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Geographic Object Based Image Analysis of WorldView-3 Imagery for Urban Hydrologic Modelling at the Catchment Scale

1
Section for Landscape Architecture and Planning, Department of Geosciences and Natural Resources Management, University of Copenhagen, 1958 Frederiksberg, Denmark
2
Sino-Danish Center for Education and Research, Chinese Academy of Sciences, Huairou district, Beijing 101408, China
3
Section for Geography, Department of Geosciences and Natural Resources Management, University of Copenhagen, 1350 Copenhagen, Denmark
4
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Water 2019, 11(6), 1133; https://doi.org/10.3390/w11061133
Received: 28 March 2019 / Revised: 20 May 2019 / Accepted: 21 May 2019 / Published: 30 May 2019
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Abstract

China’s Sponge City initiative will involve widespread installation of new stormwater infrastructure including green roofs, permeable pavements and rain gardens in at least 30 cities. Hydrologic modelling can support the planning of Sponge Cities at the catchment scale, however, highly detailed spatial data for model input can be challenging to compile from the various authorities, or, if available, may not be sufficiently detailed or updated. Remote sensing methods show great promise for mitigating this challenge due to their ability to efficiently classify satellite images into categories relevant to a specific application. In this study Geographic Object Based Image Analysis (GEOBIA) was applied to WorldView-3 satellite imagery (2017) to create a detailed land cover map of an urban catchment area in Beijing. While land cover classification results based on a Bayesian machine learning classifier alone provided an overall land cover classification accuracy of 63%, the subsequent inclusion of a series of refining rules in combination with supplementary data (including elevation and parcel delineations), yielded the significantly improved overall accuracy of 76%. Results of the land cover classification highlight the limitations of automated classification based on satellite imagery alone and the value of supplementary data and additional rules to refine classification results. Catchment scale hydrologic modelling based on the generated land cover results indicated that 61 to 82% of rainfall volume could be captured for a range of 24 h design storms under varying degrees of Sponge City implementation. View Full-Text
Keywords: Sponge City; Low Impact Development; urban hydrology; SWMM; remote sensing; Geographic Object Based Image Analysis Sponge City; Low Impact Development; urban hydrology; SWMM; remote sensing; Geographic Object Based Image Analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Randall, M.; Fensholt, R.; Zhang, Y.; Bergen Jensen, M. Geographic Object Based Image Analysis of WorldView-3 Imagery for Urban Hydrologic Modelling at the Catchment Scale. Water 2019, 11, 1133.

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