Detecting trend and seasonal changes in satellite image time series
Jan Verbesselt and Rob Hyndman and Glenn Newnham and Darius Culvenor

1-s2.0-S003442570900265X-main.pdf1.48MB
Type: Paper
Tags: Phenology

Bibtex:
@article{Verbesselt2010106,
title = "Detecting trend and seasonal changes in satellite image time series ",
journal = "Remote Sensing of Environment ",
volume = "114",
number = "1",
pages = "106 - 115",
year = "2010",
note = "",
issn = "0034-4257",
doi = "http://dx.doi.org/10.1016/j.rse.2009.08.014",
url = "http://www.sciencedirect.com/science/article/pii/S003442570900265X",
author = "Jan Verbesselt and Rob Hyndman and Glenn Newnham and Darius Culvenor",
keywords = "Change detection",
keywords = "NDVI",
keywords = "Time series",
keywords = "Trend analysis",
keywords = "MODIS",
keywords = "Piecewise linear regression",
keywords = "Vegetation dynamics",
keywords = "Phenology ",
abstract = "A wealth of remotely sensed image time series covering large areas is now available to the earth science community. Change detection methods are often not capable of detecting land cover changes within time series that are heavily influenced by seasonal climatic variations. Detecting change within the trend and seasonal components of time series enables the classification of different types of changes. Changes occurring in the trend component often indicate disturbances (e.g. fires, insect attacks), while changes occurring in the seasonal component indicate phenological changes (e.g. change in land cover type). A generic change detection approach is proposed for time series by detecting and characterizing Breaks For Additive Seasonal and Trend (BFAST). \{BFAST\} integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting change within time series. \{BFAST\} iteratively estimates the time and number of changes, and characterizes change by its magnitude and direction. We tested \{BFAST\} by simulating 16-day Normalized Difference Vegetation Index (NDVI) time series with varying amounts of seasonality and noise, and by adding abrupt changes at different times and magnitudes. This revealed that \{BFAST\} can robustly detect change with different magnitudes (> 0.1 NDVI) within time series with different noise levels (0.01–0.07 σ) and seasonal amplitudes (0.1–0.5 NDVI). Additionally, \{BFAST\} was applied to 16-day \{NDVI\} Moderate Resolution Imaging Spectroradiometer (MODIS) composites for a forested study area in south eastern Australia. This showed that \{BFAST\} is able to detect and characterize spatial and temporal changes in a forested landscape. \{BFAST\} is not specific to a particular data type and can be applied to time series without the need to normalize for land cover types, select a reference period, or change trajectory. The method can be integrated within monitoring frameworks and used as an alarm system to flag when and where changes occur. "
}

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