Investigating correlation LST -VI for dense area [620613]

Investigating correlation LST -VI for dense area
vegetation in Iasi county

Macarof Paul1, Statescu Florian1

Techinical University Gh. Asachi Iasi, Faculty of Hydrotehnics, Geodesy and Environmental Engineering
(Romania)
E-mail: [anonimizat]

Abstract
In this paper is investigate d correlation LST -Enhanced V egetation Index -2 (EVI -2), LST -Modified Soil
Adjusted Vegetation Index (MSAVI) and LST -Optimized Soil -Adjusted Vegetation Index (OSAVI). Land surface
temperature is a very important indicator of the earth's environmental analysis . Vegetation indices combine
reflectance measurements from different electromagnetic spectrum to provide information about vegetation cover on
Earth . Iași county is considered as study area in this research . Landsat -8 OLI was used in this study for modeling.
Images was taken on April 4th and September 27th 2017. The images was corrected radiometric and atmospheric. R2
proves that the vegetation was healthier on September because correlation LST -VI was stronger than on A pril.
In conclusion, these correlations can be used to study vegetation healthy, drought damage, and areas where
Urban Heat Island can occur.

Key words: Landsat 8, LST, VI, R2

1. Introduction

Remote sensed (RS) information of increase, vigor, and their dynamics from terrestrial vegetation can
provide extremely useful comprehensions for applications in urban green infrastructures, environmental monitoring,
agriculture, forestry, biodiversity conser vation and other related fields [1 ,2].
Land surface temperature (LST) is a very important indicator of the earth's environmental analysis. Land
surface emissivity (LSE) is one of the key factors of RS retrieving LST [3]. LST, calculated from RS data is used in
a lot of sphere of science, such as : biodiversity, precision agriculture , climate change, hydrology, forest ry /
deforestation , urban green infrastructures , urban planning, oceanography , different academics research etc.
Obtaining LST and using them in different analysis is important to dete rmine the problem associated with t he
environment [ 4, 5].
Vegetation indices (VI) combine reflectance measurements from different electromagnetic spectrum to provide
information about vegetation cover (VC) on Earth [6]. A VI is a spectral combination of 2 or more bands designed
to improve the contribution of vegetation properties and allow reliable spatial -temporal inter -comparisons of
terrestrial photosynthetic activity and canopy structural variations [7]. Each environment, according to Bannari, has
its own characteristics and each index is a good indicator of green vegetation in its own right [8].
According to Yue, generally, correlation between LST and VI is strong and negative [ 9]. In this paper is
investigate d correlation LST – Enhanced Vegetation Index -2 (EVI -2), LST – Modified Soil Adjusted Vegetation
Index (MSAVI) and LST – Optimized Soil -Adjusted Vegetation Index (OSAVI), using Landsat data. In conclusion,
these correlation can be used to study vegetation health, drought damage, and areas where Urban Heat Island can
occur.

2.Data and methods
2.1. Study Area

Iași county is considered as study area in this research. Study Area is geographically situated on latitude
46°48'N to 47°35'N and longitude 26°29'E to 28°07'E. Neighborings Iași county are Botosani to the north, Neamt to
the west, Vaslui to the south and Rep. of Moldova to the east.

2.2. Landsat data

In this paper is investigated corelation LST – V.I. for Iași county using remotely sensed data. The present is
focused on the thermal remot e sensing application of Landsat satellite data. Landsat -8 OLI was used in this study for
modeling. Images was taken on April 4th and September 27th 2017.

2.3.Data processing
2.3.1.Image preprocessing

The goal of image preprocessing is to make all of the remote sensing data similar so that imagies can be
considered to be taken in same environmental co nditions with the same sensors [10] . To fill the gaps with Landsat 7
images, a specialized toolbox of ArcMap 10.1 was used.
Preprocessing of Landsat 8 data s tage represent that process that prepare images for subsequent analysis that
attempts to compensate/correct for systematic errors. The images was corrected radiometric and atmospheric .
Radiometric correction is done to decrease/correct errors in the digita l numbers (DNs) of images. Atmospheric
correction can significantly enhance the interp retability and usage of images [11].

2.3.2. Vegetation Indices (VI)
2.3.2.1.Normalized Difference Vegetation Index (NDVI)

The NDVI algorithm subtracts the red (Red) reflectance values from the near -infrared (NIR) and divides it by
the sum of that bands [12].

NDVI=
) () (
RED NIRRED NIR


2.3.2.2. Enhanced Vegetation Index 2 (EVI -2)

EVI-2, without a blue band, which has the best similarity with the 3 band s EVI, particularly when
atmospheric effects are insignificant and data quality is good [13]. To determine this index follow the formula:

EVI-2 = 2.5× (NIR–RED )/(NIR+2.4 ×RED+1 )

2.3.2.3. Modified Soil Adjusted Vegetation Index (MSAVI)

MSAVI is determined as a ratio between the R and NIR values with an inductive L function applied to
maximize reduction of soil effects on the vegetation signal [14]. MSAVI is soil adjusted vegetation indices that look
for to addre ss some of the limitation of NDVI when applied to areas with a high degree of exposed soil surface.
Thus, a modified SAVI (MSAVI) replaces 𝐿 factor in the SAVI equation with a variable L function [15]. In this
way, MSAVI reduces the influence of bare soil on SAVI [16], which can be expressed as follows:

MSAVI=(2 × NIR + 1 – sqrt ((2 × NIR + 1)2 – 8 × (NIR – R))) / 2

2.3.2.4. Optimized Soil Adjusted Vegetation Index (OSAVI)

OSAVI [17] that can be expressed as follows:

OSAVI= (1+0.16) × (NIR−𝑅 )/( NIR+𝑅+0.16),

Where, OSAVI does not depend on the soil line and can eliminate the influence of the soil background effectively.
However, the applications of OSAVI are not extensive; it is mainly used for the calculation of above ground
biomass, leaf nitr ogen content, and chlorophyll content, amongothers [18].

2.3.3. Method Selection for Estimating LST

To estimate the LST from TIRs band data of Landsat -8 OLI, DNs of sensors were transformed to spectral
radiance using equation [19].

Lλ =M L×Q cal+A L-Qi

Where: 𝑀𝐿= rescaling factor, 𝑄cal = the Band 10/11 image, 𝐴𝐿=the band -specific additive rescaling factor and 𝑂𝑖
= the correction for Band 10/11
Spectral radiance is converted to brightness temperature by assuming the earth of surface is a black body
[20,21] :

BT=
15.273
)1) ln((12
LKK

Where: BT = the brightness temperature, L λ = the cell value as radiance and K 1,K2 = calibration constant of
Landsat 8 calibration
There are algorithms applied to transfer Brightness Temperature (BT) for LST:

LST=BT/[1+(λ (BT) /ρ) ×ln(LSE)]

Where: , λ(11.5 µm) = the wavelength of emitted radiance: ρ = h × c/σ = 1.438 × 10−2 mK, σ is the Stefan –
Boltzmann constant, h is Planck’s constant, c is the velocity of light, and LSE=the land surface emissivity . NDVI
was used to extract LSE, which is an adjustable parameter in correcting LST in the next step. Values of LSE were
calculated based on PV [22].

LSE=0.004 ×PV+0.986

PV=(
min maxmin
NDVI NDVINDVI NDVI
 )2

3.Results and discussions

NDVI values range from +1.0 to -1.0. Areas of barren rock, sand, or snow usually show very low NDVI
values (for example, 0.1 or less). Sparse vegetation such as shrubs and grasslands or senescing crops may result in
moderate NDVI values (approximately 0.2 to 0.5). High NDVI values (over 0.5) correspond to dense vegetation
such as that found in temperate forests or crops at their peak growth stage [23].
Figure 1 and tables 1 and 2 shows NDVI, LST maps and data statistics. For generated dense vegetation
map ( figure 1) was applied a filter. For areas resulted was extracted LST and VI (EVI -2, MSAVI and OSAVI) maps
(figure 2) .
Analyzing the statistical data of NDVI and LST, the maximum temperature was 31.32oC for Iasi county
and the minimum temperature is 1.76oC. The mean NDVI parameter indicates the high er vegetation level in
September than in April .

NDVI -14.04.2017 NDVI -27.09.2017 Dense vegetation -14.04.2017 Dense vegetation -27.09.2017

LST-14.04.2017 LST-27.09.2017 LST dense veg. -14.04.2017 LST dense veg -27.09.2017
Fig. 1.NDVI and LST maps for Iasi county and LST dense vegetation

Table 1.NDVI data statistics
NDVI
Date Minimum Maximum Mean
14.04.2017 -0.84 0.84 0.367
27.09.2017 -0.36 0.77 0.408

Table 2.LST data statistics
NDVI for Iasi county
Date Minimum Maximum Mean
14.04.2017 10.80 31.32 21.346
27.09.2017 1.76 25.05 16.846
LST for dense vegetation
Date Minimum Maximum Mean
14.04.2017 14.50 29.35 20.063
27.09.2017 3.18 24.17 14.233

Figure 2 shows VI (EVI -2, MSAVI and OSAVI) maps for areas with dense vegetation in Iasi county and
correlation LST -VI, while table 3 shows data statistics for VI .

EVI-2 14.04.2017 EVI-2 27.09.2017 MSAVI 14.04.2017 MSAVI 27.09.2017

OSAVI 14.04.2017 OSAVI 27.09.2017 LST-EVI-2 14.04.2017 LST-EVI-2 27.09.2017

LST-MSAVI 14.04.2017 LST-MSAVI 27.09.2017 LST-OSAVI 14.04.2017 LST-OSAVI 27.09.2017
Fig. 2.VI maps and LST -VI correlation

Table 3.Data statistics for VI for dense vegetation
EVI2 MSAVI OSAVI
Min Max Mean Min Max Mean Min Max Mean
14.04.2017 0.16 0.82 0.36 0.14 0.77 0.35 0.29 0.79 0.47
27.09.2017 0.22 0.70 0.38 -0.11 0.80 0.16 0.34 0.71 0.48

Data statistics for VI dense vegetation areas reveals that for September vegetation was healthy.
Many researches prove that R2 for relationship LST -VI has value over 0.4 and is strong negative
correlation . Correlation coefficient on April 14TH for relationship LST -VI has values about 0.00, because t he
vegetation is no t so developed and the lack of soil water . R2 for LST -MSAVI, LST -OSAVI in September is about
0.1 because the vegetation is in decline.
These correlations can be used to study vegetation health, drought damage, and areas where Urban Heat
Island can occur.

4.Conclusions

In this paper was studying relationship LST -VI for dense vegetation in Iasi county using Landsat 8 OLI
data. Dense vegetation areas was estabilish using NDVI. Data statistics for VI dense vegetation areas reveals that f or
September vegetation was healthier than on April . R2 proves that the vegetation was healthier on September because
correlation LST -VI was stronger than on April.
In conclusion, these correlations can be used to study vegetation healthy, drought damage, and areas where
Urban Heat Island can occur.

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