scholarly journals Predicting Forest Inventory Attributes Using Airborne Laser Scanning, Aerial Imagery, and Harvester Data

2019 ◽  
Vol 11 (7) ◽  
pp. 797 ◽  
Author(s):  
Atte Saukkola ◽  
Timo Melkas ◽  
Kirsi Riekki ◽  
Sanna Sirparanta ◽  
Jussi Peuhkurinen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Inventory ◽  
Prediction Models ◽  
Basal Area ◽  
Sample Plot ◽  
Weighted Mean ◽  
Individual Tree ◽  
Plot Size ◽  
Head Positions ◽  
Mean Square Errors

The aim of the study was to develop a new method to use tree stem information recorded by harvesters along operative logging in remote sensing-based prediction of forest inventory attributes in mature stands. The reference sample plots were formed from harvester data, using two different tree positions: harvester positions (XYH) in global satellite navigation system and computationally improved harvester head positions (XYHH). Study materials consisted of 158 mature Norway-spruce-dominated stands located in Southern Finland that were clear-cut during 2015–16. Tree attributes were derived from the stem dimensions recorded by the harvester. The forest inventory attributes were compiled for both stands and sample plots generated for stands for four different sample plot sizes (254, 509, 761, and 1018 m2). Prediction models between the harvester-based forest inventory attributes and remote sensing features of sample plots were developed. The stand-level predictions were obtained, and basal-area weighted mean diameter (Dg) and basal-area weighted mean height (Hg) were nearly constant for all model alternatives with relative root-mean-square errors (RMSE) roughly 10–11% and 6–8%, respectively, and minor biases. For basal area (G) and volume (V), using either of the position methods, resulted in roughly similar predictions at best, with approximately 25% relative RMSE and 15% bias. With XYHH positions, the predictions of G and V were nearly independent of the sample plot size within 254–761 m2. Therefore, the harvester-based data can be used as ground truth for remote sensing forest inventory methods. In predicting the forest inventory attributes, it is advisable to utilize harvester head positions (XYHH) and a smallest plot size of 254 m2. Instead, if only harvester positions (XYH) are available, expanding the sample plot size to 761 m2 reaches a similar accuracy to that obtained using XYHH positions, as the larger sample plot moderates the uncertainties when determining the individual tree position.

Fertilized Midrotation-Aged Slash Pine Plantations—Stand Structure and Yield Prediction Models

1989 ◽  
Vol 13 (2) ◽  
pp. 76-80 ◽  
Author(s):  
Robert L. Bailey ◽  
Thomas M. Burgan ◽  
Eric J. Jokela
Keyword(s):  
Prediction Models ◽  
Stand Structure ◽  
Basal Area ◽  
Slash Pine ◽  
Pine Plantations ◽  
Individual Tree ◽  
Investment Opportunities ◽  
User Friendly ◽  
Volume Equations ◽  
Soil Groups

Abstract Data from 263 plots in a regional fertilization study of midrotation-aged slash pine plantations were used to fit prediction equations for basal area, trees per acre, stand average dominant height, diameter distributions, and individual tree heights. The equations include N and P fertilizationrates and CRIFF soil groups as predictor variables. The survival model also accounts for the accelerating effect of fusiform rust on mortality rate. Using published tree volume equations, the prediction of volumes by dbh class for fertilized slash pine plantations is now possible. This integratedsystem of equations is available as a user-friendly computer program that can calculate expected yields by diameter class and aid the forester in evaluating investment opportunities that include forest fertilization. South. J. Appl. For. 13(2):76-80.

scholarly journals Estimating the Height and Basal Area at Individual Tree and Plot Levels in Canadian Subarctic Lichen Woodlands Using Stereo WorldView-3 Images

2019 ◽  
Vol 11 (3) ◽  
pp. 248 ◽  
Author(s):  
Benoît St-Onge ◽  
Simon Grandin
Keyword(s):  
Remote Sensing ◽  
Basal Area ◽  
Ground Cover ◽  
Structural Data ◽  
Tree Level ◽  
Individual Tree ◽  
Visual Contrast ◽  
The Relationship ◽  
Height Data

Lichen woodlands (LW) are sparse forests that cover extensive areas in remote subarctic regions where warming due to climate change is fastest. They are difficult to study in situ or with airborne remote sensing due to their remoteness. We have tested a method for measuring individual tree heights and predicting basal area at tree and plot levels using WorldView-3 stereo images. Manual stereo measurements of tree heights were performed on short trees (2–12 m) of a LW region of Canada with a residual standard error of ≈0.9 m compared to accurate field or UAV height data. The number of detected trees significantly underestimated field counts, especially in peatlands in which the visual contrast between trees and ground cover was low. The heights measured from the WorldView-3 images were used to predict the basal area at individual tree level and summed up at plot level. In the best conditions (high contrast between trees and ground cover), the relationship to field basal area had a R2 of 0.79. Accurate estimates of above ground biomass should therefore also be possible. This method could be used to calibrate an extensive remote sensing approach without in-situ measurements, e.g., by linking precise structural data to ICESAT-2 footprints.

scholarly journals Effects of Basal Area Factor and Plot Size on Precision and Accuracy of Forest Inventory Estimates

2011 ◽  
Vol 28 (3) ◽  
pp. 152-156 ◽  
Author(s):  
Peter Becker ◽  
Tom Nichols
Keyword(s):  
Forest Inventory ◽  
Basal Area ◽  
Cost Effective ◽  
Sampling Errors ◽  
Plot Size ◽  
Accuracy And Precision ◽  
Coefficients Of Variation ◽  
Fixed Radius ◽  
Tree Number ◽  
Biased Estimates

Abstract We tested the effects of plot size (0.05-0.30 ac) and basal area factor (BAF) (5-30) on the accuracy and precision of per-acre estimates of tree number, basal area, biomass (all for trees ≥4.5 in. dbh), and sawtimber volume (for trees ≥11.6 in. dbh). Field sampling errors, such as missing in-trees, did not affect our tests. Virtual variable- and fixed-radius plots were randomly located within an artificial matrix of 130 real plots in well-stocked upland hardwood forests of sawtimber-sized trees in the Missouri Ozarks. Inventory parameters were essentially independent of plot size and BAF, whereas their coefficients of variation decreased with plot size and increased with BAF. Thus, our results for random plots agreed with sampling theory, unlike a previous study using concentric virtual plots in West Virginia forests. A very concentrated zone of high tree density around some plot centers apparently caused the biased estimates by concentric plots. Compared with the entire composite forest, inventory means were accurately estimated (to within 5%) and size class distributions were well represented for plots ≥0.1 ac or ≤15 BAF. Our procedures provide a basis for selecting an efficient and cost-effective sampling design suited to forest characteristics and the inventory's purpose.

LiDAR – A new tool for forest measurements?

2006 ◽  
Vol 82 (2) ◽  
pp. 211-218 ◽  
Author(s):  
David L Evans ◽  
Scott D Roberts ◽  
Robert C Parker
Keyword(s):  
Remote Sensing ◽  
Forest Inventory ◽  
Measurement Tool ◽  
Mississippi State University ◽  
State University ◽  
Individual Tree ◽  
Sensing Technology ◽  
High Measurement ◽  
Application Potential ◽  
Forest Measurement

LiDAR (Light Detection and Ranging) is a remote sensing technology with strong application potential in forest resource management. It provides high measurement precision that can be used for tree and stand measurements. Although LiDAR has not been used widely as an operational measurement tool, there is a significant body of research and a number of projects at Mississippi State University (MSU) that illustrate the potential for this technology to be incorporated into operational forest assessments. This paper provides basic background on the capabilities of LiDAR in a forest measurement context that illustrates specific examples of LiDAR use including: 1) individual tree assessments, 2) a forest inventory protocol currently being operationally tested, 3) forest structure analysis, and 4) forest typing. Key words: LiDAR, remote sensing, tree identification, tree measurements, forest inventory, forest types

Modelling growing space requirement for Alnus nepalensis D. Don in Nepal

1970 ◽  
Vol 16 (2) ◽  
pp. 30-36 ◽  
Author(s):  
Ram Prasad Sharma
Keyword(s):  
Prediction Models ◽  
Growth Models ◽  
Basal Area ◽  
Individual Tree ◽  
Area Density ◽  
Space Requirement ◽  
Space Model ◽  
Alnus Nepalensis ◽  
Crown Diameter ◽  
Individual Trees

Relationship between crown diameter and stem diameter of individual trees can be translated into mathematical model, and used to generate information of growing space requirement for individual trees and crown competition index for growth models. Nine different crown diameter prediction models were developed using inventory data of Alnus nepalensis trees from a part of Parbat and Syanja districts in Nepal. Among those developed, a non-linear three parameter-based model (W = β0 {1 – exp( - β1D)}β2) explained the greatest proportion of variations of crown diameter (R2adj = 0.78), and showed desirable behaviour of flexibility and robustness. An individual tree growing space model was then derived from crown model to generate important information of shocking limits and stand basal area density for monoculture plantation or natural stands of Alnus nepalensis. Because of its flexibility, crown model is seemed potentially useful for extrapolation purpose also. However, the model cannot be applied for buttressed, wolfed and malformed trees. Key words: Alnus nepalensis; crown model; growing space model; stocking limit; basal area density Banko Janakari Vol.16(2) 2006 pp.30-36

scholarly journals Generalized Nonlinear Mixed-Effects Individual Tree Diameter Increment Models for Beech Forests in Slovakia

Forests ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 451 ◽  
Author(s):  
Ram P. Sharma ◽  
Igor Štefančík ◽  
Zdeněk Vacek ◽  
Stanislav Vacek
Keyword(s):  
Basal Area ◽  
European Beech ◽  
Site Index ◽  
Development Stage ◽  
Mixed Effects ◽  
Site Quality ◽  
Stand Age ◽  
Sample Plot ◽  
Individual Tree ◽  
Diameter Increment

Individual tree growth and yield models precisely describe tree growth irrespective of stand complexity and are capable of simulating various silvicultural alternatives in the stands with diverse structure, species composition, and management history. We developed both age dependent and age independent diameter increment models using long-term research sample plot data collected from both monospecific and mixed stands of European beech (Fagus sylvatica L.) in the Slovak Republic. We used diameter at breast height (DBH) as a main predictor and other characteristics describing site quality (site index), stand development stage (dominant height and stand age), stand density or competition (ratio of individual tree DBH to quadratic mean diameter), species mixture (basal area proportion of a species of interest), and dummy variable describing stand management regimes as covariate predictors to develop the models. We evaluated eight versatile growth functions in the first stage using DBH as a single predictor and selected the most suitable one, i.e., Chapman-Richards function for further analysis through the inclusion of covariate predictors. We introduced the random components describing sample plot-level random effects and stochastic variations on the diameter increment, into the models through the mixed-effects modelling. The autocorrelation caused by hierarchical data-structure, which is assumed to be partially reduced by mixed-effects modelling, was removed through the inclusion of the parameter accounting for the autoregressive error-structures. The models described about two-third parts of a total variation in the diameter increment without significant trends in the residuals. Compared to the age independent mixed-effects model (conditional coefficient of determination, R c 2 = 0.6566; root mean square error, RMSE = 0.1196), the age dependent model described a significantly larger proportion of the variations in diameter increment ( R c 2 = 0.6796, RMSE = 0.1141). Diameter increment was significantly influenced differently by covariate predictors included into the models. Diameter increment decreased with the advancement of stand development stage (increased dominant height and stand age), increasing intraspecific competition (increased basal area proportion of European beech per sample plot), and diameter increment increased with increasing site quality (increased site index) and decreased competition (increased ratio of DBH to quadratic mean diameter). Our mixed-effects models, which can be easily localized with the random effects estimated from prior measurement of diameter increments of four randomly selected trees per sample plot, will provide high prediction accuracies. Our models may be used for simulating growth of European beech irrespective of its stand structural complexity, as these models have included various covariate variables describing both tree-and stand-level characteristics, thinning regimes, except the climate characteristics. Together with other forest models, our models will be used as inputs to the growth simulator to be developed in the future, which is important for decision-making in forestry.

scholarly journals Measuring Individual Tree Diameter and Height Using GatorEye High-Density UAV-Lidar in an Integrated Crop-Livestock-Forest System

2020 ◽  
Vol 12 (5) ◽  
pp. 863 ◽  
Author(s):  
Ana Paula Dalla Corte ◽  
Franciel Eduardo Rex ◽  
Danilo Roberti Alves de Almeida ◽  
Carlos Roberto Sanquetta ◽  
Carlos A. Silva ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Point Cloud ◽  
Tree Height ◽  
High Density ◽  
Forest Plantations ◽  
Density Point ◽  
Individual Tree ◽  
Total Height ◽  
Area Of Interest ◽  
Mean Square Errors

Accurate forest parameters are essential for forest inventory. Traditionally, parameters such as diameter at breast height (DBH) and total height are measured in the field by level gauges and hypsometers. However, field inventories are usually based on sample plots, which, despite providing valuable and necessary information, are laborious, expensive, and spatially limited. Most of the work developed for remote measurement of DBH has used terrestrial laser scanning (TLS), which has high density point clouds, being an advantage for the accurate forest inventory. However, TLS still has a spatial limitation to application because it needs to be manually carried to reach the area of interest, requires sometimes challenging field access, and often requires a field team. UAV-borne (unmanned aerial vehicle) lidar has great potential to measure DBH as it provides much higher density point cloud data as compared to aircraft-borne systems. Here, we explore the potential of a UAV-lidar system (GatorEye) to measure individual-tree DBH and total height using an automatic approach in an integrated crop-livestock-forest system with seminal forest plantations of Eucalyptus benthamii. A total of 63 trees were georeferenced and had their DBH and total height measured in the field. In the high-density (>1400 points per meter squared) UAV-lidar point cloud, we applied algorithms (usually used for TLS) for individual tree detection and direct measurement of tree height and DBH. The correlation coefficients (r) between the field-observed and UAV lidar-derived measurements were 0.77 and 0.91 for DBH and total tree height, respectively. The corresponding root mean square errors (RMSE) were 11.3% and 7.9%, respectively. UAV-lidar systems have the potential for measuring relatively broad-scale (thousands of hectares) forest plantations, reducing field effort, and providing an important tool to aid decision making for efficient forest management. We recommend that this potential be explored in other tree plantations and forest environments.

Effects of Sample Plot Size and Prediction Models on Diameter Distribution Recovery

2021 ◽  
Author(s):  
Josh B Bankston ◽  
Charles O Sabatia ◽  
Krishna P Poudel
Keyword(s):  
Loblolly Pine ◽  
Method Of Moments ◽  
Prediction Models ◽  
Unit Area ◽  
Hybrid Approach ◽  
Diameter Distribution ◽  
Sample Plot ◽  
Plot Size ◽  
Model Form ◽  
Recovery System

Abstract Distribution of tree diameters in a stand is characterized using models that predict diameter moments and/or percentiles in conjunction with a mathematical system to recover the parameters of an assumed statistical distribution. Studies have compared Weibull diameter distribution recovery systems but arrived at different conclusions regarding the best approach for recovering a stand’s diameter distribution from predicted stand-level statistics. We assessed the effects of sample plot size and diameter moments/percentiles prediction models on the accuracy of three approaches used in recovering Weibull distribution parameters—method of moments, percentile method, and moments-percentile hybrid method. Data from five plot sizes, four of which were virtually created from existing larger plots, from unthinned loblolly pine (Pinus taeda) plantations, were used to fit moments/percentile prediction models and to evaluate the accuracy of the diameter distribution recovered using three approaches. Both plot size and prediction model form affected the accuracy of the recovery approaches as indicated by the changes in their ranking from one plot size to another for the same model form. The method of moments approach ranked best when the evaluation error index did not account for tree stumpage value, but the moments-percentile hybrid approach ranked best when stumpage value was considered. Study Implications Diameter distribution recovery techniques make it possible to disaggregate trees per unit area, predicted by the whole stand growth and yield models, into diameter and utilization product classes. Thus, the techniques provide insights into stand structure, which can guide management decisions such as thinning and selection harvesting. The techniques are also used to generate yield tables by product class, which are important inputs into harvest scheduling optimization programs. An accurate diameter recovery technique is therefore critical to forest management and planning. Based on the findings of this study, the best approach of developing a diameter distribution recovery system for unthinned loblolly pine plantations would be to use the hybrid approach, with tree diameter data collected from plots of at least one-tenth hectare. The well-known (and, most likely, widely used) method of moments approach may not be the best choice. For predicting stand diameter moments and order statistics used in a diameter distribution recovery system, it would be best to use a linear additive model that incorporates a measure of stand density, such as relative spacing and/or number of trees per unit area, and a measure of the stand’s stage of development, such as dominant height and/or age.

scholarly journals An individual-tree linear mixed-effects model for predicting the basal area increment of major forest species in Southern Europe

2020 ◽  
Vol 29 (3) ◽  
pp. e019
Author(s):  
Lucio Di Cosmo ◽  
Diego Giuliani ◽  
Maria Michela Dickson ◽  
Patrizia Gasparini
Keyword(s):  
Forest Inventory ◽  
Basal Area ◽  
Ordinary Least Squares ◽  
Mixed Effects ◽  
Forest Growth ◽  
Mixed Effects Model ◽  
Basal Area Increment ◽  
Individual Tree ◽  
Diameter At Breast Height ◽  
Breast Height

Aims of the study. Assessment of growth is essential to support sustainability of forest management and forest policies. The objective of the study was to develop a species-specific model to predict the annual increment of tree basal area through variables recorded by forest surveys, to assess forest growth directly or in the context of more complex forest growth and yield simulation models.Area of the study. Italy.Material and methods. Data on 34638 trees of 31 different forest species collected in 5162 plots of the Italian National Forest Inventory were used; the data were recorded between 2004 and 2006. To account for the hierarchical structure of the data due to trees nested within plots, a two-level mixed-effects modelling approach was used.Main results. The final result is an individual-tree linear mixed-effects model with species as dummy variables. Tree size is the main predictor, but the model also integrates geographical and topographic predictors and includes competition. The model fitting is good (McFadden’s Pseudo-R2 0.536), and the variance of the random effect at the plot level is significant (intra-class correlation coefficient 0.512). Compared to the ordinary least squares regression, the mixed-effects model allowed reducing the mean absolute error of estimates in the plots by 64.5% in average.Research highlights. A single tree-level model for predicting the basal area increment of different species was developed using forest inventory data. The data used for the modelling cover 31 species and a great variety of growing conditions, and the model seems suitable to be applied in the wider context of Southern Europe.   Keywords: Tree growth; forest growth modelling; forest inventory; hierarchical data structure; Italy.Abbreviations used: BA - basal area; BAI – five-year periodic basal area increment; BALT - basal area of trees larger than the subject tree; BASPratio - ratio of subject tree species basal area to stand basal area; BASTratio - ratio of subject tree basal area to stand basal area; CRATIO - crown ratio; DBH – diameter at breast height ; DBH0– diameter at breast height corresponding to five years before the survey year; DBHt– diameter at breast height measured in the survey year; DI5 - five-year, inside bark, DBH increment; HDOM - dominant height; LULUCF - Land Use, Land Use Changes and Forestry; ME - mean error; MAE - mean absolute error; MPD - mean percent deviation; MPSE - mean percent standard error; NFI(s) - National Forest Inventory/ies; OLS - ordinary least squares regression; RMSE - root mean squared error; UNFCCC - United Nation Framework Convention on Climate Change.

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