Assessment of vegetation trends in drylands from time series of earth observation data
(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.
(Less)
- author
- publishing date
- 2015-01-01
- 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}}, }