In this study was used Normalized Vegetation Supply Water Index (NVSWI) and Vegetation Health Index (VHI) to evaluated drought of Iasi county. [310271]

1.INTRODUCTION

Extreme climatic episodes are predicted to growth in frequency and magnitude (Min et al. 2011) and are anticipated to have strong ecological implications (Jentsch et al. 2007; Knapp et al. 2008; Smith 2011; Reichstein et al. 2013).

Drought is a stochastic natural phenomenon that appears from considerable lacking in precipitation. [anonimizat] a important number of people (Wilhite, 1993). As a climatic anomaly originating from a [anonimizat]’s surface, even in humid regions. [anonimizat]. Moreover, sustained drought also encourages desertification (Hirche et al. 2011), [anonimizat] (Pandey et al. 2013). This phenomenon is a relative, [anonimizat]. [anonimizat] (Knutson et al., 1998). [anonimizat] 2000, [anonimizat]. Drought ranked as the principal among all natural hazards (Bryant, 1991). [anonimizat]. There is no universally acceptable and applicable definition for drought as yet. Many attempts to define drought have led to numerous definitions of the term (Nagarajan, 2003). In 2008, UNDP, defines drought as the naturally phenomenon happening that exists when precipitation has been meaningfully below normal recorded levels causing serious hydrological disequilibrium that adversely affect land resources production systems. Therefore, [anonimizat], [anonimizat] (Heim et al., Zhang et al. 2013). As a result, many techniques for monitoring drought conditions have been developed.

[anonimizat]-based parameters to represent soil moisture status when the soil is often obscured by a vegetation cover. [anonimizat]-red wavebands. Have been developed and applied, a [anonimizat], [anonimizat], intensity, and severity. [anonimizat] (NDVI) has been one of most usually used approaches to drought episode monitoring and as a probe for vegetation health. Combining vegetation index and temperature offer the possible to improve the approach. A mix of land surface temperature (LST) and NDVI provides strong correlation and offers useful information to description of agricultural drought as an early warning system.

In this study was used Normalized Vegetation Supply Water Index (NVSWI) and Vegetation Health Index (VHI) to evaluated drought of Iasi county.

2.MATERIAL 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. [anonimizat] the west, Vaslui to the south and Republic of Moldova to the east. Figure 1 represents the study area. Iași county is situated in easten of Romania and it has an area of 5.476 km2.

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

2.2. Data resources

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. Instead of measuring the temperature of the air, such as weather stations do, it report on the ground itself, which is often hotter (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

Pre-processing of Landsat-8 OLI/TIRS data stage consists of those operations that prepare data for subsequent analysis that attempts to correct or compensate for systematic errors. The digital images are subjected to several corrections such as radiometric and atmospheric. The 90-m resolution TIR bands were resampled to correspond to 30-m spatial dimensions for some image processing applications. Nearest neighbor resampling was used to preserve the original pixel values in the resampled images (miningeology.blogspot).

Radiometric correction is done to reduce or correct errors in the digital numbers of images. The process improves the interpretability and quality of remote sensed data. Atmospheric correction is the process of removing the effects of the atmosphere to produce surface reflectance values. Atmospheric correction can significantly improve the interpretability and use of an image. Ideally this process requires knowledge of the atmospheric conditions and aerosol properties at the time the image 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)

2.3.3. Land surface temperature (LST)

To estimate the LST from the Landsat-8 thermal infrared (TIR) band data, 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

• 𝑄 cal = 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:

(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.

(6)

2.3.4. Normalized Vegetation Supply Water Index (NVSWI)

The Vegetation Supply Water Index (VSWI) combines NDVI with the thermal image-based parameter land surface temperature (LST) and is regularly used to its simplicity and ability to illustrate two potential properties of vegetation stress in one index, but suffer from the misfit in time scales, since vegetation greenness is pretty stable in the short to medium term but temperature oscillate diurnally, and according to weather conditions as well as slope, aspect and terrain properties. VSWI is also characteristic to the land cover type and measurement time of the image scene, and can not be used as an perfect measure of drought severity. So, attempts to normalize the VSWI have contextualized the index within a defined period of available records.

VSWI= (7)

NVSWI= (8)

NVSWI range from 0 (driest) to 100 (wet) with next five drought classes: severe dry (0-20), moderate drought (20-40), slight drought (40-60), normal (60-80), wet (80-100).

2.3.5. Vegetation Health Index (VHI), Vegetation Condition Index (VCI) and Temperature Condition Index (TCI)

The VCI is defined as follows:

VCI=100*(NDVI−NDVImin)/(NDVImax−NDVImin) (9)

where NDVImax and NDVImin represent absolute maximum and minimum values. The VCI was used as one of the important vegetation indicators when monitoring weather-related variations, such as droughts. On the condition of excessive soil wetness and/or long cloudiness, the NDVI is normally very depressed and the VCI has low values which can be interpreted erroneously as a drought event. For distinguishing droughts from excessive soil wetness and/or long cloudiness, VHI was developed through a combination of VCI and temperature condition index (TCI) (Kogan, 1995; Kogan et al., 2011):

TCI=100*(LSTmax−LST)/(LSTmax−LSTmin) (10)

VHI=a*VCI+(1−a)*TCI (11)

where the TCI reflects the stress of temperature, a is a coefficient to quantify the relative contributions of moisture and temperature to the vegetation health; LSTmax and LSTmin represent the absolute maximum and minimum. Since the contributions of moisture and temperature to vegetation health are unknown for a specific location at some periods, the proportion was often assumed equal for simplicity (i.e., a = 0.5). The detailed description of this algorithm can be found in the study of Kogan et al. (2011).

VHI range from 0 to 100 with next five drought classes: extreme drought (0-10), severe drought (10-20), moderate dought (20-30), mid drought (30-40), no drought (40-100).

3. RESLUTS AND DISCUSSION

Figure 2 shows LST, NDVI, VSWI, NVSWI, VCI, TCI and VHI maps and table 2 shows statistical data of LST and NDVI. On may 4th the maximum value of temperature was about 35oC and on june 5th the parameter mean of temperature was over 26oC. Analizying the parameter “mean” of NDVI we notice it grew from 0.51 to 0.6 between 04.05.2017 and 05.06.2017, that fact indicate an increase for vegetation coverage of the land.

Applying NVSWI to determine the degree of drought noticed that for the satellite image of May prevailed “slight drought” and for june “normal”. Second index, VHI indicate that in both months, may and june, is “no drought”.

Figure 2.LST, NDVI, VSWI, NVSWI, VCI, TCI, VHI maps

4. Conclusions

In this paper was evaluate drought, for Iasi county, using Normalized Vegetation Supply Water Index (NVSWI) and Vegetation Health Index (VHI). We discovered that both indicies can be successfully used for determine the spatiotemporal extent of agricultural drought. Applying NVSWI to determine the degree of drought noticed that for the satellite image of May prevailed “slight drought” and for june “normal”. Second index, VHI indicate that in both months, may and june, is “no drought”. It can be concluded that Vegetation Health Index is very good indicator for studing extreme drought and Normalized Vegetation Supply Water Index offer information about areas “normal” and “wet”.

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