ABSTRACT:The Landsat-8 satellite imagery is now highly developed compares to the former of Landsat projects. Both land and water area are possibly mapped using this satellite sensor. Considerable approaches have been made to obtain a more accurate method for extracting the information of water area from the images. It is difficult to generate an accurate water quality information from Landsat images by using some existing algorithm provided by researchers. Even though, those algorithms have been validated in some water area, but the dynamic changes and the specific characteristics of each area make it necessary to get them evaluated and validated over another water area. This paper aims to make a new algorithm by correlating the measured and estimated TSS and Chla concentration. We collected in-situ remote sensing reflectance, TSS and Chl-a concentration in 9 stations surrounding the Poteran islands as well as Landsat 8 data on the same acquisition time of April 22, 2015. The regression model for estimating TSS produced high accuracy with determination coefficient (R 2 ), NMAE and RMSE of 0.709; 9.67 % and 1.705 g/m 3 respectively. Whereas, Chla retrieval algorithm produced R 2 of 0.579; NMAE of 10.40% and RMSE of 51.946 mg/m 3 . By implementing these algorithms to Landsat 8 image, the estimated water quality parameters over Poteran island water ranged from 9.480 to 15.801 g/m 3 and 238.546 to 346.627 mg/m 3 for TSS and Chl-a respectively.

ABSTRACT:The Sea Surface Temperature (SST) retrieval from satellites data Thus, it could provide SST data for a long time. Since, the algorithms of SST estimation by using Landsat 8 Thermal Band are sitedependence, we need to develop an applicable algorithm in Indonesian water. The aim of this research was to develop SST algorithms in the North Java Island Water. The data used are in-situ data measured on April 22, 2015 and also estimated brightness temperature data from Landsat 8 Thermal Band Image (band 10 and band 11). The algorithm was established using 45 data by assessing the relation of measured in-situ data and estimated brightness temperature. Then, the algorithm was validated by using another 40 points. The results showed that the good performance of the sea surface temperature algorithm with coefficient of determination (R 2 ) and Root Mean Square Error (RMSE) of 0.912 and 0.028, respectively.

AbstractTSS and Chl-a are globally known as a key parameter for regular seawater monitoring. Considering the high temporal and spatial variations of water constituent, the remote sensing technique is an efficient and accurate method for extracting water physical parameters. The accuracy of estimated data derived from remote sensing depends on an accurate atmospheric correction algorithm and physical parameter retrieval algorithms. In this research, the accuracy of the atmospherically corrected product of USGS as well as the developed algorithms for estimating TSS and Chl-a concentration using Landsat 8-OLI data were evaluated. The data used in this study was collected from Poteran's waters (9 stations) on April 22, 2015 and Gili Iyang's waters (6 stations) on October 15, 2015. The low correlation between in situ and Landsat Rrs(λ) (R 2 = 0.106) indicated that atmospheric correction algorithm performed by USGS has a limitation. The TSS concentration retrieval algorithm produced an acceptable accuracy both over Poteran's waters (RE of 4.60% and R 2 of 0.628) and over Gili Iyang's waters (RE of 14.82% and R 2 of 0.345). Although the R 2 lower than 0.5, the relative error was more accurate than the minimum requirement of 30%. Whereas, the Chl-a concentration retrieval algorithm produced an acceptable result over Poteran's waters (RE of 13.87% and R 2 of 0.416) but failed over Gili Iyang's waters (RE of 99.14% and R 2 of 0.090). The low correlation between measured and estimated TSS or Chl-a concentrations were caused not only by the performance of developed TSS and Chl-a estimation retrieval algorithms but also the accuracy of atmospherically corrected reflectance of Landsat product. Keywordsremote sensing; water quality; TSS; Chl-a.Abstrak TSS dan Chl-a secara global dikenal sebagai parameter utama dalam pemantauan kualitas air laut. Mengingat tingginya variasi temporal dan spasial dari konstituen perairan, teknik penginderaan jauh adalah metode yang efisien dan akurat untuk mengekstrak parameter fisik air tersebut. Akurasi dari parameter fisik yang diturunkan dari data penginderaan jauh tergantung pada algoritma koreksi atmosfer dan algoritma estimasi parameter fisik yang akurat. Dalam penelitian ini, akurasi dari produk USGS yang terkoreksi secara atmosfer serta algoritma yang dikembangkan untuk menghitung konsentrasi TSS dan Chl-a menggunakan Landsat 8-OLI data telah dikaji. Data yang digunakan dalam penelitian ini dikumpulkan dari Perairan Poteran (9 stasiun) pada tanggal 22 April 2015, dan Perairan Gili Iyang (6 stasiun) pada tanggal 15 Oktober 2015. Korelasi yang rendah antara data in situ dan Landsat Rrs(λ) (R 2 = 0,106) menunjukkan algoritma koreksi atmosfer yang digunakan oleh USGS memiliki keterbatasan. Algoritma estimasi konsentrasi TSS menghasilkan akurasi yang dapat diterima di Perairan Poteran (RE sebesar 4,60% dan R 2 sebesar 0,628) dan di perairan Gili Iyang (RE sebesar 14,82% dan R 2 sebesar 0,345). Meskipun R 2 lebih rendah dari 0,5, kesalahan relatifnya lebih akurat dari persyaratan minimum seb...

No abstract

In recent years efforts on reducing fuel consumption has become the greatest issue related to energy crisis and global warming. The reduction of fuel consumption can be obtained, if the ship propulsion could be operated in its best performance level. Generally this is done by an appropriate analysis of engine propeller matching (EPM). In this study an EPM based on neural-network method, or NN-EPM, is established to predict the best performance of main engines, leading at minimum fuel oil consumption. A trimaran patrol ship is selected as a case study. This patrol ship is equipped with two 2720 kW main engines each connected to a controllable pitch propeller (CPP) through a reduction gear. The input parameters are ship speedVand service margin SM, with the corresponding output parameters comprise of engine speednE, engine break horse powerPB, propeller pitchP/D, and the fuel consumptionFC. An NN-EPM 2-20-15-4 configuration has been constructed out of 100 training data and then validated by 30 testing data. The maximum relative error between results from NN-EPM and EPM analysis is 2.1%, that is in term of the fuel consumption. For other parameters the errors are well below 1.0%. These facts indicate that the use of NN-EPM to predict the main engines's performance for trimaran patrol ship is satisfactory.