Remote Sensing Estimation of Forest Aboveground Biomass Based on Lasso-SVR

Wang, Ping; Tan, Sanqing; Gui, Zhilun; Wang, Shuang; Wu, Xin

doi:10.3390/f13101597

Cited by 13 publications

(5 citation statements)

References 34 publications

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“…This is in contrast to the studies conducted by other scholars on the correlation between the construction of the aboveground biomass estimation model and the remotely sensed characterization factors or the combination of remotely sensed characterization factors and the point cloud characterization variables. For example, Wang et al [39] extracted vegetation indices based on Sentinel2 image data to construct an aboveground biomass estimation model, and Du et al [40] extracted point cloud feature variables such as canopy cover and leaf area index, as well as texture features such as variance and mean, to construct an aboveground biomass model based on Landsat image data and airborne LiDAR point cloud data; the methodology of this paper has great potential to study the direct correlation between point cloud feature variables and carbon stock.…”

Section: Discussionmentioning

confidence: 99%

Research on Estimation Model of Carbon Stock Based on Airborne LiDAR and Feature Screening

Liu,

Wang,

Shi

et al. 2024

Sustainability

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The rapid and accurate estimation of forest carbon stock is important for analyzing the carbon cycle. In order to obtain forest carbon stock efficiently, this paper utilizes airborne LiDAR data to research the applicability of different feature screening methods in combination with machine learning in the carbon stock estimation model. First, Spearman’s Correlation Coefficient (SCC) and Extreme Gradient Boosting tree (XGBoost) were used to screen out the variables that were extracted via Airborne LiDAR with a higher correlation with carbon stock. Then, Bagging, K-nearest neighbor (KNN), and Random Forest (RF) were used to construct the carbon stock estimation model. The results show that the height statistical variable is more strongly correlated with carbon stocks than the density statistical variables are. RF is more suitable for the construction of the carbon stock estimation model compared to the instance-based KNN algorithm. Furthermore, the combination of the XGBoost algorithm and the RF algorithm performs best, with an R2 of 0.85 and an MSE of 10.74 on the training set and an R2 of 0.53 and an MSE of 21.81 on the testing set. This study demonstrates the effectiveness of statistical feature screening methods and Random Forest for carbon stock estimation model construction. The XGBoost algorithm has a wider applicability for feature screening.

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Section: Discussionmentioning

confidence: 99%

Research on Estimation Model of Carbon Stock Based on Airborne LiDAR and Feature Screening

Liu,

Wang,

Shi

et al. 2024

Sustainability

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“…The Pearson correlation coefficient is a measure of linear correlation between two variables and is a filter for indirectly assessing regression problems [59]. The closer its value is to 1 (−1), the stronger positive (negative) correlation between the two variables [60].…”

Section: Variable Selection Methodsmentioning

confidence: 99%

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

Sa,

Nie,

Chumachenko

et al. 2024

Remote Sensing

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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.

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“…Accuracy assessment for the spaceborne platform datasets was carried out for datasets, and a regression model on the article's coefficient of determination (R 2 ) was used. Fortyeight (48) articles [10,16,17,63,[65][66][67][68][69][70][71][72][73][74][75][76][78][79][80]82,[84][85][86]88] were employed for further analysis, as they had R 2 values among the total classified articles (54). The error bar plot of R 2 and the regression model used, as shown in Figure 14, showed that the random forest regression model ( 21) was the only model with a reasonable number for analysis, followed by linear regression (5).…”

Section: Spaceborne Analysismentioning

confidence: 99%

Forest Aboveground Biomass Estimation and Inventory: Evaluating Remote Sensing-Based Approaches

Khan,

Tan,

Gul

et al. 2024

Forests

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Remote sensing datasets offer robust approaches for gaining reliable insights into forest ecosystems. Despite numerous studies reviewing forest aboveground biomass estimation using remote sensing approaches, a comprehensive synthesis of synergetic integration methods to map and estimate forest AGB is still needed. This article reviews the integrated remote sensing approaches and discusses significant advances in estimating the AGB from space- and airborne sensors. This review covers the research articles published during 2015–2023 to ascertain recent developments. A total of 98 peer-reviewed journal articles were selected under the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Among the scrutinized studies, 54 were relevant to spaceborne, 22 to airborne, and 22 to space- and airborne datasets. Among the empirical models used, random forest regression model accounted for the most articles (32). The highest number of articles utilizing integrated dataset approaches originated from China (24), followed by the USA (15). Among the space- and airborne datasets, Sentinel-1 and 2, Landsat, GEDI, and Airborne LiDAR datasets were widely employed with parameters that encompassed tree height, canopy cover, and vegetation indices. The results of co-citation analysis were also determined to be relevant to the objectives of this review. This review focuses on dataset integration with empirical models and provides insights into the accuracy and reliability of studies on AGB estimation modeling.

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Remote Sensing Estimation of Forest Aboveground Biomass Based on Lasso-SVR

Cited by 13 publications

References 34 publications

Research on Estimation Model of Carbon Stock Based on Airborne LiDAR and Feature Screening

Research on Estimation Model of Carbon Stock Based on Airborne LiDAR and Feature Screening

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

Forest Aboveground Biomass Estimation and Inventory: Evaluating Remote Sensing-Based Approaches

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