Assessing drivers of vegetation changes in drylands from time series of earth observation data
(2015) In Remote Sensing and Digital Image Processing 22. p.183-202- Abstract
This chapter summarizes methods of inferring information about drivers of global dryland vegetation changes observed from remote sensing time series data covering from the 1980s until present time. Earth observation (EO) based time series of vegetation metrics, sea surface temperature (SST) (both from the AVHRR (Advanced Very High Resolution Radiometer) series of instruments) and precipitation data (blended satellite/rain gauge) are used for determining the mechanisms of observed changes. EO-based methods to better distinguish between climate and human induced (land use) vegetation changes are reviewed. The techniques presented include trend analysis based on the Rain-Use Efficiency (RUE) and the Residual Trend Analysis (RESTREND) and... (More)
This chapter summarizes methods of inferring information about drivers of global dryland vegetation changes observed from remote sensing time series data covering from the 1980s until present time. Earth observation (EO) based time series of vegetation metrics, sea surface temperature (SST) (both from the AVHRR (Advanced Very High Resolution Radiometer) series of instruments) and precipitation data (blended satellite/rain gauge) are used for determining the mechanisms of observed changes. EO-based methods to better distinguish between climate and human induced (land use) vegetation changes are reviewed. The techniques presented include trend analysis based on the Rain-Use Efficiency (RUE) and the Residual Trend Analysis (RESTREND) and the methodological challenges related to the use of these. Finally, teleconnections between global sea surface temperature (SST) anomalies and dryland vegetation productivity are illustrated and the associated predictive capabilities are discussed.
(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
- 20 pages
- publisher
- Springer
- external identifiers
-
- scopus:84980010201
- ISSN
- 1567-3200
- 2215-1842
- DOI
- 10.1007/978-3-319-15967-6_9
- language
- English
- LU publication?
- no
- id
- 8b75103c-a1b3-4509-acc2-35c16e176ee8
- date added to LUP
- 2018-06-08 13:53:08
- date last changed
- 2024-09-16 22:49:37
@inbook{8b75103c-a1b3-4509-acc2-35c16e176ee8, abstract = {{<p>This chapter summarizes methods of inferring information about drivers of global dryland vegetation changes observed from remote sensing time series data covering from the 1980s until present time. Earth observation (EO) based time series of vegetation metrics, sea surface temperature (SST) (both from the AVHRR (Advanced Very High Resolution Radiometer) series of instruments) and precipitation data (blended satellite/rain gauge) are used for determining the mechanisms of observed changes. EO-based methods to better distinguish between climate and human induced (land use) vegetation changes are reviewed. The techniques presented include trend analysis based on the Rain-Use Efficiency (RUE) and the Residual Trend Analysis (RESTREND) and the methodological challenges related to the use of these. Finally, teleconnections between global sea surface temperature (SST) anomalies and dryland vegetation productivity are illustrated and the associated predictive capabilities are discussed.</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 = {{1567-3200}}, language = {{eng}}, month = {{01}}, pages = {{183--202}}, publisher = {{Springer}}, series = {{Remote Sensing and Digital Image Processing}}, title = {{Assessing drivers of vegetation changes in drylands from time series of earth observation data}}, url = {{https://lup.lub.lu.se/search/files/45734289/Fensholt_et_al_2015_book_chapters_EARSel.pdf}}, doi = {{10.1007/978-3-319-15967-6_9}}, volume = {{22}}, year = {{2015}}, }