scholarly journals IMPACTS OF TREE HEIGHT-DBH ALLOMETRY ON LIDAR-BASED TREE ABOVEGROUND BIOMASS MODELING

2016 ◽  
Vol XLI-B8 ◽  
pp. 625-628
Author(s):  
R. Fang
Keyword(s):  
Aboveground Biomass ◽  
Vertical Structure ◽  
Model Performance ◽  
Tree Height ◽  
Allometric Model ◽  
Individual Tree ◽  
Tree Diameter ◽  
Lidar Measurements ◽  
Major Variable ◽  
Model Residuals

Lidar has been widely used in tree aboveground biomass (AGB) estimation at plot or stand levels. Lidar-based AGB models are usually constructed with the ground AGB reference as the response variable and lidar canopy indices as predictor variables. Tree diameter at breast height (dbh) is the major variable of most allometric models for estimating reference AGB. However, lidar measurements are mainly related to tree vertical structure. Therefore, tree height-dbh allometric model residuals are expected to have a large impact on lidar-based AGB model performance. This study attempts to investigate sensitivity of lidar-based AGB model to the decreasing strength of height-dbh relationship using a Monte Carlo simulation approach. Striking decrease in <i>R</i><sup>2</sup> and increase in relative RMSE were found in lidar-based AGB model, as the variance of height-dbh model residuals grew. I, therefore, concluded that individual tree height-dbh model residuals fundamentally introduce errors to lidar-AGB models.

scholarly journals IMPACTS OF TREE HEIGHT-DBH ALLOMETRY ON LIDAR-BASED TREE ABOVEGROUND BIOMASS MODELING

2016 ◽  
Vol XLI-B8 ◽  
pp. 625-628
Author(s):  
R. Fang
Keyword(s):  
Aboveground Biomass ◽  
Vertical Structure ◽  
Model Performance ◽  
Tree Height ◽  
Allometric Model ◽  
Individual Tree ◽  
Tree Diameter ◽  
Lidar Measurements ◽  
Major Variable ◽  
Model Residuals

Lidar has been widely used in tree aboveground biomass (AGB) estimation at plot or stand levels. Lidar-based AGB models are usually constructed with the ground AGB reference as the response variable and lidar canopy indices as predictor variables. Tree diameter at breast height (dbh) is the major variable of most allometric models for estimating reference AGB. However, lidar measurements are mainly related to tree vertical structure. Therefore, tree height-dbh allometric model residuals are expected to have a large impact on lidar-based AGB model performance. This study attempts to investigate sensitivity of lidar-based AGB model to the decreasing strength of height-dbh relationship using a Monte Carlo simulation approach. Striking decrease in &lt;i&gt;R&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt; and increase in relative RMSE were found in lidar-based AGB model, as the variance of height-dbh model residuals grew. I, therefore, concluded that individual tree height-dbh model residuals fundamentally introduce errors to lidar-AGB models.

scholarly journals Developing and Validating Nonlinear Height–Diameter Models for Major Tree Species of Ontario's Boreal Forests

2001 ◽  
Vol 18 (3) ◽  
pp. 87-94 ◽  
Author(s):  
Changhui Peng ◽  
Lianjun Zhang ◽  
Jinxun Liu
Keyword(s):  
Tree Species ◽  
Boreal Forests ◽  
Model Performance ◽  
Tree Height ◽  
Volume Estimation ◽  
Absolute Difference ◽  
Model Parameters ◽  
Individual Tree ◽  
Permanent Sample Plots ◽  
Nonlinear Growth

Abstract Six commonly used nonlinear growth functions were fitted to individual tree height-diameter data of nine major tree species in Ontario's boreal forests. A total of 22,571 trees was collected from new permanent sample plots across the northeast and northwest of Ontario.The available data for each species were split into two sets: the majority (90%) was used to estimate model parameters, and the remaining data (10%) were reserved to validate the models. The performance of the models was compared and evaluated by model, R2, mean difference, and mean absolute difference. The results showed that these six sigmoidal models were able to capture the height–diameter relationships and fit the data equally well, but produced different asymptote estimates. Sigmoidal models such as Chapman–Richards, Weibull, and Schnute functions provided the most satisfactory height predictions. The effect of model performance on tree volume estimation was also investigated. Tree volumes of different species were computed by Honer's volume equations using a range of diameters and the predicted tree total height from the six models. For trees with diameter less than 55 cm, the six height-diameter models produced very similar results for all species, while more differentiation among the models was observed for large-sized trees (e.g., diameters > 80 cm). North. J. Appl. For. 18:87–94.

scholarly journals Allometric Equations for Estimating Aboveground Biomass of Eucalyptus urophylla S.T. Blake in East Nusa Tenggara

2021 ◽  
Vol 27 (1) ◽  
pp. 24-31
Author(s):  
R Sadono ◽  
◽  
W Wahyu ◽  
F Idris
Keyword(s):  
Aboveground Biomass ◽  
Tree Height ◽  
Allometric Equation ◽  
Allometric Equations ◽  
Accurate Estimation ◽  
Eucalyptus Urophylla ◽  
Individual Tree ◽  
Eucalyptus Plantation ◽  
Diameter At Breast Height ◽  
Breast Height

Understanding the essential contribution of eucalyptus plantation for industry development and climate change mitigation requires the accurate quantification of aboveground biomass at the individual tree species level. However, the direct measurement of aboveground biomass by destructive method is high cost and time consuming. Therefore, developing allometric equations is necessary to facilitate this effort. This study was designed to construct the specific allometric models for estimating aboveground biomass of Eucalyptus urophylla in East Nusa Tenggara. Forty two sample trees were utilized to develop allometric equations using regression analysis. Several parameters were selected as predictor variables, i.e. diameter at breast height (D), quadrat diameter at breast height combined with tree height (D2H), as well as D and H separately. Results showed that the mean aboveground biomass of E. urophylla was 143.9 ± 19.44 kg tree-1. The highest biomass were noted in stem (80.06%), followed by bark (11.89%), branch (4.69%), and foliage (3.36%). The relative contribution of stem to total aboveground biomass improved with the increasing of diameter class while the opposite trend was recorded in bark, branch, and foliage. The equation lnŶ = lna + b lnD was best and reliable for estimating the aboveground biomass of E. urophylla since it provided the highest accurate estimation (91.3%) and more practical than other models. Referring to these findings, this study concluded the use of allometric equation was reliable to support more efficient forest mensuration in E. urophylla plantation.

scholarly journals Integration of UAV, Sentinel-1 and Sentinel-2 Data for Mangrove Plantations Aboveground Biomass Monitoring in Senegal

2018 ◽  
Author(s):  
José Antonio Navarro ◽  
Nur Algeet ◽  
Alfredo Fernández-Landa ◽  
Jéssica Esteban ◽  
Pablo Rodríguez-Noriega ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Auxiliary Information ◽  
Tree Height ◽  
Cost Effective ◽  
Point Clouds ◽  
Support Vector ◽  
Monitoring Methods ◽  
Individual Tree ◽  
Entire Study ◽  
Sentinel 2

Due to the increasing importance of mangroves in climate change mitigation projects, more accurate and cost-effective aboveground biomass (AGB) monitoring methods are required. However, field measurement of AGB may be a challenge because of its remote location and the difficulty to walk in these areas. This study is based on the Livelihoods Fund&rsquo; Oceanium project of 10,000 hectare mangrove plantations monitoring. In a first step, the possibility of replacing traditional field measurements of sample plots in a young mangrove plantation by a semiautomatic processing of UAV-based photogrammetric point clouds was assessed. In a second step, Sentinel-1 radar and Sentinel-2 optical imagery were used as auxiliary information to estimate AGB and its variance for the entire study area under a model-assisted framework. AGB was measured using UAV imagery in a total of 95 sample plots. UAV plot data was used in combination with non-parametric Support Vector Regression (SVR) models for the estimation of the study area AGB using model-assisted estimators. Purely UAV-based AGB estimates and their associated standard error (SE) were compared with model-assisted estimates using (1) Sentinel-1, (2) Sentinel-2 and (3) a combination of Sentinel-1 and Sentinel-2 data as auxiliary information. The validation of the UAV-based individual tree height and crown diameter measurements showed a root mean square error (RMSE) of 0.21 m and 0.32 m respectively. Relative efficiency of the three model-assisted scenarios ranged between 1.61 and 2.15. Although all SVR models improved the efficiency of the monitoring over UAV-based estimates, the best results were achieved when a combination of Sentinel-1 and Sentinel-2 data was used. Results indicated that the methodology used in this research can provide accurate and cost-effective estimates of AGB in mangrove young plantations.

scholarly journals A practical measure for determining if diameter (D) and height (H) should be combined into D2H in allometric biomass models

2019 ◽  
Vol 92 (5) ◽  
pp. 627-634 ◽  
Author(s):  
I Dutcă ◽  
R E McRoberts ◽  
E Næsset ◽  
V N B Blujdea
Keyword(s):  
Aboveground Biomass ◽  
Prediction Accuracy ◽  
Fixed Ratio ◽  
Tree Height ◽  
Parameter Estimates ◽  
Tree Biomass ◽  
Variable Model ◽  
Individual Tree ◽  
Model Constraint ◽  
Practical Measure

AbstractTree diameter at breast height (D) and tree height (H) are often used as predictors of individual tree biomass. Because D and H are correlated, the combined variable D2H is frequently used in regression models instead of two separate independent variables, to avoid collinearity related issues. The justification for D2H is that aboveground biomass is proportional to the volume of a cylinder of diameter, D, and height, H. However, the D2H predictor constrains the model to produce parameter estimates for D and H that have a fixed ratio, in this case, 2.0. In this paper we investigate the degree to which the D2H predictor reduces prediction accuracy relative to D and H separately and propose a practical measure, Q-ratio, to guide the decision as to whether D and H should or should not be combined into D2H. Using five training biomass datasets and two fitting approaches, weighted nonlinear regression and linear regression following logarithmic transformations, we showed that the D2H predictor becomes less efficient in predicting aboveground biomass as the Q-ratio deviates from 2.0. Because of the model constraint, the D2H-based model performed less well than the separate variable model by as much as 12 per cent with regard to mean absolute percentage residual and as much as 18 per cent with regard to sum of squares of log accuracy ratios. For the analysed datasets, we observed a wide variation in Q-ratios, ranging from 2.5 to 5.1, and a large decrease in efficiency for the combined variable model. Therefore, we recommend using the Q-ratio as a measure to guide the decision as to whether D and H may be combined further into D2H without the adverse effects of loss in biomass prediction accuracy.

scholarly journals Deriving Tree Size Distributions of Tropical Forests from Lidar

2021 ◽  
Vol 13 (1) ◽  
pp. 131
Author(s):  
Franziska Taubert ◽  
Rico Fischer ◽  
Nikolai Knapp ◽  
Andreas Huth
Keyword(s):  
Size Distribution ◽  
Spatial Scales ◽  
Basal Area ◽  
Tree Height ◽  
Tree Size ◽  
Diameter Distribution ◽  
Forest Inventories ◽  
Allometric Relations ◽  
Lidar Measurements ◽  
Derived Data

Remote sensing is an important tool to monitor forests to rapidly detect changes due to global change and other threats. Here, we present a novel methodology to infer the tree size distribution from light detection and ranging (lidar) measurements. Our approach is based on a theoretical leaf–tree matrix derived from allometric relations of trees. Using the leaf–tree matrix, we compute the tree size distribution that fit to the observed leaf area density profile via lidar. To validate our approach, we analyzed the stem diameter distribution of a tropical forest in Panama and compared lidar-derived data with data from forest inventories at different spatial scales (0.04 ha to 50 ha). Our estimates had a high accuracy at scales above 1 ha (1 ha: root mean square error (RMSE) 67.6 trees ha−1/normalized RMSE 18.8%/R² 0.76; 50 ha: 22.8 trees ha−1/6.2%/0.89). Estimates for smaller scales (1-ha to 0.04-ha) were reliably for forests with low height, dense canopy or low tree height heterogeneity. Estimates for the basal area were accurate at the 1-ha scale (RMSE 4.7 tree ha−1, bias 0.8 m² ha−1) but less accurate at smaller scales. Our methodology, further tested at additional sites, provides a useful approach to determine the tree size distribution of forests by integrating information on tree allometries.

scholarly journals Development and Performance Evaluation of a Very Low-Cost UAV-Lidar System for Forestry Applications

2020 ◽  
Vol 13 (1) ◽  
pp. 77
Author(s):  
Tianyu Hu ◽  
Xiliang Sun ◽  
Yanjun Su ◽  
Hongcan Guan ◽  
Qianhui Sun ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Low Cost ◽  
Canopy Cover ◽  
Tree Height ◽  
Flying Height ◽  
Tree Level ◽  
Forest Site ◽  
Lidar System ◽  
Individual Tree ◽  
Gap Fraction

Accurate and repeated forest inventory data are critical to understand forest ecosystem processes and manage forest resources. In recent years, unmanned aerial vehicle (UAV)-borne light detection and ranging (lidar) systems have demonstrated effectiveness at deriving forest inventory attributes. However, their high cost has largely prevented them from being used in large-scale forest applications. Here, we developed a very low-cost UAV lidar system that integrates a recently emerged DJI Livox MID40 laser scanner (~$600 USD) and evaluated its capability in estimating both individual tree-level (i.e., tree height) and plot-level forest inventory attributes (i.e., canopy cover, gap fraction, and leaf area index (LAI)). Moreover, a comprehensive comparison was conducted between the developed DJI Livox system and four other UAV lidar systems equipped with high-end laser scanners (i.e., RIEGL VUX-1 UAV, RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE). Using these instruments, we surveyed a coniferous forest site and a broadleaved forest site, with tree densities ranging from 500 trees/ha to 3000 trees/ha, with 52 UAV flights at different flying height and speed combinations. The developed DJI Livox MID40 system effectively captured the upper canopy structure and terrain surface information at both forest sites. The estimated individual tree height was highly correlated with field measurements (coniferous site: R2 = 0.96, root mean squared error/RMSE = 0.59 m; broadleaved site: R2 = 0.70, RMSE = 1.63 m). The plot-level estimates of canopy cover, gap fraction, and LAI corresponded well with those derived from the high-end RIEGL VUX-1 UAV system but tended to have systematic biases in areas with medium to high canopy densities. Overall, the DJI Livox MID40 system performed comparably to the RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE systems in the coniferous site and to the Velodyne Puck LITE system in the broadleaved forest. Despite its apparent weaknesses of limited sensitivity to low-intensity returns and narrow field of view, we believe that the very low-cost system developed by this study can largely broaden the potential use of UAV lidar in forest inventory applications. This study also provides guidance for the selection of the appropriate UAV lidar system and flight specifications for forest research and management.

Deriving Tree Diameter Growth and Probability of Survival Equations from Successive Diameter Distributions

2008 ◽  
Vol 54 (1) ◽  
pp. 31-35
Author(s):  
Thomas G. Matney ◽  
Emily B. Schultz
Keyword(s):  
General Procedure ◽  
Growth And Yield ◽  
Diameter Distribution ◽  
Diameter Growth ◽  
Individual Tree ◽  
Statistical Probability ◽  
Growth And Survival ◽  
Tree Diameter ◽  
Growth And Yield Models ◽  
Diameter Distributions

Abstract Many growth and yield models have used statistical probability distributions to estimate the diameter distribution of a stand at any age. Equations for approximating individual tree diameter growth and survival probabilities from dbh can be derived from these models. A general procedure for determining the functions is discussed and illustrated using a loblolly pine spacing study. The results from the spacing study show that it is possible to define tree diameter growth and survival probability functions from diameter distributions with an accuracy sufficient to obtain a link between the individual tree and diameter growth and yield models.

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