Abstract
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.
Highlights
The ability to continuously monitor land cover and land use conditions and change from satellite imagery is an ongoing effort in the remote sensing community
We aimed to answer the following major research questions: (1) How would differences in image signal-to-noise ratio (SNR) impact mapping of the developed impervious surface in terms of spatial extent and cover intensity? (2) Does the impact of SNR vary with spatial resolution in ISA mapping? (3) How does image SNR impact ISA density mapping? We compared the performances of these simulated images in quantifying ISA in two typical urban environments where impervious surfaces are mixed with vegetation canopies and other land cover types
WV3 images and spatial distributions of ISA estimated from regression tree (RT) models using WV3 imagery at 10-m, 20-m, and 30-m resolutions with three different SNRs are presented in Figures 4–8 for the two study areas
Summary
The ability to continuously monitor land cover and land use conditions and change from satellite imagery is an ongoing effort in the remote sensing community. The complexity of urban landscape types limits the use of coarse geometric resolution remote sensing information because many sub-pixel patches in urban areas are smaller than the effective pixel size of a sensor These sub-pixel features can be effectively characterized as a continuous field or fractional developed impervious surface (ISA) [21,22]. With the arrival of very high resolution satellite imagery, such as IKONOS (Greek for image), QuickBird, and WorldView (WV), efforts have been made to characterize heterogeneous landscape conditions, including urban landscapes, and to implement the information for environmental research and applications [24,25,26,27,28,29] These fine spatial resolution images encompass rich spatial information and have a greater potential to extract much more detailed thematic information and cartographic features associated with urban landscapes [30,31]. Radar data have been used successfully to map the global urban footprint [35] and ISA in regions with a relatively high frequency of cloud cover [36,37]
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