Volume 7, Issue 11, November – 2022 International Journal of Innovative Science and Research Technology

ISSN No:-2456-2165

Assessment of Inland Water Quality In Banjar

Regency Using Remotely Sensed Satellite Image
Rony Riduan, Riza Miftahul Khair, Andre Anantama Irawan, and Farrel Alta Ferisza Siregar
Environmental Engineering Department
Lambung Mangkurat University
Banjarbaru, Indonesia

Abstract:- The main environmental issues in Banjar is compared to 2018, from 97 rivers in Indonesia, 67 rivers
Regency Indonesia are the handling of residential are lightly polluted, 5 rivers are lightly polluted and 25 rivers
wastewater and environmental degradation due to are lightly polluted and heavily polluted [1].
mining activities. Efforts to monitor and analyze water
quality status with a large area and diverse land use Banjar Regency is one of the areas in South Kalimantan
require a lot of time and money. This research will help which has a large river, namely the Martapura River with an
provide alternative solutions for predicting the quality of area of 453.88 km2, and a length of 36,566 meters [2]. Banjar
inland waters using remote sensing satellite image Regency itself has an area of 4,668.50 km2 with a potential
interpretation. The research was conducted to obtain the dry land area of 13,757 ha, tidal 32,252 ha, rainfed rice fields
distribution pattern of water quality parameters such as 13,446 ha, irrigation 5,497 ha, swamp or lebak 8,538 ha, and
Turbidity, TSS (Total Suspended Solid), organic CDOM non-rice field agricultural land covering an area of 320,602
(Colored Dissolved Organic Matter Absorption), and ha. In addition to strategic utilization in the agricultural
Chlorophyll-a (Chl-a). These four parameters are sector, the development of residential areas is also a strategic
generally monitored through field sampling which is issue of the Banjar Regency with one of the main problems,
time-consuming and costly. Remote sensing provides the namely the handling of residential wastewater disposal, and
possibility to provide water quality information over a environmental degradation due to mining activities [3]. The
wide area and identify trends in changing water quality Environmental Service of South Kalimantan Province noted
patterns through historical data. The research was that there are dozens of companies operating in the Martapura
conducted through the interpretation of Sentinel-2 sub-watershed area, three of which are coal mining
satellite imagery and other sources such as Landsat and companies. The existence of these dozens of companies has
SRTM. Data were collected over the last 5 years and the potential to trigger pollution of the Martapura river which
processed using the GEE (Google Earth Engine), ESA- is the source of PDAM's raw water, transportation,
SNAP (European Space Agency - Sentinel Application agriculture and fisheries as well as other community
Platform), and QGIS applications. The interpretation economic activities. In addition to coal mining, dozens of
results for current conditions are calibrated using field companies operating along the Martapura sub-watershed are
measurement data, then mapped to display patterns of plantations, rubber, plywood, shrimp freezing, hotel and
water quality parameter conditions spatially and home industries. Pollution of the Martapura River is a priority
temporally. The last step is to perform spatial statistical for the handling of the South Kalimantan Provincial
analysis and the trend of changes in water quality using Government [4].
the GEODA application. The distribution of water
quality parameter values shows various patterns for Efforts to monitor and analyze the status of water
inland waters in Banjar Regency but tends to increase in quality with a large area and quite diverse land uses require a
locations close to residential and agricultural areas. All large amount of time and money. This research will help
water quality parameters reviewed also show an provide alternative solutions for predicting the quality of
increasing trend in the last 5 years according to the inland waters using remote sensing satellite image
analysis period. interpretation. It is necessary to conduct research to obtain the
distribution pattern of water quality on the parameters of
Keywords:- Banjar Regency, Inland Water, Remote Sensing, turbidity (Turbidity), TSS (Total Suspended Solid), organic
Water Quality, Sentinel-2. CDOM (Coloured Dissolved Organic Matter Absorption),
and Chlorophyll-a (Chl-a). These four indicators are
I. INTRODUCTION generally monitored through field sampling which is time-
consuming and costly. Remote sensing provides the
The condition of inland water quality, based on data possibility to provide water quality information over a wide
from the Ministry of Environment and Forestry in 2019 stated area, and identify trends in changing water quality patterns
that the quality of river water in Indonesia is deteriorating through historical data. Remote sensing applications in
every year. Of the 98 rivers in Indonesia, 54 rivers are lightly waters have been used as an effective alternative to monitor
polluted, 6 rivers are lightly polluted-moderately polluted, water quality. The color of the waters captured by remote
and 38 rivers are lightly polluted-severely polluted. This data sensing applications provides information about the optical

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Volume 7, Issue 11, November – 2022 International Journal of Innovative Science and Research Technology
ISSN No:-2456-2165
properties of the waters [5]. Remote sensing technology analysis to determine the trend of changes in the condition of
which has experienced rapid development has an important inland water quality in the Banjar Regency area.
role in supporting and covering the shortcomings of
conventional sampling techniques. The location of sampling and measurement points must
represent the presence of rivers, lakes, and swamps in the
The research is expected to assist efforts to monitor the Banjar Regency area. The location of sampling and
quality of inland waters in Banjar Regency spatially and measurement points in GCP (Ground Control Points) can be
temporally by utilizing Sentinel-2A satellite imagery, seen in Fig. 1.
Landsat, and SRTM. Sentinel-2A was chosen because it has
a spatial resolution of 10 meters for the Green, Red, Blue, and
NIR channels. Sentinel-2A acts as the main data used to
determine the value and distribution of TSS, turbidity,
CDOM, and Chl-a. Data were collected for the last 5 years,
and processed using the Google Earth Engine, ESA-SNAP,
and QGIS applications. The interpretation results for current
conditions are calibrated using field measurement data, then
mapped to display patterns of water quality conditions
spatially and temporally. The last step is to perform a spatial
statistical analysis of the trend in water quality changes using
GEODA application.

II. METHOD

The research phase is divided into three research stages.

The first stage is to perform water quality prediction
calculations based on remote sensing satellite imagery data
for turbidity, TSS, CDOM, and Chlorophyll-a in inland Fig 1:- Sampling Location
waters in the Banjar Regency area. The next stage is mapping
the distribution pattern of inland water quality conditions in Analysis of water condition data for the last 5 years
the Banjar Regency spatially from remote sensing satellite representing variations in time differences and the results of
image data processing for turbidity, TSS, CDOM, and interpretation of water quality representing spatial variations
Chlorophyll-a. The last stage is to analyze the trend of were carried out by cluster analysis (CA). CA is a group of
changes in inland water quality conditions in Banjar Regency multivariate analyses whose main purpose is to classify
for the last 5 years for the parameters of turbidity, TSS, objects based on the characteristics they have. CA is
CDOM, and Chlorophyll-a. performed on data that has been standardized through Z-scale
transformation (Z-scale). This transformation is used to avoid
The results of measurements and laboratory tests from misclassification due to unit variations of each water quality
the first research phase are not only used to determine the parameter; In addition, through this standardization, it can
adsorption algorithm for water quality parameters but are also increase the effect of parameters that have small variances
used for the calibration process. The satellite image obtained and vice versa reducing the effect of parameters with large
then goes through the stages of atmospheric, radiometric, and variances [6]. The CA results obtained are then mapped
geometric correction before being processed by the spatially using QGIS.
adsorption algorithm for the prediction of water quality
parameters. Before analyzing the interpretation of water III. RESULTS AND DISCUSSION
quality, identification of the probability of the presence of
inland waters carried a certain threshold. The research A. Field Measurement and Laboratory Test Result
activity was continued by mapping the distribution pattern of Table 1 shows the field measurement and laboratory
inland water quality for the 4 specified parameters. After the water sample analysis results for several water quality
spatial analysis was carried out, it was followed by a temporal parameters from sampling point locations

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Volume 7, Issue 11, November – 2022 International Journal of Innovative Science and Research Technology
ISSN No:-2456-2165
Field Measurement Laboratory Test
No. GCP Point X Coordinates Y Coordinates Turbidity Conductivitys Salinitys TDS TSS DO Nitrate Phosphate TOM BOD COD
pH
(NTU) (µS/cm) (ppt) (ppm) (mg/l) (mg/l) (mg/l) (mg/l) (mg/l) (mg/l) (mg/l)
1 River 1 3°17'47.48"S 114°41'33.15"E 28.30 0.220 0.1 117.00 7.05 24 6.1 <0.3 0.79 15.64 17.12 22.34
2 River 2 3°21'10.34"S 114°48'57.55"E 30.93 0.207 0.1 105.00 7.74 19 6.2 <0.3 0.19 14.97 17.12 21.39
3 River 3 3°21'50.47"S 114°55'19.70"E 160.00 0.318 0.1 168.00 7.60 77 6.3 <0.3 0.27 15.30 17.12 21.86
4 Pond 1 3°30'38.75"S 114°39'1.70"E 6.09 17.320 0.0 9.20 6.11 3 6.0 0.02 1.13 14.97 17.12 21.39
5 Pond 2 3°29'4.51"S 114°40'29.05"E 30.95 120.600 0.1 63.70 8.25 5 6.2 0.01 0.18 15.64 8.11 22.34
6 Pond 3 3°30'9.25"S 114°39'9.99"E 2.66 16.700 0.0 9.05 8.40 2 6.6 0.01 0.05 14.64 6.31 20.91
7 Swamp 1 3°24'26.86"S 114°51'28.36"E 13.04 144.600 0.1 55.00 7.27 12 6.3 <0.03 0.13 15.97 8.11 22.81
8 Swamp 2 3°18'43.79"S 114°51'1.49"E 4.27 30.400 0.1 16.70 4.94 8 6.2 0.01 0.08 18.30 18.02 26.14
9 Swamp 3 3°26'42.61"S 114°54'30.02"E 40.46 159.400 0.1 84.60 7.67 19 6.6 0.01 0.43 14.97 9.91 21.39
10 Lake 1 3°31'15.29"S 115° 0'30.08"E 6.66 161.900 0.1 85.90 8.12 2 6.0 0.01 0.17 14.64 16.22 20.91
11 Lake 2 3°32'4.57"S 115° 3'41.35"E 3.45 158.000 0.1 83.40 7.51 2 6.5 0.01 0.16 14.64 18.02 20.91
12 Lake 3 3°29'54.02"S 115° 5'13.78"E 3.77 156.100 0.1 82.50 7.58 3 6.7 0.01 2.70 14.64 12.61 20.91
Table 1:- Field and laboratory water quality on GCP points

Phosphate parameters at some points exceed the Based on the resulting image, the existing bands are
national water quality standard [7]. High levels of phosphate then processed to obtain the values of NDWI (Normalized
are generally caused by human or animal waste, and domestic Difference Water Index), NDTI (Normalized Difference
activities. Excessive phosphate concentration will increase Turbidity Index), Turbidity, TSS (Total Suspended Solid),
the growth of algae which results in reduced sunlight entering CDOM (Coloured Dissolved Organic Matter), and Chl-a
the water bodies [8]. BOD (Biological Oxygen Demand) (Chlorophyll-a).
parameter exceeds the national water quality standard for all
measurement points. The higher the BOD level, the higher C. River Water Quality Profile
the activity of organisms to decompose organic matter. COD The profile line in Fig. 3 is determined along the axis of
(Chemical Oxygen Demand) content showed a higher value the Martapura river with a total length of about 120 km. This
than the national water quality standard at certain locations of profile is used to provide an overview of the pattern of
the swamp. Similar to BOD, COD levels in water are changes in water quality parameters along the Martapura
associated with a decrease in dissolved oxygen content in the River from upstream to downstream.
waters [9]. TSS (Total Suspended Solid) level at point river 3
also exceeded the national water quality standard limit.
domestic and agricultural activities show a large influence on
water quality conditions at the sampling points.

B. Satellite Imagery
The satellite image is taken from the Sentinel-2 satellite
data with the help of the Google Earth Engine script. The
image obtained through this script then goes through the
stages of converting the coordinate reference system to UTM
coordinates (WGS84/UTM Zone 50S). The image is also
resampled to a resolution of 10 m. The images obtained are
satellite images for Bands 1 to 12 which are averaged per year
of observation. The image obtained is still divided into
several images, so it must undergo a merging process. The
merging process is carried out with the SNAP application
through mosaicking. The combined satellite images are then
processed and displayed in the SNAP application (Fig. 2).
Fig 3:- River profile line
Observations were made from early 2018 to mid-September
2022.
The results of satellite image processing along the river
profile line (Fig. 4) showed a more varied pattern of
fluctuations in the turbidity, TSS, and Chl-a parameters
upstream. The CDOM parameter is very volatile along the
Martapura river in 2018. CDOM shows an increase in
locations close to residential and agricultural areas. In 2019
the pattern of changes that occur along the river profile is still
fluctuating, especially for TSS and CDOM parameters. The
turbidity parameter still has a higher tendency in the upstream
area, and the CHL-a parameter is relatively not subject to
various fluctuations. Conditions in 2020 compared to the
previous year experienced an increase in TSS, CDOM, and
Fig 2:- RGB image from SNAP Chl-a concentration.

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Volume 7, Issue 11, November – 2022 International Journal of Innovative Science and Research Technology
ISSN No:-2456-2165
several locations in the upstream and middle part of the river
near the downstream. Conditions in 2022 have a different
pattern compared to 2021. At some points, there are some
increases in the concentration of turbidity, TSS, and CDOM,
especially in the middle of the longitudinal profile of the
Martapura river. Some spikes in water quality parameters are
found, especially in areas adjacent to residential, agricultural,
and industrial areas.

D. Lake Water Quality Profile

Fig. 5 shows the location of lakes in Banjar Regency
area. the lake has an area of 97.3 km2. The water quality
distribution pattern in the lake is determined through the
results of cluster analysis which divides the lake area into 9
clusters. Histogram data for each cluster was obtained
through analysis using the GEODA application based on the
distribution map of each water quality parameter.

The lake organic concentration of CDOM in 2018 was

higher at the top of the cluster than the others. However, the
highest amount of data occurred in the lower cluster at the
sides of the lake. The same pattern also occurred in 2019, but
there was a decrease in the concentration of CDOM. The
distribution of the highest concentration is still dominant in
the upper and lower clusters of the reservoir. Conditions in
2019 described a decrease in the value of the CDOM
concentration, especially in the right cluster part of the lake.
This condition still occurred until 2021, but in 2022 the
concentration of CDOM increased in almost all of the lake
clusters (Fig. 6).

The distribution pattern of Chl-a in the lake shows a

fairly uniform pattern, although the highest number of Chl-a
occurs more in the upper cluster. This condition continued
until the end of the observation year. Chl-a concentrations
decreased in 2020 and 2021 compared to 2018 and 2019 but
increased again in 2022 (Fig. 7).

Fig 4:- Water quality profile for Martapura River

TSS and turbidity fluctuation patterns still showed high

fluctuations, but still had higher values upstream than
downstream. The CDOM pattern shows an increase in the
middle of the river although it is quite varied. In the year
2021, there is an increase in Turbidity, TSS, and Chl-a and a
decrease in the average value for CDOM. In general, the
pattern of change for the turbidity and TSS parameters still
shows variations in values, but tends to increase in the
upstream part of the river. the number of CDOM parameters
has increased in the middle of the river, especially in
residential and agricultural areas. The CHL-a parameter did Fig 5:- Location of the lake in Banjar Regency
not show significant variation, but there was an increase in

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Volume 7, Issue 11, November – 2022 International Journal of Innovative Science and Research Technology
ISSN No:-2456-2165

Fig 7:- Distribution of CDOM in the lake

Water quality data from satellite imagery at observation

points are then averaged based on the year of observation.
This value was analyzed using linear regression to obtain the
trend of changes in water quality conditions in the Banjar
Fig 6:- Distribution of Chl-a in the lake Regency area. The regression results show that all water
quality parameters tend to increase in concentration
following the linear equation slope of 0.2664 and an intercept
of - 537.42 for the turbidity parameter. The TSS parameter
has a linear equation with a slope of 46,847 and an intercept
of -94458. The CDOM parameter follows a linear equation
with a slope of 1.8112 and an intercept of -3642.7 and Chl-a
with a slope of 0.3813 and an intercept value of -768.07. This
shows that the condition of water quality in Banjar Regency
is experiencing a declining trend in quality in the coming
year.

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ISSN No:-2456-2165
[6]. M. Varol, and B. Şen, “Assessment of surface water
quality using multivariate statistical techniques: a case
study of Behrimaz Stream, Turkey,” Environmental
Monitoring Assessment, vol. 159, no. 1, pp. 543-553,
2009.
[7]. "Appendix VI Government Regulation of the Republic
of Indonesia Number 22 year 2001 concerning the
Implementation of Environmental Protection and
Management," S. S. M. o. t. R. o. Indonesia, ed., 2021.
[8]. K. Ngibad, “Analysis of Phosphate Levels in Ngelom
River Water, Sidoarjo Regency, East Java,” Jurnal
Pijar Mipa, vol. 14, no. 3, pp. 197-201, 2019.
[9]. W. Atima, “BOD and COD as water pollution
parameters and wastewater quality standards,” BIOSEL
: Journal of Science and Education Research, vol. 4,
no. 1, pp. 83-93, 2015.

Fig 8:- Water quality trendline

IV. CONCLUSION

The distribution of water quality parameter values

shows a diverse pattern for inland waters in Banjar Regency
but tends to increase in locations close to the settlements and
agriculture area. In general, all water quality parameters
showed an increasing trend. This result gives the possibility
of a water quality decrease in Banjar Regency.

ACKNOWLEDGMENT

This research was supported by Lambung Mangkurat

University Research Institute and Community Service
(LPPM ULM). We also thank our colleagues from ULM
Environmental Engineering Department who provided
insight and expertise that greatly assisted the research.

REFERENCES

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[3]. A. Hidayati, R. Riduan, and M. A. Firdausy,
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