scholarly journals Spatial Variability and Optimal Number of Rain Gauges for Sampling Throughfall under Single Oak Trees during the Leafless Period

Forests ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 585
Author(s):  
Omid Fathizadeh ◽  
Seyed Mohammad Moein Sadeghi ◽  
Iman Pazhouhan ◽  
Sajad Ghanbari ◽  
Pedram Attarod ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Optimal Number ◽  
Tree Level ◽  
Individual Tree ◽  
Rain Gauges ◽  
Oak Trees ◽  
Interception Loss ◽  
Quercus Brantii ◽  
The Individual ◽  
Zagros Region

This study examined the spatial variability of throughfall (Tf) and its implications for sampling throughfall during the leafless period of oak trees. To do this, we measured Tf under five single Brant’s oak trees (Quercus brantii var. Persica), in the Zagros region of Iran, spanning a six-month-long study period. Overall, the Tf amounted to 85.7% of gross rainfall. The spatial coefficient of variation (CV) for rainstorm total Tf volumes was 25%, on average, and it decreased as the magnitude of rainfall increased. During the leafless period, Tf was spatially autocorrelated over distances of 1 to 3.5 m, indicating the benefits of sampling with relatively elongated troughs. Our findings highlight the great variability of Tf under the canopies of Brant’s oaks during their leafless period. We may also conclude that the 29 Tf collectors used in the present study were sufficient to robustly estimate tree-scale Tf values within a 10% error of the mean at the 95% confidence level. Given that a ±10% uncertainty in Tf is associated with a ±100% uncertainty in interception loss, this underscores the challenges in its measurement at the individual tree level in the leafless season. These results are valuable for determining the number and placement of Tf collectors, and their expected level of confidence, when measuring tree-level Tf of scattered oak trees and those in forest stands.

scholarly journals SISFLOR: A COMPUTATIONAL SYSTEM TO DETERMINE THE OPTIMAL TREE BUCKING

2020 ◽  
Vol 28 ◽  
pp. 192-201
Author(s):  
Rodrigo Freitas Silva ◽  
Marcelo Otone Aguiar ◽  
Mayra Luiza Marques Da Silva ◽  
Gilson Fernandes Da Silva ◽  
Adriano Ribeiro De Mendonça
Keyword(s):  
Optimization Technique ◽  
Wood Products ◽  
Tree Level ◽  
Economic Losses ◽  
Computational System ◽  
Individual Tree ◽  
Optimal Tree ◽  
Cutting Pattern ◽  
Forest Enterprises ◽  
The Individual

A continuously competitive forest market and tied to the demands for wood products promotes the study and development of applications that increase the revenue of the forest enterprises. At harvesting, the cutting pattern (forest assortment) in which the trees are traced is traditionally determined by the experience of the chainsaw operator without using any optimization technique, which may result in economic losses in relation to the commercialized products. In general, there are numerous distinct assortments that can be chosen and hardly processed by a brute-force algorithm. This is the forest assortment problem at the individual tree level with the objetice of maximizing the commercial values of the felled trees. stem-level bucking optimization problem. The aim is to maximize the sales value of harvested trees. Dynamic Programming (DP) is an efficient optimization technique to determine the optimum bucking tree as it significantly reduces the number of calculations to be made. Thus, the objective of this work was to develop a modern and intuitive computational system that is able to find the optimum tree stem bucking through DP to help companies over the bole tracing, therefore, characterizing itself as a tool that supports decision making. After the execution of the system, the optimum assortment is shown by sequentially detailing all products that should be removed from the analyzed bole as well as their respective volumes and revenue.

scholarly journals Finer Resolution Estimation and Mapping of Mangrove Biomass Using UAV LiDAR and WorldView-2 Data

Forests ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 871 ◽  
Author(s):  
Qiu ◽  
Wang ◽  
Zou ◽  
Yang ◽  
Xie ◽  
...  
Keyword(s):  
Tree Level ◽  
Inference Method ◽  
Mangrove Species ◽  
Individual Tree ◽  
Model Method ◽  
Lower Accuracy ◽  
Field Samples ◽  
The Individual ◽  
Mangrove Biomass ◽  
Grid Based

To estimate mangrove biomass at finer resolution, such as at an individual tree or clump level, there is a crucial need for elaborate management of mangrove forest in a local area. However, there are few studies estimating mangrove biomass at finer resolution partly due to the limitation of remote sensing data. Using WorldView-2 imagery, unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) data, and field survey datasets, we proposed a novel method for the estimation of mangrove aboveground biomass (AGB) at individual tree level, i.e., individual tree-based inference method. The performance of the individual tree-based inference method was compared with the grid-based random forest model method, which directly links the field samples with the UAV LiDAR metrics. We discussed the feasibility of the individual tree-based inference method and the influence of diameter at breast height (DBH) on individual segmentation accuracy. The results indicated that (1) The overall classification accuracy of six mangrove species at individual tree level was 86.08%. (2) The position and number matching accuracies of individual tree segmentation were 87.43% and 51.11%, respectively. The number matching accuracy of individual tree segmentation was relatively satisfying within 8 cm ≤ DBH ≤ 30 cm. (3) The individual tree-based inference method produced lower accuracy than the grid-based RF model method with R2 of 0.49 vs. 0.67 and RMSE of 48.42 Mg ha–1 vs. 38.95 Mg ha–1. However, the individual tree-based inference method can show more detail of spatial distribution of mangrove AGB. The resultant AGB maps of this method are more beneficial to the fine and differentiated management of mangrove forests.

Flexible open-source software for modelling wind disturbance and damage to trees and forests

2020 ◽  
Author(s):  
Tom Locatelli ◽  
Sophie Hale ◽  
Bruce Nicoll ◽  
Barry Gardiner
Keyword(s):  
Risk Model ◽  
Mechanistic Model ◽  
Wind Damage ◽  
Tree Level ◽  
Projection Data ◽  
Wind Disturbance ◽  
Experimental Conditions ◽  
Individual Tree ◽  
Tree Stability ◽  
The Individual

<p>Wind disturbance to forests extends across spatial and temporal scales and encompasses direct and indirect wind effects on the dynamics of forest ecosystems. It is detrimental to the provision of ecosystem services and reduces forest resistance and resilience to future natural disturbances. Historically, in the ecological and land-use scientific communities, forecasting the extent and probability of wind disturbance to forests has represented a serious challenge, with most studies electing to adopt qualitative or statistical approaches. The low degree of portability of statistical assessments of vulnerability to wind has limited their applicability and use, but it is recognised that they have a role in building hypotheses of the processes involved in wind damage that can be subsequently tested under experimental conditions. Results from tree stability experiments have contributed, in the last two decades, to the development of a mechanistic model of wind damage - ForestGALES. This is a process-based wind risk model that was originally created to inform the management of commercial forest plantations in the UK. Built on principles of forest science, physics, and ecology, ForestGALES requires a simple set of inputs and it has now been expanded to cover more than 20 common conifer species from across three continents, and multiple broadleaved species (e.g. Oak, Beech, Birch, and Eucalypts). Two methods of assessing vulnerability to wind damage are available in ForestGALES, one designed for application at stand level, and a novel approach that estimates vulnerability to wind at the individual tree within a stand – the latter allowing for use in complex forest stands, and for the effect of competition between trees in a stand. Until recently, ForestGALES was only available as desktop software and as an online tool as part of forest decision support systems (only for selected countries and species). These formats can be limiting for research and academic projects that aim to model and understanding wind disturbance dynamics across diverse landscapes, and that require a bespoke approach with a high degree of flexibility. To accommodate these broader requirements, ForestGALES has recently been redeveloped and released as a FOSS R package (“<em>fgr</em>”) that is fully customisable and easily integrated in R and modelling workflows and FOSS GIS frameworks. With this poster we present two exemplar studies of assessing wind damage risk to forested landscapes, one for each ForestGALES method (stand- and individual trees level), to showcase the capabilities and flexibility of the model in working with e.g. climate projection data, with other process-based models (e.g. 3PG) within an R modelling framework, and with LiDAR data, at the individual tree level.</p>

Agrilus auroguttatus exit hole distributions on Quercus agrifolia boles and a sampling method to estimate their density on individual trees

2012 ◽  
Vol 144 (6) ◽  
pp. 733-744 ◽  
Author(s):  
Laurel J. Haavik ◽  
Tom W. Coleman ◽  
Mary Louise Flint ◽  
Robert C. Venette ◽  
Steven J. Seybold
Keyword(s):  
Southern California ◽  
Tree Level ◽  
Reliable Estimate ◽  
Hole Density ◽  
Exit Hole ◽  
Individual Tree ◽  
Quercus Agrifolia ◽  
Goldspotted Oak Borer ◽  
The Individual ◽  
Agrilus Auroguttatus

AbstractIn recent decades, invasive phloem and wood borers have become important pests in North America. To aid tree sampling and survey efforts for the newly introduced goldspotted oak borer, Agrilus auroguttatus Schaeffer (Coleoptera: Buprestidae), we examined spatial patterns of exit holes on the boles (trunks) of 58 coast live oak, Quercus agrifolia Née (Fagaceae), trees at five sites in San Diego County, southern California, United States of America. Agrilus auroguttatus exit hole densities were greater at the root collar than at mid-boles (6.1 m above ground). Dispersion patterns of exit holes on lower boles (≤1.52 m) were random for trees with low exit hole densities and aggregated for trees with high exit hole densities. The mean exit hole density measured from three randomly chosen quadrats (0.09 m2) provided a statistically reliable estimate of the true mean exit hole density on the lower bole, with <25% error from the true mean. For future sampling and survey efforts in southern California oak forests and woodlands, exit hole counts within a 0.09 m2 quadrat could be made at any three locations on lower Q. agrifolia boles to accurately estimate A. auroguttatus exit hole densities at the individual tree level.

scholarly journals Comparison of Tree Species Classifications at the Individual Tree Level by Combining ALS Data and RGB Images Using Different Algorithms

2016 ◽  
Vol 8 (12) ◽  
pp. 1034 ◽  
Author(s):  
Songqiu Deng ◽  
Masato Katoh ◽  
Xiaowei Yu ◽  
Juha Hyyppä ◽  
Tian Gao
Keyword(s):  
Tree Species ◽  
Tree Level ◽  
Individual Tree ◽  
Rgb Images ◽  
The Individual

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.

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