sum up the features of four commonly used methods from other dissertations. Then, a spatial comparison method based on Google Earth is adopted to determine the optimum value of light threshold for urban built-up area extraction. Because factors such as terrain and traffic have significant impacts on the urban form, and the urban form influences the value of light threshold, the results of threshold for urban built-up area extraction are divided into two categories according to the urban form. The optimum thresholds for crumb city and strip city are 43 and 47 respectively. Compared to other methods, the spatial comparison method based on Google Earth has two advantages in innovation. Firstly, the classification of threshold result is based on the urban form; meanwhile, it not only breaks the limit of statistical data, but also improves the accuracy of urban built-up area extraction. Secondly, the data sources such as DMSP-OLS nighttime data and Google Earth image used in this method are free and easy to acquire. The method picks a series of sampling points around the whole city on Google Earth as the reference data, which displays not only the results of traditional statistics, but also the spatial differences between the real boundary and the urban area extracted by DMSP-OLS nighttime data. In a word, this method is suitable for extracting urban built-up area in regions with complicated terrains and diverse urba