2010 journal article
High-resolution land cover change detection based on fuzzy uncertainty analysis and change reasoning
INTERNATIONAL JOURNAL OF REMOTE SENSING, 31(2), 455–475.
TL;DR:
This work presents an efficient fuzzy framework for transforming map uncertainty into accurate and practical change analysis and creates a continuous representation of change, a product type that retains more information and flexibility than discrete maps of change.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
13. Climate Action
(Web of Science)
15. Life on Land
(Web of Science)
Source: Web Of Science
Added: August 6, 2018
Land cover change detection is an important research and application area for analysts of remote sensing data. The primary objective of the research described here was to develop a change detection method capable of accommodating spatial and classification uncertainty in generating an accurate map of land cover change using high resolution satellite imagery. As a secondary objective, this method was designed to facilitate the mapping of particular types and locations of change based on specific study goals. Urban land cover change pertinent to surface water quality in Raleigh, North Carolina, was assessed using land cover classifications derived from pan-sharpened, 0.61 m QuickBird images from 2002 and 2005. Post-classification map errors were evaluated using a fuzzy logic approach. First, a ‘change index’ representing a quantitative gradient along which land cover change is characterized by both certainty and relevance, was created. The result was a continuous representation of change, a product type that retains more information and flexibility than discrete maps of change. Finally, fuzzy logic and change reasoning results were integrated into a binary change/no change map that quantified the most certain, likely, and relevant change regions within the study area. A ‘from-to’ change map was developed from this binary map inserting the type of change identified in the raw post-classification map. A from-to change map had an overall accuracy of 78.9% (κ = 0.747) and effectively mapped land cover changes posing a threat to water quality, including increases in impervious surface. This work presents an efficient fuzzy framework for transforming map uncertainty into accurate and practical change analysis.