Water Quality Assessment of Remote Sensing Techniques: A Comparative Insight Between Yangtze of China and Porong of Indonesia

Authors

  • Niken Anissa Putri Department of Geography, University of Indonesia
  • Raldi Hendro Koestoer School of Environmental Science, University of Indonesia

DOI:

https://doi.org/10.15408/aism.v6i2.31386

Keywords:

Water quality, remote sensing, total suspended solid, China, Indonesia

Abstract

The river water quality, exceptionally the total suspended solid (TSS) in China and Indonesia, has deteriorated due to human activities. Remote sensing makes it easier for observers to monitor river water quality, especially TSS. However, measuring the river water quality by remote sensing is still in the model and algorithm development stage in China and Indonesia. This study aims to identify the river water quality on remote sensing in China's Yangtze River and Porong River, East Java, Indonesia, and to analyze comparisons of river water quality on remote sensing in Yangtze River, China, and Porong River, East Java, Indonesia. This method uses a literature review based on journals, articles, and primary sources to review related literature on TSS concentrations in rivers and remote sensing in China and Indonesia. River water monitoring methods can measure the TSS in China and Indonesia using remote sensing. Many water quality models for waterways are based on different satellite images. In the Yangtze Downstream River, the algorithm of TSS uses the latest random forest on Landsat-8. The algorithm of TSS in the Porong River estuary used linear regression on sentinel-2 imagery. These TSS algorithms can more precisely assess TSS in water quality for scientific studies. The results show that the latest random forest is a more precise remote sensing algorithm in China than Linear regression in Indonesia. The suspended solid models and remote sensing images such as China's MODIS, Landsat-8, and MERIS are accurate in China. Therefore, developing more precise remote sensing techniques, total suspended solid models composed of Wiggin's Algorithm and Markert Algorithm, NDWI Algorithm, and remote sensing imagery such as Sentinel-2 and Landsat-8 in Indonesia is crucial to determine total suspended solids. The researchers additionally contribute to advanced research toward advancing suitable remote sensing techniques in various areas in Indonesia.

Downloads

Download data is not yet available.

References

G. E. Adjovu, H. Stephen, D. James, and S. Ahmad, “Overview of the Application of Remote Sensing in Effective Monitoring of Water Quality Parameters,” Remote Sensing, vol. 15, no. 7. 2023, doi: 10.3390/rs15071938.

B. Hamuna, R. H. R. Tanjung, S. Suwito, H. K. Maury, and A. Alianto, “Study of Seawater Quality and Pollution Index Based on Physical-Chemical Parameters in the Waters of the Depapre District, Jayapura,” J. Ilmu Lingkung., vol. 16, no. 1, pp. 35–43, 2018, doi: 10.14710/jil.16.135-43.

K. N. Markert et al., “Historical and operational monitoring of surface sediments in the Lower Mekong Basin using Landsat and Google Earth Engine cloud computing,” Remote Sens., vol. 10, no. 6, pp. 1–19, 2018, doi: 10.3390/rs10060909.

S. N. Topp, T. M. Pavelsky, D. Jensen, M. Simard, and M. R. V. Ross, “Research trends in the use of remote sensing for inland water quality science: Moving towards multidisciplinary applications,” Water (Switzerland), vol. 12, no. 1, pp. 1–34, 2020, doi: 10.3390/w12010169.

J. Chen et al., “Remote Sensing Big Data for Water Environment Monitoring: Current Status, Challenges, and Future Prospects,” Earth’s Futur., vol. 10, no. 2, pp. 1–33, 2022, doi: 10.1029/2021EF002289.

M. K. Mukhtar, Supriatna, and M. D. M. Manessa, “The validation of water quality parameter algorithm using Landsat 8 and Sentinel-2 image in Palabuhanratu Bay,” IOP Conf. Ser. Earth Environ. Sci., vol. 846, no. 1, 2021, doi: 10.1088/1755-1315/846/1/012022.

Y. Du et al., “Using Remote Sensing to Understand the Total Suspended Matter Dynamics in Lakes across Inner Mongolia,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 14, pp. 7478–7488, 2021, doi: 10.1109/JSTARS.2021.3097083.

Q. Cao, G. Yu, S. Sun, Y. Dou, H. Li, and Z. Qiao, “Monitoring water quality of the haihe river based on ground-based hyperspectral remote sensing,” Water (Switzerland), vol. 14, no. 1, pp. 1–13, 2022, doi: 10.3390/w14010022.

F. Bioresita, M. H. Ummah, M. Wulansari, and N. A. Putri, “Monitoring Seawater Quality in the Kali Porong Estuary as an Area for Lapindo Mud Disposal leveraging Google Earth Engine,” IOP Conf. Ser. Earth Environ. Sci., vol. 936, no. 1, 2021, doi: 10.1088/1755-1315/936/1/012011.

Y. Du et al., “Total suspended solids characterization and management implications for lakes in East China,” Sci. Total Environ., vol. 806, p. 151374, 2022, doi: https://doi.org/10.1016/j.scitotenv.2021.151374.

R. Maru, I. I. Baharuddin, N. Badwi, S. Nyompa, and Sudarso, “Analysis of Water Well Quality Drilling Around Waste Disposal Site in Makassar City Indonesia,” J. Phys. Conf. Ser., vol. 954, no. 1, 2018, doi: 10.1088/1742-6596/954/1/012025.

X. Wang, Z. Wen, G. Liu, H. Tao, and K. Song, “Remote estimates of total suspended matter in China’s main estuaries using Landsat images and a weight random forest model,” ISPRS J. Photogramm. Remote Sens., vol. 183, no. July 2021, pp. 94–110, 2022, doi: 10.1016/j.isprsjprs.2021.11.001.

L. O. L. Putri and E. Wardhani, “Analysis of groundwater quality in Cimahi City of West Java Province,” IOP Conf. Ser. Earth Environ. Sci., vol. 894, no. 1, 2021, doi: 10.1088/1755-1315/894/1/012037.

H. S. D. Kospa and Rahmadi, “Influence of Community Behaviour on Water Quality in Sekanak River, Palembang,” IOP Conf. Ser. Earth Environ. Sci., vol. 306, no. 1, 2019, doi: 10.1088/1755-1315/306/1/012008.

A. P. Mishra, H. Khali, S. Singh, C. B. Pande, R. Singh, and S. K. Chaurasia, “An Assessment of In-situ Water Quality Parameters and its variation with Landsat 8 Level 1 Surface Reflectance datasets,” Int. J. Environ. Anal. Chem., vol. 00, no. 00, pp. 1–23, 2021, doi: 10.1080/03067319.2021.1954175.

V. K. Singh et al., “Development of fuzzy analytic hierarchy process based water quality model of Upper Ganga river basin, India,” J. Environ. Manage., vol. 284, no. February, p. 111985, 2021, doi: 10.1016/j.jenvman.2021.111985.

N. M. DeLuca, B. F. Zaitchik, and F. C. Curriero, “Can multispectral information improve remotely sensed estimates of total suspended solids? A statistical study in Chesapeake Bay,” Remote Sens., vol. 10, no. 9, pp. 1–16, 2018, doi: 10.3390/rs10091393.

S. Zare, S. R. Fallah Shamsi, and S. A. Abtahi, “Weakly-coupled geo-statistical mapping of soil salinity to Stepwise Multiple Linear Regression of MODIS spectral image products,” J. African Earth Sci., vol. 152, no. June 2017, pp. 101–114, 2019, doi: 10.1016/j.jafrearsci.2019.01.008.

L. Bi, B. L. Fu, P. Q. Lou, and T. Y. Tang, “Delineation Water of Pearl River Basin Using Landsat Images from Google Earth Engine,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch., vol. 42, no. 3/W10, pp. 5–10, 2020, doi: 10.5194/isprs-archives-XLII-3-W10-5-2020.

Z. N. Ghuvita Hadi, T. Hariyanto, and N. Hayati, “Estimation of Total Suspended Sediment Solid in Porong River Waters Using Multitemporal Satellite Imagery,” IOP Conf. Ser. Earth Environ. Sci., vol. 936, no. 1, 2021, doi: 10.1088/1755-1315/936/1/012006.

A. Singh and V. Vyas, “A Review on remote sensing application in river ecosystem evaluation,” Spat. Inf. Res., vol. 30, no. 6, pp. 759–772, 2022, doi: 10.1007/s41324-022-00470-5.

V. Sagan et al., “Monitoring inland water quality using remote sensing: potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing,” Earth-Science Rev., vol. 205, p. 103187, 2020, doi: https://doi.org/10.1016/j.earscirev.2020.103187.

Y. Gao, W. Zhang, Y. Li, H. Wu, N. Yang, and C. Hui, “Dams shift microbial community assembly and imprint nitrogen transformation along the Yangtze River,” Water Res., vol. 189, p. 116579, 2021, doi: https://doi.org/10.1016/j.watres.2020.116579.

M. Yao, Z. Li, and Y. Wang, “Features of Industrial Green Technology Innovation in the Yangtze River Economic Belt of China Based on Spatial Correlation Network,” Sustainability, vol. 15, no. 7. 2023, doi: 10.3390/su15076033.

C. B. Pribadi and T. Hariyanto, “The evaluation of the physical condition in coastal area as a result of changes of suspended sediment (Case study: Coastal area of Surabaya and Gresik),” Int. J. Geoinformatics, vol. 15, no. 3, pp. 81–90, 2019.

N. Andika and P. Y. Julien, “Sediment Propagation in the Porong River below the Sidoardjo Mud Volcano Diversion in Indonesia,” J. Hydraul. Eng., vol. 147, no. 11, pp. 1–12, 2021, doi: 10.1061/(asce)hy.1943-7900.0001931.

H. Snyder, “Literature review as a research methodology: An overview and guidelines,” J. Bus. Res., vol. 104, pp. 333–339, 2019, doi: https://doi.org/10.1016/j.jbusres.2019.07.039.

J. M. Grimshaw et al., “Pravila PRISMA 2020.,” Med. Flum., vol. 57, no. 4, pp. 444–465, 2021, doi: 10.21860/medflum2021_264903.

Q. Chen, B. Zhou, Z. Yu, J. Wu, and S. Tang, “Detection of the Minute Variations of Total Suspended Matter in Strong Tidal Waters Based on GaoFen-4 Satellite Data,” Remote Sensing, vol. 13, no. 7. 2021, doi: 10.3390/rs13071339.

M. D. Larson, A. Simic Milas, R. K. Vincent, and J. E. Evans, “Multi-depth suspended sediment estimation using high-resolution remote-sensing UAV in Maumee River, Ohio,” Int. J. Remote Sens., vol. 39, no. 15–16, pp. 5472–5489, Aug. 2018, doi: 10.1080/01431161.2018.1465616.

T. Aris, D. A. Mamahit, A. R. Ras, and A. Widodo, “Predicting Tanjung Piai Coastline Changes Using Digital Shoreline Analysis System Method: Impact of Indonesia’s Maritime Security,” Appl. Inf. Syst. Manag., vol. 5, no. 1, pp. 53–62, 2022, doi: 10.15408/aism.v5i1.24863.

S. Wang et al., “Developing remote sensing methods for monitoring water quality of alpine rivers on the Tibetan Plateau,” GIScience Remote Sens., vol. 59, no. 1, pp. 1384–1405, Dec. 2022, doi: 10.1080/15481603.2022.2116078.

K. T. Peterson, V. Sagan, P. Sidike, A. L. Cox, and M. Martinez, “Suspended sediment concentration estimation from landsat imagery along the lower missouri and middle Mississippi Rivers using an extreme learning machine,” Remote Sens., vol. 10, no. 10, 2018, doi: 10.3390/rs10101503.

Y. Zhang, Y. Zhang, K. Shi, Y. Zhou, and N. Li, “Remote sensing estimation of water clarity for various lakes in China,” Water Res., vol. 192, p. 116844, 2021, doi: https://doi.org/10.1016/j.watres.2021.116844.

Downloads

Published

2023-09-16

How to Cite

Water Quality Assessment of Remote Sensing Techniques: A Comparative Insight Between Yangtze of China and Porong of Indonesia. (2023). Applied Information System and Management (AISM), 6(2), 83-90. https://doi.org/10.15408/aism.v6i2.31386