[Vegetation and Remote Sensing] Vegetation

7/29/2019 [Vegetation and Remote Sensing] Vegetation

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Botanical Institute, BB209 H2002

Vegetation

Vegetation and Remote Sensing

Botanical Institute, BB209 - H2002

Vegetation

Leaf-radiation interference

Factors affecting reflectance Spectral signature

Vegetation indices

Leaf Area Index

Global vegetation monitoring

Botanical Institute, BB209 -H2002

Vegetation and RS

Inter- and intra-annual global vegetation monitoring on a periodic basis

Global biogeochemical, climate and hydrological modeling

Net primary production and carbon balance

Anthropogenic and climate change detection

Agricultural activities (plant stress, harvest yields, precisionagriculture)

Famine early warning systems

Drought studies

Landscape disturbance (volcanic, fire scars, etc.)

Land cover and land cover change products

Biophysical estimates of vegetation parameters (% cover, LAI,fAPAR)

Public health issues (rift valley fever, mosquito producing rice fields)

Ability to map the presence, amount, type, condition etc. ofvegetation


The plant leaf

Vegetation interferes with

sunlight in several ways

in areas of high vegetation

density the leaf is the most

characteristic and influential

component of this

interference

highly specialised structure

adapted to photosynthetic

activity


The Plant Leaf

four different layers upper and lower epidermis

stomatalcells, secures gasexchange (CO2, O2)

largely transparent to incidentPhotosynthetically ActiveRadiation (PAR; 0.4-0.7m).

a layer of elongatedparenchymatic cells

chloroplasts do the solar energyabsorption

a layer of spongy mesophylliccells

large air filled volumespredominate between cells

hydrated mesophyll cellscontribute water tophotosynthesis

Schematic cross section of typicalcitrus

leaf (after Harris1987)


Leaf reflectance

The function (photosynthesis)

and structure of the leaf

generate a special spectral

signature

Chlorophyll absorption

Leaf reflectance


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Leaf reflectance

Three zones in leaf reflectance spectrum

1. PAR, high absorption (sub-regions a-c)

very high leaf absorption (pigments of chlorophyll a and b &

caretenoids in the chloroplasts)

Red wavelengths (0,62-0,70 m) strongest contrast to soil reflection

due to high chlorophyll absorption.

high scatter of the PAR give pigments multiple chances to absorb the active

wavelengths

High scatter is due mainly to

differences in the refractive index

between the air spaces(1.0),

hydrated cells (1.4), and the irregular

facets of the exteriors of cells


Leaf reflectance

2. near-IR (0,74-1,1 m)

reflectance very high/ absorption minimal

scattering amplifies the spectral reflectance, especially for dense

canopies.

0,79-0,90 m avoids atmospheric water vapour absorption

most appropriate for monitoring vegetation

reflectance can reach fifty per cent on the IR plateau

level of IR plateau depends on the internal structure of the leaf.

The level increases with the number of layers of cells, their size

and the orientation of cell walls (Guyot and Riom 1988)

3. mid-IR ( 1.3-2.5 m)

high absorption due to the liquid water of the mesophyllic cells.


Leaf reflectance

soil reflectance

visible and near-IR:increases steadily

over wavelengths;

mid-IR:oscillates like, but above

reflectance from vegetation.

best regions to obtain vegetation

information separately from background

information

Red (LOW) and the near-IR(HIGH)

(0,79-0,90m)

closely connected to chlorophyll density

and green leaf density respectively

(Tucker and Sellers 1986).

RED NIR


RGB color composite

A multi-spectral,optical satellite imageis usually displayed asa false colourcomposite of threelayers

Blue layer: greenchannel

green layer: redchannel

red layer: IRchannel

this combination ofhigh values in the redlayer and low valuesin the green layergives vegetation a redcolour.

SPOT XS, july 1986


Vegetation indices

quantitative measurements indicating vigour ofvegetation

Better sensitivity than ind. Spectral bands for detectionof biomass

Ideal VI: the index should be particularly sensitive to

vegetative covers, insensitive to soil brightness,insensitive to soil color, little affected by atmosphericeffects, environmental effects and solar illuminationgeometry and sensor viewing conditions (Jackson etal., 1983)

I.e. Other factors than leaf reflectance are ofimportance!

Biological domain/internal

Physical domain/external

RED

NIR

SAVI


Biological domain

several other parameters can changevegetation canopy reflectance even ifthe leaf hemispherical reflectanceremains constant, or vice versa(Colwell, 1974)

Optical properties of vegetation Indivdual level

Species level

Physiognomy and phenology

Temporal changes

Water content, age, mineral deficiency,parasitic attacks,

Vegetation parameter

VI

Vegetation parameter

VI


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Vegetation canopy reflectance

Leaf vs. Canopy

Layers of leaves; size, orientation, shape,

coverage of ground surface shadowing

within layers and canopy Shadowing reduces reflectance, reduction

relatively lower in NIR than RED

Related to transmittance!

1. Leaf hemispherical transmittance:

correlated to hemispherical reflectance of

leaves

Red is positively correlated

IR is negatively correlated

red

NIR

trans

Refl



2. Leaf area and orientation

A change

smaller leaves or

more vertical orientation of the leaves

might increase Red and decrease near-IR reflectance,

making vegetation approach the soil reflectance curve.

These parameters are important at the individual level.



3. Hemispherical reflectance and transmittance of

supporting structures (stalk, trunks, limbs,

petioles):

Guyot and Riom (1988) have studied the reflectance

of bark on spruce. The reflectance increases

progressively from visible to mid-IR and resembles

soil reflectance. When the density (of needles) is low,

the effect of bark reflectance is particularly sensitive

in the near-IR and mid-IR (Guyot and Riom 1988).



The red shift

Maturation induced change

of the chlorophyll absorption

edge toward longer

wavelengths

Red transition zone

Complex causes: conc.

of chlorophyll change in

molecular structure

absorption bands added



Senescence

Deterioration of cell walls in the mesophyll tissuedecline in NIR reflectance

Accompanying increase in visible reflectance decline in

abundance and effectiveness of chlorophyll


Physical domain/external

Atmosphere Uniform surface and atmosphere

without clouds

NIR darkening of bright surface

Scattering and water vapor absorption(20%)

RED brightening dark surface

Added path radiance

Same effect as turbidity!

Critical surface reflectance (radiancevs. Optical thickness)

Aerosol scattering (0.04-0.20 unitdecrease in NDVI), water vapor (0.04-0.08) and Rayleigh scattering (0.02-0.04)


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Background Effective background reflectance:

Its importance increases as coverage becomes thinner; aridity, agriculture,LAI below 2

Difficult to extract vegetation information for coverages below 30%

(hutchinson, 1989) resolution! Brightness

shadowing, texture, material, wetness

Soi l line

Color

Width of soil line

The effect is local


Angular effects

Three angles are important for the registered reflectance

effects are inter-related

solar zenith angle: angle between the direction of incidentsunlight and the vertical line from nadir to zenith

look angle: angle between the sensor and the vertical line fromnadir to zenith

azimuth angle: anglebetweenthe planes definedby solar zenith and lookangle


Angular effects

The solar zenith angle changes daily and

seasonally.

point of insensitivity:

When vegetation coverage rises beyond a certain

threshold, the reflectance is saturated and thus

insensitive to a further increase in coverage

varies with the solar zenith- and look-angles

look-angle 0 (nadir viewing) & increasing zenith

angle: the point of insensitivity is at a lesser

coverage (the same reflectance is registered for

vegetation coverages that it was possible to

distinguish with a smaller zenith angle).

The same trend is seen if the solar zenith angle is

kept constant and the look angle increases

a

b


Angular effects

The sensitivity of the IR band isgreater than that of the Red band.

effects of the azimuth and look-angles has to be consideredtogether:

azimuth is 90 (b), a change in look-angle gives a symmetrical spectralresponse around the nadir axis.However, the symmetry is differentfor visible and IR wavelengths.

azimuth is 180 (c), looking up-sun,gives a lower reflectance than whenlooking down-sun, i.e. when theazimuth is 0 (a) (Colwell 1974,Guyot andRiom1988)

a

b

c



pixel size

High spatial resolution better reflects the

diversity of the environment monitored, while

increasing the pixel size better catches thehomogeneity of the environment. Observed

variability is reduced as the pixel size increases.


Spectral signature

Greeness and openess of vegetation high VI

Internal variation hetereogenity of veg type

Age, height, density etc.


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Botanical Institute, BB209 - H2002 Botanical Institute, BB209 - H2002

Vegetation indices, VI

VI highlight vegetation

Integrative functions ofcanopy, structural (%cover, LAI, LAD) and

physiological (pigments,photosynthesis) parameters

Red IR scatterdiagram tasseled cap; high IRand low Red

VI = mathematicalcombination of these

bandsThe tasseled cap of the IR-red data-

space (data are extracted from a

mid-summer MSS data-set from

Western Norway)

Water

Vegetation

Soil line


R-NIR Feature Space

Sudan

MSS-1

09/29/72

path 183

row 48


R-NIR Feature Space

Egypt

MSS-2

04/14/79

path 186

row 43


Vegetation indices

Agricultural crops observed

throughout growing seasonWide range of land surface cover

types, Landsat TM

High vegetation cover, lowest red and highest IR; RED sensitivity


Vegetation indices

Several VI exists

Amplify vegetation by normalising data/

topographic effects

Amount of data reduced Categorisation different schemes

Difference indices

Ratio indices

Orthogonal indices

Complex combinations

First generation (Bannari et al. 1995)

second generation (Bannari et al. 1995)


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Vegetation indices

Difference indices = IR Red;vegetation high positive values

Ratio indices = IR/Red Variations on this simple ratio

Adding constants, squaring, squareroot etc. to normalise (positivevalues, 0-1)

All produce lines radiating outfrom an origo; equal value on thisline

+ Normalisation good (varyingsoil and irradiance conditions)

- overestimation over dark soil


Vegetation indices

Orthogonal indices

Soil line; soil/background

plots on single line Vegetation emerges

orthogonally

Axis of variation by

Linear regression

PCA

Gram-Schmidt

orthogonalisation

1. Axis = soil line

(IR=aR+b)

2. Axis = vegetation cover


First generation VI


First generation VI


Vegetation indices

Linear combinations

Not considering - Exterior factorsor soil-vegetation interactions

Designed for specific sensors and applications

Interpretation distinct differences between ratio and orthogonal

indices

BUT small differences seen betweenstudies applying one or another

VI!

Perry and Lautenschlager (1984)

Discussing 48 diff. VIs

functional equivalence


IR

RVi=

Vegetation indices

IR

Red


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Second generation VI

Based on knowledge of physical phenomena

Interaction between EM radiation, atmosphere,

vegetative cover and soil background

Generally based on reflectance values, corrected

for sensor calibration and atmospheric effects

NDVI most widely used, reference for

evaluating new indices


Second generation VI


PVI

-50,00

0,00

50,00

100,00

150,00

200,00

250,00

300,00

350,00

400,00

0 50 100 150 200 250 300

Serie1

Soil line y=ax+b

A and b from regression

non-vegetation file/interpretation of scatterdiagram


PVI - model

PVI=(NIR-aR-b)/

(SQRT(a**2+1))


PVI


Vegetation isolines

Pairs of RED and NIR

representing equal amounts of a particular vegetation parameter

changing the optical properties of the background

fixed Leaf Area Index (LAI ) and Leaf Angle Distribution (LAD)

constant external conditions Represent the true behavior of a constant vegetation

condition against a wide range of canopy background

conditions

Vegetation Index invariant to background:

vegetation isolines = vegetation index isolines!


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Vegetation isolines

Plot of the vegetation

points with the SAIL

model (marks) for variousLAI and soil reflectance

and the NDVI isolines

(dotted lines). (Huete el al,

1999)


Vegetation isolines

Vegetation isolineequations

Simulations (SAIL) canhelp understanding optical

properties of vegetationand to develop better/moreresistant VIs

Background/soilreflectance (red) 0.05, 0.2and 0.35: increasing

brightness

Vegetation isolines do notinclude soil reflectance!

NDVI SAVI


Leaf Area Index, LAI

Amount of foliage per unit ground surface area / one halfthe total green leaf area per unit ground area (1.28 2) Optical instruments response

A driving biophysical variable

Input to models; hydrological, ecological, climate

Varies with plant/tree species, mean annual temperature,length of season, water supply and stock age

Range 0 16, maximum reached in evergreen forests atwestern coast of USA

Quantification directly/indirectly in field

Indirectly relationship to surface reflectance and therefore SVI


Indirect LAI estimation

Deterministic or stochastic canopy radiation models Homogenous canopies

Empirical spectral vegetation indices (SVI) RED, NIR, MIR/SWIR (TM3, 4, 5)

RED and MIR; strong inverse curvilinear relationship (absorption -pigments and water content)


NIR low LAI inverserelationship, high LAI

positive relationship (veg.Cover + shadowing)

SVI LAI relation Understory and background

influences

Saturation of SVI at LAI 3-5:vegetation with LAI > 3-5occupy 1/3 of terrestrial landsurface!

Indirect LAI estimation


LAI - SVI

Several SVIs related to LAI

Red/NIR combinations (NDVI, SR, SAVI)

Turner et al. 1999

Atmospheric and topographic correction

Optimal relation depends on vegetation type and density

Huete et al. 1997

NDVI faPAR

SARVI LAI

Brown et al. Xxxx

SAVI < SR

Chen and Cihlar, 1996

NDVI ~ SR background problems


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LAI SVI

SVIs including MIR band

Similar reflectance across backgrounds

Larger sensitivity to LAI

Including MIR performs better than equivalent SVIs without

MIR (Boyd et al. 2000, Brown et al. 2000, Nemani et al. 1993,

Spanner et al. 1990)

Texture stronger relation!


LAI



AVHRR (NASA) daily global coverage

Global Area Coverage

4km resolution

transmitted daily

Local Area Coverage

Full resolution 1,1km at nadir

Selectedregions (receiving station)

Global Vegetation Index, since 1982!

From GAC, 7 days composites of highest values (clouds, atmosphere)

NDVI, 15 km resolution



AVHRR applications

Observe major ecological zones and seasonal changes

Crop phenology and agricultural practices in the Nile Delta (tucker

et al. 1984)

Desertification

Long term trends (onset of spring etc.)



MODIS

The Moderate Resolution Imaging Spectroradiometer instrument

Terra platform December 18th, 1999

(wide spectral range, moderate spatial reso lution (250m 1km)

and near daily global coverage

The second MODIS is planned for launch o n the Aqua platform in April 2002


MODIS VI

How are global ecosystems changing?

What changes are occurring in global land cover andland use, and what are their causes?

How do ecosystems respond to and affect globalenvironmental change and the carbon cycle?

Products: NDVI, Normalized Difference VI. Continuity index

that will extend 20 years of AVHRR

EVI, Enhanced Vegetation index takes advantages of MODIS radiometric characteristics,

corrected surface reflectance.

expected to give improved sensitivity in high biomass regionsand improved vegetation monitoring through a de-coupling ofthe canopy background signal and a reduction in atmosphereinfluences.


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April 2001

September 2000

December 2000

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MODIS Leaf Area Index

LAI is defined as the one sided green leaf area per unit

ground area in broadleaf canopies and as the projected

needle leaf area in coniferous canopies.


April 2001

September 2000

December 2000

0 0.90.5

MODIS faPAR

faPAR is defined as the fraction of incident photosynthetically

active radiation (0.4 - 0.7 m) absorbed by the vegetation

canopy.

[Vegetation and Remote Sensing] Vegetation

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