Time Series Analysis of Forest Dynamics at the Ecoregion Level

Abstract

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.

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

“…In statistical terms, this latter behaviour is known as a 'random walk'. Such time-series concepts have only recently been used to model forest growth [59,60]. We diagnosed the benefit of this approach by testing the residuals of the model for autocorrelation [56] using the acf function in R Studio, to test whether the current level of green woody cover shows a correlation with its own past levels at a range of time lags.…”

Stepwise Multidimensional Climate Envelop Modeling of Pitch Pine (Pinus rigida)

“…The data mining procedure employed in our study closely aligns with the methodology utilized in our previous research endeavors [12][13][14]. We utilized two datasets: the USDA Forest Inventory and Analysis (FIA) dataset and the WorldClim dataset covering climate data for 1970-2000, which can be accessed at the WorldClim data website.…”

“…Employing data mining and data-driven analyses of spatially explicit climatic datasets and individual-based forest inventories serves as an effective tool for investigating the connections between various quantitative features of climate and vegetation [12,13]. For example, data-intensive modeling allowed us to investigate multidimensional forest dynamics [14], succession [15], and tolerance patterns [12,15]. In another data-driven study, we employed multivariate statistics and machine learning to rank climatic factors by their effects on forest basal area in different ecoregions [13].…”