Determining an Accurate and Cost-Effective Individual Height-Diameter Model for Mongolian Pine on Sandy Land

Height-diameter (H-D) models are important tools for forest management practice. Sandy Mongolian pine plantations (Pinus sylvestris var. mongolica) are a major component of the Three-North Afforestation Shelterbelt in Northern China. However, few H-D models are available for Mongolian pine plantations. In this paper we compared different equations found in the literature for predicting tree height, using diameter at breast height and additional stand-level predictor variables. We tested if the additional stand-level predictor variable is necessary to produce more accurate results. The dominant height was used as a stand-level predictor variable to describe the variation of the H-D relationship among plots. We found that the basic mixed-effects H-D model provided a similar predictive accuracy as the generalized mixed-effects H-D model. Moreover, it had the advantage of reducing the sampling effort. The basic mixed-effects H-D model calibration, in which the heights of the two thickest trees in the plot were included to calibrate the random effects, resulted in accurate and reliable individual tree height estimations. Thus, the basic mixed-effects H-D model with the above-described calibration design can be an accurate and cost-effective solution for estimating the heights of Mongolian pine trees in northern China.

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Predictive Ability of Mixed-Effects Height–Diameter Models Fit Using One Species but Calibrated for Another Species

Forest Science ◽

10.1093/forsci/fxz058 ◽

2019 ◽

Author(s):

Curtis L Vanderschaaf

Keyword(s):

South Carolina ◽

Predictive Ability ◽

Pinus Palustris ◽

Tree Height ◽

Pinus Elliottii ◽

Mixed Effects ◽

Pinus Echinata ◽

Individual Tree ◽

Height Estimates ◽

Model Form

Abstract Mixed-effects individual tree height–diameter models are presented for important pines in the Western Gulf, USA. Equations are presented for plantations of loblolly (Pinus taeda L.), longleaf (Pinus palustris P. Mill.), shortleaf (Pinus echinata Mill.), and slash (Pinus elliottii Engelm.) pine. To produce localized individual tree height estimates, these models can be calibrated after obtaining height–diameter measurements from a plot/stand of interest. These equations can help answer an interesting question of whether a model fit for one species can be calibrated to produce reasonable height estimates of another species. In situations where mixed-effects models have not been developed for a particular species, perhaps an equation from another species can be used. This question was addressed by calibrating these models using independent data of loblolly, longleaf, and slash pine plantations located in South Carolina. For each calibration species, in addition to the models developed described above, previously published models, but of the same model form, fit using other species from across the USA were examined. Results show that models of a variety of species can be calibrated to provide reasonable predictions for a particular species. Predictions using this particular model form indicate that model calibration is more important than species-specific height–diameter relations.

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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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Modeling Effects of Overstory Density and Competing Vegetation on Tree Height Growth

Forest Science ◽

10.1093/forestscience/54.1.107 ◽

2008 ◽

Vol 54 (1) ◽

pp. 107-122

Author(s):

Christian Salas ◽

Albert R. Stage ◽

Andrew P. Robinson

Keyword(s):

Habitat Type ◽

Predictor Variable ◽

Tree Height ◽

Height Growth ◽

Tree Age ◽

Individual Tree ◽

Vegetation Control ◽

Index Site ◽

Linear Differential ◽

Tree Height Growth

Abstract We developed and evaluated an individual-tree height growth model for Douglas-fir [Pseudotsuga menziesii (Mirbel) Franco] in the Inland Northwest United States. The model predicts growth for all tree sizes continuously, rather than requiring a transition between independent models for juvenile and mature growth phases. The model predicts the effects of overstory and understory vegetative competition on height growth. Our model requires attained height rather than tree age as a predictor variable, thereby avoiding the problems of site index. Site effects are introduced as a function of ecological habitat type, elevation, aspect, and slope. We used six data sets totaling 3,785 trees in 314 plots. The structure of the data and the model indicated the need for a mixed-effects, nonlinear modeling approach using maximum likelihood in a linear differential equation with a power transformation. Behavior of the model was analyzed using a state-space approach. Our results show that both overstory and understory density affect height growth, allowing a manager to make informed decisions about vegetation control.

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UAV-Based LiDAR Scanning for Individual Tree Detection and Height Measurement in Young Forest Permanent Trials

Remote Sensing ◽

10.3390/rs14010170 ◽

2021 ◽

Vol 14 (1) ◽

pp. 170

Author(s):

Francisco Rodríguez-Puerta ◽

Esteban Gómez-García ◽

Saray Martín-García ◽

Fernando Pérez-Rodríguez ◽

Eva Prada

Keyword(s):

Pinus Radiata ◽

Repeated Measures ◽

Pinus Pinaster ◽

Tree Height ◽

Cost Effective ◽

Permanent Plots ◽

Height Measurement ◽

Measured Variable ◽

Individual Tree ◽

Tree Detection

The installation of research or permanent plots is a very common task in growth and forest yield research. At young ages, tree height is the most commonly measured variable, so the location of individuals is necessary when repeated measures are taken and if spatial analysis is required. Identifying the coordinates of individual trees and re-measuring the height of all trees is difficult and particularly costly (in time and money). The data used comes from three Pinus pinaster Ait. and three Pinus radiata D. Don plantations of 0.8 ha, with an age ranging between 2 and 5 years and mean heights between 1 and 5 m. Five individual tree detection (ITD) methods are evaluated, based on the Canopy Height Model (CHM), where the height of each tree is identified, and its crown is segmented. Three CHM resolutions are used for each method. All algorithms used for individual tree detection (ITD) tend to underestimate the number of trees. The best results are obtained with the R package, ForestTools and rLiDAR. The best CHM resolution for identifying trees was always 10 cm. We did not detect any differences in the relative error (RE) between Pinus pinaster and Pinus radiata. We found a pattern in the ITD depending on the height of the trees to be detected: the accuracy is lower when detecting trees less than 1 m high than when detecting larger trees (RE close to 12% versus 1% for taller trees). Regarding the estimation of tree height, we can conclude that the use of the CHM to estimate height tends to underestimate its value, while the use of the point cloud presents practically unbiased results. The stakeout of forestry research plots and the re-measurement of individual tree heights is an operation that can be performed by UAV-based LiDAR scanning sensors. The individual geolocation of each tree and the measurement of heights versus pole and/or hypsometer measurement is highly accurate and cost-effective, especially when tree height reaches 1–1.5 m.

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

10.20944/preprints201812.0227.v1 ◽

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’ 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.

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A Mixed-Effects Model for the dbh–Height Relationship of Shortleaf Pine (Pinus echinata Mill.)

Southern Journal of Applied Forestry ◽

10.1093/sjaf/32.1.5 ◽

2008 ◽

Vol 32 (1) ◽

pp. 5-11 ◽

Cited By ~ 11

Author(s):

Chakra B. Budhathoki ◽

Thomas B. Lynch ◽

James M. Guldin

Keyword(s):

Tree Height ◽

Ordinary Least Squares ◽

Mixed Effects ◽

Mixed Effects Model ◽

Tree Level ◽

Permanent Plots ◽

Pinus Echinata ◽

Shortleaf Pine ◽

Individual Tree ◽

Total Height

Abstract Individual tree measurements were available from over 200 permanent plots established during 1985–1987 and later remeasured in naturally regenerated even-aged stands of shortleaf pine (Pinus echinata Mill.) in western Arkansas and eastern Oklahoma. The objective of this study was to model shortleaf pine growth in natural stands for the region. As a major component of the shortleaf modeling effort, an individual tree-level dbh–total height model was developed in which plot-specific random parameters were fitted using maximum-likelihood methods. The model predicts tree height on the basis of dbh and dominant stand height (which could be obtained from a site-index model). The mixed-effects model approach was found to predict the total height better than the similar models developed previously for this species using ordinary least-squares methods. Moreover, such a model has the appeal of generalization of the results over a region from which the plots were sampled; and also of calibration of parameters for newly sampled stands with minimal measurements.

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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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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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