Abdul Malik(1*), Muhammad Ichsan Ali(2), Abd. Rasyid Jalil(3), Sulaiman Zhiddiq(4), Abdul Mannan(5), Rahma Musyawarah(6),

(1) Universitas Negeri Makassar
(2) Jurusan Pendidikan Teknik Sipil dan Perencanaan, Fakultas Teknik, Universitas Negeri Makassar
(3) Universitas Hasanuddin Pusat Penelitian dan Pengembangan Sumberdaya Alam, Lembaga Penelitian dan Pengabdian, Universitas Hasanuddin
(4) Universitas Negeri Makassar
(5) Universitas Negeri Makassar
(6) Universitas Negeri Makassar
(*) Corresponding Author



The application of the Normalized Difference Vegetation Index (NDVI) on multispectral satellite imagery has been extensively used to assess the quantitative and qualitative aspects of mangrove vegetation. However, the use of Sentinel-2 imagery for this purpose is still relatively new. This research aims to monitor the distribution and density of mangrove vegetation in West Sulawesi by implementing NDVI transformation on Sentinel-2 imagery. The mangrove forest in Mamuju Regency, located in West Sulawesi, is one of the rich areas of mangrove forests on the island of Sulawesi, Indonesia. However, this region also exhibits disturbances in the mangrove ecosystem, resulting in limited monitoring efforts. By utilizing NDVI transformation, we identified the distribution and density of mangrove vegetation using Sentinel-2 imagery. The accuracy of image classification was evaluated using the confusion matrix method, and further analysis was conducted using linear regression to test the relationship between NDVI and mangrove density values obtained from field surveys. The results indicate that the total area of the mangrove forest reaches 1,798 hectares distributed along the coastal areas in the districts of Sampaga, Papalang, Kalukku, Mamuju, Simboro, Tapalang Barat, and Tapalang. Nearly 60% of this area has high mangrove density, while approximately 7% to 9% falls into the low and lowest density categories. NDVI values range from 0.06 to 0.81, with the highest value found in the Mamuju District and the lowest in the Papalang District. The correlation between NDVI and mangrove density shows a strong positive relationship (R=0.78). Therefore, Sentinel-2 imagery demonstrates high accuracy and potential for the development of predictive models for mangrove vegetation density. These findings have significant implications for the development of conservation policies and environmental management, as well as raising public awareness of the importance of preserving mangrove forests.


Mangrove; Spatial analysis; NDVI; Sentinel-2; West Sulawesi

Full Text:



Alongi, D. M. (2018). Impact of global change on nutrient dynamics in Mangrove Forests. Forests, 9(10), 1–13.

BPS Kabupaten Mamuju. (2023). Kabupaten Mamuju Dalam Angka 2023.

Chellamani, P., Singh, C. P., & Panigrahy, S. (2014). Assessment of the health status of Indian mangrove ecosystems using multi temporal remote sensing data. Tropical Ecology, 55(2), 245–253.

Delegido, J., Verrelst, J., Alonso, L., & Moreno, J. (2011). Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors, 11(7), 7063–7081.

Duke, N. C., Meynecke, J.-O., Dittmann, S., Ellison, A. M., Anger, K., Berger, U., Cannicci, S., Diele, K., Ewel, K. C., Field, C. D., Koedam, N., Lee, S. Y., Marchand, C., Nordhaus, I., & Dahdouh-Guebas, F. (2007). A World Without Mangroves? Science, 317(5834), 41–42.

Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1), 185–201.

Getzner, M., & Islam, M. S. (2020). Ecosystem Services of Mangrove Forests: Results of a Meta-Analysis of Economic Values. International Journal of Environmental Research and Public Health, 17(16), 5830.

Giri, C., Ochieng, E., Tieszen, L. L., Zhu, Z., Singh, A., Loveland, T., Masek, J., & Duke, N. (2011). Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecology and Biogeography, 20(1), 154–159.

Hamilton, S. E., & Casey, D. (2016). Creation of a high spatio‐temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC‐21). Global Ecology and Biogeography, 25(6), 729–738.

Himes-Cornell, A., Pendleton, L., & Atiyah, P. (2018). Valuing ecosystem services from blue forests: A systematic review of the valuation of salt marshes, sea grass beds and mangrove forests. Ecosystem Services, 30, 36–48.

Jalil, A. R., Malik, A., Nurdin, N., Saru, A., & Yunus, I. (2020). Assessment of Seawater Level, Inundation Duration and Substrate Elevation for Mangrove Rehabilitation Program in The Spermonde Archipelago South Sulawesi Indonesia. International Journal of Conservation Science, 11(4).

Kawamuna, A., Suprayogi, A., & Wijaya, A. P. (2017). Analisis Kesehatan Hutan Mangrove Berdasarkan Metode Klasifikasi NDVI Pada Citra Sentinel-2 (Studi Kasus: Teluk Pangpang Kabupaten Banyuwangi). Geodesi Undip, 6, 277–284.

Kuenzer, C., Bluemel, A., Gebhardt, S., Quoc, T. V., & Dech, S. (2011). Remote Sensing of Mangrove Ecosystems: A Review. Remote Sensing, 3(5), 878–928.

Leal, M., & Spalding, M. D. (2022). The State of World’s Mangroves 2022. 94.

Malik, A., Fensholt, R., & Mertz, O. (2015a). Economic Valuation of Mangroves for Comparison with Commercial Aquaculture in South Sulawesi, Indonesia. Forests, 6(12), 3028–3044.

Malik, A., Fensholt, R., & Mertz, O. (2015b). Mangrove exploitation effects on biodiversity and ecosystem services. Biodiversity and Conservation, 24(14).

Malik, A., Jalil, A. R., Arifuddin, A., & Syahmuddin, A. (2020). Biomass Carbon Stocks In The Mangrove Rehabilitated Area of Sinjai District, South Sulawesi, Indonesia. Geography, Environment, Sustainability, 13(3), 32–38.

Malik, A., Mertz, O., & Fensholt, R. (2017). Mangrove forest decline: consequences for livelihoods and environment in South Sulawesi. Regional Environmental Change, 17(1), 157–169.

Malik, A., Rahim, A., Sideng, U., Rasyid, A., & Jumaddin, J. (2019). Biodiversity assessment of mangrove vegetation for the sustainability of ecotourism in West Sulawesi, Indonesia. AACL Bioflux, 12(4), 1458–1466.

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.

Razali, S. M., Nuruddin, A. A., & Lion, M. (2019). Mangrove vegetation health assessment based on remote sensing indices for Tanjung Piai, Malay peninsular. Journal of Landscape Ecology(Czech Republic), 12(2), 26–40.

Richards, D. R., & Friess, D. A. (2016). Rates and drivers of mangrove deforestation in Southeast Asia, 2000–2012. Proceedings of the National Academy of Sciences, 113(2), 344–349.

Sari, S. P., & Rosalina, D. (2016). Mapping and Monitoring of Mangrove Density Changes on tin Mining Area. Procedia Environmental Sciences, 33, 436–442.

Spalding, M., & Parrett, C. L. (2019). Global patterns in mangrove recreation and tourism. Marine Policy, 110(June), 103540.

Stratoulias, D., Balzter, H., Sykioti, O., Zlinszky, A., & Tóth, V. R. (2015). Evaluating sentinel-2 for lakeshore habitat mapping based on airborne hyperspectral data. Sensors (Switzerland), 15(9), 22956–22969.

Valderrama-Landeros, L., Flores-de-Santiago, F., Kovacs, J. M., & Flores-Verdugo, F. (2018). An assessment of commonly employed satellite-based remote sensors for mapping mangrove species in Mexico using an NDVI-based classification scheme. Environmental Monitoring and Assessment, 190(1).

Valiela, I., Bowen, J. L., & York, J. K. (2001). Mangrove forests: One of the world’s threatened major tropical environments. BioScience, 51(10), 807–815.[0807:MFOOTW]2.0.CO;2

Wachid, M. N., Hapsara, R. P., Cahyo, R. D., Wahyu, G. N., Syarif, A. M., Umarhadi, D. A., Fitriani, A. N., Ramadhanningrum, D. P., & Widyatmanti, W. (2017). Mangrove canopy density analysis using Sentinel-2A imagery satellite data. IOP Conference Series: Earth and Environmental Science, 70(1).

Article Metrics

Abstract view : 22 times | PDF view : 6 times


  • There are currently no refbacks.

Copyright (c) 2024 Abdul Malik, M. Ichsan Ali Abd. Rasyid Jalil, Sulaiman Zhiddiq, Abdul Mannan, Rahma Musyawarah

 Diterbitkan Oleh:

Prodi Geografi, Jurusan Geografi

Fakultas Matematika dan Ilmu Pengetahuan alam

Universitas Negeri Makassar

Editorial Office:


Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License