Though we often take the plants and trees around us for granted, almost every aspect of our lives depends upon them. They feed us, clothe us, absorb… [310266]

1.[anonimizat]. [anonimizat], [anonimizat], and give us building materials and medications. [anonimizat], economy, and environment are all affected. (USGS)

Vegetation, [anonimizat]-steppe. [anonimizat] a layered arrangement (steppe, silvosteap, forest), [anonimizat].(PUG iasi)

The Normalized Difference Vegetation Index (NDVI) is a [anonimizat], and is adopted to analyze remote sensing measurements and assess whether the target being observed contains live green vegetation or not.(j. rousse, 1973).

[anonimizat], [anonimizat] (land cover change) in relation to the elementary physical properties as regards of the emissivity data and surface radiance (Orhan, 2016). [anonimizat]-derived (like Landsat-5 TM/ 7 ETM +/8 OLI) surface temperature data have been utilized for local and regional climate study on different scale (Carlson, 1977). [anonimizat] (LST) in wordwide since 1972. Consequently the Landsat-5/7/8 imagenary were used in research. A lot of papers showed that the surface temperature of the work was confirmed from Landsat 5/7/8 (Mallick, 2012, Macarof et al. 2017)

[anonimizat]. Also, [anonimizat]. LST, determined from remote sensing data can be use in a [anonimizat]: agriculture, [anonimizat], hydrology, forestry, urban planning etc. (Orhan et al. 2014).

Many researches indicated that between LST and NDVI is a linear relationship. [anonimizat] (Carlson et al., 1994).

[anonimizat]/[anonimizat] 2017. As based date was used Landsat 8 images.

2.Data and methods

2.1.Study area

Iași county is considered as study area in this research (figure 1). Study Area is geographically situated on latitude 46°48'N to 47°35'N and longitude 26°29'E to 28°07'E. [anonimizat], Vaslui to the south and Republic of Moldova to the east.

Figure 1.Study Area (www.wikipedia.com)

2.2. Landsat data

Landsat 8 measures different ranges of frequencies along the electromagnetic spectrum – a color, although not necessarily a color visible to the human eye. Each range is called a band. Landsat 8 has 11 bands and the tenth and eleventh band are in the thermal infrared (TIR). These bands see heat. [anonimizat], [anonimizat] (NASA Landsat Science).

Landsat data was offered free by USGS. Table 1 shows Landsat data that was used in this research.

Table 1. Landsat data

2.3. Data processing

2.3.1. Image preprocessing

Preprocessing of Landsat-8 OLI images stage represent that operations that prepare images for subsequent analysis that attempts to compensate/correct for systematic errors. The images are subjected to several corrections like radiometric and atmospheric. The 90m resolution TIR bands were resampled to "fit" to 30m spatial dimensions for some image processing applications. Is used to "keep" the original pixel values in the resampled images nearest neighbor resampling (miningeology.blogspot).

Radiometric correction is done to decrease/correct errors in the digital numbers (DN) of images. This process improves quality of remote sensed data. Process of eliminate the effects of the atmosphere to obtain surface reflectance values represents atmospheric correction. Atmospheric correction can significantly enhance the interpretability and usage of images. Perfectly for this process would to knowledge of aerosol properties and the atmospheric conditions at the time the remote sensing data was acquired (miningeology.blogspot).

2.3.2. Normalized Difference Vegetation Index

NDVI, according to Rouse, is a numerical indicator that uses the visible (Vis) and near-infrared (NIR) bands of the electromagnetic spectrum, and is adopted to analyze remote sensing measurements and assess whether the aim being observed contains live green vegetation or not (John Rouse, 1973). The NDVI algorithm subtracts the red reflectance values from the near-infrared (NIR) and divides it by the sum of them.

NDVI= (1)

Generally, healthy vegetation will absorb most of the Vis light that falls on it, and reflects a large portion of the NIR light. Unhealthy or sparse vegetation reflects more visible light and less NIR light. (Holme et al 1987).

2.3.3. Land surface temperature (LST)

To estimate the LST from thermal infrared (TIR) band data of Landsat-8 OLI, digital numbers (DN) of sensors were transformed to spectral radiance using equation (Barsi et al. 2014).

Ll=ML×Qcal+AL-Qi …(2)

Where:

• 𝑀𝐿 = the band-specific multiplicative rescaling factor

• 𝑄cal = the Band 10/11 image

• 𝐴𝐿 = the band-specific additive rescaling factor

• 𝑂𝑖 = the correction for Band 10/11

Spectral radiance is converted to brightness temperature by assuming the earth of surface is a black body (Chander et al, 2009; Coll et al, 2010):

Tb= …(3)

Where:

• Tb = the brightness temperature

• Lλ = the cell value as radiance

• K1 and K2 = Calibration constant of Landsat8 calibration

There are algorithms applied to transfer BT for LST:

LST=BT/[1+(λ*E/ ρ)*ln(LSE)] (4)

Whereas, BT is Brightness Temperature (4) (Kevin) λ is the band wavelength (μm), ρ = 14380; LSE is Land Surface Emissivity, ρ = h*c/ς, with h is Plank’s constant (6.626*10−34Js), c is light velocity (3*108 m/s) and ς is the Boltzmann constant (1.38 *10–23 J/K).

NDVI was used to extract Land Surface Emissivity (LSE), which is an adjustable parameter in correcting Land Surface Temperature in the next step. Values of LSE were calculated based on the proportion of vegetation (Jiménez-Muñoz et al. 2014).

LSE=0.004PV+0.986 (5)

Whereas, Pv is the proportion of vegetation, based on a normalized NDVI value of each pixel.

PV=()2

3. RESLUTS AND DISCUSSION

Figure 2 and tables 2, 3 shows NDVI, LST maps and data statistics.

Figure 2.NDVI and LST maps

Analyzing the statistical data of NDVI and LST, the maximum temperature is 40.1oC and the minimum temperature is 1.76oC. The mean NDVI parameter indicates the high vegetation level in June.

Table 2.NDVI data statistics

Table 3.LST data statistics

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 0.6) correspond to dense vegetation such as that found in temperate forests or crops at their peak growth stage (Stefano FERRI et al.1 2016)

For generated sparse/dense vegetation map (figure 3) was applied a filter. Sparse vegetation map was extracted from NDVI map, putting condition to extracted areas with values which ranged between 0.2-0.5, respectively over 0.6 for dense vegetation. For areas resulted was extracted and LST maps area.

Table 4a and 4b shows LST data statistics for sparse/dense area vegetation and figure 3 LST and NDVI maps for sparse/dense area vegetation and correlation between NDVI and LST for that area.

Table 4a.LST data statistics for sparse areas vegetation

Table 4b.LST data statistics-for dense areas vegetation

Figure 3. LST and NDVI maps for sparse/dense area vegetation

Next step was studied correlation between LST si NDVI sparse/dense vegetation for three moments of year. Figure 4 shows scatter plot and correlation coefficient between LST and NDVI sparse/dense vegetation.

It is noticeable that correlation coefficient has closer values to 1 for relationship LST-NDVI dense, R2 is about 0.9, while R2 is about 0.7 for relationship LST-NDVI sparse.

4.Conclusions

In this paper was studying relationship LST-NDVI sparse/dense using Landsat 8 OLI data. An analysis based on statistical data indicates that, from the three times surveyed, the highest vegetation is in June, while in September vegetation is declining according to statistical data. LST data indicates that the maximum temperature is 40.1oC, in June, and the minimum temperature is 1.76oC in September.

It is noticeable that correlation coefficient has closer values to 1 for relationship LST-NDVI dense, R2 is about 0.9, while R2 is about 0.7 for relationship LST-NDVI sparse.

In conclusion, this correlation can be used to study vegetation health, drought damage, and areas where Urban Heat Island can occur.

Reference

Carlson T., Gillies R., Perry M., 1994. A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover. www.tandfoline.com

Carlson, J. Augustine, and F. E. Boland, 1977. Potential application of satellite temperature measurements in the analysis of land use over urban areas, Bulletin of the American Meteorological Society, vol. 58, pp. 1301–1303

Chander G., Markham B., Helder D., 2009. Summary of current radiometric calibrationbcoefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. www.sciencedirect.com

Chiara Piroddi, Jeroen Steenbeek, Kristin Kaschner, Frida Ben Rais Lasram, Jacopo Aguzzi, Enric Ballesteros, Carlo Nike Bianchi, Jordi Corbera, Thanos Dailianis, Roberto Danovaro, Marta Estrada, Carlo Froglia, Eleni Voultsiadou 2010. The Biodiversity of the Mediterranean Sea: Estimates, Patterns, and Threats. PLoS ONE 5(8)

Ferri S., Siragusa A., Halkia M., 2016. The ESM green components

Holme A.M., Burnside D.G., Mitchell A.A., 1987. The development of a system for monitoring trend in range condition in the arid shrublands of Western Australia. Australian Rangeland Journal 9:14-20.

J. A. Barsi, J. R. Schott, S. J. Hook, N. G. Raqueno, B. L. Markham, R. G. Radocinski., 2014. Landsat-8 thermal infrared sensor (TIRS) vicarious radiometric calibration, www.mdpi.com

Jiménez-Muñoz J.C., Sobrino J., Skokovic D., Mattar C., Cristóbal J., 2014. Land Surface Temperature Retrieval Methods From Landsat-8 Thermal Infrared Sensor Data, IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 11, NO. 10, OCTOBER

Macarof P., Birlica C.I, 2017. .Investigating Land Surface Temperature and Vegetation Indices Changes Using Landsat Data: A Case Study of Iași County, www.georeview.ro

Mallick J., Singh K., Mukherjee S., Shashtri S., Rahman, 2012. LSE retrieval based on moisture index from LANDSAT TM satellite data over heterogeneous surfaces of Delhi city. Int. Journal of Applied Earth Obs. and Geoinformation, http://www.sciencedirect.com/

Orhan O., Ekercin S., Dadaser-Celik F., 2014. Use of Landsat Land Surface Temperature and Vegetation Indices for Monitoring Drought in the Salt Lake Basin Area, 2014. www.academia.edu

Orhan O., Yakar M., 2016. Investigating Land Surface Temperature (LST) Changes Using Landsat Data in Konya, Turkey, doi: https://www.academia.edu

Rouse J.W., Haas R.H., Schell J.A., Deering D.W., 1973. Monitoring vegetation systems in the Great Plains with ERTS. NASA SP-351 I: 309–317.

***Macarof P., Statescu F., 2017. Investigating Land Surface Temperature Changes Using Landsat Data: A Case Study of Iași County

***www.wikipedia.com

***USGS

***PUG Iasi

***www.miningeology.blogspot

***NASA Landsat Science

Though we often take the plants and trees around us for granted, almost every aspect of our lives depends upon them. They feed us, clothe us, absorb carbon dioxide, provide us with oxygen, and give us building materials and medications. Vegetation, in Iasi county – the study area in this paper, is predominantly of forest-steppe. In this study is investigating correlation LST – sparse / dense vegetation extraction from NDVI, in three different moments of 2017. As based date was used Landsat 8 images, which have undergone preprocessing. A lot of papers showed that the surface temperature of the work was confirmed from Landsat 5 / 7 / 8. Many researches indicated that between LST and NDVI is a linear relationship. It is noticeable that correlation coefficient has closer values to 1 for relationship LST – NDVI dense, R2 is about 0.9, while R2 is about 0.7 for relationship LST – NDVI sparse. In conclusion, this correlation can be used to study vegetation health, drought damage, and areas where Urban Heat Island can occur.