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Investigating spatial error structures in continuous raster data

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journal contribution
posted on 24.09.2019, 12:47 by Narumasa Tsutsumida, Pedro Rodriguez-Veiga, Paul Harris, Heiko Balzter, Alexis Comber
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.


This research is funded by KAKENHI Grant Number 15K21086; KU SPIRITS project; ROIS-DS-JOINT (006RP2018); and joint research program of CEReS, Chiba university(2018). P. Rodríguez Veiga and H. Balzter were supported by the UK’s National Centre for Earth Observation (NCEO). A. Comber and P. Harris were supported by the Natural Environment Research Council Newton Fund grant (NE/N007433/1). We thank M. Castillo, I. Cruz, and M. Olguin for giving advice on the Mexican case study. All statistical analyses and mapping were conducted in the R open source software. Functions to calculate GW mae and GW rmse and their Monte Carlo permutation tests required a series of adaptions to the functions gwss and gwss.montecarlo in the GWmodel R package (Gollini et al., 2015; Lu et al., 2014). These adapted functions are available to interested researchers on request. Options for GW msd and GW r and their tests are already provided in the same functions of the GWmodel.



International Journal of Applied Earth Observation and Geoinformation, 2019, 74, pp. 259-268 (10)

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/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/School of Geography, Geology and the Environment/GIS and Remote Sensing


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International Journal of Applied Earth Observation and Geoinformation





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