Many agro-climatological applications are based on detailed climatological data grids generally not available as raw data. But appropriately generated. Classical geostatistical techniques normally applied to stationary monovariable cases, e.g. ordinary kriging or sample kriging, are already widely used in many fields involved in spatial data processing. The use or IRF-k with external drift seems very cumbersome and specialized in the case of multi-processing of variables like mean temperature. Its standard deviation and elevations. In effect mean temperature is not a spatially stationary variable and it is knownin sparse points, but elevations which are also not stationary, are known in considerably more detail and they are well correlated with mean temperatures. Theresults obtained are discussed with reference to the results obtainable by applying other multi-variable techniques. The procedure and examples have been implemented on PC.
INTEGRATION BETWEEN GEOSTATISTICAL METHODOLOGIES AND GIS ENVIRONMENTAL GEODATA: THE EXTERNAL DRIFT / Bruno, R.; Raspa, Giuseppe. - STAMPA. - 2:(1993), pp. 1067-1075. (Intervento presentato al convegno Fourth European Conference and Exhibition on Geographical Information Systems tenutosi a Genova, Italy nel March 29 - April 1, 1993).
INTEGRATION BETWEEN GEOSTATISTICAL METHODOLOGIES AND GIS ENVIRONMENTAL GEODATA: THE EXTERNAL DRIFT
RASPA, Giuseppe
1993
Abstract
Many agro-climatological applications are based on detailed climatological data grids generally not available as raw data. But appropriately generated. Classical geostatistical techniques normally applied to stationary monovariable cases, e.g. ordinary kriging or sample kriging, are already widely used in many fields involved in spatial data processing. The use or IRF-k with external drift seems very cumbersome and specialized in the case of multi-processing of variables like mean temperature. Its standard deviation and elevations. In effect mean temperature is not a spatially stationary variable and it is knownin sparse points, but elevations which are also not stationary, are known in considerably more detail and they are well correlated with mean temperatures. Theresults obtained are discussed with reference to the results obtainable by applying other multi-variable techniques. The procedure and examples have been implemented on PC.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
