Integrating Landsat and Sentinel Data to Map Mangroves and Estimate Biomass and Carbon Stocks in Ca Mau, Vietnam
DOI:
https://doi.org/10.5281/zenodo.17224281Keywords:
Wetlands, Mangrove forests, Remote sensing, Above-ground biomass, Carbon and oxygen reservesAbstract
Wetlands are among the most important ecosystems on Earth, playing an essential role in biodiversity conservation, water purification, carbon sequestration, and oxygen production for the atmosphere. In the context of increasingly severe climate change, monitoring and assessing these areas has become critically important. Remote sensing has long been widely applied in the monitoring of natural resources and the environment, especially with the support of freely available data sources such as Landsat and Sentinel. This study focuses on the use of multispectral remote sensing data from Landsat 8 and Sentinel-2 to identify coastal mangrove forest areas in Ca Mau, Vietnam. By calculating vegetation indices such as NDVI, NDWI, etc., the research team estimated above-ground biomass, carbon storage, carbon sequestration capacity, and oxygen concentration. The results include the generation of spatial distribution maps of mangrove forests, as well as maps of carbon and oxygen reserves. These findings contribute to the effective monitoring, conservation, and management of wetland ecosystems, while also providing essential input data for climate and environmental studies in Vietnam.
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[1] Grace, J. Carbon Cycle. In S. A. Levin (Ed.), Encyclopedia of Biodiversity (2nd ed., Vol. 1, pp. 674–684). Academic Press, 2013.
[2] IPCC. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Institute for Global Environmental Strategies (IGES), Japan, 2006.
[3] Shokoufeh Salimi; Suhad A. N. Almuktar; Miklas Scholz. Impact of climate change on wetland ecosystems: A critical review of experimental wetlands. Journal of Environmental Management 286, 112160-112175, 2021.
[4] Pan, Y., et al. A large and persistent carbon sink in the world’s forests. Science, 333(6045), 988–993, 2011.
[5] Bindu, G., et al. Carbon stocks assessment of mangroves using remote sensing and GIS. Egypt Journal of Remote Sensing and Space Sciences, 23, 1–9, 2020.
[6] Vườn quốc gia Cà Mau, https://vuonqgmcm.camau.gov.vn (ngày truy cập cuối: 28/09/2025)
[7] Alisa, L.G. (2015). The Challenges of Remote Monitoring of Wetlands. Remote Sensing, 7(8), 10938-10950, 2015.
[8] Meng, G., et al. A Review of Wetland Remote Sensing. Sensors, 17(4), 777, 2017.
[9] Kaplan, G., & Avdan, U. Monthly Analysis of Wetlands Dynamics Using Remote Sensing Data. Int. Journal Geo-Information, 7(10), 411, 2018.
[10] Ansa, S., et al. Leveraging machine learning and remote sensing to monitor long-term spatial-temporal wetland changes. Applied Geography, 151, 102868, 2023.
[11] Bo-cai, G. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257–266, 1996.
[12] Huete, A. R. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309, 1988.
[13] Reichle, D.E.Dynamic properties of the global carbon cycle. In The Global Carbon Cycle and Climate Change (2nd ed.), Elsevier, 2023
[14] Michael, S., & Malcolm, W.J.D. Aboveground Forest Biomass Estimation Combining L- and P-Band SAR. Remote Sensing, 10(7), 1151, 2018.
[15] Ghosh, S.M., & Behera, M.D. Aboveground biomass estimation using multi-sensor data synergy and machine learning algorithms. Applied Geography, 98, 29–40, 2018.
[16] Avitabile, V., Herold, M., Heuvelink, G. B. M., et al. An integrated pan-tropical biomass map using multiple reference datasets. Global Change Biology, 22(4), 1406–1420, 2016.
[17] IPCC. Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Intergovernmental Panel on Climate Change, 2019.
[18] Brown, S. Measuring carbon in forests: current status and future challenges. Environmental Pollution, 116(3), 363–372, 2002.
[19] Lu, D. The Potential and Challenges of Remote Sensing-Based Biomass Estimation. International Journal of Remote Sensing, 27(7), 1297–1328. 2006.
[20] Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58(3), 289–298, 2003.
[21] Le Toan, T., Quegan, S., Davidson, M. W. J., et al. The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle. Remote Sensing of Environment, 115(11), 2850–2860, 2011.
[22] Imhoff, M. L.Radar backscatter and biomass saturation: Ramifications for global biomass inventory. IEEE Transactions on Geoscience and Remote Sensing, 33(2), 511–518, 1995.
[23] Lucas, R., et al. The development of a forest biomass retrieval algorithm using L-band SAR and model inversion. Remote Sensing of Environment, 114(6), 1387–1402, 2010.
[24] Asner, G. P., Kellner, J. R., Kennedy-Bowdoin, T., et al. High-resolution mapping of forest carbon stocks in the Colombian Amazon. Biogeosciences, 9(7), 2683–2696, 2012.
[25] Dubayah, R. O., et al. The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth’s forests and topography. Science of Remote Sensing, 1, 100002, 2020.
[26] Saatchi, S. S., et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proceedings of the National Academy of Sciences, 108(24), 9899–9904, 2011.
[27] Landsat 8. https://www.usgs.gov/ (ngày truy cập cuối 28/09/2025)
[28] Sentinel - 2 Level-2A. https://sentinels.copernicus.eu/ (ngày truy cập 28/09/2025)
[29] Giri, C., Zhu, Z., & Tieszen, L. L. Mangrove forest distributions and dynamics (1975–2005) of the tsunami-affected region of Asia. Journal of Biogeography, 34(3), 519–528, 2007
[30] Thenkabail, P. S., Lyon, J. G., & Huete, A. (Eds.). Hyperspectral Remote Sensing of Vegetation. CRC Press, 2011.
[31] Tucker, C. J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150, 1979.
[32] Gao, B. C. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266, 1996.
[33] Nguyen, H. T., Le, Q. T., & Vo, Q. T. A novel coastal mangrove recognition index (CMRI) using Sentinel-2 imagery: Case study in Vietnam. Remote Sensing Letters, 11(4), 345–354, 2020.
[34] Rahman, M. M., Ahmed, M., Ismail, H., et al. Estimating above-ground biomass in mangrove ecosystems: A comparison of multiple regression and machine learning algorithms. International Journal of Remote Sensing, 34(22), 7885–7906, 2013.
[35] Makundi, W. R., & Sathaye, J. A. GHG mitigation potential and cost in tropical forestry—Relative role for agroforestry. Environment, Development and Sustainability, 6(1), 235–260, 2004.
[36] Drusch, M., et al. Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment, 120, 25–36, 2012.
[37] Roy, D. P., et al. Landsat-8: Science and Product Vision for Terrestrial Global Change Research. Remote Sensing of Environment, 145, 154–172, 2014.
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