SOIL AND VEGETATION OPTICAL PROPERTIES

Brigitte Leblon, Ph.D.

Remote Sensing and GIS laboratory, Faculty of Forestry and Environmental Management
University of New Brunswick, Fredericton (NB), Canada, E3B 6C2
Phone: (506) 453-4924; Fax: (506) 453-3538; E-mail: bleblon @ unb.ca
Exercise # 1

Exercise # 1

Let us consider the following reflectances (in percent) measured on bare soil, during a clear day (series I) and a cloudy day (series II), in the three SPOT-HRV MLA wavebands (green (500-590 nm), red (620-680 nm) and near-infrared (790-890 nm)).

Series I: Clear days

Object
Green
Red
Near-infrared
Dry sandy soil
19.6
17.8
14.2

20.0
18.0
15.3
26.1
23.3
20.4
Half-wet soil
7.1
7.7
11.0
Brownish wet soil
9.5
10.8
15.8
Wet soil
3.3
6.5
3.1
6.5
7.7
8.3
Dark brown wet soil (with high content of organic matter)
6.7
7.1
11.9
Gray wet soil
9.8
6.8
8.3

Series II: Cloudy days

Object
Green
Red
Near-infrared
Wet soil
7.1
4.8
8.5
Sand
9.0
5.9
10.3
Gravel
10.2
4.3
7.4

1.1. Which bands are the most correlated? Verify using (i) all the data and (ii) each data series. In each case, present your results on the form of a correlation matrix. Draw the scatterplot between the most correlated bands, by using data from both series and define a possible method to distinguish among both data series.

1.2. By calculating a relative reflectance difference between the most contrasted cases, determine on which band the influence of the soil color is the less important for series #1? Determine on which band the influence of the soil type is the less important for series #2? Use mean reflectance values for a given object, if necessary. In each case, present your results in a table.
A relative reflectance difference between object A and B in the band i is:

(8)


where:
Ai = reflectance of the object A in the band i
Bi = reflectance of the object B in the band i

1.3. Calculate and draw the soil line, using (i) all the data and (ii) each data series. In each case, do wet and dry soils belong to the same line? Where are they positionned on each line?

Exercise #2

Let us consider the following reflectances (in percent) measured on various vegetated targets, during a clear day (series I) and a cloudy day (series II), in the three SPOT-HRV MLA wavebands (green (500-590 nm), red (620-680 nm) and near-infrared (790-890 nm)).

Series I: Clear days

Object
Green
Red
Near-infrared
Yellowish-green grass
7.5
6.7
42.5
Green grass
5.7
4.6
44.7
Pine needles grass on
10.1
11.4
39.3
Beech leaves in fall on grass
8.8
12.5
38.0
Yellow grass
11.75
13.15
40.7
Yellow leaves on 95% grass
7.0
5.35
60.5
Yellow leaves on 50% grass
12.3
14.5
53.5
Water
4.7
3.1
4.2
2.1
3.05
1.80


Series II: Cloudy days

Object
Green
Red
Near- infrared
Yellowish-green grass
8.2
7.2
43.1
Green grass
6.1
4.2
41.6
Pine needles on grass
8.3
7.5
34.8
Beech leaves in fall on grass
7.5
10.0
34.7
Yellow leaves on grass
15.4
15.6
10.0
19.6
18.1
11.4
48.4
47.0
36.0
Water
5.5
6.0
3.5
3.6
3.7
3.5

2.1. Verify which bands are the most correlated (i) for each data series and (ii) for both series together. In each case, present your results on the form of a correlation matrix. In each case, do not use data acquired on water, because we are looking for correlations related to vegetated targets. Try to explain the difference with the results of Question 1.1 of Exercise#1. Draw the scatterplot between the most correlated bands, by using both data series together.

2.2. Add the data of Exercise#1. Let us consider all these data as typical spectral signatures of classes (water, vegetation, soil, ... ). Which 2D-scatterplot cannot be used to discriminate these classes?

2.3. Calculate the following vegetation indices (RVI, NDVI, GEMI, PVI, TSAVI) for each object and each series. For PVI and TSAVI, use adequate soil line parameters with regard to the series number. Present your results in a table (one per series). For each series, which index is the best to distinguish vegetation from soil-type objects, vegetation from water-type objects as well as water from soil-type objects. You may use relative difference between mean values of vegetation indices to solve this question. Present your results in a table (one per series). r

2.4. Demonstrate mathematically that NDVI = tan (α-45°), if RVI = tan (α)

i) consider the definition of NDVI and RVI;
ii) determine the relationship between NDVI and RVI;
iii) calculate trigonbmetric transformations; and iv) Eureka! you are done!;
Be careful: tan (α - 45°) ≠ tan (α) - tan ( 45°)]

Solutions to Exercises