General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.
Users may download and print one copy of any publication from the public portal for the purpose of private study or research.
You may not further distribute the material or use it for any profit-making activity or commercial gain
You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.
Downloaded from orbit.dtu.dk on: Dec 05, 2020
Remote sensing estimates of impervious surfaces for hydrological modelling ofchanges in flood risk during high-intensity rainfall events
Kaspersen, Per Skougaard; Fensholt, Rasmus; Drews, Martin
Publication date:2014
Document VersionPeer reviewed version
Link back to DTU Orbit
Citation (APA):Kaspersen, P. S., Fensholt, R., & Drews, M. (2014). Remote sensing estimates of impervious surfaces forhydrological modelling of changes in flood risk during high-intensity rainfall events. Poster session presented atIARU Sustainability Science Congress, Copehagen, Denmark.
Per Skougaard Kaspersen1, Rasmus Fensholt2, Martin Drews1 1Technical University of Denmark (DTU), 2University of Copenhagen (KU)
INTRODUCTION
References - Angel, S., Parent, J., Civco, D. L., Blei, A., & Potere, D. (2011). The dimensions of global urban expansion: Estimates and projections for all
countries, 2000–2050. Progress in Planning, 75(2), 53–107. doi:10.1016/j.progress.2011.04.001
- Arnold, C. L., & Gibbons, C. J. (1996). Impervious Surface Coverage: The Emergence of a Key Environmental Indicator. Journal of the
American Planning Association, 62(2), 243–258. doi:10.1080/01944369608975688.
- IPCC (2012). Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working
Groups I and II of the Intergovernmental Panel on Climate Change [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D.
Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, UK, and
New York, NY, USA, 582 pp.
- Weng, Q. (2012). Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends. Remote Sensing of
Environment, 117, 34–49. doi:10.1016/ j.rse.2011.02.030.
CONCLUSION
As major European urban areas are almost exclusively characterized by a combination of impervious
surfaces and green vegetation, information on vegetation cover from remote sensors can be utilised to
provide accurate and cost-efficient estimates of the quantity and spatial distribution of impervious surfaces
and changes herein. Such information is useful for a wide range of applications including analysis of the
importance of urbanisation for the exposure of cities towards the occurrence and impacts of climate
extremes, such as flooding during high-intensity rainfall events.
METHODOLOGY
Information on the extent and quantity of vegetation cover
is used to provide estimates of sub-pixel imperviousness
for several urban areas in Europe. This is based on the
assumption of a strong inverse relationship between
vegetation cover and impervious surface, i.e. it is implicitly
assumed that non-impervious surfaces within urban areas
are covered with green vegetation.
Three different Landsat-based datasets (NDVI, SAVI and
fractional vegetation cover) have been developed based
on information on vegetation cover from the Landsat 8
sensor. Eight cities are analyzed in the following,
representing the major vegetative and climatic conditions
in Europe. The accuracy and spatial transferability
potential of the three methods are evaluated at 30m
spatial resolution. Landsat satellite imagery is publicly
available and covers the period from 1972 and onwards. IMPERVIOUS SURFACE MAPPING
Remote sensing estimates of impervious surfaces for hydrological modelling of changes in flood risk during high-intensity rainfall events
Abstract
Impervious surfaces (IS) such as road infrastructure,
buildings and other paved areas typically dominate urban
environments (Weng, 2012) and subsequently are often
used as an indicator of urbanisation (Angel et al., 2011).
IS may generally be defined as man-made surfaces
through which water cannot infiltrate. The quantity and
location of impervious surfaces within urban areas are
important for the hydrological response during high-
intensity rainfall as it affects the amount and velocity of
run-off, and consequently influences the exposure of
cities towards flooding (Arnold and Gibbons, 1996). For
this reason past and present city development patterns
may prove to have had (and will continue to have)
important implications for the exposure of urban systems
to the risk of flooding. At the same time, climate change is
expected to increase the frequency and intensity of
extreme rainfall events in many locations and thus further
increase the exposure of cities to flooding
(Intergovernmental Panel on Climate Change, 2012).
Increased knowledge of the importance of both urban
land cover changes and climate change for the risk of
urban areas towards flooding will provide substantial
insight to city administrations and governments in how to
plan for future climate proof cities.
This paper addresses the accuracy and applicability of
medium resolution (MR) remote sensing estimates of
impervious surfaces (IS) for urban land cover change
analysis. Landsat-based vegetation indices (VI) are found
to provide fairly accurate measurements of sub-pixel
imperviousness for urban areas at different geographical
locations within Europe, and to be applicable for cities with
diverse morphologies and dissimilar climatic and
vegetative conditions. Detailed data on urban land cover
changes can be used to examine the diverse
environmental impacts of past and present urbanisation,
including the importance of such changes for the exposure
of cities towards the occurrence and impacts of climate
extremes like high-intensity rainfall events.
0
20
40
60
80
100
0 20 40 60 80 100
Oslo
0
20
40
60
80
100
0 20 40 60 80 100
Exeter
0
20
40
60
80
100
0 20 40 60 80 100
Oslo
0
20
40
60
80
100
0 20 40 60 80 100
Exeter
0
20
40
60
80
100
0 20 40 60 80 100
Hamburg
0
20
40
60
80
100
0 20 40 60 80 100
Odense
0
20
40
60
80
100
0 20 40 60 80 100
Hamburg
0
20
40
60
80
100
0 20 40 60 80 100
Odense
0
20
40
60
80
100
0 20 40 60 80 100
Strasbourg
0
20
40
60
80
100
0 20 40 60 80 100
Nice
0
20
40
60
80
100
0 20 40 60 80 100
Strasbourg
0
20
40
60
80
100
0 20 40 60 80 100
Nice
0
20
40
60
80
100
0 20 40 60 80 100
Vienna
0
20
40
60
80
100
0 20 40 60 80 100
Barcelona
0
20
40
60
80
100
0 20 40 60 80 100
Vienna
0
20
40
60
80
100
0 20 40 60 80 100
Barcelona
Refe
ren
ce IS
(%
)
NDVI SAVI Linear Perfect fit
Landsat-based imperviousness (%)
Ob
serv
ed im
pe
rvio
usn
ess (
%)
NDVIHigh : 0.8
Low : 0
Impervious surface (%)
High : 100
Low : 0
Obs. imperviousness - high-resolution imagery
Vegetation - Landsat 8 satellite imagery
Reg
ress
ion
mo
del
ling
¯¯
¯ ¯
STUDY AREAS
Mean bias error (%)
Mean absolute error (%)
ACCURACY ASSESSMENT DATA & ANALYSIS PROCEDURES
Urban sub-areas included in the analysis
IMPACT OF URBANISATION ON FLOOD RISK (ongoing work) OVERVIEW OF FLOOD MODEL SIMULATIONS LAND COVER CHANGE ANALYSIS
Change in imperviousness- City of Odense 1984-2014
-79.9 - -60
-59.9 - -40
-39.9 - -20
-19.9 - -10
-9.9 - 10
10.1 - 20
20.1 - 40
40.1 - 60
60.1 - 80
80.1 - 100
-100 - -80
Absolute
change (%)
FLOOD HAZARD MAPPING
8.6 8.1
0
5
10
15
20
0-30 30-60 60-90 90-100 Mean
Observed imperviousness (%)
NDVI
SAVI
FR
-0.5
0.4
-5
0
5
10
15
0-30 30-60 60-90 90-100 Mean
Observed imperviousness (%)
NDVI
SAVI
FR
Flooding in the city of Odense during high-intensity rainfall occurring once
every 100 years (RP100)
Flood risk with urban
land cover as in 1984
Change in flood risk due to
urbanisation (1984-2014)
Change in flood risk due
to climate change (RCP
8.5, ≈ +3.5C)
Maps are produced by VCS Denmark for the municipality of Odense
10 19 9