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Development and Evaluation of Phenological Change Indices Derived from Time Series of Image Data
AbstractChanges in vegetation phenology are recognized as being possibly the most important early indicator of the impact of climate change on ecosystems. Time series of image data are accepted as being the best way to map these changes, if we can derive suitable indices from the huge volumes of time series data that make the interpretation of these time series difficult. The problem is thus to derive a set of indices from the time series of image data that depict changes in vegetation phenology in a way that is easy to analyze and use. This problem has been solved by deriving a set of six Phenological Change Indices that measure the five ways that the phenological curve of vegetation can change over time. These indices were then tested using simulated data based on sample phenological profiles for a set of land covers and showed that four of the indices measured four of the ways that the phenological profile can change, and two of the indices gave similar results in measuring the last way that the phenological profiles can change. A time series of image data was then used to map the Phenological Change Indices for Eurasia and a sample of land covers was used to relate the changes in phenology to location for each land cover. This work showed that the detected changes in phenology are similar to those found in other papers. The benefit of these indices is that we can now analyze changes in phenology in a much more detailed and accurate way than has been possible until now.
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McCloy, K.R. Development and Evaluation of Phenological Change Indices Derived from Time Series of Image Data. Remote Sens. 2010, 2, 2442-2473.View more citation formats
McCloy KR. Development and Evaluation of Phenological Change Indices Derived from Time Series of Image Data. Remote Sensing. 2010; 2(11):2442-2473.Chicago/Turabian Style
McCloy, Keith R. 2010. "Development and Evaluation of Phenological Change Indices Derived from Time Series of Image Data." Remote Sens. 2, no. 11: 2442-2473.
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