Indirect methods of large-scale forest biomass estimation

To estimate the responses of forest ecosystems, most relationships in biological systems are described by allometric relationships, the parameters of which are determined based on field measurements. The use of existing observed data errors may occur during the scaling of fine-scale relationships to describe ecosystem properties at a larger ecosystem scale. Here, we analyzed the scaling error in the estimation of forest ecosystem biomass based on the measurement of plots (biomass or volume per hectare) using an improved allometric equation with a scaling error compensator. The efficiency of the compensator on reducing the scaling error was tested by simulating the forest stand populations using pseudo-observation. Our experiments indicate that, on average, approximately 94.8% of the scaling error can be reduced, and for a case study, an overestimation of 3.6% can be removed in practice from a large-scale estimation for the biomass of Pinus yunnanensis Franch.

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Generic additive allometric models and biomass allocation for two natural oak species in northeastern China

10.21203/rs.3.rs-114961/v1 ◽

2020 ◽

Author(s):

Shengwang Meng ◽

Fan Yang ◽

Haibin Wang ◽

Wei Wang ◽

Sheng Hu ◽

...

Keyword(s):

Biomass Allocation ◽

Large Scale ◽

Forest Biomass ◽

Tree Height ◽

Northeastern China ◽

Carbon Accounting ◽

Biomass Estimation ◽

Stem Biomass ◽

Biomass Models ◽

Mongolian Oak

Abstract Background: Accurate quantification of forest biomass through allometric equations is crucial for global carbon accounting and climate change mitigation. Current models for oak species could not accurately estimate biomass in northeastern China, since they were usually established limited to Mongolian oak (Quercus mongolica) on local sites, and specifically, no biomass models were available for Liaodong oak (Quercus wutaishanica). The goal of this study was, therefore, to develop generic biomass models for both oak species on large scale and evaluate biomass allocation patterns within tree components. Results: The stem biomass accounts for about two-thirds of the aboveground biomass. The ratio of wood biomass holds constant and that of branch increases with increasing D, H, CW, CL, while a reverse trend was found for bark and foliage. The root-shoot ratio nonlinearly decreased with D, ranging from 1.06 to 0.11. Tree diameter proved to be a good predictor, especially for root biomass. Tree height is more prominent than crown size for improving stem biomass models, yet it puts negative effects on crown biomass models with non-significant coefficients. Crown width could help improve fitting results of branch and foliage biomass models. Conclusion: We conclude that the selected generic biomass models for Mongolian oak and Liaodong oak will vigorously promote the accuracy of biomass estimation.

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Tropical-Forest Biomass Estimation at X-Band From the Spaceborne TanDEM-X Interferometer

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2014.2334140 ◽

2015 ◽

Vol 12 (2) ◽

pp. 239-243 ◽

Cited By ~ 54

Author(s):

Robert Treuhaft ◽

Fabio Gonzalves ◽

Joao Roberto dos Santos ◽

Michael Keller ◽

Michael Palace ◽

...

Keyword(s):

Tropical Forest ◽

Forest Biomass ◽

Biomass Estimation ◽

X Band

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Understory biomass estimation based on Structure from Motion data by Multi-layered forest drone survey

10.5194/egusphere-egu21-14670 ◽

2021 ◽

Author(s):

Yupan Zhang ◽

Yuichi Onda ◽

Hiroaki Kato ◽

Xinchao Sun ◽

Takashi Gomi

Keyword(s):

Structure From Motion ◽

Forest Structure ◽

Large Scale ◽

Forest Canopy ◽

Point Clouds ◽

Understory Vegetation ◽

Biomass Estimation ◽

Canopy Openness ◽

Cubic Model ◽

Overlap Analysis

Understory vegetation is an important part of evapotranspiration from forest floor. Forest management changes the forest structure and then affects the understory vegetation biomass (UVB). Quantitative measurement and estimation of&#160; UVB is a step cannot be ignored in the study of forest ecology and forest evapotranspiration. However, large-scale biomass measurement and estimation is challenging. In this study, Structure from Motion (SfM) was adopted simultaneously at two different layers in a plantation forest made by Japanese cedar and Japanese cypress to reconstruct forest structure from understory to above canopy: i) understory drone survey in a 1.1h sub-catchment to generate canopy height model (CHM) based on dense point clouds data derived from a manual low-flying drone under the canopy; ii) Above-canopy drone survey in whole catchment (33.2 ha) to compute canopy openness data based on point clouds of canopy derived from an autonomous flying drone above the canopy. Combined with actual biomass data from field harvesting to develop regression models between the CHM and UVB, which was then used to map spatial distribution of&#160; UVB in sub-catchment. The relationship between UVB and canopy openness data was then developed by overlap analysis. This approach yielded high resolution understory over catchment scale with a point cloud density of more than 20 points/cm2. Strong coefficients of determination (R-squared = 0.75) of the cubic model supported prediction of UVB from CHM, the average UVB was 0.82kg/m2 and dominated by low ferns. The corresponding forest canopy openness in this area was 42.48% on average. Overlap analysis show no significant interactions between them in a cubic model with weak predictive power (R-squared < 0.46). Overall, we reconstructed the multi-layered structure of the forest and provided models of UVB. Understory survey has high accuracy for biomass measurement, but it&#8217;s inherently difficult to estimate UVB only based on canopy openness result.

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Large-scale bioenergy from additional harvest of forest biomass is neither sustainable nor greenhouse gas neutral

GCB Bioenergy ◽

10.1111/j.1757-1707.2012.01169.x ◽

2012 ◽

Vol 4 (6) ◽

pp. 611-616 ◽

Cited By ~ 176

Author(s):

Ernst-Detlef Schulze ◽

Christian Körner ◽

Beverly E. Law ◽

Helmut Haberl ◽

Sebastiaan Luyssaert

Keyword(s):

Greenhouse Gas ◽

Large Scale ◽

Forest Biomass

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Forest Biomass Estimation Based on Forest Inventory Data in Middle and Last Scales

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.183-185.220 ◽

2011 ◽

Vol 183-185 ◽

pp. 220-224

Author(s):

Ming Ze Li ◽

Wen Yi Fan ◽

Ying Yu

Keyword(s):

Tree Species ◽

Forest Inventory ◽

Forest Biomass ◽

Biomass Estimation ◽

Total Biomass ◽

Inventory Data ◽

Biomass Equation ◽

Tree Species Composition ◽

Forest Inventory Data ◽

Ecosystem Carbon

The forest biomass (which is referred to the arbor aboveground biomass in this research) is one of the most primary factors to determine the forest ecosystem carbon storages. There are many kinds of estimating methods adapted to various scales. It is a suitable method to estimate forest biomass of the farm or the forestry bureau in middle and last scales. First each subcompartment forest biomass should be estimated, and then the farm or the forestry bureau forest biomass was estimated. In this research, based on maoershan farm region, first the single tree biomass equation of main tree species was established or collected. The biomass of each specie was calculated according to the materials of tally, such as height, diameter and so on in the forest inventory data. Secondly, each specie’s biomass and total biomass in subcompartment were calculated according to the tree species composition in forest management investigation data. Thus the forest biomass spatial distribution was obtained by taking subcompartment as a unit. And last the forest total biomass was estimated.

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Forest Biomass Estimation Using SAR and Optical Images

Journal of Geospatial Information Technology ◽

10.29252/jgit.3.1.15 ◽

2015 ◽

Vol 3 (1) ◽

pp. 15-26

Author(s):

Mohamad Reza Ramezani ◽

Mahmood Reza Sahebi ◽

◽

Keyword(s):

Forest Biomass ◽

Biomass Estimation ◽

Optical Images

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Using synthetic data to evaluate the benefits of large field plots for forest biomass estimation with LiDAR

Remote Sensing of Environment ◽

10.1016/j.rse.2018.05.007 ◽

2018 ◽

Vol 213 ◽

pp. 115-128 ◽

Cited By ~ 13

Author(s):

Fabian Ewald Fassnacht ◽

Hooman Latifi ◽

Florian Hartig

Keyword(s):

Forest Biomass ◽

Synthetic Data ◽

Large Field ◽

Biomass Estimation ◽

Field Plots

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SAR and Optical Images for Forest Biomass Estimation

Biomass - Detection, Production and Usage ◽

10.5772/18930 ◽

2011 ◽

Cited By ~ 2

Author(s):

Jalal Amini ◽

Josaphat Tetuko Sri Sumantyo

Keyword(s):

Forest Biomass ◽

Biomass Estimation ◽

Optical Images

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Influence of Variable Selection and Forest Type on Forest Aboveground Biomass Estimation Using Machine Learning Algorithms

Forests ◽

10.3390/f10121073 ◽

2019 ◽

Vol 10 (12) ◽

pp. 1073 ◽

Cited By ~ 10

Author(s):

Li ◽

Liu

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Variable Selection ◽

Aboveground Biomass ◽

Forest Type ◽

Learning Algorithms ◽

Forest Biomass ◽

Machine Learning Algorithms ◽

Biomass Estimation ◽

Landsat 8

Forest biomass is a major store of carbon and plays a crucial role in the regional and global carbon cycle. Accurate forest biomass assessment is important for monitoring and mapping the status of and changes in forests. However, while remote sensing-based forest biomass estimation in general is well developed and extensively used, improving the accuracy of biomass estimation remains challenging. In this paper, we used China’s National Forest Continuous Inventory data and Landsat 8 Operational Land Imager data in combination with three algorithms, either the linear regression (LR), random forest (RF), or extreme gradient boosting (XGBoost), to establish biomass estimation models based on forest type. In the modeling process, two methods of variable selection, e.g., stepwise regression and variable importance-base method, were used to select optimal variable subsets for LR and machine learning algorithms (e.g., RF and XGBoost), respectively. Comfortingly, the accuracy of models was significantly improved, and thus the following conclusions were drawn: (1) Variable selection is very important for improving the performance of models, especially for machine learning algorithms, and the influence of variable selection on XGBoost is significantly greater than that of RF. (2) Machine learning algorithms have advantages in aboveground biomass (AGB) estimation, and the XGBoost and RF models significantly improved the estimation accuracy compared with the LR models. Despite that the problems of overestimation and underestimation were not fully eliminated, the XGBoost algorithm worked well and reduced these problems to a certain extent. (3) The approach of AGB modeling based on forest type is a very advantageous method for improving the performance at the lower and higher values of AGB. Some conclusions in this paper were probably different as the study area changed. The methods used in this paper provide an optional and useful approach for improving the accuracy of AGB estimation based on remote sensing data, and the estimation of AGB was a reference basis for monitoring the forest ecosystem of the study area.

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