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Detecting changes in vegetation trends using time series segmentation

Jamali, Sadegh LU ; Jönsson, Per LU ; Eklundh, Lars LU ; Ardö, Jonas LU and Seaquist, Jonathan LU (2015) In Remote Sensing of Environment 156(January). p.182-195
Abstract
Although satellite-based sensors have made vegetation data series available for several decades, the detection of vegetation trend and change is not yet straightforward. This is partly due to the scarcity of available change detection algorithms suitable for identifying and characterizing both abrupt and non-abrupt changes, without sacrificing accuracy or computational speed. We propose a user-friendly program for analysing vegetation time series, with two main application domains: generalising vegetation trends to main features, and characterizing vegetation trend changes. This program, Detecting Breakpoints and Estimating Segments in Trend (DBEST) uses a novel segmentation algorithm which simplifies the trend into linear segments using... (More)
Although satellite-based sensors have made vegetation data series available for several decades, the detection of vegetation trend and change is not yet straightforward. This is partly due to the scarcity of available change detection algorithms suitable for identifying and characterizing both abrupt and non-abrupt changes, without sacrificing accuracy or computational speed. We propose a user-friendly program for analysing vegetation time series, with two main application domains: generalising vegetation trends to main features, and characterizing vegetation trend changes. This program, Detecting Breakpoints and Estimating Segments in Trend (DBEST) uses a novel segmentation algorithm which simplifies the trend into linear segments using one of three user-defined parameters: a generalisation-threshold parameter δ, the m largest changes, or a threshold β for the magnitude of changes of interest for detection. The outputs of DBEST are the simplified trend, the change type (abrupt or non-abrupt), and estimates for the characteristics (time and magnitude) of the change. DBEST was tested and evaluated using simulated Normalized Difference Vegetation Index (NDVI) data at two sites, which included different types of changes. Evaluation results demonstrate that DBEST quickly and robustly detects both abrupt and non-abrupt changes, and accurately estimates change time and magnitude.



DBEST was also tested using data from the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI image time series for Iraq for the period 1982–2006, and was able to detect and quantify major change over the area. This showed that DBEST is able to detect and characterize changes over large areas. We conclude that DBEST is a fast, accurate and flexible tool for trend detection, and is applicable to global change studies using time series of remotely sensed data sets. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
trend analysis, satellite imagery, segmentation, time series, NDVI, GIMMS, DBEST, change detection, vegetation dynamics
in
Remote Sensing of Environment
volume
156
issue
January
pages
182 - 195
publisher
Elsevier
external identifiers
  • wos:000347579900015
  • scopus:84908316467
ISSN
0034-4257
DOI
10.1016/j.rse.2014.09.010
language
English
LU publication?
yes
id
218c4b41-46fd-4e23-b8b8-65df0d546bd4 (old id 4698732)
alternative location
http://www.sciencedirect.com/science/article/pii/S0034425714003538
date added to LUP
2014-11-28 17:03:44
date last changed
2017-11-05 03:00:08
@article{218c4b41-46fd-4e23-b8b8-65df0d546bd4,
  abstract     = {Although satellite-based sensors have made vegetation data series available for several decades, the detection of vegetation trend and change is not yet straightforward. This is partly due to the scarcity of available change detection algorithms suitable for identifying and characterizing both abrupt and non-abrupt changes, without sacrificing accuracy or computational speed. We propose a user-friendly program for analysing vegetation time series, with two main application domains: generalising vegetation trends to main features, and characterizing vegetation trend changes. This program, Detecting Breakpoints and Estimating Segments in Trend (DBEST) uses a novel segmentation algorithm which simplifies the trend into linear segments using one of three user-defined parameters: a generalisation-threshold parameter δ, the m largest changes, or a threshold β for the magnitude of changes of interest for detection. The outputs of DBEST are the simplified trend, the change type (abrupt or non-abrupt), and estimates for the characteristics (time and magnitude) of the change. DBEST was tested and evaluated using simulated Normalized Difference Vegetation Index (NDVI) data at two sites, which included different types of changes. Evaluation results demonstrate that DBEST quickly and robustly detects both abrupt and non-abrupt changes, and accurately estimates change time and magnitude.<br/><br>
<br/><br>
DBEST was also tested using data from the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI image time series for Iraq for the period 1982–2006, and was able to detect and quantify major change over the area. This showed that DBEST is able to detect and characterize changes over large areas. We conclude that DBEST is a fast, accurate and flexible tool for trend detection, and is applicable to global change studies using time series of remotely sensed data sets.},
  author       = {Jamali, Sadegh and Jönsson, Per and Eklundh, Lars and Ardö, Jonas and Seaquist, Jonathan},
  issn         = {0034-4257},
  keyword      = {trend analysis,satellite imagery,segmentation,time series,NDVI,GIMMS,DBEST,change detection,vegetation dynamics},
  language     = {eng},
  number       = {January},
  pages        = {182--195},
  publisher    = {Elsevier},
  series       = {Remote Sensing of Environment},
  title        = {Detecting changes in vegetation trends using time series segmentation},
  url          = {http://dx.doi.org/10.1016/j.rse.2014.09.010},
  volume       = {156},
  year         = {2015},
}