Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach

Abstract

Remote sensing images are useful for monitoring the spatial distribution and growth of urban built-up areas because they can provide timely and synoptic views of urban land cover. Although the normalized difference built-up index (NDBI) is useful to map urban built-up areas, it still has some limitations. This study sought to improve the NDBI by using a semiautomatic segmentation approach. The proposed approach had more than 20% higher overall accuracy than the original method when both were implemented simultaneously at the National Olympic Park (NOP), Beijing, China. One reason for the improvement is that the proposed NDBI approach separates urban areas from barren and bare land to some extent. More importantly, the proposed method eliminates the original assumption that a positive NDBI value should indicate built-up areas and a positive normalized difference vegetation index (NDVI) value should indicate vegetation. The new method has improved universality and lower commission error compared with the original method.