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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)(
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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.