Journal of Northwest Forestry University, Volume. 40, Issue 4, 97(2025)

Estimation of Rubber Forest Biomass Based on Machine Learning Algorithms

ZHAO Yongchen1, HU Wengfeng1,2, WANG Chao2, and TANG Rongnian1、*
Author Affiliations
  • 1School of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, Hainan, China
  • 2School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
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    References(34)

    [2] [2] YANG X Q, BLAGODATSKY S, LIPPE M, et al. Land-use change impact on time-averaged carbon balances: Rubber expansion and reforestation in a biosphere reserve, South-West China[J]. Forest Ecology and Management, 2016, 372: 149-163.

    [4] [4] WALTER J, EDWARDS J, MCDONALD G, et al. Photogrammetry for the estimation of wheat biomass and harvest index[J]. Field Crops Research, 2018, 216: 165-174.

    [5] [5] ZHANG Y Z, MA J, LIANG S L, et al. An evaluation of eight machine learning regression algorithms for forest aboveground biomass estimation from multiple satellite data products[J]. Remote Sensing, 2020, 12(24): 4015.

    [6] [6] LI Y, LI M, LI C, et al. Forest aboveground biomass estimation using Landsat 8 and Sentinel-1 A data with machine learning algorithms[J]. Scientific Reports, 2020, 10(1): 9952.

    [7] [7] PHAM T, YOKOYA N, BUI D, et al. Remote sensing approaches for monitoring mangrove species, structure, and biomass: Opportunities and challenges[J]. Remote Sensing, 2019, 11(3): 230.

    [8] [8] WANG L A, ZHOU X D, ZHU X K, et al. Estimation of biomass in wheat using random forest regression algorithm and remote sensing data[J]. The Crop Journal, 2016, 4(3): 212-219.

    [9] [9] GENG L Y, CHE T, MA M G, et al. Corn biomass estimation by integrating remote sensing and long-term observation data based on machine learning techniques[J]. Remote Sensing, 2021, 13(12): 2352.

    [10] [10] HOSSEINI M, MCNAIRN H, MITCHELL S, et al. Synthetic aperture radar and optical satellite data for estimating the biomass of corn[J]. International Journal of Applied Earth Observation and Geoinformation, 2019, 83: 101933.

    [11] [11] LIANG Y Y, KOU W L, LAI H Y, et al. Improved estimation of aboveground biomass in rubber plantations by fusing spectral and textural information from UAV-based RGB imagery[J]. Ecological Indicators, 2022, 142: 109286.

    [12] [12] HAN L, YANG G, DAI H, et al. Modeling maize aboveground biomass based on machine learning approaches using UAV remote-sensing data[J]. Plant methods, 2019, 15: 1-19.

    [13] [13] HAN L, YANG G J, DAI H Y, et al. Modeling maize aboveground biomass based on machine learning approaches using UAV remote-sensing data[J]. Plant Methods, 2019, 15: 10.

    [14] [14] XU D D, WANG H B, XU W X, et al. LiDAR applications to estimate forest biomass at individual tree scale: Opportunities, challenges and future perspectives[J]. Forests, 2021, 12(5): 550.

    [18] [18] YASEN K, KOEDSIN W. Estimating aboveground biomass of rubber tree using remote sensing in phuket province, Thailand[J]. Journal of Medical and Bioengineering, 2015, 4(6): 451-456.

    [19] [19] CHEN B Q, YUN T, MA J, et al. High-precision stand age data facilitate the estimation of rubber plantation biomass: A case study of Hainan Island, China[J]. Remote Sensing, 2020, 12(23): 3853.

    [20] [20] MOHMED G, HEYNES X, NASER A, et al. Modelling daily plant growth response to environmental conditions in Chinese solar greenhouse using Bayesian neural network[J]. Scientific Reports, 2023, 13: 4379.

    [21] [21] MA Y C, ZHANG Z, KANG Y H, et al. Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach[J]. Remote Sensing of Environment, 2021, 259: 112408.

    [22] [22] DOS SANTOS J P R, FERNANDES S B, MCCOY S, et al. Novel Bayesian networks for genomic prediction of developmental traits in biomassSorghum[J]. G3 Genes Genomes Genetics, 2020, 10(2): 769-781.

    [23] [23] XU X Q, LU J S, ZHANG N, et al. Inversion of rice canopy chlorophyll content and leaf area index based on coupling of radiative transfer and Bayesian network models[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 150: 185-196.

    [24] [24] HAJEB M, HAMZEH S, KAZEM ALAVIPANAH S, et al. Simultaneous retrieval of sugarcane variables from Sentinel-2 data using Bayesian regularized neural network[J]. International Journal of Applied Earth Observation and Geoinformation, 2023, 116: 103168.

    [27] [27] ZHANG L J, ZHANG X X, SHAO Z F, et al. Integrating Sentinel-1 and 2 with LiDAR data to estimate aboveground biomass of subtropical forests in northeast Guangdong, China[J]. International Journal of Digital Earth, 2023, 16(1): 158-182.

    [28] [28] KGANYAGO M, ADJORLOLO C, MHANGARA P. Optimizing Sentinel-2 feature space for improved crop biophysical and biochemical variables retrieval using the novel spectral triad feature selection algorithm[J]. Geocarto International, 2024, 39(1): 2309174.

    [29] [29] KURSA M B, JANKOWSKI A, RUDNICKI W R. Boruta-A system for feature selection[J]. Fundamenta Informaticae, 2010, 101(4): 271-285.

    [30] [30] HEARST M A, DUMAIS S T, Osuna E, et al. Support vector machines[J]. IEEE Intelligent Systems and Their Applications, 1998, 13(4): 18-28.

    [31] [31] RIGATTI S J. Random forest[J]. Journal of Insurance Medicine, 2017, 47(1): 31-39.

    [32] [32] LI S W, CHEN T, WANG L, et al. Effective tourist volume forecasting supported by PCA and improved BPNN using Baidu index[J]. Tourism Management, 2018, 68: 116-126.

    [34] [34] QIN G X, WU J, LI C B, et al. Comparison of the hybrid of radiative transfer model and machine learning methods in leaf area index of grassland mapping[J]. Theoretical and Applied Climatology, 2024, 155(4): 2757-2773.

    [35] [35] ZENG N, REN X L, HE H L, et al. Estimating grassland aboveground biomass on the Tibetan Plateau using a random forest algorithm[J]. Ecological Indicators, 2019, 102: 479-487.

    [36] [36] JOSPIN L V, LAGA H, BOUSSAID F, et al. Hands-on Bayesian neural networks: A tutorial for deep learning users[J]. IEEE Computational Intelligence Magazine, 2022, 17(2): 29-48.

    [37] [37] UNIYAL S, PUROHIT S, CHAURASIA K, et al. Quantification of carbon sequestration by urban forest using Landsat 8 OLI and machine learning algorithms in Jodhpur, India[J]. Urban Forestry & Urban Greening, 2022, 67: 127445.

    [38] [38] RANA P, POPESCU S, TOLVANEN A, et al. Estimation of tropical forest aboveground biomass in Nepal using multiple remotely sensed data and deep learning[J]. International Journal of Remote Sensing, 2023, 44(17): 5147-5171.

    [39] [39] HYTNEN J, NURMI J, KAAKKURIVAARA N, et al. Rubber tree (Hevea brasiliensis) biomass, nutrient content, and heating values in southern Thailand[J]. Forests, 2019, 10(8): 638.

    [40] [40] CHEN L, REN C Y, ZHANG B, et al. Estimation of forest above-ground biomass by geographically weighted regression and machine learning with sentinel imagery[J]. Forests, 2018, 9(10): 582.

    [41] [41] MASJEDI A, CRAWFORD M M, CARPENTER N R, et al. Multi-temporal predictive modelling ofSorghumbiomass using UAV-based hyperspectral and LiDAR data[J]. Remote Sensing, 2020, 12(21): 3587.

    [42] [42] MUSTHAFA M, SINGH G. Improving forest above-ground biomass retrieval using multi-sensor L- and C- band SAR data and multi-temporal spaceborne LiDAR data[J]. Frontiers in Forests and Global Change, 2022, 5: 822704.

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    ZHAO Yongchen, HU Wengfeng, WANG Chao, TANG Rongnian. Estimation of Rubber Forest Biomass Based on Machine Learning Algorithms[J]. Journal of Northwest Forestry University, 2025, 40(4): 97

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    Paper Information

    Received: Jun. 3, 2024

    Accepted: Sep. 12, 2025

    Published Online: Sep. 12, 2025

    The Author Email: TANG Rongnian (rn.tang@hainanu.edu.cn)

    DOI:10.3969/j.issn.1001-7461.2025.04.11

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