How else can we assess exposure to vegetation and

green spaces?

Alexandra Chudnovsky

AIRO Lab

Department of Geography and Human Environment

School of Geosciences, Tel-Aviv University

Munich 2016: Exploring potential pathways linking greenness and green spaces to health

•Presentation outline

• Why to use satellite-derived index?

• Some basic RS concepts

• Satellite-derived vegetation data (modelling the regional scale)

• Field in-situ optical observation of the vegetation (modeling from the local scale to the regional)

GIS Data: still need pre-processing

Evaluation of the building layers. Buildings are delineated in black. On the left image inner spaces marked in

cyan. On the right image, greenhouses marked in cyan. All the cyan marked object were removed from the

original layer.

GIS Data: still need pre-processing

Categories according to USGS and NLCD

Energy-matter interactions in the

atmosphere, at the study area,

and at the remote sensor

detector

Jensen 2009

Energy recorded by remote

sensing systems undergoes

fundamental interactions that

should be understood to properly

interpret the remotely sensed

data.

Discrimination between surfaces vs spectral resolution

1 2 3

1 2 3 4

321

0.0

20.0

40.0

60.0

80.0

100.0

0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4

Wavelength in Microns

% R

efle

ctan

ce r

el. to

Hal

on

5 7

7654

HRVIR/HRG

TM-7

ASTER

Fe

Al-OH

Vegetation

C-O

kaolinite

grass

carbonate

goethite

8 9

4

Spectral Signature

Beyond the

human ability

Visible, NearIR and Middle IR Interactions

RGB vs False color composite

multi-spectral sensors: record energy over several separate wavelength ranges

NDVINormalized Difference Vegetation Index

DN4-DN3 is a measure of how

much chlorophyll absorption

is present, but it is sensitive

to cos(i) unless the difference

is divided by the sum

DN4+DN3.

3344

3344

3344

3344

333

444

)cos()cos(

)cos()cos(

)cos();cos(

rIrI

rIrINDVI

irIirI

irIirINDVI

irI

DNirI

DN

+

−=

+

−=

==

π

π

ππ

Biophysical Variables used in Environmental Studies• Vegetation: pigment concentration, biomass, foliar water content

• Temperature

• Soil moisture

• Surface roughness

• Evapotranspiration

• Atmosphere: aerosols concentrations, gaseous pollutants, temperature, water vapor, wind speed/direction, energy inputs, precipitation, cloud and aerosol properties

• BRDF

• Ocean: color, phytoplankton, chemistry

• Spatial: x,y, and potentially z

• Temporal: time the image was acquired

• Directional: sensor and sun angle

• Polarization: in RADAR

SATELLITE IMAGERY: different resolutions

• Landscape Scale: Landsat 7/ETM+ (30m)

• Sentinel (20m)

• Aster (30 m)

• Regional Scale: Terra/ Aqua platforms: MODIS (1000m, 500m, and 250m)

http://glovis.usgs.gov /

https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table

MODIS 1km (LST, LAI/FPAR, GPP/NPP, Reflectance, NDVI)

MODIS 500m water stress (NDSVI, LSWI)

MODIS 250m EVI, NDVI

L8,30

L8, 90m (LST)

Sentinel 20m

Freely available data

2015

2000 20032001 2005

-0.3 0.82007 2011

NDVI

MOD13A, First week of July

Baghdad

Baghdad

Baghdad Baghdad Baghdad

Baghdad Baghdad

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0

0.05

0.1

0.15

0.2

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

ndvi aodN

DV

I AO

D

Time series analyses of NDVI averaged over

Baghdad and surrounding cities

High resolution data require Image pre-processing (work flow)

• Radiometric correction -

atmospheric correction

• Geometric correction -

rectification & georeferencing

• Display & Enhancement -

Contrast stretching

• Information extraction –

image classification (supervised/unsupervised)

data mining, feature extraction, spectral

vegetation indices (SVIs)

• Analysis

outside RS/GIS, data staged in text files/excel from imagery

and analyzed in statistics package

Sentinel: spatial and spectral configuration (data since 2013)

Landsat: since 1972

RGB

False Color Composite (R:7,G:4,B:2-landsat5, R:7,G:5,B:3-landsat8)

Landsat 5

1999

Landsat 8

2015

Landsat 8

2013

Landsat 5

2011

Landsat 5

2009

Landsat 5

2006

Landsat 5

2003

NDVI (Vegetation Index)

Halab, Syria :Time series analyses

Cautions about NDVI

• Saturates over dense vegetation

• Less information than original data

• Any factor that unevenly influences the red and NIR reflectance will influence the NDVI

• such as atmospheric path radiance, soil wetness

• Pixel-scale values may not represent plant-scale processes

• Derivatives of NDVI (FAPAR, LAI) are not physical quantities and should be used with caution

Other vegetation indices:• Soil-adjusted Vegetation Index (SAVI)

• Soil and Atmospherically-Resistant Vegetation Index (SARVI)

• Moisture Stress Index (MSI or NDMI)- information on vegetation water content

• Enhanced Vegetation Index (EVI)

LLSAVI

redNIR

redNIR

++

−+=

ρρ

ρρ)1(

Where L is an adjustment factor for soil. Huete (1988) found the optimal value for L is 0.5

LRR

RRSARVI

rbNIR

rbNIR

++

−=

Huete and Liu, 1994

)1(2Re1

Re LLRCRCR

RREVI

BluedNIR

dNIR ++−+

−=

Huete and Justice, 1999

EVI has improved sensitivity to high biomass regions

http://www.harrisgeospatial.com/docs/BroadbandGreenness.html#Infrared

• Aerosol Free Vegetation Index (Karnieli et al. 2001)

the atmospheric resistant vegetation index (ARVI)

rbNIR

rbNIR

RR

RRARVI

+

−= )( redblueredrb RRRR −−= γ

Kaufman and Tanre, 1992. Atmospherically Resistant Vegetation Index (ARVI) for

EOS-MODIS. IEEE Trans. Geosci. Rem. Sen. 30(2):261-270.

More indexes….

VARI1 = (green – red)/(red + green)

red edge NDVI with two Sentinel-2 bands: 705 and 740nm:

RE NDVI1 = (NIR -705)/(NIR+705)

RE NDVI2 = (NIR -740)/(NIR+740) and certainly NDVI.

It’s also worth to use VARI with blue band (443 nm) that is atmospherically resistant :

VARI2 = (green – red)/(red + green-blue)

Select the best – for your study area and application

Landsat-based vegetation indexes: Tel-

Aviv and suburbs

SAVI

-0.7

0.5

-0.3

0.45

-0.1

0.4

Brightness

1.3

-0.1

NDVIGreenness Index

What are challenges we face when working with different spatial resolutions data?

We are able better to estimate different vegetation

types with increasing of spectral resolution

Red, blue and

green polygons

represent

different types of

vegetation

¯

0 300 600150Meters

¯

0 300 600150MetersSource: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus

DS, USDA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo,and the GIS User Community

¯

0 25 5012.5MetersSource: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus

DS, USDA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo,and the GIS User Community

¯

0 25 5012.5Meters

Mixed pixel

concept

¯

0 25 5012.5Meters

NDVI:

0.36 0.33 0.37

Air-Photo

Air-Photo

RGB image

RGB image

NDVI

image

0.28

1

0.29

Vegetation

1

A BC

Mixed Pixel Concept:

Mixed Energies

Build-up 0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.4 0.9 1.4 1.9 2.4

Wavelength, µm

Refl

ecta

nce

2Vegetation:

End-member

1

3

Concrete- end member

0.19

NDVIFalse colorTrue color

10 m

Sentinel

20 m

Sentinel

60 m

Sentinel

Spatial Resolution: decreasing the spatial resolution increase the contribution of

mixed pixels

10 m

Sentinel

20 m

Sentinel

60 m

Sentinel

NDVI above the same area sampled at different resolutions: small urban parks

NDVI

Count

Chudnovsky, A., and Lugassi, R. in progress

5

15 Mean= 0.69

10 m resolution

6

Mean= 0.64

20 m resolution

Mean= 0.59

Mean= 0.50

1

60 m resolution

30 m resolution

10 m 20 m 60 m 60 m20 m10 m

ND

VI

Densely populated Neighborhood with high

vegetation cover

How we can estimate the contribution of different land uses/coverages inside of a single pixel?

Linear Spectral Unmixing

Basic Assumptions:

• Spectral variation is caused by a limited number of surface materials (i.e. soil, water, shadow, vegetation)

• The pixel is a linear mixture of endmember constituents

• All endmembers possibly contained in the pixel have been included in the analysis

• A unique solution is possible if the number of spectral components DO NOT exceed the number of spectral bands +1.

Fi = F1 + F2 + ...+ FN =1i=1

N

∑

DNλ = F1DNλ,1 + F2DNλ,2 + ...+ FN DNλ,N + Eλ

0

0.1

0.2

0.3

0.4

0.5

0.6

0.45 0.95 1.45 1.95 2.45

90%concr10%veg 80%concr20%veg 70%concr30%veg

60%concr40%veg 50%concr50%veg 40%concr60%veg

30%concr70%veg 20%concr80%veg 10%concr90%veg

concr1 veg1

Wavelength, µm

Ref

lect

ance

LSU approximation/ Estimation

LSU

0

1

concrete vegetation RMSE

Spectral Angle Mapper

Field in-situ studies

Not only for Validation

Imaging Spectrometer Data of Healthy Green Vegetation in the San Luis Valley of

Colorado Obtained on September 3, 1993 Using AVIRIS

224 channels each 10 nm wide with 20 x 20 m pixels

High soot content settled on a leaf

Chemometric approach/ NIRS analyses

nn XAXAXAXAAY +++++= ......332211

where Y is the chemical constituent, A is an empirical coefficient, and X1-n are wavelengths.

Reflectance response of a single Magnolia leaf

(Magnolia grandiflora) to decreased relative water content

Demonstration of total absorption area of two vegetation samples. The

higher area (black) is of sample with low Cl and Na content whereas

lower area (red) is of sample with high content.

0.00

0.25

0.50

0.75

1.00

1750 1940 2130 2320

Ref

lect

ance

(C

R)

Wavelength (nm)

2235 nm

1840 nm straight line

absorption line

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

350 850 1350 1850 2350Wavelength, nm

Ref

lect

an

ce

No fertilization

With fertilization

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

350 850 1350 1850 2350Wavelength, nm

Ref

lect

an

ceNo fertilization

With fertilization

Wet Dry

NDI: 550-

686 nm

NDI: 850-

420 nmNDI: 700-

900 nmSlope: 686-

775 nmNDI: 550-

686 nm

NDI: 850-

425 nm NDI: 700-

900 nm

Slope: 470-

530 nm

Slope: 686-

775 nm

Shefer S. Israel A., Goldberg A., Chudnovsky, A. Botanica Marina 2016 12

12

xx

yySlope

−

−=

Dry1

Dry2Dry3Dry4

Dry5

Dry7Dry9

Dry11

Dry12Dry13

Dry15

Dry16

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

Measured (Log (Glc))

Pred

icte

d (

Lo

g (

Glc

))y=0.87x + 0.16

R2=0.88

-0.00020

-0.00015

-0.00010

-0.00005

0.00000

0.00005

0.00010

0.00015

-0.0010 -0.0005 0.0000 0.0005 0.0010

Dry 16

Dry 9

Dry 10

Dry 11

Dry 12

Dry 14

Dry 13Dry 15

Dry 7

Dry 5

Dry 3

Dry 4

Dry 1

Dry 8

Dry 6

Dry 2 PC1

PC

2

B-1000

-800

-600

-400

-200

0

200

400

600

800

400 900 1400 1900 2400

Wavelength, nm

A B

C

Reg

ress

ion

coeff

icie

nts

Lantana

Ficus Ibiscus

University

Ironi D

Shai Agnon Str

Independence

Garden

Park

HaYarkon

Lantana

Ibiscus

Ficus

1. Sampling vegetation (the

same type)

2. Measuring gravimetric

weight of “dusty” samples

vs cleaned

AISA-ES 2003

Fabric + Chrstolite (asbstos)

Fabric

Asbestos

Conclusions

To estimate several indexes for study region and select the best

suited

To select the most appropriate sensor for the studied application

Field validation is necessary. Validation using existing GIS

layers is also helpful

40% of

urban+

60% of

vegetation