Determining thresholds of vegetation indexes for mangrove cover mapping and change detection in Ha Tinh province

Authors

  • Chu Quynh Anh Vietnam National University of Forestry
  • Nguyen Hai Hoa Vietnam National University of Forestry
  • Nguyen Ngoc Linh Vietnam National University of Forestry
  • Vu Van Truong Vietnam National University of Forestry
  • Nguyen Thi Hai Van F-School, Vietnam Natonal University of Forestry

DOI:

https://doi.org/10.55250/Jo.vnuf.11.1.2026.047-054

Keywords:

Coastal management, Ha Tinh, mangrove mapping, Sentinel-2, threshold analysis, vegetation indices

Abstract

Accurate monitoring of mangrove ecosystems is crucial for coastal zone management, yet region-specific methodologies remain underdeveloped for many vulnerable coastlines. This study developed optimized vegetation index thresholds from Sentinel-2 imagery for accurate mapping of mangrove cover changes in Ha Tinh province, Vietnam. Eight vegetation indices were calculated and evaluated using field survey data combined with remote sensing data. Through threshold analysis and accuracy assessment, the Combined Mangrove Recognition Index (CMRI ≥ 0.15) was identified as the most effective index for mangrove detection in the study area. The mangrove classification for 2025 achieved high accuracy (Overall Accuracy = 95%, Kappa = 0.91). Change detection analysis between 2020 and 2025 revealed a net increase in mangrove area by 27.86% (from 687.97 ha to 879.65 ha). This expansion is attributed to active government-led restoration programs and supporting initiatives. However, persistent threats from illegal logging, aquaculture expansion, and climate-induced hazards highlight ongoing challenges. This study proposes a practical and transferable remote sensing approach to mangrove monitoring. It underscores the need to integrate robust spectral thresholding with effective policy enforcement to ensure long-term mangrove conservation in the vulnerable coastal areas of central Vietnam.

References

[1]. Alongi, D. M. (2014). Carbon cycling and storage in mangrove forests. Annual Review of Marine Science. 6: 195-219. DOI: 10.1146/annurev-marine-010213-135020.

[2]. Goldberg, L., Lagomasino, D., Thomas, N., & Fatoyinbo, T. (2020). Global declines in human-driven mangrove loss. Global Change Biology. 26(10): 5844-5855. DOI: 10.1111/gcb.15275

[3]. McLeod, E., Chmura, G. L., Bouillon, S., Salm, R., Björk, M., Duarte, C. M., ... & Silliman, B. R. (2011). A blueprint for blue carbon: Toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2. Frontiers in Ecology and the Environment. 9(10): 552-560. DOI: 10.1890/110004

[4]. 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. DOI: 10.1111/geb.12449

[5]. 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.

DOI: 10.1073/pnas.1510272113

[6]. Hong PN & San HT (1993). Mangroves of Vietnam. IUCN, Bangkok, Thailand.

[7]. MARD (2021). Decision No. 1558/QD-BNN-TCLN dated April 13, 2021, by the Ministry of Agriculture and Rural Development on the Promulgation of National Forest Status in 2020 (In Vietnamese).

[8]. 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. DOI: 10.3390/rs3050878

[9]. Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., ... & Bargellini, P. (2012). Sentinel-2: ESA's optical high-resolution mission for GMES operational services. Remote Sensing of Environment. 120: 25-36.

DOI: 10.1016/j.rse.2011.11.026

[10]. Huete, A. R., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment. 83(1-2): 195-213. DOI: 10.1016/S0034-4257(02)00096-2

[11]. Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing. 27(14): 3025-3033. DOI: 10.1080/01431160600589179

[12]. Pham, T. D., Yokoya, N., Bui, D. T., Yoshino, K., & Friess, D. A. (2019). Remote sensing approaches for monitoring mangrove species, structure, and biomass: Opportunities and challenges. Remote Sensing. 11(3): 230. DOI: 10.3390/rs11030230

[13]. Ha, T. S., Nguyen, H. H., & Vu, V. T. (2023). Mangrove cover-based vegetation indices mapping using PlanetScope data in Tien Yen district, Quang Ninh province. Journal of Forestry Science and Technology. 15: 127-138. DOI: 10.55250/jo.vnuf.2023.15.127-138

[14]. General Statistics Office (2022). Socio-economic statistical data of 63 provinces and cities 2015-2021.

[15]. Rouse, J.W., Haas, R.H., Schell, J.A. & Deering, D.W. (1973). Monitoring vegetation systems in the Great Plains with ERTS. NASA Special Publication. 351: 309-317.

[16]. Gao, B.C. (1996). NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment. 58(3): 257-266. DOI: 10.1016/S0034-4257(96)00067-3

[17]. Huete, A.R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment. 25(3): 295-309. DOI: 10.1016/0034-4257(88)90106-X.

[18]. Gupta, K., Mukhopadhyay, A., Giri, S., Chanda, A., Datta Majumdar, S., Samanta, S., ... & Hazra, S. (2018). An index for discrimination of mangroves from non-mangroves using LANDSAT 8 OLI imagery. Methods X. 5: 1129-1139. DOI: 10.1016/j.mex.2018.09.011

[19]. Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment. 55(2): 95-107.

DOI: 10.1016/0034-4257(95)00186-7

[20]. Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment. 58(3): 289-298. DOI: 10.1016/S0034-4257(96)00072-7

[21]. Jordan, C. F. (1969). Derivation of leaf-area index from quality of light on the forest floor. Ecology. 50(4): 663-666. DOI: 10.2307/1936256

[22]. Congalton RG, Green K (1999) Assessing the accuracy of remotely sensed data: principles and practices. Lewis Publishers, Boca Raton. 137.

[23]. Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment. 37(1): 35-46.

DOI: 10.1016/0034-4257(91)90048-b.

[24]. Ha Tinh Newspaper (2020). Ha Tinh adds nearly 66 hectares of coastal mangrove forests. Ha Tinh Provincial Portal. https://hatinh.gov.vn/vi/tin-tuc-su-kien/tin-bai/8660 (In Vietnamese).

[25]. Kim Oanh (2020). Ha Tinh plants mangrove forests along rivers and estuaries. Nature and Environment Electronic Magazine.

https://thiennhienmoitruong.vn/ha-tinh-trong-rung-ngap-man-ven-song-cua-bien.html (In Vietnamese).

[26]. IPCC. (2022). Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.

Downloads

Published

15-05-2026

How to Cite

Chu Quynh Anh, Nguyen Hai Hoa, Nguyen Ngoc Linh, Vu Van Truong, & Nguyen Thi Hai Van. (2026). Determining thresholds of vegetation indexes for mangrove cover mapping and change detection in Ha Tinh province. Journal of Forestry Science and Technology, 11(1), 047–054. https://doi.org/10.55250/Jo.vnuf.11.1.2026.047-054

Issue

Section

Resource management & Environment

Categories

Most read articles by the same author(s)

Similar Articles

<< < 5 6 7 8 9 10 11 12 13 14 > >> 

You may also start an advanced similarity search for this article.