Prediksi Konsentrasi Klorofil-a Menggunakan Data Citra Satelit Sentinel-2A di Waduk Jatiluhur Kabupaten Purwakarta Prediction of Chlorophyll-a Using Satellite Imagery Data Sentinel-2A in Jatiluhur Reservoir Purwakarta Regency
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Abstract
Lakes, reservoirs, and rivers are among the most changing ecosystems on the earth’s surface. Chlorophyll-a as the main pigment in phytoplankton is often used to describe the water quality of lake and reservoirs and as an important element that indicates the tropical status of a waters. Spatial and temporal changes in algae blooms in a lake make it difficult to use in situ monitoring. The aim of the study is to predict the concentration of chlorophyll-a in Jatiluhur Reservoirs. The data used in this study were chlorophyll-a data from in situ measurements and satellite data Sentinel-2A. The method to obtain the value of the concentration of chlorophyll-a are an empirical and semi-analytical method. The empirical method is carried out by correlating in situ data with ratio of band 5 (red edge) and band 4 (red) (B5/B4). While the semi-analytical method is carried out by analyzing the Case 2 Regional Coast Colour (C2RCC) algorithm that integration in Sentinel Application Platform (SNAP) software. Correlation between in situ data and prediction of chlorophyll-a data has a strong relationship with the coefficient of determination R2=0.67 (in situ data with ratio prediction data) and R2=0.56 (in situ data with C2RCC prediction data). Based on this, remote sensing data on Sentinel-2A imagery with the application of several algorithms, can be used to support water quality monitoring activities in lakes and reservoirs, especially chlorophyll-a.
Keywords: Chlorophyll-a, Sentinel-2A satellite imagery, Jatiluhur reservoirs, Empirical, Semi-analytical
ABSTRAK
Danau, waduk, dan sungai merupakan salah satu ekosistem yang paling banyak berubah di permukaan bumi. Klorofil-a sebagai pigmen utama fitoplankton sering digunakan untuk menggambarkan kualitas perairan danau dan waduk. Selain itu, klorofil-a merupakan unsur penting yang menandakan status tropik suatu perairan. Perubahan secara spasial dan temporal blooming alga pada suatu danau/waduk membuat sulit untuk melakukan monitoring secara in situ. Penelitian dilakukan untuk memprediksi nilai konsentrasi klorofil-a di Waduk Jatiluhur menggunakan data penginderaan jauh. Data yang digunakan dalam penelitian ini adalah data klorofil-a hasil pengukuran in situ dan data citra satelit Sentinel-2A. Metode yang digunakan untuk memprediksi nilai konsentrasi klorofil-a adalah metode empiris dan semi analisis. Metode empiris dilakukan dengan mengkorelasikan antara nilai klorofil-a in situ dengan nilai rasio band 5 (red edge) dan band 4 (red) (B5/B4). Sedangkan metode semi analisis dilakukan dengan analisis algoritma Case 2 Regional Coast Colour (C2RCC) yang terintegrasi pada perangkat lunak Sentinel Application Platform (SNAP). Hasil korelasi antara data in situ dan data prediksi klorofil-a diperoleh hubungan yang kuat dengan koefisien determinasi R2=0,67 (data in situ dengan data prediksi rasio) dan R2=0,56 (data in situ dengan data prediksi C2RCC). Berdasarkan hal tersebut, data penginderaan jauh citra Sentinel-2A dengan aplikasi beberapa algoritma, dapat digunakan untuk mendukung kegiatan monitoring kualitas perairan di danau dan waduk terutama klorofil-a.
Kata kunci: Klorofil-a, Citra Sentinel-2A, Waduk Jatiluhur, Empiris, Semi analisis
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References
Abell, J. M., Özkundakci, D., Hamilton, D. P., & Miller SD. (2011). Relationships between land use and nitrogen and phosphorus in New Zealand lakes. Marine and Freshwater Research 62: 162–175. DOI: 10.1071/MF10180.
Ansper, A., & Alikas, K. (2019). Retrieval of chlorophyll a from Sentinel-2 MSI data for the European Union water framework directive reporting purposes. Remote Sensing 11. DOI: 10.3390/rs11010064.
APHA. (2005). Standard Methods for the Examination of Water and Waste Water Including Bottom Sediment and Sludges. Health Association Inc.: New York.
Astuti, Lismining, P. A., Sugianti, Y., Warsa, A., Rahman, A., & Hendrawan ALS. (2016). Tata Kelola Perikanan Berkelanjutan di Waduk Jatiluhur. Penerbit Deepublish. PENERBIT DEEPUBLISH: Yogyakarta.
Binding, C. E., Greenberg, T. A., & Bukata, R.P. (2013). The MERIS Maximum Chlorophyll Index; its merits and limitations for inland water algal bloom monitoring. Journal of Great Lakes Research 39: 100–107. DOI: 10.1016/j.jglr.2013.04.005.
Binding, C. E., Jerome, J.H., Bukata, R.P., & Booty, W.G. (2010). Suspended particulate matter in Lake Erie derived from MODIS aquatic colour imagery. International Journal of Remote Sensing 31: 5239–5255. DOI: 10.1080/01431160903302973.
Birk, S., & Ecke, F. (2014). The potential of remote sensing in ecological status assessment of coloured lakes using aquatic plants. Ecological Indicators 46: 398–406. DOI: 0.1016/j.ecolind.2014.06.035.
Bolpagni, R., Bresciani, M., Laini, A., Pinardi, M, Matta, E., Ampe, E. M., Giardino, C., Viaroli, P., & Bartoli, M. (2014). Remote sensing of phytoplankton-macrophyte coexistence in shallow hypereutrophic fluvial lakes. Hydrobiologia 737: 67–76. DOI: 10.1007/s10750-013-1800-6.
Bresciani, M., Cazzaniga, I., Austoni, M., Sforzi, T., Buzzi, F., Morabito, G., & Giardino, C. (2018). Mapping phytoplankton blooms in deep subalpine lakes from Sentinel-2A and Landsat-8. Hydrobiologia 824: 197–214. DOI: 10.1007/s10750-017-3462-2.
Buma, W.G., & Lee, S. Il. (2020). Evaluation of Sentinel-2 and Landsat 8 images for estimating Chlorophyll-a concentrations in Lake Chad, Africa. Remote Sensing 12. DOI: 10.3390/RS12152437.
Carneiro, F. M., Nabout, J.C., Vieira, L. C. G., Roland, F., & Bini, L. M. (2014). Determinants of chlorophyll-a concentration in tropical reservoirs. Hydrobiologia 740: 89–99. DOI: 10.1007/s10750-014-1940-3.
Doerffer, R., & Schiller, H. (2007). The MERIS Case 2 water algorithm. International Journal of Remote Sensing 28: 517–535. DOI: 10.1080/01431160600821127.
Green, E.P., Mumby, P.J., Edwards, A.J., & Clark, C. D. (1996). A review of remote sensing for the assessment and management of tropical coastal resources. Coastal Management 24: 1–40. DOI: 10.1080/08920759609362279.
Ha, N. T. T., Thao, N. T. P., Koike, K., & Nhuan, M. T. (2017). Selecting the best band ratio to estimate chlorophyll-a concentration in a tropical freshwater lake using sentinel 2A images from a Case study of Lake Ba Be (Northern Vietnam). ISPRS International Journal of Geo-Information 6. DOI: 10.3390/ijgi6090290.
IOCCG. (2000). IOCCG Report Number 03: Remote Sensing of Ocean Colour in Coastal, and Other Optically-Complex, Waters. IOCCG web page.
Lins, R. C., Martinez, J. M., Marques, D da. M., Cirilo, J. A., & Fragoso, C. R. (2017). Assessment of chlorophyll-a remote sensing algorithms in a productive tropical estuarine-lagoon system. Remote Sensing 9: 1–19. DOI: 10.3390/rs9060516.
Liu, W., Li, S., Bu, H., Zhang, Q., & Liu, G. (2012). Eutrophication in the Yunnan Plateau lakes: The influence of lake morphology, watershed land use, and socioeconomic factors. Environmental Science and Pollution Research 19: 858–870. DOI: 10.1007/s11356-011-0616-z.
Matthews, M. W. (2011). A current review of empirical procedures of remote sensing in Inland and near-coastal transitional waters. International Journal of Remote Sensing, 6855–6899. DOI: 10.1080/01431161.2010.512947.
Matthews, M. W., Bernard, S., & Winter, K. (2010). Remote sensing of cyanobacteria-dominant algal blooms and water quality parameters in Zeekoevlei, a small hypertrophic lake, using MERIS. Remote Sensing of Environment 114: 2070–2087. DOI: 10.1016/j.rse.2010.04.013.
Morel A, & Prieur L. (1977). Analysis of variations in ocean color. Limnology and Oceanography 22: 709–722. DOI: 10.4319/lo.1977.22.4.0709.
Nduru, R. E., Situmorang, M., & Tarigan, G. (2014). Analisa Faktor-Faktor Yang Mempengaruhi Hasil Produksi Padi Di Deli Serdang. Saintia Matematika 2: 71–83.
Ogashawara, I., Kiel, C., Jechow, A., Kohnert, K., Ruhtz, T., Grossart, H. P., Hölker, F., Nejstgaard, J. C., Berger, S. A., & Wollrab, S. (2021). The use of sentinel-2 for chlorophyll-A spatial dynamics assessment: A comparative study on different lakes in northern Germany. Remote Sensing 13: 1–26. DOI: 10.3390/rs13081542.
Paerl, H. W., Scott, J. T., McCarthy, M. J., Newell, S. E., Gardner, W. S., Havens, K. E., Hoffman, D. K., Wilhelm, S. W., & Wurtsbaugh, W. A. (2016). It Takes Two to Tango: When and Where Dual Nutrient (N & P) Reductions Are Needed to Protect Lakes and Downstream Ecosystems. Environmental Science and Technology 50: 10805–10813. DOI: 10.1021/acs.est.6b02575.