Non-destructive method for above-ground biomass estimation of Fraxinus uhdei (Wenz.) Lingelsh in an urban forest

López-López, Stephanie Fabiola; Martínez-Trinidad, Tomás; Benavides-Meza, Héctor Mario; García-Nieto, Moisés; Santos-Posadas, Héctor M. De los

doi:10.1016/j.ufug.2017.03.025

Cited by 11 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This type of analysis is difficult to perform using satellite images; hence, this is an advantage of using LiDAR measurements in real time. A study on estimating the biomass from the length, tree trunk diameter, and inclination angle of each branch has also been reported [41]. In addition to biomass monitoring, information regarding branches and tree trunks can be used for regular tree inspections.…”

Section: Branch Segmentationmentioning

confidence: 99%

Estimating Tree Structural Parameters via Automatic Tree Segmentation From LiDAR Point Cloud Data

Itakura

Miyatani²,

Hosoi

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

In this article, we proposed an automated tree segmentation method using light detection and ranging (LiDAR) point cloud data. Tree segmentation was performed accurately even with bumpy ground, and was validated on more than 1000 samples. For example, 371 out of 374 trees were detected from dataset 2, and the error was caused by the trees with low point densities located in the area far from the LiDAR. Segmentation was accurately performed, including the branches, leading to the retrieval of high-level parameters such as the leaf areas. To obtain the parameters regarding the leaf area from the segmented trees, a method for classifying the leaf and branch points in the three-dimensional point clouds obtained using a terrestrial LiDAR method was proposed. After preprocessing the input point cloud, such as by voxelization, the fast point feature histogram (FPFH) features were calculated. Then, the classifier for classification into leaves and branches was trained using the training dataset to calculate the test accuracy with the test data. Moreover, an unsupervised method for classification using the FPFH feature and k-means algorithm was also performed. Consequently, the recall and precision values of the classification were determined as 98.14% and 96.03%, respectively, with the supervised approach.

show abstract

Section: Branch Segmentationmentioning

confidence: 99%

Estimating Tree Structural Parameters via Automatic Tree Segmentation From LiDAR Point Cloud Data

Itakura

Miyatani²,

Hosoi

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Currently, the estimation methods of forest biomass include the clear-cutting method, mean wood method, relative growth method, and remote sensing estimation method [17,[19][20][21][22][23][24][25]. The clear-cutting method is very accurate, and although some scholars later proposed a non-destructive method for measuring tree biomass, it appears to be less accurate in the calculation of forest biomass in large areas [26]. The mean wood method is relatively simple but offers low reliability.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Regional Forest Biomass Using Machine Learning: A Case Study of Beijing, China

Liu

Yue

Pei

et al. 2023

Forests

View full text Add to dashboard Cite

Dynamic changes in forest biomass are closely related to the carbon cycle, climate change, forest productivity and biodiversity. However, most previous studies mainly focused on the calculation of current forest biomass, and only a few studies attempted to predict future dynamic changes in forest biomass which obtained uncertain results. Therefore, this study comprehensively considered the effects of multi-stage continuous survey data of forest permanent sample plots, site condition factors and corresponding meteorological factors using Beijing as an example. The geographic detector method was used to screen the key interfering factors that affect the growth of forest biomass. Then, based on the back-propagation artificial neural network (BP-ANN) and support vector machine (SVM) learning methods, 80% of the sample data were extracted to train the model, and thereby verify the prediction accuracy of different modeling methods using different training samples. The results showed that the forest biomass prediction models based on both the machine learning algorithms had good fitting accuracy, and there was no significant difference in the prediction results between the two models. However, the SVM model was better than the BP-ANN. While the BP-ANN model provided more volatile predictions, and the accuracy was above 80%, the prediction results of the SVM model were relatively stable, and the accuracy was above 90%. This study not only provides good technical support for the scientific estimation of regional forest biomass in the future, but also offers reliable basic data for sustainable forest management, planning decisions, forest carbon sequestration and sustainable development.

show abstract

“…First, an impartial forest inventory project is required, with reliable measurements of trees' attributes; further, requires biomass estimation models to accurately represent forest inventory data (Dutcâ et al, 2020). The methodologies usually used are defi ned as destructive, when trees inside a plot or trees previous selected from diametric classes are harvested and measured (Singh et al, 2011); and non-destructive, when is not necessary to cut trees (López-López et al, 2017). Non-destructive methodologies for estimating biomass are faster, cheaper, and avoid environmental problems resulting from tree felling (Mòntes, 2009).…”

Section: Introductionmentioning

confidence: 99%

Use of Destructive and Non-Destructive Methodologies to Estimate Stem Biomass Accumulation and Carbon Stock in an Eucalyptus Forest

et al. 2022

View full text Add to dashboard Cite

Predicting wood biomass and carbon stock contents in planted forests can vary due to limitations associated with the measurement of parameters. Therefore, reducing possible errors generated over biomass and carbon stock quantification is an important step in obtaining reliable data. The study aimed to compare the use of destructive and non-destructive methodologies for predicting biomass and carbon stock in a planted Eucalyptus forest. Scaling was performed on 21 trees and 3 methodologies for carbon stock estimation were compared. For methodology 1, a control sample was harvested, sectioned, weighted in the field, and the carbon stock calculated based on these data. Methodology 2 was also destructive, as trees were harvested, scaled and the carbon stock predicted based on these data. Methodology 3 was non-destructive, as trees were scaled upright with the aid of equipment and the predicted carbon stock was based on these data. Biomass and carbon stock were compared by Test F and no statistical difference was observed. The data were separated according to diametric classes and compared by the Kolmogorov-Smirnov test, and again no significant difference was observed. Furthermore, three equations were generated based on the Schumacher & Hall model and compared by the identity test model and no differences between the methodologies were observed. Thus, both nondestructive and destructive methodologies herein evaluated were effective and showed equal results to the control sample. Moreover, the use of the non-destructive methodology reduces time and cost destined to predicting biomass and carbon stock.

show abstract

Non-destructive method for above-ground biomass estimation of Fraxinus uhdei (Wenz.) Lingelsh in an urban forest

Cited by 11 publications

References 19 publications

Estimating Tree Structural Parameters via Automatic Tree Segmentation From LiDAR Point Cloud Data

Estimating Tree Structural Parameters via Automatic Tree Segmentation From LiDAR Point Cloud Data

Prediction of Regional Forest Biomass Using Machine Learning: A Case Study of Beijing, China

Use of Destructive and Non-Destructive Methodologies to Estimate Stem Biomass Accumulation and Carbon Stock in an Eucalyptus Forest

Contact Info

Product

Resources

About