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\title{Application of remote sensing technique for drought assessment based on normalized difference drought index, a case study of Bac Binh district, Binh Thuan province (Vietnam)}

\author{Le Hung Trinh$^{1}$, Danh Tuyen Vu$^{2}$}

\affil{1}{Le Quy Don Technical University, Hanoi, Vietnam}

\affil{2}{Hanoi University of Natural Resources and Environment, Hanoi, Vietnam}


\abstract{Drought is a natural phenomenon, which occurs in most regions in the world, caused
immense damage in agricultural production and seriously affected on the
environment. Droughts often occur on a large scale, so traditional observation
techniques for drought monitoring are difficult, time consuming and costly. Remote
sensing data provides information on the Earth's surface at different spectrum
channels and and wide coverage has been used effectively in monitoring and severe
drought. This paper presents the results of the study on drought assessment in Bac
Binh district, Binh Thuan province using LANDSAT multispectral imagery based on
normalized difference drought index (NDDI). The results obtained in the study can
be used to create drought level map and provides information to help managers set
measures to minimize damage caused by drought.}


\section{1. Introduction}

    Drought is one of the most costly natural disasters in Vietnam. In recent years,
drought tends to happen both in rainy season and dry season due to the impact of climate
change
[\itc{Ngo et al.,} \reflink{Ngo17}{2017};
\itc{Pham and Nguyen,} \reflink{Pham12}{2012};
\itc{Trinh,} \reflink{Trin14}{2014}].
Droughts are
particularly severe in the Central and Central Highlands of Vietnam that causes great
damage in the socio-economic and environment sectors. According to the Ministry of
Agriculture and Rural Development Vietnam, more than 240,000 hectares of rice, 18,000
hectares of crops, 55,000 hectares of fruit trees and more than 100,000 hectares of
industrial crops were damaged by water shortages only in drought years 2015--2016
[\itc{Ministry...,} \reflink{Mini08}{2008}].


\begin{sloppypar}
    The traditional drought monitoring methods used in Vietnam are based on data on
precipitation, temperature, and soil moisture collected from the meteorological stations.
Ground-based observations reflect only drought situation of local area around the station
and in fact cannot establish the number of meteorological stations with expected density
due to the high cost. Therefore, such data-sets fail to monitor drought conditions
accurately and in a timely fashion. Remote sensing technology with advantages such as
wide area coverage and short revisit interval has been used effectively in the study of
drought monitoring.
\end{sloppypar}

\begin{sloppypar}
    Optical remote sensing data such as MODIS, LANDSAT and SPOT have been used
for the large-scale drought monitoring. During the past few years, many studies in
Vietnam have demonstrated the effectiveness of remote sensing techniques for drought
monitoring and assessment of soil/vegetation moisture
[\itc{Ngo et al.,} \reflink{Ngo17}{2017};
\itc{Nguyen et al.,} \reflink{Nguy16}{2016};
\itc{Tran,} \reflink{Tran07}{2007};
\itc{Trinh,} \reflink{Trin14}{2014}].
\end{sloppypar}

    More than 100 drought indices have so far been proposed, some of which are
operationally used to characterize drought using gridded maps at regional and national
levels. These indices correspond to different types of drought, including meteorological,
agricultural, and hydrological drought
[\itc{Gulacsi and Kovacs,} \reflink{Gula15}{2015};
\itc{Zagar et al.,} \reflink{Zaga11}{2011}].
 Some of the indices derived
from satellite data and used for drought monitoring include vegetation condition index --
VCI
[\itc{Kogan,} \reflink{Koga90}{1990}],
crop water stress index -- CWSI
[\itc{Idso et al.,} \reflink{Idso81}{1981}],
 normalized
difference infrared index -- NDII
[\itc{Hardisky et al.,} \reflink{Hard83}{1983}],
vegetation health index -- VHI
[\itc{Kogan,} \reflink{Koga95}{1995}],
normalized difference water index -- NDWI
[\itc{Gao,} \reflink{Gao96}{1996}],
global vegetation moisture index -- GVMI
[\itc{Ceccato et al.,} \reflink{Cecc02}{2002}],
soil water index -- SWI
[\itc{Wagner,} \reflink{Wagn03}{2003}],
remote sensing drought risk index -- RDRI
[\itc{Liu et al.,} \reflink{Liu08}{2008}]. Some
drought indices are based on the relationship between land surface temperature and land
cover, such as the temperature condition index -- TCI
[\itc{Kogan,} \reflink{Koga95}{1995}],
vegetation
temperature condition index -- VTCI
[\itc{Wang et al.,} \reflink{Wang01}{2001}],
temperature vegetation
dryness index -- TVDI
[\itc{Lambin and Ehrlich,} \reflink{Lamb96}{1996};
\itc{Sandholt et al.,} \reflink{Sand02}{2002}].

    \itc{Gu et al.} [\reflink{Gu07}{2007}]
    proposed the normalized difference drought index (NDDI),
    which
combines both vegetation greenness (NDVI)
[\itc{Rouse et al.,} \reflink{Rous74}{1974}]           %%% ??? 73
and wetness conditions
(NDWI)
[\itc{Gao,} \reflink{Gao96}{1996}].
This drought index can be suitable for long-term drought
monitoring, particularly for agricultural drought
[\itc{Hazaymeh and Hassan,} \reflink{Haza16}{2016}].               %%% ???

    This paper focuses on the application of normalized difference drought index to
assess and monitor drought in Bac Binh district, Binh Thuan province (South Central
Coast of Vietnam) in period 1988--2017.

\section{2. Materials and Methods}

\subsection{2.1. The Study Area}


\begin{sloppypar}
     Bac Binh is a rural district of Binh Thuan province in the South Central Coast
region of Vietnam
(\figref{1}).           %   (Fig. 1).
 The area is bounded between latitude 10\deg $58'27''$ N to
11\deg $31'38''$ N and longitude 108\deg $06'30''$ E to 108\deg $37'34''$ E. The district covers an area of
1868.82~km$^2$ and had a population over of 120,000 people
(\href{http://bacbinh.binhthuan.gov.vn}{http://bacbinh.binhthuan.gov.vn}).
\end{sloppypar}

\begin{sidewaysfigure}
\figurewidth{140mm}
\setimage{}{}{155mm}{}{2018es000647-f01}
\fcaplabel{The study area, Bac Binh district, Binh Thuan province (Vietnam).}
\end{sidewaysfigure}

     Bac Binh has three geographical terrains: plain, semi-mountain and mountain. It
located in tropical monsoon region and is characterized by semi-arid climate in the south
central region. The climate is divided into two seasons: the rainy season from May until
October and the dry season from November to April. According the monitoring
results, the average annual rainfall in Bac Binh district is very low and the average
temperature is high. This is one of the most regions severely affected by drought in
Southeast region of Vietnam. The major form of desertification in Bac Binh district is
desertification of sand dunes. The total red sandy area is 32,600~ha (17.5\%
total area of district).


\subsection{2.2. Materials}

     In this study, nine multispectral cloud -- free LANDSAT~5 TM, LANDSAT~7
ETM$+$ and 8~OLI-TIRS images (path 124, row 52) with a spatial resolution of $30\times30$
meters were acquired from 1988, 1993, 1996, 1998, 2002, 2004, 2009, 2011 and 2017
(\figref{2}           %   (Fig. 2
and
\tabref{1}).           %   Tab. 1).
The LANDSAT data was the standard terrain correction products
(L1T), downloaded from United States Geological Survey (USGS --
\href{http://glovis.usgs.gov}{http://glovis.usgs.gov})
website. These satellite images were taken during the peak time of
the dry season in South Central Coast of Vietnam (January to March).\vspace*{1pc}

\begin{figure}                 % Fig 2
\figurewidth{90mm}
\setimage{}{}{115mm}{}{2018es000647-f02}\vspace*{2pc}
\fcaplabel{LANDSAT multispectral images in Bac Binh district, RGB=NIR: RED: GREEN.}
\end{figure}


\begin{table}[t]                                   % Table 1
\tablewidth{100mm}
\ftablabel{The LANDSAT Multispectral Images Used in This Study}
\vspace{5pt}
\begin{tabular}
{@{}l@{\hspace{10pt}}
c@{\hspace{10pt}}
c@{}}
\hline
\\ [-7pt]
No.	&	Data type		&	Time of data acquisition	\\
   [7pt]  \hline   \\ [-7pt]
1	&	Landsat 5 TM	&	January 07, 1988			\\
2	&	Landsat 5 TM	&	March 09, 1993				\\
3	&	Landsat 5 TM	&	March 01, 1996				\\
4	&	Landsat 5 TM	&	January 01, 1998			\\
5	&	Landsat 7 ETM+	&	January 05, 2002			\\
6	&	Landsat 5 TM	&	January 03, 2004			\\
7	&	Landsat 5 TM	&	January 16, 2009			\\
8	&	Landsat 5 TM	&	February 07, 2011			\\
9	&	Landsat 8 OLI	&	January 30, 2017			\\
[4pt] \hline
\end{tabular}
\end{table}




\subsection{2.3. Methods}

     Image processing started with radiometric and geometric correction. In first step,
the brightness of the pixel value (digital number) is converted into the spectral radiance
value (Wm$^{-2}$~$\mu$m$^{-1}$).
Then, the spectral radiance value is used to calculate the top of
atmosphere value -- TOA. The surface reflectance value can be calculated using ``dark
object subtraction'' (DOS) atmospheric correction method
[\itc{Chavez,} \reflink{Chav88}{1988}, \reflink{Chav96}{1996}].
This method estimates the atmospheric contributions to a surface spectrum by measuring
homogeneous surfaces over a range of illumination conditions.

     The reflectance values of green, red, near infrared (NIR) and short wave infrared
(SWIR) bands were used to calculate normalized difference vegetation index\linebreak (NDVI)
[\itc{Rouse et al.,} \reflink{Rous74}{1974}]
and normalized difference water index  % (NDWI)
[\itc{McFeeters,} \reflink{McFe96}{1996}]
following the equations:

\begin{eqnarray*}  %\label{1}
 \mathrm{NDVI} = \frac{\rho_{\mathrm{NIR}} - \rho_{\mathrm{RED}}}
 {\rho_{\mathrm{NIR}} + \rho_{\mathrm{RED}}}
 \end{eqnarray*}                                     %   (1)
\begin{eqnarray*}  %\label{2}
 \mathrm{NDWI} =
 \frac{\rho_{\mathrm{GREEN}} - \rho_{\mathrm{SWIR}}}
 {\rho_{\mathrm{GREEN}} + \rho_{\mathrm{SWIR}}}
 \end{eqnarray*}                                     %   (2)
where $\rho_{\mathrm{GREEN}}$, $\rho_{\mathrm{RED}}$, $\rho_{\mathrm{NIR}}$, $\rho_{\mathrm{SWIR}}$ are reflectance values of green, red, near infrared
(NIR) and short wave infrared (SWIR) bands of LANDSAT multispectral image.


     Finally, the NDDI index combines information from both the NDVI and NDWI
indices following the equations
[\itc{Gu et al.}, \reflink{Gu07}{2007}]:

\begin{equation}\label{1}
 \mathrm{NDDI} =
 \frac{\mathrm{NDVI} - \mathrm{NDWI}}
 {\mathrm{NDVI} + \mathrm{NDWI}}
 \end{equation}                                             %   (3)


\begin{sloppypar}
    According to Drought Categories
[\itc{Gu et al.}, \reflink{Gu07}{2007}],
     NDDI index was classified
into 3 classes: ``non-drought'' (NDDI value is smaller than 0.1); ``drought-moderate''
(NDDI value range from 0.1 to 0.3) and ``drought-severe'' (NDDI value larger than 0.3)
(\tabref{2}).           %   (Tab. 2).
\end{sloppypar}


\begin{table}[t]                                   % Table 2
\tablewidth{105mm}
\ftablabel{Classification of Drought Levels Based on NDDI Index  [\itc{Gu et al.}, \reflink{Gu07}{2007}]}
\vspace{5pt}
\begin{tabular}
{@{}l@{\hspace{5pt}}
c@{\hspace{5pt}}
c@{\hspace{5pt}}
c@{}}
\hline
\\ [-7pt]
No.	&	NDDI value			&	Drought level		&	Legend	\\
   [7pt]  \hline   \\ [-7pt]
1	&	NDDI $< 0.1$			&	Non-drought			& Green			\\
2	&	$0.1 \leq$ NDDI $< 0.3$	&	Drought-moderate	& Yellow			\\
3	&	$0.3 \leq$ NDDI		&	Drought-severe		    & Red			\\
[4pt] \hline
\end{tabular}
\end{table}

     In this study, image processing is done by using ERDAS Imagine 2014 program,
and drought maps were created using ArcGIS 10 program.

\section{3. Result and Discussion}

     The LANDSAT multi-temporal data after pre-processing was cut along the
boundary of study area. The reflectance values for red and NIR bands were used to
calculate normalized difference vegetation index % (NDVI)
and the reflectance values of
green and SWIR bands were used to calculate normalized difference water index. % (NDWI).
Subsequently, the NDVI and NDWI indices are used to calculate the
normalized difference drought index % (NDDI)
 following
formula
\eqref{1}.
The result of NDDI
index in Bac Binh district retrieval from LANDSAT satellite images in period \mbox{1988--2017} is present in
\figref{3}.           %   Fig. 3.


\begin{figure}                     % Fig 3
\figurewidth{95mm}
\setimage{}{}{115mm}{}{2018es000647-f03}\vspace*{2pc}
\fcaplabel{NDDI index over study area in period 1988--2017 retrieval from LANDSAT data.}
\end{figure}

The dark pixels of NDDI index have low drought level, and the
light pixels have high drought level. The inter-annual variation in the percentage of
drought impacted areas in the Bac Binh district from 1988--2017 due to drought
categories
[\itc{Gu et al.}, \reflink{Gu07}{2007}]
are shown in
\figref{4}           %   Fig. 4
and
\tabref{3}.           %   Tab. 3.







\begin{figure}                    % Fig 4
\figurewidth{95mm}
\setimage{}{}{115mm}{}{2018es000647-f04}\vspace*{2pc}
\fcaplabel{Map of drought in the Bac Binh district in period 1988--2017.}
\end{figure}

\begin{sidewaystable}                                   % Table 3
\tablewidth{155mm}
\ftablabel{Result of Drought Assessment in Bac Binh District in Period 1988--2017}
\vspace{5pt}
\small
\begin{tabular}
{@{}l@{\hspace{15pt}}
c@{\hspace{12pt}}
c@{\hspace{15pt}}
c@{\hspace{15pt}}
c@{\hspace{15pt}}
c@{\hspace{12pt}}
c@{}}
\hline
\\ [-7pt]
Year	&	\multicolumn{2}{c}{Non-drought}\qquad \qquad	&	\multicolumn{2}{c}{Drought--moderate} \qquad \qquad	&	\multicolumn{2}{c}{Drought--severe}	\\
		&	Area (ha)	&	Area (\%)	&	Area (ha)	&	Area (\%)	&	Area (ha)	&	Area (\%)	\\
   [7pt]  \hline   \\ [-7pt]
1988	&	150327.54	&	80.17		&	32623.74	&	17.40		&	4559.31		&	2.43		\\
1993	&	111467.88	&	59.45		&	61433.73	&	32.76		&	14609.07	&	7.79		\\
1996	&	115221.69	&	61.45		&	47866.23	&	25.53		&	24422.58	&	13.02		\\
1998	&	92821.86	&	49.50		&	59465.25	&	31.71		&	35223.48	&	18.78		\\
2002	&	119893.95	&	63.94		&	49296.96	&	26.29		&	18319.59	&	9.77		\\
2004	&	135093.15	&	72.05		&	44641.80	&	23.81		&	7775.73		&	4.15		\\
2009	&	131340.33	&	70.04		&	43304.13	&	23.09		&	12866.13	&	6.86		\\
2011	&	122642.46	&	65.41		&	51354.63	&	27.39		&	13513.59	&	7.21		\\
2017	&	110013.39	&	58.67		&	64184.49	&	34.23		&	13312.71	&	7.10		\\
[4pt] \hline
\end{tabular}
\end{sidewaystable}


     Thus, it can be seen that relative large drought areas recorded in the middle of the
1993--2002 period, in which the area affected by drought severe was highest in 1996
(13.02\%
of total area) and 1998 (18.78\%
of total area)
(\tabref{3}).           %   (Tab. 3).
 This is also the time
when the south central coast region of Vietnam is severely affected by the El Nino
phenomenon (El Nino years in period 1993--1995 and 1997--1998).

\begin{sloppypar}
     Analysis of the obtained results showed that, the regions with ``non-drought'' level
are mainly concentrated in forested areas, where distributed in the north and a part in the
south of the study area (vegetation cover is shown by red color on the
\figref{2}).           %   Fig. 2).
 The areas
with ``non-drought'' level strongly decreased in period 1988--1998, from 80.17\%
of total
area to 49.50\%
of total area due to the decline of forest area, especially in the northwest
region of Bac Binh district. These areas are likely to increase in period 1998--2009, from
49.50\%
to 70.04\%
of total area of district and then continue to decrease in 2011 (65.41\%
of total area) and 2017 years (58.67\%
of total area).
\end{sloppypar}


     The results obtained in this study also show the important role of vegetation cover
in reducing drought risk. In planted forest areas (Phan Hiep, Hoa Thang, Hong Thai
regions) in east of the district, drought level is at ``severe'' in period 1988--1996 to
``moderate'' and ``non-drought'' in 2011 and 2017 years
(\figref{4}).           %   (Fig. 4).


\begin{figure}[t]                     % Fig 5
\figurewidth{95mm}
\setimage{}{}{100mm}{}{2018es000647-f05}
\fcaplabel{Location of the soil moisture and land surface temperature monitoring points.}
\end{figure}

     Measurement data of soil moisture and land surface temperature at 10 sample
points
(\figref{5})           %   (Fig. 5)
at 23 February 2017 were used in the comparison with NDDI values
retrieval from LANDSAT 8 image, which acquired from same day. This in situ data was
observed in the framework of the ministry-level project (Ministry of Natural Resources
and Environment (Vietnam), No.~2015.08.10). Comparison between NDDI values and
measurement data of soil moisture and land surface temperature is presented in
\tabref{4}.           %   Table 4.
It can be seen, 8 out of 10 measurement points are located at ``drought-severe'' level and 2
measurement points are located at ``drought-
moderate'' level. The soil moisture at the all
10 sampling points is less than 10\%,
 and land surface temperature is high than 305~(K).
The soil moisture at sampling points at ``drought-moderate'' level is highest, and the land
surface temperature at these points is lowest in comparison with the measurement points
with drought level at ``severe''.


\begin{sidewaystable}                                   % Table 4
\tablewidth{150mm}
\ftablabel{Comparison Between NDDI Values and Measurement Data of Soil Moisture and Land Surface Temperature}
\vspace{5pt}
\footnotesize
\begin{tabular}
{@{}r@{\hspace{10pt}}
c@{\hspace{8pt}}
c@{\hspace{10pt}}
c@{\hspace{10pt}}
c@{\hspace{10pt}}
c@{\hspace{10pt}}
c@{}}
\hline
\\ [-7pt]
No.	&	\multicolumn{2}{c}{Coordinate} (m)\qquad	&	Land surface  	&	Soil moisture 	&	NDDI value	 &	 Drought level	\\
	&	$X$	&	$Y$	                        &     temperature (K)                        &      W (\%)       & & \\
   [7pt]  \hline   \\ [-7pt]
1	&	869931	&	1237474	&	314.3	&	3.1	&	0.398	&	Severe drought	\\
2	&	877403	&	1237824	&	313.9	&	3.6	&	0.318	&	Severe drought	\\
3	&	876936	&	1229069	&	310.2	&	4.6	&	0.302	&	Severe drought	\\
4	&	876469	&	1224166	&	308.6	&	5.8	&	0.189	&	Moderate drought	\\
5	&	890010	&	1243894	&	315.8	&	3.4	&	0.435	&	Severe drought	\\
6	&	890504	&	1243387	&	311.4	&	4.3	&	0.323	&	Severe drought	\\
7	&	868531	&	1247280	&	305.0	&	3.5	&	0.392	&	Severe drought	\\
8	&	861526	&	1236423	&	313.2	&	6.6	&	0.247	&	Moderate drought	\\
9	&	851837	&	1240859	&	315.9	&	4.2	&	0.345	&	Severe drought	\\
10	&	866226	&	1224538	&	312.0	&	3.4	&	0.540	&	Severe drought	\\
[4pt] \hline
\end{tabular}
\end{sidewaystable}


\begin{sloppypar}
     We also compared the results obtained in the study with the
     meteorology index
(MI) values which calculated from rainfall data at 13 meteorological stations in the
period 1960--2010
\itc{Pham and Nguyen,} \reflink{Pham12}{2012}].
These meteorological stations are located
in Bac Binh district and some neighboring districts in Binh Thuan province. The results
showed that the northern part of study area has drought level ``non-drought'' and ``less
drought'' (the MI values higher than 1.2). Meanwhile, the central and southern part of
Bac Binh district suffered severe drought (the MI values less than 0.4). This result is also
consistent with the distribution map of drought in the Bac Binh district
(\figref{4}).           %   (Fig. 4).
\end{sloppypar}


\section{4. Conclusion}

     In this study, we used Landsat time series data in period 1988--2017 to assess
drought conditions in Bac Binh district, Binh Thuan province (South Central Coast of
Vietnam). The results obtained show that NDDI index can be used effectively for drought
monitoring due to simplicity in calculation.

     The results show that relative large drought areas recorded in the middle of the
1993--2002 period, in which the area affected by drought severe was highest in 1996 and
1998 (En Nino years). The drought-prone areas are mainly distributed in agricultural and
sandy lands, which is concentrated in the center and southern part of the study area. In
spite of the large inter-annual variability, the long-term trends in the drought impacted
areas in the Bac Binh district as a whole increased dramatically over the period 1988--2017,
especially in the El Nino years of 1993, 1996 and 1998.

     The results obtained in the study can be used to create drought distribution map
and provide information to effectively respond and minimize damage caused by drought.

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