Biomass Estimation and Saturation Value Determination Based on Multi-Source Remote Sensing Data

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 12; p. 2250
• Main Authors: Sa, Rula, Nie, Yonghui, Chumachenko, Sergey, Fan, Wenyi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2024
• Subjects: aboveground biomass; AGB; Algorithms; ALOS2 PALSAR; Artificial neural networks; Biomass; Carbon; Carbon cycle; China; Coniferous forests; Correlation coefficient; Correlation coefficients; Ecosystems; Environmental factors; Estimation; Forest biomass; forests; Landsat; Landsat 8; Machine learning; Neural networks; Regression analysis; Remote sensing; Saturation; Sentinel-2A; Support vector machines; Synthetic aperture radar; Variables; Vegetation; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16122250

Forest biomass estimation is undoubtedly one of the most pressing research subjects at present. Combining multi-source remote sensing information can give full play to the advantages of different remote sensing technologies, providing more comprehensive and rich information for aboveground biomass (AGB) estimation research. Based on Landsat 8, Sentinel-2A, and ALOS2 PALSAR data, this paper takes the artificial coniferous forests in the Saihanba Forest of Hebei Province as the object of study, fully explores and establishes remote sensing factors and information related to forest structure, gives full play to the advantages of spectral signals in detecting the horizontal structure and multi-dimensional synthetic aperture radar (SAR) data in detecting the vertical structure, and combines environmental factors to carry out multivariate synergistic methods of estimating the AGB. This paper uses three variable selection methods (Pearson correlation coefficient, random forest significance, and the least absolute shrinkage and selection operator (LASSO)) to establish the variable sets, combining them with three typical non-parametric models to estimate AGB, namely, random forest (RF), support vector regression (SVR), and artificial neural network (ANN), to analyze the effect of forest structure on biomass estimation, explore the suitable AGB of artificial coniferous forests estimation of machine learning models, and develop the method of quantifying saturation value of the combined variables. The results show that the horizontal structure is more capable of explaining the AGB compared to the vertical structure information, and that combining the multi-structure information can improve the model results and the saturation value to a great extent. In this study, different sets of variables can produce relatively superior results in different models. The variable set selected using LASSO gives the best results in the SVR model, with an R2 values of 0.9998 and 0.8792 for the training and the test set, respectively, and the highest saturation value obtained is 185.73 t/ha, which is beyond the range of the measured data. The problem of saturation in biomass estimation in boreal medium- and high-density forests was overcome to a certain extent, and the AGB of the Saihanba area was better estimated.