%0 Conference Proceedings
%4 urlib.net/www/2017/12.05.12.49.16
%2 urlib.net/www/2017/12.05.12.49.17
%F lattes: 2916855460918534 1 FelgueirasCamaOrti:2017:ExGeMe
%T Exploring geostatistical methods to improve the altimetry accuracies of digital elevation models
%D 2017
%A Felgueiras, Carlos Alberto,
%A Camargo, Eduardo Celso Gerbi,
%A Ortiz, Jussara de Oliveira,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress carlos@dpi.inpe.br
%@electronicmailaddress eduardo.camargo@inpe.br
%@electronicmailaddress jussara.ortiz@inpe.br
%B International Conference on GeoComputation
%C Leeds, England
%8 4-7 Sept.
%S Proceedings
%K Digital Elevation Modeling, DEM accuracy, Geostatistics, kriging, CoKriging.
%X This article explores computational geostatistical methods to improve the accuracy of the altimetry attribute of Digital Elevation Models (DEMs). The geostatistical procedures, ordinary kriging, kriging with external drift and cokriging, are used to obtain uncertainty representations related to altimetry predictions. The data modeling is performed from existing DEMs, mainly available for free in the internet, and additional high accurate set of 3D sample points. Although the freeware DEMs are dense and generally have good spatial distributions, the accuracy of their altimetry information might not be suitable for many applications. A way of mitigating this problem is to combine the available DEM data along with additional information, coming from reliable sources and having better quality, in the data modeling processes. Generally, high accurate altimetry data are collected, often with a higher cost, in field works at specific points located inside the spatial region of interest. In short, this work aims to integrate spatial elevation information from different through geostatistical methods to obtain better quality DEMs. The methods addressed in this research work were applied to a case study in a Brazilian southeast geographical region.
%@language en
%3 felgueiras_exploring.pdf
%U http://www.geocomputation.org/2017/papers/45.pdf
%O Informações Adicionais: Abstract
%O This article explores computational geostatistical methods to improve the accuracy of the
%O altimetry attribute of Digital Elevation Models (DEMs). The geostatistical procedures,
%O ordinary kriging, kriging with external drift and cokriging, are used to obtain uncertainty
%O representations related to altimetry predictions. The data modeling is performed from
%O existing DEMs, mainly available for free in the internet, and additional high accurate set
%O of 3D sample points. Although the freeware DEMs are dense and generally have good
%O spatial distributions, the accuracy of their altimetry information might not be suitable for
%O many applications. A way of mitigating this problem is to combine the available DEM
%O data along with additional information, coming from reliable sources and having better
%O quality, in the data modeling processes. Generally, high accurate altimetry data are
%O collected, often with a higher cost, in field works at specific points located inside the
%O spatial region of interest. In short, this work aims to integrate spatial elevation
%O information from different through geostatistical methods to obtain better quality DEMs.
%O The methods addressed in this research work were applied to a case study in a Brazilian
%O southeast geographical region..