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Gradients in urban material composition: A new concept to map cities with spaceborne imaging spectroscopy data

Jilge, Marianne and Heiden, Uta and Neumann, Carsten and Feilhauer, Hannes (2019) Gradients in urban material composition: A new concept to map cities with spaceborne imaging spectroscopy data. Remote Sensing of Environment, 223, pp. 179-193. Elsevier. doi: 10.1016/j.rse.2019.01.007. ISSN 0034-4257.

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Official URL: https://www.sciencedirect.com/science/article/pii/S0034425719300070?via%3Dihub


To understand processes in urban environments, such as urban energy fluxes or surface temperature patterns, it is important to map urban surface materials. Airborne imaging spectroscopy data have been successfully used to identify urban surface materials mainly based on unmixing algorithms. Upcoming spaceborne Imaging Spectrometers (IS), such as the Environmental Mapping and Analysis Program (EnMAP), will reduce the time and cost-critical limitations of airborne systems for Earth Observation (EO). However, the spatial resolution of all operated and planned IS in space will not be higher than 20 to 30 m and, thus, the detection of pure Endmember (EM) candidates in urban areas, a requirement for spectral unmixing, is very limited. Gradient analysis could be an alternative method for retrieving urban surface material compositions in pixels from spaceborne IS. The gradient concept is well known in ecology to identify plant species assemblages formed by similar environmental conditions but has never been tested for urban materials. However, urban areas also contain neighbourhoods with similar physical, compositional and structural characteristics. Based on this assumption, this study investigated (1) whether cover fractions of surface materials change gradually in urban areas and (2) whether these gradients can be adequately mapped and interpreted using imaging spectroscopy data (e.g. EnMAP) with 30 m spatial resolution. Similarities of material compositions were analysed on the basis of 153 systematically distributed samples on a detailed surface material map using Detrended Correspondence Analysis (DCA). Determined gradient scores for the first two gradients were regressed against the corresponding mean reflectance of simulated EnMAP spectra using Partial Least Square regression models. Results show strong correlations with R2 = 0.85 and R2 = 0.71 and an RMSE of 0.24 and 0.21 for the first and second axis, respectively. The subsequent mapping of the first gradient reveals patterns that correspond to the transition from predominantly vegetation classes to the dominance of artificial materials. Patterns resulting from the second gradient are associated with surface material compositions that are related to finer structural differences in urban structures. The composite gradient map shows patterns of common surface material compositions that can be related to urban land use classes such as Urban Structure Types (UST). By linking the knowledge of typical material compositions with urban structures, gradient analysis seems to be a powerful tool to map characteristic material compositions in 30 m imaging spectroscopy data of urban areas.

Item URL in elib:https://elib.dlr.de/126601/
Document Type:Article
Title:Gradients in urban material composition: A new concept to map cities with spaceborne imaging spectroscopy data
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Heiden, UtaUNSPECIFIEDhttps://orcid.org/0000-0002-3865-1912UNSPECIFIED
Journal or Publication Title:Remote Sensing of Environment
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:pp. 179-193
Keywords:EnMAP Gradient analysis Imaging spectroscopy Urban Surface materials
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface Dynamics
Deposited By: Jilge, Marianne
Deposited On:11 Mar 2019 12:35
Last Modified:03 Nov 2023 09:25

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