Dynamic Change of Mangroves in Aceh Tamiang Regency using Landsat Temporal Data, 2000 to 2023
Article Sidebar
-
Random forest, Spatiotemporal, Mangrove dynamics change
Abstract
Mangroves, known for their high productivity, play vital roles in physical, ecological, and economic aspects that benefit human life. However, these ecosystems are currently threatened by climate change and human activities. To address this challenge, Indonesia aims to rehabilitate 600,000 hectares of mangroves by 2024. Effectively monitoring changes in mangrove dynamics is crucial for achieving this goal. This study focuses on understanding the dynamic change of the mangrove land cover in Aceh Tamiang from 2000 to 2023. Mangrove dynamics in Aceh Tamiang are important because it has the largest mangrove area in East Aceh, which is decreasing due to conversion to the oil palm industry. The classification using random forest (RF) algorithm by utilizing VWB-IC (Vegetation-Water-Built-up Index Combined), which area NDVI, SAVI, ARVI, GNDVI, SLAVI, and EVI as vegetation indices; MNDWI and ANDWI as water indices; and NDBI as built-up index. The employment of this combination is necessary to enhance the accuracy of classification due to the addition of more input parameters to machine learning. The image data are acquired through Landsat 5 for 2000 and 8 and 9 satellites for 2023. The observed dynamics include mangroves transitioning into fishponds (768 ha) and plantations (2,679 ha) between 2000 and 2023. The processed data indicates a decrease in the Aceh Tamiaang mangrove area from 13,270 ha in 2000 to 9,386 ha in 2023. These results can be used to determine mangrove rehabilitation policies in Aceh Tamiang, Indonesia.
Full text article
References
Clough B. 2013. Continuing the Journey Amongst Mangroves. Okinawa: ISME.
Lu, C.; Liu, J.; Jia, M.; Liu, M.; Man, W.; Fu, W.; Zhong, L.; Lin, X.; Su, Y.; Gao, Y. Dynamic Analysis of Mangrove Forests Based on an Optimal Segmentation Scale Model and Multi-seasonal Images in Quanzhou Bay, China. 2018, 10, doi:10.3390/RS10122020.
Spalding M, McIvor A, Tonneijck FH, Tol S and van Eijk P. 2014. Mangroves for Coastal Defence. Guidelines for Coastal Managers & Policy Makers. Online: Wetlands International and The Nature Conservancy.
Nunoo, F.K.E.; Agyekumhene, A. Mangrove Degradation and Management Practices Along the Coast of Ghana. 2022, 13, doi:10.4236/as.2022.1310065.
Hendy, I.W.; Michie, L.A.; Taylor, B.W. Habitat Creation and Biodiversity Maintenance in Mangrove Forests: Teredinid Bivalves as Ecosystem Engineers. 2014, 2, doi:10.7717/PEERJ.591.
Dinilhuda, A.; Akbar, A.A.; J.; Herawaty, H. Potentials of Mangrove Ecosystem as Storage of Carbon for Global Warming Mitigation. 2020, 21, doi:10.13057/BIODIV/D211141.
Meng, Y.; Bai, J.; Gou, R.; Cui, X.; Feng, J.; Dai, Z.; Diao, X.; Diao, X.; Zhu, X.; Lin, G. Relationships Between Above- and Below-ground Carbon Stocks in Mangrove Forests Facilitate Better Estimation of Total Mangrove Blue Carbon. 2021, 16, doi:10.1186/S13021-021-00172-9.
Adriyanti, D.T.; Putri, T.W.; Mindaryani, A.; Prasetya. A. ; Petrus, H.T. Community Empowerment through Sustainable Utilisation of Mangrove Forests for the Production of Natural Dyes with An Environmentally Friendly System (Phase I: Increasing Awareness and Knowledge Enhancement). Jurnal Pengabdian kepada Masyarakat (Indonesian Journal of Community Engagement). 2023, 9(1):38-45.
Habiba, A.S.; Yemul, N.B.; Ghalme, R.L. Use of mangrove leaves (Rhizophora mucronath) extract as a biofertilizer for seed germination. 2020 Jun.
Tahang, H.; Amiluddin, A.; Amir, F.; Firman, F. Economic Valuation of Mangrove Forest Ecosystem in Sinjai. 2018, 1, doi:10.35911/TORANI.V1I2.4444.
Khaine, I.; Woo, S.Y. An Overview of Interrelationship Between Climate Change and Forests. 2015, 11, doi:10.1080/21580103.2014.932718.
FAO. 2023. The world’s mangroves 2000–2020. Rome: FAO.
Contessa, V.; Dyson, K.; Vivar Mulas, P. P.; Kindgard, A.; Liu, T.; Saah, D.; Tenneson, K.; Pekkarinen, A. Uncovering dynamics of global mangrove gains and losses. Remote Sensing, 2023, 15(15):1-22.
Bunting, P. Global Mangrove Extent Change 1996–2020: Global Mangrove Watch Version 3.0. 2022, 14, doi:10.3390/rs14153657.
KLHK. 2022. The State of Indonesia’s Forests 2022 Towards FOLU Net Sink 2030. Jakarta: KLHK.
Sasmito, S,D.; Basyuni, M.; Kridalaksana, A.; Saragi-Sasmito, M.F.; Lovelock, C.E.; Murdiyarso, D. Challenges and opportunities for achieving Sustainable Development Goals through restoration of Indonesia’s mangroves. Nature Ecology & Evolution. 2023, 7(1):62-70.
Arifanti, V.B.; Sidik, F.; Mulyanto, B.; Susilowati, A.; Wahyuni, T.; Subarno; Yulianti; Yuniarti, N.; Aminah, A.; Suita, E. Challenges and Strategies for Sustainable Mangrove Management in Indonesia: A Review. 2022, 13, doi:10.3390/f13050695.
Pham, T.D.; Yokoya, N.; Bui, D.T.; Yoshino, K.; Friess, D.A. Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges. 2019, 11, doi:10.3390/RS11030230.
Sunkur, R.; Kantamaneni, K.; Bokhoree, C.; Rathnayake, U.; Fernando, M. Mangrove mapping and monitoring using remote sensing techniques towards climate change resilience. Scientific Reports, 2014, 14(1):1-14.
Elmahdy, S.I.; Ali, T.; Mohamed, M.M.; Howari, F.M.; Abouleish, M.; Simonet, D. Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes from 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression and Naive Bayes Tree Models. 2020, 8, doi:10.3389/FENVS.2020.00102.
Junianto, M., Sugianto, S., Basri H. Analysis of Changes in Mangrove Land Cover in West Langsa District, Langsa. Jurnal Penelitian Pendidikan IPA. 2023 Mar 31;9(3):1155-62.
Irawan, S.; Kurniawan, D.E.; Anurogo, W.; Lubis, M.Z. Mangrove Distribution in Riau Islands Using Remote Sensing Technology. 2017, 1, doi:10.30871/JAGI.V1I2.456.
Aritonang, L.; Septyani, E.; Maria, L. Pemetaan Perubahan Luasan Mangrove Melalui Analisis Citra Satelit Landsat Di Tangkolak Barat, Karawang, Jawa Barat. 2022, 3, doi:10.23960/jgrs.2022.v3i1.69.
Febrianto, S.; Syafina, H.A.; Latifah, N.A.; Puroso, P. Dinamika Perubahan Luasan Dan Kerapatan Ekosistem Mangrove Di Kawasan Taman Nasional Sembilang Menggunakan Citra Satelit Landsat 8. 2022, 25, doi:10.14710/jkt.v25i3.14909.
Febriandi, F. The Estimation of Mangroves Area and Density of Mangroves Changes Use the Remote Sensing Data at Northheast Province of Aceh, Indonesia. 2017, 1, doi:10.24036/SJDGGE.V1I1.43.
Haikal, B.F.; Susilo, S.B.; Agus, S.B.; Oktavian, R.Z. Mapping mangrove distribution using remote sensing technology in Harapan, Kelapa and Pamegaran Seribu Islands National Park. In IOP Conference Series: Earth and Environmental Science. 2021. 944, 1, 012046
Raynaldo, A.; Mukhtar, E.; Novarino, W. Mapping and change analysis of mangrove forest by using Landsat imagery in Mandeh Bay, West Sumatra, Indonesia. Aquaculture, Aquarium, Conservation & Legislation, 2020, 13(4), 2144-2151.
Ramandalush, F.; Santoso, A.I.; Hutahean, A.A. Analisis Data Penginderaan Jauh untuk Mendeteksi Perubahan Luasan Mangrove Sebagai Sarana Pelindung Ekosistem Pantai (Studi Kasus di Kema, Kabupaten Minahasa Utara, Sulawesi Utara): Analysis of Remote Sensing Data to Detect Changes in Mangrove Area as a Protective Means of Coastal Ecosystems (Case Study in Kema, North Minahasa Regency, North Sulawesi). 2016, 2, doi:https://doi.org/10.37875/chartdatum.v2i1.78.
Natsir, M.; Ulya, Z.; Fitriani, R.D. Mangrove Forest Utilization Policies Reconceptualized with a View to Improving the Regional Economy in Aceh Tamiang District, Indonesia. 2023, 23, doi:10.13057/biodiv/d231256.
Charles, A. C.; Armstrong, A.; Nnamdi, O. C.; Innocent, M. T.; Obiageri, N. J.; Begianpuye, A. F.; Timothy, E. E. Review of Spatial Analysis as a Geographic Information Management Tool. American Journal of Engineering and Technology Management, 2024, 9(1), 8-20.
Balabied, S. A. A.; Eid, H. F. Utilizing random forest algorithm for early detection of academic underperformance in open learning environments. PeerJ Computer Science, 2023, 9, e1708.
Gibbons, C.; Richards, S.; Valderas, J.M.; Campbell, J. Supervised Machine Learning Algorithms Can Classify Open-Text Feedback of Doctor Performance With Human-Level Accuracy. J Med Internet Res. 2017;19:3. e65.
Cho, J.; Lee, K.; Shin, E.; Choy, G.; Do. S.. How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?. arXiv preprint ararXiv. 2017, 1511.06348.
Mukherjee, S.; Tamayo, P.; Rogers, S.; Rifkin, R.; Engle, A.; Campbell, C.; Golub, T.R.; Mesirov, J.P. Estimating Dataset Size Requirements for Classifying DNA Microarray Data. 2003, 10, doi:10.1089/106652703321825928.
Davoudi Moghaddam, D.; Rahmati, O.; Panahi, M.; Tiefenbacher, J.P.; Darabi, H.; Haghizadeh, A.; Haghighi, A.T.; Nalivan, O.A.; Bui, D.T. The Effect of Sample Size on Different Machine Learning Models for Groundwater Potential Mapping in Mountain Bedrock Aquifers. 2020, 187, doi:10.1016/J.CATENA.2019.104421.
Adadi, A. A Survey on Data‐efficient Algorithms in Big Data Era. 2021, 8, doi:10.1186/S40537-021-00419-9.
Moraes, D.; Benevides, P.; Costa, H.; Moreira, F.D.; Caetano, M. Influence of sample size in land cover classification accuracy using random forest and sentinel-2 data in Portugal. 2021, doi:https://doi.org/10.1109/IGARSS47720.2021.9553924.
Alam, M.Z.; Rahman, M.S.; Rahman, M.S. A Random Forest Based Predictor for Medical Data Classification Using Feature Ranking. 2019, 15, doi:10.1016/J.IMU.2019.100180.
Short, N.M., P.D. Lowan Jr., S.C. Freden, and W.A. Finch Jr., 1976. Mission to Earth: Landsat Views the World, NASA Special Publication 360, NASA Science and Technical Information Office, Washington, D.C.
Murphy, J.M.; Butlin, T.; Duff, P.F.; Fitzgerald, A.J. Revised Radiometric Calibration Technique for LANDSAT-4 Thematic Mapper Data. 1984, 22, doi:10.1109/TGRS.1984.350617.
Acharya, T. D.; Yang, I. Exploring landsat 8. International Journal of IT, Engineering and Applied Sciences Research (IJIEASR), 2015, 4(4), 4-10.
[USGS] United State Geological Survey, 2013, Landsat 8: U.S. Geological Survey Fact Sheet 2013–3060, 4 p., https://pubs.usgs.gov/fs/2013/3060/.
Reuter, D.C.; Richardson, C.; Pellerano, F.A.; Irons, J.R.; Allen, R.G.; Anderson, M.C.; Jhabvala, M.D.; Lunsford, A.W.; Montanaro, M.; Smith, R. The Thermal Infrared Sensor (TIRS) on Landsat 8: Design Overview and Pre-launch Characterization. 2015, 7, doi:10.3390/RS70101135.
Xu, H.; Ren, M.; Lin, M. Cross-comparison of Landsat-8 and Landsat-9 data: a three-level approach based on underfly images. 2024, 61(1), doi:https://doi.org/10.1080/15481603.2024.2318071.
Khyami, A. Impact of Land Cover Change on Land Surface Temperature over Greater Beirut Area – Lebanon. 2021, 2, doi:10.38094/JGIER2121.
OGUZ, H. 2020. Retrieving actual evapotranspiration from landsat 8 imagery: A case study of Adana, Turkey. Theory and Research in Agriculture, Forestry and Aquaculture Sciences II, 147.
Gorelick, N.; Hancher, M.; Dixon, M.J.; Ilyushchenko, S.; Thau, D.; Moore, R. Google Earth Engine: Planetary-scale Geospatial Analysis for Everyone. 2017, 202, doi:10.1016/J.RSE.2017.06.031.
Kumar, L.; Mutanga, O. Google Earth Engine Applications Since Inception: Usage, Trends, and Potential. 2018, 10, doi:10.3390/RS10101509.
Parente, L.; Taquary, E.C.; Silva, A.P.F. da .; Souza, C.; Ferreira, L.G. Next Generation Mapping: Combining Deep Learning, Cloud Computing, and Big Remote Sensing Data. 2019, 11, doi:10.3390/RS11232881.
Padarian, J.; Minasny, B.; McBratney, A.B. Using Google's Cloud-based Platform for Digital Soil Mapping. 2015, 83, doi:10.1016/J.CAGEO.2015, 06, 023.
Velastegui-Montoya, A.; Montalván-Burbano, N.; Carrión-Mero, P.; Rivera-Torres, H.; Sadeck, L.; Adami, M. Google Earth Engine: a global analysis and future trends. Remote Sensing, 2023, 15, doi:https://doi.org/10.3390/rs15143675
Belgiu, M.; Drăguţ, L. Random Forest in Remote Sensing: A Review of Applications and Future Directions. 2016, 114, doi:10.1016/J.ISPRSJPRS.2016.01.011.
Breiman, L. Random forests. Machine learning, 2001, 45, doi:https://doi.org/10.1023/A:1010933404324.
Wang, P.; Zhang, X.; Shi, L.; Liu, M.; Liu, G.; Cao, C.; Wang, R. Assessment of Sea-Ice Classification Capabilities during Melting Period Using Airborne Multi-Frequency PolSAR Data. 2024, 16, doi:https://doi.org/10.3390/rs16061100.
Chan, J.C.-W.; Beckers, P.; Spanhove, T.; Borre, J.V. An Evaluation of Ensemble Classifiers for Mapping Natura 2000 Heathland in Belgium Using Spaceborne Angular Hyperspectral (chris/proba) Imagery. 2012, 18, doi:10.1016/J.JAG.2012.01.002.
Shao, Y.; Campbell, J.B.; Taff, G.N.; Zheng, B. An Analysis of Cropland Mask Choice and Ancillary Data for Annual Corn Yield Forecasting Using MODIS Data. 2015, 38, doi:10.1016/J.JAG.2014.12.017.
Rahmawati, A.D. Vegetation-water-built up Index Combined: Algorithm Indices Combination for Characterization and Distribution of Mangrove Forest Through Google Earth Engine. 2022, 3, doi:10.52045/jca.v3i1.298.
Tran, T.V.; Reef, R.; Zhu, X. A Review of Spectral Indices for Mangrove Remote Sensing. 2022, 14, doi:10.3390/rs14194868.
Aulia, Z.S.; Pribadi, R.; Helmi, M. The Use of Vegetation Indices on Temporal Mangrove Condition: A Case Study on Timbulsloko and Bedono, Demak. Scientific Journal of Fisheries & Marine, 2024, 16(1).
Moriya, É.A.S.; Imai, N.N.; Tommaselli, A.M.G.; Honkavaara, E.; Rosalen, D.L. Design of Vegetation Index for Identifying the Mosaic Virus in Sugarcane Plantation: A Brazilian Case Study. 2023, 13, doi:10.3390/agronomy13061542.
Yang, Y.; Yang, D.; Wang, X.; Zhang, Z.; Nawaz, Z. Testing accuracy of land cover classification algorithms in the qilian mountains based on gee cloud platform. Remote Sensing, 2021, 13(24), 5064.
Li, Y.; Zhu, X.; Pan, Y.; Gu, J.; Zhao, A.; Liu, X. A Comparison of Model-assisted Estimators to Infer Land Cover/use Class Area Using Satellite Imagery. 2014, 6, doi:10.3390/RS6098904.
Mawardi, M.; & Elisa, E. Keanekaragaman mangrove di pantai kupang desa Lubuk Damar Kecamatan Seruway Kabupaten Aceh Tamiang. Jurnal Jeumpa, 2017, 4(2), 61-67.
Darmarini, A. S.; Wardiatno, Y.; Prartono, T.; Soewardi, K.; Samosir, A. M.; Zainuri, M. Mangrove community structure in Lubuk Damar Coast, Seruway, Aceh Tamiang. Journal of Natural Resources and Environmental Management, 2021, 12(1), 72-81.
Kasnadiya, K.; Subhan, S.; Erida, G. Identifikasi dan Pemanfaatan Hasil Hutan Bukan Kayu pada Ekosistem Mangrove dalam Kawasan Kesatuan Pengelolaan Hutan Wilayah III Aceh (Studi Kasus: Kabupaten Aceh Tamiang). Jurnal Ilmiah Mahasiswa Pertanian, 2023, 8(2), 590-596.
Aprilia, K.; As, A.P.; Rosmaiti, R. Studi Keanekaragaman Jenis Ikan Di Sungai Mati Alur Cucur Aceh Tamiang. 2023, 13, doi:10.29303/jp.v13i1.433.
Giri, C.; Long, J.; Abbas, S.; Murali, M.; Qamer, F.M.; Pengra, B.W.; Thau, D. Distribution and Dynamics of Mangrove Forests of South Asia. 2015, 148, doi:10.1016/J.JENVMAN.2014.01.020.
Osland, M.J.; Feher, L.C.; López-Portillo, J.; Day, R.H.; Suman, D.O.; Guzmán Menéndez, J.M.; Rivera-Monroy, V.H. Mangrove Forests in a Rapidly Changing World: Global Change Impacts and Conservation Opportunities Along the Gulf of Mexico Coast. 2018, 214, doi:10.1016/J.ECSS.2018.09.006.
Raw, J.; Godbold, J.A.; Van Niekerk, L.; Van Niekerk, L.; Adams, J.B. Drivers of Mangrove Distribution at the High-energy, Wave-dominated, Southern African Range Limit. 2019, 226, doi:10.1016/J.ECSS.2019.106296.
Dewinta, D.; Warlina, L. Dampak Alih Fungsi Lahan Pertanian Terhadap Ketahanan Pangan Di Kabupaten Cianjur. 2017, 4, doi:10.34010/JWK.V4I02.2450.
Arico, Z.; Rahman, A.; Akob, B.; Jayanthi, S. Sustainable development based on indigenous knowledge in the coastal a'rea of Aceh Tamiang, Aceh, Indonesia. Kasetsart Journal of Social Sciences, 2023, 44(3), 739-750.
Asy’Ari, R.; Rahmawati, A. D.; Dzulfigar, A.; Marfi, K. P.; Ulfa, A.; Puspitasari, R. F.; Raihan, F.; Aslam, M. F.; Saputri , H. R.; Madinu, A. M. A.; Jouhary, N. A.; Hidayat, M. A. D.; Fadhil, M. H.; Nurrahmah, A. C.; Inanda, I. A.; Zamani, N. P.; Pramulya, R.; Setiawan, Y. Mangrove Damage vs Succession: An Opinion on the Journey of Mangrove Investigation Studies in Subang Regency Coast Area . SSRS Journal A: Agro-Environmental Research, 2023, 1, 1–6. Retrieved from https://publishing.ssrs.or.id/ojs/index.php/ssrs-a/article/view/11
Ward, R.D.; Friess, D.A.; Day, R.H.; MacKenzie, R.A. Impacts of Climate Change on Mangrove Ecosystems: A Region by Region Overview. 2016, 2, doi:10.1002/EHS2.1211.
He, M.; Wang, X.; Zou, C.; Dai, B.; Jin, L. A Commodity Classification Framework Based on Machine Learning for Analysis of Trade Declaration. 2021, 13, doi:10.3390/SYM13060964.
Smith, T.J.; Robblee, M.B.; Wanless, H.R.; Doyle, T.W. Mangroves, Hurricanes and Lightning Strikes. 1994, 44, doi:10.2307/1312230.
Doyle, T.W.; Smith, T.J.; Robblee, M.B. Wind Damage Effects of Hurricane Andrew on Mangrove Communities Along the Southwest Coast of Florida, USA. 1995.
Xiong, L.; Lagomasino, D.; Charles, S.P.; Castañeda-Moya, E.; Cook, B.D.; Redwine, J.; Fatoyinbo, L. Quantifying Mangrove Canopy Regrowth and Recovery After Hurricane Irma with Large-scale Repeat Airborne Lidar in the Florida Everglades. 2022, 114, doi:10.1016/j.jag.2022.103031.
Madinu, A.M.A,.; Jouhary, N.A.; Ulfa, A.; Rahmadhanti, I.N.; Pudjawati, N.H.; Asy’Ari, R.; Zamani, N.P.; Pramulya, R.; Setiawan Y. Monitoring of coastal dynamics at Subang Regency using Landsat Collection Data and Cloud Computing Based. BIO Web of Conferences. 2024, 106: 04005.
Field, C., 1995. Impacts of expected climate change on mangroves. Hydrobiologia 295, 75–81.
Ellison, J.C. Vulnerability Assessment of Mangroves to Climate Change and Sea-level Rise Impacts. 2015, 23, doi:10.1007/S11273-014-9397-8.
Gilman, E.; Ellison, J.C.; Duke, N.C.; Field, C.D. Threats to Mangroves from Climate Change and Adaptation Options: A Review. 2008, 89, doi:10.1016/J.AQUABOT.2007.12.009.
Smith, T.J.; Duke, N.C. Physical Determinants of Inter-estuary Variation in Mangrove Species Richness Around the Tropical Coastline of Australia. 1987, 14, doi:10.2307/2844783.
Ball, M.C.; Sobrado, M.A. 2002. Ecophysiology of mangroves: challenges in linking physiological processes with patterns in forest structure. Pages 331–346 in M. C. Press, J. D. Scholes and M. G. Barker, editors. Advances in plant physiological ecology. Blackwell Science, Oxford, UK.
Zulham, A.; Shafitri, N.; Wardono, B.; Pramoda, R.; Yuliati, C.; Purnomo, A. H. Collective actions to support sustainable aquaculture: Evidence from brackish water ponds area in Aceh Tamiang. In BIO Web of Conferences. 2023, 74, 01008.
Fanbudy, A.R.; Solfiyeni; Mukhtar, E. Mangrove Distribution in North Sumatera, Indonesia A Review. IJPSAT . 2024, 42, 158-170.
Zhang, Z.; Ahmed, M.R.; Zhang, Q.; Li, Y.; Li, Y. Monitoring of 35-year Mangrove Wetland Change Dynamics and Agents in the Sundarbans Using Temporal Consistency Checking. 2023, 15, doi:10.3390/rs15030625.
Ellison, J. C. 2021. Factors influencing mangrove ecosystems. (eds) Mangroves: Ecology, Biodiversity and Management. Singapore. 2021, doi:https://doi.org/10.1007/978-981-16-2494-0_4
Eddy, S.; Setiawan, A. A.; Taufik, M.; Oktavia, M.; utomo, B.; Milantara, N. Loss of carbon stock as an impact of anthropogenic activities in a protected mangrove forest. 2023, 24 doi:https://doi.org/10.13057/biodiv/d241211.
Andiny, P.; Wahyuningsih, P.; Meutia, R.; Junita, A. Perancangan Destinasi Branding Desa Wisata Pusong Kapal Kecamatan Seruway Kabupaten Aceh Tamiang. 2022, 19
Gunawan, R. H.; Muchlisin, Z. A.; Mellisa, S. Kebiasaan Makan Ikan Lemeduk (Barbonymus schwanenfeldii) di Sungai Tamiang, Kecamatan Sekerak, Kabupaten Aceh Tamiang, Provinsi Aceh. 2017, 2.
Rahmawati, A. D.; Asy’Ari, R. Google Earth Engine: Pemanfaatan platform pemetaan berbasis Cloud Computing dalam mendeteksi distribusi mangrove dengan citra Sentinel-2 di Jakarta. 2021.
Authors
Media Konservasi is an open access journal, meaning that all content is freely available without charge to the user or their institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal without needing to request prior permission from the publisher or the author.
All articles published by Media Konservasi are licensed under the Creative Commons Attribution 4.0 International License. This allows for unrestricted use, distribution, and reproduction in any medium, provided proper credit is given to the original authors.
Authors submitting manuscripts should understand and agree that the copyright of published manuscripts is retained by the authors. Copyright encompasses the exclusive rights of authors to reproduce, distribute, and sell any part of the journal articles in all forms and media. Reproduction of any part of this journal, its storage in databases, and its transmission by any form or media is allowed without written permission from Media Konservasi.
Article Details
How to Cite
