Identification of drought extent using NVSWI and VHI in Iași county area, Romania [310278]

[anonimizat] – Ph.D Student: [anonimizat]., „Gheorghe Asachi” [anonimizat], [anonimizat], e-mail [anonimizat]

Bartic (Lazăr) [anonimizat].D Student: [anonimizat]., „Gheorghe Asachi” [anonimizat], [anonimizat], e-mail bartic.georgiana91@gmail.[anonimizat].D Student: [anonimizat]., „Gheorghe Asachi” [anonimizat], [anonimizat], e-mail 00000000000000000

[anonimizat].D. Eng.,”Gheorghe Asachi” [anonimizat], [anonimizat], e-mail: [anonimizat]

Abstract: Drought is a stochastic natural phenomenon that appears from considerable lacking in precipitation. [anonimizat] a important number of people. [anonimizat]-based parameters to represent soil moisture status when the soil is often obscured by a vegetation cover. [anonimizat]-red wavebands. In this study were used remote sensing images from the Landsat 8 Operational Land Imager (OLI), taken in may and june 2017. The study area was the county of Iasi. [anonimizat], Normalized Vegetation Supply Water Index (NVSWI) and Vegetation Health Index (VHI), were used. VSWI is derived from The Vegetation Supply Water Index (VSWI). This index was developed to combine the NDVI and the land surface temperature (LST) to detect the moisture condition. VHI was developed through a combination of Vegetation Condition Index (VCI), [anonimizat], [anonimizat] (TCI), [anonimizat]. After applying NVSWI to determine the degree of drought we noticed that for the satellite image of May prevailed “slight drought” and for june “normal”. [anonimizat], [anonimizat] “no drought”. It can be concluded that Vegetation Health Index is a very good indicator for studing extreme drought and Normalized Vegetation Supply Water Index offer information about areas “normal” and “wet”.

Keywords: Drought, Remote Sensing; Landsat 8, NVSWI, VHI

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. This phenomenon can disrupt economical and ecological systems, leading to population displacement. Moreover, sustained drought also favors desertification, according to Hirche, and land degradation, which are especially harmful for vulnerable landscapes bordering arid and semiarid areas (Pandey et al. 2013). This phenomenon is a relative, rather than absolute, condition that should be described for each region. Each drought differs in duration, intensity and spatial extent (Knutson et al., 1998). Wilhite reported, in 2000, that the onset and end, as well as severity are often hard to determine. Drought ranked as the principal among all natural hazards (Bryant, 1991). Drought is not only limit to arid and semi-arid regions but often visits potentially good rainfall zones. 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, drought is the most costly disaster that can affect natural habitats, ecosystems, agricultural systems, and urban water supplies (Heim et al., Zhang et al. 2013). As a result, many techniques for monitoring drought conditions have been developed.

Techniques for monitoring agricultural drought from remote sensing are indirect, as they depend on using image-based parameters to represent soil moisture status when the soil is often obscured by a vegetation cover. The techniques are mainly based on measuring vegetation health or greenness using vegetation indices, often in combination with canopy temperature anomalies using thermal infra-red wavebands. Have been developed and applied, a few remote-sensed drought indices, which including duration, spatial extent, intensity, and severity. One of these, the Normalized Difference Vegetation Index (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 possibility to improve the approach. A mix of land surface temperature (LST) and NDVI provides strong correlation and offers useful information to describe the agricultural drought as an early warning system.

In this study Normalized Vegetation Supply Water Index (NVSWI) and Vegetation Health Index (VHI) were used 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. Neighborings of Iași county are Botosani to the north, Neamt to 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

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. In order to be used by some image processing aplication, the 90m resolution TIR bands were resampled to "fit" to 30m spatial. Resampling is used to "keep" the original pixel values in the resampled images nearest neighbor (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. The process of eliminating the effects of the atmosphere to obtain surface reflectance values represents the atmospheric correction. Atmospheric correction can significantly enhance the interpretability and usage of images. Perfectly for this process would be to have knowledge about the 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)

2.3.3. Land surface temperature (LST)

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

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 is used as one of the most important vegetation indicators when monitoring weather related variations, like drought. On the condition of "exaggerated" soil wetness or cloudiness, the NDVI is improper and the VCI has low values which can be interpreted wrongfully as a drought event. To differentiate droughts from excessive soil wetness or cloudiness, was developed VHI, a combination of VCI and 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 (a = 0.5).

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.

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

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

Table 2.Statistic data

4. Conclusions

In this paper drought was evaluated, for Iasi county, using Normalized Vegetation Supply Water Index (NVSWI) and Vegetation Health Index (VHI). We discovered that both indicies can be successfully used to determine the spatiotemporal extent of agricultural drought. After applying NVSWI to determine the degree of drought, it was noticed that for the satellite image of May prevailed “slight drought” and for june “normal”. Second index, VHI indicates 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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