scholarly journals The Comparison of RGB 564 and RGB 573 Band Composite of Landsat 8 for Mangrove Vegetation Distribution Identification on Pahawang Island, Lampung

2021 ◽  
Vol 830 (1) ◽  
pp. 012017
Author(s):  
Mohammad Ashari Dwiputra ◽  
Adib Mustofa
Keyword(s):  
Landsat 8 ◽  
Vegetation Distribution ◽  
Mangrove Vegetation

scholarly journals AUTOMATIC EXTRACTION OF MANGROVE VEGETATION FROM OPTICAL SATELLITE DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 555-561
Author(s):  
Mayank Agrawal ◽  
Devireddy Sushma Reddy ◽  
Ram Chandra Prasad
Keyword(s):  
Coastal Vegetation ◽  
Landsat 8 ◽  
Data Sets ◽  
Visual Interpretation ◽  
Hybrid Approaches ◽  
Mangrove Vegetation ◽  
Current Scenario ◽  
Liss Iii ◽  
Nicobar Islands ◽  
Interpretation Method

Mangrove, the intertidal halophytic vegetation, are one of the most significant and diverse ecosystem in the world. They protect the coast from sea erosion and other natural disasters like tsunami and cyclone. In view of their increased destruction and degradation in the current scenario, mapping of this vegetation is at priority. Globally researchers mapped mangrove vegetation using visual interpretation method or digital classification approaches or a combination of both (hybrid) approaches using varied spatial and spectral data sets. In the recent past techniques have been developed to extract these coastal vegetation automatically using varied algorithms. In the current study we tried to delineate mangrove vegetation using LISS III and Landsat 8 data sets for selected locations of Andaman and Nicobar islands. Towards this we made an attempt to use segmentation method, that characterize the mangrove vegetation based on their tone and the texture and the pixel based classification method, where the mangroves are identified based on their pixel values. The results obtained from the both approaches are validated using maps available for the region selected and obtained better accuracy with respect to their delineation. The main focus of this paper is simplicity of the methods and the availability of the data on which these methods are applied as these data (Landsat) are readily available for many regions. Our methods are very flexible and can be applied on any region.

scholarly journals MAPPING OF MANGROVE VEGETATION USING LANDSAT SATELLITE IMAGERYIN THE ESTUARY OF GANGSAL RIVER INDRAGIRI HILIR REGENCY RIAU PROVINCE

2020 ◽  
Vol 2 (3) ◽  
pp. 181-189
Author(s):  
Hendri Susilo ◽  
Musrifin Ghalib ◽  
Aras Mulyadi
Keyword(s):  
Community Structure ◽  
Satellite Imagery ◽  
Landsat 8 ◽  
Sampling Point ◽  
Landsat 8 Oli ◽  
Transect Line ◽  
Solid Density ◽  
Mangrove Vegetation ◽  
Landsat 5 Tm ◽  
Landsat Satellite

The research was conducted in January - March 2019. This study aims to map and analyze changes in the area and density of mangrove vegetation based on NDVI values and community structure in the Muara Sungai Gangsal, Indragiri Hilir Regency. To analyze the area and density of NDVI using Landsat 5 TM satellite imagery in 2008 and Landsat 8 OLI/TIRS in 2018. Analysis using ArcGis 10.3 software. The calculation of mangroves based on community structure used the Transect Line Plot method at 6 stations for community structure sampling. The area of mangrove vegetation in 2008 was 2,706 ha and in 2018 it was 2,693 ha. The results of the analysis of mangrove vegetation area from 2008 to 2018 there was a reduction of 13 ha. The NDVI value for 2008 criteria is rarely 133 ha, while 2.009 ha are wide and 564 ha is dense. The NDVI value of the 2018 mangrove vegetation is rarely 16 hectares, while 2,135 hectares are in the area and 542 hectares are dense. Based on the analysis of mangrove density in 2018 at 6 sampling point stations ranging from 866 ind/ha to 1,522 ind/ha. Density criteria are rarely detected at station I with a density of 922 ind/ha and station II with a density of 866 ind/ha. The criterion of moderate density was detected at station V with a density of 1,255 ind/ha and station VI with a density of 1,044 ind/ha. Criteria for solid density were detected at station III with a density of 1,522 ind/ha and station IV with a density of 1,511 ind/ha.

scholarly journals AUTOMATIC EXTRACTION OF MANGROVE VEGETATION FROM OPTICAL SATELLITE DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 555-561
Author(s):  
Mayank Agrawal ◽  
Devireddy Sushma Reddy ◽  
Ram Chandra Prasad
Keyword(s):  
Coastal Vegetation ◽  
Landsat 8 ◽  
Data Sets ◽  
Visual Interpretation ◽  
Hybrid Approaches ◽  
Mangrove Vegetation ◽  
Current Scenario ◽  
Liss Iii ◽  
Nicobar Islands ◽  
Interpretation Method

Mangrove, the intertidal halophytic vegetation, are one of the most significant and diverse ecosystem in the world. They protect the coast from sea erosion and other natural disasters like tsunami and cyclone. In view of their increased destruction and degradation in the current scenario, mapping of this vegetation is at priority. Globally researchers mapped mangrove vegetation using visual interpretation method or digital classification approaches or a combination of both (hybrid) approaches using varied spatial and spectral data sets. In the recent past techniques have been developed to extract these coastal vegetation automatically using varied algorithms. In the current study we tried to delineate mangrove vegetation using LISS III and Landsat 8 data sets for selected locations of Andaman and Nicobar islands. Towards this we made an attempt to use segmentation method, that characterize the mangrove vegetation based on their tone and the texture and the pixel based classification method, where the mangroves are identified based on their pixel values. The results obtained from the both approaches are validated using maps available for the region selected and obtained better accuracy with respect to their delineation. The main focus of this paper is simplicity of the methods and the availability of the data on which these methods are applied as these data (Landsat) are readily available for many regions. Our methods are very flexible and can be applied on any region.

scholarly journals ANALISIS TUTUPAN VEGETASI MANGROVE DI PULAU MANTEHAGE, TAMAN NASIONAL BUNAKEN, SULAWESI UTARA

2018 ◽  
Vol 6 (2) ◽  
pp. 37
Author(s):  
Jufran Sapsuha ◽  
Rignolda Djamaluddin ◽  
Calvyn F.A. Sondak ◽  
Royke M. Rampengan ◽  
Esri T. Opa ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Image Interpretation ◽  
River Mouth ◽  
Middle Part ◽  
Landsat 8 ◽  
Clear Cutting ◽  
North Sulawesi ◽  
Mangrove Vegetation ◽  
Landsat 7 ◽  
Vegetation Cover Change

Mangrove forest are typical forest growing on along coasts, river mouth saffected by tides. This study was conducted on April to September 2018 in Mantehage Island, Bunaken National Park, North Sulawesi. It was aimed to evaluate vegetation cover change during the period from 1995 to 2017 and to deskrip condition of mangrove vegetation at locations where the vegetation covers were identified to have been change. Image interpretation method and ground checks were applied in the study. RGB composite results of 473 1995 Landsat-5 images, RGB 453 of Landsat-7 images in 2005 and RGB 564 of Landsat-8 images in 2017 showed the area of mangrove vegetation cover in 1995, 2005 and 2017 respectively were 1333.95 ha, 1371.53 and 1383.21 ha. There was an increase in the area of mangrove vegetation cover in 1995-2005 covering an area of 37.58 ha and in the years 2005-2017 covering an area of 11.68 ha. In total there was an additional vegetation cover change of 49,26 ha for 22 years. Result from ground check indicated that the change in vegetation covers occurred at locations subjected to sedimentation, natural recovery at previous clear-cutting areas and area of artificial plantation. The phenomenan of mangrove diebach was found in the middle part between the two mainlands of Mantehage Island.Hutan mangrove merupakan tipe hutan yang khas dan tumbuh di sepanjang pantai atau muara sungai yang dipengaruhi oleh pasang surut air laut. Penelitian ini dilakukan dari bulan April-September 2018 di Pulau Mantehage, Taman Nasional Bunaken, Sulawesi Utara dengan tujuan untuk mengevaluasi perubahan tutupan vegetasi mangrove di Pulau Mantehage dengan selang waktu 1995, 2005 dan 2017sertaMendeskripsikan kondisi vegetasi mangrove yang teridentifikasi mengalami perubahan tutupan. Penelitian dilakukan dengan metode interpretasi citra dan survei lapangan (Ground check). Hasil komposit RGB 473 citra Landsat-5 tahun 1995, RGB 453 citra Landsat-7 tahun 2005 dan RGB 564 citra Landsat-8 tahun 2017 menunjukkan luas tutupan vegetasi mangrove pada tahun 1995, 2005 dan 2017 secara berturut-turut adalah 1333,95 ha, 1371,53 dan 1383,21 ha. Terjadi penambahan luas tutupan vegetasi mangrove pada tahun 1995-2005 seluas 37,58 ha dan pada tahun 2005-2017 seluas 11,68 ha, jika dijumlahkan dalam kurun waktu 22 tahun terjadi penambahan luas tutupan vegetasi mangrove sebesar 49,26 ha. Hasil Ground Check di lapangan menunjukkan penambahan luas tutupan vegetasi mangrove terjadi pada lahan yang mengalami sedimentasi, lahan terbuka bekas penebangan yang mengalami pemulihan kembali dan pada lokasi tertentu yang ditanami secara artifisial. Fenomena mangrove dieback ditemukan di lokasi bagian tengah antara dua daratan Pulau Mantehage.

scholarly journals KUALITAS LINGKUNGAN PERAIRAN DAN POTENSI PRODUKSI IKAN KAWASAN KONSERVASI TERUBUK BENGKALIS (Tenualosa macrura Bleeker, 1852)

2018 ◽  
Vol 1 (1) ◽  
pp. 37
Author(s):  
Khairul Amri ◽  
Gathot Winarso ◽  
Muchlizar Muchlizar
Keyword(s):  
Water Quality ◽  
Satellite Imagery ◽  
Primary Productivity ◽  
Landsat 8 ◽  
Productivity Level ◽  
Mangrove Vegetation ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Terubuk Bengkalis (Tenualosa macrura) yang hidup di perairan Bengkalis dinyatakan terancam punah akibat eksploitasi berlebih dan penurunan kualitas perairan. Penelitian ini bertujuan untuk menganalisa kualitas perairan habitat terubuk Bengkalis, menggunakan data parameter oseanografi hasil pengukuran in-situ. Selain itu, data penginderaan jauh berupa citra Landsat 8 digunakan untuk analisa tutupan mangrove (hutan bakau) serta citra MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer) untuk analisis produktivitas primer (NPP). Penelitian dilaksanakan selama April-November 2015. Hasil penelitian menunjukkan total luas tutupan mangrove yang teridentifikasi citra Satelit Landsat 8 (2015) mencapai 11.736,2 Ha, berkurang sekitar 4.470,8 Ha dalam waktu 12 tahun (2003-2015) dengan laju kehilangan 372,5 Ha/tahun. Dari aspek oseanografi, kawasan konservasi ikan terubuk merupakan perairan dangkal dengan tingkat kecerahan rendah (0,54-0,95 m); suhu perairan relatif tinggi berkisar 29,15-31,87 0C (rata-rata 300C) dan salinitas rata-rata tergolong rendah (28,77-29,22 ppt). Nilai sebaran pH dan oksigen terlarut/DO juga rendah yakni pH 6,3-8,9 (rata-rata pH 7) dan DO 3,90-4,98 mg/l (di bawah Baku Mutu Air Laut). Komposisi substrat dasar didominasi lumpur, dengan prosentase 67,4-89,8%, sehingga perairan ini umumnya keruh. Perairan ini tergolong subur (eutropik) dengan kelimpahan fitoplankton tinggi (23.584 - 95.616 sel/l) terdiri dari 32-52 genera. Produktivitas primer juga tinggi, rata-rata 288,87 mgC/m2/hari dengan potensi produksi ikan 3.680,2 ton/tahun. Terubuk Bengkalis (Tenualosa macrura) is an endemic tropical shad fish that live in Bengkalis waters.This species has been declared endangered due to over exploitation and environmental degradation. The current research aimed to analyze the environmental quality of the species. The data used in this research were consited of in-situ measurement and remote sensing data: Landsat 8 Satellite imagery for mangrove cover observation and MODIS (Moderate-resolution Imaging Spectroradiometer) imagery for Net Primary Productivity (NPP). The results showed that the cover of mangrove vegetation along the coast of Bengkalis Island dentified by Landsat 8 Satellite imagery was11.736,24 Ha. The total loss of cover mangrove vegetation is estimated about 4.470,83 Ha, decreased drastically in 12 years (2003-2015) with a loss rate of 372.5 Ha/year. The habitat of shad fish is shallow water category. The water quality was too turbid (brightness level 0.54-0.95 m); warm water temperature of 29.,15-31.87 0C (average 300C); and low salinity (28,77-29,22 ppt). The relatively low pH and dissolved oxygen content were determined: pH ranged between 6,3-8,9 (mean7) and the DO: 3,90-4,98 mg/l (under the Sea Water Quality Standard).The substrat was dominated by mud (67,4-89,8%) in Bengkalis Strait sub area due to the turbidity. However, these waters are euthropic level category with a high abundance of phytoplankton ranging from 23,584-95,616 cells/l and the species richness varies from 32-52 species. The primary productivity level of waters was also quite high was average of 288,87 mgC/m2/day resulting an estimation of potential of fish biomass value about 3.680,2 ton/year.

scholarly journals EVALUATION OF MANGROVE DAMAGE LEVEL BASED ON LANDSAT 8 IMAGE

2017 ◽  
Vol 11 (2) ◽  
pp. 105 ◽  
Author(s):  
Gathot Winarso ◽  
Anang D. Purwanto
Keyword(s):  
Mangrove Forest ◽  
Field Survey ◽  
Damage Identification ◽  
High Tide ◽  
Actual Condition ◽  
Landsat 8 ◽  
Identification Algorithm ◽  
Land Uses ◽  
Damage Level ◽  
Mangrove Vegetation

Monitoring of mangrove damage in Java requires special attention because the mangrove vegetation has been under pressure from various other land uses which are considered more productive. This paper applied quick-mangrove-damage-detection technique using Landsat 8. The purpose of this study is to develop mangrove damage identification algorithm using Landsat 8. The findings from field survey in Segara Anakan-Cilacap show that major mangrove logging generates the growth of minor mangrove, specifically Derris and Acanthus type; the minor mangrove cover area is categorized as high density based on NDVI value. The index use does not meet the actual condition in the field. This study proposes a new index as mangrove quality indicator. The new proposed mangrove index is derived from 2 bands that could differentiate mangrove vegetation where different digital number of two bands is higher from mangrove forest than non-mangrove forest. That phenomenon is caused the low of SWIR spectral on mangrove forest due to absorption by wet soil below the mangrove forest where flooded in high tide.  The new mangrove index is formulated as (NIR – SWIR / NIR x SWIR) x 10000. The new mangrove index has good correlation with density of major mangrove in the field, and also good correlation with mangrove degradation map. Mangrove index has been functioning properly and can be applied in Segara Anakan, Cilacap and potentially can be applied in other locations.

Analisis Pengaruh Kerapatan Vegetasi Terhadap Suhu Permukaan dan Keterkaitannya Dengan Fenomena UHI

2020 ◽  
Vol 21 (1) ◽  
pp. 99
Author(s):  
Dewi Miska Indrawati ◽  
Suharyadi Suharyadi ◽  
Prima Widayani
Keyword(s):  
Urban Heat Island ◽  
Landsat 8 ◽  
Heat Island ◽  
Landsat 8 Oli ◽  
Urban Heat ◽  
Split Window Algorithm

Kota Mataram adalahpusat dan ibukota dari provinsi Nusa Tenggara Barat yang tentunya menjadi pusat semua aktivitas masyarakat disekitar daerah tersebut sehingga menyebabkan peningkatan urbanisasi. Semakin meningkatnya peningkatan urbanisasi yan terjadi di perkotaan akan menyebabkan perubahan penutup lahan, dari awalnya daerah bervegetasi berubah menjadi lahan terbangun. Oleh karena itu, akan memicu peningkatan suhu dan menyebabkan adanya fenomena UHI dikota Mataram.Tujuan dari penelitian ini untuk mengetahui hubungan kerapatan vegetasi dengan kondisi suhu permukaan yang ada diwilayah penelitian dan memetakan fenomena UHI di Kota Mataram. Citra Landsat 8 OLI tahun 2018 yang digunakan terlebih dahulu dikoreksi radiometrik dan geometrik. Metode untuk memperoleh data kerapatan vegetasi menggunakan transformasi NDVI, LST menggunakan metode Split Window Algorithm (SWA) dan identifikasi fenomena urban heat island. Hasil penelitian yang diperoleh menunjukkan kerapatan vegetasi mempunyai korelasi dengan nilai LST. Hasil korelasi dari analisis pearson yang didapatkan antara kerapatan vegetasi terhadap suhu permukaan menghasilkan nilai -0,744. Fenomena UHIterjadi di pusat Kota Mataram dapat dilihat dengan adanya nilai UHI yaitu 0-100C. Semakin besar nilai UHI, semakin tinggi perbedaan LSTnya.

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