Development and Evaluation of Phenological Change Indices Derived from Time Series of Image Data

Abstract

Changes 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.

Study of the Variations of Archaeological Marks at Neolithic Site of Lucera, Italy Using High-Resolution Multispectral Datasets

“…A critical aspect for the true identification of crop marks is the phenological stage of the crops [16,17]. Diverse parameters, such as the weather conditions, the date of onset of greenness, the soil and crop characteristics, and the changes in the climate of an area (e.g., the length of the growing season), may cause variations in the phenological cycle of crops [18]. The latter can also affect researchers' interpretation, since features/crop marks detected from a satellite image taken during a specific phenological stage (e.g., boot stage), might be difficult to differentiate from a succeeding one taken during a different phenological stage.…”

Global Biogeographical Pattern of Ecosystem Functional Types Derived From Earth Observation Data

“…Especially the de-convolution of the original time series into phenological and productivity metrics yield additional information on various aspects of vegetation dynamics and ecosystem functioning [18] that can be related to land use [19]. Monitoring vegetation phenology using satellite remote sensing [20][21][22][23][24][25] offers an optimal framework for ecosystem studies because in situ phenological data are comparatively scarce in many parts of the world [26] and because long time and large spatial scales can be addressed simultaneously. Monitoring vegetation phenology has also improved the understanding of the interactions between the biosphere, the climate and biogeochemical cycles [16,[27][28][29].…”

Mapping Changes in Plant Phenology across Eurasia, Africa, North and South America from Time Series Image Data

“…This development and the validation of the indices are given in McCloy (2010). It has been shown in McCloy (2010) that most of the indices measure closely but not perfectly the five ways that the phenological profile as described above can change. The exception to this is the Offset Index that is a precise measure of the average change in the Phenological Profile.…”

“…Such validation was reported in McCloy (2010) in which sample profiles were derived for selected land covers, and then modified in three specific ways using specific formulae to do so; to change the relative amplitude, the absolute value and the length of the season. How the Phenological Change Indices changed in response to these rigorous changes to the phenological profiles is given in McCloy (2010) and can be summarized as:…”