Using Remote Sensing and Multivariate Statistics in Analyzing the Relationship between Land Use Pattern and Water Quality in Tien Giang Province, Vietnam

Abstract

This study was carried out to understand how land use patterns influence surface water quality in Tien Giang Province using remote sensing and statistical approaches. Surface water quality data were collected at 34 locations with the frequency of four times (March, June, September, and November) in 2019. Water quality parameters were used in the analysis, including pH, temperature, electrical conductivity (EC), total suspended solids (TSS), dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), ammonium (N-NH4+), nitrite (N-NO2−), nitrate (N-NO3−), sulfate (SO42−), orthophosphate (P-PO43−), chloride (Cl−), total nitrogen (TN), total phosphorus (TP), and coliform. The relationship between land use patterns and water quality was analyzed using geographic information techniques (GIS), remote sensing (RS), statistical approaches (cluster analysis (CA), principal component analysis (PCA), and Krustal–Wallis), and weighted entropy. The results showed water quality was impaired by total suspended solids, nutrients (N-NH4+, N-NO2−, P-PO43−), organic matters (BOD, COD), and ions (Cl− and SO42−). Kruskal–Wallis analysis results showed that all water quality parameters in the water bodies in Tien Giang Province were seasonally fluctuated, except for BOD and TN. The highest levels of water pollutants were found mostly in the dry season (March and June). The majority of the land in the study area was used for rice cultivation (40.64%) and residential (27.51%). Water quality in the study area was classified into nine groups corresponding to five combined land use patterns comprising residential–aquaculture, residential–rice cultivation, residential–perennials, residential–rice–perennial, and residential–rice–perennial crops–aquacultural. The concentrations of the water pollutants (TSS, DO, BOD, COD, N-NH4+, N-NO2−, Cl−, and coliform) in the locations with aquaculture land use patterns (Clusters 1 and 2) were significantly larger than those of the remaining land use patterns. PCA analysis presented that most of the current water quality monitoring parameters had a great impact on water quality in the water bodies. The entropy weight showed that TSS, N-NO2−, and coliform are the most important water quality parameters due to residential–aquaculture and residential–rice cultivation; EC, DO, N-NH4+, N-NO2−, Cl−, and coliform were the significant variables for the land use type of residential–perennial crops; N-NO2−, P-PO43−, and coliform for the land use pattern of residential–rice cultivation–perennial crops) and N-NH4+, N-NO2−, Cl−, and coliform for the land use pattern of residential–rice cultivation–perennial crops–aquaculture. The current findings showed that that surface water quality has been influenced by the complex land use patterns in which residential and rice cultivation may have major roles in causing water impairment. The results of the water quality assessment and the variation in water properties of the land use patterns found in this study provide scientific evidence for future water quality management.

The Variation of Water Quality in Three Land Use Types in U Minh Ha National Park, Ca Mau Province, Vietnam Using Multivariate Statistical Approaches

“…However, the link between water quality and land use types has not been studied in U Minh Ha National Park, Ca Mau province, Vietnam. Meanwhile, the influence of land use patterns on the water quality has also been found in a previous study by Giao et al (2021) [25]. Therefore, this study was conducted to monitor the water quality in disturbed (Acacia hybrid-AH and planted Melaleuca cajuputi-PMC) and undisturbed areas (natural Melaleuca cajuputi-NMC) (1) to assess the water quality characteristics in areas with different land use patterns, (2) to analyze the correlation among the water quality parameters, (3) to assess the similarity of the water quality among land use patterns, and (4) to identify key water parameters resulting in seasonal variations of the water quality in the study area.…”

Analysis of Surface Water Quality in Upstream Province of Vietnamese Mekong Delta Using Multivariate Statistics

“…The pH value in the study was assessed as neutral for different water bodies. In addition, the pH value in the water bodies of An Giang has a more stable fluctuation level than other provinces/cities in the Mekong Delta, such as Hau Giang (6.8-7.1) [31], Ben Tre (6.53-8.02) [32], Dong Thap (7.15-7.36) [33], and Tien Giang (7.2-7.8) [34]. This can be explained by its geographic location in the upstream area, with large water flow, mainly alluvial soil, less affected by acid sulfate soil through leaching and overflow processes, which helps maintain the pH value as more stable in the water.…”

“…It is toxic to aquatic life, especially in alkaline environments [39]. The NH 4 + -N values in the water bodies in Hau Giang, Tien Giang, and Dong Thap provinces were 0.27 mg L −1 [31], 0.3-0.5 mg L −1 [33], and 0.36-0.40 mg L −1 [32], respectively. It can be deduced that surface water quality in the water bodies in the Vietnamese Mekong delta was contaminated with nutrients.…”

“…According to Ly and Giao (2018) [6], coliform in the surface water of the An Giang province in the period 2009-2016 was over the allowable limit by 2.14-7.02 times. It can be observed that microbial contamination has been a serious problem not only in the study area but also other water bodies in the Mekong Delta [28,31,33,36]. High levels of coliform can either directly or indirectly affect human health via using this water source for domestic purposes, irrigation and aquaculture [40].…”

Integrated Approach to Quantify the Impact of Land Use and Land Cover Changes on Water Quality of Surma River, Sylhet, Bangladesh

“…To explore the landscape pattern's impacts on the lakes and rivers water quality several studies have been conducted [15,16], and it seems that the riparian buffer zone landscape patterns are more powerful in explaining water quality variations [17]. Land use types have an influence on surface water quality which can be analyzed by using statistical methods, remote sensing (RS), and geographic information system (GIS) [18][19][20][21]. Li et al [12] stated that in riparian zones, the landscape category has a significant impact on water quality, and the alterations in the landscape through urban spread have put a lot of pressure on undeveloped land.…”