Assessing drivers of vegetation changes in drylands from time series of earth observation data
Publikation: Bidrag til bog/antologi/rapport › Bidrag til bog/antologi › Forskning › fagfællebedømt
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Assessing drivers of vegetation changes in drylands from time series of earth observation data. / Fensholt, Rasmus; Horion, Stephanie; Tagesson, Torbern; Ehammer, Andrea; Grogan, Kenneth; Tian, Feng; Huber, Silvia; Verbesselt, Jan; Prince, Stephen D.; Tucker, Compton J.; Rasmussen, Kjeld.
Remote Sensing and Digital Image Processing. red. / Claudia Kuenzer; Stefan Dech; Wolfgang Wagner. Cham : Springer, 2015. s. 183-202 (Remote Sensing and Digital Image Processing, Bind 22).Publikation: Bidrag til bog/antologi/rapport › Bidrag til bog/antologi › Forskning › fagfællebedømt
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TY - CHAP
T1 - Assessing drivers of vegetation changes in drylands from time series of earth observation data
AU - Fensholt, Rasmus
AU - Horion, Stephanie
AU - Tagesson, Torbern
AU - Ehammer, Andrea
AU - Grogan, Kenneth
AU - Tian, Feng
AU - Huber, Silvia
AU - Verbesselt, Jan
AU - Prince, Stephen D.
AU - Tucker, Compton J.
AU - Rasmussen, Kjeld
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-319-15967-6_9
DO - 10.1007/978-3-319-15967-6_9
M3 - Book chapter
AN - SCOPUS:84980010201
SN - 978-3-319-15966-9
T3 - Remote Sensing and Digital Image Processing
SP - 183
EP - 202
BT - Remote Sensing and Digital Image Processing
A2 - Kuenzer, Claudia
A2 - Dech, Stefan
A2 - Wagner, Wolfgang
PB - Springer
CY - Cham
ER -
ID: 234283266