Jan Haas (jhaas@abe.kth.se) and Yifang Ban, Division of Geoinformatics, Department of Urban Planning and Environment

KTH Royal Institute of Technology, Stockholm, Sweden

The objective of this study is to evaluate the potential use and synergetic effects of

novel ESA Sentinel-1A C-band SAR and Sentinel-2A MSI data for mapping of

ecologically important urban and peri-urban space at medium to high spatial

resolutions. The combined use of Sentinel-1A SAR in Interferometric Wide Swath

mode and simulated Sentinel-2A MSI data is being evaluated in classification of an

urban area over Zürich, Switzerland. Based on the outcome from fused, segmented

and SVM-classified data, landscape metrics and ecosystem services are used to

characterize ecosystem service provision and demand budgets, drawing spatial and

topological considerations of service providing landscape patches into account.

INTRODUCTION

Zürich is the largest city in Switzerland with an increasing urban population of over

400,000 inhabitants and about 1.8 million people residing in the metropolitan area.

The city centre lies about 400 m above sea-level in the temperate climate zone. The

living quality in Zürich is considered high with noticeable progress in terms of

environmental quality over the past two decades. Major urban classes are continuous

and discontinuous urban fabric, industrial/commercial areas, the infrastructural

road/railroad network including Zürich airport, construction sites, green urban

spaces, sports/leisure facilities and allotments. The urban hinterland is characterized

by Lake Zürich, agricultural land and forest.

Synergies of Sentinel-1A SAR and Sentinel-2A MSI Data for

Urban Ecosystem Mapping

METHODOLOGY

characteristics of natural green and blue land cover patches that are expected to alter ecosystem service provision capacities were derived under these following assumptions:

Figure 3. Classification result

• The combined use of Sentinel-1 SAR and simulated Sentinel-2 MSI data has

proven effective in urban land cover and ecosystem services mapping and

continued use of the data is endorsed for future ecological and urban applications.

• Spatial characteristics and topological aspects of landscape patches that are believed

to influence the provision of ecosystem services have been integrated to the

concept of ecosystem service supply and demand budgets.

RESULTS AND DISCUSSION

CONCLUSIONS

Range-Doppler

Terrain Correction

Sentinel-1 IW Level-1

GRD

VH and VV Intensity

2015-03-16

Simulated Sentinel-2

level 1c product (APEX)

RGB, NIR and SWIR

2011-06-26

5x5 adaptive Lee

Speckle filtering

Coregistration, RMSE X/Y

<0.5 pixels, CH1903

Resampling (8-bit,

2% tail-trim for S1, 10m2)

Canny Edge Detection

Accuracy Assessment

Continuity

Area

Perimeter-to-area

Distance

Ecosystem Service Budgets

(accounting for space/topology)

Final

classification

14 classes

Ecosystem Service Budgets

(Burkhard et al., 2012)

Segmentation with KTH-SEG

Object-based SVM Classification

Class aggregation and post-

classification

STUDY AREA AND DATA

Figure 2. Methodology Flowchart

Figure 1. RGB true-colour-composite of a Sentinel-2A APEX scene from June 2011 (left) and

Sentinel-1A 5x5 adaptive Lee speckle filtered intensity data from March 2015 (VV-VH-VV) (right)

For this study, a 16x22 km2 Sentinel-2A scene, simulated with high resolution

airborne imaging spectrometer (APEX) data and including all spatial and spectral

characteristics corresponding to a Sentinel-2 level 1c product dating from 2011-06-

26 was used in combination with Sentinel-1A C-band SAR IW mode data as a

Level-1 GRD product from 2015-03-16.

This research is supported by a grant from FORMAS. The research is also part of

the project “Satellite Monitoring of Urbanization for Sustainable Urban

Development” within the European Space Agency (ESA) and the Chinese

Ministry of Science and Technology (MOST) Dragon III program.

ACKNOWLEDGEMENT

The classification into twelve urban and peri-urban classes resulted in an overall

accuracy of 79.81% with a Kappa coefficient of 0.78 indicating the suitability of

the chosen classification method and underlying data. Largest confusions occurred

between construction sites and industrial/commercial areas and between the built-

up classes discontinuous urban fabric, sport and leisure facilities and between

airport runways and roads. The classified image was sieve-filtered and agriculture

and mixed forest classes were transformed into urban green spaces and urban

forests, respectively under an urban mask. From the final classification, four spatial

• Distance: Increased ecosystem service provision of patches close to urban dwellers.

• Perimeter-to-area: The lower the ratio, the higher the service provision because less

edge is shared with other classes and patch centres are more pristine.

• Area: Larger patches are capable of providing more services.

• Contiguity: High patch connectivity and less fragmented landscapes are considered

beneficent in several ways, e.g. for species dispersal or recreational purposes.

Figure 4. Spatial patch characteristics influencing ecosystem service provision

Figure 5. Ecosystem service budgets

Ecosystem service budgets were then

generated by modifying the supply

values initially presented in Burkhard et

al. (2012).

REFERENCE

Burkhard, B., Kroll, F., Nedkov, S. & Müller, F. (2012). Mapping ecosystem service

supply, demand and budgets. Ecological Indicators 21:17–29.