Novel approach to assess the representativeness and methodologies to design an environmental observatory network
Date
2019
Authors
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Publisher
University of Delaware
Abstract
Environmental monitoring, especially long-term monitoring programs are a backbone component for environmental science and policy. Where environmental observatory networks (EONs) are entities that coordinates environmental monitoring efforts and provides useful information that helps to develop knowledge at a regional to global scale. However, due to intrinsic environmental variability and EONs organizational structure, the ability of EONs to properly represents environmental dynamic is prompt to poorly represents certain regions or ecosystems. This dissertation focuses on developed a different approach to assess EONs representativeness and design, by using a time-varying land-cover surface classification that characterize ecosystem functional heterogeneity based on carbon uptake dynamics (i.e., ecosystem functional types; EFTs), and by using machine learning techniques (i.e., maxent, random forest) to assess EONs representativeness. This study is divided into three main objectives; A) assess the representativeness of AmeriFlux and the National Ecological Observatory Network (NEON) to monitor the spatial and temporal variability of EFTs across the conterminous Unites States; B) propose a flexible framework to optimize the design of an EON using a publicly available data in a high-diverse country (i.e., Mexico; and C) Assess the representativeness of ecosystem states factors (i.e., climate, topography and soil resources) along with ecosystem processes (i.e., gross primary productivity and evapotranspiration) of FLUXNET eddy-covariance sites in Latin America. Results indicate that this dissertation provides valuable information for EONs management as identifies spatial information gaps and could guide an optimal EONs design. Also, is based on a reproducible framework using publicly available information and it could be applied anywhere in the world.