The three drivers of environmental change: climate change, population growth and economic growth, result in a range of pressures on our coastal environment. Coastal development for industry and farming are a major pressure on terrestrial and environmental quality. In their process most of industry using sea water as cooling water. When water used as a coolant is returned to the natural environment at a higher temperature, the change in temperature decreases oxygen supply and affects marine ecosystem. This research is presents results from ongoing study on application of Landsat 8 for monitoring the intensity and distribution area of sea surface temperature changed by the heated effluent discharge from the power plant on Paiton coast, Probolinggo, East Java province. Remote sensing technology using a thermal band in Operational Land Imager (OLI) sensor of Landsat 8 sattelite imagery (band 10 and band 11) are used to determine the intensity and distribution of temperature changes. Estimation of sea surface temperature (SST) using remote sensing technology is applied to provide ease of marine temperature monitoring with a large area coverage. The method used in this research using the Split Window Algorithm (SWA) methods which is an algorithm with ability to perform extraction of sea surface temperature (SST) with brigthness temperature (BT) value calculation on the band 10 and band 11 of Landsat 8. Formula which was used in this area is Ts = BT10 + (2.946*(BT10 - BT11)) - 0.038 (Ts is the surface temperature value (°C), BT10 is the brightness temperature value (°C) Band 10, BT11 is the brightness temperature value (°C) Band 11. The result of this algorithm shows the good performance with Root Mean Square Error (RMSE) amount 0.406.

The decrease of coastal-water quality in the Surabaya coastal region can be recognized from the conceentration of Total Suspended Sediment(TSS ) . As a result we need a system for monitoring sediment concentration in the coastal region of Surabaya which regularly measures TSS. The principle to model and monitor TSSconcentration using remote sensing methods is by the integration of Landsat-8OLI satellites image processing using some ofTSS-models then those are analyzed for looking its suitability with TSS value direcly measured in the field ( in-situ measurement). The TSS value modeled from all algorithms validated usingcorrelation analysis and linear regression . The result shows that TSS model with the highest correlation value is TSS algorithm by Budiman (2004)with r value 0.991. Hence this algorithm can be used to investigate TSS-distribution which represent the coastal water quality of Surabaya with TSS value between 75 mg/L to 125 mg/L. Abstrak Penurunan kualitas perairan pantai dapat ditunjukkan oleh tingginya konsentrasi sedimen yang dinyatakan oleh Total Suspended Sedimen(TSS). Untuk keperluan ini diperlukan sistem monitoring dan pengukuran konsentrasi sedimen wilayah pantai Surabaya secara spasial dan non spasial, dimana pengukuran TSS dapat dilakukan secara regular dengan ketelitian yang baik. Prinsip monitoring konsentrasi TSS dengan metode penginderaan jauh adalah memproses citra dari satelit Landsat 8OLI dengan menggunakan beberapa model TSS, kemudian dilihat kesesuaiannya konsentrasi TSS yang diukur langsung di lapangan (pengukuran insitu).Penelitian ini menggunakan empat algoritma TSS untuk mendapatkan konsentrasi TSS. Nilai konsentrasi TSS dari semua algortima diuji dengan data in-situ

Pantai merupakan suatu kawasan peralihan atau pertemuan antara darat dan laut. Pada Kabupaten Gianyar, Bali membentang laut sepanjang selatan Pulau Bali yang merupakan daerah yang berbatasan langsung dengan wilayah pesisir. Tentunya hal tersebut tidak lepas dari adanya dinamika perubahan pada fisik pantai yang disebabkan seperti pengikisan daratan oleh air laut (abrasi) maupun adanya angkutan sedimen dari darat (akresi) yang pada umumnya menjadi sorotan terhadap perubahan garis pantai. Untuk itu diperlukan penelitian guna mengetahui besarnya perubahan yang terjadi sepanjang garis pantai tahun 2002 sampai 2017 sehingga menghasilkan peta perubahan garis pantai. Metode yang digunakan adalah menggunakan interpretasi ratio pada kanal SWIR dan hijau pada citra Landsat 7 dan Landsat 8 ditambah dengan melakukan klasifikasi, dapat dilakukan untuk mengidentifikasi garis pantai beserta menganalisis besarnya perubahan yang terjadi. Hasil analisis tumpang susun identifikasi garis pantai di Kabupaten Gianyar menunjukkan luas pesisir pada tahun 2002 sebesar 42,441 km 2 dan pada tahun 2017 sebesar 42,285 km 2 dimana terjadi abrasi sebesar 0,195 km 2 yang diakibatkan oleh faktor alam yaitu pesisir Kabupaten Gianyar berada di zona laut lepas.

During the recent years, maritime surveillance has been receiving a growing interest. Ship detection and identification are parts of maritime surveillance in order to dealing with illegal fishery, maritime traffic, sea border activity, or oil spill detection and monitoring. Nowadays, Synthetic Aperture Radar (SAR) as one of active remote sensing technology provide signals to penetrate cloud, can be advantage to be used in tropical region with the intention to monitor sea objects on the sea surface from the space. The availability of Sentinel-1 as SAR imaging mission, providing continuous all-weather, day-and-night imagery, makes it ideal for precise cueing and location of ship activities at sea. Utilization of CFAR (Constant False Alarm Rate) algorithm provided by SNAP (Sentinel Application Platform) software from ESA show rapid detection of ship in the study areas (Madura Strait and Lamong Gulf). Compared with manual ship extraction method, it gives sufficient results.

Sidoarjo mud disaster is an occurrence of hot mud bursts at drilling location of Lapindo Brantas Inc., Sidoarjo, Indonesia since 29th May 2006. In order to overcome the continuous mud flow, Indonesian government built embankment around the center of the mudflow. They also throw mud materials into the Porong River. The large and continuous disposal of mud material leads to sedimentation in Porong River. Remote sensing method with satellite imagery can be a solution to find out how much sedimentation occurred in Porong River as a result of mud’s disposal. Total Suspended Solid (TSS) calculation from satellite image can be indicator of sedimentation distribution.In previous studies, TSS distribution has been observed from Landsat-7 and Landsat 8 data. Nowadays, with ability of Sentinel-2 which has higher spatial resolution (10 m) and higher revisit time (up to 6 days), optimization of TSS distribution in Porong river can be done. Thus the objective of this research is analysis of Sentinel-2 imagery application to estimate TSS in Porong River. Result showed good correlation between in-situ data and TSS estimation from Sentinel-2 with value 0.72. Keywords : Sentinel-2, TSS, Porong River.