Improving change vector analysis by cross-correlogram spectral matching for accurate detection of land-cover conversion

Abstract

Time series of vegetation index (VI) information derived from remote sensing is important for land-cover change detection. Although traditional change vector analysis (TCVA) is an effective method for extracting land-cover change information from a time series of VI data, it has the disadvantage of being too sensitive to temporal fluctuations in VI values. The method tends to overestimate the changes and confuse the actual land-cover conversion with the land covers that have not been converted but experience significant VI changes. Cross-correlogram spectral matching (CCSM) can tell the degree of shape similarity between VI profiles and be used to detect land-cover conversion. However, this method may omit some land conversion in which the before and after land-cover types are rather similar in VI profile shape but differ significantly in absolute VI values. This article proposes a new approach that improves TCVA with an adapted use of CCSM. First, TCVA is employed for preliminary detection of land-cover changes. Second, the changes caused by temporal fluctuations of VI values are identified through the CCSM analysis and excluded to only keep the most likely land-cover conversions. Finally, classification is performed to map the different types of land-cover conversions. The improved change vector analysis (ICVA) was applied to detect land-cover conversions from 2000 to 2008, using a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced VI images for the Beijing–Tianjin–Tangshan urban agglomeration district, China. The results show that ICVA is able to detect land-cover conversion with a significantly higher accuracy (78.00%, κ = 0.56) than TCVA (64.00%, κ = 0.35) or CCSM (66.60%, κ = 0.27). The proposed approach is of particular value in distinguishing actual land-cover conversion from land-cover modifications resulting from phenological changes.

Acknowledgements

This research was supported by the National High-Tech Research Programme of China (Grant No. 2009AA122004), the National Basic Research Programme of China (Grant No. 2010CB950901), and the Natural Science Foundation of China (Grant No. 40971059).

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