Abstract
The area of urban impervious surfaces is one of the most important indicators for determining the level of urbanisation and the quality of the environment and is rapidly increasing with the acceleration of urbanisation in developing countries. This paper proposes a novel remote sensing index based on the coastal band and normalised difference vegetation index for extracting impervious surface distribution from Landsat 8 multispectral remote sensing imagery. The index was validated using three images covering urban areas of China and was compared with five other typical index methods for the extraction of impervious surface distribution, namely, the normalised difference built-up index, index-based built-up index, normalised difference impervious surface index, normalised difference impervious index, and combinational built-up index. The results showed that the novel index provided higher accuracy and effectively distinguished impervious surfaces from bare soil, and the average values of the recall, precision, and F1 score for the three images were 95%, 91%, and 93%, respectively. The novel index provides better applicability in the extraction of urban impervious surface distribution from Landsat 8 multispectral remote sensing imagery.
Highlights
Impervious surfaces are artificial surfaces that water cannot infiltrate to reach the soil, such as parking lots, streets, and highways [1]
9, 2631 growth of developing countries, the rapid urbanisation of rural land and its conversion to urban land directly lead to an increase in the area of impervious surfaces, which may lead to the urban heat island effect
This study proposed a novel index, namely, the ratio-based impervious surface index (RISI), for extracting impervious surface
Summary
Impervious surfaces are artificial surfaces that water cannot infiltrate to reach the soil, such as parking lots, streets, and highways [1]. Changes in land use and land cover caused by the expansion of impervious surfaces are likely to influence the regional climate [2]. Accurate and fast extraction methods for impervious surface distribution are important which is helpful for detecting regional environmental changes in urban areas and achieving sustainable urban development. They can be divided into two categories, namely, field surveys and remote sensing [1]. Field surveys can provide more detailed information on impervious surface distribution but are time-consuming, laborious, and difficult to apply to the assessment of large areas. When extracting impervious surface distribution in a large urban area, Landsat
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