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ANALYSIS OF CLASSIFICATION METHODS FOR MAPPING SHALLOW WATER HABITATS USING SPOT-7 SATELLITE IMAGERY IN NUSA LEMBONGAN ISLAND, BALI Kuncoro Teguh Setiawan; Gathot Winarso; Andi Ibrahim; Anang Dwi Purwanto; I Made Parsa
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 19, No.1 (2022)
Publisher : Ikatan Geografi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2022.v19.a3748

Shallow water habitat maps are crucial for the sustainable management purposes of marine resources. The use of a better digital classification method can provide shallow water habitat maps with the best accuracy rate that is able to indicate actual conditions. Experts use the object-based classification method as an alternative to the pixel-based method. However, the pixel-based classification method continues to be relied upon by experts in obtaining benthic habitat conditions in shallow water. This study aims to analyze the classification results and examine the accuracy rate of shallow-water habitats distribution using SPOT-7 satellite imagery in Nusa Lembongan Island, Bali. Water column correction by Lyzenga 2006 was opted, while object-based and pixel-based classification was used in this study. The benthic habitat classification scheme uses four classes: substrate, seagrass, macroalgae, and coral. The results show different accuracy is obtained between pixel-based classification with maximum likelihood models and object-based classification with decision tree models. Mapping benthic habitats in Nusa Lembongan, Bali, with object-based classification and decision tree models, has higher accuracy than the other with 68%.

Perubahan sebaran dan kerapatan hutan mangrove di Pesisir Pantai Bama, Taman Nasional Baluran menggunakan citra satelit SPOT 4 dan SPOT 6 Andhika Rahmatullah Laksmana Fudloly; Mochammad Arif Zainul Fuad; Anang Dwi Purwanto
Depik Vol 9, No 2 (2020): August 2020
Publisher : Faculty of Marine and Fisheries, Universitas Syiah Kuala

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

Perubahan sebaran dan kerapatan hutan mangrove di Pesisir Pantai Bama, Taman Nasional Baluran menggunakan citra satelit SPOT 4 dan SPOT 6 Andhika Rahmatullah Laksmana Fudloly; Mochammad Arif Zainul Fuad; Anang Dwi Purwanto
Depik Vol 9, No 2 (2020): August 2020
Publisher : Faculty of Marine and Fisheries, Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13170/depik.9.2.14494

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

Perubahan sebaran dan kerapatan hutan mangrove di Pesisir Pantai Bama, Taman Nasional Baluran menggunakan citra satelit SPOT 4 dan SPOT 6 Andhika Rahmatullah Laksmana Fudloly; Mochammad Arif Zainul Fuad; Anang Dwi Purwanto
Depik Vol 9, No 2 (2020): August 2020
Publisher : Faculty of Marine and Fisheries, Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13170/depik.9.2.14494

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

ANALYSIS OF CLASSIFICATION METHODS FOR MAPPING SHALLOW WATER HABITATS USING SPOT-7 SATELLITE IMAGERY IN NUSA LEMBONGAN ISLAND, BALI Kuncoro Teguh Setiawan; Gathot Winarso; Andi Ibrahim; Anang Dwi Purwanto; I Made Parsa
International Journal of Remote Sensing and Earth Sciences Vol. 19 No. 1 (2022)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2022.v19.a3748

Shallow water habitat maps are crucial for the sustainable management purposes of marine resources. The use of a better digital classification method can provide shallow water habitat maps with the best accuracy rate that is able to indicate actual conditions. Experts use the object-based classification method as an alternative to the pixel-based method. However, the pixel-based classification method continues to be relied upon by experts in obtaining benthic habitat conditions in shallow water. This study aims to analyze the classification results and examine the accuracy rate of shallow-water habitats distribution using SPOT-7 satellite imagery in Nusa Lembongan Island, Bali. Water column correction by Lyzenga 2006 was opted, while object-based and pixel-based classification was used in this study. The benthic habitat classification scheme uses four classes: substrate, seagrass, macroalgae, and coral. The results show different accuracy is obtained between pixel-based classification with maximum likelihood models and object-based classification with decision tree models. Mapping benthic habitats in Nusa Lembongan, Bali, with object-based classification and decision tree models, has higher accuracy than the other with 68%.

ANALYSIS OF POTENTIAL FISHING ZONES IN COASTAL WATERS: A CASE STUDY OF NIAS ISLAND WATERS Anang Dwi Purwanto; Teguh Prayogo; Sartono Marpaung; Argo Galih Suhada
International Journal of Remote Sensing and Earth Sciences Vol. 17 No. 1 (2020)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2020.v17.a3298

The need for information on potential fishing zones based on remote sensing satellite data (ZPPI) in coastal waters is increasing. This study aims to create an information model of such zones in coastal waters (coastal ZPPI). The image data used include GHRSST, SNPP-VIIRS and MODIS-Aqua images acquired from September 1st-30th, 2018 and September 1st-30th, 2019, together with other supporting data. The coastal ZPPI information is based on the results of thermal front SST detection and overlaying this with chlorophyll-a. The method of determining the thermal front sea surface temperature (SST) used Single Image Edge Detection (SIED). The chlorophyll-a range used was in the mesotropic area (0.2-0.5 mg/m3). Coastal ZPPI coordinates were determined using the polygon centre of mass, while the coastal ZPPI information generated was only for coastal areas with a radius of between 4-12 nautical miles and was divided into two criteria, namely High Potential (HP) and Low Potential (LP). The results show that the coastal ZPPI models were suitable to determine fishing locations around Nias Island. The percentage of coastal ZPPI information generated was around 90% information monthly. In September 2018, 27 days of information were produced, consisting of 11 HP sets of coastal ZPPI information and 16 sets of LP information, while in September 2019 it was possible to produce 29 days of such information, comprising 11 sets of HP coastal ZPPI information and 18 LP sets. The use of SST parameters of GHRSST images and the addition of chlorophyll-a parameters to MODIS-Aqua images are very effective and efficient ways of supporting the provision of coastal ZPPI information in the waters of Nias Island and its surroundings.

ANALYSIS OF WATER PRODUCTIVITY IN THE BANDA SEA BASED ON REMOTE SENSING SATELLITE DATA Sartono Marpaung; Rizky Faristyawan; Anang Dwi Purwanto; Wikanti Asriningrum; Argo Galih Suhadha; Teguh Prayogo; Jansen Sitorus
International Journal of Remote Sensing and Earth Sciences Vol. 17 No. 1 (2020)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2020.v17.a3280

This study examines the density of potential fishing zone (PFZ) points and chlorophyll-a concentration in the Banda Sea. The data used are those on chlorophyll-a from the Aqua MODIS satellite, PFZ points from ZAP and the monthly southern oscillation index. The methods used are single image edge detection, polygon center of mass, density function and a Hovmoller diagram. The result of the analysis show that productivity of chlorophyll-a in the Banda Sea is influenced by seasonal factors (dry season and wet season) and ENSO phenomena (El NiÃ±o and La NiÃ±a). High productivity of chlorophyll-a Â occurs during in the dry season with the peak in August, while low productivity occurs in the wet season and the transition period, with the lowest levels in April and December. The variability in chlorophyll-a production is influenced by the global El NiÃ±o and La NiÃ±a phenomena; production increases during El NiÃ±o and decreases during La NiÃ±a. Tuna conservation areas have as lower productivity of chlorophyll-a and PFZ point density compared to the northern and southern parts of the Banda Sea. High density PFZ point regions are associated with regions that have higher productivity of chlorophyll-a, namely the southern part of the Banda Sea, while low density PFZ point areasÂ are associated with regions that have a low productivity of chlorophyll-a, namely tuna conservation areas. The effect of the El NiÃ±o phenomenon in increasing chlorophyll-a concentration is stronger in the southern part of study area than in the tuna conservation area. On the other hand, the effect of La NiÃ±a phenomenon in decreasing chlorophyll-a concentration is stronger in the tuna conservation area than in the southern and northern parts of the study area.

IDENTIFICATION OF MANGROVE FORESTS USING MULTISPECTRAL SATELLITE IMAGERIES Anang Dwi Purwanto; Wikanti Asriningrum
International Journal of Remote Sensing and Earth Sciences Vol. 16 No. 1 (2019)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2019.v16.a3097

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.

TIME SERIES ANALYSIS OF TOTAL SUSPENDED SOLID (TSS) USING LANDSAT DATA IN BERAU COASTAL AREA, INDONESIA Ety Parwati; Anang Dwi Purwanto
International Journal of Remote Sensing and Earth Sciences Vol. 14 No. 1 (2017)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2017.v14.a2676

Water quality information is usually used for the first examination of the pollution. Â 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 Â 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.

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