Developing additive systems of biomass equations for nine hardwood species in Northeast China

Dong, Lihu; Zhang, Lianjun; Li, Fengri

doi:10.1007/s00468-015-1196-1

Cited by 80 publications

(106 citation statements)

References 57 publications

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“…First, field AGB was calculated based on models of the relationship between the measured data (tree species and DBH) and aboveground tree biomass [22][23][24][25][26][27] (Figure 3), as described in Section 2.1. The remote-sensing data parameters (GLAS metrics and TM variables) corresponding to field plots were then extracted using the methods described in Sections 2.2 and 2.3…”

Section: Methodsmentioning

confidence: 99%

“…The average aboveground biomass of each plot was then obtained by aggregating all single-tree biomass values in this plot and dividing by the area of the sampling plot. In the region studied in the current research, the single-tree biomass for each tree species was estimated using species-specific allometric equations [22][23][24][25][26][27] (Figure 3) obtained from the literature. The average aboveground biomass of each plot was then obtained by aggregating all single-tree biomass values in this plot and dividing by the area of the sampling plot.…”

Section: Icesat/glas Datamentioning

confidence: 99%

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Comparison and Evaluation of Three Methods for Estimating Forest above Ground Biomass Using TM and GLAS Data

et al. 2017

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Medium spatial resolution biomass is a crucial link from the plot to regional and global scales. Although remote-sensing data-based methods have become a primary approach in estimating forest above ground biomass (AGB), many difficulties remain in data resources and prediction approaches. Each kind of sensor type and prediction method has its own merits and limitations. To select the proper estimation algorithm and remote-sensing data source, several forest AGB models were developed using different remote-sensing data sources (Geoscience Laser Altimeter System (GLAS) data and Thematic Mapper (TM) data) and 108 field measurements. Three modeling methods (stepwise regression (SR), support vector regression (SVR) and random forest (RF)) were used to estimate forest AGB over the Daxing'anling Mountains in northeastern China. The results of models using different datasets and three approaches were compared. The random forest AGB model using Landsat5/TM as input data was shown the acceptable modeling accuracy (R 2 = 0.95 RMSE = 17.73 Mg/ha) and it was also shown to estimate AGB reliably by cross validation (R 2 = 0.71 RMSE = 39.60 Mg/ha). The results also indicated that adding GLAS data significantly improved AGB predictions for the SVR and SR AGB models. In the case of the RF AGB models, including GLAS data no longer led to significant improvement. Finally, a forest biomass map with spatial resolution of 30 m over the Daxing'anling Mountains was generated using the obtained optimal model.

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Section: Methodsmentioning

confidence: 99%

Section: Icesat/glas Datamentioning

confidence: 99%

Comparison and Evaluation of Three Methods for Estimating Forest above Ground Biomass Using TM and GLAS Data

et al. 2017

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“…Based on these parameters, we calculated the biomass for different ages in sample plots using the individual biomass models produced by Dong et al (2014Dong et al ( , 2015. Richards' equation was used to fit the relationship between biomass and age for each species.…”

Section: Iforest -Biogeosciences and Forestrymentioning

confidence: 99%

Quantitative simulation of C budgets in a forest in Heilongjiang province, China

Wang¹,

Li²,

Fan³

et al. 2017

iForest

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“…Consequently, DBH is usually measured for all trees in ground-based forest inventories, experimental and permanent growth plots, but tree biomass measurements are usually obtained from an affordable number of sample trees [3]. Correspondingly, a large number of aboveground biomass-, belowground biomass-, and total biomass-DBH equations have been developed for numerous species in many forest types over the last 20 years [2][3][4][5][6][7][8]. As a result of these studies, predicting tree biomass from DBH has become a well-established technique, widely applied in forest inventory and growth and yield predictions.…”

Section: Introductionmentioning

confidence: 99%

Development of a System of Compatible Individual Tree Diameter and Aboveground Biomass Prediction Models Using Error-In-Variable Regression and Airborne LiDAR Data

Liu

Sun

et al. 2018

Remote Sensing

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Estimating individual tree diameters at breast height (DBH) from delineated crowns and tree heights on the basis of airborne light detection and ranging (LiDAR) data provides a good opportunity for large-scale forest inventory. Generally, ground-based measurements are more accurate, but LiDAR data and derived DBH values can be obtained over larger areas for a relatively smaller cost if a right procedure is developed. A nonlinear least squares (NLS) regression is not an appropriate approach to predict the aboveground biomass (AGB) of individual trees from the estimated DBH because both the response variable and the regressor are subject to measurement errors. In this study, a system of compatible individual tree DBH and AGB error-in-variable models was developed using error-in-variable regression techniques based on both airborne LiDAR and field-measured datasets of individual Picea crassifolia Kom. trees, collected in northwestern China. Two parameter estimation algorithms, i.e., the two-stage error-in-variable model (TSEM) and the nonlinear seemingly unrelated regression (NSUR), were proposed for estimating the parameters in the developed system of compatible individual tree DBH and AGB error-in-variable models. Moreover, two model structures were applied to estimate AGB for comparison purposes: NLS with AGB estimation depending on DBH (NLS&DD) and NLS with AGB estimation not depending on DBH (NLS&NDD). The results showed that both TSEM and NSUR led to almost the same parameter estimates for the developed system. Moreover, the developed system effectively accounted for the inherent correlation between DBH and AGB as well as for the effects of measurement errors in the DBH on the predictions of AGB, whereas NLS&DD and NLS&NDD did not. A leave-one-out cross-validation indicated that the prediction accuracy of the developed system of compatible individual tree DBH and AGB error-in-variable models with NSUR was the highest among those estimated by the four methods evaluated, but, statistically, the accuracy improvement was not significantly different from zero. The main reason might be that, except for the measurement errors, other source errors were ignored in the modeling. This study implies that, overall, the proposed method provides the potential to expand the estimations of both DBH and AGB from individual trees to stands by combining the error-in-variable modeling and LiDAR data and improve their estimation accuracies, but its application needs to be further validated by conducting a systematical uncertainty analysis of various source errors in a future study.

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Developing additive systems of biomass equations for nine hardwood species in Northeast China

Cited by 80 publications

References 57 publications

Comparison and Evaluation of Three Methods for Estimating Forest above Ground Biomass Using TM and GLAS Data

Comparison and Evaluation of Three Methods for Estimating Forest above Ground Biomass Using TM and GLAS Data

Quantitative simulation of C budgets in a forest in Heilongjiang province, China

Development of a System of Compatible Individual Tree Diameter and Aboveground Biomass Prediction Models Using Error-In-Variable Regression and Airborne LiDAR Data

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