城市化是社会经济和科学技术发展的结果，过快的城市扩张产生了一系列的土地、环境问题，如何平衡城市扩张与环境保护之间的关系是现下研究的热点问题。近二十年来，随着对地观测技术的快速发展，使得长时序、动态监测城市空间扩展和城市形态变化成为可能。

本文考虑到NPP/VIIRS夜间灯光数据存在背景噪声、异常值及年际数据不连续的问题，对数据进行校正，以山东省为研究对象，基于校正后的NPP/VIIRS灯光数据和MODIS EVI数据，采用植被修正灯光指数方法提取了城市建成区，对2012-2019年山东省城市空间动态演变特征进行了分析，分别构建了灰色驱动力预测模型和BP神经网络预测模型，探究城市发展与经济社会因子之间的驱动力机制。主要的研究结论如下：

（1）利用掩膜去噪法对原始灯光数据进行降噪处理，随后进行异常值剔除、连续性校正，得到长时间序列、可用的灯光数据，并对校正后的灯光亮值像元进行统计和GDP线性拟合，结果表明亮值像元的个数和总量都呈现持续增长的趋势，与GDP经济参数的拟合程度较高，相关系数R²大于0.9，说明校正后的灯光数据具有较高的适用性及可靠性。

（2）采用邻域分析法、支持向量机法、指数构建方法进行建成区提取实验，并对三种方法进行精度对比分析，结果表明结合植被指数构建的修正灯光指数在建成区提取方面具有较高精度，与统计数据相差0.33%，准确率达到83%，提取结果可用于研究城市扩展变化规律。

（3）从空间拓展时序变化、空间形态测度变化、城市用地规模三个方面探讨山东省城市空间扩展的时空规律。研究结果表明山东省城市扩展主要经历了三个阶段：以济南为中心的省会都市圈先发展，然后向东部沿海迁移，后期向内部鲁西南地区发展，城市发展趋于平衡，区域间协调可持续发展，建成区边界范围向着集中、规范化发展。

（4）利用灰色关联度分析探索山东省城市扩张与经济驱动力因子之间的关联程度，总体分析，地区生产总值、市辖区人口数量、社会消费零售额与建成区面积之间的关联度都超过0.7，具有较高的关联性。选取排名前五的经济驱动力因子，分别构建了灰色驱动力预测模型和BP神经网络预测模型，对山东省城市扩张进行模拟预测，结果表明BP神经网络模型得到的模拟值与实际值之间的相对误差在2%左右，精准度更高，可以适用于山东省城市扩张分析。

Urbanization is the result of the development of social economy and science and technology. Excessive urban expansion has produced a series of land and environmental problems. How to balance the relationship between urban expansion and environmental protection is a hot topic of current research. In the past two decades, with the rapid development of earth observation technology, it is possible to monitor urban spatial expansion and urban form change in a long time sequence and dynamically.

This article takes into account the background noise, abnormal value and inter-annual data discontinuity in the NPP/VIIRS night light data, and corrects the data. Taking Shandong Province as the research object, based on the corrected NPP/VIIRS light data and MODIS EVI data, The vegetation correction light index method was used to extract the urban built-up area, and the dynamic evolution characteristics of the urban space in Shandong Province from 2012 to 2019 were analyzed, and the gray driving force prediction model and the BP neural network prediction model were constructed to explore the urban development and economic and social factors. The driving force mechanism between. The main research conclusions are as follows:

(1) Use the mask denoising method to denoise the original light data, then perform outlier elimination and continuity correction to obtain long-term sequence and usable light data, and perform statistics on the corrected light brightness value pixels It is linearly fitted to GDP. The result shows that the number and total amount of bright value pixels show a continuous growth trend, and the degree of fitting with GDP economic parameters is relatively high. The correlation coefficient R² is greater than 0.9, indicating that the corrected light data has a relatively high High applicability and reliability.

(2) The neighborhood analysis method, support vector machine method, and index construction method are used to carry out built-up area extraction experiments, and the accuracy of the three methods is compared. The results show that the modified light index constructed in conjunction with the vegetation index has a better performance in built-up area extraction. High accuracy, 0.33% difference from statistical data, and accuracy rate of 83%. The extracted results can be used to study the law of urban expansion.

(3) Discuss the temporal and spatial laws of urban spatial expansion in Shandong Province from three aspects: temporal changes in spatial expansion, changes in spatial form measurement, and urban land use scale. The research results show that the urban expansion of Shandong Province has experienced three main stages. The provincial capital metropolitan area with Jinan as the center develops first, then migrates to the eastern coastal area, and then develops to the southwestern part of Shandong. The urban development tends to be balanced, and the inter-regional coordination is sustainable. Development, the boundary and scope of built-up areas are developing towards concentration and standardization.

(4) Use gray correlation analysis to explore the degree of correlation between urban expansion and economic driving factors in Shandong Province. Overall analysis, the correlation degree between regional GDP, population of municipal districts, social consumer retail sales and built-up area Both are more than 0.7, with high relevance. Select the top five economic driving force factors, respectively construct the gray driving force prediction model and the BP neural network prediction model to simulate and predict the urban expansion of Shandong Province. The results show that the relative error between the simulated value and the actual value obtained by the BP neural network model is about 2%, and the accuracy is higher, which can be applied to the analysis of urban expansion in Shandong Province.

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