Analysis of Dissolved Oxygen on Tuban Coast Using Remote Sensing Algorithms from Landsat 8 Satellite Image Data and Interpolated Polynomials
DOI:
https://doi.org/10.24857/rgsa.v18n8-049Keywords:
Remote Sensing, Landsat 8 OLI, Dissolved Oxygen, District of TubanAbstract
Dissolved oxygen is one of the parameters to detect whether a water body is experiencing interference or pollution due to the influence of pollutants and the presence of dirt from outside such as garbage discharges from local communities. Dissolved oxygen plays an important role in the life of biota in the aquatic environment, especially in coastal areas because with sufficient oxygen levels in water bodies, living things will be able to grow and multiply properly. This study wants to try to analyze dissolved oxygen levels in the coastal area of Tuban, where this coast is good for study because the morphology of the beach is relatively gentle and close to the recreation area of Boom Tuban beach. The method used in analyzing the distribution of dissolved oxygen is by remote sensing technology to obtain reflectance values from satellite images used to compile algorithms where later the most optimal mathematical model of dissolved oxygen can be obtained on the coast of Boom. Furthermore, it will also be modeled with a polynomial interpolation approach to obtain the best results from the reflectance relationship of the image with dissolved oxygen levels. The results obtained show that the polynomial approach of degrees 2 and 3 does not provide a significant increase in the correlation value besides that dissolved oxygen levels are still within normal limits, and have not experienced significant pollution so that it does not require expensive coastal handling, only needs monitoring every year to get a healthy water situation to support coastal areas intended for regional tourism.
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Akbarpour Shirazi, M., Samieifard, R., Abduli, M. A., & Omidvar, B. (2016). Mathematical modeling in municipal solid waste management: case study of Tehran. Journal of Environmental Health Science and Engineering, 14(1), 8. https://doi.org/10.1186/s40201-016-0250-2 DOI: https://doi.org/10.1186/s40201-016-0250-2
Aurin, D., Mannino, A., & Lary, D. J. (2018). Remote sensing of CDOM, CDOM spectral slope, and dissolved organic carbon in the global ocean. Appl Sci (Basel), 8(12), 2687. https://doi.org/10.3390/app8122687 DOI: https://doi.org/10.3390/app8122687
Cao, L., Liang, R. feng, Tuo, Y. cai, Li, Y., & Li, K. feng. (2016). Influence of total dissolved gas-supersaturated water on silver carp (Hypophthalmichthys molitrix). Water Science and Engineering, 9(4), 324–328. https://doi.org/10.1016/j.wse.2017.01.001 DOI: https://doi.org/10.1016/j.wse.2017.01.001
Chen, Z., Liu, J., Qian, Z., Li, L., Zhang, Z., Feng, G., Ruan, S., & Sun, G. (2023). Monitoring Land Degradation through Vegetation Dynamics Mathematical Modeling: Case of Jornada Basin (in the U.S.). Remote Sensing 2023, Vol. 15, Page 978, 15(4), 978. https://doi.org/10.3390/RS15040978 DOI: https://doi.org/10.3390/rs15040978
Dennison, P. E., Roberts, D. A., & Peterson, S. H. (2007). Spectral shape-based temporal compositing algorithms for MODIS surface reflectance data. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2007.02.009 DOI: https://doi.org/10.1016/j.rse.2007.02.009
Flood, N. (2014). Continuity of reflectance data between landsat-7 ETM+ and landsat-8 OLI, for both top-of-atmosphere and surface reflectance: A study in the australian landscape. Remote Sensing, 6(9), 7952–7970. https://doi.org/10.3390/rs6097952 DOI: https://doi.org/10.3390/rs6097952
Guang, J., Xue, Y., Yang, L., Mei, L., & He, X. (2013). A method for retrieving land surface reflectance using MODIS data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/JSTARS.2013.2259145 DOI: https://doi.org/10.1109/JSTARS.2013.2259145
Martins, A. M. P., Heijnen, J. J., & Van Loosdrecht, M. C. M. (2003). Effect of dissolved oxygen concentration on sludge settleability. Applied Microbiology and Biotechnology 2003 62:5, 62(5), 586–593. https://doi.org/10.1007/S00253-003-1384-6 DOI: https://doi.org/10.1007/s00253-003-1384-6
Pearce, M. W., & Schumann, E. H. (2003). Dissolved Oxygen Characteristics of the Gamtoos Estuary. African Journal of Marine Science, 25(1), 99–109. https://doi.org/10.2989/18142320309504003 DOI: https://doi.org/10.2989/18142320309504003
Petitcolin, F., & Vermote, E. (2002). Land surface reflectance, emissivity and temperature from MODIS middle and thermal infrared data. Remote Sensing of Environment. https://doi.org/10.1016/S0034-4257(02)00094-9 DOI: https://doi.org/10.1016/S0034-4257(02)00094-9
Rochelle-Newall, E. J., Fisher, T. R., Fan, C., & Glibert, P. M. (1999). Dynamics of chromophoric dissolved organic matter and dissolved organic carbon in experimental mesocosms. International Journal of Remote Sensing, 20(3), 627–641. https://doi.org/10.1080/014311699213389 DOI: https://doi.org/10.1080/014311699213389
Stansbury, J., Kozimor, L., Admiraal, D., & Dove, E. (2009). Water quality modeling of the effects of macrophytes on dissolved oxygen in a shallow tailwater reservoir. Http://Dx.Doi.Org/10.1080/07438140809354844, 24(4), 339–348. https://doi.org/10.1080/07438140809354844 DOI: https://doi.org/10.1080/07438140809354844
Wenhai, L. U., Xianquan, X., Lu, Y., Yan, X. U., Xiao, L. I., & Shuming, L. I. U. (2017). The temporal-spatial distribution and changes of dissolved oxygen in the Changjiang Estuary and its adjacent waters for the last 50 a. 36(5), 90–98. https://doi.org/10.1007/s13131-017-1063-6 DOI: https://doi.org/10.1007/s13131-017-1063-6
Wibisana, H., Bangun, M. S., & Lasminto, U. (2020). Mathematical models selection on the total suspended solid mapping using reflective satellite image data. International Journal on Advanced Science, Engineering and Information Technology, 1, 185–190. https://doi.org/10.18517/IJASEIT.10.1.6750 DOI: https://doi.org/10.18517/ijaseit.10.1.6750
Xiao, M., Wu, F., Yi, Y., Han, Z., & Wang, Z. (2019). Optical properties of dissolved organic matter and controlling factors in Dianchi LakeWaters. Water (Switzerland), 11(10). https://doi.org/10.3390/w11101967 DOI: https://doi.org/10.3390/w11101967
Zainab, S., Handajani, N., & Wibisana, H. (2022). Acidity (pH) and Dissolved Oxygen Levels as Indicators of Water Quality Around the Tuban Coastal Area. Nusantara Science and Technology Proceedings. https://doi.org/10.11594/NSTP.2022.2719 DOI: https://doi.org/10.11594/nstp.2022.2719
Zhang, W., Li, A., Jin, H., Bian, J., Zhang, Z., Lei, G., Qin, Z., & Huang, C. (2013). An enhanced spatial and temporal data fusion model for fusing landsat and modis surface reflectance to generate high temporal landsat-like data. Remote Sensing. https://doi.org/10.3390/rs5105346 DOI: https://doi.org/10.3390/rs5105346
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