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Special Issue "Modeling of Forest Structure and Dynamics"

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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 (1 November 2019) | Viewed by 14662

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Special Issue Editors

Dr. Nikolay Strigul

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Guest Editor

Department of Mathematics and Statistics, College of Arts and Sciences, Washington State University Vancouver, Vancouver, WA 98686, USA
Interests: forest modeling; remote sensing; photogrammetry; 3-D modeling of vegetation; individual-based models; stochastic modeling
Special Issues, Collections and Topics in MDPI journals

Dr. Demetrios Gatziolis

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Guest Editor

Research Forester US Forest Service | FS · Pacific Northwest Research Station
Interests: remote sensing (primarily using active technologies, e.g. LiDAR); photogrammetry (digital aerial photography, structure from motion); Geographic Information Science (GIS); forest inventory and modeling

Special Issue Information

Dear Colleagues,

Forests are complex adaptive systems and their modelling involves substantial modeling challenges. In recent years, large datasets recording ecological variables have become widely available, e.g., forest inventories, and remote sensing vegetation surveys coupled with climatic datasets. These datasets provides opportunities to complement traditional experimental approaches with new generation predictive models of forest dynamics and data-driven discovery and hypothesis testing methods. These new approaches aim to evaluate vegetation and biochemistry dynamics at different spatial scales, from forests stands to the regional and continental scales. The underlying modeling challenges include three major components: (1) the use of individual-based models, as they are among the most suitable and promising tools for simulating complex-adaptive systems and interactions on multiple scales, (2) the development of different scaling methods that approximate individual-based processes, and (3) the investigation of various inverse problems to connect models with empirical data including imagery and 3D modeling. This Special Issue will provide a cross-disciplinary platform for foresters, ecologists, modelers, statisticians, and data analysts who are involved in forest modeling research.

Prof. Nikolay Strigul
Dr. Demetrios Gatziolis
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 2000 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 modeling
scaling of forest dynamics
forest inventories
data-intensive modeling
individual-based models
process-based models
forest dynamics
hierarchical patch dynamics
remote sensing-based modeling

Published Papers (6 papers)

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Research

Open AccessArticle

Modelling Post-Disturbance Successional Dynamics of the Canadian Boreal Mixedwoods

Kobra Maleki

Mohamadou Alpha Gueye

Benoit Lafleur

Alain Leduc

and

Yves Bergeron

Forests 2020, 11(1), 3; https://doi.org/10.3390/f11010003 - 18 Dec 2019

Cited by 8 | Viewed by 2357

Abstract

Natural disturbances, such as fire and insect outbreaks, play important roles in natural forest dynamics, which are characterized over long time scales by changes in stand composition and structure. Individual-based forest simulators could help explain and predict the response of forest ecosystems to different disturbances, silvicultural treatments, or environmental stressors. This study evaluated the ability of the SORTIE-ND simulator to reproduce post-disturbance dynamics of the boreal mixedwoods of eastern Canada. In 1991 and 2009, we sampled all trees (including seedlings and saplings) in 431 (256 m²) plots located in the Lake Duparquet Research and Teaching Forest (western Quebec). These plots were distributed in stands originating from seven wildfires that occurred between 1760 and 1944, and which represented a chronosequence of post-disturbance stand development. We used the 1991 inventory data to parameterize the model, and simulated short- to long-term natural dynamics of post-fire stands in both the absence and presence of a spruce budworm outbreak. We compared short-term simulated stand composition and structure with those observed in 2009 using a chronosequence approach. The model successfully generated the composition and structure of empirical observations. In long-term simulations, species dominance of old-growth forests was not accurately estimated, due to possible differences in stand compositions following wildfires and to differences in stand disturbance histories. Mid- to long-term simulations showed that the secondary disturbance incurred by spruce budworm did not cause substantial changes in early successional stages while setting back the successional dynamics of middle-aged stands and accelerating the dominance of white cedar in late-successional post-fire stands. We conclude that constructing a model with appropriate information regarding stand composition and disturbance history considerably increases the strength and accuracy of the model to reproduce the natural dynamics of post-disturbance boreal mixedwoods. Full article

(This article belongs to the Special Issue Modeling of Forest Structure and Dynamics)

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Open AccessArticle

Autoregressive Modeling of Forest Dynamics

Olga Rumyantseva

Andrey Sarantsev

and

Nikolay Strigul

Forests 2019, 10(12), 1074; https://doi.org/10.3390/f10121074 - 26 Nov 2019

Cited by 2 | Viewed by 1634

Abstract

In this work, we employ autoregressive models developed in financial engineering for modeling of forest dynamics. Autoregressive models have some theoretical advantage over currently employed forest modeling approaches such as Markov chains and individual-based models, as autoregressive models are both analytically tractable and operate with continuous state space. We performed a time series statistical analysis of forest biomass and basal areas recorded in Quebec provincial forest inventories from 1970 to 2007. The geometric random walk model adequately describes the yearly average dynamics. For individual patches, we fit an autoregressive process (AR) of order 1 capable to model negative feedback (mean-reversion). Overall, the best fit also turned out to be geometric random walk; however, the normality tests for residuals failed. In contrast, yearly means were adequately described by normal fluctuations, with annual growth on average of 2.3%, but with a standard deviation of order of 40%. We used a Bayesian analysis to account for the uneven number of observations per year. This work demonstrates that autoregressive models represent a valuable tool for the modeling of forest dynamics. In particular, they quantify the stochastic effects of environmental disturbances and develop predictive empirical models on short and intermediate temporal scales. Full article

(This article belongs to the Special Issue Modeling of Forest Structure and Dynamics)

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Open AccessArticle

Stand Diameter Distribution Modeling and Prediction Based on Maximum Entropy Principle

Yuling Chen

Baoguo Wu

and

Zhiqiang Min

Forests 2019, 10(10), 859; https://doi.org/10.3390/f10100859 - 02 Oct 2019

Cited by 4 | Viewed by 1644

Abstract

Research Highlights: Improving the prediction accuracy represents a popular forest simulation modeling issue, and exploring the optimal maximum entropy (MaxEnt) distribution is a new effective method for improving the diameter distribution model simulation precision to overcome the disadvantages of Weibull. Background and Objectives: The MaxEnt distribution is the closest to the actual distribution under the constraints, which are the main probability density distributions. However, relatively few studies have addressed the optimization of stand diameter distribution based on MaxEnt distribution. The objective of this study was to introduce application of the MaxEnt distribution on modeling and prediction of stand diameter distribution. Materials and Methods: The long-term repeated measurement data sets consisted of 260 diameter frequency distributions from China fir (Cunninghamia lanceolate (Lamb.) Hook) plantations in the southern China Guizhou. The Weibull distribution and the MaxEnt distribution were applied to the fitting of stand diameter distribution, and the modeling and prediction characteristics of Weibull distribution and MaxEnt distribution to stand diameter distribution were compared. Results: Three main conclusions were obtained: (1) MaxEnt distribution presented a more accurate simulation than three-parametric Weibull function; (2) the Chi-square test showed diameter distributions of unknown stands can be well estimated by applying MaxEnt distribution based on the plot similarity index method (PSIM) and Weibull distribution based on the parameter prediction method (PPM); (3) the MaxEnt model can deal with the complex nonlinear relationship and show strong prediction ability when predicting the stand distribution structure. Conclusions: With the increase of sample size, the PSIM has great application prospects in the dynamic prediction system of stand diameter distribution. Full article

(This article belongs to the Special Issue Modeling of Forest Structure and Dynamics)

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Open AccessArticle

Estimating Crown Structure Parameters of Moso Bamboo: Leaf Area and Leaf Angle Distribution

and

Forests 2019, 10(8), 686; https://doi.org/10.3390/f10080686 - 14 Aug 2019

Cited by 7 | Viewed by 2829

Abstract

Both leaf area (LA) and leaf angle distribution are the most important eco-physiological measures of tree crowns. However, there are limited published investigations on the two parameters of Moso bamboo (Phyllostachys edulis (Carrière) J. Houz., abbreviated as MB). The aim of this study was to develop allometric equations for predicting crown LA of MB by taking the diameter at breast height (DBH) and tree height (H) as predictors and to investigate the leaf angle distribution of a MB crown based on direct leaf angle measurements. Data were destructively sampled from 29 MB crowns including DBH, H, biomass and the area of sampled leaves, biomass of total crown leaves, and leaf angles. The results indicate that (1) the specific leaf area (SLA) of a MB crown decreases from the bottom to the top; (2) the vertical LA distribution of MB crowns follow a “Muffin top” shape; (3) the LA of MB crowns show large variations, from 7.42 to 74.38 m²; (4) both DBH and H are good predictors in allometry-based LA estimations for a MB crown; (5) linear, exponential, and logarithmic regressions show similar capabilities for the LA estimations; (6) leaf angle distributions from the top to the bottom of a MB crown can be considered as invariant; and (7) the leaf angle distribution of a MB crown is close to the planophile case. The results provide an important tool to estimate the LA of MB on the standing scale based on DBH or H measurements, provide useful prior knowledge for extracting leaf area indexes of MB canopies from remote sensing-based observations, and, therefore, will potentially serve as a crucial reference for calculating carbon balances and other ecological studies of MB forests. Full article

(This article belongs to the Special Issue Modeling of Forest Structure and Dynamics)

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Open AccessArticle

Modeling Dynamics of Structural Components of Forest Stands Based on Trivariate Stochastic Differential Equation

Petras Rupšys

Forests 2019, 10(6), 506; https://doi.org/10.3390/f10060506 - 14 Jun 2019

Cited by 13 | Viewed by 2710

Abstract

Research Highlights: Today’s approaches to modeling of forest stands are in most cases based on that the regression models and they are constructed as static sub-models describing individual stands variables. The disadvantages of this method; it is laborious because too many different equations need to be assessed and empirical choices of candidate equations make the results subjective; it does not relate to the stand variables dynamics against the age dimension (time); and does not consider the underlying covariance structure driving changes in the stand variables. In this study, the dynamical model defined by a fixed-and mixed effect parameters trivariate stochastic differential equation (SDE) is introduced and described how such a model can be used to model quadratic mean diameter, mean height, number of trees per hectare, self-thinning line, stand basal area, stand volume per hectare and much more. Background and Objectives: New developed marginal and conditional trivariate probability density functions, combining information generated from an age-dependent variance-covariance matrix of quadratic mean diameter, mean height and number of trees per hectare, improve stand growth prediction, and forecast (in forecast the future is completely unavailable and must only be estimated from historical patterns) accuracies. Materials and Methods: Fixed-and mixed effect parameters SDE models were harmonized to predict and forecast the dynamics of quadratic mean diameter, mean height, number of trees per hectare, basal area, stand volume per hectare, and their current and mean increments. The results and experience from applying the SDE concepts and techniques in an extensive whole stand growth and yield analysis are described using a Scots pine (Pinus sylvestris L.) experimental dataset in Lithuania. Results: The mixed effects scenario SDE model showed high accuracy, the percentage root mean square error values for quadratic mean diameter, mean height, number of trees per hectare, stand basal area and stand volume per hectare predictions (forecasts) were 3.37% (10.44%), 1.82% (2.07%), 1.76% (2.93%), 6.65% (10.41%) and 6.50% (8.93%), respectively. In the same way, the quadratic mean diameter, mean height, number of trees per hectare, stand basal area and stand volume per hectare prediction (forecast) relationships had high values of the coefficient of determination, R², 0.998 (0.987), 0.997 (0.992), 0.997 (0.988), 0.968 (0.984) and 0.966 (0.980), respectively. Conclusions: The approach presented in this paper can be used for developing a new generation stand growth and yield models. Full article

(This article belongs to the Special Issue Modeling of Forest Structure and Dynamics)

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Open AccessArticle

Modeling Diameter Distribution of Black Alder (Alnus glutinosa (L.) Gaertn.) Stands in Poland

Piotr Pogoda

Wojciech Ochał

and

Stanisław Orzeł

Forests 2019, 10(5), 412; https://doi.org/10.3390/f10050412 - 13 May 2019

Cited by 15 | Viewed by 3152

Abstract

We present diameter distribution models for black alder (Alnus glutinosa (L.) Gaertn.) derived from diameter measurements made at breast height in 844 circular sample plots set in 163 managed stands located in south-eastern Poland. A total of 22,530 trees were measured. Stand age ranged from six to 89 years. The model formulation was based on the two-parameter Weibull function and a non-parametric percentile-based method. Weibull function parameters were recovered from the first raw and second central moments estimated using the stand quadratic mean diameter. The same stand characteristic was used to predict values of 12 percentiles in the percentile-based method. The model performance was assessed using the k-fold cross-validation method. The goodness-of-fit statistics include the Kolmogorov–Smirnov statistic, mean error, root mean squared error, and two variants of the error index introduced by Reynolds. The percentile model developed, accurately predicted diameter distributions in 88.4% of black alder stands, as compared to 81.9% for the Weibull model (Kolmogorov–Smirnov test). Alternative statistical metrics assessing goodness-of-fit to empirical distributions suggested that the non-parametric percentile model was superior to the parametric Weibull model, especially in stands older than 20 years. In younger stands, the two models were accurate only in 57% of the cases, and did not differ significantly with respect to goodness-of-fit measures. Full article

(This article belongs to the Special Issue Modeling of Forest Structure and Dynamics)

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