Decadal Monitoring of Upwelling Dynamics in Satonda Island Waters Using Landsat-8 and Machine Learning Regression
Abstract
Global warming and associated weather changes, notably the El Niño Southern Oscillation (ENSO), significantly impact marine ecosystems by altering water quality parameters such as chlorophyll-a (Chl-a) and sea surface temperature (SST). These changes are crucial in understanding the biogeochemical and ecological dynamics of marine environments, especially in regions affected by upwelling. This study aims to monitor upwelling events on Satonda Island, a volcanic island with unique central lake and status as a protected area using remote sensing. Utilizing Landsat-8 imagery and machine learning regression techniques—Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART)—this research evaluates the water quality in Satonda waters over a decade (2013– 2023). The RF method emerged as the most accurate in estimating Chl-a and SST, indicating its efficacy in monitoring marine ecosystems with the result (RMSE = 0.309 and 0.274). The analysis reveals seasonal upwelling patterns, characterized by decreased SST and increased Chl-a concentration, with peaks varying annually between June and November. This study highlights the crucial role of remote sensing and machine learning in monitoring the effects of climate change on marine biodiversity. It provides valuable insights into the temporal dynamics of upwelling in the shallow waters of Indonesia.
References
Ampou, E. E., Manessa, M. D. M., Hamzah, F., & Widagti, N. (2020). Study of sea surface temperature (Sst), does it affect coral reefs? Jurnal Ilmiah Perikanan Dan Kelautan, 12(2), 199–213. https://doi.org/10.20473/jipk.v12i2.20316
Androulidakis, Y. S., & Krestenitis, Y. N. (2022). Sea surface temperature variability and marine heat waves over the Aegean, Ionian, and Cretan Seas from 2008–2021. Journal of Marine Science and Engineering, 10(1), 42. https://doi.org/10.3390/jmse10010042
Antoni, S., Bantan, R. A., Al-Dubai, T. A., Lubis, M. Z., Anurogo, W., & Silaban, R. D. (2019). Chlorophyll-a, and Sea Surface Temperature (SST) as proxies for Climate Changes: Case Study in Batu Ampar waters, Riau Islands. IOP Conference Series: Earth and Environmental Science, 273(1). https://doi.org/10.1088/1755-1315/273/1/012012
Bakun, A., Black, B. A., Bograd, S. J., García-Reyes, M., Miller, A. J., Rykaczewski, R. R., & Sydeman, W. J. (2015). Anticipated effects of climate change on coastal upwelling ecosystems. Current Climate Change Reports, 1(2), 85–93. https://doi.org/10.1007/s40641-015-0008-4
Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011
Biau, G., & Scornet, E. (2016). A random forest guided tour. TEST, 25(2), 197–227. https://doi.org/10.1007/s11749-016-0481-7
Dabuleviciene, T., Vaiciute, D., & Kozlov, I. E. (2020). Chlorophyll-a Variability during upwelling events in the South-Eastern Baltic Sea and in the Curonian Lagoon from Satellite Observations. Remote Sensing, 12(21), 3661. https://doi.org/10.3390/rs12213661
Decree of the Minister of Environment and Forestry of the Republic of Indonesia, (2022). Perubahan Fungsi Dalam Fungsi Pokok Kawasan Hutan dari Kawasan Taman Buru Pulau Moyo, Taman Wisata Alam Laut Pulau Moyo, dan Taman Wisata Alam Pulau Satonda Menjadi Taman Nasional Moyo Satonda di Kabupaten Sumbawa. Minister of Environment and Forestry of the Republic of Indonesia.
Dyba, K., Ermida, S., Ptak, M., Piekarczyk, J., & Sojka, M. (2022). Evaluation of methods for estimating lake surface water temperature using landsat 8. Remote Sensing, 14(15), 3839. https://doi.org/10.3390/rs14153839
Efriana, A. F., Manessa, M. D. M., & Ayu, F. (2024). Development of empirical CDOM algorithm for Sentinel-2 using the Gloria dataset. Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet, 12977, 129770V. SPIE. https://doi.org/10.1117/12.3009622
Free, G., Bresciani, M., Pinardi, M., Ghirardi, N., Luciani, G., Caroni, R., & Giardino, C. (2021). Detecting climate driven changes in chlorophyll-a in deep subalpine lakes using long term satellite data. Water, 13(6), 866. https://doi.org/10.3390/w13060866
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
Ha, N. T. T., Koike, K., Nhuan, M. T., Canh, B. D., Thao, N. T. P., & Parsons, M. (2017). Landsat 8/OLI Two Bands Ratio Algorithm for Chlorophyll-A Concentration Mapping in Hypertrophic Waters: An Application to West Lake in Hanoi (Vietnam). IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(11), 4919–4929. https://doi.org/10.1109/JSTARS.2017.2739184
Hedley, J. D., Roelfsema, C. M., Chollett, I., Harborne, A. R., Heron, S. F., Weeks, S. J., Skirving, W. J., Strong, A. E., Mark Eakin, C., Christensen, T. R. L., Ticzon, V., Bejarano, S., & Mumby, P. J. (2016). Remote sensing of coral reefs for monitoring and management: A review. Remote Sensing, 8(2). https://doi.org/10.3390/rs8020118
Henson, S. A., Beaulieu, C., & Lampitt, R. (2016). Observing climate change trends in ocean biogeochemistry: when and where. Global Change Biology, 22(4), 1561–1571. https://doi.org/10.1111/gcb.13152
Jacox, M. G., Edwards, C. A., Hazen, E. L., & Bograd, S. J. (2018). Coastal Upwelling Revisited: Ekman, Bakun, and Improved Upwelling Indices for the U.S. West Coast. Journal of Geophysical Research: Oceans, 123(10), 7332–7350. https://doi.org/10.1029/2018JC014187
Jang, J.-C., & Park, K.-A. (2019). High-resolution sea surface temperature retrieval from landsat 8 OLI/TIRS data at coastal regions. Remote Sensing, 11(22), 2687. https://doi.org/10.3390/rs11222687
Karimi, B., Hashemi, S. H., & Aghighi, H. (2022). Performance of Sentinel-2 and Landsat-8 satellites in estimating Chlorophyll-a concentration in a shallow freshwater lake. Preprint (Researchsquare), 1-15. https://doi.org/10.21203/rs.3.rs-1968542/v1
Katlane, R., El Kilani, B., Dhaoui, O., Kateb, F., & Chehata, N. (2023). Monitoring of sea surface temperature, chlorophyll, and turbidity in Tunisian waters from 2005 to 2020 using MODIS imagery and the Google Earth Engine. Regional Studies in Marine Science, 66, 103143. https://doi.org/10.1016/j.rsma.2023.103143
Kniebusch, M., Meier, H. E. M., Neumann, T., & Börgel, F. (2019). Temperature variability of the Baltic Sea Since 1850 and attribution to atmospheric forcing variables. Journal of Geophysical Research: Oceans, 124(6), 4168–4187. https://doi.org/10.1029/2018JC013948
Lakshmi, G. J., Bodapati, V., Baji, S. S., & Panuganti, T. S. (2023). Water quality monitoring using remote-sensing. International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES), 624–628. https://doi.org/10.1109/CISES58720.2023.10183395
Liu, Y., Ortega-Farías, S., Tian, F., Wang, S., & Li, S. (2021). Estimation of surface and near-surface air temperatures in arid northwest china using landsat satellite images. Frontiers in Environmental Science, 9, 791336. https://doi.org/10.3389/fenvs.2021.791336
Manzar Abbas, M., Melesse, A. M., Scinto, L. J., & Rehage, J. S. (2019). Satellite estimation of chlorophyll-a using Moderate Resolution Imaging Spectroradiometer (MODIS) Sensor in shallow coastal water bodies: validation and improvement. Water, 11(8), 1621. https://doi.org/10.3390/w11081621
McDowell, R. W., & Hamilton, D. P. (2013). Nutrients and eutrophication: introduction. Marine and Freshwater Research, 64(5), iii-vi. https://doi.org/10.1071/MF13059
Mohebzadeh, H., Yeom, J., & Lee, T. (2020). Spatial downscaling of MODIS chlorophyll-a with genetic programming in South Korea. Remote Sensing, 12(9), 1412. https://doi.org/10.3390/rs12091412
Mountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 247–259. https://doi.org/10.1016/j.isprsjprs.2010.11.001
Radoux, J., Waldner, F., & Bogaert, P. (2020). How response designs and class proportions affect the accuracy of validation data. Remote Sensing, 12(2), 257. https://doi.org/10.3390/rs12020257
Rahman, M. A., S. Laksmini, M., Agung, M. U. K., & Sunarto. (2019). The effect of seasons on oceanographic conditions in determining the fishing area of cakalang fish (Katsuwonus Pelamis) in South West Java Waters. Jurnal Perikanan dan Kelautan, 10(1), 92–102.
Rutledge, K., McDaniel, M., Teng, S., Hall, H., Ramroop, T., Sprout, E., Hunt, J., Boudreau, D., & Costa, H. (2024). Upwelling. National Geographic. Retrieved from https://education.nationalgeographic.org/resource/upwelling/
Sari, L. A., Al Diana, N., Arsad, S., Pursetyo, K., & Cahyoko, Y. (2021). Monitoring of phytoplankton abundance and chlorophyll-a content in the estuary of Banjar Kemuning River, Sidoarjo Regency, East Java. Journal of Ecological Engineering, 22(1), 29–35. https://doi.org/10.12911/22998993/128877
Sari, Q. W., Siswanto, E., Utari, P. A., Saputra, O. F., Lestiana, H., Holidi, Subiyanto, Yuniarti, & Iskandar, I. (2022). Seasonal Driven mechanism of the surface chlorophyll-a distribution along the Western Coast of Sumatra. Journal of Ecological Engineering, 23(11), 254–260. https://doi.org/10.12911/22998993/153995
Satar, M. N., Akhir, M. F., Zainol, Z., & Chung, J. X. (2023). Upwelling in marginal seas and its association with climate change scenario—A comparative review. Climate, 11(7), 151. https://doi.org/10.3390/cli11070151
Schourup-Kristensen, V., Maar, M., Larsen, J., Mohn, C., Murawski, J., She, J., & Jakobsen, H. H. (2021). Methodology for defining homogeneous water bodies for management purposes. Marine Pollution Bulletin, 173, 113004. https://doi.org/10.1016/j.marpolbul.2021.113004
Simanjuntak, F., & Lin, T. H. (2022). Monsoon effects on chlorophyll-a, sea surface temperature, and ekman dynamics variability along the southern coast of lesser Sunda islands and its relation to ENSO and IOD based on satellite observations. Remote Sensing, 14(7), 1682. https://doi.org/10.3390/rs14071682
Suhermat, M., Dimyati, M., Supriatna, S., & Martono, M. (2021). Impact of climate change on sea surface temperature and chlorophyll-a concentration in South Sukabumi Waters. Jurnal Ilmu Lingkungan, 19(2), 393-398.. https://doi.org/10.14710/jil.19.2.393-398
Umasangaji, H., & Ramili, Y. (2021). Mini review: Characteristics of upwelling in several coastal areas in the world. IOP Conference Series: Earth and Environmental Science, 890(1), 012004. https://doi.org/10.1088/1755-1315/890/1/012004
USGS. (2014). Specification Landsat-8. Retrieved from http://landsat.usgs.gov
Welliken, M. A., Melmambessy, E. H. ., Merly, S. L., Pangaribuan, R. D., Lantang, B., Hutabarat, J., & Wirasatriya, A. (2018). Variability chlorophyll-a and sea surface temperature as the fishing ground basis of mackerel fish in The Arafura Sea. E3S Web of Conferences, 73, 04004. https://doi.org/10.1051/e3sconf/20187304004
Wirasatriya, A, Kunarso, Maslukah, L., Satriadi, A., & Armanto, R. D. (2018). Different responses of chlorophyll-a concentration and Sea Surface Temperature (SST) on southeasterly wind blowing in the Sunda Strait. IOP Conference Series: Earth and Environmental Science, 139, 012028. https://doi.org/10.1088/1755-1315/139/1/012028
Wirasatriya, Anindya, Susanto, R. D., Kunarso, K., Jalil, A. R., Ramdani, F., & Puryajati, A. D. (2021). Northwest monsoon upwelling within the Indonesian seas. International Journal of Remote Sensing, 42(14), 5433–5454. https://doi.org/10.1080/01431161.2021.1918790
Zacharis, N. Z. (2018). Classification and regression trees (CART) for predictive modeling in blended learning. International Journal of Intelligent Systems and Applications, 10(3), 1–9. https://doi.org/10.5815/ijisa.2018.03.01
Zhang, T., Huang, M., & Wang, Z. (2020). Estimation of chlorophyll-a Concentration of lakes based on SVM algorithm and Landsat 8 OLI images. Environmental Science and Pollution Research, 27, 14977-14990. https://doi.org/10.1007/s11356-020-07706-7
Androulidakis, Y. S., & Krestenitis, Y. N. (2022). Sea surface temperature variability and marine heat waves over the Aegean, Ionian, and Cretan Seas from 2008–2021. Journal of Marine Science and Engineering, 10(1), 42. https://doi.org/10.3390/jmse10010042
Antoni, S., Bantan, R. A., Al-Dubai, T. A., Lubis, M. Z., Anurogo, W., & Silaban, R. D. (2019). Chlorophyll-a, and Sea Surface Temperature (SST) as proxies for Climate Changes: Case Study in Batu Ampar waters, Riau Islands. IOP Conference Series: Earth and Environmental Science, 273(1). https://doi.org/10.1088/1755-1315/273/1/012012
Bakun, A., Black, B. A., Bograd, S. J., García-Reyes, M., Miller, A. J., Rykaczewski, R. R., & Sydeman, W. J. (2015). Anticipated effects of climate change on coastal upwelling ecosystems. Current Climate Change Reports, 1(2), 85–93. https://doi.org/10.1007/s40641-015-0008-4
Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011
Biau, G., & Scornet, E. (2016). A random forest guided tour. TEST, 25(2), 197–227. https://doi.org/10.1007/s11749-016-0481-7
Dabuleviciene, T., Vaiciute, D., & Kozlov, I. E. (2020). Chlorophyll-a Variability during upwelling events in the South-Eastern Baltic Sea and in the Curonian Lagoon from Satellite Observations. Remote Sensing, 12(21), 3661. https://doi.org/10.3390/rs12213661
Decree of the Minister of Environment and Forestry of the Republic of Indonesia, (2022). Perubahan Fungsi Dalam Fungsi Pokok Kawasan Hutan dari Kawasan Taman Buru Pulau Moyo, Taman Wisata Alam Laut Pulau Moyo, dan Taman Wisata Alam Pulau Satonda Menjadi Taman Nasional Moyo Satonda di Kabupaten Sumbawa. Minister of Environment and Forestry of the Republic of Indonesia.
Dyba, K., Ermida, S., Ptak, M., Piekarczyk, J., & Sojka, M. (2022). Evaluation of methods for estimating lake surface water temperature using landsat 8. Remote Sensing, 14(15), 3839. https://doi.org/10.3390/rs14153839
Efriana, A. F., Manessa, M. D. M., & Ayu, F. (2024). Development of empirical CDOM algorithm for Sentinel-2 using the Gloria dataset. Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet, 12977, 129770V. SPIE. https://doi.org/10.1117/12.3009622
Free, G., Bresciani, M., Pinardi, M., Ghirardi, N., Luciani, G., Caroni, R., & Giardino, C. (2021). Detecting climate driven changes in chlorophyll-a in deep subalpine lakes using long term satellite data. Water, 13(6), 866. https://doi.org/10.3390/w13060866
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
Ha, N. T. T., Koike, K., Nhuan, M. T., Canh, B. D., Thao, N. T. P., & Parsons, M. (2017). Landsat 8/OLI Two Bands Ratio Algorithm for Chlorophyll-A Concentration Mapping in Hypertrophic Waters: An Application to West Lake in Hanoi (Vietnam). IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(11), 4919–4929. https://doi.org/10.1109/JSTARS.2017.2739184
Hedley, J. D., Roelfsema, C. M., Chollett, I., Harborne, A. R., Heron, S. F., Weeks, S. J., Skirving, W. J., Strong, A. E., Mark Eakin, C., Christensen, T. R. L., Ticzon, V., Bejarano, S., & Mumby, P. J. (2016). Remote sensing of coral reefs for monitoring and management: A review. Remote Sensing, 8(2). https://doi.org/10.3390/rs8020118
Henson, S. A., Beaulieu, C., & Lampitt, R. (2016). Observing climate change trends in ocean biogeochemistry: when and where. Global Change Biology, 22(4), 1561–1571. https://doi.org/10.1111/gcb.13152
Jacox, M. G., Edwards, C. A., Hazen, E. L., & Bograd, S. J. (2018). Coastal Upwelling Revisited: Ekman, Bakun, and Improved Upwelling Indices for the U.S. West Coast. Journal of Geophysical Research: Oceans, 123(10), 7332–7350. https://doi.org/10.1029/2018JC014187
Jang, J.-C., & Park, K.-A. (2019). High-resolution sea surface temperature retrieval from landsat 8 OLI/TIRS data at coastal regions. Remote Sensing, 11(22), 2687. https://doi.org/10.3390/rs11222687
Karimi, B., Hashemi, S. H., & Aghighi, H. (2022). Performance of Sentinel-2 and Landsat-8 satellites in estimating Chlorophyll-a concentration in a shallow freshwater lake. Preprint (Researchsquare), 1-15. https://doi.org/10.21203/rs.3.rs-1968542/v1
Katlane, R., El Kilani, B., Dhaoui, O., Kateb, F., & Chehata, N. (2023). Monitoring of sea surface temperature, chlorophyll, and turbidity in Tunisian waters from 2005 to 2020 using MODIS imagery and the Google Earth Engine. Regional Studies in Marine Science, 66, 103143. https://doi.org/10.1016/j.rsma.2023.103143
Kniebusch, M., Meier, H. E. M., Neumann, T., & Börgel, F. (2019). Temperature variability of the Baltic Sea Since 1850 and attribution to atmospheric forcing variables. Journal of Geophysical Research: Oceans, 124(6), 4168–4187. https://doi.org/10.1029/2018JC013948
Lakshmi, G. J., Bodapati, V., Baji, S. S., & Panuganti, T. S. (2023). Water quality monitoring using remote-sensing. International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES), 624–628. https://doi.org/10.1109/CISES58720.2023.10183395
Liu, Y., Ortega-Farías, S., Tian, F., Wang, S., & Li, S. (2021). Estimation of surface and near-surface air temperatures in arid northwest china using landsat satellite images. Frontiers in Environmental Science, 9, 791336. https://doi.org/10.3389/fenvs.2021.791336
Manzar Abbas, M., Melesse, A. M., Scinto, L. J., & Rehage, J. S. (2019). Satellite estimation of chlorophyll-a using Moderate Resolution Imaging Spectroradiometer (MODIS) Sensor in shallow coastal water bodies: validation and improvement. Water, 11(8), 1621. https://doi.org/10.3390/w11081621
McDowell, R. W., & Hamilton, D. P. (2013). Nutrients and eutrophication: introduction. Marine and Freshwater Research, 64(5), iii-vi. https://doi.org/10.1071/MF13059
Mohebzadeh, H., Yeom, J., & Lee, T. (2020). Spatial downscaling of MODIS chlorophyll-a with genetic programming in South Korea. Remote Sensing, 12(9), 1412. https://doi.org/10.3390/rs12091412
Mountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 247–259. https://doi.org/10.1016/j.isprsjprs.2010.11.001
Radoux, J., Waldner, F., & Bogaert, P. (2020). How response designs and class proportions affect the accuracy of validation data. Remote Sensing, 12(2), 257. https://doi.org/10.3390/rs12020257
Rahman, M. A., S. Laksmini, M., Agung, M. U. K., & Sunarto. (2019). The effect of seasons on oceanographic conditions in determining the fishing area of cakalang fish (Katsuwonus Pelamis) in South West Java Waters. Jurnal Perikanan dan Kelautan, 10(1), 92–102.
Rutledge, K., McDaniel, M., Teng, S., Hall, H., Ramroop, T., Sprout, E., Hunt, J., Boudreau, D., & Costa, H. (2024). Upwelling. National Geographic. Retrieved from https://education.nationalgeographic.org/resource/upwelling/
Sari, L. A., Al Diana, N., Arsad, S., Pursetyo, K., & Cahyoko, Y. (2021). Monitoring of phytoplankton abundance and chlorophyll-a content in the estuary of Banjar Kemuning River, Sidoarjo Regency, East Java. Journal of Ecological Engineering, 22(1), 29–35. https://doi.org/10.12911/22998993/128877
Sari, Q. W., Siswanto, E., Utari, P. A., Saputra, O. F., Lestiana, H., Holidi, Subiyanto, Yuniarti, & Iskandar, I. (2022). Seasonal Driven mechanism of the surface chlorophyll-a distribution along the Western Coast of Sumatra. Journal of Ecological Engineering, 23(11), 254–260. https://doi.org/10.12911/22998993/153995
Satar, M. N., Akhir, M. F., Zainol, Z., & Chung, J. X. (2023). Upwelling in marginal seas and its association with climate change scenario—A comparative review. Climate, 11(7), 151. https://doi.org/10.3390/cli11070151
Schourup-Kristensen, V., Maar, M., Larsen, J., Mohn, C., Murawski, J., She, J., & Jakobsen, H. H. (2021). Methodology for defining homogeneous water bodies for management purposes. Marine Pollution Bulletin, 173, 113004. https://doi.org/10.1016/j.marpolbul.2021.113004
Simanjuntak, F., & Lin, T. H. (2022). Monsoon effects on chlorophyll-a, sea surface temperature, and ekman dynamics variability along the southern coast of lesser Sunda islands and its relation to ENSO and IOD based on satellite observations. Remote Sensing, 14(7), 1682. https://doi.org/10.3390/rs14071682
Suhermat, M., Dimyati, M., Supriatna, S., & Martono, M. (2021). Impact of climate change on sea surface temperature and chlorophyll-a concentration in South Sukabumi Waters. Jurnal Ilmu Lingkungan, 19(2), 393-398.. https://doi.org/10.14710/jil.19.2.393-398
Umasangaji, H., & Ramili, Y. (2021). Mini review: Characteristics of upwelling in several coastal areas in the world. IOP Conference Series: Earth and Environmental Science, 890(1), 012004. https://doi.org/10.1088/1755-1315/890/1/012004
USGS. (2014). Specification Landsat-8. Retrieved from http://landsat.usgs.gov
Welliken, M. A., Melmambessy, E. H. ., Merly, S. L., Pangaribuan, R. D., Lantang, B., Hutabarat, J., & Wirasatriya, A. (2018). Variability chlorophyll-a and sea surface temperature as the fishing ground basis of mackerel fish in The Arafura Sea. E3S Web of Conferences, 73, 04004. https://doi.org/10.1051/e3sconf/20187304004
Wirasatriya, A, Kunarso, Maslukah, L., Satriadi, A., & Armanto, R. D. (2018). Different responses of chlorophyll-a concentration and Sea Surface Temperature (SST) on southeasterly wind blowing in the Sunda Strait. IOP Conference Series: Earth and Environmental Science, 139, 012028. https://doi.org/10.1088/1755-1315/139/1/012028
Wirasatriya, Anindya, Susanto, R. D., Kunarso, K., Jalil, A. R., Ramdani, F., & Puryajati, A. D. (2021). Northwest monsoon upwelling within the Indonesian seas. International Journal of Remote Sensing, 42(14), 5433–5454. https://doi.org/10.1080/01431161.2021.1918790
Zacharis, N. Z. (2018). Classification and regression trees (CART) for predictive modeling in blended learning. International Journal of Intelligent Systems and Applications, 10(3), 1–9. https://doi.org/10.5815/ijisa.2018.03.01
Zhang, T., Huang, M., & Wang, Z. (2020). Estimation of chlorophyll-a Concentration of lakes based on SVM algorithm and Landsat 8 OLI images. Environmental Science and Pollution Research, 27, 14977-14990. https://doi.org/10.1007/s11356-020-07706-7
Published
2024-06-23
How to Cite
EFRIANA, Anisya Feby et al.
Decadal Monitoring of Upwelling Dynamics in Satonda Island Waters Using Landsat-8 and Machine Learning Regression.
Geosfera Indonesia, [S.l.], v. 9, n. 2, p. 144-156, june 2024.
ISSN 2614-8528.
Available at: <https://jurnal.unej.ac.id/index.php/GEOSI/article/view/47203>. Date accessed: 21 dec. 2024.
doi: https://doi.org/10.19184/geosi.v9i2.47203.
Section
Original Research Articles
