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Remote Sensing for Fire Management

FOR 435: Remote Sensing for Fire Management

3. Reflectance Properties of Unburned Surfaces

• Vegetation Properties

• Soil Properties

• Spectral Indices

FOR 435: Reflectance of Healthy Vegetation

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FOR 435: Reflectance of Healthy Vegetation

FOR 435: Reflectance of Healthy Vegetation

Source: Goetz (1992)

0.3 to 0.8 Microns:

This region is governed by the absorption of theincoming EM radiation by pigments:

FOR 435: Reflectance of Healthy Vegetation

Wavelength (μm)

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0.7 to 0.8 Microns:

The Red Edge

0.8 to 1.3 Microns:

The NIR Plateau

FOR 435: Reflectance of Healthy Vegetation

Wavelength (μm)

1.3 to 2.5 Microns:

This region is governed by the absorption ofthe incoming EM radiation by water and thethickness of the vegetation leaves

FOR 435: Reflectance of Healthy Vegetation

Wavelength (μm)

Pigments

Palisade

Mesophyll

Pigments

Palisade

FOR 435: Reflectance of Healthy Vegetation

Mesophyll

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Young Leaves Old Leaves

FOR 435: Reflectance of Healthy Vegetation

FOR 435: Reflectance of Healthy Vegetation

Source: Colwell (1983)

Green Vegetation Senesced Vegetation

FOR 435: Reflectance of Senesced Vegetation

Source: Elvidge (1990)

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FOR 435: Reflectance of Senesced Vegetation

The senescence of Justicia brandegeana.

As the chlorophyll content decreases the pigment carotene, which is yellow, becomes dominant.

FOR 435: Variations of Reflectance – Water Content

FOR 435: Variations of Reflectance – Water Content

Source: Colwell (1983)

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FOR 435: Reflectance of Soils

Source: Clark et al. (2003)

FOR 435: Reflectance of Soils – Moisture Content

Source: Bowers and Hanks (1965)

Soils that are poor in organic matter haveconvex spectral reflectance curves(between 0.5 and 1.3 μm)

FOR 435: Reflectance of Soils – Organic Matter

Soils that are rich in organic matter haveconcave spectral reflectance curves(between 0.5 and 1.3 μm)

Source: Nagler et al. (2000)

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FOR 435: Reflectance of Soils – Grain Size

Source: Clark et al. (2003)

FOR 435: Comparison of Unburned Surfaces

FOR 435: Comparison of Unburned Surfaces

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Spectral indices are used to:• Maximize the sensitivity of a particularsurface of interest• Normalize or reduce effects due to sun

It is very common in fire management to use maps that useone spectral index or another. Common spectral indices are NDVIand the Normalized Burn Ratio (NBR).

FOR 435: Spectral Indices

angle, viewing angle, the atmosphere, topography, instrument noise, etc, to allow consistent spatial and temporal comparisons• Be related to a physical process

NOTE: spectral indices will always produce regression results than simply using the separate bands

Source: Jenson (2000)

The development of spectral indices to aid the interpretation ofremotely sensed imagery broadly advanced in four stages.

Intrinsic indices were developed from simple band ratios, which highlighted the spectral properties of vegetation at different stages of growth and senescence.

FOR 435: Spectral Indices

Indices were then produced to compensate for background effects such as that caused in areas in which the soil response dominates over the vegetation.

Thirdly indices were developed to compensate for the effects of atmospheric distortion.

Finally, new spectral indices to applications other than vegetation health. These include indices for burned area assessment and fire severity.

The most widely applied spectral index is the NormalizedDifference Vegetation Index (NDVI)

FOR 435: Spectral Indices - NDVI

The NDVI index was first applied by Rouse et al (1974). The NDVI identifies the photosynthetic affinity or ‘greenness’ of the vegetation through the reflective proprieties of the chlorophyll and mesophyll layers within the plants in themesophyll layers within the plants in the NIR and red part of the EM spectrum.

In cases of photo-synthetically active vegetation, low red reflectance is observed along with very high NIR reflectance producing a NDVI, approaching 1.

Soil is observed to have NDVI values close to zero, whilst water is normally is associated with negative values.

Source: Goetz (1992)

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The most widely applied spectral index is the NormalizedDifference Vegetation Index (NDVI)

RNIR

RNIRNDVIρρρρ

+−

=

FOR 435: Spectral Indices - NDVI

You must always use calculate NDVI and other spectral indices with reflectance data.

This is because the transformation between digital numbers to radiance to reflectance differ for each band.

NDVI calculated with digital numbers will not equal NDVI calculated with reflectance. Ideally, you would calculate NDVI on atmospherically corrected imagery.

True Color Landsat Image:

B3 B2 B1

FOR 435: Spectral Indices - NDVI

NIR Color Landsat Image:

B4 B3 B2

NDVI Image:

1 = Very Green

Remote sensing researchers are not just interested in vegetation. Spectral indices have been used to monitor a variety of land change processes such as identifying and characterizing burned areas.

Burned Area Mapping:

GEMI3, VI3T, MIRBI, etc (Fire

FOR 435: Spectral Indices – Burned Area

Indices)

Fire Severity Mapping:

NBR, BSI (Burn Severity Indices)

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Separablity tests are used to determine how similar the distributions of two groups of pixels are. They are also used to test the discriminative ability of an index or technique at detecting between two distinct classes (e.g. say grass and trees).

FOR 435: Spectral Indices – Are they any good?

Testing how ‘good’ your training data is useful before a classification – i.e. the more separable training data is the better you would expect a classification that reliesyou would expect a classification that relies on that data to be.

Before using collected pixels or classes you first need to answer the following questions:

• What type of distribution do you have?

• Do the classes have a good separablity?

• Is the separablity statistically significant?

In separablity tests you measure statistical difference or distance between two or more distributions.

FOR 435: Spectral Indices – Are they any good?

Many options exist:

• Some measures look at the distance between class means (e.g., Euclidean Distance, Divergence). • Others quantify the differences between class means AND the distribution of the values about those means (i.e. noise) (e.g., M statistic, J-M Distance, Bhattacharyya Distance)• Some methods only work with one band at a time (e.g. Euclidean Distance, M statistic).• Others can work on any number of bands (e.g. J-M Distance, multi-spectral plots).

The M-statistic tests the separation between the histograms produced by plotting the frequency of all the pixel values within two classes (Kaufman and Remer, 1994)

FOR 435: Spectral Indices – Are they any good?

M 1 M 1

Frequency

M = (μ1-μ2) / (σ1+σ2)

Burned UnburnedPixel Classes Pixel Classes

Burned UnburnedPixel Classes Pixel Classes

M<1 M>>1

ixel Values Pixel Values

M<1 classes significantly overlap and the ability to separate is poor.

M>1 Histogram means are well separated and regions easy to discriminate.

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The J-M distance is a function of separablity that directly relates to the probability of how good a resultant classification will be (Swain et al 1971). The J-M distance is asymptotic to √2, where values of √2 suggest complete separablity.

FOR 435: Spectral Indices – Are they any good?

⎥⎤

⎢⎡ +CC1

1

)1(2M-J α−−= eub

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

×

++−⎟

⎠⎞

⎜⎝⎛ +

−=−

bu

bu

bubuT

buCC

CCμμCCμμ 2ln

21)(

2)(

81 1

α

Where,

u and b (i.e. classes 1 and 2) are the two signatures (classes) being compared,

Cu is the covariance matrix of signature u,

mu is the mean vector of signature u,

T is the transposition function.