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A spatially explicit methodology for a priori estimation of field survey effort in environmental observation networks
Authors:Carlos Guerra  Marc J Metzger  João Honrado  Joaquim Alonso
Institution:1. Mediterranean Ecosystems and Landscapes, Institute of Mediterranean Agricultural and Environmental Sciences, évora, Portugal;2. Center of Geomatics and Environmental Systems Analysis, Polytechnic Institute of Viana do Castelo, Ponte de Lima, Portugalcarlosguerra@esa.ipvc.pt;4. School of GeoSciences, The University of Edinburgh, Edinburgh, UK;5. Centro de Investiga??o em Biodiversidade e Recursos Genéticos (CIBIO) &6. Faculdade de Ciências, Universidade do Porto, Porto, Portugal;7. Center of Geomatics and Environmental Systems Analysis, Polytechnic Institute of Viana do Castelo, Ponte de Lima, Portugal
Abstract:When establishing environmental monitoring programmes, it crucial to make reliable cost estimates, especially where a field survey is involved. This paper presents a methodology for creating a spatial measure of a field survey effort (SE). A set of relevant variables affecting a SE (e.g. areas with rough terrain, or distant from the main road network) was classified using fuzzy sets and then combined to produce spatially explicit effort indicators, which were integrated to a single measure using an analytic hierarchy process (AHP). To evaluate this approach and identify the limits for its application, three spatially nested case studies were used to test the spatial expression of SE and the scalable capacity of the method itself. The presented methodology could cope with variations in the scale and data resolution, retrieving a coherent estimate of SE across the different case studies. The presented methodology is therefore useful for (i) testing the network designs for sampling bias related to SE, (ii) comparing alternative sampling designs, (iii) assessing the sampling costs and (iv) supporting the human and logistical resource management.
Keywords:ecological field survey  environmental monitoring  monitoring effort  logistic optimization
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