Extraction of Impermeable Surfaces Based on Multi-Source Nighttime Light Images of Different Geomorphological Partitions

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Abstract
Accurate extraction of impermeable surfaces is important for assessing land use change and improving the urban heat island effect. Nighttime light imagery has the advantage of being efficient and cost effective, providing a new perspective for monitoring and extracting impermeable surfaces and analyzing urban expansion processes. However, for the vast Karst terrain fragmentation area located in southwest China, the extraction of impermeable surface information faces many challenges due to surface landscape fragmentation and nighttime light image resolution. These challenges include light spillover, oversaturation and limited understanding of spatial links with surface types at fine scales. This study uses Luojia1-01, NPP-VIIRS, and Flint as remote sensing data sources to examine the applicability of nighttime light images in extracting impermeable surfaces from geomorphologically complex areas. The results show that Luojia1-01 data can provide finer spatial details and more accurate impermeable surface extraction results than NPP-VIIRS and Flint data. The relative error of extracted area in regions with large topographic relief is higher than that in regions with flat topographic cuttings. The extraction results of the three images are spatially similar; however, the overall accuracy is poor, and a single nighttime light image is not the best solution for obtaining impermeable surface information in large scale terrain fragmentation areas. However, the integrated application of multi-source light images is a trend for future regional research and development, and the best way to extract impermeable surfaces in complex terrain areas should be explored in conjunction with other remote sensing data sources in the future.
Funding Information
  • National Natural Science Foundation of China Regional Projects (41661088, (2016)5674)
  • Guizhou Province “High-level Innovative Talents Training Program ‘100’ Level Talents” Project (41661088, (2016)5674)

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