Comparing Non-Destructive Methods to Estimate Volume of Three Tree Taxa in Beijing, China

Liu, Jincheng; Zhong-ke, Feng; Mannan, Abdul; Khan, Tauheed Ullah; Cheng, Zhuxin

doi:10.3390/f10020092

Cited by 14 publications

(11 citation statements)

References 37 publications

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“…Using these inventory data, forest biomass can be estimated at provincial or national scales [7]. However, with the continuous change in the forest resource structure of China, there have been some problems with these inventory data in regional biomass estimation [8].To obtain the total volume or biomass of forest, the volume or biomass of one tree is calculated, and then the volumes or biomasses of all the trees in the sample plot are added together. Obviously, this method requires a high amount of manpower and material resources [9].…”

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confidence: 99%

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Estimation of Forest Biomass in Beijing (China) Using Multisource Remote Sensing and Forest Inventory Data

Zhu

Zhong-ke

et al. 2020

Forests

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Forest biomass reflects the material cycle of forest ecosystems and is an important index to measure changes in forest structure and function. The accurate estimation of forest biomass is the research basis for measuring carbon storage in forest systems, and it is important to better understand the carbon cycle and improve the efficiency of forest policy and management activities. In this study, to achieve an accurate estimation of meso-scale (regional) forest biomass, we used Ninth Beijing Forest Inventory data (FID), Landsat 8 OLI Image data and ALOS-2 PALSAR-2 data to establish different forest types (coniferous forest, mixed forest, and broadleaf forest) of biomass models in Beijing. We assessed the potential of forest inventory, optical (Landsat 8 OLI) and radar (ALOS-2 PALSAR-2) data in estimating and mapping forest biomass. From these data, a wide range of parameters related to forest structure were obtained. Random forest (RF) models were established using these parameters and compared with traditional multiple linear regression (MLR) models. Forest biomass in Beijing was then estimated. The results showed the following: (1) forest inventory data combined with multisource remote sensing data can better fit forest biomass than forest inventory data alone. Among the three forest types, mixed forest has the best fitting model. Forest inventory variables and multisource remote sensing variables can match each other in time and space, capturing almost all spatial variability. (2) The 2016 forest biomass density in Beijing was estimated to be 52.26 Mg ha−1 and ranged from 19.1381–195.66 Mg ha−1. The areas with high biomass were mainly distributed in the north and southwest of Beijing, while the areas with low biomass were mainly distributed in the southeast and central areas of Beijing. (3) The estimates from the RF model are better than those from the MLR model, showing a high R 2 and a low root mean square error (RMSE). The R 2 values of the MLR models of three forest types were greater than 0.5, and RMSEs were less than 15.5 Mg ha−1, The R 2 values of the RF models were higher than 0.6, and the RMSEs were lower than 13.5 Mg ha−1. We conclude that the methods in this paper can help improve the accurate estimation of regional biomass and provide a basis for the planning of relevant forestry decision-making departments.

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confidence: 99%

Estimation of Forest Biomass in Beijing (China) Using Multisource Remote Sensing and Forest Inventory Data

Zhu

Zhong-ke

et al. 2020

Forests

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“…Hu et al [16] proposed a method for measuring the leaf geometric characteristics of poplar seedlings based on 3D visualization via the use of time-of-flight (ToF) and digital cameras. A new method was proposed for calculating the tree volume model with high precision by using the electronic theodolite [17]. Mugasha et al [18] Constructed a variety of D-H relationship equations.…”

Section: A Backgroundmentioning

confidence: 99%

Trunk Model Establishment and Parameter Estimation for a Single Tree Using Multistation Terrestrial Laser Scanning

Pan

Zhang

et al. 2020

IEEE Access

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Parameter estimation and effective tree trunk model establishment are significant to tree biomass measurements and three-dimensional (3D) reconstruction. In this study, we proposed a trunk model establishment and parameter estimation method for a single tree through multistation terrestrial laser scanning (TLS). This approach obtains a more accurate and dense point cloud and yields several improvements. First, to correct the tree position, the angle between the trunk centreline and the vertical line was extracted to normalize the tree point clouds. Subsequently, some tree model parameters were accurately calculated. The tree height was extracted based on individual differences rather than directly adopting the Digital Elevation Model (DEM) model; The B-Spline curve fitting was added to the diameters at breast height (DBH) measure, which better determined the shape of the DBH compared with circle fitting; The CBH was measured by multi-view voxel projection instead of Single-view fixed threshold segmentation, which confirmed the position of the CBH more accurately. To highlight the effectiveness and pertinence of the proposed method, we analysed three plots that the proposed method can achieve more accurate results, where the mean relative error (MRE) of the tree height, DBH and CBH were 1.89%, 1.74% and 2.91% satisfying the accuracy standard specified by the technical indicators (± 5%). The experimental results validate the trunk model accuracy and the effectiveness of the parameter measurements (close to the field measurements). The proposed method is significantly different from the accuracy of the previous method. The proposed method has the best accuracy in Multistation Terrestrial Laser Scanning. Hence the proposed method has outstanding performance in the high-precision extraction of trunk parameters and the construction of trunk models which will perform forest inventory and construction of virtual forest more accurately.

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“…volumes, aiming to improve the current destructive, costly, time-consuming volume model. Liu et al (2019) proposed a new non-destructive, low-cost, efficient method for calculating tree volume models with high precision; they used only the electronic theodolite to create volume models for Platycladus orientalis, Larix principis-rupprechtii, and Populus spp in China. Similar methodologies were used in plantations and for young trees.…”

Section: Introductionmentioning

confidence: 99%

“…With advances in science and technology, new methods to measure volumes of tree stands have been created. Optical electronic total stations, photogrammetry technology, three-dimensional laser scanning technology, unmanned aerial systems, and other non-contact measurement methods have been introduced in the field of forestry (Liu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Constructing single-entry stem volume models for four economically important tree species of Greece

Koulelis¹,

Ioannidis²

2021

Folia Oecologica

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Three different nonlinear regression models were tested for their ability to predict stem volume for economically important native tree species in Greece. Τhe models were evaluated using adjusted R square (Adj Rsqr) root mean square error (RMSE) and Akaike information criterion (AICc), where necessary. In general, the quadratic polynomial and cubic polynomial models and the two-parameter power models fit the data well. Although the two-parameter power function fit best for fir, oak, and beech trees, the cubic polynomial model produced the best fit statistics for black pine. Making forest inventory estimates often involves predicting tree volumes from only the diameter at breast height (DBH) and merchantable height. This study covers important gaps in fast and cost-effective methods for calculating the volume of tree species at national level. However, the increasing need for reliable estimates of inventory components and volume changes requires more accurate volume estimation techniques. Especially when those estimates concern the national inventory, those models must be validated using an entire range of age/diameter and site classes of each species before their extended use across the country to promote the sustainable use of forest resources.

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Comparing Non-Destructive Methods to Estimate Volume of Three Tree Taxa in Beijing, China

Cited by 14 publications

References 37 publications

Estimation of Forest Biomass in Beijing (China) Using Multisource Remote Sensing and Forest Inventory Data

Estimation of Forest Biomass in Beijing (China) Using Multisource Remote Sensing and Forest Inventory Data

Trunk Model Establishment and Parameter Estimation for a Single Tree Using Multistation Terrestrial Laser Scanning

Constructing single-entry stem volume models for four economically important tree species of Greece

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