J
Jan Verbesselt
Researcher at Wageningen University and Research Centre
Publications - 125
Citations - 7664
Jan Verbesselt is an academic researcher from Wageningen University and Research Centre. The author has contributed to research in topics: Normalized Difference Vegetation Index & Satellite Image Time Series. The author has an hindex of 37, co-authored 117 publications receiving 6161 citations. Previous affiliations of Jan Verbesselt include Ghent University & Commonwealth Scientific and Industrial Research Organisation.
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Detecting trend and seasonal changes in satellite image time series
TL;DR: Breaks For Additive Seasonal and Trend (BFAST) as mentioned in this paper is a change detection approach for time series by detecting and characterizing Breaks for Additive seasonal and trend, which integrates the decomposition of time series into trend, seasonal and remainder components with methods for detecting change within time series.
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Phenological change detection while accounting for abrupt and gradual trends in satellite image time series
TL;DR: BFAST as mentioned in this paper integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting change within time series, showing that the phenological change detection is influenced by the signal-to-noise ratio of the time series.
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Near real-time disturbance detection using satellite image time series
TL;DR: In this article, a multi-purpose time-series-based disturbance detection approach is proposed to identify and model stable historical variation to enable change detection within newly acquired data, which can analyse in-situ or satellite data time series of biophysical indicators from local to global scale since it is fast, does not depend on thresholds and does not require time series gap filling.
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Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology
Matthias Forkel,Nuno Carvalhais,Jan Verbesselt,Miguel D. Mahecha,Christopher S.R. Neigh,Markus Reichstein +5 more
TL;DR: A breakpoint detection analysis reveals that an overestimation of breakpoints in NDVI trends can result in wrong or even opposite trend estimates, and gives practical recommendations for the application of trend methods on long-term NDVI time series.
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Trend changes in global greening and browning: contribution of short-term trends to longer-term change
TL;DR: In this article, the authors used trend changes in normalized difference vegetation index (NDVI) satellite data between 1982 and 2008 to detect abrupt and gradual changes in vegetation greenness over time.