1Pusat Riset Oseanografi, Badan Riset dan Inovasi Nasional, Indonesia
2Pusat Riset Geoinformatika, Badan Riset dan Inovasi Nasional, Indonesia
3Departemen SIG, Fakultas Geografi, Universitas Gadjah Mada, Indonesia
BibTex Citation Data :
@article{JKT22830, author = {Devica Natalia Br Ginting and Rizky Faristyawan and Siti Nurulita Mutiara Safitri and Gathot Winarso}, title = {Identifikasi Spesies Mangrove dengan menggunakan Metode Principal Component Analysis (PCA) pada Citra Landsat-8 di Taman Nasional Sembilang, Sumatera Selatan, Indonesia}, journal = {Jurnal Kelautan Tropis}, volume = {27}, number = {2}, year = {2024}, keywords = {spesies mangrove; Landsat-8; principal component analysis; Taman Nasional Sembilang}, abstract = { Mangroves are coastal plants influenced by tidal cycles. One of the regions in South Sumatra Province with a mangrove ecosystem is Sembilang National Park. As a national park, this location is strategically positioned for research related to mangrove species. The utilization of the Principal Component Analysis (PCA) method is considered to enhance the capabilities of remote sensing data in mangrove mapping. However, its application has been limited to the mangrove level. The research objective is to identify mangrove species in Sembilang National Park using the PCA method by leveraging Landsat-8 image data acquired on September 9, 2019. Mangrove distribution is obtained through the Mangrove Vegetation Index (MVI) and vector data from Global Mangrove Watch. The image is then overlaid with field species data to obtain species spectral patterns. Additionally, the correlation between spectral band values and eigenvalues (PC) is analyzed to detect parameters correlated with eigenvalues. The research results show that PC values correlate with mangrove species and can be used for mangrove species identification. This is demonstrated by Bruguiera Gymnorrhiza species with canopy coverage of 60.8% and 62.46% at ST7 and ST8 having the same PC values, while mangroves with canopy coverage of 62.5% in different species have different PC values. Moreover, the PCA method can indicate two crucial factors in identifying mangrove species, namely vegetation and soil factors. Mangrove merupakan tumbuhan pesisir yang dipengaruhi oleh pasang surut. Salah satu wilayah di Provinsi Sumatera Selatan yang memiliki ekosistem mangrove adalah Taman Nasional Sembilang. Sebagai taman nasional, lokasi ini sangat strategis untuk dilakukan penelitian terkait spesies mangrove. Pemanfaatan metode principal component analysis (PCA) dinilai mampu meningkatkan kemampuan data penginderaan jauh dalam pemetaan mangrove. Namun selama ini, pemanfaatan terbatas pada level mangrove. Adapun tujuan penelitian adalah mengidentifikasi spesies mangrove di Taman Nasional Sembilang menggunakan metode PCA dengan memanfaatkan data citra Landsat-8 yang diakusisi pada 09 September 2019. Sebaran mangrove diperoleh melalui indeks vegetasi mangrove (MVI) dan data vektor dari Global Mangrove Watch. Citra tersebut kemudian di overlay dengan data spesies lapangan untuk mendapatkan pola spektral species. Selain itu, korelasi nilai spektral band dan eigen (PC) dianalisis untuk mendeteksi parameter yang berkorelasi dengan nilai eigen. Hasil penelitian menunjukkan nilai PC memiliki korelasi dengan spesies mangrove dan dapat digunakan untuk mengidentifikasi spesies mangrove. Hal ini ditunjukkan spesies Bruguiera Gymnorrhiza dengan tutupan kanopi 60,8% dan 62,46% pada ST7 dan ST8 memiliki nilai PC yang sama serta mangrove dengan besaran tutupan kanopi 62,5% pada spesies nilai PC berbeda. Selain itu, metode PCA mampu menunjukkan dua faktor penting dalam mengidentifikasi spesies mangrove, yaitu faktor vegetasi dan tanah. }, issn = {2528-3111}, pages = {345--356} doi = {10.14710/jkt.v27i2.22830}, url = {https://ejournal2.undip.ac.id/index.php/jkt/article/view/22830} }
Refworks Citation Data :
Mangroves are coastal plants influenced by tidal cycles. One of the regions in South Sumatra Province with a mangrove ecosystem is Sembilang National Park. As a national park, this location is strategically positioned for research related to mangrove species. The utilization of the Principal Component Analysis (PCA) method is considered to enhance the capabilities of remote sensing data in mangrove mapping. However, its application has been limited to the mangrove level. The research objective is to identify mangrove species in Sembilang National Park using the PCA method by leveraging Landsat-8 image data acquired on September 9, 2019. Mangrove distribution is obtained through the Mangrove Vegetation Index (MVI) and vector data from Global Mangrove Watch. The image is then overlaid with field species data to obtain species spectral patterns. Additionally, the correlation between spectral band values and eigenvalues (PC) is analyzed to detect parameters correlated with eigenvalues. The research results show that PC values correlate with mangrove species and can be used for mangrove species identification. This is demonstrated by Bruguiera Gymnorrhiza species with canopy coverage of 60.8% and 62.46% at ST7 and ST8 having the same PC values, while mangroves with canopy coverage of 62.5% in different species have different PC values. Moreover, the PCA method can indicate two crucial factors in identifying mangrove species, namely vegetation and soil factors.
Mangrove merupakan tumbuhan pesisir yang dipengaruhi oleh pasang surut. Salah satu wilayah di Provinsi Sumatera Selatan yang memiliki ekosistem mangrove adalah Taman Nasional Sembilang. Sebagai taman nasional, lokasi ini sangat strategis untuk dilakukan penelitian terkait spesies mangrove. Pemanfaatan metode principal component analysis (PCA) dinilai mampu meningkatkan kemampuan data penginderaan jauh dalam pemetaan mangrove. Namun selama ini, pemanfaatan terbatas pada level mangrove. Adapun tujuan penelitian adalah mengidentifikasi spesies mangrove di Taman Nasional Sembilang menggunakan metode PCA dengan memanfaatkan data citra Landsat-8 yang diakusisi pada 09 September 2019. Sebaran mangrove diperoleh melalui indeks vegetasi mangrove (MVI) dan data vektor dari Global Mangrove Watch. Citra tersebut kemudian di overlay dengan data spesies lapangan untuk mendapatkan pola spektral species. Selain itu, korelasi nilai spektral band dan eigen (PC) dianalisis untuk mendeteksi parameter yang berkorelasi dengan nilai eigen. Hasil penelitian menunjukkan nilai PC memiliki korelasi dengan spesies mangrove dan dapat digunakan untuk mengidentifikasi spesies mangrove. Hal ini ditunjukkan spesies Bruguiera Gymnorrhiza dengan tutupan kanopi 60,8% dan 62,46% pada ST7 dan ST8 memiliki nilai PC yang sama serta mangrove dengan besaran tutupan kanopi 62,5% pada spesies nilai PC berbeda. Selain itu, metode PCA mampu menunjukkan dua faktor penting dalam mengidentifikasi spesies mangrove, yaitu faktor vegetasi dan tanah.
Article Metrics:
Last update:
Upon acceptance for publication, authors agree to transfer the copyright of their article to Jurnal Kelautan Tropis, while retaining the right to reuse their work under the terms of the open license applied.
From the date of publication, the copyright for each article is held by Jurnal Kelautan Tropis. This transfer allows the journal to manage, disseminate, and preserve scholarly content in accordance with international standards and open access best practices.
Although copyright is held by the journal, all published articles are made available under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). Under this license, anyone may:
Copy and redistribute the material in any medium or format
Remix, transform, and build upon the material for any purpose, even commercially
provided that:
Appropriate credit is given to the original author(s) and the source
Indications are made of any changes that were made
Derivative works are distributed under the same license (CC BY-SA 4.0)
While copyright is held by the journal, authors retain important reuse rights. Authors may:
Reuse the published version of their article in future works, including books, compilations, and lectures
Deposit the published version in institutional or subject repositories
Share the article freely, including on personal websites or academic networks
as long as the original publication in Jurnal Kelautan Tropis is cited and the CC BY-SA 4.0 license terms are respected.
Authors must ensure that any third-party content included in the article (e.g., figures, images, datasets) is either original, in the public domain, or licensed for reuse under compatible terms. If specific permissions are required, authors must obtain them prior to submission.
For questions regarding copyright or licensing, please contact the editorial office at: j.kelautantropis@gmail.com
View My Stats
Jurnal Kelautan Tropis is published by Departement of Marine Science, Faculty of Fisheries and Marine Science, Universitas Diponegoro under a Creative Commons Attribution-ShareAlike 4.0 International License.
