KTH ROYAL INSTITUTEOF TECHNOLOGY

ESA DUE INNOVATOR III: EO4UrbanMultitemporal Sentinel-1A SAR & Sentinel-2A MSI Data

for Global Urban Services

1KTH Royal Institute of TechnologyStockholm, Sweden

2University of Pavia, Italy

Yifang Ban1 and Paolo Gamba2

ESA EOEP DUE Program

The Data User Element (DUE) is a programmatic component of the Earth Observation Envelope Programme (EOEP), an optional programme of the European Space Agency, currently subscribed by 18 ESA Member States.

The DUE mission is to favour the establishment of a long-term relationship between the User communities and Earth Observation. It is a continuation on a larger scale of the Data User Programme (DUP).

ESA DUE Innovator Program

ESA DUE Innovator III Program

ESA DUE Innovator Program

ESA DUE Innovator Program

ESA DUE Innovator III Projects

Global Urban Services

Team KTH Royal Institute of Technology, SwedenUniversity of Pavia, Italy

Users Stockholm County Administration, SwedenNational Geomatics Center, China

ESA DUE Innovator III Projects

http://due.esrin.esa.int/page_news321.php

Global Urbanization Trend In 2008, more than 50% of the world population live in cities. By 2050, the world is expected to add an additional 2.5 billion

urban dwellers; with nearly 90 percent of the increase concentrated in Asia and

Africa. (United Nations, 2014).

Shanghai, 1979

Shanghai, 2010

Environmental Consequences

High concentrations of aerosols, exhaust gases, pollution and dust Hazardous to health Increased smog, haze, fog, clouds

Source: The Associated Press

Source: Suicup via Wikimedia Source: zmescience.com

Environmental Consequences

Source: BBC News Source: www.theatlanticcities.com

Source: rendezvous.blogs.nytimes.com

Paved surfaces -> rainfall water -> flooding– Urbanization results in more impervious surfaces, thus

reducing the area where infiltration to ground water canoccur. Thus, more storm water runoff occurs.

– 79 people died in July 2012 Beijing flooding

Environmental ConsequencesUrban heat island (UHI) and heat waves UHI – urban air temperatures higher than surrounding rural areas. The average air temperature in a city with 1 million inhabitants is 1-

3 degrees warmer. Heat waves: In the afternoon, the difference can be 12 degrees

warmer, no night time cooling. Death rate increases during heat waves.

Environmental Impact

The growth of urban areas & subsequent transportationnetworks generates a host of environmental impacts

Deforestation, habitat fragmentation and loss of biodiversity Loss of high quality farmland Contamination of Lakes and other waterways Increases in fossil fuel consumption & emissions of greenhouse

gases

Derived from optical data (TM, MERIS, MODIS, etc.)

Data gaps: difficulties to acquire images in appropriate seasons

Information gaps: confusions among various classes such as baresoil and builtup areas, and among vegetation classes

Existing Global Urban Data in GLC Products

Existing Global Urban Data in GLC Products

Spaceborne SAR Systems

SEASATNASA/JPL (USA)

L-Band, 1978

ERS-1European Space Agency (ESA)

C-Band, 1991-2000

SIR-C/X-SARNASA/JPL, L- and C-Band (quad)

DLR / ASI, X-bandApril and October 1994

J-ERS-1Japanese Space Agency (NASDA)

L-Band, 1992-1998

RadarSAT-1Canadian Space Agency (CSA)

C-Band, 1995-today

ERS-2European Space Agency (ESA)

C-Band, 1995-today

Shuttle Radar Topography Mission (SRTM)NASA/JPL (C-Band), DLR (X-Band)

February 2000

ENVISAT / ASAREuropean Space Agency (ESA)

C-Band (dual), 2002-today

ALOS / PALSARJapanese Space Agency (NASDA)

L-Band (quad), 2004

TerraSAR-XGerman Aerospace Center (DLR) / Astirum

X-Band (quad), 2005

RadarSAT-IICanadian Space Agency (CSA)

C-Band (quad), 2005

SAR-LupeBWB, GermanyX-Band, 2005

Range / Azimuth Resolution

10m 3m 1m 10cm

ENVISAT / ASAR

ERS 1&2 RADARSAT 1 RADARSAT 2

ALOS SENTINEL-1 Cosmo-Skymed SAR-Lupe

HJ-1-C TerraSAR-X

FGAN - PAMIR

ONERA - RAMSES

ONERA - SETHI

TerraSAR-X

Urban Extraction: ENVISAT ASAR Data

Beijing Berlin Jakarta Lagos Mexico City

Mumbai New York City Rio de Janeiro Stockholm Sydney

Average values Kappa Overall Accuracy Std. Dev. Comission Omission

KTH - UNIPV 0,707 85,36% 4% 5,47 23,75

GlobCover 0,471 72,67% 13% 17,10 40,47

MODIS 500 0,525 76,31% 11% 20,03 31,12

Ban, Y. and A. Jacob & P. Gamba, 2014. Spaceborne SAR Data for Global Urban Mapping at 30m Resolution Using a Robust Urban Extractor. ISPRS J. of Photogrammetry & Remote Sensing

KTH-Pavia vs. MODIS 500 & GlobCover

KTH‐Pavia Modis 500 Glob Cover false colorx x x redx o x bluex x o bluex o o yellowo x x greeno o x greeno x o green

Beijing 2009 Lagos 2010

Sentinel-1A SAR & -2A MSI Data

EO4Urban: Objectives

The overall objective is to evaluate multi-temporalmulti-resolution Sentinel-1A SAR and Sentinel-2AMSI data for developing a pilot global urbanservices based on user requirements to supportsustainable urban development.

The Stockholm County Administrative Board's Challenges

Very high demand for housing and regional development

VSNational interests for sustainable developmentclimate impactInfrastructureGreen structure and biodiversity

Stockholm County User Needs

Up-to-date, accurate information on urban development in the region (Urban Structures map).• No viable way to see what areas are being

developed after approved planning.• Construction process is often several years.• Up to two years delay for finished

construction to be registered in the national database.

Temporal information regarding green structure• SCAB is charged with maintaining the biodiversity and

oversee green structure• Mapping green structure and monitoring changes• No accurate biotope database exists for the region• Lots of field work, lots of manual image interpretation

Stockholm County User Needs

Accurate spatiotemporal information on waterbodies and their developments.• Climate change is a very important factor in

sustainable planning.• We lack good information on impervious

surface classes for flooding analysis.• We lack a good information on soil moisture for

risk analysis. • The coastline today is derived from Swedish

National Land Survey data and provides staticmaps. We wish to follow the developments.

Stockholm County User Needs

National Geomatics Center of China User Needs

Study Areas

Sentinel-1A SAR Data

Study area Image Date Image Type Orbit Type Polarization Incidence Angle

Bei jing 2015‐05‐12 IW  DSC  VV IW(1‐2)

Jakarta 2015‐05‐12 IW  DSC  VV IW(1‐2)

Mexico 2015‐05‐15 IW  DSC  VV IW(3)

Milan 2015‐03‐10 SM  DSC HH/HV ~ 35° (S4)

2015‐03‐11 SM  ASC HH/HV ~ 43° (S6)

Stockholm 2015‐05‐16 IW  DSC  VV IW(2‐3)

ENVISAT ASAR: NanchangSentinel-1A SAR: Milan

Sentinel-1A SAR: Nanchang

Sentinel-1A SAR DataStockholm Beijing

Date Orientation Polarization2014-10-08 Descending VV2014-10-22 Ascending VV2015-04-18 Descending VV2015-05-02 Ascending VV2015-05-12 Descending VV2015-05-24 Descending VH VV2015-05-26 Ascending VV

Date Orientation Polarization2014-10-12 Descending VH VV2014-10-31 Descending VH VV2015-04-10 Ascending VH VV2015-04-22 Ascending VH VV2015-05-16 Ascending VH VV2015-05-23 Descending VH VV

Methodlogy

36

Methodology

Methodology

Ban, Y. and A. Jacob, 2013. Object-based Fusion of Multitemporal Multi-angle ENVISAT ASAR and HJ-1 Multispectral Data for Urban Land-Cover Mapping. IEEE Transaction on GeoScience and Remote Sensing, Vol. 51, No. 4, pp. 1998-2006.

KTH-SEG: Stepwise Example

Preliminary Results

Study area Observation Image Overall Accuracy Urban Precision

Milan (1 image) SM ASC HH 81.81% 69.9%

Milan (dual pol) SM ASC HH & HV 78.57% 55.1%

Milan (2 images) SM ASC & DSC HH 83.21% 79.5%

Stockholm IW ASC VV 77.29% 52.3%

Preliminary Results

2015‐06‐18 4035th  EARSeL Symposium, Stockholm

Preliminary Results

Preliminary Results

Preliminary Results

Preliminary Results

Preliminary Results

2015‐06‐15 4535th  EARSeL Symposium, Stockholm

Preliminary Results

2015‐06‐15 4635th  EARSeL Symposium, Stockholm

Preliminary ResultsChengdu 1998 -> 2003 ->2008->2011

Results: Stockholm

2015-06-15 48EARSEL SYMPOSIUM 2015 STOCKHOLM

Results: Stockholm

KTH-SEG (2015) Urban Atlas (2010)

Results: StockholmName Testing Urban AtlasWater 97,6 83Roads 36,5 17,6

High Density Builtup 72,3 31,7Low Density Builtup 59,4 53,8

Golf course 89,3 29,5Forest 81 61,9

Urban Green Structure 22,5 20,2Agricultural Open Land 77,7 37,6

Airport 55,8 36,4Average Accurracy 65,8 41,3

KTH‐SEG Urban Classification Stockholm

Confustion Matrix with Producer's and User's Accuracy in % 

Name ID 1 2 3 4 5 6 7 8 9 UserWater 1 97,6 0 0 0 0,5 0 0 0 1,8 97,6Roads 2 0 36,5 6 34,2 4,9 2,6 9,4 4,2 2,2 36,5

High Density Builtup 3 0 7,4 72,3 18,7 0 0 1,6 0 0 72,3Low Density Builtup 4 0 4,1 3,6 59,4 0,2 19 12,7 1,1 0 59,4

Golf course 5 1,2 3,5 0 0,2 89,3 0,2 1,3 0,2 4,1 89,3Forest 6 0 0,2 0 10,1 0 81 8,7 0 0 81,0

Urban Green Structure 7 0 21,4 1,4 25,1 7,3 18,9 22,5 1,2 2,2 22,5Agricultural Open Land 8 0 8,4 0 3,6 2,1 0,1 1,5 77,7 6,7 77,7

Airport 9 0 4 0 4,1 33 0 0,2 2,9 55,8 55,8Producer's Acc. 100,0 14,0 79,5 64,0 60,0 83,4 10,2 98,6 14,9

Results: Beijing

2015-06-15 51EARSEL SYMPOSIUM 2015 STOCKHOLM

Results: Beijing

Sentinel-1A SAR Data are promising for urbanextent extraction, especially when multiple images intwo orbit orientations were used, the urbanextraction accuracies were significantly improved.

The preliminary results also show higher resolutionSM mode to be advantageous.

Further research and testing will be performed toevaluate and compare multitemporal Sentintel-1ASM and IW data in dual polarization in bothascending and descending orbits for urban extentextraction when more data become available

Conclusions

Conclusion

Sentinel-1A IW mode imagery showed promising potential for urban classifications.

However, Sentinel-1A IW images lack the spatial resolution required for detailed urban land cover classification

Future Research

Fusion of Sentinel-1A SAR and Sentinel-2A MSI data

Improved implementation of KTH-Pavia Urban Extractor and KTH-SEG for parallel processing environments (GPU & cluster computing)

Multi-scale segmentation & multi-level classification