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Triple-feature fusion from UAV multispectral imagery enhances species-level mangrove carbon assessment

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Abstract

Accurate estimation of mangrove ecosystem carbon stocks is essential for effective blue carbon management. Significant interspecific variations in carbon storage capacity and estimation methods arise due to species-specific biophysical characteristics, highlighting the need for precise mangrove species identification and species-level carbon stock assessment. However, limited studies assessed mangrove carbon stocks at species-level. This study, conducted in the Gaoqiao Mangrove Nature Reserve in Zhanjiang, Guangdong Province, applied UAV multispectral technology to simultaneously acquire spectral, structural and textural vegetation feature variables for mangrove species identification and established species-specific carbon stock models, thereby achieving species-level carbon stock estimation. Results showed that (1) by integrating spectral and structural features, the study achieved 89.87% overall accuracy in species identification. (2) Species-level carbon stock estimation models, incorporating spectral, structural and textural feature variables alongside field-measured carbon data, demonstrated strong predictive performance (R2 = 0.48-0.95). (3) The most effective vegetation feature variables for carbon estimation varied significantly across species, emphasizing the necessity of accounting for species heterogeneity in mangrove carbon stock estimations. (4) Carbon stocks exhibited significant interspecific variation, with Rhizophora stylosa demonstrating the highest aboveground (97.06 t hm⁻2) and belowground (37.22 t hm⁻2) stocks, compared to Aegiceras corniculatum’s minimum values of 49.14 and 19.88 t hm⁻2, respectively. This study established a UAV-based multispectral framework for mangrove species-level carbon stock estimation and provided new insights for mangrove carbon assessment and management by demonstrating the importance of considering species-specific influences on carbon stocks and their estimation.

Data availability

Data provided in the manuscript and supplementary file. Further details, the corresponding author can provide the data used and analyzed in this study upon request.

Reference

  1. Mcleod, E. et al. A blueprint for blue carbon: Toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2. Front. Ecol. Environ. 9, 552–560. https://doi.org/10.1890/110004 (2011).

    Google Scholar 

  2. Madhavan, C., Meera, S. P. & Kumar, A. Anatomical adaptations of mangroves to the intertidal environment and their dynamic responses to various stresses. Biol. Rev. https://doi.org/10.1111/brv.13172 (2024).

    Google Scholar 

  3. Donato, D. C. et al. Mangroves among the most carbon-rich forests in the tropics. Nat. Geosci. 4, 293–297. https://doi.org/10.1038/ngeo1123 (2011).

    Google Scholar 

  4. Bai, J. et al. Mangrove diversity enhances plant biomass production and carbon storage in Hainan island, China. Funct. Ecol. 35(3), 774–786. https://doi.org/10.1111/1365-2435.13753 (2021).

    Google Scholar 

  5. Sitthi, A., Pimple, U., Piponiot, C. & Gond, V. Assessing the effectiveness of mangrove rehabilitation using above-ground biomass and structural diversity. Sci. Rep. 15, 7839. https://doi.org/10.1038/s41598-025-92514-7 (2025).

    Google Scholar 

  6. Li, Z. et al. Remote estimation of mangrove aboveground carbon stock at the species level using a low-cost unmanned aerial vehicle system. Remote Sens. 11, 1018. https://doi.org/10.3390/rs11091018 (2019).

    Google Scholar 

  7. Zhang, R. & Fan, J. Classification and carbon-stock estimation of mangroves in dongzhaigang based on multi-source remote sensing data using google earth engine. Remote Sens. 17(6), 964. https://doi.org/10.3390/rs17060964 (2025).

    Google Scholar 

  8. Rao, K., Ramanathan, A. L. & Raju, N. J. Assessment of blue carbon stock of Coringa mangroves: Climate change perspective. J. Clim. Change 8, 41–58. https://doi.org/10.3233/JCC220013 (2022).

    Google Scholar 

  9. Binh, N. A. et al. Monitoring mangrove traits through optical Earth observation: Towards spatio-temporal scalability using cloud-based Sentinel-2 continuous time series. ISPRS J. Photogramm. Remote Sens. 214, 135–152. https://doi.org/10.1016/j.isprsjprs.2024.06.007 (2024).

    Google Scholar 

  10. Roy, A. D. et al. Remote sensing-based mangrove blue carbon assessment in the Asia-Pacific: A systematic review. Sci. Total Environ. 938, 173270. https://doi.org/10.1016/j.scitotenv.2024.173270 (2024).

    Google Scholar 

  11. Ruwaimana, M. et al. The advantages of using drones over space-borne imagery in the mapping of mangrove forests. Plos One 13, 0200288. https://doi.org/10.1371/journal.pone.0200288 (2018).

    Google Scholar 

  12. Lu, D. et al. A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. Int. J. Dig. Earth 9, 63–105. https://doi.org/10.1080/17538947.2014.990526 (2014).

    Google Scholar 

  13. Lu, D. The potential and challenge of remote sensing-based biomass estimation. Int. J. Remote Sens. 27, 1297–1328. https://doi.org/10.1080/01431160500486732 (2007).

    Google Scholar 

  14. Zhu, Y. et al. Estimating and mapping mangrove biomass dynamic change using WorldView-2 images and digital surface models. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 2123–2134. https://doi.org/10.1109/JSTARS.2020.2989500 (2020).

    Google Scholar 

  15. Hidayatullah, M. F., Kamal, M. & Wicaksono, P. Species-based aboveground mangrove carbon stock estimation using WorldView-2 image data. Remote Sens. Appl.: Soc. Environ. 30, 100959. https://doi.org/10.1016/j.rsase.2023.100959 (2023).

    Google Scholar 

  16. Navarro, A. et al. The application of unmanned aerial vehicles (UAVs) to estimate above-ground biomass of mangrove ecosystems. Remote Sens. Environ. 242, 111747. https://doi.org/10.1016/j.rse.2020.111747 (2020).

    Google Scholar 

  17. Tian, Y. et al. Aboveground mangrove biomass estimation in Beibu Gulf using machine learning and UAV remote sensing. Sci. Total Environ. 781, 146816. https://doi.org/10.1016/j.scitotenv.2021.146816 (2021).

    Google Scholar 

  18. Tait, L. et al. Unmanned aerial vehicles (UAVs) for monitoring macroalgal biodiversity: Comparison of RGB and multispectral imaging sensors for biodiversity assessments. Remote Sens. 11, 2332. https://doi.org/10.3390/rs11192332 (2019).

    Google Scholar 

  19. Cao, J. et al. Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models. Remote Sens. 10, 89. https://doi.org/10.3390/rs10010089 (2018).

    Google Scholar 

  20. Yang, Y. et al. Fine-scale mangrove species classification based on uav multispectral and hyperspectral remote sensing using machine learning. Remote Sens. 16(16), 3093. https://doi.org/10.3390/rs16163093 (2024).

    Google Scholar 

  21. Luo, S. et al. Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation. Ecol. Ind. 73, 378–387. https://doi.org/10.1016/j.ecolind.2016.10.001 (2017).

    Google Scholar 

  22. De Almeida, D. R. A. et al. Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and LiDAR fusion. Remote Sens. Environ. 264, 112582. https://doi.org/10.1016/j.rse.2021.112582 (2021).

    Google Scholar 

  23. Xiang, H. et al. Community identification and carbon storage monitoring of Heritiera littoralis with UAV hyperspectral imaging. Ecol. Ind. 167, 112653. https://doi.org/10.1016/j.ecolind.2024.112653 (2024).

    Google Scholar 

  24. Tao, H. et al. Estimation of crop growth parameters using UAV-based hyperspectral remote sensing data. Sensors 20(5), 1296. https://doi.org/10.3390/s20051296 (2020).

    Google Scholar 

  25. Dalla Corte, A. P. et al. Measuring individual tree diameter and height using GatorEye high-density UAV-LiDAR in an integrated crop-livestock-forest system. Remote Sens. 12, 863. https://doi.org/10.3390/rs12050863 (2020).

    Google Scholar 

  26. Salum, R. B. et al. Improving mangrove above-ground biomass estimates using LiDAR. Estuarine, Coastal Shelf Sci. 236, 106585. https://doi.org/10.1016/j.ecss.2020.106585 (2020).

    Google Scholar 

  27. Li, S. et al. Estimation of aboveground biomass of different vegetation types in mangrove forests based on UAV remote sensing. Sustain. Horiz. 11, 100100. https://doi.org/10.1016/j.horiz.2024.100100 (2024).

    Google Scholar 

  28. Yin, D., Wang, L., Lu, Y. & Shi, C. Mangrove tree height growth monitoring from multi-temporal UAV-LiDAR. Remote Sens. Environ. 303, 114002. https://doi.org/10.1016/j.rse.2024.114002 (2024).

    Google Scholar 

  29. Nesha, M. K., Hussin, Y. A., van Leeuwen, L. M. & Sulistioadi, Y. B. Modeling and mapping aboveground biomass of the restored mangroves using ALOS-2 PALSAR-2 in East Kalimantan, Indonesia. Int. J. Appl. Earth Obs. Geoinform. 91, 102158. https://doi.org/10.1016/j.jag.2020.102158 (2020).

    Google Scholar 

  30. Hickey, S. M., Callow, N. J., Phinn, S., Lovelock, C. E. & Duarte, C. M. Spatial complexities in aboveground carbon stocks of a semi-arid mangrove community: A remote sensing height-biomass-carbon approach. Estuarine, Coastal Shelf Sci. 200, 194–201. https://doi.org/10.1016/j.ecss.2017.11.004 (2018).

    Google Scholar 

  31. Chen, R., Zhang, R., Zhao, C., Wang, Z. & Jia, M. High-resolution mapping of mangrove species height in fujian zhangjiangkou national mangrove nature reserve combined GF-2, GF-3, and UAV-LiDAR. Remote Sens. 15(24), 5645. https://doi.org/10.3390/rs15245645 (2023).

    Google Scholar 

  32. Wang, Z. et al. Regional mangrove vegetation carbon stocks predicted integrating UAV-LiDAR and satellite data. J. Environ. Manag. 368, 122101. https://doi.org/10.1016/j.jenvman.2024.122101 (2024).

    Google Scholar 

  33. Yin, D. & Wang, L. Individual mangrove tree measurement using UAV-based LiDAR data: Possibilities and challenges. Remote Sens. Environ. 223, 34–49. https://doi.org/10.1016/j.rse.2018.12.034 (2019).

    Google Scholar 

  34. Jain, K. & Pandey, A. Calibration of satellite imagery with multispectral UAV imagery. J. Indian Soc. Remote Sens. 49, 479–490. https://doi.org/10.1007/s12524-020-01251-z (2020).

    Google Scholar 

  35. Fu, B. et al. Comparison of RFE-DL and stacking ensemble learning algorithms for classifying mangrove species on UAV multispectral images. Int. J. Appl. Earth Obs. Geoinform. 112, 102890. https://doi.org/10.1016/j.jag.2022.102890 (2022).

    Google Scholar 

  36. Li, Y. et al. Comparison of different transfer learning methods for classification of mangrove communities using MCCUNet and UAV multispectral images. Remote Sens. 14, 5533. https://doi.org/10.3390/rs14215533 (2022).

    Google Scholar 

  37. Chen, X. et al. An approach for detecting mangrove areas and mapping species using multispectral drone imagery and deep learning. Sensors 25(8), 2540. https://doi.org/10.3390/s25082540 (2025).

    Google Scholar 

  38. Kolanuvada, S. R. & Ilango, K. K. Automatic extraction of tree crown for the estimation of biomass from UAV imagery using neural networks. J. Indian Soc. Remote Sens. 49, 651–658. https://doi.org/10.1007/s12524-020-01242-0 (2021).

    Google Scholar 

  39. Lian, X. et al. Biomass calculations of individual trees based on unmanned aerial vehicle multispectral imagery and laser scanning combined with terrestrial laser scanning in complex stands. Remote Sens. 14, 4715. https://doi.org/10.3390/rs14194715 (2022).

    Google Scholar 

  40. Tan, H. et al. Improved estimation of aboveground biomass in rubber plantations using deep learning on UAV multispectral imagery. Drones 9(1), 32. https://doi.org/10.3390/drones9010032 (2025).

    Google Scholar 

  41. Zhao, D. et al. Spatiotemporal dynamics and geo-environmental factors influencing mangrove gross primary productivity during 2000–2020 in Gaoqiao Mangrove Reserve, China. For. Ecosyst. 10, 100137. https://doi.org/10.1016/j.fecs.2023.100137 (2023).

    Google Scholar 

  42. Cao Q. The estimation of mangrove biomass and carbon storageusing remote sensing data in Beibu Gulf coast. Chinese Academy of Forestry. (2010).

  43. Tran, T. V., Reef, R. & Zhu, X. A review of spectral indices for mangrove remote sensing. Remote Sens. 14, 4868. https://doi.org/10.3390/rs14194868 (2022).

    Google Scholar 

  44. Haralick, R. M., Shanmugam, K. & Dinstein, I. H. Textural features for image classification. IEEE Trans. Syst., Man, Cybern. 6, 610–621. https://doi.org/10.1109/TSMC.1973.4309314 (2007).

    Google Scholar 

  45. Huang, X., Zhang, L. & Wang, L. Evaluation of morphological texture features for mangrove forest mapping and species discrimination using multispectral IKONOS imagery. IEEE Geosci. Remote Sens. Lett. 6, 393–397. https://doi.org/10.1109/LGRS.2009.2014398 (2009).

    Google Scholar 

  46. Eckert, S. Improved forest biomass and carbon estimations using texture measures from WorldView-2 satellite data. Remote Sens. 4(4), 810–829. https://doi.org/10.3390/rs4040810 (2012).

    Google Scholar 

  47. Li, H., Han, Y. & Chen, J. Combination of google earth imagery and Sentinel-2 data for mangrove species mapping. J. Appl. Remote Sens. 14, 010501. https://doi.org/10.1117/1.JRS.14.010501 (2020).

    Google Scholar 

  48. Li, Q., Wong, F. K. K. & Fung, T. Classification of mangrove species using combined WordView-3 and LiDAR data in Mai Po Nature Reserve, Hong Kong. Remote Sens. 11(18), 2114. https://doi.org/10.3390/rs11182114 (2019).

    Google Scholar 

  49. Simpson, G. et al. Species-level classification of peatland vegetation using ultra-high-resolution UAV imagery. Drones 8(3), 97. https://doi.org/10.3390/drones8030097 (2024).

    Google Scholar 

  50. Bhadra, T. et al. Monitoring the mangroves of Indian Sundarbans using geospatial techniques. Int. Arch. Photogramm., Remote Sens. Spatial Inform. Sci. 48, 405–412. https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-405-2023 (2023).

    Google Scholar 

  51. Gao, L., Chai, G. & Zhang, X. Above-ground biomass estimation of plantation with different tree species using airborne LiDAR and hyperspectral data. Remote Sens. 14, 2568. https://doi.org/10.3390/rs14112568 (2022).

    Google Scholar 

  52. Pan, H. L., Huang, C. M. & Huang, C. Y. Mapping aboveground carbon density of subtropical subalpine dwarf bamboo (Yushania niitakayamensis) vegetation using UAV-lidar. Int. J. Appl. Earth Obs. Geoinform. 123, 103487. https://doi.org/10.1016/j.jag.2023.103487 (2023).

    Google Scholar 

  53. Niu, X. et al. Estimation of coastal wetland vegetation aboveground biomass by integrating UAV and satellite remote sensing data. Remote Sens. 16(15), 2760. https://doi.org/10.3390/rs16152760 (2024).

    Google Scholar 

  54. Shao, J. Linear model selection by cross-validation. J. Am. Stat. Assoc. 88, 486–494. https://doi.org/10.1080/01621459.1993.10476299 (1993).

    Google Scholar 

  55. Wang, G., Guan, D., Peart, M. R., Chen, Y. & Peng, Y. Ecosystem carbon stocks of mangrove forest in Yingluo Bay, Guangdong Province of South China. For. Ecol. Manag. 310, 539–546. https://doi.org/10.1016/j.foreco.2013.08.045 (2013).

    Google Scholar 

  56. Wang, G. et al. Changes in mangrove community structures affecting sediment carbon content in Yingluo Bay of South China. Mar. Pollut. Bull. 149, 110581. https://doi.org/10.1016/j.marpolbul.2019.110581 (2019).

    Google Scholar 

  57. Simarmata, N. et al. Mangrove ecosystem species mapping from integrated Sentinel-2 imagery and field spectral data using random forest algorithm. J. Appl. Remote Sens. 18(1), 014509–014509. https://doi.org/10.1117/1.JRS.18.014509 (2024).

    Google Scholar 

  58. Chen, Z., Zhang, M., Zhang, H. & Liu, Y. Mapping mangrove using a red-edge mangrove index (REMI) based on Sentinel-2 multispectral images. IEEE Trans. Geosci. Remote Sens. 61, 1–11. https://doi.org/10.1109/TGRS.2023.3323741 (2023).

    Google Scholar 

  59. Zhang, P., Shang, X. & Wu, Z. Morphological characteristics of leaves and estimation of relative chlorophyll content in five species of mangrove based on image processing technology. Chin. J. Trop. Crops 41(3), 496–503 (2020).

    Google Scholar 

  60. Wolff, F., Lorenz, S., Korpelainen, P., Eltner, A. & Kumpula, T. UAV and field hyperspectral imaging for Sphagnum discrimination and vegetation modelling in Finnish aapa mires. Int. J. Appl. Earth Obs. Geoinform. 134, 104201. https://doi.org/10.1016/j.jag.2024.104201 (2024).

    Google Scholar 

  61. Gan, Y., Wang, Q. & Song, G. Structural complexity significantly impacts canopy reflectance simulations as revealed from reconstructed and sentinel-2-monitored scenes in a temperate deciduous forest. Remote Sens. 16(22), 4296. https://doi.org/10.3390/rs16224296 (2024).

    Google Scholar 

  62. Zhang, W., Chen, Y., Shang, X., Xu, F. & Wu, Z. Comparison on SPAD values of leaves in five species of mangroves. Subtrop. Plant Sci. 45(03), 212 (2016).

    Google Scholar 

  63. Jiang, D. et al. A trade-off between leaf carbon economics and plant size among mangrove species in dongzhaigang, China. Ecol. Evol. 14(11), e70559. https://doi.org/10.1002/ece3.70559 (2024).

    Google Scholar 

  64. Wang, T., Zhang, H., Lin, H. & Fang, C. Textural–spectral feature-based species classification of mangroves in Mai Po Nature Reserve from Worldview-3 imagery. Remote Sens. 8(1), 24. https://doi.org/10.3390/rs8010024 (2015).

    Google Scholar 

  65. Sun, Y. et al. Species classification and carbon stock assessment of mangroves in Qi’ao Island with Worldview-3 imagery. Forests 14(12), 2356. https://doi.org/10.3390/f14122356 (2023).

    Google Scholar 

  66. Liu, Y., Zhang, D., Lu, G. Ma, W. Y. Study on texture feature extraction in region-based image retrieval system. In 2006 12th International Multi-Media Modelling Conference, 8-pp. IEEE https://doi.org/10.1109/MMMC.2006.1651329 (2006).

  67. Franklin, S. E., Hall, R. J., Moskal, L. M., Maudie, A. J. & Lavigne, M. B. Incorporating texture into classification of forest species composition from airborne multispectral images. Int. J. Remote Sens. 21, 61–79. https://doi.org/10.1080/014311600210993 (2010).

    Google Scholar 

  68. Purnamasari, E., Kamal, M. & Wicaksono, P. Comparison of vegetation indices for estimating above-ground mangrove carbon stocks using PlanetScope image. Reg. Stud. Mar. Sci. 44, 101730. https://doi.org/10.1016/j.rsma.2021.101730 (2021).

    Google Scholar 

  69. Mariano Neto, M., da Silva, J. B. & de Brito, H. C. Carbon stock estimation in a Brazilian mangrove using optical satellite data. Environ. Monit. Assess. 196(1), 9. https://doi.org/10.1007/s10661-023-12151-3 (2024).

    Google Scholar 

  70. Li, Q. et al. Tree-level carbon stock estimations across diverse species using multi-source remote sensing integration. Comput. Electron. Agric. 231, 109904. https://doi.org/10.1016/j.compag.2025.109904 (2025).

    Google Scholar 

  71. Wu, P. et al. Fine identification and biomass estimation of mangroves based on UAV multispectral and LiDAR. Natl. Remote Sens. Bull 26, 1169–1181 (2022).

    Google Scholar 

  72. Wang, M. et al. Potential of texture metrics derived from high-resolution Pleiades satellite data for quantifying aboveground carbon of Kandelia candel mangrove forests in Southeast China. Wetlands Ecol. Manag. 26, 789–803. https://doi.org/10.1007/s11273-018-9610-2 (2018).

    Google Scholar 

  73. Ruiz-Jaen, M. C. & Potvin, C. Tree diversity explains variation in ecosystem function in a neotropical forest in Panama. Biotropica 42(6), 638–646. https://doi.org/10.1111/j.1744-7429.2010.00631.x (2010).

    Google Scholar 

  74. Chen, S. et al. Higher soil organic carbon sequestration potential at a rehabilitated mangrove comprised of Aegiceras corniculatum compared to Kandelia obovata. Sci. Total Environ. 752, 142279. https://doi.org/10.1016/j.scitotenv.2020.142279 (2021).

    Google Scholar 

  75. Kang, L. et al. Carbon storage potential and influencing factors of mangrove plantation in Kaozhouyang, Guangdong Province, South China. Front. Mar. Sci. 11, 1439266. https://doi.org/10.3389/fmars.2024.1439266 (2025).

    Google Scholar 

  76. Lv, Z., Duan, A. & Zhang, J. Influence of forest age, tree size, and climate factors on biomass and carbon storage allocation in Chinese fir forests. Ecol. Ind. 163, 112096. https://doi.org/10.1016/j.ecolind.2024.112096 (2024).

    Google Scholar 

  77. Stephenson, N. L. et al. Rate of tree carbon accumulation increases continuously with tree size. Nature 507, 90–93. https://doi.org/10.1038/nature12914 (2014).

    Google Scholar 

  78. Rani, V. et al. Carbon stock in biomass pool of fragmented mangrove habitats of Kochi, Southern India. Environ. Sci. Pollut. Res. 30(43), 96746–96762. https://doi.org/10.1007/s11356-023-29069-5 (2023).

    Google Scholar 

  79. Mukherjee, J., Ray, S. & Ghosh, P. B. A system dynamic modeling of carbon cycle from mangrove litter to the adjacent Hooghly estuary, India. Ecol. Model. 252, 185–195. https://doi.org/10.1016/j.ecolmodel.2012.06.036 (2013).

    Google Scholar 

  80. Wang, G. et al. Spatial patterns of biomass and soil attributes in an estuarine mangrove forest (Yingluo Bay, South China). Eur. J. For. Res. 133, 993–1005. https://doi.org/10.1007/s10342-014-0817-3 (2014).

    Google Scholar 

  81. Yu, C. et al. Development of ecosystem carbon stock with the progression of a natural mangrove forest in Yingluo Bay, China. Plant Soil 460, 391–401. https://doi.org/10.1007/s11104-020-04819-3 (2021).

    Google Scholar 

  82. Kamal, M., Hidayatullah, M. F., Mahyatar, P. & Ridha, S. M. Estimation of aboveground mangrove carbon stocks from WorldView-2 imagery based on generic and species-specific allometric equations. Remote Sens. Appl.: Soc. Environ. 26, 100748. https://doi.org/10.1016/j.rsase.2022.100748 (2022).

    Google Scholar 

  83. Salum, R. B., Robinson, S. A. & Rogers, K. A validated and accurate method for quantifying and extrapolating mangrove above-ground biomass using LiDAR data. Remote Sens. 13(14), 2763. https://doi.org/10.3390/rs13142763 (2021).

    Google Scholar 

  84. Jones, A. R. et al. Estimating mangrove tree biomass and carbon content: A comparison of forest inventory techniques and drone imagery. Front. Mar. Sci. 6, 784. https://doi.org/10.3389/fmars.2019.00784 (2020).

    Google Scholar 

  85. Wang, G., Guan, D., Xiao, L. & Peart, M. R. Ecosystem carbon storage affected by intertidal locations and climatic factors in three estuarine mangrove forests of South China. Reg. Environ. Change 19, 1701–1712. https://doi.org/10.1007/s10113-019-01515-6 (2019).

    Google Scholar 

  86. Miraki, M., Sohrabi, H. & Immitzer, M. Tree species mapping in mangrove ecosystems using UAV-RGB imagery and object-based image classification. J. Indian Soc. Remote Sens. 51, 2095–2103. https://doi.org/10.1007/s12524-023-01752-7 (2023).

    Google Scholar 

  87. Zhen, J. et al. Performance of xgboost ensemble learning algorithm for mangrove species classification with multisource spaceborne remote sensing data. J. Remote Sens. 4, 0146. https://doi.org/10.34133/remotesensing.0146 (2024).

    Google Scholar 

  88. Tunca, E., Köksal, E. S., Akay, H., Öztürk, E. & Taner, S. Ç. Novel machine learning framework for high-resolution sorghum biomass estimation using multi-temporal UAV imagery. Int. J. Environ. Sci. Technol. 22, 13673–13688. https://doi.org/10.1007/s13762-025-06498-y (2025).

    Google Scholar 

  89. Wei, L. et al. Wheat biomass, yield, and straw-grain ratio estimation from multi-temporal UAV-based RGB and multispectral images. Biosyst. Eng. 234, 187–205. https://doi.org/10.1016/j.biosystemseng.2023.08.002 (2023).

    Google Scholar 

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Acknowledgments

We thank the funding support of the Program of Science and Technology of Shenzhen (grant numbers KCXFZ20211020165000001), Hainan Province Science and Technology Special Fund (ZDYF2024SHFZ146), and the National Natural Science Foundation of China (grant numbers 42201361). We also like to thank our colleagues from Guangdong mangrove engineering technology research center at Peking University for the assistance with field investigation and data analysis.

Funding

This research was supported by the Program of Science and Technology of Shenzhen (grant numbers KCXFZ20211020165000001), Hainan Province Science and Technology Special Fund (ZDYF2024SHFZ146), and the National Natural Science Foundation of China (grant numbers 42201361).

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Authors and Affiliations

  1. School of Environment and Energy, Shenzhen Graduate School, Peking University, Shenzhen, 518055, China

    Yu Chen, Xiaoxue Shen, Biqian Jiang, Ruili Li & Minwei Chai

  2. Guangdong Mangrove Engineering Technology Research Center, Peking University, Shenzhen, 518055, China

    Yu Chen, Xiaoxue Shen, Biqian Jiang, Ruili Li & Minwei Chai

  3. School of Ecology, Sun Yat-sen University, Guangzhou, 510275, China

    Chunhua Yan

  4. Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, China

    Chunhua Yan

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  1. Yu Chen
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  2. Xiaoxue Shen
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  3. Chunhua Yan
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  4. Biqian Jiang
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  5. Ruili Li
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  6. Minwei Chai
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Yu Chen: Conceptualization, Methodology, Formal analysis, Visualization, Writing-original draft, Writing-review & editing. Xiaoxue Shen: Methodology, Investigation, Writing-review & editing. Chunhua Yan: Methodology, Formal analysis, Visualization, Writing-review & editing. Biqian Jiang: Conceptualization, Methodology, Investigation, Formal analysis, Visualization. Ruili Li: Conceptualization, Methodology, Investigation, Supervision, Writing-review & editing. Minwei Chai: Methodology, Investigation, Writing-review & editing.

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Ruili Li.

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Chen, Y., Shen, X., Yan, C. et al. Triple-feature fusion from UAV multispectral imagery enhances species-level mangrove carbon assessment.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-40303-1

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  • DOI: https://doi.org/10.1038/s41598-026-40303-1

Keywords

  • Carbon stock
  • Mangrove
  • Species level
  • UAV multispectral technology

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