Agricultural and Biosystems Engineering Publications

Campus Units

Agricultural and Biosystems Engineering

Document Type

Article

Publication Version

Published Version

Publication Date

4-2017

Journal or Book Title

Boletim de Ciências Geodésicas

Volume

23

Issue

2

First Page

296

Last Page

308

DOI

10.1590/s1982-21702017000200019

Abstract

Almost every researcher has come through observations that “drift” from the rest of the sample, suggesting some inconsistency. The aim of this paper is to propose a new inconsistent data detection method for continuous geospatial data based in Geostatistics, independently from the generative cause (measuring and execution errors and inherent variability data). The choice of Geostatistics is based in its ideal characteristics, as avoiding systematic errors, for example. The importance of a new inconsistent detection method proposal is in the fact that some existing methods used in geospatial data consider theoretical assumptions hardly attended. Equally, the choice of the data set is related to the importance of the LiDAR technology (Light Detection and Ranging) in the production of Digital Elevation Models (DEM). Thus, with the new methodology it was possible to detect and map discrepant data. Comparing it to a much utilized detections method, BoxPlot, the importance and functionality of the new method was verified, since the BoxPlot did not detect any data classified as discrepant. The proposed method pointed that, in average, 1,2% of the data of possible regionalized inferior outliers and, in average, 1,4% of possible regionalized superior outliers, in relation to the set of data used in the study.

Comments

This article is from Bol. Ciênc. Geod. vol.23 no.2 Curitiba Apr./June 2017, http://dx.doi.org/10.1590/s1982-21702017000200019.

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Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Copyright Owner

Adriana Maria Rocha Trancoso Santos, Gerson Rodrigues dos Santos, Paulo César Emiliano, Nilcilene das Graças Medeiros, Amy L. Kaleita, Lígia de Oliveira Serrano Pruski

Language

en

File Format

application/pdf

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