Predicting Future Diameter Distributions Given Current Stand Attributes

2022 ◽  
Author(s):  
Quang V. Cao
Keyword(s):  
Prediction Method ◽  
Diameter Distribution ◽  
Tree Level ◽  
Individual Tree ◽  
Distribution Parameters ◽  
Level Model ◽  
Novel Method ◽  
Quadratic Mean Diameter ◽  
Stand Attributes ◽  
Individual Trees

This study discussed four methods to project a diameter distribution from age A1 to age A2. Method 1 recovers parameters of the distribution at age A2 from stand attributes at that age. Method 2 uses a stand-level model to grow the quadratic mean diameter, and then recovers the distribution parameters from that prediction. Method 3 grows the diameter distribution by assuming tree-level survival and diameter growth functions. Method 4 first converts the diameter distribution at age A1 into a list of individual trees before growing these trees to age A2. In a numerical example employing the Weibull distribution, methods 3 and 4 produced better results based on two types of error indices and the relative predictive error for each diameter class. Method 4 is a novel method that converts a diameter distribution into a list of individual-trees, and in the process, successfully links together diameter distribution, individual-tree, and whole stand models.

scholarly journals Prediction Comparison of Stand Parameters and Two Ecosystem Services through New Growth and Yield Model System for Mixed Nothofagus Forests in Southern Chile

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1236
Author(s):  
Paulo Moreno-Meynard ◽  
Sebastian Palmas ◽  
Salvador A. Gezan
Keyword(s):  
Ecosystem Services ◽  
Basal Area ◽  
Growth And Yield ◽  
Diameter Distribution ◽  
Tree Level ◽  
Yield Model ◽  
Individual Tree ◽  
Nothofagus Forests ◽  
Level Model ◽  
Stand Parameters

Forest managers need tools to predict the behavior of forests not only for the main stand parameters, such as basal area and volume, but also for ecosystem services such as timber volume and carbon sequestration. Useful tools to predict these parameters are growth and yield model systems with several possible options for modeling, such as the whole stand-level model, with or without diameter distribution generation, individual tree-level model, and compatibility models. However, those tools are scarce or developed mainly for forest plantations that are mostly located in the northern hemisphere. Thus, this study focuses on analyzing predictions of several growth and yield models built for native mixed Nothofagus forests from southern Chile, using the simulator Nothopack. A dataset of 19 permanent plots with three measurements were used for comparing the different models. Individual tree-level simulation presented the best goodness-of-fit statistics for stand parameters and ecosystem services. For example, the basal area gave an R2emp of 0.97 and 0.87 at 6 and 12 years of projection. However, the stand-level simulations with a generation of diameter distribution and both compatibility models showed satisfactory performance, both in accuracy and bias control. The simulator Nothopack, which has the capability of obtaining detailed outputs, is a useful tool to support management plans for these forest ecosystems.

scholarly journals Spatial Models With Inter-Tree Competition From Airborne Laser Scanning Improve Estimates of Genetic Variance

2021 ◽  
Vol 11 ◽  
Author(s):  
David Pont ◽  
Heidi S. Dungey ◽  
Mari Suontama ◽  
Grahame T. Stovold
Keyword(s):  
Laser Scanning ◽  
Management Practices ◽  
Spatial Models ◽  
Airborne Laser Scanning ◽  
Tree Level ◽  
Residual Variance ◽  
Spatial Effects ◽  
Individual Tree ◽  
Airborne Laser ◽  
Individual Trees

Phenotyping individual trees to quantify interactions among genotype, environment, and management practices is critical to the development of precision forestry and to maximize the opportunity of improved tree breeds. In this study we utilized airborne laser scanning (ALS) data to detect and characterize individual trees in order to generate tree-level phenotypes and tree-to-tree competition metrics. To examine our ability to account for environmental variation and its relative importance on individual-tree traits, we investigated the use of spatial models using ALS-derived competition metrics and conventional autoregressive spatial techniques. Models utilizing competition covariate terms were found to quantify previously unexplained phenotypic variation compared with standard models, substantially reducing residual variance and improving estimates of heritabilities for a set of operationally relevant traits. Models including terms for spatial autocorrelation and competition performed the best and were labelled ACE (autocorrelation-competition-error) models. The best ACE models provided statistically significant reductions in residuals ranging from −65.48% for tree height (H) to −21.03% for wood stiffness (A), and improvements in narrow sense heritabilities from 38.64% for H to 14.01% for A. Individual tree phenotyping using an ACE approach is therefore recommended for analyses of research trials where traits are susceptible to spatial effects.

scholarly journals Individual Tree Attribute Estimation and Uniformity Assessment in Fast-Growing Eucalyptus spp. Forest Plantations Using Lidar and Linear Mixed-Effects Models

2020 ◽  
Vol 12 (21) ◽  
pp. 3599
Author(s):  
Rodrigo Vieira Leite ◽  
Carlos Alberto Silva ◽  
Midhun Mohan ◽  
Adrián Cardil ◽  
Danilo Roberti Alves de Almeida ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Tree Level ◽  
Forest Plantations ◽  
Forest Plantation ◽  
Projected Area ◽  
Individual Tree ◽  
Lorenz Curves ◽  
Fast Growing ◽  
Crown Volume ◽  
Individual Trees

Fast-growing Eucalyptus spp. forest plantations and their resultant wood products are economically important and may provide a low-cost means to sequester carbon for greenhouse gas reduction. The development of advanced and optimized frameworks for estimating forest plantation attributes from lidar remote sensing data combined with statistical modeling approaches is a step towards forest inventory operationalization and might improve industry efficiency in monitoring and managing forest resources. In this study, we first developed and tested a framework for modeling individual tree attributes in fast-growing Eucalyptus forest plantation using airborne lidar data and linear mixed-effect models (LME) and assessed the gain in accuracy compared to a conventional linear fixed-effects model (LFE). Second, we evaluated the potential of using the tree-level estimates for determining tree attribute uniformity across different stand ages. In the field, tree measurements, such as tree geolocation, species, genotype, age, height (Ht), and diameter at breast height (dbh) were collected through conventional forest inventory practices, and tree-level aboveground carbon (AGC) was estimated using allometric equations. Individual trees were detected and delineated from lidar-derived canopy height models (CHM), and crown-level metrics (e.g., crown volume and crown projected area) were computed from the lidar 3-D point cloud. Field and lidar-derived crown metrics were combined for ht, dbh, and AGC modeling using an LME. We fitted a varying intercept and slope model, setting species, genotype, and stand (alone and nested) as random effects. For comparison, we also modeled the same attributes using a conventional LFE model. The tree attribute estimates derived from the best LME model were used for assessing forest uniformity at the tree level using the Lorenz curves and Gini coefficient (GC). We successfully detected 96.6% of the trees from the lidar-derived CHM. The best LME model for estimating the tree attributes was composed of the stand as a random effect variable, and canopy height, crown volume, and crown projected area as fixed effects. The %RMSE values for tree-level height, dbh, and AGC were 8.9%, 12.1%, and 23.7% for the LFE model and improved to 7.3%, 7.1%, and 13.6%, respectively, for the LME model. Tree attributes uniformity was assessed with the Lorenz curves and tree-level estimations, especially for the older stands. All stands showed a high level of tree uniformity with GC values approximately 0.2. This study demonstrates that accurate detection of individual trees and their associated crown metrics can be used to estimate Ht, dbh, and AGC stocks as well as forest uniformity in fast-growing Eucalyptus plantations forests using lidar data as inputs to LME models. This further underscores the high potential of our proposed approach to monitor standing stock and growth in Eucalyptus—and similar forest plantations for carbon dynamics and forest product planning.

scholarly journals LiDAR Applications to Estimate Forest Biomass at Individual Tree Scale: Opportunities, Challenges and Future Perspectives

Forests ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 550
Author(s):  
Dandan Xu ◽  
Haobin Wang ◽  
Weixin Xu ◽  
Zhaoqing Luan ◽  
Xia Xu
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
Forest Biomass ◽  
Remotely Sensed ◽  
Tree Level ◽  
Biomass Estimation ◽  
Future Perspectives ◽  
Individual Tree ◽  
The Individual ◽  
Individual Trees

Accurate forest biomass estimation at the individual tree scale is the foundation of timber industry and forest management. It plays an important role in explaining ecological issues and small-scale processes. Remotely sensed images, across a range of spatial and temporal resolutions, with their advantages of non-destructive monitoring, are widely applied in forest biomass monitoring at global, ecoregion or community scales. However, the development of remote sensing applications for forest biomass at the individual tree scale has been relatively slow due to the constraints of spatial resolution and evaluation accuracy of remotely sensed data. With the improvements in platforms and spatial resolutions, as well as the development of remote sensing techniques, the potential for forest biomass estimation at the single tree level has been demonstrated. However, a comprehensive review of remote sensing of forest biomass scaled at individual trees has not been done. This review highlights the theoretical bases, challenges and future perspectives for Light Detection and Ranging (LiDAR) applications of individual trees scaled to whole forests. We summarize research on estimating individual tree volume and aboveground biomass (AGB) using Terrestrial Laser Scanning (TLS), Airborne Laser Scanning (ALS), Unmanned Aerial Vehicle Laser Scanning (UAV-LS) and Mobile Laser Scanning (MLS, including Vehicle-borne Laser Scanning (VLS) and Backpack Laser Scanning (BLS)) data.

scholarly journals Influence of timber harvesting costs on the layout of cuttings and economic return in forest planning based on dynamic treatment units

2018 ◽  
Vol 27 (1) ◽  
pp. e001 ◽  
Author(s):  
Adrián Pascual ◽  
Timo Pukkala ◽  
Sergio De-Miguel ◽  
Annukka Pesonen ◽  
Petteri Packalen
Keyword(s):  
Laser Scanning ◽  
Spatial Optimization ◽  
Timber Harvesting ◽  
Diameter Distribution ◽  
Forest Planning ◽  
Timber Production ◽  
Individual Tree ◽  
Dynamic Treatment ◽  
Stand Attributes ◽  
Harvesting Costs

Aim of study: To analyze the influence of harvesting costs on the distribution and type of cuttings when forest management planning is based on the dynamic treatment units (DTUs) approach.Area of study: A Mediterranean pine forest in Central Spain.Materials and methods: Airborne laser scanning data were used in area-based approach to predict stand attributes and delineate segments that were used as calculation units. Predicted stand attributes and existing models for diameter distribution and individual-tree growth were used to simulate alternative management schedules for each segment for a 60-year planning horizon divided into three 20-year periods. Three alternative forest planning problems were formulated. They aimed to maximize or minimize net income, or maximize timber production with a constant flow of harvested timber. Spatial goals were used in all cases to enhance the clustering of treatments.Main results: Maxizing timber production without considering harvesting costs can be costly, even close to the plan that minimized net incomes. Maximizing net incomes led to frequent use of final felling instead of thinnings, placing cuttings near forest roads and creating more compact DTUs than obtained in the plan that maximized timber production.Research highlights: Compared to previous studies on DTUs, this study integrated felling and forwarding costs, which depended on distance to road and stand attributes, in the process of creating DTUs by means of spatial optimization.

scholarly journals Detection of Pine Shoot Beetle (PSB) Stress on Pine Forests at Individual Tree Level using UAV-Based Hyperspectral Imagery and Lidar

2019 ◽  
Vol 11 (21) ◽  
pp. 2540 ◽  
Author(s):  
Qinan Lin ◽  
Huaguo Huang ◽  
Jingxu Wang ◽  
Kan Huang ◽  
Yangyang Liu
Keyword(s):  
Forest Health ◽  
Pine Forests ◽  
Radiative Transfer Model ◽  
Tree Level ◽  
Lidar Data ◽  
Transfer Model ◽  
Combined Approach ◽  
Individual Tree ◽  
Pine Shoot Beetle ◽  
Individual Trees

In recent years, the outbreak of the pine shoot beetle (PSB), Tomicus spp., has caused serious shoots damage and the death of millions of trees in Yunnan pine forests in southwestern China. It is urgent to develop a convincing approach to accurately assess the shoot damage ratio (SDR) for monitoring the PSB insects at an early stage. Unmanned airborne vehicles (UAV)-based sensors, including hyperspectral imaging (HI) and lidar, have very high spatial and spectral resolutions, which are very useful to detect forest health. However, very few studies have utilized HI and lidar data to estimate SDRs and compare the predictive power for mapping PSB damage at the individual tree level. Additionally, the data fusion of HI and lidar may improve the detection accuracy, but it has not been well studied. In this study, UAV-based HI and lidar data were fused to detect PSB. We systematically evaluated the potential of a hyperspectral approach (only-HI data), a lidar approach (only-lidar data), and a combined approach (HI plus lidar data) to characterize PSB damage of individual trees using the Random Forest (RF) algorithm, separately. The most innovative point is the proposed new method to extract the three dimensional (3D) shadow distribution of each tree crown based on a lidar point cloud and the 3D radiative transfer model RAPID. The results show that: (1) for the accuracy of estimating the SDR of individual trees, the lidar approach (R2 = 0.69, RMSE = 12.28%) performed better than hyperspectral approach (R2 = 0.67, RMSE = 15.87%), and in addition, it was useful to detect dead trees with an accuracy of 70%; (2) the combined approach has the highest accuracy (R2 = 0.83, RMSE = 9.93%) for mapping PSB damage degrees; and (3) when combining HI and lidar data to predict SDRs, two variables have the most contributions, which are the leaf chlorophyll content (Cab) derived from hyperspectral data and the return intensity of the top of shaded crown (Int_Shd_top) from lidar metrics. This study confirms the high possibility to accurately predict SDRs at individual tree level if combining HI and lidar data. The 3D radiative transfer model can determine the 3D crown shadows from lidar, which is a key information to combine HI and lidar. Therefore, our study provided a guidance to combine the advantages of hyperspectral and lidar data to accurately measure the health of individual trees, enabling us to prioritize areas for forest health promotion. This method may also be used for other 3D land surfaces, like urban areas.

scholarly journals Modelling the Diameter Distribution of Savanna Trees with Drone-Based LiDAR

2021 ◽  
Vol 13 (7) ◽  
pp. 1266
Author(s):  
Mitchel L. M. Rudge ◽  
Shaun R. Levick ◽  
Renee E. Bartolo ◽  
Peter D. Erskine
Keyword(s):  
Allometric Scaling ◽  
Scale Up ◽  
Point Clouds ◽  
Stem Diameter ◽  
Diameter Distribution ◽  
Tree Level ◽  
Individual Tree ◽  
Tree Crowns ◽  
Tree Metrics ◽  
Diameter Distributions

The diameter distribution of savanna tree populations is a valuable indicator of savanna health because changes in the number and size of trees can signal a shift from savanna to grassland or forest. Savanna diameter distributions have traditionally been monitored with forestry techniques, where stem diameter at breast height (DBH) is measured in the field within defined sub-hectare plots. However, because the spatial scale of these plots is often misaligned with the scale of variability in tree populations, there is a need for techniques that can scale-up diameter distribution surveys. Dense point clouds collected from uncrewed aerial vehicle laser scanners (UAV-LS), also known as drone-based LiDAR (Light Detection and Ranging), can be segmented into individual tree crowns then related to stem diameter with the application of allometric scaling equations. Here, we sought to test the potential of UAV-LS tree segmentation and allometric scaling to model the diameter distributions of savanna trees. We collected both UAV-LS and field-survey data from five one-hectare savanna woodland plots in northern Australia, which were divided into two calibration and three validation plots. Within the two calibration plots, allometric scaling equations were developed by linking field-surveyed DBH to the tree metrics of manually delineated tree crowns, where the best performing model had a bias of 1.8% and the relatively high RMSE of 39.2%. A segmentation algorithm was then applied to segment individual tree crowns from UAV-LS derived point clouds, and individual tree level segmentation accuracy was assessed against the manually delineated crowns. 47% of crowns were accurately segmented within the calibration plots and 68% within the validation plots. Using the site-specific allometry, DBH was modelled from crown metrics within all five plots, and these modelled results were compared to field-surveyed diameter distributions. In all plots, there were significant differences between field-surveyed and UAV-LS modelled diameter distributions, which became similar at two of the plots when smaller trees (<10 cm DBH) were excluded. Although the modelled diameter distributions followed the overall trend of field surveys, the non-significant result demonstrates a need for the adoption of remotely detectable proxies of tree size which could replace DBH, as well as more accurate tree detection and segmentation methods for savanna ecosystems.

scholarly journals A simulation-based approach to a near-optimal thinning strategy: allowing harvesting times to be determined for individual trees

2019 ◽  
pp. 320-331
Author(s):  
Peter Fransson ◽  
Oskar Franklin ◽  
Ola Lindroos ◽  
Urban Nilsson ◽  
Åke Brännström
Keyword(s):  
Optimal Strategy ◽  
Management Strategy ◽  
Basal Area ◽  
Forest Growth ◽  
Tree Level ◽  
Individual Tree ◽  
Area Reduction ◽  
Forest Growth Model ◽  
The Individual ◽  
Individual Trees

As various methods for precision inventories, including light detection and ranging (LiDAR), are becoming increasingly common in forestry, planning at the individual-tree level is becoming more viable. In this study, we present a method for finding the optimal thinning times for individual trees from an economic perspective. The method utilizes a forest growth model based on individual trees that has been fitted to Norway spruce (Picea abies (L.) Karst.) stands in northern Sweden. We find that the optimal management strategy is to thin from above (i.e., harvesting trees that are larger than average). We compare our optimal strategy with a conventional management strategy and find that the optimal strategy results in approximately 20% higher land expectation value. Furthermore, we find that for the optimal strategy, increasing the discount rate will reduce the final harvest age and increase the basal area reduction. Decreasing the cost to initiate a thinning (e.g., machinery-related transportation costs) increases the number of thinnings and delays the first thinning.

scholarly journals An End to End Process Development for UAV-SfM Based Forest Monitoring: Individual Tree Detection, Species Classification and Carbon Dynamics Simulation

Forests ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 680 ◽  
Author(s):  
Fujimoto ◽  
Haga ◽  
Matsui ◽  
Machimura ◽  
Hayashi ◽  
...  
Keyword(s):  
Forest Management ◽  
Process Model ◽  
Carbon Dynamics ◽  
Forest Degradation ◽  
Dynamics Simulation ◽  
Forest Monitoring ◽  
Tree Level ◽  
Individual Tree ◽  
End To End ◽  
Individual Trees

To promote Bio-Energy with Carbon dioxide Capture and Storage (BECCS), which aims to replace fossil fuels with bio energy and store carbon underground, and Reducing Emissions from Deforestation and forest Degradation (REDD+), which aims to reduce the carbon emissions produced by forest degradation, it is important to build forest management plans based on the scientific prediction of forest dynamics. For Measurement, Reporting and Verification (MRV) at an individual tree level, it is expected that techniques will be developed to support forest management via the effective monitoring of changes to individual trees. In this study, an end-to-end process was developed: (1) detecting individual trees from Unmanned Aerial Vehicle (UAV) derived digital images; (2) estimating the stand structure from crown images; (3) visualizing future carbon dynamics using a forest ecosystem process model. This process could detect 93.4% of individual trees, successfully classified two species using Convolutional Neural Network (CNN) with 83.6% accuracy and evaluated future ecosystem carbon dynamics and the source-sink balance using individual based model FORMIND. Further ideas for improving the sub-process of the end to end process were discussed. This process is expected to contribute to activities concerned with carbon management such as designing smart utilization for biomass resources and projecting scenarios for the sustainable use of ecosystem services.

scholarly journals Rating Introgression between Lodgepole and Jack Pine at the Individual Tree Level Using Morphological Traits

2011 ◽  
Vol 28 (3) ◽  
pp. 138-145 ◽  
Author(s):  
Katherine P. Bleiker ◽  
Allan L. Carroll
Keyword(s):  
Pinus Banksiana ◽  
Pinus Contorta ◽  
Morphological Traits ◽  
Function Analysis ◽  
Introgressive Hybridization ◽  
Principal Component ◽  
Jack Pine ◽  
Tree Level ◽  
Individual Tree ◽  
Individual Trees

Abstract Introgressive hybridization between species generates novel gene combinations and phenotypes. We required an accessible, objective method of rating introgression between lodgepole pine (Pinus contorta var. latifolia [Engelm.] Critchfield) and jack pine (Pinus banksiana Lamb.) for individual trees where their ranges overlap in Canada for use in another study on host species effects on resistance to an eruptive herbivore that has recently expanded its range. We adapted, simplified, and fully quantified a morphological index developed to rate introgression of pine populations and applied it to individual trees. In addition to principal component analysis (PCA), we also used discriminant function analysis (DFA), a potentially more powerful method given a priori knowledge of parent taxa, to generate introgression ratings. Among-tree variation in morphological traits and introgression was high at sites within the hybrid zone but very low at pure parent sites. PCA and DFA produced similar introgression ratings at the stand level, but ratings differed substantially for some individual trees. Certain morphological traits may be omitted from both PCA and DFA with little impact on stand-level ratings. The discriminant functions presented here are based on easy-to-measure, fully quantifiable morphological traits and can be used by other researchers to produce relative introgression ratings for lodgepole and jack pine. The approach may also be applied to other plant hybrid systems.

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