ampling plots. The
osen for analysis
of northeast Iran
naturally. Annual
and the study site
en 379203] |9"-—
0"E. The elevation
21 to 27 degrees.
m) in a 13 x |3
lomly chosen for
ically 3-4 m high,
These forests are
sharpening)
mpling plots. The
ysen for analysis
] in July 2008 by
2006. ALOS data
and multispectral
ea, and are not too
truments on-board
trument for Stereo
‘and Near Infrared
r digital elevation
ions, respectively.
ith 2.5-m spatial
| a wavelength of
a visible and near
yastal zones with a
four multispectral
0 pm), red (0.61-
JAXA EORC).
Vegetation indices
In remote sensing applications, the most commonly used
vegetation index for detecting vegetation. Here you can see the
properties of each vegetation index that introduce by others
scientist (Table 1).
New vegetation index
In arid and semi-arid regions, soil background has more
reflectance in the near infrared (NIR) and red (RED)
wavelengths of vegetation. Soil components that affect spectral
reflectance include color, roughness, and water content.
Roughness also has the effect of decreasing reflectance because
of an increase in multiple scattering and shading. RED-NIR
scattergrams, termed the “soil line”, are used as a reference
point in most vegetation studies. The problem is that real soil
surfaces are not homogeneous and contain a composite of
several types. Analysis has shown that for a given soil
characteristic, variability in one wavelength is often functionally
related to reflectance in another wavelength. Vegetation cover is
usually sparse compared to soil background and soil and plant
spectral signatures tend to mix non-linearly. Thus, arid plants
tend to lack the strong red edge found in plants of humid
regions due to ecological adaptations to the harsh desert
environment. We decided to introduce a new vegetation index
based on total wavelength (visible and NIR). The TRVI is the
ratio of NIR and the sum of visible and NIR wavelengths, and is
calculated using the following equation:
TRVIZ4 —— ——5——— —
RR G+B)
NIR-RED (1)
where RED and NIR stand for spectral reflectance
measurements acquired in the red and near-infrared regions,
respectively. For this equation, the normalized difference is
divided by the total of visible and near infrared wavelengths. In
this equation, “4” is the measured reflectance. In fact, this
équation shows the ratio of the normalized difference of
reflectance and measured reflectance of all bands, i.e., the four
bands in the multispectral image. In this study, TRVI was used
fo estimate the stand density of Persian juniper and wild
pistachio trees.
Vls
Formula Presented | Summary
Ta Rouse et al. | RED and NIR stand for
Differenee | A DVI = NIR- RED (1974) spectral reflectance
Vegetation TT NIR+ RED measurements acquired
Index in the red and near-
(NDVI) infrared regions,
respectively
Soil- Heute (1988) L indicates the soil-
Adjusted brightness dependent
Vegetation correction factor that
Index (NIR - RED) (1 + L) compensates for differences
(SAVD SAVIZ— AL in soil background
NIR+RED+L condition, L=1 low
vegetation densities, L=0.5
intermediate vegetation
densities and L=0.25 higher
voi densities
ae Qictal (1994) | The dynamic range of the
: MSAVI - 12x; -((ZXNIR 3) -8x(NIR-RED)| inductive MSAVI was
Sen BENANNT SUR RED] slightly lower than that of
ue the empirical L function
E due to differences in L
NNI boundary conditions
um Rondeaux et al. The value of 0.16 in this
= NIR-RED (1996) formula was found to
med OSAVI = . (NIR-RED) . produce a satisfactory
VERTU NIR+RED+0.16 reduction in soil noise, both
ANT for low and high vegetation
L(OSAVD | cover
Table 1. The properties of conventional vegetation indices
Tree counting
Using a GPS device, we located the centres and corners of each
9-ha plot based on satellite imagery input to the GPS. Then,
starting in a plot corner, we measured densities of juniper and
pistachio trees on all the plots. This was done in cooperation
with the regional natural resources organization.
Methods of analysis
We calculated linear regression coefficients between tree
density and vegetation index values. Tree density for each plot
was obtained from the field surveys. Vegetation indices were
calculated using ALOS satellite data for each 9-ha plot (14400
data points). Subsequently, we used 5x5 maximum filtering
algorithms for vegetation index data from each plot to
determine the optimum maximum spectral value for pistachio
and juniper trees. We also calculated frequency results for 5x5
maximum filtering of vegetation indices from each plot. Finally,
simple linear regressions between tree density and vegetation
index values were calculated based on the best threshold
vegetation index values. All of these analyses were performed
using ENVI (Environment for Visualizing Images) software,
service pack 1, and Microsoft Excel 2007.
3. RESULTS AND DISCUSSION
3.1 Relationship between vegetation indices and tree
density for the 5x5 maximum filtering algorithm for
pistachio
A simple linear regression between the conventional vegetation
indices, new TRVI vegetation index and tree density based on
the 5x5 maximum filtering algorithm was calculated. For all of
the vegetation indices, the relationship between tree density and
vegetation value was positive. NDVI and OSAVI had similar R?
values and plot distributions and higher than other vegetation
indices.
According to Colwell (1974), background reflectance can have
an important effect on canopy reflectance, especially with low
values of percentage vegetation cover (Figure 5).
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