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Defining, Quantifying, Observing and Modeling Forest Canopy Traits

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A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: closed (30 June 2018) | Viewed by 26685

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Special Issue Editors

Dr. Gil Bohrer

E-Mail Website
Guest Editor

Department of Civil, Environmental & Geodetic Engineering, Ohio State University, 2070 Neil Ave., Columbus, OH 43210, USA
Interests: ecohydrology; transpiration; plant hydrodynamics; forest meteorology; greenhouse-gas fluxes; eddy-covariance measurements; land-surface modeling

Dr. Ashley Matheny

E-Mail Website
Co-Guest Editor

Department of Geological Sciences, Jackson School of Geosciences, The University of Texas at Austin, Austin, TX, USA
Interests: ecohydrology; forest water use; species-specific hydraulic strategies; ecohydrological modeling; land-atmosphere exchange
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forest are responsible for a large portion of terrestrial ecosystem function, including net primary production, surface energy, and water exchanges with the weather system, and affecting hydrological systems. Forest canopies are complex ecosystems, composed of millions of individuals from tens of hundreds of species. While species-specific physiological traits describe the properties of tissues within individuals, understanding and modeling forest function requires the identification and classification of emergent forest-level traits. Trait-based modeling approaches are successful in representing community function and ecosystem-level environmental interactions and dynamics. Such approaches are dependent on observation and quantification of these emergent forest-level traits. We encourage studies from all fields, including experimental studies, monitoring approaches, remote sensing, and models, which describe, define, observe, quantify or apply forest canopy traits, to contribute to this Special Issue. We strive to promote knowledge for the preservation, management, and future development of forest ecosystems, and to generate knowledge for improved forest modeling and for incorporating the effects of forests in estimations and predictions of the global climate and ecosystem function.

Dr. Gil Bohrer
Prof. Dr. Ashley Matheny
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Forest Canopy
Canopy Structure
Tree Physiology
Trait-Based Modeling
Ecosystem Modeling
Carbon Sequestration
Transpiration
Competition
Ecohydrology
Remote Sensing

Published Papers (6 papers)

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Research

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16 pages, 1901 KiB

Open AccessArticle

A Levenberg–Marquardt Backpropagation Neural Network for Predicting Forest Growing Stock Based on the Least-Squares Equation Fitting Parameters

by Ruyi Zhou, Dasheng Wu, Luming Fang, Aijun Xu and Xiongwei Lou

Forests 2018, 9(12), 757; https://doi.org/10.3390/f9120757 - 05 Dec 2018

Cited by 20 | Viewed by 3885

Abstract

Traditional field surveys are expensive, time-consuming, laborious, and difficult to perform, especially in mountainous and dense forests, which imposes a burden on forest management personnel and researchers. This study focuses on predicting forest growing stock, one of the most significant parameters of a forest resource assessment. First, three schemes were designed—Scheme 1, based on the study samples with mixed tree species; Scheme 2, based on the study samples divided into dominant tree species groups; and Scheme 3, based on the study samples divided by dominant tree species groups—the evaluation factors are fitted by least-squares equations, and the non-significant fitted-factors are removed. Second, an overall evaluation indicator system with 17 factors was established. Third, remote sensing images of Landsat Thematic Mapper, digital elevation model, and the inventory for forest management planning and design were integrated in the same database. Lastly, a backpropagation neural network based on the Levenberg–Marquardt algorithm was used to predict the forest growing stock. The results showed that the group estimation precision exceeded 90%, which is the highest standard of total sampling precision of inventory for forest management planning and design in China. The prediction results for distinguishing dominant tree species were better than for mixed dominant tree species. The results also showed that the performance metrics for prediction could be improved by least-squares equation fitting and significance filtering of the evaluation factors. Full article

(This article belongs to the Special Issue Defining, Quantifying, Observing and Modeling Forest Canopy Traits)

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12 pages, 2344 KiB

Open AccessArticle

Spatial Variation in Canopy Structure across Forest Landscapes

by Brady S. Hardiman, Elizabeth A. LaRue, Jeff W. Atkins, Robert T. Fahey, Franklin W. Wagner and Christopher M. Gough

Forests 2018, 9(8), 474; https://doi.org/10.3390/f9080474 - 03 Aug 2018

Cited by 26 | Viewed by 4736

Abstract

Forest canopy structure (CS) controls many ecosystem functions and is highly variable across landscapes, but the magnitude and scale of this variation is not well understood. We used a portable canopy LiDAR system to characterize variation in five categories of CS along N = 3 transects (140–800 m long) at each of six forested landscapes within the eastern USA. The cumulative coefficient of variation was calculated for subsegments of each transect to determine the point of stability for individual CS metrics. We then quantified the scale at which CS is autocorrelated using Moran’s I in an Incremental Autocorrelation analysis. All CS metrics reached stable values within 300 m but varied substantially within and among forested landscapes. A stable point of 300 m for CS metrics corresponds with the spatial extent that many ecosystem functions are measured and modeled. Additionally, CS metrics were spatially autocorrelated at 40 to 88 m, suggesting that patch scale disturbance or environmental factors drive these patterns. Our study shows CS is heterogeneous across temperate forest landscapes at the scale of 10 s of meters, requiring a resolution of this size for upscaling CS with remote sensing to large spatial scales. Full article

(This article belongs to the Special Issue Defining, Quantifying, Observing and Modeling Forest Canopy Traits)

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16 pages, 2124 KiB

Open AccessArticle

Plant Hydraulic Trait Covariation: A Global Meta-Analysis to Reduce Degrees of Freedom in Trait-Based Hydrologic Models

by A. Rio Mursinna, Erica McCormick, Katie Van Horn, Lisa Sartin and Ashley M. Matheny

Forests 2018, 9(8), 446; https://doi.org/10.3390/f9080446 - 25 Jul 2018

Cited by 13 | Viewed by 4228

Abstract

Current vegetation modeling strategies use broad categorizations of plants to estimate transpiration and biomass functions. A significant source of model error stems from vegetation categorizations that are mostly taxonomical with no basis in plant hydraulic strategy and response to changing environmental conditions. Here, we compile hydraulic traits from 355 species around the world to determine trait covariations in order to represent hydraulic strategies. Simple and stepwise regression analyses demonstrate the interconnectedness of multiple vegetative hydraulic traits, specifically, traits defining hydraulic conductivity and vulnerability to embolism with wood density and isohydricity. Drought sensitivity is strongly (Adjusted R² = 0.52, p < 0.02) predicted by a stepwise linear model combining rooting depth, wood density, and isohydricity. Drought tolerance increased with increasing wood density and anisohydric response, but with decreasing rooting depth. The unexpected response to rooting depth may be due to other tradeoffs within the hydraulic system. Rooting depth was able to be predicted from sapwood specific conductivity and the water potential at 50% loss of conductivity. Interestingly, the influences of biome or growth form do not increase the accuracy of the drought tolerance model and were able to be omitted. Multiple regression analysis revealed 3D trait spaces and tradeoff axes along which species’ hydraulic strategies can be analyzed. These numerical trait spaces can reduce the necessary input to and parameterization of plant hydraulics modules, while increasing the physical representativeness of such simulations. Full article

(This article belongs to the Special Issue Defining, Quantifying, Observing and Modeling Forest Canopy Traits)

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18 pages, 2307 KiB

Open AccessArticle

Effect of Vertical Canopy Architecture on Transpiration, Thermoregulation and Carbon Assimilation

by Tirtha Banerjee and Rodman Linn

Forests 2018, 9(4), 198; https://doi.org/10.3390/f9040198 - 11 Apr 2018

Cited by 18 | Viewed by 5687

Abstract

Quantifying the impact of natural and anthropogenic disturbances such as deforestation, forest fires and vegetation thinning among others on net ecosystem—atmosphere exchanges of carbon dioxide, water vapor and heat—is an important aspect in the context of modeling global carbon, water and energy cycles. The absence of canopy architectural variation in horizontal and vertical directions is a major source of uncertainty in current climate models attempting to address these issues. This manuscript demonstrates the importance of considering the vertical distribution of foliage density by coupling a leaf level plant biophysics model with analytical solutions of wind flow and light attenuation in a horizontally homogeneous canopy. It is demonstrated that plant physiological response in terms of carbon assimilation, transpiration and canopy surface temperature can be widely different for two canopies with the same leaf area index (LAI) but different leaf area density distributions, under several conditions of wind speed, light availability, soil moisture availability and atmospheric evaporative demand. Full article

(This article belongs to the Special Issue Defining, Quantifying, Observing and Modeling Forest Canopy Traits)

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3280 KiB

Open AccessArticle

Countering Negative Effects of Terrain Slope on Airborne Laser Scanner Data Using Procrustean Transformation and Histogram Matching

by Endre Hofstad Hansen, Liviu Theodor Ene, Terje Gobakken, Hans Ole Ørka, Ole Martin Bollandsås and Erik Næsset

Forests 2017, 8(10), 401; https://doi.org/10.3390/f8100401 - 21 Oct 2017

Cited by 2 | Viewed by 4610

Abstract

Forest attributes such as tree heights, diameter distribution, volumes, and biomass can be modeled utilizing the relationship between remotely sensed metrics as predictor variables, and measurements of forest attributes on the ground. The quality of the models relies on the actual relationship between the forest attributes and the remotely sensed metrics. The processing of airborne laser scanning (ALS) point clouds acquired under heterogeneous terrain conditions introduces a distortion of the three-dimensional shape and structure of the ALS data for tree crowns and thus errors in the derived metrics. In the present study, Procrustean transformation and histogram matching were proposed as a means of countering the distortion of the ALS data. The transformations were tested on a dataset consisting of 192 field plots of 250 m² in size located on a gradient from gentle to steep terrain slopes in western Norway. Regression models with predictor variables derived from (1) Procrustean transformed- and (2) histogram matched point clouds were compared to models with variables derived from untransformed point clouds. Models for timber volume, basal area, dominant height, Lorey’s mean height, basal area weighted mean diameter, and number of stems were assessed. The results indicate that both (1) Procrustean transformation and (2) histogram matching can be used to counter crown distortion in ALS point clouds. Furthermore, both techniques are simple and can easily be implemented in the traditional processing chain of ALS metrics extraction. Full article

(This article belongs to the Special Issue Defining, Quantifying, Observing and Modeling Forest Canopy Traits)

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Review

Jump to: Research

14 pages, 1489 KiB

Open AccessFeature PaperReview

Neighbor and Height Effects on Crown Properties Associated with the Uniform-Stress Principle of Stem Formation

by Thomas J. Dean

Forests 2018, 9(6), 334; https://doi.org/10.3390/f9060334 - 07 Jun 2018

Cited by 1 | Viewed by 2973

Abstract

According to the uniform-stress principle of stem formation, the amount of leaf area a tree carries and the leverage it exerts on the stem determine the stem dimensions. Within an even-aged monoculture, the leaf area per tree and the leverage placed on the stem are functions of tree density and tree height. The uniform-stress principle presents the means to translate density effects on crown characteristics into stem dimensions and total standing volume. This approach is truly a top-down method of simulating growth tree and stand growth because leaf area and other crown properties must be determined before stem size and taper can be calculated. Each crown property influences either the sail area or the leverage placed on the stem, but the degree to which a specific crown property affects these parameters changes with stand density and height. Leverage is the more complicated of the two variables, being a function of the height to the base of the live crown and the vertical distribution of leaf area. The purpose of this brief review is to summarize the effects of stand density on the height to the base of the live tree and the vertical distribution of leaf area and the various ways these variables have been quantified. Full article

(This article belongs to the Special Issue Defining, Quantifying, Observing and Modeling Forest Canopy Traits)

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