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Open AccessArticle

Assessment of the Impacts of Image Signal-to-Noise Ratios in Impervious Surface Mapping

1
U.S. Geological Survey (USGS) Earth Resources Observation and Science Center (EROS), Sioux Falls, SD 57198, USA
2
ASRC Federal Data Solutions (AFDS), Contractor to the USGS EROS, Sioux Falls, SD 57198, USA
3
USGS Land Imaging Program, Flagstaff, AZ 86001, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(22), 2603; https://doi.org/10.3390/rs11222603
Received: 11 September 2019 / Revised: 31 October 2019 / Accepted: 2 November 2019 / Published: 6 November 2019
(This article belongs to the Special Issue Advancements in Remote Sensing of Land Surface Change)
Medium spatial resolution satellite images are frequently used to characterize thematic land cover and a continuous field at both regional and global scales. However, high spatial resolution remote sensing data can provide details in landscape structures, especially in the urban environment. With upgrades to spatial resolution and spectral coverage for many satellite sensors, the impact of the signal-to-noise ratio (SNR) in characterizing a landscape with highly heterogeneous features at the sub-pixel level is still uncertain. This study used WorldView-3 (WV3) images as a basis to evaluate the impacts of SNR on mapping a fractional developed impervious surface area (ISA). The point spread function (PSF) from the Landsat 8 Operational Land Imager (OLI) was used to resample the WV3 images to three different resolutions: 10 m, 20 m, and 30 m. Noise was then added to the resampled WV3 images to simulate different fractional levels of OLI SNRs. Furthermore, regression tree algorithms were incorporated into these images to estimate the ISA at different spatial scales. The study results showed that the total areal estimate could be improved by about 1% and 0.4% at 10-m spatial resolutions in our two study areas when the SNR changes from half to twice that of the Landsat OLI SNR level. Such improvement is more obvious in the high imperviousness ranges. The root-mean-square-error of ISA estimates using images that have twice and two-thirds the SNRs of OLI varied consistently from high to low when spatial resolutions changed from 10 m to 20 m. The increase of SNR, however, did not improve the overall performance of ISA estimates at 30 m. View Full-Text
Keywords: impervious surface; signal-to-noise ratios; WorldView-3; Landsat impervious surface; signal-to-noise ratios; WorldView-3; Landsat
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MDPI and ACS Style

Xian, G.; Shi, H.; Anderson, C.; Wu, Z. Assessment of the Impacts of Image Signal-to-Noise Ratios in Impervious Surface Mapping. Remote Sens. 2019, 11, 2603.

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