Spatiotemporal Dynamics of Mangrove Cover Change in Tanjungpinang City, Riau Islands Province

Lili Sudjana, Sodikin Sodikin, Rina Astarika

Abstract


Tanjungpinang City is a coastal area surrounded by the sea and characterized by a mangrove ecosystem. Coastal regions are more dynamic and vulnerable compared to other areas, both naturally and due to human activities. This study aims to determine the rate of mangrove cover change in Tanjungpinang City from 2007 to 2023 and to predict changes in mangrove cover by the year 2035. The methodology employed involves spatiotemporal analysis of land cover change using Landsat 7 ETM+ imagery from 2007, Landsat 8 OLI imagery from 2015, and Sentinel-2A MSI imagery from 2023. The prediction for mangrove cover change in 2035 was conducted using the Land Change Modeler. The results indicate that from 2007 to 2023, there was a decrease in mangrove area of 323.40 hectares, with a rate of 20.21 hectares per year. The predicted mangrove coverage in Tanjungpinang City for the year 2035 is estimated to be 977.72 hectares, representing a reduction of 461.21 hectares (32.05%) from the coverage in 2023.

 

Keywords: Land Change Modeler; Google Earth Engine; mangrove cover prediction; Landsat; Sentinel-2A


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Adawiyah, H., Mutia, T., Subhani, A., Kabul, L. M., & Saputra, A. M. (2021). Analisis Sistem Informasi Geografis Perubahan Penggunaan Lahan di Kecamatan Labuhan Haji. Geodika: Jurnal Kajian Ilmu Dan Pendidikan Geografi, 5(1), 174–184.

Afrita, N., & Ramadhan, R. (2024). Perubahan Penutup Lahan Berdasarkan Citra Landsat Multiwaktu Menggunakan Land Change Modeler ( LCM ) di Kabupaten Merangin. Jurnal Pendidikan Tambusai, 8(Lcm), 7446–7454.

Akdeniz, H. B., Sag, N. S., & Inam, S. (2023). Analysis of land use/land cover changes and prediction of future changes with land change modeler: Case of Belek, Turkey. Environmental Monitoring and Assessment, 195(1), 135.

Akram, A. M., & Hasnidar, H. (2022). Identifikasi Kerusakan Ekosistem Mangrove Di Kelurahan Bira Kota Makassar. JOURNAL OF INDONESIAN TROPICAL FISHERIES (JOINT-FISH) : Jurnal Akuakultur, Teknologi Dan Manajemen Perikanan Tangkap, Ilmu Kelautan, 5(1), 1–11. https://doi.org/10.33096/joint-fish.v5i1.101

Altman, D. G. (1990). Practical statistics for medical research. Chapman and Hall/CRC.

Awal, E. E., Sitanggang, I. S., & Syaufina, L. (2023). Model Prediksi Perubahan Tutupan Lahan Pada Area Kebakaran Lahan Gambut Menggunakan Model Cellular Automata Markov. Jurnal Informatika Dan Teknologi Informasi, 1(2). https://doi.org/10.56854/jt.v1i2.141

Bengen, D., Yonvitner, Y., & Rahman, R. (2023). Pedoman Teknis Pengenalan dan Pengelolaan Mangrove.

Craig, B. A., & Sendi, P. P. (2002). Estimation of the transition matrix of a discrete‐time Markov chain. Health Economics, 11(1), 33–42.

Darmawan, S., Nasing, E. N., & Tridawati, A. (2022). Prediksi perubahan kawasan hutan mangrove menggunakan model land change modeler berbasis citra satelit penginderaan jauh. Jurnal Tekno Insentif, 16(1), 54–68.

Dimyati, R. D. (2022). TEKNOLOGI PEMANTAUAN MANGROVE YANG EFISIEN DI INDONESIA BERBASIS DATA PENGINDERAAN JAUH OPTIK.

Doodee, M. D. D., Rughooputh, S. D. D. V, & Jawaheer, S. (2023). Remote sensing monitoring of mangrove growth rate at selected planted sites in Mauritius. South African Journal of Science, 119(1–2), 1–7.

Fariz, T. R., Permana, P. I., Daeni, F., & Putra, A. C. P. (2021). Pemetaan ekosistem mangrove di Kabupaten Kubu Raya menggunakan machine learning pada Google Earth Engine. Jurnal Geografi: Media Informasi Pengembangan Dan Profesi Kegeografian, 18(2), 83–89.

Friess, D. A., Rogers, K., Lovelock, C. E., Krauss, K. W., Hamilton, S. E., Lee, S. Y., Lucas, R., Primavera, J., Rajkaran, A., & Shi, S. (2019). The state of the world’s mangrove forests: past, present, and future. Annual Review of Environment and Resources, 44(1), 89–115.

Giri, C. (2016). Observation and monitoring of mangrove forests using remote sensing: Opportunities and challenges. Remote Sensing, 8(9), 783.

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27.

Han, H., Yang, C., & Song, J. (2015). Scenario simulation and the prediction of land use and land cover change in Beijing, China. Sustainability, 7(4), 4260–4279.

Mappanganro, F., Asbar, A., & Danial, D. (2018). INVENTARISASI KERUSAKAN DAN STRATEGI REHABILITASI HUTAN MANGROVE DI DESA KEERA KECAMATAN KEERA KABUPATEN WAJO. Jurnal Pendidikan Teknologi Pertanian. https://api.semanticscholar.org/CorpusID:133828781

Mohammad Malik, Kuncahyo, B., & Puspaningsih, N. (2023). Dinamika Perubahan Tutupan Hutan Mangrove Sebagai Kawasan Lindung Menggunakan Citra Satelit di Pulau Peleng Sulawesi Tengah. Journal of Tropical Silviculture, 14(03), 183–190. https://doi.org/10.29244/j-siltrop.14.03.183-190

Mutanga, O., & Kumar, L. (2019). Google earth engine applications. In Remote sensing (Vol. 11, Issue 5, p. 591). MDPI.

Nguyen, H. T. T., Pham, T. A., Doan, M. T., & Tran, P. T. X. (2020). Land use/land cover change prediction using multi-temporal satellite imagery and multi-layer perceptron Markov model. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 44, 99–105.

Papilaya, P. P. E. (2022). Aplikasi Google Earth Engine Dalam Menyediakan Citra Satelit Sumberbedaya Alam Bebas Awan. MAKILA, 16(2), 96–103.

Pontius, R. G. (2001). Quantification error versus location error in comparison of categorical maps (vol 66, pg 1011, 2000). Photogrammetric Engineering and Remote Sensing, 67(5), 540.

Prabowo, D. P., Bachri, S., & Wiwoho, B. S. (2017). Prediksi Perubahan Penggunaan Lahan Dan Pola Berdasarkan Citra Landsat Multiwaktu Dengan Land Change Modeler (Lcm) Idrisi Selva 17: Studi Kasus Sub-Das Brantas Hulu. Jurnal Pendidikan Geografi, 22(1), 32–48. https://doi.org/10.17977/um017v22i12017p032

Prasetya, F. A., & Wibowo, A. (2024). Analisis Spasial-Temporal Perubahan Penggunaan Lahan Akibat Pembangunan Bandara Internasional Dhoho Kediri Berbasis Data Google Earth. Geodika: Jurnal Kajian Ilmu Dan Pendidikan Geografi, 8(No.1), 65–74. https://doi.org/10.29408/geodika.v8i1.25731

Rahadian, A., Prasetyo, L. B., Setiawan, Y., & Wikantika, K. (2019). A historical review of data and information of Indonesian mangroves area. Media Konservasi, 24(2), 163–178.

Raynaldo, A., Mukhtar, E., & Novarino, W. (2020). Mapping and change analysis of mangrove forest by using Landsat imagery in Mandeh Bay, West Sumatra, Indonesia. Aquaculture, Aquarium, Conservation & Legislation, 13(4), 2144–2151.

Rosalina, D., Hawati, Rombe, K. H., Surachmat, A., Awaluddin, Amiluddin, M., Leilani, A., & Asriyanti. (2023). Application of remote sensing and GIS for mapping changes in land area and mangrove density in the Kuri Caddi Mangrove tourism, South Sulawesi Province, Indonesia. Biodiversitas, 24(2), 1049–1056. https://doi.org/10.13057/biodiv/d240246

Sodikin, S., Sitorus, S. R. P., Prasetyo, L. B., & Kusmana, C. (2017). Spatial analysis of mangrove deforestation and mangrove rehabilitation directive in Indramayu Regency, West Java, Indonesia.

Uddin, M. S., Van Steveninck, E. de R., Stuip, M., & Shah, M. A. R. (2013). Economic valuation of provisioning and cultural services of a protected mangrove ecosystem: A case study on Sundarbans Reserve Forest, Bangladesh. Ecosystem Services, 5, 88–93.

Verburg, P. H., Schot, P. P., Dijst, M. J., & Veldkamp, A. (2004). Land use change modelling: current practice and research priorities. GeoJournal, 61, 309–324.

Zhang, Z., Ahmed, M. R., Zhang, Q., Li, Y., & Li, Y. (2023). Monitoring of 35-year mangrove wetland change dynamics and agents in the sundarbans using temporal consistency checking. Remote Sensing, 15(3), 625.




DOI: http://dx.doi.org/10.23960%2Fjpg.v12i2.31028

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