An Integrated Approach for Saltwater Intrusion Monitoring based on Remote Sensing combined with Multivariable Analysis: A Case Study of Coastal Zone in Southern Vietnam45229

An Integrated Approach for Saltwater Intrusion Monitoring based on Remote Sensing combined with Multivariable Analysis: A Case Study of Coastal Zone in Southern Vietnam Quoc Huy Nguyen

An Integrated Approach for Saltwater Intrusion

Monitoring based on Remote Sensing combined with Multivariable Analysis: A Case Study of Coastal

Zone in Southern Vietnam

Quoc Huy Nguyen (1) , Tien Yin Chou (2) , Mei Ling Yeh (2) , Thanh Van Hoang (2) , Xuan Linh

Nguyen (1) , Huyen Ai Tong (3) , Quang Thanh Bui (4)

(1) PhD program in Civil and Hydraulic Engineering, Feng Chia University, Taichung, Taiwan R.O.C

(2) GIS Research Center, Feng Chia University, Taichung, Taiwan R.O.C

(3) Space Technology Institute, Hanoi, Vietnam

(4) Faculty of Geography, Hanoi University of Science, Hanoi, Vietnam

* Correspondence: st_huy@gis.tw

Abstract: Saltwater intrusion is a basic concern in many parts of Vietnam relative to long-term

dependable water supplies It affects many sides of human life and the ecosystem Remote sensing is

a useful tool for saltwater intrusion monitoring In this study, we proposed an integrated approach

to estimate EC (Electrical Conductivity) value from multi-temporal optical remote sensing data for monitoring saltwater intrusion of coastal zone in southern Vietnam Multiple variable analysis was used to discover the relation between EC and different indices groups which were extracted from LANDSAT satellite images, including: original bands group, PCA (principle component analysis) group, brightness group, vegetation group, salinity group, ratio group and combined group All results were validated by field survey data This research indicated that group of combined indices from LANDSAT (EC6) had the highest correlation to EC index with R2 = 0.77 and could be used for multi-temporal saltwater intrusion monitoring A set of maps from 2005 to 2019 were established for Ben Tre province where is one of coastal zones in southern Vietnam to support policy manager to make decision for reducing damage from saltwater intrusion

Keywords: saltwater intrusion; remote sensing; multiple variable analysis; Mekong Delta; Vietnam

1 Introduction

Saltwater intrusion is an essential problem concern for humans and the ecosystem

It is a factor that changes the properties of the soil, which adversely affects the development

of crops, causing damage to agricultural production Besides, it also causes human and animal health impacts and influences industrial production activities using natural water sources (Chhabra 2017) There are many definitions of saltwater intrusion In general, saltwater intrusion is a phenomenon of saltwater intruding deeply into the interior of areas when sea level rises at high tide It is the process when saltwater seeps into coastal groundwater systems and mixes with freshwater Salty land is an area where contains soluble salts at concentrations higher than usual and causes adverse effects on crops Saltwater intrusion is a common soil degradation It’s a process of accumulation of salts and dissolved alkali metals on the soil surface and upper soil layers This process usually starts from the lower soil layers and then slowly spreads to the surface

In the past, saltwater intrusion monitoring was usually based on field measurement

of salinity indicators but it was expensive that need to find other methods Remote sensing technology is one of the tools to solve this problem There has been a lot of research on

developing correlation models to observe saltwater intrusion through extracted indices from satellite images (An et al 2016) used NIR and SWIR index data from LANDSAT7 and LANDSAT8 images to estimate Soil Salinity Index (SSI) Meanwhile, (Scudiero et al 2015) found a correlation between Canopy Response Salinity Index (CRSI) from LANDSAT7-TM

to monitor saltwater intrusion And many other studies have demonstrated benefits in combining indicators extracted from remote sensing images to monitor multi-temporal saltwater intrusion (Wu et al 2020; Elhag et al 2017; Fan et al 2015; Liu et al 2016; El Harti

et al 2016; Nawar et al 2015; Peng et al 2019) However, the results of this researches are not applicable to all areas because of different local conditions in each region

In this study, we proposed an integrated approach for saltwater intrusion monitoring in Mekong Delta Vietnam base on using multiple variable analysis to discover relation between EC and 6 indices groups which are extracted from LANDSAT satellite images All of the results will be verified by collected sample data in our research area

2 Methodology

2.1 Study area

3 islands (An Hoa, Bao, Minh) where were deposited by alluvial from 4 branches of Mekong river (Tien, Ba Lai, Ham Luong, Co Chien) The location of Ben Tre ranges from 9°48' to 10°20' latitude and from 105°57' to 106°48' longitude and it’s in the coastal zone in southern Vietnam This region has natural factors which are directly affected the coastal zone Especially when water level in the river is low, saltwater can be pushed back into the river and canal system to create a salinization area with different concentrations because of flows

is not strong enough to prevent salty water from sea with high tide

Figure 1 The location of Ben Tre province in the Mekong Delta Vietnam

2.2 Materials

LANDSAT5-TM and LANDSAT8 satellite images used for this study because of advantages in time series analysis for regional with an average spatial and temporal resolution, the medium of spectral bands and free also

Table 1 Description of LANDSAT5-TM and LANDSAT8

LANDSAT8

LANDSAT5-TM

LANDSAT8

LANDSAT5-TM

(coastal/aerosol)

(SWIR) 1

(SWIR) 2

(TIRS) 1

(TIRS) 2

A field salinity survey was conducted by taking soil samples in the study site to verify results One sample is designed in a 90m x 90m cell with 5 positions (Figure 2)

Figure 2 Survey locations and Design of sample locations

EM31-MK2 device was used to measure EC data with 556 samples over different land uses It will be standardized by reference to soil samples analysis

Table 2 Mean of EC over Land Uses.

Land Use Number of Samples EC Mean Salty Level

2.3 Data processing

To discover the relation between indices group of LANDSAT and EC, we divided them into 5 physical indices groups including: original index, principle component analysis index, brightness index, vegetation, salty index and ratio index See in Table 3 for more details

Table 2 Indices Group which are extracted from LANDSAT

Brightness

Index

Vegetation

Index

2019) SI4 = sqrt (((NIR * R) - (G * B)) / ((NIR * R) - (G

* B)))

(Samiee et al 2018)

B/NIR B/R

B/SWIR1 B/SWIR2 G/R G/NIR G/SWIR1 G/SWIR2 R/NIR R/SWIR1 R/SWIR2 NIR/SWIR1 NIR/SWIR2 SWIR1/SWIR2

After calculated, these indicators will be extracted base on measured EC locations to establish in regression analysis model where EC is dependent variable and others are independent variables Criteria(s) for regression assessment include Sig coefficient

Multiple variable regression model applied for separate indices groups to identify which group has the highest correlation with EC One combined group of all indices will also

be used in this progress to assessment 70% samples data will be used for model input and 30% for validation

Figure 3 Logical framework of study

3 Results

3.1 Relation between EC and Indices Group from LANDSAT

The results demonstrate that a combined group has the highest correlation with EC

for saltwater intrusion monitoring Lowest correlation with EC is PCA group (R = 0.675 and

with EC

Table 3 Regression models from indices group Indices

Group

Original

Index

L_EC1 = 11,609 + 40,220B3 - 82,156B5 + 87,971B6

PCA

Index

L_EC2 = 13,223 - 30,522 * PCA1 + 7,836 * PCA2 + 50,068 * PCA3

Vegetation

Index

L_EC3 = 18,822 - 13,088 *

GDVI

Salty

Index

L_EC4 = 16,816 + 8,475 * SI5 -

65,088 * SI6 + 533,248 * SI1 - 314,669 * SI3 + 0,594 * SI4

Ratio

Index

L_EC5 = 17,970 + 22,547 * (G/NIR) - 2,638 * (G/R) - 10,087 * (SWIR1/SWIR2) + 2,304 * (NIR/SWIR2) - 3,879 *

(B/G)

All

Combined

Index

L_EC6 = -6,489 + 20,492 * (G/NIR) + 0,383 * T + 28,615 * B6 + 6,247 * (NIR/SWIR1) - 20,053 * SI2 - 3,505 * (SWIR1/SWIR2)

Figure 4 Correlation of surveyed EC and Indices group

3.2 Validation

30% samples data are used for validating EC estimation model from LANDSAT The

can be accepted This model is applied in EC estimation from LANDSAT from 2005 to 2019 with 4 classification levels of EC values by Ministry of Agriculture and Rural Development

Table 4 Salty Level Classification

Figure 5 Correlation of estimated EC and surveyed EC

3.3 Saltwater intrusion Mapping

After validation, saltwater intrusion maps were established by using LANDSAT and L_EC6 model to estimate EC value In particular, salty regions are mainly distributed in 3 coastal districts over mangrove and aquaculture areas including Binh Dai, Ba Tri and Thanh Phu

Figure 6 Map of saltwater intrusion in Ben Tre in 2005

Figure 7 Map of saltwater intrusion in Ben Tre in 2010

Figure 8 Map of saltwater intrusion in Ben Tre in 2015

Figure 9 Map of saltwater intrusion in Ben Tre in 2019

4 Conclusions and discussion

The general trend of saltwater intrusion in Ben Tre province showed an increase of salty area and a decrease of the non-salty area which is mainly concentrated in coastal districts The conversion of land use from rice to aquaculture is one of the leading causes of saltwater intrusion in soils Within 13 years from 2005 - 2019, a total of non-salty areas decreased by nearly 15824.84 ha while salty area increased where the lowest was 8169.98 ha and the highest was 10314.19 ha During 2010 - 2015 periods, high salty area expanded fastest with 6861.46 (ha) due to the inefficient production of rice and aquaculture which was converted to industrial shrimp farming Another reason is the alluvial land between large rivers such as Ham Luong, Co Chien and Tien has also been changed from plant to aquaculture The leading cause of saltwater intrusion in Ben Tre province is an extension of aquaculture area to the land Saltwater which is taken from canals will lead to saltwater intrusion deeper from the sea Moreover, canal system is intertwined while irrigation system

is not closed and makes some places become saltwater holes in the dry season Therefore, saltwater intrusion occurs every year along the river and agriculture land in Ben Tre province

Saltwater intrusion is a severe worldwide problem and directly affecting the natural environment, agriculture, and food security It is important to establish saltwater intrusion map which provides useful information about salty area and can be useful for land use planning and management Results demonstrated that indices group is very important in estimating EC from optical satellite images In this study, we reviewed remote sensing applications related to analysis and assessment of saltwater intrusion using indices group

from LANDSAT which integrated with field survey data, spatial analysis and statistical methods However, they are not really suitable for coastal zones such as Ben Tre because of cloudy In the future, radar satellite images may be considered to solve this problem

Acknowledgments

The data in this article was supported by project VT-UD.03/16-20, entitled:

“Studying, Assessing, and Zoning Soil Salinity by Using Multi-Temporal Satellite Imagery:

A Case Study in Ben Tre Province”, which belongs to the national program on Space Science and Technology (2016 - 2020)

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