A comparative analysis of modeling approaches and canopy height-based data sources for mapping forest growing stock volume in a northern subtropical ecosystem of China

LIN, Wenke; Lu, Yagang; Li, Guiying; Jiang, Xiandie; Lu, Dengsheng

doi:10.1080/15481603.2022.2044139

Cited by 10 publications

(6 citation statements)

References 83 publications

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“…LASSO uses constraint forms to identify smaller subsets of estimated variance and predictor variables with good variable selection and regularization capabilities [103]. The results show that optimal results were obtained for the LASSO variable set, which indicates that LASSO is a more efficient variable selection method [62,104].…”

Section: Analysis Of the Model Resultsmentioning

confidence: 96%

“…LASSO is a regression method that performs both regularization and variable selection to improve the prediction accuracy and enhance the interpretability of the model. It removes the coefficients of some ineffective variables from the model by making them smaller or even compressing them to 0 to deal with multi-collinearity and retains only the most useful features [62].…”

Section: Variable Selection Methodsmentioning

confidence: 99%

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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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Section: Analysis Of the Model Resultsmentioning

confidence: 96%

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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“…These unique LiDAR properties make it more attractive than other high-resolution sensors for challenging forest stands [26][27][28]. For modeling forest properties, variables are typically extracted from airborne LiDAR data using two common methods [29]: (a) extracting height metrics, density metrics, and forest profile features directly from point clouds [30][31][32][33] and (b) extracting statistics from canopy height model (CHM) data, which is the distinction between a digital surface model (DSM) and a digital terrain model (DTM) derived from LIDAR data [34,35]. Despite these contributions, LiDAR data are cost-prohibitive, thus limiting its use in nations with limited resources.…”

Section: Historical Research Trendsmentioning

confidence: 99%

Remote Sensing Applications in Monitoring Poplars: A Review

Mapuru,

Xulu,

Gebreslasie

2023

Forests

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Given the ability of remote sensing to detect distinctive plant traits, it has emerged in recent decades as a useful and attractive research tool for forest trees such as poplars. Although poplars have been extensively studied using remote sensing over the past thirty years, no reviews have been conducted to understand the results of multiple applications. Here, we present a review and synthesis of poplar studies in this regard. We searched the Scopus, Google Scholar, and Science Direct databases and found 266 published articles, of which 148 were eligible and analyzed. Our results show a rapid increase in remote sensing-based poplar publications over the period of 1991–2022, with airborne platforms, particularly LiDAR, being predominantly used, followed by satellite and ground-based sensors. Studies are widespread in the Global North, accounting for more than two-thirds of studies. The studies took place mainly in agricultural landscapes, followed by forest areas and riparian areas, with a few in mountain and urban areas. Commonly studied biophysical parameters were mostly obtained from LiDAR data. On the other hand, spectral indicators have been widely used to monitor the health and vitality of poplar trees, integrating various machine learning algorithms. Overall, remote sensing has been widely used in poplar studies, and the increasing use of free satellite data and processing platforms is expected to pave the way for data-poor countries to monitor poplar in the Global South, where resources are mainly limited.

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“…In recent years, remote sensing technology has been widely applied in forest surveys across large areas, and a variety of remotely sensed data including optical, Remote Sens. 2022, 14, 4410 2 of 14 radar, and Lidar with different modeling approaches from simple linear regression to complicated deep learning have been used to estimate forest attributes [2][3][4][5][6]. No matter what remotely sensed data or algorithms are used, sample plot data aggregated from single trees are required for both model development and model validation [7].…”

Section: Introductionmentioning

confidence: 99%

“…No matter what remotely sensed data or algorithms are used, sample plot data aggregated from single trees are required for both model development and model validation [7]. By building relationships between remote sensing-derived variables and forest attributes of in situ plots, the continuous distribution of the forest attributes in a study area can be predicted [6,8]. Therefore, accurate measurements of sample plots are crucial for both traditional forest inventories and remote sensing-based modeling approaches.…”

Section: Introductionmentioning

confidence: 99%

Examining the Role of UAV Lidar Data in Improving Tree Volume Calculation Accuracy

Liao

Zou³

et al. 2022

Remote Sensing

Self Cite

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Traditional forest inventories are based on field surveys of established sample plots, which involve field measurements of individual trees within a sample plot and the selection of proper allometric equations for tree volume calculation. Thus, accurate field measurements and properly selected allometric equations are two crucial factors for providing high-quality tree volumes. One key problem is the difficulty in accurately acquiring tree height data, resulting in high uncertainty in tree volume calculation when the diameter at breast height (DBH) alone is used. This study examined the uncertainty of tree height measurements using different means and the impact of allometric models on tree volume estimation accuracy. Masson pine and eucalyptus plantations in Fujian Province, China, were selected as examples; their tree heights were measured three ways: using an 18-m telescopic pole, UAV Lidar (unmanned aerial vehicle, light detection and ranging) data, and direct measurement of felled trees, with the latest one as a reference. The DBH-based and DBH–height-based allometric equations corresponding to specific tree species were used for the calculations of tree volumes. The results show that (1) tree volumes calculated from the DBH-based models were lower than those from the DBH–height-based models. On average, tree volumes were underestimated by 0.018 m3 and 0.117 m3 for Masson pine and eucalyptus, respectively, while the relative root-mean-squared errors (RMSEr) were 24.04% and 33.90%, respectively, when using the DBH-based model; (2) the tree height extracted from UAV Lidar data was more accurate than that measured using a telescopic pole, because the pole measurement method generally underestimated the tree height, especially when the trees were taller than the length of the pole (18 m in our study); (3) the tree heights measured using different methods greatly impacted the accuracies of tree volumes calculated using the DBH–height model. The telescopic-pole-measured tree heights resulted in a relative error of 9.1–11.8% in tree volume calculations. This research implies that incorporation of UAV Lidar data with DBH field measurements can effectively improve tree volume estimation and could be a new direction for sample plot data collection in the future.

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A comparative analysis of modeling approaches and canopy height-based data sources for mapping forest growing stock volume in a northern subtropical ecosystem of China

Cited by 10 publications

References 83 publications

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

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

Remote Sensing Applications in Monitoring Poplars: A Review

Examining the Role of UAV Lidar Data in Improving Tree Volume Calculation Accuracy

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