Assessment of vegetation trends in drylands from time series of earth observation data
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Assessment of vegetation trends 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. ed. / Claudia Kuenzer; Stefan Dech; Wolfgang Wagner. Springer, 2015. p. 159-182 (Remote Sensing and Digital Image Processing, Vol. 22).Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review
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TY - CHAP
T1 - Assessment of vegetation trends 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 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.
AB - 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.
U2 - 10.1007/978-3-319-15967-6_8
DO - 10.1007/978-3-319-15967-6_8
M3 - Book chapter
AN - SCOPUS:84979982345
T3 - Remote Sensing and Digital Image Processing
SP - 159
EP - 182
BT - Remote Sensing and Digital Image Processing
A2 - Kuenzer, Claudia
A2 - Dech, Stefan
A2 - Wagner, Wolfgang
PB - Springer
ER -
ID: 239904472