Time Series Analysis of Forest Dynamics at the Ecoregion Level

Olga Rumyantseva, Andrey Sarantsev and Nikolay Strigul
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Olga Rumyantseva: Department of Mathematics and Statistics, Washington State University Vancouver, 14204 NE Salmon Creek Avenue, Vancouver, WA 98686, USA
Andrey Sarantsev: Department of Mathematics and Statistics, University of Nevada in Reno, Reno, NV 89557, USA
Nikolay Strigul: Department of Mathematics and Statistics, Washington State University Vancouver, 14204 NE Salmon Creek Avenue, Vancouver, WA 98686, USA

Forecasting, 2020, vol. 2, issue 3, 1-23

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.

Keywords: time series forecasting; North American ecoregions; forest dynamics; autoregressive models; random walk model; AR(1) process; ARIMA (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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