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Assessment of vegetation trends in drylands from time series of earth observation data

Fensholt, Rasmus ; Horion, Stephanie ; Tagesson, Torbern LU ; Ehammer, Andrea ; Grogan, Kenneth ; Tian, Feng LU ; Huber, Silvia ; Verbesselt, Jan ; Prince, Stephen D. and Tucker, Compton J. , et al. (2015) In Remote Sensing and Digital Image Processing 22. p.159-182
Abstract

This chapter summarizes approaches to the detection of dryland vegetation change and methods for observing spatio-temporal trends from space. An overview of suitable long-term Earth Observation (EO) based datasets for assessment of global dryland vegetation trends is provided and a status map of contemporary greening and browning trends for global drylands is presented. The vegetation metrics suitable for per-pixel temporal trend analysis is discussed, including seasonal parameterisation and the appropriate choice of trend indicators. Recent methods designed to overcome assumptions of long-term linearity in time series analysis (Breaks For Additive Season and Trend(BFAST)) are discussed. Finally, the importance of the spatial scale when... (More)

This chapter summarizes approaches to the detection of dryland vegetation change and methods for observing spatio-temporal trends from space. An overview of suitable long-term Earth Observation (EO) based datasets for assessment of global dryland vegetation trends is provided and a status map of contemporary greening and browning trends for global drylands is presented. The vegetation metrics suitable for per-pixel temporal trend analysis is discussed, including seasonal parameterisation and the appropriate choice of trend indicators. Recent methods designed to overcome assumptions of long-term linearity in time series analysis (Breaks For Additive Season and Trend(BFAST)) are discussed. Finally, the importance of the spatial scale when performing temporal trend analysis is introduced and a method for image downscaling (Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM)) is presented.

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publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Remote Sensing and Digital Image Processing
series title
Remote Sensing and Digital Image Processing
volume
22
pages
24 pages
publisher
Springer
external identifiers
  • scopus:84979982345
ISSN
2215-1842
1567-3200
DOI
10.1007/978-3-319-15967-6_8
language
English
LU publication?
no
id
5aead0d9-a321-4011-9987-2e9f6f14d0e4
date added to LUP
2018-06-08 13:52:22
date last changed
2024-04-15 08:58:18
@inbook{5aead0d9-a321-4011-9987-2e9f6f14d0e4,
  abstract     = {{<p>This chapter summarizes approaches to the detection of dryland vegetation change and methods for observing spatio-temporal trends from space. An overview of suitable long-term Earth Observation (EO) based datasets for assessment of global dryland vegetation trends is provided and a status map of contemporary greening and browning trends for global drylands is presented. The vegetation metrics suitable for per-pixel temporal trend analysis is discussed, including seasonal parameterisation and the appropriate choice of trend indicators. Recent methods designed to overcome assumptions of long-term linearity in time series analysis (Breaks For Additive Season and Trend(BFAST)) are discussed. Finally, the importance of the spatial scale when performing temporal trend analysis is introduced and a method for image downscaling (Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM)) is presented.</p>}},
  author       = {{Fensholt, Rasmus and Horion, Stephanie and Tagesson, Torbern and Ehammer, Andrea and Grogan, Kenneth and Tian, Feng and Huber, Silvia and Verbesselt, Jan and Prince, Stephen D. and Tucker, Compton J. and Rasmussen, Kjeld}},
  booktitle    = {{Remote Sensing and Digital Image Processing}},
  issn         = {{2215-1842}},
  language     = {{eng}},
  month        = {{01}},
  pages        = {{159--182}},
  publisher    = {{Springer}},
  series       = {{Remote Sensing and Digital Image Processing}},
  title        = {{Assessment of vegetation trends in drylands from time series of earth observation data}},
  url          = {{https://lup.lub.lu.se/search/files/45734347/Fensholt_et_al_2015_book_chapters_EARSel.pdf}},
  doi          = {{10.1007/978-3-319-15967-6_8}},
  volume       = {{22}},
  year         = {{2015}},
}