Extracting Impervious Surface from Aerial Imagery Using Semi-Automatic Sampling and Spectral Stability

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dc.contributor.authorZhang, Hua
dc.contributor.authorGorelick, Steven M.
dc.contributor.authorZimba, Paul V.
dc.contributor.authorZhang, Hua
dc.contributor.authorGorelick, Steven M.
dc.contributor.authorZimba, Paul V.
dc.creator.orcidhttps://orcid.org/0000-0002-6470-933X
dc.creator.orcidhttps://orcid.org/0000-0001-6541-2055
dc.creator.orcidhttps://orcid.org/0000-0002-6470-933X
dc.creator.orcidhttps://orcid.org/0000-0001-6541-2055
dc.date.accessioned2020-04-30T19:43:06Z
dc.date.accessioned2020-04-30T19:43:06Z
dc.date.available2020-04-30T19:43:06Z
dc.date.available2020-04-30T19:43:06Z
dc.date.issued2020-02-04
dc.date.issued2020-02-042020-02-04
dc.date.issued2020-02-04
dc.description.abstractThe quantification of impervious surface through remote sensing provides critical information for urban planning and environmental management. The acquisition of quality reference data and the selection of effective predictor variables are two factors that contribute to the low accuracies of impervious surface in urban remote sensing. A hybrid method was developed to improve the extraction of impervious surface from high-resolution aerial imagery. This method integrates ancillary datasets from OpenStreetMap, National Wetland Inventory, and National Cropland Data to generate training and validation samples in a semi-automatic manner, significantly reducing the effort of visual interpretation and manual labeling. Satellite-derived surface reflectance stability is incorporated to improve the separation of impervious surface from other land cover classes. This method was applied to 1-m National Agriculture Imagery Program (NAIP) imagery of three sites with different levels of land development and data availability. Results indicate improved extractions of impervious surface with user’s accuracies ranging from 69% to 90% and producer’s accuracies from 88% to 95%. The results were compared to the 30-m percent impervious surface data of the National Land Cover Database, demonstrating the potential of this method to validate and complement satellite-derived medium-resolution datasets of urban land cover and land use.en_US
dc.description.abstractThe quantification of impervious surface through remote sensing provides critical information for urban planning and environmental management. The acquisition of quality reference data and the selection of effective predictor variables are two factors that contribute to the low accuracies of impervious surface in urban remote sensing. A hybrid method was developed to improve the extraction of impervious surface from high-resolution aerial imagery. This method integrates ancillary datasets from OpenStreetMap, National Wetland Inventory, and National Cropland Data to generate training and validation samples in a semi-automatic manner, significantly reducing the effort of visual interpretation and manual labeling. Satellite-derived surface reflectance stability is incorporated to improve the separation of impervious surface from other land cover classes. This method was applied to 1-m National Agriculture Imagery Program (NAIP) imagery of three sites with different levels of land development and data availability. Results indicate improved extractions of impervious surface with user’s accuracies ranging from 69% to 90% and producer’s accuracies from 88% to 95%. The results were compared to the 30-m percent impervious surface data of the National Land Cover Database, demonstrating the potential of this method to validate and complement satellite-derived medium-resolution datasets of urban land cover and land use.
dc.identifier.citationZhang, H.; Gorelick, S.M.; Zimba, P.V. Extracting Impervious Surface from Aerial Imagery Using Semi-Automatic Sampling and Spectral Stability. Remote Sens. 2020, 12, 506en_US
dc.identifier.citationZhang, H.; Gorelick, S.M.; Zimba, P.V. Extracting Impervious Surface from Aerial Imagery Using Semi-Automatic Sampling and Spectral Stability. Remote Sens. 2020, 12, 506
dc.identifier.doi10.3390/rs12030506
dc.identifier.doi10.3390/rs1203050610.3390/rs12030506
dc.identifier.doi10.3390/rs12030506
dc.identifier.urihttps://hdl.handle.net/1969.6/87833
dc.identifier.urihttps://hdl.handle.net/1969.6/87833https://hdl.handle.net/1969.6/87833
dc.identifier.urihttps://hdl.handle.net/1969.6/87833
dc.language.isoen_USen_US
dc.language.isoen_US
dc.publisherRemote Sensingen_US
dc.publisherRemote Sensing
dc.rightsAttribution 3.0 United States*
dc.rightsAttribution 3.0 United States
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/
dc.subjectimpervious surfaceen_US
dc.subjectland coveren_US
dc.subjectnational agriculture imagery program (naip)en_US
dc.subjectspectral stabilityen_US
dc.subjectopenstreetmap (osm)en_US
dc.subjectlandsaten_US
dc.subjectgoogle earth engineen_US
dc.subjectimpervious surface
dc.subjectland cover
dc.subjectnational agriculture imagery program (naip)
dc.subjectspectral stability
dc.subjectopenstreetmap (osm)
dc.subjectlandsat
dc.subjectgoogle earth engine
dc.titleExtracting Impervious Surface from Aerial Imagery Using Semi-Automatic Sampling and Spectral Stabilityen_US
dc.titleExtracting Impervious Surface from Aerial Imagery Using Semi-Automatic Sampling and Spectral Stability
dc.typeArticleen_US
dc.typeArticle

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