Anang Dwi Purwanto, Anang Dwi
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DETEKSI AWAL HABITAT PERAIRAN LAUT DANGKAL MENGGUNAKAN TEKNIK OPTIMUM INDEX FACTOR PADA CITRA SPOT 7 DAN LANDSAT 8 Purwanto, Anang Dwi; Setiawan, Kuncoro Teguh
Jurnal Kelautan Vol 12, No 2 (2019)
Publisher : Department of Marine Sciences, Trunojoyo University of Madura, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1490.837 KB) | DOI: 10.21107/jk.v12i2.5400

Abstract

ABSTRACTInformation of the existence of the shallow water habitat  is required especially in environmental conservation and monitoring of activities in coastal areas. The component of the shallow water habitat including coral reefs and seagrass. Interpretation of the shallow water habitat is constrained by the location of ecosystem associated with other objects. The aim of study is to determine the best combination of band composites in identifying the shallow water habitat in Pemuteran Beach, Bali. The study used SPOT 7 imagery (acquisition on April 11, 2018) and Landsat 8  imagery (acquisition on April 14, 2018). The data of the shallow water habitat based on the result of field survey was conducted on 7-13 April 2018 at Pemuteran Beach, Bali.  Image data obtained from Remote Sensing Technology and Data Center of LAPAN. Determination of combination of 3 (three) bands the shallow water habitat using Optimum Index Factor (OIF) method where this method used standard deviation value and correlation coefficient from combination of 3 (three) bands. The results show the composite combinations of band 2 (green), band 3 (red) and band 4 (NIR) have the highest OIF values for SPOT 7 image, while the composite combinations of band 2 (blue), band 4 (red) and band 6 (SWIR 1) have the highest OIF values for Landsat 8 image. Interpretation of distribution of shallow water habitat can be done effectively using RGB 423 composite image (SPOT 7) and RGB 642 composite image (Landsat 8). Keywords: Shallow Water Habitat, OIF, SPOT 7, Landsat 8, PemuteranABSTRAKInformasi keberadaan habitat perairan laut dangkal semakin dibutuhkan terutama dalam kegiatan pelestarian lingkungan dan monitoring di wilayah pesisir. Komponen penyusun ekosistem habitat dasar perairan laut dangkal di antaranya terumbu karang dan lamun. Dalam interpretasi ekosistem habitat dasar perairan laut dangkal terkendala oleh lokasi keberadaan ekosistem yang berasosiasi dengan obyek lainnya. Tujuan penelitian ini adalah menentukan kombinasi komposit kanal terbaik dalam mengidentifikasi obyek habitat dasar perairan laut dangkal di Pantai Pemuteran, Bali. Data citra satelit yang digunakan dalam penelitian ini adalah citra SPOT 7 akuisisi tanggal 11 April 2018  dan citra Landsat 8 akuisisi tanggal 14 April 2018, sedangkan data terkait informasi sebaran habitat dasar perairan laut dangkal diperoleh berdasarkan hasil survei lapangan yang telah dilakukan pada tanggal 7-13 April 2018 di Pantai Pemuteran, Bali. Data citra satelit diperoleh dari Pusat Teknologi dan Data LAPAN. Untuk menentukan kombinasi dari 3 (tiga) kanal terbaik dalam interpretasi habitat dasar perairan laut dangkal digunakan metode Optimum Index Factor (OIF) dimana metode ini menggunakan nilai standar deviasi dan koefisien korelasi dari kombinasi 3 (tiga) kanal citra yang digunakan. Hasil penelitian menunjukkan kombinasi komposit 2 (hijau), 3 (merah) dan 4 (NIR) mempunyai nilai OIF tertinggi untuk citra SPOT 7, sedangkan kombinasi komposit 2 (biru), 4 (merah) dan 6 (SWIR 1) mempunyai nilai OIF tertinggi untuk citra Landsat 8. Interpretasi sebaran habitat dasar perairan laut dangkal dapat dilakukan secara efektif dengan menggunakan citra komposit RGB 423 untuk citra SPOT 7 dan RGB 642 untuk citra Landsat 8.Kata kunci: Habitat Dasar Perairan Dangkal, OIF, SPOT 7, Landsat 8, Pemuteran
Identifikasi Gosong Karang Mengggunakan Citra Satelit Sentinel 2A (Studi Kasus: Perairan Pesisir Nias Utara) Purwanto, Anang Dwi; Prayogo, Teguh; Marpaung, Sartono
Jurnal Teknologi Lingkungan Vol. 21 No. 1 (2020)
Publisher : Center for Environmental Technology - Agency for Assessment and Application of Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1895.207 KB) | DOI: 10.29122/jtl.v21i1.3769

Abstract

ABSTRACTThe waters of Northern Nias, North Sumatra Province have a great potential for natural resources, one of which is the reef which is often used as a fishing ground. This study aims to identify and monitor the distribution of coral reefs around the waters of Northern Nias. The location of study is limited by coordinates 97° 0'31'' - 97° 16'54'' E and 1° 29'2'' LU - 1° 6'24'' N. The study locations were grouped in 6 (six) areas including Mardika reef, Wunga reef, Mausi1 reef, Mausi2 reef, Tureloto reef and Senau reef. The data used were Sentinel 2A imagery acquisition on 19 September 2018 and field observations made on 6-12 September 2018. Data processing includes geometric correction, radiometric correction, water column correction and classification using pixel-based and object-based methods as well as by delineating on the image. One classification method will be chosen that is most suitable for the location of the reef. The results show Sentinel 2A was very helpful in mapping the distribution of coral reefs compared to direct observation in the field. The use of image classification method rightly is very helpful in distinguishing coral reef objects from surrounding objects. The estimated area of coral reefs was 1,793.20 ha with details of the Mardika reef 143.27 ha, Wunga reef 627.06 ha, Mausi1 reef 299.84 ha, Mausi2 reef 141.873 ha, Tureloto reef 244.73 ha, Senau reef 336.44 ha. The existence of coral reefs have a high potential as a fishing ground and a natural tourist attraction.Keywords: coral reefs, sentinel 2A, lyzenga 1978, image classification, Northern NiasABSTRAKPerairan Nias Utara yang terletak di Provinsi Sumatra Utara memiliki potensi kekayaan alam yang besar dimana salah satunya adalah gosong karang yang sering dijadikan lokasi penangkapan ikan oleh nelayan. Penelitian ini bertujuan untuk mengidentifikasi dan monitoring sebaran gosong karang di sekitar perairan Nias Utara. Lokasi penelitian dibatasi dengan koordinat 97°0’31’’ - 97°16’54’’ BT dan 1°29’2’’LU – 1°6’24’’  LU. Untuk mempermudah dalam pengolahan data maka lokasi kajian dikelompokkan dalam 6 (enam) kawasan diantaranya gosong Mardika, gosong Wunga, gosong Mausi1, gosong Mausi2, gosong Tureloto dan gosong Senau. Data yang digunakan adalah citra satelit Sentinel 2A hasil perekaman tanggal 19 September 2018 dan hasil pengamatan lapangan yang telah dilakukan pada tanggal 6 - 12 September 2018. Pengolahan data meliputi koreksi geometrik, koreksi radiometrik, koreksi kolom air dan klasifikasi menggunakan metode klasifikasi berbasis piksel dan berbasis objek serta deliniasi citra. Dari ketiga metode klasifikasi tersebut akan dipilih satu metode klasifikasi yang sesuai dengan lokasi gosong karang. Hasil penelitian menunjukkan citra Sentinel 2A sangat membantu dalam memetakan sebaran gosong karang dibandingkan dengan pengamatan langsung di lapangan. Pemilihan metode klasifikasi citra satelit yang tepat sangat membantu dalam membedakan objek gosong karang dengan objek di sekitarnya. Estimasi total luasan gosong karang di perairan Nias Utara adalah 1,793.20 ha dengan rincian luasan gosong karang Mardika 143.27 ha, gosong Wunga 627.06 ha, gosong Mausi1 299.84 ha, gosong Mausi2 141.873 ha, gosong Tureloto 244.73 ha, gosong Senau 336.44 ha. Keberadaan gosong karang memiliki potensi yang tinggi sebagai lokasi penangkapan ikan dan memiliki daya tarik sebagai tempat wisata alam.Kata kunci: gosong karang, sentinel 2A, lyzenga 1978, klasifikasi citra, Nias Utara
TIME SERIES ANALYSIS OF TOTAL SUSPENDED SOLID (TSS) USING LANDSAT DATA IN BERAU COASTAL AREA, INDONESIA Parwati, Ety; Purwanto, Anang Dwi
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 14, No 1 (2017)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (968.28 KB) | DOI: 10.30536/j.ijreses.2017.v14.a2676

Abstract

Water quality information is usually used for the first examination of the pollution.  One of the parameters of water quality is Total Suspended Solid (TSS), which describes the amount of matter of particles suspended in the water. TSS information is also used as initial information about waters condition of a region. TSS could be derive from Landsat data with several combinations of spectral channels to evaluate the condition of the observation area for both the waters and the surrounding land. The study aimed to evaluate Berau waters condition in Kalimantan, Indonesia, by utilizing TSS dynamics extracted from Landsat data. Validated TSS extraction algorithm was obtained by choosing the best correlation between  field data and image data. Sixty pairs of points had been used to build validated TSS algorithms for the Berau Coastal area. The algorithm was TSS = 3.3238 * exp (34 099 * Red Band Reflectance). The data used for this study were Landsat 5 TM, Landsat 7 ETM and Landsat 8 data acquisition in 1994, 1996, 1998, 2002, 2004, 2006, 2008 and 2013. For detailed evaluation, 20 regions were created along the watershed up to the coast. The results showed the fluctuation of TSS values in each selected region. TSS value increased if there was a change of any kind of land cover/land used into bareland, ponds, settlements or shrubs. Conversely, TSS value decreased if there was a wide increase of mangrove area or its position was very closed to the ocean.
Adaptasi Masyarakat terhadap Perubahan Penutup Lahan di Kecamatan Kelapa Kampit, Belitung Timur Rahmawati, Emma; Purwanto, Anang Dwi
Forum Ilmu Sosial Vol 46, No 2 (2019): December 2019
Publisher : Faculty of Social Science, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/fis.v46i2.21223

Abstract

Land cover changes occur in Indonesia where one of them is in Kelapa Kampit, East Belitung Regency. This area is very famous for large-scale tin mining activities carried out by tin companies. Along with the policy on mining activities that must care about the surrounding environment and reclamation and revegetation activities carried out in post-mining land, the massive tin mining activities in the region are decreasing. This study aims to analyze changes in land cover and find out the adaptation of the community carried out due to changes in land cover and to know the factors that influence the adaptation strategy. This study uses the mixed methods approach. The data used include in the form of Landsat TM satellite imagery acquisition in 1995 and Landsat 8 acquisition in 2015 and interview data. The method of separating land cover objects using the technique of not guided digital classification. After the reclassification process has been carried out, 8 (eight) classes of land cover are produced. The results of the study show (1) there are contradictory conditions between mining land and forests with oil palm plantations. The condition of the mining area and forests from 1994 - 2015 decreased, while the area of oil palm plantations increased significantly. (2) The community adapts to varying sources of income, changes mining areas and relies on assistance from various parties (3) The factors that determine community adaptation are education, knowledge, environmental conditions, people's understanding of job other than mining, sources of income and assistance provided to the community.
Pemanfaatan Data Penginderaan Jauh untuk Ekstraksi Habitat Perairan Laut Dangkal di Pantai Pemuteran, Bali, Indonesia Purwanto, Anang Dwi; Setiawan, Kuncoro Teguh; Ginting, Devica Natalia Br.
Jurnal Kelautan Tropis Vol 22, No 2 (2019): JURNAL KELAUTAN TROPIS
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (381.698 KB) | DOI: 10.14710/jkt.v22i2.5092

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Indonesia had a large diversity of coastal ecosystems. One part of the them is the coral reef. The concept of mapping coral reef ecosystems has been outlined in the RSNI document about the mapping of shallow marine waters. The aim of this study is to map shallow marine waters using the 1981 and 2006 lyzenga methods. The mapping was made based on three classes including coral reef, mixed seagrass and macroalgae, and substrate. The location of the study was conducted at Pemuteran Beach, Bali. The data used were Landsat 8 imagery acquisition on 14 April 2018. Stages of data processing include atmospheric correction, radiometric correction, pansharpening, masking, cropping, and water column correction and classification. Water column correction used the Lyzenga 1981 and 2006. Classification methods to distinguish objects of shallow marine waters using the unsupervised method. The results showed differences in the results of extraction of shallow marine waters information using the Lyzenga 1981 with the 2006 Lyzenga method. The extraction results with the Lyzenga 2006 method provide more detailed information in identifying the three classes of shallow marine waters. Indonesia memiliki keanekaragaman ekosistem pesisir yang cukup besar. Salah satu bagian dari ekosistem tersebut adalah ekosistem terumbu karang. Konsep pemetaan ekosistem terumbu karang telah dituangkan dalam RSNI tentang pemetaan habitat dasar perairan laut dangkal. Tujuan penelitian ini adalah untuk melakukan pemetaan habitat perairan laut dangkal dengan menggunakan metode lyzenga 1981 dan 2006.  Pemetaan tersebut dibuat berdasarkan tiga kelas diantaranya: kelas terumbu karang, kelas campuran padang lamun dan makro alga, serta kelas substrat dasar. Lokasi penelitian dilaksanakan di Pantai Pemuteran, Bali. Data yang digunakan adalah citra Landsat 8 akuisisi 14 April 2018. Tahapan pengolahan data meliputi, koreksi atmosferik, koreksi radiometrik, proses pansharpening, proses masking darat air, cropping, serta koreksi kolom air serta klasifikasi. Koreksi kolom air menggunakan metode Lyzenga 1981 dan 2006. Klasifikasi untuk membedakan obyek habitat perairan laut dangkal menggunakan metode unsupervised . Hasil penelitian menunjukkan adanya perbedaan hasil ekstraksi informasi habitat perairan laut dangkal menggunakan metode Lyzenga 1981 dengan metode Lyzenga 2006. Hasil ekstraksi dengan metode Lyzenga 2006 memberikan informasi yang lebih detail dalam mengidentifikasi tiga kelas habitat perairan laut dangkal tersebut.
DETEKSI AWAL HABITAT PERAIRAN LAUT DANGKAL MENGGUNAKAN TEKNIK OPTIMUM INDEX FACTOR PADA CITRA SPOT 7 DAN LANDSAT 8 Purwanto, Anang Dwi; Setiawan, Kuncoro Teguh
JURNAL ENGGANO Vol 4, No 2
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (934.567 KB) | DOI: 10.31186/jenggano.4.2.174-192

Abstract

Informasi keberadaan habitat perairan laut dangkal semakin dibutuhkan terutama dalam kegiatan pelestarian lingkungan dan monitoring di wilayah pesisir. Komponen penyusun ekosistem habitat dasar perairan laut dangkal di antaranya terumbu karang dan lamun dimana lokasi keberadaan obyek habitat ini cenderung berdekatan. Dalam interpretasi ekosistem habitat dasar perairan laut dangkal terkendala oleh lokasi keberadaan ekosistem yang berasosiasi dengan obyek lainnya. Tujuan penelitian ini adalah menentukan kombinasi komposit kanal terbaik dalam mengidentifikasi obyek habitat dasar perairan laut dangkal di Pantai Pemuteran, Bali. Data citra satelit yang digunakan dalam penelitian ini adalah citra SPOT 7 akuisisi tanggal 11 April 2018 dan citra Landsat 8 akuisisi tanggal 14 April 2018, sedangkan data terkait informasi sebaran habitat dasar perairan laut dangkal diperoleh berdasarkan hasil survei lapangan yang telah dilakukan pada tanggal 7-13 April 2018 di Pantai Pemuteran, Bali. Data citra satelit diperoleh dari Pusat Teknologi dan Data LAPAN. Untuk menentukan kombinasi dari 3 (tiga) kanal terbaik dalam interpretasi habitat dasar perairan laut dangkal digunakan metode Optimum Index Factor (OIF) dimana metode ini menggunakan nilai standar deviasi dan koefisien korelasi dari kombinasi 3 (tiga) kanal citra yang digunakan. Hasil penelitian menunjukkan kombinasi komposit 2 (hijau), 3 (merah) dan 4 (NIR) mempunyai nilai OIF tertinggi untuk citra SPOT 7, sedangkan kombinasi komposit 2 (biru), 4 (merah) dan 6 (SWIR 1) Mempunyai nilai OIF tertinggi untuk citra Landsat 8. Interpretasi sebaran habitat dasar perairan laut dangkal dapat dilakukan secara efektif dengan menggunakan citra komposit RGB 423 untuk citra SPOT 7 dan RGB 642 untuk citra Landsat 8.DETECTION OF SHALLOW WATER HABITATS USING OPTIMUM INDEX FACTORS TECHNIQUE ON SPOT 7 AND LANDSAT 8 IMAGERY. Information of the existence of the shallow water habitat is required especially in environmental conservation and monitoring of activities in coastal areas. The component of the shallow water habitat including coral reefs and seagrass where the location of the existence of these relatively close together. Interpretation of the shallow water habitat is constrained by the location of ecosystem associated with other objects. The aim of study is to determine the best combination of band composites in identifying the shallow water habitat in Pemuteran Beach, Bali. The study used SPOT 7 imagery (acquisition on April 11, 2018) and Landsat 8 imagery (acquisition on April 14, 2018). The data of the shallow water habitat based on the result of field survey was conducted on 7-13 April 2018 at Pemuteran Beach, Bali. Image data obtained from Remote Sensing Technology and Data Center of LAPAN. Determination of combination of 3 (three) bands the shallow water habitat using Optimum Index Factor (OIF) method where this method used standard deviation value and correlation coefficient from combination of 3 (three) bands. The results show the composite combinations of band 2 (green), band 3 (red) and band 4 (NIR) have the highest OIF values for SPOT 7 image, while the composite combinations of band 2 (blue), band 4 (red) and band 6 (SWIR 1) have the highest OIF values for Landsat 8 image. Interpretation of distribution of shallow water habitat can be done effectively using RGB 423 composite image (SPOT 7) and RGB 642 composite image (Landsat 8).
IDENTIFICATION OF MANGROVE FORESTS USING MULTISPECTRAL SATELLITE IMAGERIES Purwanto, Anang Dwi; Asriningrum, Wikanti
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 1 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1900.958 KB) | DOI: 10.30536/j.ijreses.2019.v16.a3097

Abstract

The visual identification of mangrove forests is greatly constrained by combinations of RGB composite. This research aims to determine the best combination of RGB composite for identifying mangrove forest in Segara Anakan, Cilacap using the Optimum Index Factor (OIF) method. The OIF method uses the standard deviation value and correlation coefficient from a combination of three image bands. The image data comprise Landsat 8 imagery acquired on 30 May 2013, Sentinel 2A imagery acquired on 18 March 2018 and images from SPOT 6 acquired on 10 January 2015. The results show that the band composites of 564 (NIR+SWIR+Red) from Landsat 8 and 8a114 (Vegetation Red Edge+SWIR+Red) from Sentinel 2A are the best RGB composites for identifying mangrove forest, in addition to those of 341 (Red+NIR+Blue) from SPOT 6. The near-infrared (NIR) and short-wave infrared (SWIR) bands play an important role in determining mangrove forests. The properties of vegetation are reflected strongly at the NIR wavelength and the SWIR band is very sensitive to evaporation and the identification of wetlands.
Perubahan sebaran dan kerapatan hutan mangrove di Pesisir Pantai Bama, Taman Nasional Baluran menggunakan citra satelit SPOT 4 dan SPOT 6 Fudloly, Andhika Rahmatullah Laksmana; Fuad, Mochammad Arif Zainul; Purwanto, Anang Dwi
DEPIK Jurnal Ilmu-Ilmu Perairan, Pesisir dan Perikanan Vol 9, No 2 (2020): August 2020
Publisher : Faculty of Marine and Fisheries, Syiah Kuala University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (853.399 KB) | DOI: 10.13170/depik.9.2.14494

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The condition of mangrove forests in the Baluran National Park area is always changing. Mapping changes of mangrove area and density is needed to find out areas that need attention for mangrove conservation. The study aimed to determine the distribution and the density of mangrove forests in coastal waters of Bama, Baluran National Park. The image data used were SPOT 4 acquisition in 2007 and SPOT 6 acquisition in 2017 as well as field data that have been collected on 23-25 January 2019. The method of separating mangrove and non-mangrove objects used supervised classification, whereas for estimating the density of mangrove using the Normalized Difference Vegetation Index (NDVI) algorithm. The results showed the distribution of mangrove forests in coastal waters of Bama, Baluran National Park from 2007-2017 decreased in area by 8.9 ha. In contrast, the condition of mangrove density increased significantly, where the changes in mangrove density were dominated in the high-density class. The results of the accuracy tests using the method confusion matrix obtained an overall accuracy of 88%, while the accuracy-test with the kappa method obtained an accuracy of 87.76%. The resulting accuracy value indicates a high level of accuracy (more than 85%) and according to the specified requirements.Keywords: Mangrove, NDVI, SPOT 4, SPOT 6, Baluran National Park ABSTRAKKondisi luasan hutan mangrove di kawasan Taman Nasional Baluran terus mengalami perubahan. Pemetaan perubahan luasan dan kerapatan mangrove sangat diperlukan untuk mengetahui area yang membutuhkan perhatian untuk pelestarian mangrove. Penelitian ini bertujuan untuk mengetahui sebaran dan kerapatan hutan mangrove di  pesisir pantai Bama, Taman Nasional Baluran. Data yang digunakan dalam penelitian adalah citra SPOT 4 akuisisi tahun 2007 dan citra SPOT 6 akuisisi tahun 2017 dan data hasil survei lapangan yang telah dilakukan pada tanggal 23 - 25 Januari 2019. Metode pemisahan obyek mangrove dan non mangrove menggunakan klasifikasi terbimbing (supervised), sedangkan untuk pendugaan tingkat kerapatan mangrove menggunakan algoritma Normalized Difference Vegetation Index (NDVI). Hasil penelitian menunjukkan sebaran hutan mangrove di pesisir pantai Bama, Taman Nasional Baluran dari tahun 2007-2017 mengalami penurunan luasan sebesar 8,9 ha, sedangkan kondisi tingkat kerapatan mangrove mengalami peningkatan yang cukup signifikan dimana perubahan kerapatan mangrove didominasi pada kelas kerapatan rapat. Hasil uji akurasi menggunakan metode matriks kesalahan (confusion matrix) memperoleh overall accuracy sebesar 88%, sedangkan uji akurasi dengan metode kappa diperoleh tingkat akurasi sebesar 87,76%. Nilai akurasi yang dihasilkan menunjukkan tingkat ketelitian yang cukup tinggi (lebih dari 85%) dan telah memenuhi syarat yang ditetapkan.Kata kunci: Mangrove, NDVI, SPOT 4, SPOT 6, Taman Nasional Baluran
IDENTIFICATION OF MANGROVE FORESTS USING MULTI-RESOLUTION SATELLITE IMAGERY Purwanto, Anang Dwi
Jurnal Segara Vol 16, No 2 (2020): Agustus
Publisher : Pusat Riset Kelautan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15578/segara.v16i2.7512

Abstract

The development of remote sensing technology for identifying various of coastal and marine ecosystems which one of them is mangrove forest increasing rapidly. Identification of mangrove forests visually is constrained by much of combinations of RGB composite. The aims of this research is to determine the best combination of RGB composite for identifying mangrove forest in Segara Anakan, Cilacap using Optimum Index Factor (OIF) method. The image data used represents 3 levels of intermediate to high resolution spatial resolution including Landsat 8 imagery (30 m) acquisition on 30 May 2013, Sentinel 2A image (10 m) acquisition on 18 March 2018 and SPOT 6 image (6 m) acquisition on 10 January 2015. Data of mangrove distributions used were the results of field measurements in the period 2013-2015. The results showed that the band composites of 564 (NIR+SWIR+Red) of Landsat 8 image and the band composites of 8a114 (Vegetation Red Edge+SWIR+Red) of Sentinel 2A are the best RGB composites for identifying mangrove forest, while the band composites of 341 (Red+NIR+Blue) of SPOT 6 image is  also the best colour composites (R-G-B) for identifying mangrove forest in Segara Anakan, Cilacap. The RGB composites of images developed from Landsat 8 and Sentinel 2A image are able to distinguish objects of mangrove forest from surrounding objects more clearly, but image composites from SPOT 6 image still require additional of association elements to identify mangrove objects.The development of remote sensing technology for identifying various of coastal and marine ecosystems which one of them is mangrove forest increasing rapidly. Identification of mangrove forests visually is constrained by much of combinations of RGB composite. The aims of this research is to determine the best combination of RGB composite for identifying mangrove forest in Segara Anakan, Cilacap using Optimum Index Factor (OIF) method. The image data used represents 3 levels of intermediate to high resolution spatial resolution including Landsat 8 imagery (30 m) acquisition on 30 May 2013, Sentinel 2A image (10 m) acquisition on 18 March 2018 and SPOT 6 image (6 m) acquisition on 10 January 2015. Data of mangrove distributions used were the results of field measurements in the period 2013-2015.The results showed that the band composites of 564 (NIR+SWIR+Red) of Landsat 8 image and the band composites of 8a114 (Vegetation Red Edge+SWIR+Red) of Sentinel 2A are the best RGB composites for identifying mangrove forest, while the band composites of 341 (Red+NIR+Blue) of SPOT 6 image is  also the best colour composites(R-G-B) for identifying mangrove forest in Segara Anakan, Cilacap. The RGB composites of images developed from Landsat 8 and Sentinel 2A image are able to distinguish objects of mangrove forest from surrounding objects more clearly, but imagecomposites from SPOT 6 image still require additional of association elements to identify mangrove objects.