Access count of this item: 74

Files in This Item:
File Description SizeFormat 
j.jag.2018.09.020.pdf3.77 MBAdobe PDFView/Open
Title: Investigating spatial error structures in continuous raster data
Authors: Tsutsumida, Narumasa
Rodríguez-Veiga, Pedro
Harris, Paul
Balzter, Heiko
Comber, Alexis
Author's alias: 堤田, 成政
Keywords: Error distribution
Spatial accuracy
Local error diagnostics
Spatial heterogeneity
Issue Date: Feb-2019
Publisher: Elsevier BV
Journal title: International Journal of Applied Earth Observation and Geoinformation
Volume: 74
Start page: 259
End page: 268
Abstract: The objective of this study is to investigate spatial structures of error in the assessment of continuous raster data. The use of conventional diagnostics of error often overlooks the possible spatial variation in error because such diagnostics report only average error or deviation between predicted and reference values. In this respect, this work uses a moving window (kernel) approach to generate geographically weighted (GW) versions of the mean signed deviation, the mean absolute error and the root mean squared error and to quantify their spatial variations. Such approach computes local error diagnostics from data weighted by its distance to the centre of a moving kernel and allows to map spatial surfaces of each type of error. In addition, a GW correlation analysis between predicted and reference values provides an alternative view of local error. These diagnostics are applied to two earth observation case studies. The results reveal important spatial structures of error and unusual clusters of error can be identified through Monte Carlo permutation tests. The first case study demonstrates the use of GW diagnostics to fractional impervious surface area datasets generated by four different models for the Jakarta metropolitan area, Indonesia. The GW diagnostics reveal where the models perform differently and similarly, and found areas of under-prediction in the urban core, with larger errors in peri-urban areas. The second case study uses the GW diagnostics to four remotely sensed aboveground biomass datasets for the Yucatan Peninsula, Mexico. The mapping of GW diagnostics provides a means to compare the accuracy of these four continuous raster datasets locally. The discussion considers the relative nature of diagnostics of error, determining moving window size and issues around the interpretation of different error diagnostic measures. Investigating spatial structures of error hidden in conventional diagnostics of error provides informative descriptions of error in continuous raster data.
Rights: © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (
DOI(Published Version): 10.1016/j.jag.2018.09.020
Appears in Collections:Journal Articles

Show full item record

Export to RefWorks

Export Format: 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.