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Open AccessArticle

A Concept for Uncertainty-Aware Analysis of Land Cover Change Using Geovisual Analytics

Lab for Geoinformatics and Geovisualization, HafenCity University, Überseeallee 16, 20457 Hamburg, Germany
ISPRS Int. J. Geo-Inf. 2014, 3(3), 1122-1138; https://doi.org/10.3390/ijgi3031122
Received: 1 June 2014 / Revised: 13 August 2014 / Accepted: 2 September 2014 / Published: 19 September 2014
Analysis of land cover change is one of the major challenges in the remote sensing and GIS domain, especially when multi-temporal or multi-sensor analyses are conducted. One of the reasons is that errors and inaccuracies from multiple datasets (for instance caused by sensor bias or spatial misregistration) accumulate and can lead to a high amount of erroneous change. A promising approach to counter this challenge is to quantify and visualize uncertainty, i.e., to deal with imperfection instead of ignoring it. Currently, in GIS the incorporation of uncertainty into change analysis is not easily possible. We present a concept for uncertainty-aware change analysis using a geovisual analytics (GVA) approach. It is based on two main elements: first, closer integration of change detection and analysis steps; and second, visual communication of uncertainty during analysis. Potential benefits include better-informed change analysis, support for choosing change detection parameters and reduction of erroneous change by filtering. In a case study with a change scenario in an area near Hamburg, Germany, we demonstrate how erroneous change can be filtered out using uncertainty. For this, we implemented a software prototype according to the concept presented. We discuss the potential and limitations of the concept and provide recommendations for future work. View Full-Text
Keywords: remote sensing; change analysis; uncertainty; geovisualization; geovisual analytics remote sensing; change analysis; uncertainty; geovisualization; geovisual analytics
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Kinkeldey, C. A Concept for Uncertainty-Aware Analysis of Land Cover Change Using Geovisual Analytics. ISPRS Int. J. Geo-Inf. 2014, 3, 1122-1138.

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