{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T16:14:36Z","timestamp":1740154476734,"version":"3.37.3"},"reference-count":63,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2019,11,6]],"date-time":"2019-11-06T00:00:00Z","timestamp":1572998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"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.<\/jats:p>","DOI":"10.3390\/rs11222603","type":"journal-article","created":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T11:52:36Z","timestamp":1573127556000},"page":"2603","source":"Crossref","is-referenced-by-count":2,"title":["Assessment of the Impacts of Image Signal-to-Noise Ratios in Impervious Surface Mapping"],"prefix":"10.3390","volume":"11","author":[{"given":"George","family":"Xian","sequence":"first","affiliation":[{"name":"U.S. Geological Survey (USGS) Earth Resources Observation and Science Center (EROS), Sioux Falls, SD 57198, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7013-1565","authenticated-orcid":false,"given":"Hua","family":"Shi","sequence":"additional","affiliation":[{"name":"ASRC Federal Data Solutions (AFDS), Contractor to the USGS EROS, Sioux Falls, SD 57198, USA"}]},{"given":"Cody","family":"Anderson","sequence":"additional","affiliation":[{"name":"U.S. Geological Survey (USGS) Earth Resources Observation and Science Center (EROS), Sioux Falls, SD 57198, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7393-1832","authenticated-orcid":false,"given":"Zhuoting","family":"Wu","sequence":"additional","affiliation":[{"name":"USGS Land Imaging Program, Flagstaff, AZ 86001, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4254","DOI":"10.1080\/01431161.2018.1452075","article-title":"Land cover 2.0","volume":"39","author":"Wulder","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5309","DOI":"10.1080\/01431161.2015.1093195","article-title":"An overview of 21 global and 43 regional land-cover mapping products","volume":"36","author":"Grekousis","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2607","DOI":"10.1080\/01431161.2012.748992","article-title":"Finer resolution observation and monitoring of global land cover: First mapping results with landsat tm and etm+ data","volume":"34","author":"Gong","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","first-page":"1055","article-title":"Assessment of classification accuracies of sentinel-2 and landsat-8 data for land cover\/use mapping","volume":"41","author":"Sertel","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: Esa\u2019s optical high-resolution mission for gmes operational services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1016\/j.rse.2018.07.030","article-title":"Mapping annual urban dynamics (1985\u20132015) using time series of landsat data","volume":"216","author":"Li","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2011.11.020","article-title":"A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using spot-5 hrg imagery","volume":"118","author":"Duro","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.cosust.2009.07.012","article-title":"Global urban land-use trends and climate impacts","volume":"1","author":"Seto","year":"2009","journal-title":"Curr. Opin. Environ. Sustain."},{"key":"ref_9","unstructured":"IPCC (2013). The physical science basis. The Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1126\/science.1150195","article-title":"Global change and the ecology of cities","volume":"319","author":"Grimm","year":"2008","journal-title":"Science"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2011.08.005","article-title":"Remote sensing of urban environments: Special issue","volume":"117","author":"Weng","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1733","DOI":"10.1016\/j.rse.2010.03.003","article-title":"Mapping global urban areas using modis 500-m data: New methods and datasets based on \u2018urban ecoregions\u2019","volume":"114","author":"Schneider","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1080\/01431160310001654950","article-title":"A global analysis of urban reflectance","volume":"26","author":"Small","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2015.12.027","article-title":"Characterizing the magnitude, timing and duration of urban growth from time series of landsat-based estimates of impervious cover","volume":"175","author":"Song","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2012.10.025","article-title":"Urban growth of the Washington, D.C.\u2013baltimore, md metropolitan region from 1984 to 2010 by annual, landsat-based estimates of impervious cover","volume":"129","author":"Sexton","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1080\/10106049.2013.768300","article-title":"A comparative assessment between object and pixel-based classification approaches for land use\/land cover mapping using spot 5 imagery","volume":"29","author":"Tehrany","year":"2014","journal-title":"Geocarto Int."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Shao, Z., Fu, H., Fu, P., and Yin, L. (2016). Mapping urban impervious surface by fusing optical and sar data at the decision level. Remote Sens., 8.","DOI":"10.3390\/rs8110945"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.rse.2017.08.028","article-title":"Comparison of simulated hyperspectral hyspiri and multispectral landsat 8 and sentinel-2 imagery for multi-seasonal, regional land-cover mapping","volume":"200","author":"Clark","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2011.08.024","article-title":"A review of large area monitoring of land cover change using landsat data","volume":"122","author":"Hansen","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.scib.2019.03.002","article-title":"Stable classification with limited sample: Transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017","volume":"64","author":"Gong","year":"2019","journal-title":"Sci. Bull."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.rse.2005.04.017","article-title":"Assessments of urban growth in the tampa bay watershed using remote sensing data","volume":"97","author":"Xian","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.rse.2006.02.010","article-title":"Use of impervious surface in urban land-use classification","volume":"102","author":"Lu","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.rse.2011.04.042","article-title":"Impact of spatial resolution and satellite overpass time on evaluation of the surface urban heat island effects","volume":"117","author":"Sobrino","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.landurbplan.2006.11.009","article-title":"Mapping private gardens in urban areas using object-oriented techniques and very high-resolution satellite imagery","volume":"81","author":"Mathieu","year":"2007","journal-title":"Landsc. Urban Plan."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"016517","DOI":"10.1117\/1.JRS.13.016517","article-title":"Rule-based, hierarchical land use and land cover classification of urban and peri-urban agriculture in data-poor regions with rapideye satellite imagery: A case study of nakuru, kenya","volume":"13","author":"Willkomm","year":"2019","journal-title":"J. Appl. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.isprsjprs.2009.10.002","article-title":"Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge","volume":"65","author":"Bouziani","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1109\/JSTARS.2010.2074186","article-title":"A multilevel hierarchical image segmentation method for urban impervious surface mapping using very high resolution imagery","volume":"4","author":"Li","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3380","DOI":"10.1080\/01431161.2015.1060645","article-title":"Detailed intra-urban mapping through transferable obia rule sets using worldview-2 very-high-resolution satellite images","volume":"36","author":"Hamedianfar","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1276","DOI":"10.1016\/j.rse.2009.02.014","article-title":"A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification","volume":"113","author":"Pacifici","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.rse.2011.02.030","article-title":"Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends","volume":"117","author":"Weng","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1080\/01431160802508985","article-title":"Extraction of urban impervious surfaces from an ikonos image","volume":"30","author":"Lu","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"169","DOI":"10.14358\/PERS.71.2.169","article-title":"Shadow analysis in high-resolution satellite imagery of urban areas","volume":"71","author":"Dare","year":"2005","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2657","DOI":"10.1109\/36.975000","article-title":"Effect of spatial resolution on classification errors of pure and mixed pixels in remote sensing","volume":"39","author":"Hsieh","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/01431160600746456","article-title":"A survey of image classification methods and techniques for improving classification performance","volume":"28","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.isprsjprs.2017.10.012","article-title":"Breaking new ground in mapping human settlements from space\u2014The global urban footprint","volume":"134","author":"Esch","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.isprsjprs.2018.03.007","article-title":"A new scheme for urban impervious surface classification from sar images","volume":"139","author":"Zhang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.rse.2013.10.028","article-title":"Improving the impervious surfaces estimation with combined use of optical and sar remote sensing images","volume":"141","author":"Zhang","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_38","first-page":"611","article-title":"Remote sensing of urban\/suburb an infrastructure and socio-economic attributes","volume":"65","author":"Jensen","year":"1999","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1080\/01431160210164271","article-title":"Noise estimation in remote sensing imagery using data masking","volume":"4","author":"Corner","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1080\/01431160902922888","article-title":"Geostatistically estimated image noise is a function of variance in the underlying signal","volume":"31","author":"Asmat","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2067","DOI":"10.1364\/OL.28.002067","article-title":"Improved signal-to-noise ratio in spectral-domain compared with time-domain optical coherence tomography","volume":"28","author":"Cense","year":"2003","journal-title":"Opt. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5099","DOI":"10.1080\/01431160500254999","article-title":"Interpreting image-based methods for estimating the signal-to-noise ratio","volume":"26","author":"Atkinson","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"743","DOI":"10.5721\/EuJRS20154841","article-title":"Effects of spectral resolution and snr on the vegetation solar-induced fluorescence retrieval using fld-based methods at canopy level","volume":"48","author":"Liu","year":"2017","journal-title":"Eur. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Jorge, D., Barbosa, C., De Carvalho, L., Affonso, A., Lobo, F., and Novo, E. (2017). Snr (signal-to-noise ratio) impact on water constituent retrieval from simulated images of optically complex amazon lakes. Remote Sens., 9.","DOI":"10.3390\/rs9070644"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JRS.7.073558","article-title":"Increased potential to monitor water quality in the near-shore environment with landsat\u2019s next-generation satellite","volume":"7","author":"Gerace","year":"2013","journal-title":"J. Appl. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/S0034-4257(01)00298-X","article-title":"Impact of sensor\u2019s point spread function on land cover characterization: Assessment and deconvolution","volume":"80","author":"Huang","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2016.10.002","article-title":"Updating urban extents with nighttime light imagery by using an object-based thresholding method","volume":"187","author":"Xie","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.rse.2006.09.005","article-title":"Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil","volume":"106","author":"Powell","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/S0034-4257(02)00136-0","article-title":"Estimating impervious surface distribution by spectral mixture analysis","volume":"84","author":"Wu","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_50","unstructured":"USDA (2018, May 01). Naip Imagery, Available online: https:\/\/www.fsa.usda.gov\/programs-and-services\/aerial-photography\/imagery-programs\/naip-imagery\/index."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"096044","DOI":"10.1117\/1.JRS.9.096044","article-title":"Validation of digitalglobe worldview-3 earth imaging satellite shortwave infrared bands for mineral mapping","volume":"9","author":"Kruse","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/0924-2716(94)90013-2","article-title":"Estimation of spot p-mode point spread function and derviation of a deconvolution filter","volume":"49","author":"Forster","year":"1994","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_53","unstructured":"USGS (2018, June 01). Landsat Geometry, Available online: https:\/\/www.usgs.gov\/land-resources\/nli\/landsat\/landsat-geometry."},{"key":"ref_54","unstructured":"DigitalGlobe (2018, April 01). Worldview3. Available online: http:\/\/worldview3.digitalglobe.com\/."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2208","DOI":"10.3390\/rs70202208","article-title":"Landsat-8 operational land imager (oli) radiometric performance on-orbit","volume":"7","author":"Morfitt","year":"2015","journal-title":"Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1676","DOI":"10.1016\/j.rse.2010.02.018","article-title":"Updating the 2001 national land cover database impervious surface products to 2006 using landsat imagery change detection methods","volume":"114","author":"Xian","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.rse.2015.07.014","article-title":"Characterization of shrubland ecosystem components as continuous fields in the northwest united states","volume":"168","author":"Xian","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1080\/15481603.2018.1517445","article-title":"Geospatial data mining for digital raster mapping","volume":"56","author":"Wylie","year":"2019","journal-title":"GISci. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1111\/1752-1688.12620","article-title":"Case study comparing multiple irrigated land datasets in arizona and colorado, USA","volume":"54","author":"Shi","year":"2018","journal-title":"J. Am. Water Resour. Assoc."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Gu, Y., Wylie, B.K., Boyte, S.P., Picotte, J., Howard, D.M., Smith, K., and Nelson, K.J. (2016). An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data. Remote Sens., 8.","DOI":"10.3390\/rs8110943"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"5763","DOI":"10.1080\/01431161.2017.1346403","article-title":"Subpixel land-cover classification for improved urban area estimates using landsat","volume":"38","author":"Maclachlan","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_62","first-page":"1","article-title":"Design of near-infrared soil moisture measring instrument","volume":"31","author":"Yang","year":"2015","journal-title":"Nongye Gongcheng Xuebao Trans. Chin. Soc. Agric. Eng."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Darwish, W., Tang, S., Li, W., and Chen, W. (2017). A new calibration method for commercial rgb-d sensors. Sensors, 17.","DOI":"10.3390\/s17061204"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/22\/2603\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,22]],"date-time":"2024-06-22T00:17:32Z","timestamp":1719015452000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/22\/2603"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,6]]},"references-count":63,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["rs11222603"],"URL":"https:\/\/doi.org\/10.3390\/rs11222603","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,11,6]]}}}