DOI: https://doi.org/10.14710/geoplanning.5.2.177-188

TOTAL SUSPENDED SOLID DISTRIBUTION ANALYSIS USING SPOT-6 DATA IN SEGARA ANAKAN, CILACAP

Aisya Jaya Dhannahisvara  -  Cartography and Remote Sensing, Dept. of Geographic Information Science, Indonesia
Hartono Harjo  -  Cartography and Remote Sensing, Indonesia
*Pramaditya Wicaksono orcid  -  Remote Sensing Laboratory, Dept. of Geographic Information Science, Indonesia
Ferman Setia Nugroho  -  Stasiun Bumi Penginderaan Jauh LAPAN, Parepare, Indonesia, Indonesia

Citation Format:
Abstract

Spatial distribution and concentration of Total Suspended Solid (TSS) is one of the coastal parameters which are required to be examined in order to understand the quality of the water. Rapid development of remote sensing technology has resulted in the emergence of various methods to estimate TSS concentration. SPOT-6 data has spatial, spectral, and temporal characteristics that can be used to estimate TSS concentration. The purposes of this research are (1) to determine the best method for estimating TSS concentration, (2) to map TSS distribution, and (3) to determine the correlation between TSS concentration and chlorophyll-a concentration using SPOT-6 data in Segara Anakan. The estimation of TSS concentration in this research was performed using empirical model built from SPOT-6 and TSS field data. Bands used in this research are single band data (blue, green, red, and near infrared) and transformed bands such as band ratio (12 combinations), Normalized Difference Suspended Solid Index (NDSSI), and Suspended Solid Concentration Index (SSC). The result shows that blue, green, red, and near infrared bands and SSC index significantly correlated to TSS. Afterwards, regression analysis was performed to determine the function that can be used to predict TSS concentration using SPOT-6 data. Regression function used are linear and non-linear (exponential, logarithmic, 2nd order polynomial, and power). The best model was chosen based on the accuracy assessment using Standard Error of Estimate (SE). The selected model was used to calculate total TSS concentration and was correlated with chlorophyll-a field data. The result of accuracy test shows that the model from blue band has an accuracy of 70.68 %, green band 70.68 %, red band 75.73 %, near infrared band 65.58 %, and SSC 73.67 %. The accuracy test shows that red band produced the best prediction model for mapping TSS concentration distribution. The total TSS concentration, which was calculated using red band empirical model, is estimated to be 6.13 t. According to the correlation test, TSS concentration in Segara Anakan has no significant correlation with chlorophyll-a concentration, with a coefficient correlation value of -0.265.

Fulltext View|Download
Keywords: Band Ratio; SPOT-6; Total Suspended Solid; Chlorophyll-a; Transformation indices;

Article Metrics:

Article Info
Section: Original Research
Language : EN
Recent articles
REMOTE SENSING AND GIS FOR LAND COVER CLASSIFICATION AND CHANGE DETECTION IN GAZIPUR SADAR, BANGLADESH DURING 1973-2017 THE EFFECT OF MINIMUM NOISE FRACTION ON MULTISPECTRAL IMAGERY DATA FOR VEGETATION CANOPY DENSITY MODELLING TIME TRAVEL ESTIMATIONS USING MAC ADDRESSES OF BUS, PASSENGERS: A POINT TO PATH-QGIS ANALYSIS More recent articles
Most cited articles
LAND USE ANALYSIS USING TIME SERIES OF VEGETATION INDEX DERIVED FROM SATELLITE REMOTE SENSING IN BRANTAS RIVER WATERSHED, EAST JAVA, INDONESIA INCREASING ACCURACY VALUE IN THE ESTIMATES OF CARBON STOCK BY USING VEGETATION INDEX FROM ALOS AVNIR 2 SATELLITE IMAGERY EVALUASI CITRA WORLDVIEW-2 UNTUK PENDUGAAN KEDALAMAN PERAIRAN DANGKAL PULAU KELAPA-HARAPAN MENGGUNAKAN ALGORITMA RASIO BAND Evaluation of MODIS-Derived LST Products with Air Temperature Measurements in Cyprus QUALITY ANALYSIS OF SINGLE TREE OBJECT WITH OBIA AND VEGETATION INDEX FROM LAPAN SURVEILLANCE AIRCRAFT MULTISPECTRAL DATA IN URBAN AREA More cited articles
  1. Congalton, R. G., & Green, K. (2009). Assessing the Accuracy of Remotely Sensed Data. Boca Raton: Taylor & Francis Group
  2. Daniel. (2007). Struktur Komunitas Fitoplankton di Estuari Sungai Brantas, Jawa Timur. Skripsi. Bogor: Departemen Manajemen Sumberdaya Perairan Fakultas Perikanan dan Ilmu Kelautan Institut Pertanian Bogor
  3. Danoedoro, P. (2012). Pengantar Penginderaan Jauh Digital. Yogyakarta: Penerbit Andi
  4. Doxaran, D., Froidefon, J. M., & Castaing, P. (2002). A Reflectance Band Ratio Used to Estimate suspended sediment concentration in sediment-dominated coastal water. int. j. remote sensing vol. 23, (pp. 5079-5085). France
  5. Hossain, A. K., Jia, Y., & Chao, X. (2007). Development of Remote Sensing Based Index for Estimating/Mapping Suspended Sediment Concentration in River or Lake Environments. Mississipi: ResearchGate
  6. Hung, T. H., & Tuyen, V. D. (2014). Estimating Suspended Sediment Concentrations in Surface Water of Tri An Lake (Vietnam) using Landsar Multispectral Images. Vestnik OrelGAU, (pp. 57-64). Hanoi
  7. Jensen, J. R. (2013). Remote Sensing of The Environment An Earth Resource Perspective. England: Pearson
  8. LAPAN. (2014, November 17). Pusat Teknologi dan Data Penginderaan Jauh. Retrieved Maret 9, 2016, from Pustek Data LAPAN: http://www.pustekdata.lapan.go.id
  9. Lee, M. S., Park, K. A., Chung, J. Y., Ahn, Y. H., & Moon, J. E. (2011). Estimation of Coastal Suspended Sediment Concentration Using Satellite Data and Oceanic Insitu Measurement. Korean Journal of Remote Sensing, Vol.27, No.6, (pp. 677-692). Seoul
  10. Lehner, S., Anders, I., & Gayer, G. (2004). High Resolution Maps of Suspended Particulate Matter Concentration in The German Bight. EARSeL eProceedings 3, (pp. 118-126). Germany
  11. Li, J., Gao, S., & Wang, Y. (2010). Delineating Suspended Sediment Concentration Patterns in Surface Waters of Changjiang Estuary by Remote Sensing Analysis. Acta Oceanologica Sinica, 38-47
  12. Lo, C. P. (1986). Penginderaan Jauh Terapan. Diterjemahkan oleh : Bambang Purbowaseso. Jakarta: Penerbit Universitas Indonesia (UI Press)
  13. Nurandani, P. (2013). Pemetaan Total Suspended Solid (TSS) menggunakan CItra Satelit Muultitemporal Di Danau Rawa Pening Provinsi Jawa Tengah. Jurnal Geodesi UNDIP Voume 2 Nomor 4
  14. Nurjati, C., & Handayani, H. H. (2011). Penginderaan Jauh untuk Estimasi Kandungan TSS di Wilayah Pantai Timur Surabaya Akibat Pembuangan Lumpur Lapindo. Jurnal Seminar Nasional Aplikasi Teknologi Prasarana Wilayah
  15. Sugiyono. (2003). Statistik untuk Penelitian. Bandung: Alfabeta
  16. UNEP. (2015). Sedimentation and Erosion. Retrieved Desember 26, 2015, from United Nation Environments Programme: http://www.cep.unep.org/publications-and-resources/marine-and-coastal-issues-links/sedimentation-and-erosion
  17. Yuliarko, S. (2010). Permasalahan Kawasan Segara Anakan. 2-10
  18. Zheng, Z., Li, Y., Guo, Y., Xu, Y., Liu, G., & Du, C. (2015). Landsat-Based Long-Term Monitoring of Total Suspended Matter Concentration Pattern Change in the Wet Season for Dongting Lake, China. Remote Sensing, 13976-13999

Last update:

  1. Impact of land use and socio-economic changes in water catchment area on total suspended solid (TSS) in Lake Toba

    A H Harianja, A E Suoth, E Nazir, G S Saragih, R Fauzi, M Y Hidayat. IOP Conference Series: Earth and Environmental Science, 407 (1), 2019. doi: 10.1088/1755-1315/407/1/012005

  2. Determining effective water depth for total suspended solids (TSS) mapping using PlanetScope imagery

    Putu Wirabumi, Muhammad Kamal, Pramaditya Wicaksono. International Journal of Remote Sensing, 42 (15), 2021. doi: 10.1080/01431161.2021.1931538

Last update: 2024-11-22 03:37:45

No citation recorded.