Extraction of Urban Built-Up Areas Based on Data Fusion: A Case Study of Zhengzhou, China

Abstract

Urban built-up areas are not only the spatial carriers of urban activities but also the direct embodiment of urban expansion. Therefore, it is of great practical significance to accurately extract urban built-up areas to judge the process of urbanization. Previous studies that only used single-source nighttime light (NTL) data to extract urban built-up areas can no longer meet the needs of rapid urbanization development. Therefore, in this study, spatial location big data were first fused with NTL data, which effectively improved the accuracy of urban built-up area extraction. Then, a wavelet transform was used to fuse the data, and multiresolution segmentation was used to extract the urban built-up areas of Zhengzhou. The study results showed that the precision and kappa coefficient of urban built-up area extraction by single-source NTL data were 85.95% and 0.7089, respectively, while the precision and kappa coefficient of urban built-up area extraction by the fused data are 96.15% and 0.8454, respectively. Therefore, after data fusion of the NTL data and spatial location big data, the fused data compensated for the deficiency of single-source NTL data in extracting urban built-up areas and significantly improved the extraction accuracy. The data fusion method proposed in this study could extract urban built-up areas more conveniently and accurately, which has important practical value for urbanization monitoring and subsequent urban planning and construction.