scholarly journals PENGARUH PERUBAHAN LUAS HUTAN MANGROVE TERHADAP KONSENTRASI TOTAL SUSPENDED MATTER (TSM) DI MUARA PERANCAK, JEMBRANA – BALI

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Komang Iwan Suniada
Keyword(s):  
Suspended Matter ◽  
Mangrove Forest ◽  
Satellite Image ◽  
River Mouth ◽  
Remote Sensing Data ◽  
Image Data ◽  
Mangrove Ecosystem ◽  
Total Suspended Matter ◽  
Forest Area ◽  
Mangrove Forests

Study of the function of mangrove forests as a sediment trap has been largely undertaken using field measurement methods, but only a few researches that fully utilize remote sensing data to find out the influence of mangrove forest’s area changes against the Total Suspended Matter (TSM) making this study very interesting and important to do.  This research was conducted in Perancak estuary area which is one of mangrove ecosystem area in Bali besides West Bali National Park, Benoa Forest Park and Nusa Lembongan. The data used to generate TSM information and change of mangrove forest area in this research is medium resolution satellite image data, Landsat.  Tidal data and rainfall data were used as a supporting data. The information of TSM concentration obtained by using Budhiman (2004) algorithm, shows that along with the increasing of mangrove forest area has caused the decreasing of TSM concentration at mouth Perancak river. The decline was caused by sediments trapped and settled around trees or mangrove roots, especially the Rhizophora mangroves. In addition to the increasing of mangrove forest area, the tidal oceanography factor also greatly influences the TSM fluctuation around Perancak river mouth. 

scholarly journals IDENTIFIKASI POTENSI DAN STRATEGI PENGEMBANGAN EKOWISATA PADA KAWASAN KONSERVASI HUTAN MANGROVE DESA PASAR BANGGI KABUPATEN REMBANG

2016 ◽  
Vol 5 (4) ◽  
pp. 328-336
Author(s):  
Aprilia Kukuh Dwijayati ◽  
Djoko Suprapto ◽  
Siti Rudiyanti
Keyword(s):  
Mangrove Forest ◽  
Swot Analysis ◽  
Secondary Data ◽  
Mangrove Ecosystem ◽  
Forest Area ◽  
Survey Method ◽  
Mangrove Forests ◽  
Potential Analysis ◽  
Tourism Potential

ABSTRAK Desa Pasar Banggi memiliki potensi pariwisata hutan mangrove yang besar untuk dikembangkan. Tujuan dari penelitian ini untuk mengidentifikasi potensi, kesesuaian ekowisata dan menentukan strategi pengembangan ekowisata pada kawasan hutan mangrove Desa Pasarbanggi Kabupaten Rembang. Penelitian dilakukan pada tanggal 3-27 Juli 2016 di kawasan hutan mangrove Desa Pasar Banggi, Kabupaten Rembang. Metode yang digunakan dalam penelitian ini adalah deskriptif eksloratif, dengan menggunakan metode survei dalam bentuk data primer dan skunder. Data yang diperoleh kemudian dianalisis kesesuaian lahan dan analisis SWOT (Strength, Weakness, Opportunity dan Thread). Hasil penelitian menyatakan bahwa ketebalan hutan mangrove 100-150 ha dengan kerapatan bernilai 35-40 ind/m2. Potensi ekowisata yang terdapat pada kawasan hutan mangrove desa Pasarbanggi adanya jenis satwa dalam hal ini jenis burung yang dilindungi seperti kuntul kecil, dan kuntul kerbau.Hasil analisis kesesuaian ekowisata mangrove menunjukan pada stasiun I, II, dan III masing-masing adalah 67%, 78%, dan 70% bahwa kawasan hutan mangrove dukuh Kaliuntu termasuk dalam kategori sesuai untuk dijadikan kawasan ekowisata di Kabupaten Rembang.Menurut hasil analisis SWOT, yang menjadi prioritas utama di Desa Pasar Banggi adalah: a) Penentuan zona dalam kawasan konservasi ekosistem mangrove dan b) Peningkatan partisipasi stakeholder terhadap program konservasi ekosistem mangrove. Kata Kunci: Ekowisata; Hutan Mangrove; Pasar Banggi  ABSTRACT Pasar Banggi Village has tourism potential of mangrove forests to be developed. The purpose of this research was to identify the potential,analysis comformity of ecotourism and estabilsh the strategy of ecotourism development in the mangrove forest at Pasar Banggi area Rembang Regency. The research was conducted on July 3 - 27th 2016 in the mangrove forest  Pasar Banggi Village, Rembang Regency. The method used in this research is descriptive explorative, by using survey method in a form of primary and secondary data. The collected data was then analyzed the conformitu of the land and SWOT analysis (Strangth, Weakness, Opportunity, and Thread). The result of the research showed that the thickness of mangrove forest is 100-150 ha with the density of 35-40 ind/m2. The ecotourism potential that can be found in the mangrove forest at Pasar banggi village is the existence of protected bird such as little egrets and egrest buffalo. The result of the analysis comformity of ecotourism mangrove was shown in the station I, II, and III respectively are 67%, 78%, and 70% that the mangrove forest area of Kaliuntu Village was include in suitable catagory to be targetted as the ecotourism area in Rembang Regency. Based on the SWOT analysis, the major priority in Pasar Banggi Village are: a) Determining zone in mangrove ecosystem concervational area, and b) The increase of stakeholders participation to the mangrove ecosystem concervational program. Keywords: Ecotourism; Mangrove Forest; Pasar Banggi

scholarly journals KERUSAKAN EKOSISTEM MANGROVE DI KECAMATAN UJUNG PANGKAH KABUPATEN GRESIK PROVINSI JAWA TIMUR Degradation of Mangrove Ecosystem in Ujung Pangkah Subdistrict Gresik District East Java Province

2017 ◽  
Vol 8 (2) ◽  
pp. 130-133
Author(s):  
Arif Prasetyo ◽  
Nyoto Santoso ◽  
Lilik Budi Prasetyo
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Geographic Information System ◽  
Mangrove Forest ◽  
Mangrove Ecosystem ◽  
Geographic Information ◽  
Forest Area ◽  
Land Conversion ◽  
Mangrove Forests ◽  
Geographic Information System Application

The mangrove ecosystem in Ujung Pangkah Sub-district Gresik District has an important role in living life in the delta of Bengawan Solo River. The existence of mangrove ecosystem in this sub-district is threatened by land conversion activity, especially land conversion of mangrove forest to fishpond. In addition, sedimentation activities in the Solo River led to the formation of new land in the mouth of the river that formed the mudflat. This location is an important habitat for flora and fauna. The existence of mangrove forests and emerging lands is threatened by land conversion activities into ponds. The degradation condition of mangrove ecosystem in this research is coastal change in the form of abrasion and reduction of mangrove forest area determined by spatial approach with Geographic Information System application and remote sensing. Damage to mangrove ecosystem in the period 2006 to 2016 in the form of abrasion is 177.64 hectares, while the reduction of mangrove forest area in the same period of 101.70 hectares.Key words: Geographic Information System, remote sensing, Gresik, Bengawan Solo River

scholarly journals ASSESSMENT OF MANGROVE ECOSYSTEM POTENCY AND ITS UTILIZATION IN DOMPU REGENCY COASTAL WATERS, WEST NUSA TENGGARA PROVINCE USING SATELLITE IMAGERY APPLICATION

Jurnal Segara ◽  
2019 ◽  
Vol 15 (2) ◽  
Author(s):  
Yulius Yulius ◽  
Syahrial Nur Amri ◽  
August Daulat ◽  
Sari Indriani Putri
Keyword(s):  
Satellite Imagery ◽  
Coastal Waters ◽  
Mangrove Forest ◽  
Satellite Image ◽  
Mangrove Ecosystem ◽  
Coastal Vegetation ◽  
Coastal Protection ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Mangrove Species

Mangrove forests are tropical coastal vegetation communities, which has the ability to grow in coastal area with tidal and muddy environment. Several functions of mangrove forest such as ecological functions can be used for coastal protection, trapping sediment and strengthen the coastal ecosystems. Coastal waters in Dompu Regency, West Nusa Tenggara have natural mangrove ecosystem with a huge potency and advantages to the region. This study aimed to understand the condition of mangrove ecosystem based on satellite image analysis of Landsat 8 Operational Land Imager (OLI) in 2014 and assess the potency, information related to the utilization by community. Data collection in this study were combined from satellite imagery interpretation with interview and questionnaires. The results showed that the mangrove forest extent in Dompu Regency Coastal Waters were about 90,631 ha with uniformity index 0.68 (medium uniformity). Two mangrove species were found in the region namely Rhizopora stylosa and Rhizopora apiculata and used by the community for several purposes such as firewood, natural coastal protection from tidal, waves and abrasion, also for crabs and fish spawning ground.

scholarly journals Perubahan Lahan Mangrove di Pesisir Muara Gembong, Bekasi, Jawa Barat

2021 ◽  
Vol 10 (1) ◽  
pp. 55-63
Author(s):  
Alin Maulani ◽  
Nur Taufiq-SPJ ◽  
Ibnu Pratikto
Keyword(s):  
Data Processing ◽  
Satellite Image ◽  
Image Data ◽  
Mangrove Ecosystem ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Mangrove Ecosystems ◽  
Satellite Image Data ◽  
Sentinel 2A ◽  
Land Changes

Kecamatan Muara Gembong adalah wilayah dengan ekosistem mangrove yang cukup luas dan tersebar. Mangrove adalah kelompok jenis tumbuhan yang tumbuh di sepanjang garis pantai tropis sampai subtropis di suatu lingkungan yang mengandung garam dan bentuk lahan berupa pantai dengan reaksi tanah anaerob. Kondisi ekosistem mangrove sangat peka terhadap gangguan dari luar terutama dari kegiatan pencemaran, konversi hutan mangrove menjadi kawasan non-hutan, ekploitasi hasil mangrove yang berlebihan sehingga terjadi dinamika pada luasan lahannya. Perubahan yang terjadi pada ekosistem mangrove ini dapat berupa penambahan, pengurangan, dan lahan yang tetap. Metode yang dilakukan pada penelitian ini berupa pengolahan data satelit citra Sentinel 2A, Landsat 8, dan Landsat 5 untuk menganalisa sebaran mangrove pada tahun 2009, 2014, dan 2019, serta perubahan yang terjadi. Validasi data dilakukan dengan pengamatan kawasan langsung di lokasi penelitian berdasarkan pengolahan data yang telah dilakukan. Hasil pengolahan data menunjukan di Kecamatan Muara Gembong pada tahun 2009-2019 diketahui terjadi penambahan luasan lahan mangrove sebesar 1017,746 ha dan pengurangan luasan mangrove sebesar 275,37 ha. Selain itu, terdapat pula lahan mangrove yang tetap bertahan pada kurun waktu 2009-2019 seluas 255,057 ha. Sehingga perubahan lahan mangrove yang terjadi di Kecamatan Muara Gembong cenderung mengalami pertambahan luasan lahan mangrove, yaitu sebesar 66% lahan mangrove yang bertambah. Muara Gembong Subdistrict is an area with a wide and scattered mangrove ecosystem. Mangroves are a group of plant species that grow along tropical to subtropical coastlines in an environment that contains salt and landforms in the form of beaches with anaerobic soil reactions. The condition of mangrove ecosystems is very sensitive to outside disturbances, especially from pollution activities, conversion of mangrove forests to non-forest areas, excessive exploitation of mangrove products resulting in dynamics in the area of land. Changes that occur in this mangrove ecosystem can be in the form of addition, subtraction, and permanent land. The method used in this research is the processing of Sentinel 2A, Landsat 8, and Landsat 5 satellite image data to analyze the distribution of mangroves in 2009, 2014 and 2019, and the changes that occur. Data validation is done by direct observation of the area at the research location based on data processing that has been done. The results of data processing showed that in Muara Gembong Subdistrict in 2009-2019 it was known that there was an increase in the area of mangrove land by 1017, 746 ha and reduction in mangrove area by 275.37 ha. In addition, there are also mangrove lands that have survived in the period 2009-2019 covering 255,057 ha. So that changes in mangrove land that occur in Muara Gembong District tend to experience an increase in the area of mangrove land, which is equal to 66% of the mangrove land that is increasing.

Assessment of mangrove forest change in Ca Mau province using satellite images in the period of 1988 - 2018

2021 ◽  
Vol 66 (1) ◽  
pp. 175-187
Author(s):  
Duong Phung Thai ◽  
Son Ton
Keyword(s):  
Satellite Images ◽  
Satellite Image ◽  
Image Interpretation ◽  
Image Data ◽  
Vegetation Coverage ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Forest Change ◽  
Shrimp Ponds ◽  
Area Classification

On the basis of using practical methods, satellite image processing methods, the vegetation coverage classification system of the study area, interpretation key for the study area, classification and post-classification pro cessing, this research introduces how to exploit and process multi-temporal satellite images in evaluating the changes of forest area. Landsat 4, 5 TM and Landsat 8 OLI remote sensing image data were used to evaluate the changes in the area of mangrove forests (RNM) in Ca Mau province in the periods of 1988 - 1998, 1998 - 2013, 2013 - 2018, and 1988 - 2018. The results of the image interpretation in 1988, 1998, 2013, 2018 and the overlapping of the above maps show: In the 30-year period from 1988 to 2018, the total area of mangroves in Ca Mau province was decreased by 28% compared to the beginning, from 71,093.3 ha in 1988 reduced to 51,363.5 ha in 2018, decreasing by 19,729.8 ha. The recovery speed of mangroves is 2 times lower than their disappearance speed. Specifically, from 1988 to 2018, mangroves disappeared on an area of 42,534.9 hectares and appeared on the new area of 22,805 hectares, only 12,154.5 hectares of mangroves remained unchanged. The fluctuation of mangrove area in Ca Mau province is related to the process of deforestation to dig shrimp ponds, coastal erosion, the formation of mangroves on new coastal alluvial lands and soil dunes in estuaries, as well as planting new mangroves in inefficient shrimp ponds.

scholarly journals Detection Of Community Knowledge Level Of Economic, Ecological Benefits And Causes Of Damage To Mangrove Forest Ecosystems

2021 ◽  
Vol 748 (1) ◽  
pp. 012017
Author(s):  
E Wahyuni ◽  
Zulhafandi ◽  
Hendris ◽  
Jarin
Keyword(s):  
Mangrove Forest ◽  
Forest Ecosystems ◽  
Mangrove Ecosystem ◽  
Economic Benefits ◽  
Household Waste ◽  
Mangrove Forests ◽  
Scoring Method ◽  
Ecological Benefits ◽  
Community Knowledge ◽  
Breeding Sites

Abstract Mangrove forests are natural resources in coastal areas that have an important role in terms of social, economic and ecological aspects. However, the utilization of the mangrove ecosystem makes it vulnerable to damage. This study aims to determine the level of public knowledge of the economic, ecological benefits and damage that happened to mangrove areas in Tarakan City. The scoring method was used to determine the total score or the total score of the respondents’ answers, which amount to 50 people. that the community’s knowledge of the economic benefits of mangrove forests was categorized as know for the benefits of mangroves as firewood, mangrove areas as a place for settlement, mangroves as a place to get fish and mangrove benefits as aquaculture areas with total scores of 226, 200, 232,230 respectively. However, the level of community knowledge about the benefits of mangroves as a medicinal ingredient obtained a total score of 164, which means the level of community knowledge was in the doubtful category. While the level of community knowledge of the ecological benefits of mangrove forests as coastline guards, sea wave barriers, sea wind protectors, and animal breeding sites were included in the category of “Know” with a total score of 228, 224, 234, 240 respectively, but the level of knowledge The community regarding the benefits of mangroves that can manage household waste was in the “doubtful” category with a total score of 128. The community was aware of the damage to mangrove forest ecosystems caused by garbage, logging / mangroves, expanding aquaculture, settlements and increasing population. with a total score of 234,232, 210,228 and 200 levels of knowledge, respectively, which are included in the “Know” category.

scholarly journals MACHINE LEARNING-BASED MANGROVE LAND CLASSIFICATION ON WORLDVIEW-2 SATELLITE IMAGE IN NUSA LEMBONGAN ISLAND

2018 ◽  
Vol 14 (2) ◽  
pp. 159 ◽  
Author(s):  
Aulia Ilham ◽  
Marza Ihsan Marzuki
Keyword(s):  
Machine Learning ◽  
Satellite Image ◽  
Image Data ◽  
Information Criterion ◽  
Mangrove Forests ◽  
Mangrove Species ◽  
Rhizophora Apiculata ◽  
Distribution Type ◽  
Non Linear System ◽  
Image Data Classification

Machine learning is an empirical approach for regressions, clustering and/or classifying (supervised or unsupervised) on a non-linear system. This method is mainly used to analyze a complex system for  wide data observation. In remote sensing, machine learning method could be  used for image data classification with software tools independence. This research aims to classify the distribution, type, and area of mangroves using Akaike Information Criterion approach for case study in Nusa Lembongan Island. This study is important because mangrove forests have an important role ecologically, economically, and socially. For example is as a green belt for protection of coastline from storm and tsunami wave. Using satellite images Worldview-2 with data resolution of 0.46 meters, this method could identify automatically land class, sea class/water, and mangroves class. Three types of mangrove have been identified namely: Rhizophora apiculata, Sonnetaria alba, and other mangrove species. The result showed that the accuracy of classification was about 68.32%.

scholarly journals Society’s Participation Model For Mangrove Rehabilitation in Cempa District, Pinrang Regency

2019 ◽  
Vol 2 (1) ◽  
pp. 9
Author(s):  
Nurul Azmi
Keyword(s):  
Mangrove Forest ◽  
Sampling Technique ◽  
Mangrove Ecosystem ◽  
Mangrove Forests ◽  
Planning Stage ◽  
Working Groups ◽  
Level Of Knowledge ◽  
Mangrove Rehabilitation ◽  
The Government ◽  
Forest Rehabilitation

This study aims to determine: 1) The level of society support in the rehabilitation of mangrove forests. 2) Model ofsociety participation in the rehabilitation of mangrove forests. The population in this study was 721 people, a sample was72 people. The sampling technique used is proportional random sampling. Data collection using observation techniques, interviews, and questionnaires. Data analysis using descriptive interpretative. The results showed that the level of knowledge and society support about mangrove forest rehabilitation is good enough. The model of society  participation is that they are directly involved in the rehabilitation of mangrove forest starting from the planning stage, which is to provide aspirations in the form of concepts, ideas. Designing the concept to be implemented. Planting, they participates in the provision of seeds and planting. Establish working groups led by community leaders or chairmen of working groups. Maintenance, participate in mangrove csssare from the re-planting of mangroves that diaman dead and eradicate the pests that attack the mangrove seeds, and finally the Supervision, in this stage  they forbid anyone to remove, cut the mangrove that has been planted. The government also participates in the supervision and make the rule of giving sanctions to those who deliberately destroy the mangrove ecosystem.

scholarly journals Mapeamento e identificação de vetores responsáveis pela supressão do manguezal na Zona Costeira do Baixo Sul da Bahia, Brasil (Mapping and identification of vectors responsible for mangrove suppression in the Southern Bahia Lowlands, Brazil)

2020 ◽  
Vol 12 (7) ◽  
pp. 2503
Author(s):  
Ana Paula Sena de Souza ◽  
Ivonice Sena de Souza ◽  
George Olavo ◽  
Jocimara Souza Britto Lobão ◽  
Rafael Vinícius de São José
Keyword(s):  
Urban Areas ◽  
Satellite Image ◽  
Mangrove Ecosystem ◽  
Shrimp Farming ◽  
Mangrove Forests ◽  
Coverage Area ◽  
Southern Bahia ◽  
Global Accuracy ◽  
Landsat Satellite ◽  
Accuracy Indices

O ecossistema manguezal representa 8% de toda a linha de costa do planeta ocupando uma área total de 181.077 km2. O Brasil é o segundo país em extensão de áreas de manguezal, ficando atrás apenas da Indonésia. O objetivo do presente estudo foi mapear e identificar os principais vetores responsáveis pela supressão da cobertura das áreas de manguezal na região do Baixo Sul da Bahia, Brasil, a partir de imagens de satélite Landsat disponíveis para o período entre 1994 e 2017. Os mapeamentos foram realizados a partir de classificação supervisionada, utilizando o método Maxver. A acurácia da classificação obtida foi verificada através da verdade de campo, de índices de Exatidão Global, e dos coeficientes de concordância kappa e Tau. As classes que apresentaram maior área de cobertura no período analisado foram: vegetação ombrófila densa, agropecuária, solo exposto e manguezal. Foram identificados dois vetores principais responsáveis pela supressão dos bosques de mangue: a expansão desordenada das áreas urbanas (com destaque para o município de Valença) e o avanço da atividade de carcinicultura clandestina, devido a instalação de tanques de cultivo de camarão sem o devido processo de licenciamento ambiental (sobretudo no município de Nilo Peçanha). O uso das geotecnologias, em especial o Sensoriamento Remoto e os Sistemas de Informações Geográficas, foram ferramentas fundamentais na identificação destes vetores responsáveis pela supressão das áreas de manguezal na área de estudo região do Baixo Sul da Bahia.  Mapping and identification of vectors responsible for mangrove suppression in the Southern Bahia Lowlands, BrazilA B S T R A C TThe mangrove ecosystem represents 8% of the entire coastline of the planet and occupies a total area of 181,077 km2. Brazil is the second largest country in terms of mangrove areas, second only to Indonesia. The aim of the present study was to map and identify the main vectors responsible for the suppression of mangrove cover in the Southern Lowlands of Bahia, Brazil, from Landsat satellite images available for the period 1994-2017. based on supervised classification using the Maxver method. The accuracy of the classification obtained was verified through field truth, Global Accuracy indices, and kappa and Tau agreement coefficients. The classes that presented larger coverage area in the analyzed period were: dense ombrophilous vegetation, agriculture, exposed soil and mangrove. Two main vectors responsible for the suppression of mangrove forests were identified: the disorderly expansion of urban areas (especially the municipality of Valença) and the advance of clandestine shrimp farming due to the installation of shrimp farms without due environmental licensing process (mainly in the municipality of Nilo Peçanha). The use of geotechnologies, especially Remote Sensing and Geographic Information Systems, were fundamental tools in the identification of these vectors responsible for the suppression of mangrove areas in the study area of the Southern Bahia Lowlands.Key-words: environmental impacts, satellite image, shrimp farming.

scholarly journals Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data

2020 ◽  
Vol 12 (10) ◽  
pp. 1690 ◽  
Author(s):  
Tianyu Hu ◽  
YingYing Zhang ◽  
Yanjun Su ◽  
Yi Zheng ◽  
Guanghui Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Aboveground Biomass ◽  
Mangrove Forest ◽  
Remote Sensing Data ◽  
Dimensional Structure ◽  
Coefficient Of Determination ◽  
Free Access ◽  
Mangrove Forests ◽  
Estimation Model ◽  
Mangrove Biomass

Mangrove forest ecosystems are distributed at the land–sea interface in tropical and subtropical regions and play an important role in carbon cycles and biodiversity. Accurately mapping global mangrove aboveground biomass (AGB) will help us understand how mangrove ecosystems are affected by the impacts of climatic change and human activities. Light detection and ranging (LiDAR) techniques have been proven to accurately capture the three-dimensional structure of mangroves and LiDAR can estimate forest AGB with high accuracy. In this study, we produced a global mangrove forest AGB map for 2004 at a 250-m resolution by combining ground inventory data, spaceborne LiDAR, optical imagery, climate surfaces, and topographic data with random forest, a machine learning method. From the published literature and free-access datasets of mangrove biomass, we selected 342 surface observations to train and validate the mangrove AGB estimation model. Our global mangrove AGB map showed that average global mangrove AGB density was 115.23 Mg/ha, with a standard deviation of 48.89 Mg/ha. Total global AGB storage within mangrove forests was 1.52 Pg. Cross-validation with observed data demonstrated that our mangrove AGB estimates were reliable. The adjusted coefficient of determination (R2) and root-mean-square error (RMSE) were 0.48 and 75.85 Mg/ha, respectively. Our estimated global mangrove AGB storage was similar to that predicted by previous remote sensing methods, and remote sensing approaches can overcome overestimates from climate-based models. This new biomass map provides information that can help us understand the global mangrove distribution, while also serving as a baseline to monitor trends in global mangrove biomass.

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