Remote sensing technology can provide spatial information for mapping shallow water benthic habitat, a case study conducted on Sebaru Besar Island. The purpose of this study was to analyze mapping accuracy of shallow water benthic habitats usings WorldView 2 and SPOT 6 (201 imageries). The classification of multispectral images is carried out using the Depth Invariant Index (DII) transformation and by applying the Maximum Likelihood (MLH) algorithm to both satellite images. The number of benthic habitat classes produced are eight habitat classes from each image used. The results of the analysis show that the overall accuracy in Worldview 2 and SPOT 6 images is 61.29% and 51.61%. Results of Z-statistic comparison between Worldview-2 and SPOT-6 imagery was 1,04, means that the results did not differ significantly.

This study was conducted to observe the dynamic of shoreline changes in Sangsit Region, Bali Province using multi-temporal remote sensing datasets. The remote sensing data were acquired from several Landsat images in the period of 2000-2019 (20 years) with 30-meter spatial resolution. Digital Shoreline Analysis System (DSAS) method was used to analyse data images to determine the shoreline changes. Prior to the analysis, the images were corrected by ground check data. It revealed that the shoreline changes has occur in Sangsit Region for period of 20 years (2000-2019). The results obtained indicate that there is a change in shoreline with mild accretion to less extreme abrasion categories which occurred in the Sangsit Region in that period. From 2000-2005 it shows an accretion of 221.03 m which is categorized as mild accretion and from 2005-2019 the shoreline changes that occur is only an abrasion its categorized as mild to less extreme abrasion. The highest abrasion occurred in the period of 2010-2015 which the abrasion is around 49.65 m.

Fuzzy logic has applications in various fields, but has special meaning for remote sensing. Fuzzy logic allows partial membership, a very important property in the field of remote sensing, since partial membership is translated closely to the problem of mixed pixels. The aim of this research is to apply fuzzy logic classification algorithm to map benthic habitat in SPOT 7 and Sentinel 2A satellite imagery, test its accuracy level and compare fuzzy logic classification algorithm with maximum likelihood. Field data retrieval located in Karang Lebar and Karang Congkak, Kepulauan Seribu on 6 December until 10 December 2017. The overall accuracy test results show that fuzzy logic algorithm still has a good accuracy level compared to the maximum likelihood algorithm. Differences in pixel size (spatial resolution) of satellite imagery also affect accuracy results, where SPOT 7 satellite imagery has greater accuracy then Sentinel 2A. ABSTRAKLogika fuzzy memiliki aplikasi di berbagai bidang, namun memiliki arti khusus untuk penginderaan jarak jauh. Logika fuzzy memungkinkan keanggotaan parsial, bagian yang sangat penting dibidang penginderaan jarak jauh, karena keanggotaan parsial diterjemahkan secara dekat dengan masalah piksel campuran. Penelitian ini bertujuan untuk menerapkan algoritma klasifikasi logika fuzzy untuk memetakan habitat dasar Perairan dangkal pada Citra Satelit SPOT 7 dan Sentinel 2A, menguji tingkat akurasinya dan membandingkan algoritma klasifikasi logika fuzzy dengan maximum likelihood. Pengambilan data lapang berlokasi di gusung Karang Lebar dan Karang Congkak, Kepuluan Seribu pada tanggal 6 Desember sampai dengan 10 Desember 2017. Keseluruhan hasil uji akurasi menunjukan bahwa algoritma logika fuzzy masih memiliki tingkat akurasi yang baik dibandingkan dengan algoritma maximum likelihood. Perbedaan ukuran pixel (resolusi spasial) dari citra satelit juga mempengaruhi hasil akurasi, dimana citra satelit SPOT 7 memiliki tingkat akurasi yang lebih besar dibandingkan dengan Sentinel 2A.Kata kunci: habitat perairan dangkal, klasifikasi, logika fuzzy, Sentinel 2A, SPOT 7

Habitat perairan dangkal sangat penting dipetakan diantaranya karena: (1) mendukung perencanaan, manajemen, dan pengambilan keputusan tata ruang pemerintah; (2) mendukung dan mendesain Marine Protected Area (MPA); (3) melakukan program penelitian ilmiah yang bertujuan untuk menghasilkan pengetahuan tentang ekosistem bentik dan geologi dasar laut; (4) melakukan penilaian sumber daya dasar laut yang hidup dan tidak hidup untuk tujuan ekonomi dan menajemen, termasuk rancangan cadangan perikanan. Hingga saat ini belum ada standar untuk tingkat kedetailan peta tematik ekosistem pesisir khususnya habitat perairan dangkal sesuai kebutuhan pengelolaan wilayah pesisir dengan skema klasifikasi tertentu. Penelitian ini bertujuan untuk membandingkan akurasi peta hasil klasifikasi habitat perairan dangkal antara citra SPOT 6, Sentinel 2A, dan Landsat 8 menggunakan algoritma klasifikasi support vector machine. Lokasi penelitian terletak di Kepulauan Wakatobi, meliputi 2 lokasi yaitu Pulau Kapota dan Pulau Kompoone. Pengambilan data in-situ dilaksanakan pada tanggal 7-11 Juli 2019. Sebanyak 347 ground truth dan foto transek hasil sampling di lapangan telah dianalisis menggunakan coral point count with excel extension (CPCe). Skema klasifikasi yang dihasilkan yaitu 8 kelas habitat bentik, selanjutnya dilakukan klasifikasi dengan mengkelaskan kembali menjadi 6 dan 5 kelas. Hasil yang diperoleh pada citra SPOT-6 untuk semua kelas habitat perairan dangkal yang digunakan memiliki overall accuracy yang lebih besar. Perbedaan ukuran piksel (resolusi spasial) dan jumlah skema klasifikasi sangat memengaruhi hasil akurasi.

Object-based image analysis (OBIA) is an image classification that is oriented to object patterns that use image objects as the basis for processing, calculates characteristics per object, and extracts land cover information from remotely sensed images. This study aims to detect salt ponds using Sentinel 2 satellite data with an object-based classification model. The center of salt production, which is also an experimental area for the development of industrial salt from the ministry of maritime affairs and fisheries on the north coast of the island of Java was selected as the study area. The unit of analysis for this classification is the segmented object of sentinel image. The classification scheme built to detect salt ponds using OBIA consists of level 1, level 2, and level 3. Level 1 is to separate land and water using a Near Infrared canal. Level 2 is to separate land use from object segmentation results in land class at level 1 using NDVI transformation, and level 3 is to separate salt and non-salt ponds from the segmentation results of land use at level 2 using sentinel image transformation algorithm for the distribution of chlorophyll-a. The result shows chlorophyll-a estimation image transformation from sentinel useful to separate salt and non-salt ponds. Many researchers have been reported that chlorophyll-a does not live in the salinity range of salt ponds greater than 50 ppt, meanwhile, in non-salt ponds, chlorophyll-a is used as natural feed for cultivated animals. Furthermore, the research shows a classification scheme of salt ponds and non-salt ponds can be derived from sentinel 2 imagery with OBIA approach