Advanced Statistical and Computational Techniques in Environment and Ecology Research, 2nd Edition

Dear Colleagues,

The Earth’s environment and ecosystem are complex, multidimensional, multiscale stochastic processes in which different physical processes interact on different temporal and spatial scales. Applied mathematicians are working to extract meaningful information from large amounts of observational and simulation data using various techniques (e.g., EOF, DEA, EVA, CCA, SSA, PCA, MCMC, DFA, IPTA, Bayesian decisions, downscaling analyses, data assimilation, wavelet/framelet, spectral analyses, information entropy, and stochastic networks). Rapid advances in statistical and computational techniques have reached all aspects of the environment and ecology. At present, the ensemble statistical learning techniques are being developed to deal with the emerging big Earth datasets, which cannot be analyzed deeply using classic statistical techniques due to the size, variety, and dynamic nature of big data. In this Special Issue, we aim to collect recent results on developing and applying advanced statistical and computational techniques to reveal trends and patterns of environmental evolution, determine the links inside environment systems and ecosystems, and evaluate the uncertainty of environmental prediction to achieve sustainable development goals.

Prof. Dr. Zhihua Zhang
Prof. Dr. M. James C. Crabbe
Guest Editors

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