Autoregressive Modeling of Forest Dynamics

Rumyantseva, O. I.; Sarantsev, Andrey; Strigul, Nikolay

doi:10.3390/f10121074

Cited by 4 publications

(9 citation statements)

References 41 publications

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“…In the present paper, we extend the autoregressive modeling approach recently developed to forecast forest dynamics in Quebec [16] to the conterminous United States (see also Sections 2.2 and 2.3.1), we also examine autoregressive integrated moving average models ARIMA(p,d,q) (see Section 2.4). Within each USA ecoregion, we model forest dynamics using various autoregressive time series models.…”

Section: Our Contributionsmentioning

confidence: 99%

“…We build the regression for each a value in the interval (−2, 2) with a step 0.01, and then we find the r value and the standard error σ. Minimizing standard error for this linear regression is equivalent to maximizing the likelihood function (see [16]).…”

Section: Autoregressive Model Ar(1) For Basal Area On Individual Forest Plotsmentioning

confidence: 99%

“…Time series modeling traditionally employed in financial mathematics, meteorology and remote sensing is a promising tool for the forecasting of forest dynamics at large scales. We recently demonstrated the utility of autoregressive models for modeling forest dynamics in Quebec [16]. Time series models operate in continuous time and with continuous state space [16].…”

Section: Introductionmentioning

confidence: 99%

“…We recently demonstrated the utility of autoregressive models for modeling forest dynamics in Quebec [16]. Time series models operate in continuous time and with continuous state space [16]. Similarly to Markov chains, time series models can be parameterized from irregularly collected forest inventory data using Bayesian techniques [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…Time series models operate in continuous time and with continuous state space [16]. Similarly to Markov chains, time series models can be parameterized from irregularly collected forest inventory data using Bayesian techniques [14][15][16]. Autoregressive models are computationally efficient and analytically tractable, and, therefore, are especially suitable for large scale carbon cycle and biogeochemistry forecasting models, which do not require species-level predictions of forest dynamics [17].…”

Section: Introductionmentioning

confidence: 99%

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Time Series Analysis of Forest Dynamics at the Ecoregion Level

2020

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Forecasting of forest dynamics at a large scale is essential for land use management, global climate change and biogeochemistry modeling. We develop time series models of the forest dynamics in the conterminous United States based on forest inventory data collected by the US Forest Service over several decades. We fulfilled autoregressive analysis of the basal forest area at the level of US ecological regions. In each USA ecological region, we modeled basal area dynamics on individual forest inventory pots and performed analysis of its yearly averages. The last task involved Bayesian techniques to treat irregular data. In the absolute majority of ecological regions, basal area yearly averages behave as geometric random walk with normal increments. In California Coastal Province, geometric random walk with normal increments adequately describes dynamics of both basal area yearly averages and basal area on individual forest plots. Regarding all the rest of the USA’s ecological regions, basal areas on individual forest patches behave as random walks with heavy tails. The Bayesian approach allowed us to evaluate forest growth rate within each USA ecological region. We have also implemented time series ARIMA models for annual averages basal area in every USA ecological region. The developed models account for stochastic effects of environmental disturbances and allow one to forecast forest dynamics.

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Section: Our Contributionsmentioning

confidence: 99%

Section: Autoregressive Model Ar(1) For Basal Area On Individual Forest Plotsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Time Series Analysis of Forest Dynamics at the Ecoregion Level

2020

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Analysis of Longitudinal Forest Data on Individual-Tree and Whole-Stand Attributes Using a Stochastic Differential Equation Model

Rupšys

Petrauskas

2022

Forests

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This paper focuses on individual-tree and whole-stand growth models for uneven-aged and mixed-species stands in Lithuania. All the growth models were derived using a single trivariate diffusion process defined by a mixed-effect parameters trivariate stochastic differential equation describing the tree diameter, potentially available area, and height. The mixed-effect parameters of the newly developed trivariate transition probability density function were estimated using an approximate maximum likelihood procedure. Using the relationship between the multivariate probability density and univariate marginal (conditional) densities, the growth equations were derived to predict or forecast the individual-tree and whole-stand variables, such as diameter, potentially available area, height, basal area, and stand density. All the results are illustrated using an observed dataset from 53 permanent experimental plots remeasured from 1 to 7 times. The computed statistical measures showed high predictive and forecast accuracy compared with validation data that were not used to find parameter estimates. All the results were implemented in the Maple computer algebra system.

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Fire, Rain and CO2: Potential Drivers of Tropical Savanna Vegetation Change, with Implications for Carbon Crediting

Barber,

Edwards,

Zander

2023

Fire

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A global trend of increasing tree cover in savannas has been observed and ascribed to a range of possible causes, including CO2 levels, changing rainfall and fire frequency. We tested these explanations in the Australian tropical savanna, taking 96 savanna ‘cool burning’ projects from Australia’s emissions offset scheme as case studies. We obtained readings of tree cover and explanatory variables from published remote sensing or spatial data sources. These were analysed using time-series linear regression to obtain coefficients for the influence of severe fire occurrence, annual rainfall and prior percentage tree cover. Although statistically significant coefficients for the key variables were found in only half (severe fire) or one quarter (rainfall) of the individual project models, when comparing all the model coefficients across the rainfall gradient, ecologically coherent explanations emerge. No residual trend was observed, suggesting rising CO2 levels have not influenced tree cover over the study period. Our approach models tree cover change by separating ecological drivers from human-controlled factors such as fire management. This is an essential design feature of national emissions inventories and emissions offsets programs, where crediting must be additional to the expected baseline, and arise from human activity.

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Autoregressive Modeling of Forest Dynamics

Cited by 4 publications

References 41 publications

Time Series Analysis of Forest Dynamics at the Ecoregion Level

Time Series Analysis of Forest Dynamics at the Ecoregion Level

Analysis of Longitudinal Forest Data on Individual-Tree and Whole-Stand Attributes Using a Stochastic Differential Equation Model

Fire, Rain and CO2: Potential Drivers of Tropical Savanna Vegetation Change, with Implications for Carbon Crediting

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