Skip to main content
SpringerLink
Log in

Mapping the Above-Ground Biomass of Rhizophora apiculata plantation Forests Using PlanetScope Imagery in Thanh Phu Nature Reserve, Vietnam

  • ECOLOGY
  • Published:
Biology Bulletin Aims and scope Submit manuscript

Abstract

Estimating forest biomass is a necessary task to assess the carbon sequestration potential and storage, which serves to evaluate the environmental value of forests. It is possible to develop models for predicting forest biomass by combining remote sensing, field data and statistical models. In this study, we assessed the feasibility of estimating aboveground biomass (AGB) of Rhizophora apiculata plantation forests in Thanh Phu Nature Reserve by combining PlanetScope imagery and field inventory data obtained from 55 sample plots. Furthermore, we investigated the potential to enhance the accuracy of AGB estimation by applying four statistical models, generalized linear model (GLM), generalized exponential model (GEM), support vector machine (SVM) and random forest (RF). Our results showed that incorporating gray—level co-occurrence matrix (GLCM) texture features led to a more robust AGB estimation compared to using only spectral bands or vegetation indices. The RF model achieved the highest accuracy of AGB estimation based on the combination of spectral bands, vegetation indices and the optimum texture features, with an R2 value of 0.77. In addition, the spectral and texture features of the green and near-infrared bands were also important in predicting AGB. Finally, the results indicated that PlanetScope imagery has a great potential for mapping the AGB of R. apiculata plantations in mangrove forests, with relatively good accuracy (e.g., 78.26 and 86.36% for low and high values, respectively).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

Similar content being viewed by others

REFERENCES

  1. Alongi, D.M., Carbon sequestration in mangrove forests, Carbon Manage., 2012, vol. 3, no. 3, pp. 313–322.

    Article  CAS  Google Scholar 

  2. Argamosa, R.J.L., Blanco, A.C., Baloloy, A.B., Candido, C.G., Dumalag, J.B.L.C., Dimapilis, L.L.C., and Paringit, E.C., Modelling above ground biomass of mangrove forest using Sentinel-1 imagery, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018, vol. 4, pp. 13–20.

    Google Scholar 

  3. Avitabile, V., Herold, M., Lewis, S.L., Phillips, O.L., Aguilar-Amuchastegui, N., Asner, G.P., Brienen, R.J.W., DeVries, B.R., Gatti, R.G., Feldpausch, T.R., and Girardin, C., Comparative analysis and fusion for improved global biomass mapping, in Int. Conf. GV2M, Avignon, France, 2014, pp. 251–252.

  4. Asner, G.P., Knapp, D.E., Martin, R.E., Tupayachi, R., Anderson, C.B., Mascaro, J., Sinca, F., Chadwick, K.D., Higgins, M., Farfan, W., and Llactayo, W., Targeted carbon conservation at national scales with high-resolution monitoring. Proc. Natl. Acad. Sci. U. S. A., 2014, vol. 111, no. 47, pp. 5016–5022.

    Article  ADS  Google Scholar 

  5. Asner, G.P. and Mascaro, J., Mapping tropical forest carbon. Calibrating plot estimates to a simple LiDAR metric, Remote Sens. Environ., 2014, vol. 140, pp. 614–624.

    Article  ADS  Google Scholar 

  6. Asari, N., Suratman, M.N., Mohd Ayob, N.A., and Abdul Hamid, N.H., Mangrove as a natural barrier to environmental risks and coastal protection, in Mangroves, Ecology, Biodiversity and Management, 2021, pp. 305–322.

    Google Scholar 

  7. Baloloy, A.B., Blanco, A.C., Candido, C.G., Argamosa, R.J.L., Dumalag, J.B.L.C., Dimapilis, L.L.C., and Paringit, E.C., Estimation of mangrove forest aboveground biomass using multispectral bands, vegetation indices and biophysical variables derived from optical satellite imageries, Rapideye, planetscope and sentinel-2, ISPRS Ann. Photogram. Remote Sens. Spatial Inf. Sci., 2018, vol. 4, pp. 29–36.

    Article  Google Scholar 

  8. Breiman, L., Random forests, Machine Learning, 2001, vol. 45, pp. 5–32.

    Article  Google Scholar 

  9. Castillo, J.A.A., Apan, A.A., Maraseni, T.N., and Salmo, S.G., Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery, ISPRS J. Photogram. Remote Sens., 2017, vol. 134, pp. 70–85.

    Article  ADS  Google Scholar 

  10. Bao, T.Q., Ha, N.T., Nguyet, B.T.M., Hoan, V. M., Viet, L.H., and Hung, D.V., Aboveground biomass and carbon stock of Rhizophora apiculata forest in Ca Mau, Vietnam, Biodiversitas, J. Biol. Diversity, 2022, vol. 23, no. 1, pp. 403–414.

    Google Scholar 

  11. Christensen, B.O., Biomass and primary production of Rhizophora apiculata Bl. in a mangrove in southern Thailand, Aquat. Bot., 1978, vol. 4, pp. 43–52.

    Article  Google Scholar 

  12. Chatting, M., Al-Maslamani, I., Walton, M., Skov, M.W., Kennedy, H., Husrevoglu, Y.S., and LeVay, L., Future mangrove carbon storage under climate change and deforestation, Front. Mar. Sci., 2022, vol. 9, pp. 58–72.

    Article  Google Scholar 

  13. Chave, J., Andalo, C., Brown, S., Cairns, M.A., Chambers, J.Q., Eamus, D., Fölster, H., Fromard, F., Higuchi, N., Kir, T., and Lescure, J.P., Tree allometry and improved estimation of carbon stocks and balance in tropical forests, Oecologia, 2005, vol. 145, pp. 87–99.

    Article  CAS  PubMed  ADS  Google Scholar 

  14. Chen, H., Qin, Z., Zhai, D.L., Ou, G., Li, X., Zhao, G., Fan, J., Zhao, C., and Xu, H., Mapping forest aboveground biomass with MODIS and Fengyun-3C VIRR imageries in Yunnan Province, Southwest China using Linear Regression, K-Nearest Neighbor and Random Forest, Remote Sensing, 2022, vol. 14, no. 21, p. 5456.

    Article  ADS  Google Scholar 

  15. Chopping, M., Schaaf, C.B., Zhao, F., Wang, Z., Nolin, A.W., Moisen, G.G., Martonchik, J.V., and Bull, M., Forest structure and aboveground biomass in the southwestern United States from MODIS and MISR, Remote Sens. Environ., 2011, vol. 115, no. 11, pp. 2943–2953.

    Article  ADS  Google Scholar 

  16. Csillik, O., Kumar, P., Mascaro, J., O’Shea, T., and Asner, G.P., Monitoring tropical forest carbon stocks and emissions using Planet satellite data, Sci. Rep., 2019, vol. 9, no. 1, pp. 1–12.

    Article  CAS  Google Scholar 

  17. Cuc, N.T.K. and Steveninck, E.D., Production function of planted mangroves in Thanh Phu nature reserve, Mekong delta, Vietnam, J. Coastal Res., 2015, vol. 31, no. 5, pp. 1084–1090.

    Article  Google Scholar 

  18. Dube, T. and Mutanga, O., Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment, South Africa, ISPRS J. Photogram. Remote Sens., 2015, vol. 101, pp. 36–46.

    Article  ADS  Google Scholar 

  19. Dang, A.T.N., Nandy, S., Srinet, R., Luong, N.V., Ghosh, S., and Kumar, A.S., Forest aboveground biomass estimation using machine learning regression algorithm in Yok Don National Park, Vietnam, Ecol. Inf., 2019, vol. 50, pp. 24–32.

    Article  Google Scholar 

  20. Dantas, D., Terra, M.D.C.N.S., Schorr, L.P.B., and Calegario, N., Machine learning for carbon stock prediction in a tropical forest in Southeastern Brazil, Rev. Bosq., 2021, vol. 42, no. 1, pp. 131–140.

    Article  Google Scholar 

  21. FAO, Global Forest Resources Assessment 2020, Main Report, Rome, 2020.

  22. Feng, Y., Lu, D., Chen, Q., Keller, M., Moran, E., dos-Santos, M.N., Bolfe, E.L., and Batistella, M., Examining effective use of data sources and modeling algorithms for improving biomass estimation in a moist tropical forest of the Brazilian Amazon, Int. J. Digital Earth, 2017, vol. 10, no. 10, pp. 996–1016.

    Article  ADS  Google Scholar 

  23. Fischer, F.J., Maréchaux, I., and Chave, J., Improving plant allometry by fusing forest models and remote sensing, New Phytol., 2019, vol. 223, no. 3, pp. 1159–1165.

    Article  PubMed  Google Scholar 

  24. Gizachew, B., Solberg, S., Næsset, E., Gobakken, T., Bollandsås, O.M., Breidenbach, J., Zahabu, E., and Mauya, E.W., Mapping and estimating the total living biomass and carbon in low-biomass woodlands using Landsat 8 CDR data, Carbon Balance Manage., 2016, vol. 11, no. 1, pp. 1–14.

    Article  Google Scholar 

  25. Gitelson, A.A., Kaufman, Y.J., and Merzlyak, M.N., Use of a green channel in remote sensing of global vegetation from EOS-MODIS, Remote Sensing Environ., 1996, vol. 58, no. 3, pp. 289–298.

    Article  ADS  Google Scholar 

  26. Gao, Y., Lu, D., Li, G., Wang, G., Chen, Q., Liu, L., and Li, D., Comparative analysis of modeling algorithms for forest aboveground biomass estimation in a subtropical region, Remote Sensing, 2018, vol. 10, no. 4, p. 627.

    Article  ADS  Google Scholar 

  27. Hijmans, R.J., Van Etten, J., Cheng, J., Mattiuzzi, M., Sumner, M., Greenberg, J.A., and Hijmans, M.R.J., Package ‘raster,’ R package, 2015.

  28. Huete, A.R., A soil-adjusted vegetation index (SAVI), Remote Sensing Environ., 1988, vol. 25, no. 3, pp. 295–309.

    Article  ADS  Google Scholar 

  29. Hojas, G.L., Ceccherini, G., Garcia Haro, F.J., Avitabile, V., and Eva, H., The potential of high resolution (5 m) RapidEye optical data to estimate above ground biomass at the national level over Tanzania, Forests, 2019, vol. 10, no. 2, p. 107.

    Article  Google Scholar 

  30. Jachowski, N.R., Quak, M.S., Friess, D.A., Duangnamon, D., Webb, E.L., and Ziegler, A.D., Mangrove biomass estimation in Southwest Thailand using machine learning, Appl. Geogr., 2013,vol. 45, pp. 311–321.

    Article  Google Scholar 

  31. Komiyama, A., Poungparn, S., and Kato, S., Common allometric equations for estimating the tree weight of mangroves, J. Trop. Ecol., 2005, vol. 21, no. 4, pp. 471–477.

    Article  Google Scholar 

  32. Kauffman, J.B., Heider, C., Cole, T.G., Dwire, K.A., and Donato, D.C., Ecosystem carbon stocks of Micronesian mangrove forests, Wetlands, 2011, vol. 31, pp. 343–352.

    Article  Google Scholar 

  33. Kiyono, Y., Saito, S., Takahashi, T., Toriyama, J., Awaya, Y., Asai, H., Furuya, N., Ochiai, Y., Inoue, Y., Sato, T., and Sophal, C., Practicalities of non-destructive methodologies in monitoring anthropogenic greenhouse gas emissions from tropical forests under the influence of human intervention, Jpn. Agric. Res. Q., JARQ, 2011, vol. 45, no. 2, pp. 233–242.

    Article  Google Scholar 

  34. Liu, K., Wang, J., Zeng, W., and Song, J., Comparison and evaluation of three methods for estimating forest above ground biomass using TM and GLAS data, Remote Sensing, 2017, vol. 9, no. 4, p. 341.

    Article  ADS  Google Scholar 

  35. Lee, S.Y., Primavera, J.H., Dahdouh-Guebas, F., McKee, K., Bosire, J.O., Cannicci, S., Diele, K., Fromard, F., Koedam, N., Marchand, C., and Mendelssohn, I., Ecological role and services of tropical mangrove ecosystems, a reassessment, Global Ecol. Biogeogr., 2014, vol. 23, no. 7, pp. 726–743.

    Article  Google Scholar 

  36. Leutner, B., Horning, N., Schwalb-Willmann, J., and Hijmans, R.J., RStoolbox, tools for remote sensing data analysis, R package version 0.2, 2019.

  37. López-Serrano, P.M., López-Sánchez, C.A., Álvarez-González, J.G., and García-Gutiérrez, J., A comparison of machine learning techniques applied to landsat-5 TM spectral data for biomass estimation, Can. J. Remote Sensing, 2016, vol. 42, no. 6, pp. 690–705.

    Article  ADS  Google Scholar 

  38. Marelign, M.A., Estimating and mapping woodland biomass and carbon using Landsat 8 vegetation index, a case study in Dirmaga Watershed, Ethiopia, Comput. Ecol. Software, 2022, vol. 12, no. 2, p. 67.

    Google Scholar 

  39. Mascaro, J., Asner, G.P., Knapp, D.E., Kennedy-Bowdoin, T., Martin, R.E., Anderson, C., Higgins, M., and Chadwick, K.D., A tale of two “forests,” Random Forest machine learning aids tropical forest carbon mapping, PLoS One, 2014, vol. 9, no. 1, p. e85993.

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  40. Miller, G.J., Morris, J.T., and Wang, C., Estimating aboveground biomass and its spatial distribution in coastal wetlands utilizing planet multispectral imagery, Remote Sensing, 2019, vol. 11, no. 17, pp. 2020.

    Article  ADS  Google Scholar 

  41. Moradi, F., Sadeghi, S.M.M., Heidarlou, H.B., Deljouei, A., Boshkar, E., and Borz, S.A., Above-ground biomass estimation in a Mediterranean sparse coppice oak forest using Sentinel-2 data, Ann. For. Res., 2022, vol. 65, no. 1, pp. 165–182.

    Article  Google Scholar 

  42. Mutanga, O., Adam, E., and Cho, M.A., High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm, Int. J. App-l. Earth Observ. Geoinf., 2012, vol. 18, pp. 399–406.

    Google Scholar 

  43. Nandy, S., Ghosh, S., Kushwaha, S.P.S., and Senthil Kumar, A., Remote sensing-based forest biomass assessment in northwest Himalayan landscape, in Remote Sensing of Northwest Himalayan Ecosystems, Navalgund, R., Kumar, A., and Nandy, S., Eds., Singapore: Springer, 2019.

    Google Scholar 

  44. Nguyen, H.H., Vu, H.D., and Achim, R.Ã., Estimation of above-ground mangrove biomass using landsat-8 data-derived vegetation indices, a case study in Quang Ninh province, Vietnam, For. Soc., 2021, vol. 5, no. 2, pp. 506–525.

    Google Scholar 

  45. Ong, J.E., Gong, W.K., and Wong, C.H., Allometry and partitioning of the mangrove, Rhizophora apiculata, For. Ecol. Manage., 2004, vol. 188, nos. 1–3, pp. 395–408.

    Article  Google Scholar 

  46. Pang, Z., Zhang, G., Tan, S., Yang, Z., and Wu, X., Improving the accuracy of estimating forest carbon density using the tree species classification method, Forests, 2022, vol. 13, no. 12, p. 2004.

    Article  Google Scholar 

  47. Purnamasari, E., Kamal, M., and Wicaksono, P., Comparison of vegetation indices for estimating above-ground mangrove carbon stocks using PlanetScope image, Region. Stud. Mar. Sci., 2021, vol. 44, p. 101730.

    Article  Google Scholar 

  48. Pandey, S.K., Chand, N., Nandy, S., Muminov, A., Sharma, A., Ghosh, S., and Srinet, R., High-resolution mapping of forest carbon stock using Object-Based Image Analysis (OBIA) technique, J. Indian Soc. Remote Sensing, 2020, vol. 48, pp. 865–875.

    Article  Google Scholar 

  49. Pham, L.T. and Brabyn, L., Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms, ISPRS J. Photogram. Remote Sensing, 2017, vol. 128, pp. 86–97.

    Article  ADS  Google Scholar 

  50. Pham, T.D., Yokoya, N., Xia, J., Ha, N.T., Le, N.N., Nguyen, T.T.T., Dao, T.H., Vu, T.T.P., Pham, T.D., and Takeuchi, W., Comparison of machine learning methods for estimating mangrove above-ground biomass using multiple source remote sensing data in the Red River Delta Biosphere Reserve, Vietnam, Remote Sensing, 2020a, vol. 12, no. 8, p. 1334.

    Article  ADS  Google Scholar 

  51. Pham, T.D., Le, N.N., Ha, N.T., Nguyen, L.V., Xia, J., Yokoya, N., To, T.T., Trinh, H.X., Kieu, L.Q., and Takeuchi, W., Estimating mangrove above-ground biomass using extreme gradient boosting decision trees algorithm with fused sentinel-2 and ALOS-2 PALSAR-2 data in can Gio biosphere reserve, Vietnam, Remote Sensing, 2020b, vol. 12, no. 5, p. 777.

    Article  ADS  Google Scholar 

  52. Reis, A.A., Werner, J.P., Silva, B.C., Figueiredo, G.K., Antunes, J.F., Esquerdo, J.C., Coutinho, A.C., Lamparelli, R.A., Rocha, J.V., and Magalhães, P.S., Monitoring pasture aboveground biomass and canopy height in an integrated crop–livestock system using textural information from PlanetScope imagery, Remote Sensing, 2020a, vol. 12, no.16, p. 2534.

    Article  ADS  Google Scholar 

  53. Reis, A.A., Silva, B.C., Werner, J.P.S., Silva, Y.F., Rocha, J.V., Figueiredo, G.K.D.A., Antunes, J.F.G., Esquerdo, J.C.D.M., Coutinho, A.C., Lamparelli, R.A.C., and Magalhães, P.S.G., Exploring the potential of high-resolution planetscope imagery for pasture biomass estimation in an integrated crop–livestock system, in 2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference (LAGIRS), 2020b, pp. 675–680.

    Google Scholar 

  54. Rouse, J.W., Jr., Haas, R.H., Deering, D.W., Schell, J.A., and Harlan, J.C., Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation, 1974, US government work.

  55. Rudianto, R., Bengen, D.G., and Kurniawan, F., Causes and effects of mangrove ecosystem damage on carbon stocks and absorption in East Java, Indonesia, Sustainability, 2020, vol. 12, no. 24, p. 10319.

    Article  Google Scholar 

  56. Sinha, S., Jeganathan, C., Sharma, L.K., and Nathawat, M.S., A review of radar remote sensing for biomass estimation, Int. J. Environ. Sci. Technol., 2015, vol. 12, pp. 1779–1792.

    Article  Google Scholar 

  57. Safari, A., Sohrabi, H., Powell, S., and Shataee, S., A comparative assessment of multi-temporal Landsat 8 and machine learning algorithms for estimating aboveground carbon stock in coppice oak forests, Int. J. Remote Sensing, 2017, vol. 38, no. 22, pp. 6407–6432.

    Article  ADS  Google Scholar 

  58. Solórzano, J.V., Gallardo-Cruz, J.A., González, E.J., Peralta-Carreta, C., Hernández-Gómez, M., Fernández-Montes, de Oca A., and Cervantes-Jiménez, L.G., Contrasting the potential of Fourier transformed ordination and gray level co-occurrence matrix textures to model a tropical swamp forest’s structural and diversity attributes, J. Appl. Remote Sensing, 2018, vol. 12, no. 3, р. 036006.

  59. Theilade, I., Rutishauser, E., and Poulsen, M.K., Community assessment of tropical tree biomass, challenges and opportunities for REDD+, Carbon Balance Manage., 2015, vol. 10, pp. 1–8.

    Article  Google Scholar 

  60. Turton, A.E., Augustin, N.H., and Mitchard, E.T., Improving estimates and change detection of forest above-ground biomass using statistical methods, Remote Sensing, 2022, vol. 14, no. 19, p. 4911.

    Article  ADS  Google Scholar 

  61. UNFCCC, Report of the Conference of the Parties on its Nineteenth Session, held in Warsaw from 11 to 23 November 2013, in United Nations Framework Convention on Climate Change, 2014.

  62. Vorster, A.G., Evangelista, P.H., Stovall, A.E., and Ex S., Variability and uncertainty in forest biomass estimates from the tree to landscape scale. The role of allometric equations, Carbon Balance Manage., 2020, vol. 15, pp. 1–20.

    Article  Google Scholar 

  63. Xu, C., Ding, Y., Zheng, X., Wang, Y., Zhang, R., Zhang, H., Dai, Z., Xie, Q., A comprehensive comparison of machine learning and feature selection methods for maize biomass estimation using Sentinel-1 SAR, Sentinel-2 vegetation indices, and biophysical variables, Remote Sensing, 2022, vol. 14, no. 16, p. 4083.

    Article  ADS  Google Scholar 

  64. Yin, G., Zhang, Y., Sun, Y., Wang, T., Zeng, Z., and Piao, S., MODIS based estimation of forest aboveground biomass in China, PLoS One, 2015, vol. 10, no. 6, p. e0130143.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Zhao, P., Lu, D., Wang, G., Liu, L., Li, D., Zhu, J., and Yu, S., Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data, Int. J. Appl. Earth Observ. Geoinf., 2016, vol. 53, pp. 1–15.

    ADS  Google Scholar 

  66. Zhang, Y., Ma, J., Liang, S., Li, X., and Liu, J., A stacking ensemble algorithm for improving the biases of forest aboveground biomass estimations from multiple remotely sensed datasets, GIScience Remote Sens., 2022, vol. 59, no. 1, pp. 234–249.

    Article  Google Scholar 

  67. Zuur, A.F., Hilbe, J.M., and Ieno, E.N., A Beginner’s Guide to GLM and GLMM with R, Beginner’s Guide Series, Newburgh: Highland Statistics Limited, 2013.

Download references

ACKNOWLEDGMENTS

We are deeply grateful to the Mr. Dao Van Hai for his collaboration and help during the fieldwork. We are also thankful to all the participants in this study, particularly for their time and patience. We thank the contribution of anonymous reviewers who helped to improve this manuscript.

Funding

This research is funded by Vietnam National University of Forestry at Dongnai under grant no. 28/QĐ-PHĐHLNKHCN&HTQT.

Author information

Authors and Affiliations

  1. Vietnam National University of Forestry at Dongnai, Dongnai Province, Vietnam

    Kieu Manh Huong & Nguyen Thanh Tuan

  2. School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, BS8 1TQ, Bristol, UK

    Diego I. Rodríguez-Hernández

Authors
  1. Kieu Manh Huong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Diego I. Rodríguez-Hernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nguyen Thanh Tuan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

N.T.T. and K.M.H conceived the study, conducted the fieldwork, did all analysis and led the writing; N.T.T., K.M.H. and D.I.R.-H. wrote the manuscript.

Corresponding author

Correspondence to Nguyen Thanh Tuan.

Ethics declarations

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

This work does not contain any studies involving human and animal subjects.

CONFLICT OF INTEREST

The authors of this work declare that they have no conflicts of interest.

Additional information

Publisher’s Note.

Pleiades Publishing remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kieu Manh Huong, Rodríguez-Hernández, D.I. & Tuan, N.T. Mapping the Above-Ground Biomass of Rhizophora apiculata plantation Forests Using PlanetScope Imagery in Thanh Phu Nature Reserve, Vietnam. Biol Bull Russ Acad Sci 50 (Suppl 3), S450–S461 (2023). https://doi.org/10.1134/S1062359023601957

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1062359023601957

Keywords:

Search

Navigation