Green areas: numerous positive effects for sustainable metropolis Satellite images : an easy way to map vegetation

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1 Green areas: numerous positive effects for sustainable metropolis Satellite images : an easy way to map vegetation Copernicus Training and Information Session 10 and 11 may 2017 in Lund, Sweden Dominique HEBRARD Copernicus EU Copernicus EU Copernicus EU

2 U r b Heat a n Island a gurban e n d Agenda a f o r for t hthe e EU EU Urban Urban Agenda for the UE Through the Urban Agenda for the EU, national governments, the European Commission, European institutions and other stakeholders will be working together for a sustainable, innovative and economically powerful Europe that offers a good quality of life. The green areas contribute of the quality of life for urban inhabitants.

3 B e n e fi t s of g Green r e e nareas a r ebenefits a s a rare e numerous numerous The followings positive effects should be taken into account : - limitation of floods - biodiversity conservation policies - inhabitant well-being and positive impacts on health - urban quality - cooling effect (urban heat island)

4 K n o w l e d g e o f g r e e n a r e a s f o r u r b a n p l a n n e r s The Urban vegetation is usually not well known in the current database especially in the private areas. It s an important lack of data for the public policies makers who need Inventory of the situation of their metropolis. They need also data to asses the efficiency of the strategies implemented.

5 H o w s a t e l l i t ehow d asatellite t a c a images n c o can n t rcontribute ibute to the k n o w l e d g e o f to g the r e eknowledge n a r e a of s?green areas? Most of the optical earth observation satellites have sensors that are able to map the vegetation. Vegetation is characterized by - high absorbtion on the red - low absorption on Near Infrared.

6 How satellite data can contribute knowledge of green areas? to the A simple vegetation indicator called NDVI (Normalized Difference Vegetation Index) enables to map in a simple way the vegetation. NIR - RED NDVI = NIR + RED J. W. Rouse. the vernal advancement and retrogradation of natural vegetation, Type ii report, NASA/GSFCT, Greenbelt, MD, USA, NIR : Near Infra-Red band RED : Red Band Other vegetation index combining RED and NIR exist as : l l l RVI (Ratio Vegetation Index) RVI = NIR / RED DVI (Difference Vegetation Index) DVI = NIR - RED

7 Which image are relevant for green areas? Of course channels red and infra-red are needed, but the resolution is also a key issue. Very High Resolution images are the best solution to map the vegetation, especially at the cadastral parcel level. - Pleiades (50 cm) fit to this need for urban areas - Sentinel 2 have a lower resolution (10 m). This lower resolution can fit for rural green areas and monitoring land consumption. Sentinel 2 are easy to get and it s possible to choose easily the best season. The season for the selected image has to be adpated, depending on the local climate. (eg: in France it should be good to select an image between march and september)

8 Copernicus sources for Green Vegetation Areasmapping Satellite data Copernicus Contributing Missions Optical missions with NIR and Red bands as Pléiades with Very High Resolution for urban areas, or RapidEye, SPOT with High Resolution... Sentinel-2 mission Full constellation will provide image with NIR (band8) and RED (band4) about every day at 10-20m spatial resolution Copernicus services Copernicus Global Service provides access to Pan-european High Resolution Layers of Forests and grassland maps from 20m resolution satellite imagery updated every 3 years.

9 Using satellite data to map green areas DEMONSTRATION Use of very high resolution optical satellite data to have a global view of Green areas in urban context.

10 Data used: Pléiades image acquired the 05-MAR-2014, orthorectified Software used for the demo: Qgis software is used Free and Open Source Geographic Information System Sentinel Application Platform (SNAP) Free Toolboxes made available by ESA can be used for similar manipulation with Sentinel2

11 > Open satellite data

12 Adapt radiometric parameters to see the landscape in true colors

13 Adapt radiometric parameters to see the landscape in true colors

14 > Zoom in the area of interest

15 > NDVI computation

16 > NDVI computation =NDVI=(PIR-R)/(PIR+R)

17 > NDVI computation

18 > Vegetation extraction

19 > Vegetation layer

20 > Vegetation layer : create a mask of vegetation Mask=1 if NDVI>0.3

21 > Vegetation vector layer

22 > Vegetation vector layer

23 > Vegetation vector layer

24 > Vegetation vector layer

25 > Vegetation vector layer

26 > Vegetation vector layer

27 > Vegetation area estimation

28 > Vegetation area estimation

29 Outlook : a step towards more sophisticated products Contribution of Pléiades-HR images to the assessment of urban green infrastructures : dealing with urban ecological network issues and urban densification ecological network Area with vegetation trees roughness coefficient Vegetation : trees Increase of the roughness ceof. Depending on the buidings and roads Source : Pauline Crombette (LISST CIEU, CNES)

30 Thank you for your attention!