The Use of Geostatistics in the Study of Floral Phenology ofVulpia geniculata(L.) Link
Author(s) -
Eduardo J. León Ruiz,
Herminia Garcı́a-Mozo,
Eugenio Domínguez Vilches,
Carmen Galán
Publication year - 2012
Publication title -
the scientific world journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.453
H-Index - 93
eISSN - 2356-6140
pISSN - 1537-744X
DOI - 10.1100/2012/624247
Subject(s) - geostatistics , phenology , kriging , sampling (signal processing) , stratified sampling , physical geography , geography , ecology , cartography , spatial variability , environmental science , computer science , statistics , biology , mathematics , filter (signal processing) , computer vision
Traditionally phenology studies have been focused on changes through time, but there exist many instances in ecological research where it is necessary to interpolate among spatially stratified samples. The combined use of Geographical Information Systems (GIS) and Geostatistics can be an essential tool for spatial analysis in phenological studies. Geostatistics are a family of statistics that describe correlations through space/time and they can be used for both quantifying spatial correlation and interpolating unsampled points. In the present work, estimations based upon Geostatistics and GIS mapping have enabled the construction of spatial models that reflect phenological evolution of Vulpia geniculata (L.) Link throughout the study area during sampling season. Ten sampling points, scattered troughout the city and low mountains in the “Sierra de Córdoba” were chosen to carry out the weekly phenological monitoring during flowering season. The phenological data were interpolated by applying the traditional geostatitical method of Kriging, which was used to ellaborate weekly estimations of V. geniculata phenology in unsampled areas. Finally, the application of Geostatistics and GIS to create phenological maps could be an essential complement in pollen aerobiological studies, given the increased interest in obtaining automatic aerobiological forecasting maps.
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