An urban-rural Fringe extraction method based on Combined Urban-rural Fringe Index (CUFI)

Abstract

In this article, a multi-source data-based method for urban-rural fringe extraction is proposed to solve the problem of insufficiently accurate division of urban-rural fringe, which can conveniently and accurately realise the extraction of the area of urban-rural fringe. The method uses multi-source data such as night light remote sensing images, POI data and high-resolution remote sensing images. We constructed the Combined Urban-Rural Fringe Index (CUFI) model based on the characteristics of NDVI and NTL, POI kernel density value points that change from the city centre to the surrounding area. The calculation of CUFI involves three aspects. The initial step involves processing data, which includes reducing noise in nighttime light images and removing inaccurate information from POI data. The subsequent step involves calculating combined features of NTL and POI kernel density, which includes analyzing the kernel density of POI data, standardizing the results of POI kernel density and nighttime light data, computing NTL & POI indexes using the equal weight combined feature calculation method and reclassifying NTL & POI indexes .The third part involves rejecting misinformation using NDVI. This includes calculating the NDVI index using high-resolution satellite data, rejecting bare land information in the NDVI index using nighttime light data, and reclassifying the processed results. The reclassified results are then multiplied with the reclassification results of the NTL&POI index to obtain the CUFI calculation results. In order to verify the effectiveness and reliability of the method, Tangshan City, Hebei Province was selected as the experimental area, and the method was used to compare and analyse the accuracy with the traditional urban built-up area boundary extraction method. The results show that compared with the traditional method, the extraction accuracy of the CUFI method is higher, reaching 89.51%, which is able to effectively identify urban villages and lakes in the urban area, as well as improving the resolution of extracting Urban-Rural Fringe using NTL& POI methods, and also the urban-rural fringe which has weak night light and fewer POI points, but actually has urban-rural duality attributes Extraction.