Models for predicting individual tree height increment and tree diameter in young stands in southern Sweden

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 R2 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.

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Corrigendum to “Individual tree height increment model for managed even-aged stands of ponderosa pine throughout the western United States using linear mixed effects models” [Forest Ecol. Manage. 221 (2006) 147–154]

Forest Ecology and Management ◽

10.1016/j.foreco.2007.08.011 ◽

2007 ◽

Vol 251 (3) ◽

pp. 231

Author(s):

Fabian C.C. Uzoh ◽

William W. Oliver

Keyword(s):

United States ◽

Ponderosa Pine ◽

Tree Height ◽

Mixed Effects ◽

Mixed Effects Models ◽

Western United States ◽

Height Increment ◽

Individual Tree ◽

Linear Mixed Effects Models ◽

Linear Mixed Effects

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

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b8-625-2016 ◽

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 R2 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.

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Individual tree height increment model for managed even-aged stands of ponderosa pine throughout the western United States using linear mixed effects models

Forest Ecology and Management ◽

10.1016/j.foreco.2005.09.012 ◽

2006 ◽

Vol 221 (1-3) ◽

pp. 147-154 ◽

Cited By ~ 22

Author(s):

Fabian C.C. Uzoh ◽

William W. Oliver

Keyword(s):

United States ◽

Ponderosa Pine ◽

Tree Height ◽

Mixed Effects ◽

Mixed Effects Models ◽

Western United States ◽

Height Increment ◽

Individual Tree ◽

Linear Mixed Effects Models ◽

Linear Mixed Effects

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

Remote Sensing ◽

10.3390/rs13010077 ◽

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.

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

Forest Science ◽

10.1093/forestscience/54.1.31 ◽

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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Adjudicating Perspectives on Forest Structure: How Do Airborne, Terrestrial, and Mobile Lidar-Derived Estimates Compare?

Remote Sensing ◽

10.3390/rs13122297 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2297

Author(s):

Jonathon J. Donager ◽

Andrew J. Sánchez Meador ◽

Ryan C. Blackburn

Keyword(s):

Ponderosa Pine ◽

Laser Scanning ◽

Canopy Cover ◽

Basal Area ◽

Tree Height ◽

Absolute Error ◽

Forest Monitoring ◽

Individual Tree ◽

Structural Configurations ◽

Individual Trees

Applications of lidar in ecosystem conservation and management continue to expand as technology has rapidly evolved. An accounting of relative accuracy and errors among lidar platforms within a range of forest types and structural configurations was needed. Within a ponderosa pine forest in northern Arizona, we compare vegetation attributes at the tree-, plot-, and stand-scales derived from three lidar platforms: fixed-wing airborne (ALS), fixed-location terrestrial (TLS), and hand-held mobile laser scanning (MLS). We present a methodology to segment individual trees from TLS and MLS datasets, incorporating eigen-value and density metrics to locate trees, then assigning point returns to trees using a graph-theory shortest-path approach. Overall, we found MLS consistently provided more accurate structural metrics at the tree- (e.g., mean absolute error for DBH in cm was 4.8, 5.0, and 9.1 for MLS, TLS and ALS, respectively) and plot-scale (e.g., R2 for field observed and lidar-derived basal area, m2 ha−1, was 0.986, 0.974, and 0.851 for MLS, TLS, and ALS, respectively) as compared to ALS and TLS. While TLS data produced estimates similar to MLS, attributes derived from TLS often underpredicted structural values due to occlusion. Additionally, ALS data provided accurate estimates of tree height for larger trees, yet consistently missed and underpredicted small trees (≤35 cm). MLS produced accurate estimates of canopy cover and landscape metrics up to 50 m from plot center. TLS tended to underpredict both canopy cover and patch metrics with constant bias due to occlusion. Taking full advantage of minimal occlusion effects, MLS data consistently provided the best individual tree and plot-based metrics, with ALS providing the best estimates for volume, biomass, and canopy cover. Overall, we found MLS data logistically simple, quickly acquirable, and accurate for small area inventories, assessments, and monitoring activities. We suggest further work exploring the active use of MLS for forest monitoring and inventory.

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