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http://dx.doi.org/10.7780/kjrs.2011.27.5.601

Hotspot Detection for Land Cover Changes Using Spatial Statistical Methods  

Lee, Jeong-Hun (Department of Spatial Information Engineering, Pukyong National University)
Kim, Sang-Il (Department of Spatial Information Engineering, Pukyong National University)
Han, Kyung-Soo (Department of Spatial Information Engineering, Pukyong National University)
Lee, Yang-Won (Department of Spatial Information Engineering, Pukyong National University)
Publication Information
Korean Journal of Remote Sensing / v.27, no.5, 2011 , pp. 601-611 More about this Journal
Abstract
Land cover changes are occurring for a variety of reasons such as urbanization, infrastructure construction, desertification, drought, flood, and so on. Many researchers have studied the cause and effect of land cover changes, and also the methods for change detection. However, most of the detection methods are based on the dichotomy of "change" and "not change" according a threshold value. In this paper, we present a change detection method with the integration of probability, spatial autocorrelation, and hotspot detection. We used the AMOEBA (A Multidirectional Ecotope-Based Algorithm) and developed the AMOEBA-CH (core hotspot) because the original algorithm tends to produce too many clusters. Our method considers the probability of land cover changes and the spatial interactions between each pixel and its neighboring pixels using a local spatial autocorrelation measure. The core hotspots of land cover changes can be delineated by a contiguity-dominance model of our AMOEBA-CH method. We tested our algorithm in a simulation for land cover changes using NDVI (Normalized Difference Vegetation Index) data in South Korea between 2000 and 2008.
Keywords
change detection; NDVI; land cover change; spatial autocorrelation; AMOEBA;
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Times Cited By KSCI : 3  (Citation Analysis)
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