% Reflectance rel. to Halon

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

3 2 1

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

% Reflectance rel. to Halon

5 7

7 6 4 5

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

NDVI

Normalized 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.

3 3 4 4

3 3 4 4

3 3 4

4

3 3 4

4

3 3 3

4 4 4

) cos(

) cos(

) cos(

) cos(

) cos(

);

cos(

r I r I

r I r NDVI I

i r I i r I

i r I i r NDVI I

r i DN I

r i DN I

+

= −

+

= −

=

=

π π π π

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 2001 2003 2005

-0.3 0.8

2007 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

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

ndvi aod

NDVI AOD

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)

L L SAVI

red NIR

red NIR

+ +

+ −

= ρ ρ

ρ ) ρ

1 (

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

L R

R

R SARVI R

rb NIR

rb NIR

+ +

= −

Huete and Liu, 1994

) 1

(

2 Re

1

Re L

L R

C R

C R

R EVI R

Blue d

NIR

d

NIR +

+

− +

= −

Huete and Justice, 1999

EVI has improved sensitivity to high biomass regions

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

ed

• Aerosol Free Vegetation Index (Karnieli et al. 2001) the atmospheric resistant vegetation index (ARVI)

rb NIR

rb NIR

R R

R ARVI R

+

= − R_rb = R_red −

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 Greenness Index NDVI

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

¯

0 150 300 600

Meters

¯

0 150 300 600

Meters

Source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community

¯

0 12.5 25 50

Meters

Source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community

¯

0 12.5 25 50

Meters

Mixed pixel concept

¯

0 12.5 25 50

Meters

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 B

C

Mixed Pixel Concept:

Mixed Energies

Build-up ^0.050

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

Reflectance

2

Vegetation:

End-member 1

3

Concrete- end member

0.19

NDVI False color

True 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 10 m 20 m 60 m

NDVI

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.

F

= F

+ F

+ ... + F

= 1

i=1 N

∑

DN

= F

DN

+ F

DN

+ ... + F

DN

+ E

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

Reflectance

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

n n

X A X

A X

A X

A A

Y = +

+

+

. + ... +

where Y is the chemical constituent, A is an empirical coefficient, and X_1-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

Reflectance (CR)

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 2350

Wavelength, nm

Reflectance No fertilization

With fertilization

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

350 850 1350 1850 2350

Wavelength, nm

Reflectance

No fertilization With fertilization

Wet Dry

NDI: 550- 686 nm

NDI: 850- 420 nm

NDI: 700- 900 nm

Slope: 686-

775 nm NDI: 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 _{2 1}

1 2

x x

y Slope y

−

= −

Dry1

Dry2Dry4Dry3 Dry5

Dry7Dry9

Dry11

Dry12 Dry13 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))

Predicted (Log (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 Dry9 Dry10

Dry11 Dry12 Dry14

Dry13 Dry15

Dry7

Dry5 Dry3 Dry4

Dry¹

Dry8

Dry6

Dry2 PC1

PC2

B

-1000 -800 -600 -400 -200 0 200 400 600 800

400 900 1400 1900 2400

Wavelength, nm

A B

C

Regression coefficients

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